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0000000000000000000000000000000000000000..0b0862e70e19406328b3016b1df9068f78350af6 Binary files /dev/null and b/Adversarial Samples/Outdoor/Outdoor_Adversarial_Surfaces differ diff --git a/Adversarial Samples/main.py b/Adversarial Samples/main.py new file mode 100644 index 0000000000000000000000000000000000000000..fd2e6d4ae4d48abc46fdcddeb556be2e85d67623 --- /dev/null +++ b/Adversarial Samples/main.py @@ -0,0 +1,135 @@ +import sys +sys.path.append('../XGBoost_Prediction_Model/') + +import warnings +warnings.filterwarnings("ignore") +import Predict +import XGBoost_utils +import torch +import numpy as np +import os +from os.path import isfile, isdir, join + +text_detection_model_path = '../XGBoost_Prediction_Model/EAST-Text-Detection/frozen_east_text_detection.pb' +LDA_model_pth = '../XGBoost_Prediction_Model/LDA_Model_trained/lda_model_best_tot.model' +training_ad_text_dictionary_path = '../XGBoost_Prediction_Model/LDA_Model_trained/object_word_dictionary' +training_lang_preposition_path = '../XGBoost_Prediction_Model/LDA_Model_trained/dutch_preposition' + +Adversarial_types = ['Newspaper', 'Banner', 'Multiple Ads', 'Outdoor', 'Others'] +mypath = '../XGBoost_Prediction_Model/Adversarial Samples New' + +ad_locations_total = { + 'Newspaper': [0,0,0,0,0,0,0,0,0,0,0], + 'Banner': [2,2,2,2,2,2,2,2,2,2,2], + 'Multiple Ads': [0,0,1,0,0,1,0,0,0,0,0], + 'Outdoor': [2,2,2,2,2,2,2,2,2,2,2], + 'Others': [1,0,1,0,2,0,1,1,0,0,0] +} + +Predicted_AG = {} +Predicted_BG = {} +GT_AG = {} +GT_BG = {} + +for f in os.listdir(mypath): + # if f != 'Outdoor': + # continue + if isdir(join(mypath, f)): + print('Currently processing samples of type '+f+'......') + path_temp = join(mypath, f) + surfaces = torch.load(join(path_temp,f+'_Adversarial_Surfaces')) + print(surfaces) + categories = torch.load(join(path_temp,f+'_Adversarial_Categories')) + ad_locations_curr = ad_locations_total[f] + ad_embeddings = torch.load(join(path_temp,f+'_ad_topic_embeddings')) + ctpg_embeddings = torch.load(join(path_temp,f+'_ctpg_topic_embeddings')) + GT_AG[f] = torch.load(join(path_temp,'AGs')) + GT_BG[f] = torch.load(join(path_temp,'BGs')) + + #f is, e.g. Outdoor + #sub_f is, e.g. 1,2,...,11 + AG_predictions_per_type = np.zeros(11) + BG_predictions_per_type = np.zeros(11) + for sub_f in os.listdir(path_temp): + if isdir(join(path_temp, sub_f)): + print('Sample Number '+sub_f+'...... ') + context_pth = None + for imgs in os.listdir(join(path_temp, sub_f)): + jpg_removed_split = imgs[:-4].split(' ') + if len(jpg_removed_split) > 1: + img_type = jpg_removed_split[-1] + if img_type == 'Ad': + ad_pth = join(join(path_temp, sub_f),imgs) + print(ad_pth) + elif img_type == 'Context': + context_pth = join(join(path_temp, sub_f),imgs) + + #Start Predicting + sample_num = int(sub_f)-1 + + AG = Predict.Ad_Gaze_Prediction(input_ad_path=ad_pth, input_ctpg_path=context_pth, text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, + training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch', + ad_embeddings=ad_embeddings[sample_num].reshape(1,768), ctpg_embeddings=ctpg_embeddings[sample_num].reshape(1,768), + surface_sizes=list(surfaces[sample_num]), Product_Group=list(categories[sample_num]), + obj_detection_model_pth=None, ad_location=ad_locations_curr[sample_num], num_topic=20, Gaze_Time_Type='Ad') + AG_predictions_per_type[sample_num] = AG + + BG = Predict.Ad_Gaze_Prediction(input_ad_path=ad_pth, input_ctpg_path=context_pth, text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, + training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch', + ad_embeddings=ad_embeddings[sample_num].reshape(1,768), ctpg_embeddings=ctpg_embeddings[sample_num].reshape(1,768), + surface_sizes=list(surfaces[sample_num]), Product_Group=list(categories[sample_num]), + obj_detection_model_pth=None, ad_location=ad_locations_curr[sample_num], num_topic=20, Gaze_Time_Type='Brand') + BG_predictions_per_type[sample_num] = BG + + Predicted_AG[f] = AG_predictions_per_type + Predicted_BG[f] = BG_predictions_per_type + +print("Final results: ") +diffs_rmse = {} +diffs_rmsrpd = {} +GT_AG_tot = [] +GT_BG_tot = [] +Pred_AG_tot = [] +Pred_BG_tot = [] +for key in Predicted_AG.keys(): + print(key) + print('AGs', Predicted_AG[key]) + print('BGs', Predicted_BG[key]) + Pred_AG_tot.append(Predicted_AG[key]) + Pred_BG_tot.append(Predicted_BG[key]) + GT_AG_tot.append(GT_AG[key]) + GT_BG_tot.append(GT_BG[key]) + + rmse1 = np.sqrt(np.mean((GT_AG[key]-Predicted_AG[key])**2)) + rmse2 = np.sqrt(np.mean((GT_BG[key]-Predicted_BG[key])**2)) + diffs_rmse[key] = (rmse1, rmse2) + + rmsrpd1 = XGBoost_utils.RMSRPD(GT_AG[key],Predicted_AG[key]) + rmsrpd2 = XGBoost_utils.RMSRPD(GT_BG[key],Predicted_BG[key]) + diffs_rmsrpd[key] = (rmsrpd1, rmsrpd2) + + print() + +Pred_AG_tot = np.concatenate(Pred_AG_tot) +Pred_BG_tot = np.concatenate(Pred_BG_tot) +GT_AG_tot = np.concatenate(GT_AG_tot) +GT_BG_tot = np.concatenate(GT_BG_tot) +print("RMSE: ") +print("Total AG: ", np.sqrt(np.mean((Pred_AG_tot-GT_AG_tot)**2))) +print("Total BG: ", np.sqrt(np.mean((Pred_BG_tot-GT_BG_tot)**2))) +print() +for key in diffs_rmse.keys(): + print(key, diffs_rmse[key][0], diffs_rmse[key][1]) +print() + +print("RMSRPD: ") +print("Total AG: ", XGBoost_utils.RMSRPD(Pred_AG_tot,GT_AG_tot)) +print("Total BG: ", XGBoost_utils.RMSRPD(Pred_BG_tot,GT_BG_tot)) +print() +for key in diffs_rmsrpd.keys(): + print(key, diffs_rmsrpd[key][0], diffs_rmsrpd[key][1]) +print() + +print("Correlation: ") +print("Total AG: ", np.corrcoef(Pred_AG_tot, GT_AG_tot)) +print("Total BG: ", np.corrcoef(Pred_BG_tot, GT_BG_tot)) diff --git a/Brand_Gaze_Model/.DS_Store b/Brand_Gaze_Model/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..deff9d313a36ca1a6105f89c3a6808c1aae91b6d Binary files /dev/null and b/Brand_Gaze_Model/.DS_Store differ diff --git a/Brand_Gaze_Model/10_models/Model_1.json b/Brand_Gaze_Model/10_models/Model_1.json new file mode 100644 index 0000000000000000000000000000000000000000..b07f783b92ab06f5cedf82926a229503b30d449d --- /dev/null +++ b/Brand_Gaze_Model/10_models/Model_1.json @@ -0,0 +1 @@ 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0000000000000000000000000000000000000000..4cfd060f1dd7dc3ec7d17cee20c4bd4467b82559 --- /dev/null +++ b/CNN_Gaze_Model/Fine-tune_BG/Model_7.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0164c62de0c0a8651f9878312d1ec8b53c01e8c454003ee55f61a409c7482ac7 +size 98547310 diff --git a/CNN_Gaze_Model/Fine-tune_BG/Model_8.pth b/CNN_Gaze_Model/Fine-tune_BG/Model_8.pth new file mode 100644 index 0000000000000000000000000000000000000000..2a449af80423791c263e3267d3de62376c9d0c32 --- /dev/null +++ b/CNN_Gaze_Model/Fine-tune_BG/Model_8.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:46aa32762da46488473e4b3c13660f328d8919c18a8bf7d588845f57eb1f082b +size 98547310 diff --git a/CNN_Gaze_Model/Fine-tune_BG/Model_9.pth b/CNN_Gaze_Model/Fine-tune_BG/Model_9.pth new file mode 100644 index 0000000000000000000000000000000000000000..f940aa50fdb3b4c835029edd500595b89eae96d6 --- /dev/null +++ b/CNN_Gaze_Model/Fine-tune_BG/Model_9.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:123833979fa23aec1710aacc15fa614cc30ba76f761281c505a5252e8ebc5e3a +size 98547310 diff --git a/DL_models.py b/DL_models.py new file mode 100644 index 0000000000000000000000000000000000000000..36e46df5601048f342284cbb24e70201f5c75c54 --- /dev/null +++ b/DL_models.py @@ -0,0 +1,62 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import torch +import torch.nn as nn +import torchvision.models as models +from torchvision.models import ResNet50_Weights +import numpy as np +from torchvision import transforms + +class CustomResNet(nn.Module): + def __init__(self): + super(CustomResNet, self).__init__() + if torch.cuda.is_available(): + self.device = 'cuda' + elif torch.backends.mps.is_available(): + self.device = 'mps' + else: + self.device = 'cpu' + + self.transform = transforms.Compose([ + transforms.Resize((224, 224)), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + ]) + + self.resnet = models.resnet50(weights=ResNet50_Weights.DEFAULT) + + # Remove the final fully connected layer + self.resnet = nn.Sequential(*list(self.resnet.children())[:-1]) + + # Define MLP + self.fc1 = nn.Linear(2 * (512 * 4), 256) + self.fc2 = nn.Linear(256, 1) + + + def forward(self, x1, x2, Location): + N = x1.shape[0] + + x1 = self.transform(x1.to(torch.float).to(self.device)) + x2 = self.transform(x2.to(torch.float).to(self.device)) + Location = Location.to(torch.float).to(self.device) + + # Process both images through the same ResNet + f1 = self.resnet(x1) + f2 = self.resnet(x2) + + # Flatten the features + f1 = f1.view(f1.size(0), -1) + f2 = f2.view(f2.size(0), -1) + + f_ad = f1*Location[:,0].reshape(N,1) + f2*Location[:,1].reshape(N,1) + f_context = f1*(1-Location[:,0].reshape(N,1)) + f2*(1-Location[:,1].reshape(N,1)) + + # Concatenate the features + combined = torch.cat((f_ad, f_context), dim=1) + + # Pass through MLP + x = torch.relu(self.fc1(combined)) + x = self.fc2(x) + + return x \ No newline at end of file diff --git a/Demo.py b/Demo.py new file mode 100644 index 0000000000000000000000000000000000000000..4fa7601e0d22de08c139aaaf13009d8328d8dc1d --- /dev/null +++ b/Demo.py @@ -0,0 +1,14 @@ +import Predict + +if __name__ == "__main__": + input_ad_path = 'Demo/004.jpg' + text_detection_model_path = 'EAST-Text-Detection/frozen_east_text_detection.pb' + LDA_model_pth = 'LDA_Model_trained/lda_model_best_tot.model' + training_ad_text_dictionary_path = 'LDA_Model_trained/object_word_dictionary' + training_lang_preposition_path = 'LDA_Model_trained/dutch_preposition' + + Predict.Ad_Gaze_Prediction(input_ad_path=input_ad_path, input_ctpg_path=None, ad_location=0, + text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, + training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch', + surface_sizes=None, Product_Group=None, + obj_detection_model_pth=None, num_topic=20, Gaze_Time_Type='Ad') \ No newline at end of file diff --git a/Demo/.DS_Store b/Demo/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 Binary files /dev/null and b/Demo/.DS_Store differ diff --git a/Demo/004.jpg b/Demo/004.jpg new file mode 100644 index 0000000000000000000000000000000000000000..0e6a7f35e430d08b3d531863895908c78644eb1b Binary files /dev/null and b/Demo/004.jpg differ diff --git a/Demo/README.md b/Demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..24cbc22a3a7d0516a92cab28d44358ec388d25b6 --- /dev/null +++ b/Demo/README.md @@ -0,0 +1 @@ +Demo Ad Image diff --git a/EAST-Text-Detection/README.md b/EAST-Text-Detection/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9e61d4a6804f1266652516123899e7b6a78a7842 --- /dev/null +++ b/EAST-Text-Detection/README.md @@ -0,0 +1,3 @@ +EAST Model: \ +Code reference <- https://pyimagesearch.com/2018/08/20/opencv-text-detection-east-text-detector/ \ +EAST Paper <- https://arxiv.org/abs/1704.03155 diff --git a/EAST-Text-Detection/frozen_east_text_detection.pb b/EAST-Text-Detection/frozen_east_text_detection.pb new file mode 100644 index 0000000000000000000000000000000000000000..7ab722faf939b1f8598999cc4e0878f45ed661bc --- /dev/null +++ b/EAST-Text-Detection/frozen_east_text_detection.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9b486f3c3eee77b4c8cc91a83892c37026cca7d29b79bf3b93772ccd2db58454 +size 96662756 diff --git a/LDA_Model_trained/README.md b/LDA_Model_trained/README.md new file mode 100644 index 0000000000000000000000000000000000000000..769085a5038bc46c3393be18b89f7643cdc1d4a9 --- /dev/null +++ b/LDA_Model_trained/README.md @@ -0,0 +1 @@ +This folder contains the LDA model trained on the ads. diff --git a/LDA_Model_trained/dutch_preposition b/LDA_Model_trained/dutch_preposition new file mode 100644 index 0000000000000000000000000000000000000000..738f7dd89a92a9ced7ada88fa36f21e667168205 Binary files /dev/null and b/LDA_Model_trained/dutch_preposition differ diff --git a/LDA_Model_trained/lda_model_best_tot.model b/LDA_Model_trained/lda_model_best_tot.model new file mode 100644 index 0000000000000000000000000000000000000000..ab76b003cc675cd01cb0c6946bdcceafd60bb59f --- /dev/null +++ b/LDA_Model_trained/lda_model_best_tot.model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f47f541cfcf649be3679ae4a08e69efd0f963c603a3b4a2ea6422f5c016af123 +size 465837 diff --git a/LDA_Model_trained/lda_model_best_tot.model.expElogbeta.npy b/LDA_Model_trained/lda_model_best_tot.model.expElogbeta.npy new file mode 100644 index 0000000000000000000000000000000000000000..a6bcf84bb561eefa1728de87b2fc996f14136ef2 --- /dev/null +++ b/LDA_Model_trained/lda_model_best_tot.model.expElogbeta.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a028fd23eb3b2f607094c70883ff41de9cbd882834edb28f4945e8ab3f296929 +size 9235328 diff --git a/LDA_Model_trained/lda_model_best_tot.model.id2word b/LDA_Model_trained/lda_model_best_tot.model.id2word new file mode 100644 index 0000000000000000000000000000000000000000..1408ac167e60378314150a8a79810fbeaf777c58 --- /dev/null +++ b/LDA_Model_trained/lda_model_best_tot.model.id2word @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bf9fee0f5b5a2933da6825021f065095c5c3016373b3250ebdcf43b4c719887b +size 1763994 diff --git a/LDA_Model_trained/lda_model_best_tot.model.state b/LDA_Model_trained/lda_model_best_tot.model.state new file mode 100644 index 0000000000000000000000000000000000000000..07a5f9ada0d87483d883ed30f4a902d3c4c07b07 --- /dev/null +++ b/LDA_Model_trained/lda_model_best_tot.model.state @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1964c1ef5806fb1551f92268f1c48cbb042691dcc12380121e988cbcbe60170 +size 9697676 diff --git a/LDA_Model_trained/object_word_dictionary b/LDA_Model_trained/object_word_dictionary new file mode 100644 index 0000000000000000000000000000000000000000..46016904685bcb7ee03d2ebc1d5005af951be41a --- /dev/null +++ b/LDA_Model_trained/object_word_dictionary @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ab1525ee478c86c969996b5ac6b56037c4280ac77ca6e534b53d108f730c350 +size 2571085 diff --git a/Magazine_Optimization_Demo/.DS_Store b/Magazine_Optimization_Demo/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..776ac24e704aadc9c20bd76219b4eab04f6fbbba Binary files /dev/null and b/Magazine_Optimization_Demo/.DS_Store differ diff --git a/Magazine_Optimization_Demo/Magazine_Optimization.py b/Magazine_Optimization_Demo/Magazine_Optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..11c5f2db9c9c2cc69a15a74ebb9640bb885f6b69 --- /dev/null +++ b/Magazine_Optimization_Demo/Magazine_Optimization.py @@ -0,0 +1,347 @@ +import sys +sys.path.append('XGBoost_Prediction_Model/') + +import warnings +warnings.filterwarnings("ignore") + +import cv2 as cv +import numpy as np +from pulp import * +import Predict +import torch +from torchvision.io import read_image + +#Global Paths for Models and Dictionaries +text_detection_model_path = 'XGBoost_Prediction_Model/EAST-Text-Detection/frozen_east_text_detection.pb' +LDA_model_pth = 'XGBoost_Prediction_Model/LDA_Model_trained/lda_model_best_tot.model' +training_ad_text_dictionary_path = 'XGBoost_Prediction_Model/LDA_Model_trained/object_word_dictionary' +training_lang_preposition_path = 'XGBoost_Prediction_Model/LDA_Model_trained/dutch_preposition' + +def Preference_Matrix(Magazine_Pages, Magazine_Slots, Ad_Groups, Ad_Element_Sizes, + Ad_embeddings, Ctpg_embeddings, + Textboxes=None, Obj_and_Topics=None, Costs=None, + Method='XGBoost'): +#Magazine_Pages: A list containing all paths to Magazine Ads and Editorials +#Magazine_Slots: 0 (right), 1 (left), 2 (full-page) + + #Costs Specification + if Costs is None: + Costs = np.ones(len(Magazine_Pages)) + + #Separate Images into Ads and Counterpages + Ads = [] + Counterpages = [] + Assign_ids = [] + Costs_Ctpg = [] + + ad_locations = [] + prod_groups = [] + ad_elements = [] + ad_embeds = [] + ctpg_embeds = [] + + if Textboxes is not None: + ad_textbox = []; ctpg_textbox = [] + + if Obj_and_Topics is not None: + ad_num_obj = []; ctpg_num_obj = [] + ad_topic_weight = []; ctpg_topic_weight = [] + + double_page_ad_attention = [] + double_page_brand_attention = [] + + for i, path in enumerate(Magazine_Pages): + if Magazine_Slots[i] == 2: + if Textboxes is None: + textboxes_curr = None + else: + textboxes_curr = Textboxes[i] + + if Obj_and_Topics is None: + obj_and_topics_curr = None + else: + obj_and_topics_curr = Obj_and_Topics[i] + + if Method == 'XGBoost': + ad_attention = Predict.Ad_Gaze_Prediction(input_ad_path=path, input_ctpg_path=None, ad_location=None, + text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, + training_language='dutch', ad_embeddings=Ad_embeddings[i].reshape(1,768), ctpg_embeddings=Ctpg_embeddings[i].reshape(1,768), + surface_sizes=list(Ad_Element_Sizes[i]), Product_Group=list(Ad_Groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr, + obj_detection_model_pth=None, num_topic=20, Gaze_Time_Type='Ad', Info_printing=False) + + brand_attention = Predict.Ad_Gaze_Prediction(input_ad_path=path, input_ctpg_path=None, ad_location=None, + text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, + training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, + training_language='dutch', ad_embeddings=Ad_embeddings[i].reshape(1,768), ctpg_embeddings=Ctpg_embeddings[i].reshape(1,768), + surface_sizes=list(Ad_Element_Sizes[i]), Product_Group=list(Ad_Groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr, + obj_detection_model_pth=None, num_topic=20, Gaze_Time_Type='Brand', Info_printing=False) + + elif Method == 'CNN': + img_curr = read_image(path)[:,89:921,:].unsqueeze(0) + ad_img_CNN = img_curr[:,:,:,:640] + ctpg_img_CNN = img_curr[:,:,:,640:] + ad_location = torch.tensor([[1,1]]) + ad_attention = Predict.CNN_Prediction(ad_img_CNN, ctpg_img_CNN, ad_location, Gaze_Type='AG').item() + brand_attention = Predict.CNN_Prediction(ad_img_CNN, ctpg_img_CNN, ad_location, Gaze_Type='BG').item() + + double_page_ad_attention.append(ad_attention/Costs[i]) + double_page_brand_attention.append(brand_attention/Costs[i]) + else: + Assign_ids.append(i) + img_curr = cv.imread(path) + img_curr = cv.resize(img_curr, (1280,1024)) + _, w, _ = img_curr.shape + page_width = w // 2 + ad_locations.append(1-Magazine_Slots[i]) + ctpg_location = Magazine_Slots[i] + ad_img = img_curr[:, (Magazine_Slots[i]*page_width):((Magazine_Slots[i]+1)*page_width)] + ctpg_img = img_curr[:, (ctpg_location*page_width):((ctpg_location+1)*page_width)] + Ads.append(ad_img) + Counterpages.append(ctpg_img) + prod_groups.append(Ad_Groups[i]) + ad_elements.append(Ad_Element_Sizes[i]) + Costs_Ctpg.append(Costs[i]) + ad_embeds.append(Ad_embeddings[i]) + ctpg_embeds.append(Ctpg_embeddings[i]) + + if Textboxes is not None: + ad_textbox_curr, ctpg_textbox_curr = Textboxes[i] + ad_textbox.append(ad_textbox_curr); ctpg_textbox.append(ctpg_textbox_curr) + + if Obj_and_Topics is not None: + ad_obj_curr, ctpg_obj_curr, ad_topic_curr, ctpg_topic_curr = Obj_and_Topics[i] + ad_num_obj.append(ad_obj_curr); ctpg_num_obj.append(ctpg_obj_curr) + ad_topic_weight.append(ad_topic_curr); ctpg_topic_weight.append(ctpg_topic_curr) + + Ad_Attention_Preference = np.zeros((len(Ads),len(Counterpages))) + Brand_Attention_Preference = np.zeros((len(Ads),len(Counterpages))) + + for i, ad in enumerate(Ads): + print('Ad '+str(i)+" Assigning...") + if Method == 'CNN': + ad_images_stack = [] + ctpg_images_stack = [] + ad_locations_stack = [] + for j, ctpg in enumerate(Counterpages): + + # if ad_locations[j] == 0: + # new_image = np.concatenate((ad,ctpg),axis=1) + # else: + # new_image = np.concatenate((ctpg,ad),axis=1) + + if Textboxes is not None: + textboxes_curr = [ad_textbox[i],ctpg_textbox[j]] + else: + textboxes_curr = None + + if Obj_and_Topics is not None: + obj_and_topics_curr = [ad_num_obj[i],ctpg_num_obj[j],ad_topic_weight[i],ctpg_topic_weight[j]] + else: + obj_and_topics_curr = None + + if Method == 'XGBoost': + ad_attention = Predict.Ad_Gaze_Prediction(input_ad_path=ad, input_ctpg_path=ctpg, + text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, + training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, + training_language='dutch', ad_embeddings=ad_embeds[i].reshape(1,768), ctpg_embeddings=ctpg_embeds[i].reshape(1,768), + surface_sizes=list(ad_elements[i]), Product_Group=list(prod_groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr, + obj_detection_model_pth=None, ad_location=ad_locations[j], num_topic=20, Gaze_Time_Type='Ad', Info_printing=False) + brand_attention = Predict.Ad_Gaze_Prediction(input_ad_path=ad, input_ctpg_path=ctpg, + text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, + training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, + training_language='dutch', ad_embeddings=ad_embeds[i].reshape(1,768), ctpg_embeddings=ctpg_embeds[i].reshape(1,768), + surface_sizes=list(ad_elements[i]), Product_Group=list(prod_groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr, + obj_detection_model_pth=None, ad_location=ad_locations[j], num_topic=20, Gaze_Time_Type='Brand', Info_printing=False) + + Ad_Attention_Preference[i,j] = ad_attention/Costs_Ctpg[j] + Brand_Attention_Preference[i,j] = brand_attention/Costs_Ctpg[j] + + elif Method == 'CNN': + ad_img_CNN = torch.tensor(ad).permute(2,0,1).unsqueeze(0)[:,:,89:921,:] + ad_images_stack.append(ad_img_CNN) + ctpg_img_CNN = torch.tensor(ctpg).permute(2,0,1).unsqueeze(0)[:,:,89:921,:] + ctpg_images_stack.append(ctpg_img_CNN) + ad_locations_stack.append(torch.tensor([[1,0]])) + # ad_attention = Predict.CNN_Prediction(ad_img_CNN, ctpg_img_CNN, ad_location, Gaze_Type='AG').item() + # brand_attention = Predict.CNN_Prediction(ad_img_CNN, ctpg_img_CNN, ad_location, Gaze_Type='BG').item() + + if Method == 'CNN': + ad_images_stack = torch.cat(ad_images_stack,dim=0) + ctpg_images_stack = torch.cat(ctpg_images_stack,dim=0) + ad_locations_stack = torch.cat(ad_locations_stack,dim=0) + ad_attentions = Predict.CNN_Prediction(ad_images_stack, ctpg_images_stack, ad_locations_stack, Gaze_Type='AG').to('cpu').squeeze() + brand_attentions = Predict.CNN_Prediction(ad_images_stack, ctpg_images_stack, ad_locations_stack, Gaze_Type='BG').to('cpu').squeeze() + Ad_Attention_Preference[i] = ad_attentions.numpy()/np.array(Costs_Ctpg) + Brand_Attention_Preference[i] = brand_attentions.numpy()/np.array(Costs_Ctpg) + + + return Ad_Attention_Preference, Brand_Attention_Preference, double_page_ad_attention, double_page_brand_attention, Assign_ids + +def Preference_Matrix_different_magazine(Magzine_Target, Magzine_Ad, + Magazine_Slots_Target, Magazine_Slots_Ad, + Ad_Groups, Ad_Element_Sizes, + Textboxes_Target=None, Textboxes_Ad=None, + Obj_and_Topics_Target=None, Obj_and_Topics_Ad=None, + Costs=None): + #Separate Images into Ads and Counterpage + Ads = [] + Counterpages = [] + Assign_ids_target = [] + Assign_ids_ad = [] + + ad_locations = [] + prod_groups = [] + ad_elements = [] + + if Textboxes_Target is not None: + ad_textbox = []; ctpg_textbox = [] + + if Obj_and_Topics_Target is not None: + ad_num_obj = []; ctpg_num_obj = [] + ad_topic_weight = []; ctpg_topic_weight = [] + + double_page_ad_attention = [] + double_page_brand_attention = [] + + #Target magazine (Counterpage) + for i, path in enumerate(Magzine_Target): + if Magazine_Slots_Target[i] == 2: + continue + else: + Assign_ids_target.append(i) + img_curr = cv.imread(path) + img_curr = cv.resize(img_curr, (1280,1024)) + _, w, _ = img_curr.shape + page_width = w // 2 + ad_locations.append(Magazine_Slots_Target[i]) + ctpg_location = 1-Magazine_Slots_Target[i] + ctpg_img = img_curr[:, (ctpg_location*page_width):((ctpg_location+1)*page_width)] + Counterpages.append(ctpg_img) + + if Textboxes_Target is not None: + _, ctpg_textbox_curr = Textboxes_Target[i] + ctpg_textbox.append(ctpg_textbox_curr) + + if Obj_and_Topics_Target is not None: + _, ctpg_obj_curr, _, ctpg_topic_curr = Obj_and_Topics_Target[i] + ctpg_num_obj.append(ctpg_obj_curr) + ctpg_topic_weight.append(ctpg_topic_curr) + + #Ad magazine (Ads) + for i, path in enumerate(Magzine_Ad): + if Magazine_Slots_Ad[i] == 2: + if Textboxes_Ad is None: + textboxes_curr = None + else: + textboxes_curr = Textboxes_Ad[i] + + if Obj_and_Topics_Ad is None: + obj_and_topics_curr = None + else: + obj_and_topics_curr = Obj_and_Topics_Ad[i] + + ad_attention = Predict.Ad_Gaze_Prediction(input_ad_path=path, input_ctpg_path=None, text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, + training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch', + surface_sizes=list(Ad_Element_Sizes[i]), Product_Group=list(Ad_Groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr, + obj_detection_model_pth=None, ad_location=None, num_topic=20, Gaze_Time_Type='Ad', Info_printing=False) + + brand_attention = Predict.Ad_Gaze_Prediction(input_ad_path=path, input_ctpg_path=None, text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, + training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch', + surface_sizes=list(Ad_Element_Sizes[i]), Product_Group=list(Ad_Groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr, + obj_detection_model_pth=None, ad_location=None, num_topic=20, Gaze_Time_Type='Brand', Info_printing=False) + double_page_ad_attention.append(ad_attention) + double_page_brand_attention.append(brand_attention) + else: + Assign_ids_ad.append(i) + img_curr = cv.imread(path) + img_curr = cv.resize(img_curr, (1280,1024)) + _, w, _ = img_curr.shape + page_width = w // 2 + ad_img = img_curr[:, (Magazine_Slots_Ad[i]*page_width):((Magazine_Slots_Ad[i]+1)*page_width)] + Ads.append(ad_img) + prod_groups.append(Ad_Groups[i]) + ad_elements.append(Ad_Element_Sizes[i]) + + if Textboxes_Ad is not None: + ad_textbox_curr, _ = Textboxes_Ad[i] + ad_textbox.append(ad_textbox_curr) + + if Obj_and_Topics_Ad is not None: + ad_obj_curr, _, ad_topic_curr, _ = Obj_and_Topics_Ad[i] + ad_num_obj.append(ad_obj_curr) + ad_topic_weight.append(ad_topic_curr) + + #Check costs on Ad position + if Costs is None: + Costs = np.ones(len(Counterpages)) + + #Matrix + if len(Ads) > len(Counterpages): + return None + else: + Ad_Attention_Preference = np.zeros((len(Ads),len(Counterpages))) + Brand_Attention_Preference = np.zeros((len(Ads),len(Counterpages))) + for i, ad in enumerate(Ads): + print('Ad '+str(i)+" Assigning...") + for j, ctpg in enumerate(Counterpages): + + # if ad_locations[j] == 0: + # new_image = np.concatenate((ad,ctpg),axis=1) + # else: + # new_image = np.concatenate((ctpg,ad),axis=1) + + if Textboxes_Target is not None: + textboxes_curr = [ad_textbox[i],ctpg_textbox[j]] + else: + textboxes_curr = None + + if Obj_and_Topics_Target is not None: + obj_and_topics_curr = [ad_num_obj[i],ctpg_num_obj[j],ad_topic_weight[i],ctpg_topic_weight[j]] + else: + obj_and_topics_curr = None + + ad_attention = Predict.Ad_Gaze_Prediction(input_ad_path=ad, input_ctpg_path=ctpg, text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, + training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch', + surface_sizes=list(ad_elements[i]), Product_Group=list(prod_groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr, + obj_detection_model_pth=None, ad_location=ad_locations[j], num_topic=20, Gaze_Time_Type='Ad', Info_printing=False) + brand_attention = Predict.Ad_Gaze_Prediction(input_ad_path=ad, input_ctpg_path=ctpg, text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, + training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch', + surface_sizes=list(ad_elements[i]), Product_Group=list(prod_groups[i]), TextBoxes=textboxes_curr, Obj_and_Topics=obj_and_topics_curr, + obj_detection_model_pth=None, ad_location=ad_locations[j], num_topic=20, Gaze_Time_Type='Brand', Info_printing=False) + Ad_Attention_Preference[i,j] = ad_attention/Costs[j] + Brand_Attention_Preference[i,j] = brand_attention/Costs[j] + return Ad_Attention_Preference, Brand_Attention_Preference, double_page_ad_attention, double_page_brand_attention, Assign_ids_ad, Assign_ids_target + +def Assignment_Problem(costs, workers, jobs): + #https://machinelearninggeek.com/solving-assignment-problem-using-linear-programming-in-python/ + + prob = LpProblem("Assignment Problem", LpMinimize) + + # The cost data is made into a dictionary + costs= makeDict([workers, jobs], costs, 0) + + # Creates a list of tuples containing all the possible assignments + assign = [(w, j) for w in workers for j in jobs] + + # A dictionary called 'Vars' is created to contain the referenced variables + vars = LpVariable.dicts("Assign", (workers, jobs), 0, None, LpBinary) + + # The objective function is added to 'prob' first + prob += ( + lpSum([vars[w][j] * costs[w][j] for (w, j) in assign]), + "Sum_of_Assignment_Costs", + ) + + # There are row constraints. Each job can be assigned to only one employee. + for j in jobs: + prob+= lpSum(vars[w][j] for w in workers) == 1 + + # There are column constraints. Each employee can be assigned to only one job. + for w in workers: + prob+= lpSum(vars[w][j] for j in jobs) == 1 + + # The problem is solved using PuLP's choice of Solver + prob.solve() + + return prob + diff --git a/Magazine_Optimization_Demo/Magazines/.DS_Store b/Magazine_Optimization_Demo/Magazines/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..d6dbe55a804fb3e2438a3defa18cc3f304e5fb01 Binary files /dev/null and b/Magazine_Optimization_Demo/Magazines/.DS_Store differ diff --git a/Magazine_Optimization_Demo/Magazines/M1/.DS_Store b/Magazine_Optimization_Demo/Magazines/M1/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..618da74c65217b8c8d1ebeb51081eb312bdedf68 Binary files /dev/null and b/Magazine_Optimization_Demo/Magazines/M1/.DS_Store differ diff --git a/Magazine_Optimization_Demo/Magazines/M1/Jpg_Cosmopolitan/002.jpg b/Magazine_Optimization_Demo/Magazines/M1/Jpg_Cosmopolitan/002.jpg new file mode 100644 index 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mode 100644 index 0000000000000000000000000000000000000000..5a6455250db890e830daa075d817f30e1b6d6529 Binary files /dev/null and b/Magazine_Optimization_Demo/Optimal_Magazines_XGBoost/M5/Newly_Assigned_Page6.jpg differ diff --git a/Magazine_Optimization_Demo/README.md b/Magazine_Optimization_Demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e2a9e4617c4fb11b143c56e098525386bb0d2d81 --- /dev/null +++ b/Magazine_Optimization_Demo/README.md @@ -0,0 +1,70 @@ +Summary: +Magazine M1: +Total Time used: 146.47204303741455 +Ad Gaze: +Strategy: Assign Ad 11 to Counterpage 4; Assign Ad 12 to Counterpage 3; Assign Ad 13 to Counterpage 5; Assign Ad 14 to Counterpage 8; Assign Ad 15 to Counterpage 13; Assign Ad 16 to Counterpage 6; Assign Ad 1 to Counterpage 7; Assign Ad 2 to Counterpage 12; Assign Ad 3 to Counterpage 11; Assign Ad 4 to Counterpage 1; Assign Ad 5 to Counterpage 16; Assign Ad 6 to Counterpage 2; Assign Ad 7 to Counterpage 10; Assign Ad 8 to Counterpage 14; Assign Ad 10 to Counterpage 15; +max Attention: 36.02685832977295 +original Attention: 28.521670818328857 +------------------------ +Brand Gaze: +Strategy: Assign Ad 11 to Counterpage 12; Assign Ad 12 to Counterpage 2; Assign Ad 13 to Counterpage 5; Assign Ad 14 to Counterpage 1; Assign Ad 15 to Counterpage 16; Assign Ad 16 to Counterpage 11; Assign Ad 1 to Counterpage 7; Assign Ad 2 to Counterpage 10; Assign Ad 3 to Counterpage 4; Assign Ad 4 to Counterpage 15; Assign Ad 5 to Counterpage 14; Assign Ad 6 to Counterpage 3; Assign Ad 7 to Counterpage 6; Assign Ad 8 to Counterpage 13; Assign Ad 10 to Counterpage 8; +max Attention: 8.779523849487305 +original Attention: 6.383262395858765 +Attention under AG Assignment: 8.407692551612854 +------------------------ + +Magazine M5: +Total Time used: 25.30950903892517 +Ad Gaze: +Strategy: Assign Ad 0 to Counterpage 6; Assign Ad 1 to Counterpage 4; Assign Ad 2 to Counterpage 2; Assign Ad 4 to Counterpage 5; Assign Ad 5 to Counterpage 0; Assign Ad 6 to Counterpage 1; +max Attention: 20.937814712524414 +original Attention: 12.0139479637146 +------------------------ +Brand Gaze: +Strategy: Assign Ad 0 to Counterpage 4; Assign Ad 1 to Counterpage 1; Assign Ad 2 to Counterpage 2; Assign Ad 4 to Counterpage 6; Assign Ad 5 to Counterpage 5; Assign Ad 6 to Counterpage 0; +max Attention: 3.95954430103302 +original Attention: 2.687319040298462 +Attention under AG Assignment: 3.8800885677337646 +------------------------ + +Magazine M2: +Total Time used: 54.91435790061951 +Ad Gaze: +Strategy: Assign Ad 0 to Counterpage 7; Assign Ad 1 to Counterpage 0; Assign Ad 3 to Counterpage 3; Assign Ad 4 to Counterpage 8; Assign Ad 5 to Counterpage 5; Assign Ad 6 to Counterpage 1; Assign Ad 7 to Counterpage 6; Assign Ad 8 to Counterpage 9; Assign Ad 9 to Counterpage 4; +max Attention: 26.092838048934937 +original Attention: 21.70353937149048 +------------------------ +Brand Gaze: +Strategy: Assign Ad 0 to Counterpage 4; Assign Ad 1 to Counterpage 9; Assign Ad 3 to Counterpage 0; Assign Ad 4 to Counterpage 8; Assign Ad 5 to Counterpage 3; Assign Ad 6 to Counterpage 6; Assign Ad 7 to Counterpage 5; Assign Ad 8 to Counterpage 1; Assign Ad 9 to Counterpage 7; +max Attention: 5.299419045448303 +original Attention: 4.00095796585083 +Attention under AG Assignment: 5.10155713558197 +------------------------ + +Magazine M3: +Total Time used: 262.1063117980957 +Ad Gaze: +Strategy: Assign Ad 12 to Counterpage 20; Assign Ad 13 to Counterpage 14; Assign Ad 14 to Counterpage 17; Assign Ad 16 to Counterpage 6; Assign Ad 17 to Counterpage 24; Assign Ad 18 to Counterpage 9; Assign Ad 19 to Counterpage 11; Assign Ad 20 to Counterpage 13; Assign Ad 21 to Counterpage 18; Assign Ad 22 to Counterpage 22; Assign Ad 1 to Counterpage 4; Assign Ad 24 to Counterpage 16; Assign Ad 2 to Counterpage 7; Assign Ad 3 to Counterpage 2; Assign Ad 4 to Counterpage 12; Assign Ad 6 to Counterpage 8; Assign Ad 7 to Counterpage 3; Assign Ad 8 to Counterpage 1; Assign Ad 9 to Counterpage 21; Assign Ad 11 to Counterpage 19; +max Attention: 67.31096935272217 +original Attention: 45.341137170791626 +------------------------ +Brand Gaze: +Strategy: Assign Ad 12 to Counterpage 19; Assign Ad 13 to Counterpage 8; Assign Ad 14 to Counterpage 17; Assign Ad 16 to Counterpage 2; Assign Ad 17 to Counterpage 3; Assign Ad 18 to Counterpage 21; Assign Ad 19 to Counterpage 18; Assign Ad 20 to Counterpage 9; Assign Ad 21 to Counterpage 16; Assign Ad 22 to Counterpage 11; Assign Ad 1 to Counterpage 12; Assign Ad 24 to Counterpage 20; Assign Ad 2 to Counterpage 13; Assign Ad 3 to Counterpage 6; Assign Ad 4 to Counterpage 4; Assign Ad 6 to Counterpage 24; Assign Ad 7 to Counterpage 1; Assign Ad 8 to Counterpage 7; Assign Ad 9 to Counterpage 14; Assign Ad 11 to Counterpage 22; +max Attention: 13.022720456123352 +original Attention: 9.656069993972778 +Attention under AG Assignment: 12.365073680877686 +------------------------ + +Magazine M4: +Total Time used: 18.036799907684326 +Ad Gaze: +Strategy: Assign Ad 1 to Counterpage 2; Assign Ad 2 to Counterpage 4; Assign Ad 3 to Counterpage 1; Assign Ad 4 to Counterpage 3; Assign Ad 5 to Counterpage 5; +max Attention: 14.580140829086304 +original Attention: 10.33321762084961 +------------------------ +Brand Gaze: +Strategy: Assign Ad 1 to Counterpage 2; Assign Ad 2 to Counterpage 4; Assign Ad 3 to Counterpage 5; Assign Ad 4 to Counterpage 3; Assign Ad 5 to Counterpage 1; +max Attention: 3.2732757329940796 +original Attention: 1.7348273992538452 +Attention under AG Assignment: 3.2471154928207397 +------------------------ \ No newline at end of file diff --git a/Magazine_Optimization_Demo/__pycache__/Magazine_Optimization.cpython-310.pyc b/Magazine_Optimization_Demo/__pycache__/Magazine_Optimization.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..69012e6263bbc433725d7d02587d4fce535aafcb Binary files /dev/null and b/Magazine_Optimization_Demo/__pycache__/Magazine_Optimization.cpython-310.pyc differ diff --git a/Magazine_Optimization_Demo/main.py b/Magazine_Optimization_Demo/main.py new file mode 100644 index 0000000000000000000000000000000000000000..5e79a49d9f54ba8da911d80b00c9a2d95bfb53da --- /dev/null +++ b/Magazine_Optimization_Demo/main.py @@ -0,0 +1,127 @@ +import sys +sys.path.append('../XGBoost_Prediction_Model/') + +import warnings +warnings.filterwarnings("ignore") +import Predict +import torch +import numpy as np +import os +from os.path import isfile, isdir, join +from Magazine_Optimization import * +import time + +mypath = '../XGBoost_Prediction_Model/Magazine_Optimization_Demo/Magazines' +results = {} + +for f in os.listdir(mypath): + if isdir(join(mypath, f)): + print('Currently processing Magazine '+f+'......') + path_temp = join(mypath, f) + dir_list = [] + for sub_f in os.listdir(path_temp): + if isdir(join(path_temp, sub_f)): + sub_path_temp = join(path_temp, sub_f) + if (sub_f.split('_')[0]) == 'Jpg': + dir_list = os.listdir(sub_path_temp) + dir_list.sort() + for i in range(len(dir_list)): + dir_list[i] = join(sub_path_temp,dir_list[i]) + else: + Slots = torch.load(join(sub_path_temp,'Slots')).astype('int32') + Sizes = torch.load(join(sub_path_temp,'surfaces')) + Product_Groups = torch.load(join(sub_path_temp,'Prod_Cat')) + Textboxes = torch.load(join(sub_path_temp,'Textboxes')) + Obj_and_Topics = torch.load(join(sub_path_temp,'Obj_and_Topics')) + Ad_embeddings = torch.load(join(sub_path_temp,'Ad_Emb')) + Ctpg_embeddings = torch.load(join(sub_path_temp,'Ctpg_Emb')) + + start = time.time() + Costs = np.ones(len(dir_list)) #np.arange(len(dir_list),0,-1) + Ad_Gaze, Brand_Gaze, Double_Page_Ad_Attention, Double_Page_Brand_Attention, Assign_ids = Preference_Matrix(dir_list, Slots, Product_Groups, Sizes, + Ad_embeddings=Ad_embeddings, Ctpg_embeddings=Ctpg_embeddings, + Textboxes=Textboxes, Obj_and_Topics=Obj_and_Topics, + Costs=Costs, Method='XGBoost') + + #Assignement Problem + workers = [] + jobs = [] + N = (Ad_Gaze.shape)[0] + for i in range(N): + workers.append(i+1) + jobs.append(i+1) + + max_ad_attention = np.max(Ad_Gaze) + max_brand_attention = np.max(Brand_Gaze) + Ad_Gaze_cost = max_ad_attention - Ad_Gaze + Brand_Gaze_cost = max_brand_attention - Brand_Gaze + + Prob_solved_Ad = Assignment_Problem(Ad_Gaze_cost, workers, jobs) + Prob_solved_Brand = Assignment_Problem(Brand_Gaze_cost, workers, jobs) + end = time.time() + assigning_time = end-start + + # Print the variables optimized value + print('If based on maximizing Overall Ad Attention: ') + strategy_AG = '' + BG_under_AG_assignment = 0 + for v in Prob_solved_Ad.variables(): + if v.varValue == 1: + curr = (v.name).split('_') + temp = curr[0]+' Ad '+str(Assign_ids[int(curr[1])-1])+' to Counterpage '+str(Assign_ids[int(curr[2])-1]) + BG_under_AG_assignment += Brand_Gaze_cost[int(curr[1])-1,int(curr[2])-1] + strategy_AG += temp+'; ' + print(temp) + + # The optimised objective function value is printed to the screen + m_ad = N*max_ad_attention - value(Prob_solved_Ad.objective) + sum(Double_Page_Ad_Attention) + print("Maximized Ad Attention = ", m_ad, " sec.") + print("Maximized Average Ad attention on each Ad = ", (N*max_ad_attention - value(Prob_solved_Ad.objective) + sum(Double_Page_Ad_Attention))/(N + len(Double_Page_Ad_Attention)), " sec.") + print("Original Ad Attention = ", np.trace(Ad_Gaze), " sec.") + print() + + # Print the variables optimized value + print('If based on maximizing Overall Brand Attention: ') + strategy_BG = '' + for v in Prob_solved_Brand.variables(): + if v.varValue == 1: + curr = (v.name).split('_') + temp = curr[0]+' Ad '+str(Assign_ids[int(curr[1])-1])+' to Counterpage '+str(Assign_ids[int(curr[2])-1]) + strategy_BG += temp+'; ' + print(temp) + + # The optimised objective function value is printed to the screen + m_brand = N*max_brand_attention - value(Prob_solved_Brand.objective) + sum(Double_Page_Brand_Attention) + BG_under_AG_assignment = N*max_brand_attention - BG_under_AG_assignment + sum(Double_Page_Brand_Attention) + print("Maximized Brand Attention = ", m_brand, " sec.") + print("New Brand Gaze under AG assignment = ", BG_under_AG_assignment, " sec.") + print("Maximized Average Brand attention on each Ad = ", (N*max_brand_attention - value(Prob_solved_Brand.objective) + sum(Double_Page_Brand_Attention))/(N + len(Double_Page_Brand_Attention)), " sec.") + print("Original Brand Attention = ", np.trace(Brand_Gaze), " sec.") + print('End of Magazine '+f+'......') + + results[f] = {'AG':[strategy_AG,m_ad,np.trace(Ad_Gaze)], 'BG':[strategy_BG,m_brand,np.trace(Brand_Gaze),BG_under_AG_assignment], 'Time':assigning_time} + print() + print() + +print() +torch.save(results, '../XGBoost_Prediction_Model/Magazine_Optimization_Demo/results') +# torch.save(results, '../XGBoost_Prediction_Model/Magazine_Optimization_Demo/results_CNN') +print('Summary: ') +for f in list(results.keys()): + print('Magazine '+f+': ') + dict_curr = results[f] + print('Total Time used: ',dict_curr['Time']) + print('Ad Gaze: ') + print('Strategy: '+dict_curr['AG'][0]) + print('max Attention: ',dict_curr['AG'][1]) + print('original Attention: ',dict_curr['AG'][2]) + print('Improvement: ', (dict_curr['AG'][1]-dict_curr['AG'][2])/dict_curr['AG'][2]*100) + print('------------------------') + print('Brand Gaze: ') + print('Strategy: '+dict_curr['BG'][0]) + print('max Attention: ',dict_curr['BG'][1]) + print('original Attention: ',dict_curr['BG'][2]) + print('Attention under AG Assignment: ',dict_curr['BG'][3]) + print('Improvement: ', (dict_curr['BG'][3]-dict_curr['BG'][2])/dict_curr['BG'][2]*100) + print('------------------------') + print() \ No newline at end of file diff --git a/Magazine_Optimization_Demo/main_decipher_result.py b/Magazine_Optimization_Demo/main_decipher_result.py new file mode 100644 index 0000000000000000000000000000000000000000..31bc1ffa933ae946d8979af23c3dd0b1fd5a7448 --- /dev/null +++ b/Magazine_Optimization_Demo/main_decipher_result.py @@ -0,0 +1,48 @@ +import sys +sys.path.append('../XGBoost_Prediction_Model/') + +import warnings +warnings.filterwarnings("ignore") +import Predict +import torch +import numpy as np +import os +from os.path import isfile, isdir, join +from Magazine_Optimization import * +import cv2 as cv +import time + +mypath = '../XGBoost_Prediction_Model/Magazine_Optimization_Demo/Magazines' +# results = torch.load('../XGBoost_Prediction_Model/Magazine_Optimization_Demo/results') +results = torch.load('../XGBoost_Prediction_Model/Magazine_Optimization_Demo/results_CNN') + +for f in os.listdir(mypath): + if isdir(join(mypath, f)): + print('Currently processing Magazine '+f+'......') + path_temp = join(mypath, f) + dir_list = [] + for sub_f in os.listdir(path_temp): + if isdir(join(path_temp, sub_f)): + sub_path_temp = join(path_temp, sub_f) + if (sub_f.split('_')[0]) == 'Jpg': + dir_list = os.listdir(sub_path_temp) + dir_list.sort() + for i in range(len(dir_list)): + dir_list[i] = join(sub_path_temp,dir_list[i]) + else: + Slots = torch.load(join(sub_path_temp,'Slots')).astype('int32') + strategies = results[f]['AG'][0].split('; ')[:-1] + newpath = '../XGBoost_Prediction_Model/Magazine_Optimization_Demo/Optimal_Magazines_CNN/'+f + if not os.path.exists(newpath): + os.makedirs(newpath) + for item in strategies: + ad_id = int(item.split(' ')[2]) + ctpg_id = int(item.split(' ')[5]) + ad_loc = 1-Slots[ad_id] + ctpg_loc = Slots[ctpg_id] + ad = cv.imread(dir_list[ad_id])[:,(ad_loc*640):((ad_loc+1)*640),:] + ctpg = cv.imread(dir_list[ctpg_id])[:,(ctpg_loc*640):((ctpg_loc+1)*640),:] + img = np.concatenate((ad,ctpg),axis=1) + cv.imwrite(newpath+'/Newly_Assigned_Page'+str(ad_id)+'.jpg', img) + + diff --git a/Magazine_Optimization_Demo/main_different_mag.py b/Magazine_Optimization_Demo/main_different_mag.py new file mode 100644 index 0000000000000000000000000000000000000000..e014f7b943eada33d48686435a7991709a8dacaf --- /dev/null +++ b/Magazine_Optimization_Demo/main_different_mag.py @@ -0,0 +1,145 @@ +import sys +sys.path.append('XGBoost_Prediction_Model/') + +import warnings +warnings.filterwarnings("ignore") +import Predict +import torch +import numpy as np +import os +from os.path import isfile, isdir, join +from Magazine_Optimization import * + +mypath = 'XGBoost_Prediction_Model/Magazine_Optimization_Demo/Magazines' +results = {} + +#Target Magazine folders +Target = [join(mypath, 'M1'), join(mypath, 'M3')] + +for Tg in Target: + dir_list_target = [] + for sub_f in os.listdir(Tg): + if isdir(join(Tg, sub_f)): + sub_path_temp = join(Tg, sub_f) + if (sub_f.split('_')[0]) == 'Jpg': + dir_list_target = os.listdir(sub_path_temp) + dir_list_target.sort() + for i in range(len(dir_list_target)): + dir_list_target[i] = join(sub_path_temp,dir_list_target[i]) + else: + Slots_target = torch.load(join(sub_path_temp,'Slots')).astype('int32') + # Sizes_target = torch.load(join(sub_path_temp,'surfaces')) + # Product_Groups_target = torch.load(join(sub_path_temp,'Prod_Cat')) + Textboxes_target = torch.load(join(sub_path_temp,'Textboxes')) + Obj_and_Topics_target = torch.load(join(sub_path_temp,'Obj_and_Topics')) + + for f in os.listdir(mypath): + if isdir(join(mypath, f)) and f != 'M1' and f != 'M3': + print('Currently processing Target Magazine '+Tg+' with Ad Magazine '+f+'......') + path_temp_ad = join(mypath, f) + dir_list_ad = [] + for sub_f in os.listdir(path_temp_ad): + if isdir(join(path_temp_ad, sub_f)): + sub_path_temp = join(path_temp_ad, sub_f) + if (sub_f.split('_')[0]) == 'Jpg': + dir_list_ad = os.listdir(sub_path_temp) + dir_list_ad.sort() + for i in range(len(dir_list_ad)): + dir_list_ad[i] = join(sub_path_temp,dir_list_ad[i]) + else: + Slots_ad = torch.load(join(sub_path_temp,'Slots')).astype('int32') + Sizes_ad = torch.load(join(sub_path_temp,'surfaces')) + Product_Groups_ad = torch.load(join(sub_path_temp,'Prod_Cat')) + Textboxes_ad = torch.load(join(sub_path_temp,'Textboxes')) + Obj_and_Topics_ad = torch.load(join(sub_path_temp,'Obj_and_Topics')) + + result = Preference_Matrix_different_magazine(dir_list_target, dir_list_ad, + Slots_target, Slots_ad, + Product_Groups_ad, Sizes_ad, + Textboxes_Target=Textboxes_target, Textboxes_Ad=Textboxes_ad, + Obj_and_Topics_Target=Obj_and_Topics_target, Obj_and_Topics_Ad=Obj_and_Topics_ad) + if result is not None: + Ad_Gaze, Brand_Gaze, Double_Page_Ad_Attention, Double_Page_Brand_Attention, Assign_ids_ad, Assign_ids_target = result + + #Assignement Problem + workers = [] + jobs = [] + N = np.max(Ad_Gaze.shape) + M_small = np.min(Ad_Gaze.shape) + for i in range(N): + workers.append(i+1) + jobs.append(i+1) + zeros_aux = np.zeros((N,N)) + zeros_aux[:Ad_Gaze.shape[0],:] = Ad_Gaze + Ad_Gaze = zeros_aux + zeros_aux = np.zeros((N,N)) + zeros_aux[:Brand_Gaze.shape[0],:] = Brand_Gaze + Brand_Gaze = zeros_aux + + max_ad_attention = np.max(Ad_Gaze) + max_brand_attention = np.max(Brand_Gaze) + Ad_Gaze_cost = max_ad_attention - Ad_Gaze + Brand_Gaze_cost = max_brand_attention - Brand_Gaze + + Prob_solved_Ad = Assignment_Problem(Ad_Gaze_cost, workers, jobs) + Prob_solved_Brand = Assignment_Problem(Brand_Gaze_cost, workers, jobs) + + # Print the variables optimized value + print('If based on maximizing Overall Ad Attention: ') + strategy_AG = '' + BG_under_AG_assignment = 0 + for v in Prob_solved_Ad.variables(): + if v.varValue == 1: + curr = (v.name).split('_') + BG_under_AG_assignment += Brand_Gaze_cost[int(curr[1])-1,int(curr[2])-1] + if int(curr[1]) <= M_small: + temp = curr[0]+' Ad '+str(Assign_ids_ad[int(curr[1])-1])+' to Counterpage '+str(Assign_ids_target[int(curr[2])-1]) + strategy_AG += temp+'; ' + print(temp) + + # The optimised objective function value is printed to the screen + m_ad = N*max_ad_attention - value(Prob_solved_Ad.objective) + sum(Double_Page_Ad_Attention) + print("Maximized Ad Attention = ", m_ad, " sec.") + print("Maximized Average Ad attention on each Ad = ", (N*max_ad_attention - value(Prob_solved_Ad.objective) + sum(Double_Page_Ad_Attention))/(N + len(Double_Page_Ad_Attention)), " sec.") + print() + + # Print the variables optimized value + print('If based on maximizing Overall Brand Attention: ') + strategy_BG = '' + for v in Prob_solved_Brand.variables(): + if v.varValue == 1: + curr = (v.name).split('_') + if int(curr[1]) <= M_small: + temp = curr[0]+' Ad '+str(Assign_ids_ad[int(curr[1])-1])+' to Counterpage '+str(Assign_ids_target[int(curr[2])-1]) + strategy_BG += temp+'; ' + print(temp) + + # The optimised objective function value is printed to the screen + m_brand = N*max_brand_attention - value(Prob_solved_Brand.objective) + sum(Double_Page_Brand_Attention) + BG_under_AG_assignment = N*max_brand_attention - BG_under_AG_assignment + sum(Double_Page_Brand_Attention) + print("Maximized Brand Attention = ", m_brand, " sec.") + print("New Brand Gaze under AG assignment = ", BG_under_AG_assignment, " sec.") + print("Maximized Average Brand attention on each Ad = ", (N*max_brand_attention - value(Prob_solved_Brand.objective) + sum(Double_Page_Brand_Attention))/(N + len(Double_Page_Brand_Attention)), " sec.") + print('End of Magazine '+f+'......') + + results[Tg+' '+f] = {'AG':[strategy_AG,m_ad,np.trace(Ad_Gaze)], 'BG':[strategy_BG,m_brand,np.trace(Brand_Gaze),BG_under_AG_assignment]} + print() + print() + else: + print("Ads cannot be fully assigned!") + + +print() +print('Summary: ') +for f in list(results.keys()): + print('Magazine '+f+': ') + dict_curr = results[f] + print('Ad Gaze: ') + print('Strategy: '+dict_curr['AG'][0]) + print('max Attention: ',dict_curr['AG'][1]) + print('------------------------') + print('Brand Gaze: ') + print('Strategy: '+dict_curr['BG'][0]) + print('max Attention: ',dict_curr['BG'][1]) + print('------------------------') + print() \ No newline at end of file diff --git a/Magazine_Optimization_Demo/results b/Magazine_Optimization_Demo/results new file mode 100644 index 0000000000000000000000000000000000000000..079c087710e4ca7af75c6aa7422481bf2b74c24e Binary files /dev/null and b/Magazine_Optimization_Demo/results differ diff --git a/Magazine_Optimization_Demo/results_CNN b/Magazine_Optimization_Demo/results_CNN new file mode 100644 index 0000000000000000000000000000000000000000..7c7bec5cf81579a9c13a489e23e74fb8d0288687 Binary files /dev/null and b/Magazine_Optimization_Demo/results_CNN differ diff --git a/Magazine_Topic_Embedding_sample_size15/.DS_Store b/Magazine_Topic_Embedding_sample_size15/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..093491f487fad10174df61eaacb668eafa55dd09 Binary files /dev/null and b/Magazine_Topic_Embedding_sample_size15/.DS_Store differ diff --git a/Magazine_Topic_Embedding_sample_size15/1_Pooling/config.json b/Magazine_Topic_Embedding_sample_size15/1_Pooling/config.json new file mode 100644 index 0000000000000000000000000000000000000000..9213783a33aa495a3c2d3791c7c5888d90607a4e --- /dev/null +++ b/Magazine_Topic_Embedding_sample_size15/1_Pooling/config.json @@ -0,0 +1,10 @@ +{ + "word_embedding_dimension": 768, + "pooling_mode_cls_token": false, + "pooling_mode_mean_tokens": true, + "pooling_mode_max_tokens": false, + "pooling_mode_mean_sqrt_len_tokens": false, + "pooling_mode_weightedmean_tokens": false, + "pooling_mode_lasttoken": false, + "include_prompt": true +} \ No newline at end of file diff --git a/Magazine_Topic_Embedding_sample_size15/README.md b/Magazine_Topic_Embedding_sample_size15/README.md new file mode 100644 index 0000000000000000000000000000000000000000..07036df93f60b2245e87b3f065a09fc95c178f85 --- /dev/null +++ b/Magazine_Topic_Embedding_sample_size15/README.md @@ -0,0 +1,562 @@ +--- +base_model: distilbert/distilroberta-base +datasets: [] +language: [] +library_name: sentence-transformers +metrics: +- cosine_accuracy +- dot_accuracy +- manhattan_accuracy +- euclidean_accuracy +- max_accuracy +pipeline_tag: sentence-similarity +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:22878 +- loss:TripletLoss +widget: +- source_sentence: 'The image features an advertisement for Euro-Actie Van Sola, a + Dutch company offering various kitchenware items. The poster showcases several + images of different utensils and dishes in both English (Euro Acti) and French + text alongside the logo "SOLA" at the top right corner. Promotional offers are + also highlighted on this page such as discounts like 25% off with promo code MAXIMALJAARSVAARDIGHEID + or free delivery service when purchasing from their catalogues through Leenkooken + En Nettoelenvoorgangtakken Online Shoppe by using specific codes: €10498637 EFT/NETTOEENWOEGANGTAKKEN + OR €€...' + sentences: + - 'The image features a promotional flyer for three dessert recipes, titled "Zo + makkelijk kan leker zijn" which translates to "So easy can make it." The text + provides information about the ingredients and preparation times required for + each recipe: Aardbeienbaken (vanilla frisee vlees-toffees), Brownies with caramelized + pineapple topper; Gevulde Ananasen met frietse Vogel Toefjes ...' + - The image features a woman holding an open wallet, seemingly in distress or contemplation. + The headline "Altid geld ektor? Is het hoop hoepel met simpel-budgetplan" suggests + that the article discusses whether it's possible to make ends meet with simple + budgeting plans and possibly touches on financial matters related to money management + for individuals who may be struggling financially due to lack of funds within + their wallets at any given time during certain periods throughout January 2013 + as indicated by February hours mentioned above (hourly wages) like those associated + with NIBU which could refer either directly towards her situation described earlier + but seems more likely indirectly indicating how one might utilize these resources + efficiently over different timeslots/days if they are unable to manage finances + effectively currently; however this is purely speculative without further context + provided from what we see here only showing someone going through something quite + personal about themselves while trying out various methods such as seeking help + via phone calls made using numbers listed below title line etc... + - The image is a magazine cover featuring an advertisement for Libelle. The main + focus of the ad depicts someone walking on what appears to be farmland, suggesting + that it may promote rural or agricultural products and services associated with + farming activities in such areas. +- source_sentence: 'The image is an advertisement for a product called "Predictor". + It features a woman in black dress and tank top, standing against the backdrop + of red color with white text that reads: ''Dat krijg je evan als jezwaanger bent'' + which translates to English as ''When you want your wish comes true'', indicating + some form of predictive power or magic associated with this brand. The background + suggests it might be related to pregnancy-related products given its association + with maternity wear like knickers (underwear) from Italian brands such as Mister + & Mrs Italy and Sonia Bianca among others listed on their website www.predictorwebster.com/www_predictortorator' + sentences: + - The image is a magazine page featuring an article titled "uit". The cover of the + issue includes various elements such as photos, text in different languages and + designs like logos or graphics that contribute to its overall aesthetic appeal. + - 'The image is a magazine cover featuring an article titled " Het patron: ontcoog + moet niet langer eten het maar waard aan werk" translated to English as "[Patron]: + On the table you must not only eat but also value your work.” The author of this + piece, Door Lilien MacNackon''s picture and name are prominently displayed at + the top left corner in red text against white background with her smiling face + capturing attention towards right side center-bottom part of page 15 from Cosmopolitan + Magazine dated April/May issue for year 2003 published by Studio Consult Uitgeverij + B.V Amsterdam Netherlands.' + - The image is a promotional poster for "Van de Straat" featuring an orange and + blue background. In the center of this vibrant backdrop, there's a woman carrying + out her belongings from inside what appears to be either car or storage unit near + water bodies such as docks with boats nearby in various sizes. The text on top + reads 'VAN DE STRAAT,' while below it says that she has found something interesting + about Viva magazine which features articles like those described by Tweedehandtopstukken + voor binnenen (Second-Hand Items Toppicks For Binns) suggesting they are offering + unique items at affordable prices within their stores across Europe. +- source_sentence: The image depicts a black and white advertisement for Jil Sander's + "SUMMER IN YOU" eau de toilette. A man is standing next to the box, which prominently + displays his name in bold red letters against an off-white background that contrasts + with him. The slogan on top of this message reads "JIL SANDRER," suggesting it + may be related or endorsed by them as well. + sentences: + - 'The image is a magazine cover featuring an article titled " Het patron: ontcoog + moet niet langer eten het maar waard aan werk" translated to English as "[Patron]: + On the table you must not only eat but also value your work.” The author of this + piece, Door Lilien MacNackon''s picture and name are prominently displayed at + the top left corner in red text against white background with her smiling face + capturing attention towards right side center-bottom part of page 15 from Cosmopolitan + Magazine dated April/May issue for year 2003 published by Studio Consult Uitgeverij + B.V Amsterdam Netherlands.' + - The image depicts a news article about the city of Goudda, featuring various elements + from daily life. The headline "Stammenstrid in gouda" suggests that it's an issue + related to urban planning or development within this area. There are several people + visible throughout the scene - some walking on sidewalks and others standing near + buildings with street signs nearby. A stroller is also present among these individuals, + indicating families might be involved too. + - The image is a page from an advertisement or brochure, featuring various items + related to audio and electronics. In the top left corner of the photo are two + headphones with Sony branding on them in different colors - silver for one pair + (Bioscoop oren) and black for another set which seems like they're part of some + product display setup at Mannenzaken store. The center section shows David sitting + comfortably outdoors while wearing these same headsets as he listens intently + into his ears during wintertime days when temperatures drop below zero degrees + Celsius. Towards the bottom right side there appears to be information about MP417 + van David by Capa Coffee Shop & Playboy magazine online shop website www.capacoffeeshopw + waterproof tablecloth w/ clip designed specifically for this model laptop case + that comes along with its own carrying bag made up out of recycled materials including + plastic bottles such as those used before their recycling process starts! +- source_sentence: The image features an advertisement for Philips Flat TV, showcasing + a man relaxing in bed with his head resting on the armrest of their television. + The background includes elements like blue skies and waves as part of its visual + appeal to create relaxation or serenity feelings that align well with leisure + activities such as watching movies at home during downtime moments. + sentences: + - The image is a screenshot of an article from the E-Business section. It features + two men, likely business figures or executives based on their attire and positions + in front of computers with IBM logos visible behind them. The headline indicates + that they are discussing enterprise strategies for social media marketing within + businesses like Ibm using platforms such as LinkedIn (IBM's name). There appears + to be some text related to "Welvaartstaat" which could suggest travel services + associated with these companies but it may not fully translate into English due + to character limitations here. + - The image is a vibrant advertisement for the Pakhuis Amsterdams, showcasing its + architectural design and inviting visitors to explore. The building features various + elements like red bicycles parked outside on wheels that resemble scooters or + skateboards in different colors such as black with orange accents (red). There's + also an eye-catching display of bookshelves filled with numerous volumes arranged + neatly against one wall. A prominent figure wearing glasses stands near these + shelves under a glass roof structure inside the museum entrance area where people + can admire works from renowned artists while enjoying refreshments at nearby cafes + if they choose so. + - The image features a magazine cover page with the title "De goed-gesprek gek" + prominently displayed. The background of this colorful and engaging design includes + elements such as text, images like people's faces or objects related to fashion, + possibly suggesting an article about personal style in magazines that target young + women interested in these topics. +- source_sentence: The image features a silver hatchback car parked in the middle + of an expansive, dense forest. The trees are tall and dark green with visible + trunks that reach up into what appears to be early morning light filtering through + them due to their proximity at sunset or sunrise. There is no direct sunlight + casting shadows on the ground from above; instead, there's soft lighting coming + downwards as if it were just after dawn or dusk when natural daylight has not + yet reached its peak intensity. + sentences: + - The image is a magazine cover with various advertisements and articles. The main + focus of the advertisement in question appears to be about tomatoes, featuring + an attractive woman holding wine glasses next to them on top of what seems like + food or kitchen items. There's also text related to "Medicijn" suggesting it might + relate to health-related products as well. + - 'The image depicts a man standing among an array of white chairs, arranged in + rows and clusters. The background is green with the text "en creatie" prominently + displayed at the top left corner. There''s also some Dutch writing visible on + one side that translates to English as: + + “Voegd verwijzen en werken met vrij informatietechnologie systems op het eind + van de product te kennunnen dat er waarvoor een betere bezit moet wordt gegeven + dan ze momentoan kunnen bestaan” - This phrase roughly means “To evaluate whether + it makes sense for you or your organization to acquire products based solely upon + their immediate availability,” which suggests this might relate to software evaluation + processes within Microsoft enterprises seeking better ways to manage resources + efficiently while staying current without unnecessary waste.' + - The image is a page from the bridge puzzles section of an older edition. The top + left corner features "BRIDGE," while at its center, there's text that reads "(Noor + Helijkaal) HORIZONTAL" and then continues with more details about various aspects + related to this puzzle or game in general. In contrast, below these sections are + instructions for solving crosswords called Kettingwoord (the Dutch word meaning + 'setting up en prijswinnaars') which includes hints like identifying patterns + among clues provided on each square as well as how winning combinations can be + found by connecting them across multiple squares without crossing other words + already spelled out within those boxes. +model-index: +- name: SentenceTransformer based on distilbert/distilroberta-base + results: + - task: + type: triplet + name: Triplet + dataset: + name: Magazine + type: Magazine + metrics: + - type: cosine_accuracy + value: 0.9041628383631352 + name: Cosine Accuracy + - type: dot_accuracy + value: 0.0963318962470846 + name: Dot Accuracy + - type: manhattan_accuracy + value: 0.8027422432680755 + name: Manhattan Accuracy + - type: euclidean_accuracy + value: 0.9085447734822248 + name: Euclidean Accuracy + - type: max_accuracy + value: 0.9085447734822248 + name: Max Accuracy +--- + +# SentenceTransformer based on distilbert/distilroberta-base + +This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. + +## Model Details + +### Model Description +- **Model Type:** Sentence Transformer +- **Base model:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) +- **Maximum Sequence Length:** 512 tokens +- **Output Dimensionality:** 768 tokens +- **Similarity Function:** Cosine Similarity + + + + +### Model Sources + +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture + +``` +SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) +) +``` + +## Usage + +### Direct Usage (Sentence Transformers) + +First install the Sentence Transformers library: + +```bash +pip install -U sentence-transformers +``` + +Then you can load this model and run inference. +```python +from sentence_transformers import SentenceTransformer + +# Download from the 🤗 Hub +model = SentenceTransformer("sentence_transformers_model_id") +# Run inference +sentences = [ + "The image features a silver hatchback car parked in the middle of an expansive, dense forest. The trees are tall and dark green with visible trunks that reach up into what appears to be early morning light filtering through them due to their proximity at sunset or sunrise. There is no direct sunlight casting shadows on the ground from above; instead, there's soft lighting coming downwards as if it were just after dawn or dusk when natural daylight has not yet reached its peak intensity.", + 'The image is a page from the bridge puzzles section of an older edition. The top left corner features "BRIDGE," while at its center, there\'s text that reads "(Noor Helijkaal) HORIZONTAL" and then continues with more details about various aspects related to this puzzle or game in general. In contrast, below these sections are instructions for solving crosswords called Kettingwoord (the Dutch word meaning \'setting up en prijswinnaars\') which includes hints like identifying patterns among clues provided on each square as well as how winning combinations can be found by connecting them across multiple squares without crossing other words already spelled out within those boxes.', + 'The image is a magazine cover with various advertisements and articles. The main focus of the advertisement in question appears to be about tomatoes, featuring an attractive woman holding wine glasses next to them on top of what seems like food or kitchen items. There\'s also text related to "Medicijn" suggesting it might relate to health-related products as well.', +] +embeddings = model.encode(sentences) +print(embeddings.shape) +# [3, 768] + +# Get the similarity scores for the embeddings +similarities = model.similarity(embeddings, embeddings) +print(similarities.shape) +# [3, 3] +``` + + + + + + + +## Evaluation + +### Metrics + +#### Triplet +* Dataset: `Magazine` +* Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) + +| Metric | Value | +|:-------------------|:-----------| +| cosine_accuracy | 0.9042 | +| dot_accuracy | 0.0963 | +| manhattan_accuracy | 0.8027 | +| euclidean_accuracy | 0.9085 | +| **max_accuracy** | **0.9085** | + + + + + +## Training Details + +### Training Dataset + +#### Unnamed Dataset + + +* Size: 22,878 training samples +* Columns: sentence_0, sentence_1, and sentence_2 +* Approximate statistics based on the first 1000 samples: + | | sentence_0 | sentence_1 | sentence_2 | + |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | sentence_0 | sentence_1 | sentence_2 | + |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | The image features a model in an advertisement for Nivea body lotion. She is seated on the ground, dressed only in her underwear and smiling at the camera with blonde hair cascading down around her shoulders. The background of the photo consists mainly of blue tones that contrast against the white border surrounding it. In addition to showcasing the product's branding prominently above its logo "NIVEA BODY" which suggests this ad might be promoting skincare products or personal care items associated with beauty routines such as moisturizing creams or sunscreens. | The image is a book page with the headline "Meer dan moeder" written in bold blue letters. The accompanying text provides an introduction to Miranda Landman, who has been described as having her own unique style and approach towards parenting that differs from traditional norms. It mentions how she shares stories of being a mother during challenging times like when one child was diagnosed with cancer or dealing with difficult family dynamics such as divorce. Her perspective emphasizes self-love for herself first before focusing on raising children effectively through love instead of fear tactics which can lead to negative consequences over time. | The image displays a newspaper article from Australia, with the headline "Nieuwe Aussies gezocht" prominently featured. The text in Dutch provides details about an event where 19 million residents have thrown away approximately one billion new pieces of waste every day for several weeks at large dumpsites near towns and cities across New South Wales (NSW) due to high levels of immigration migration resulting in overcrowding conditions like those seen during World War II's displacement camps known as 'Europa', which were part of Europe’s largest refugee settlement system built by German architect Karl Lagerfeld on former Nazi concentration camp grounds after his expulsion following WWII. This is not only causing environmental problems but also social issues within these communities affected disproportionately more than other areas such as Melbourne or Sydney because they are often left behind when economic growth occurs elsewhere without adequate support systems that can accommodate them effectively; this has led many people seeking better opportunities outside their current living arrangements instead turning towards alternative locations abroad including countries located further down under its geographical location situated closer compared to North America while being farther off geographically speaking so it may become easier depending upon individual circumstances regarding travel plans between continents via airplanes offering flights connecting various points along different routes leading ultimately toward worldwide destinations far beyond our immediate vicinity here today! | + | The image is a vibrant collage of five polaroid photos, each capturing the joyous moments shared among friends at various social gatherings. The top left photo features two men clinking glasses and smiling broadly; one man holds his glass high in celebration while both are dressed casually with shirts that suggest an informal setting. In contrast to this joyful scene on its own right side stand three more people enjoying their time together: they all have drinks held up near them as if celebrating something or simply sharing laughter over some casual conversation. All these images exude warmth through candid expressions from individuals engaged within lively group interactions during what appears to be late-night get-togethers where good times abound for everyone present. | The image is a collage of four Polaroid-style photos, all featuring people enjoying drinks and having fun. The top left photo shows two individuals smiling while holding up their glasses filled with beer in unison; the bottom right corner has "de barkeepers" written on it as if they are bartenders celebrating or posing for this memorable moment together.
In addition to these main photographs, there's also text that reads: "Nu eerst een Bavaria," which translates from Dutch into English meaning something like 'Now first an experience,' indicating some sort of celebration related event taking place at Bavarian events such as sports matches involving soccer teams named Bayern Munich (Bavaria) ... | The image displays a book cover with text in Dutch. The main focus is on the name "Suzanne," which appears prominently at various points throughout the page, possibly indicating that it's about or by Suzanne Rensers from Columbia Records and 24x7 Music Publishing Bijzondere Muziekgroepen/Columbia Recordings International Nederland BV Voorheemweg Houten Netherlands-based label specializing in classical music albums for adults over age thirteen; also distributes to international markets including China via distribution partners such as Chrysalis Group plc UK based distributor of classic rock & roll artists like Pink Floyd , Queen etc) (www...chrysallismusicgroupplcometl), and Universal Japan Inc Tokyo japan( www…universaljapain com | + | The image is a promotional advertisement for the Mercedes-Benz SLK Special Edition car. It features an elegant silver sports car against a soft pink background, emphasizing its sleek design and luxury appeal. The text in Dutch at the bottom of the ad emphasizes that this edition offers exclusivity with no compromises on quality or style - "als exklusief nog niet eindelijk" translates to "as exclusive now not necessarily forever," suggesting ongoing value despite potential changes over time. | The image presents an advertisement for the Audi quattro, showcasing a silver SUV on ice. The car's design is sleek and modern with its distinctive four-ring logo prominently displayed in red at the bottom right corner of the ad. In addition to highlighting the vehicle itself, there are words like "quatto" which suggests it might be associated with performance or driving capabilities due to being named after this legendary model from Volkswagen Group history. | The image is a page from an article about Philips Lighting Poland, featured in the Dutch newspaper "Economie". The main focus of the photo appears to be on the building's architecture and its modern design elements such as large windows with reflective glass panels that enhance visibility outside this facility located at 12 Bulvarz street in Polania-Bulwarza city center area. | +* Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: + ```json + { + "distance_metric": "TripletDistanceMetric.EUCLIDEAN", + "triplet_margin": 5 + } + ``` + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `per_device_train_batch_size`: 16 +- `per_device_eval_batch_size`: 16 +- `num_train_epochs`: 10 +- `multi_dataset_batch_sampler`: round_robin + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: no +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 16 +- `per_device_eval_batch_size`: 16 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 1 +- `eval_accumulation_steps`: None +- `learning_rate`: 5e-05 +- `weight_decay`: 0.0 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `max_grad_norm`: 1 +- `num_train_epochs`: 10 +- `max_steps`: -1 +- `lr_scheduler_type`: linear +- `lr_scheduler_kwargs`: {} +- `warmup_ratio`: 0.0 +- `warmup_steps`: 0 +- `log_level`: passive +- `log_level_replica`: warning +- `log_on_each_node`: True +- `logging_nan_inf_filter`: True +- `save_safetensors`: True +- `save_on_each_node`: False +- `save_only_model`: False +- `restore_callback_states_from_checkpoint`: False +- `no_cuda`: False +- `use_cpu`: False +- `use_mps_device`: False +- `seed`: 42 +- `data_seed`: None +- `jit_mode_eval`: False +- `use_ipex`: False +- `bf16`: False +- `fp16`: False +- `fp16_opt_level`: O1 +- `half_precision_backend`: auto +- `bf16_full_eval`: False +- `fp16_full_eval`: False +- `tf32`: None +- `local_rank`: 0 +- `ddp_backend`: None +- `tpu_num_cores`: None +- `tpu_metrics_debug`: False +- `debug`: [] +- `dataloader_drop_last`: False +- `dataloader_num_workers`: 0 +- `dataloader_prefetch_factor`: None +- `past_index`: -1 +- `disable_tqdm`: False +- `remove_unused_columns`: True +- `label_names`: None +- `load_best_model_at_end`: False +- `ignore_data_skip`: False +- `fsdp`: [] +- `fsdp_min_num_params`: 0 +- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} +- `fsdp_transformer_layer_cls_to_wrap`: None +- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} +- `deepspeed`: None +- `label_smoothing_factor`: 0.0 +- `optim`: adamw_torch +- `optim_args`: None +- `adafactor`: False +- `group_by_length`: False +- `length_column_name`: length +- `ddp_find_unused_parameters`: None +- `ddp_bucket_cap_mb`: None +- `ddp_broadcast_buffers`: False +- `dataloader_pin_memory`: True +- `dataloader_persistent_workers`: False +- `skip_memory_metrics`: True +- `use_legacy_prediction_loop`: False +- `push_to_hub`: False +- `resume_from_checkpoint`: None +- `hub_model_id`: None +- `hub_strategy`: every_save +- `hub_private_repo`: False +- `hub_always_push`: False +- `gradient_checkpointing`: False +- `gradient_checkpointing_kwargs`: None +- `include_inputs_for_metrics`: False +- `eval_do_concat_batches`: True +- `fp16_backend`: auto +- `push_to_hub_model_id`: None +- `push_to_hub_organization`: None +- `mp_parameters`: +- `auto_find_batch_size`: False +- `full_determinism`: False +- `torchdynamo`: None +- `ray_scope`: last +- `ddp_timeout`: 1800 +- `torch_compile`: False +- `torch_compile_backend`: None +- `torch_compile_mode`: None +- `dispatch_batches`: None +- `split_batches`: None +- `include_tokens_per_second`: False +- `include_num_input_tokens_seen`: False +- `neftune_noise_alpha`: None +- `optim_target_modules`: None +- `batch_eval_metrics`: False +- `batch_sampler`: batch_sampler +- `multi_dataset_batch_sampler`: round_robin + +
+ +### Training Logs +| Epoch | Step | Training Loss | Magazine_max_accuracy | +|:------:|:-----:|:-------------:|:---------------------:| +| 0.3497 | 500 | 3.6095 | - | +| 0.6993 | 1000 | 2.1101 | - | +| 1.0 | 1430 | - | 0.8553 | +| 1.0490 | 1500 | 1.5426 | - | +| 1.3986 | 2000 | 1.2006 | - | +| 1.7483 | 2500 | 0.9514 | - | +| 2.0 | 2860 | - | 0.8845 | +| 2.0979 | 3000 | 0.6882 | - | +| 2.4476 | 3500 | 0.5506 | - | +| 2.7972 | 4000 | 0.4571 | - | +| 3.0 | 4290 | - | 0.8956 | +| 3.1469 | 4500 | 0.3377 | - | +| 3.4965 | 5000 | 0.304 | - | +| 3.8462 | 5500 | 0.2386 | - | +| 4.0 | 5720 | - | 0.8973 | +| 4.1958 | 6000 | 0.1906 | - | +| 4.5455 | 6500 | 0.1826 | - | +| 4.8951 | 7000 | 0.1479 | - | +| 5.0 | 7150 | - | 0.9058 | +| 5.2448 | 7500 | 0.1178 | - | +| 5.5944 | 8000 | 0.1094 | - | +| 5.9441 | 8500 | 0.0814 | - | +| 6.0 | 8580 | - | 0.9025 | +| 6.2937 | 9000 | 0.0877 | - | +| 6.6434 | 9500 | 0.0645 | - | +| 6.9930 | 10000 | 0.0601 | - | +| 7.0 | 10010 | - | 0.9029 | +| 7.3427 | 10500 | 0.0482 | - | +| 7.6923 | 11000 | 0.0407 | - | +| 8.0 | 11440 | - | 0.9099 | +| 8.0420 | 11500 | 0.0299 | - | +| 8.3916 | 12000 | 0.0309 | - | +| 8.7413 | 12500 | 0.0225 | - | +| 9.0 | 12870 | - | 0.9085 | +| 9.0909 | 13000 | 0.0158 | - | +| 9.4406 | 13500 | 0.0176 | - | + + +### Framework Versions +- Python: 3.10.12 +- Sentence Transformers: 3.0.1 +- Transformers: 4.41.2 +- PyTorch: 2.3.0+cu121 +- Accelerate: 0.32.1 +- Datasets: 2.20.0 +- Tokenizers: 0.19.1 + +## Citation + +### BibTeX + +#### Sentence Transformers +```bibtex +@inproceedings{reimers-2019-sentence-bert, + title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", + author = "Reimers, Nils and Gurevych, Iryna", + booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", + month = "11", + year = "2019", + publisher = "Association for Computational Linguistics", + url = "https://arxiv.org/abs/1908.10084", +} +``` + +#### TripletLoss +```bibtex +@misc{hermans2017defense, + title={In Defense of the Triplet Loss for Person Re-Identification}, + author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, + year={2017}, + eprint={1703.07737}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + + + + + \ No newline at end of file diff --git a/Magazine_Topic_Embedding_sample_size15/config.json b/Magazine_Topic_Embedding_sample_size15/config.json new file mode 100644 index 0000000000000000000000000000000000000000..b8ce9842c0a319041905edb72789cd58dbc40a5c --- /dev/null +++ b/Magazine_Topic_Embedding_sample_size15/config.json @@ -0,0 +1,27 @@ +{ + "_name_or_path": "distilroberta-base", + "architectures": [ + "RobertaModel" + ], + "attention_probs_dropout_prob": 0.1, + "bos_token_id": 0, + "classifier_dropout": null, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 514, + "model_type": "roberta", + "num_attention_heads": 12, + "num_hidden_layers": 6, + "pad_token_id": 1, + "position_embedding_type": "absolute", + "torch_dtype": "float32", + "transformers_version": "4.41.2", + "type_vocab_size": 1, + "use_cache": true, + "vocab_size": 50265 +} diff --git a/Magazine_Topic_Embedding_sample_size15/config_sentence_transformers.json b/Magazine_Topic_Embedding_sample_size15/config_sentence_transformers.json new file mode 100644 index 0000000000000000000000000000000000000000..0bd3040e567053e214ea431033a077738d9db968 --- /dev/null +++ b/Magazine_Topic_Embedding_sample_size15/config_sentence_transformers.json @@ -0,0 +1,10 @@ +{ + "__version__": { + "sentence_transformers": "3.0.1", + "transformers": "4.41.2", + "pytorch": "2.3.0+cu121" + }, + "prompts": {}, + "default_prompt_name": null, + "similarity_fn_name": null +} \ No newline at end of file diff --git a/Magazine_Topic_Embedding_sample_size15/merges.txt b/Magazine_Topic_Embedding_sample_size15/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..226b0752cac7789c48f0cb3ec53eda48b7be36cc --- /dev/null +++ b/Magazine_Topic_Embedding_sample_size15/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 +Ġ t +Ġ a +h e +i n +r e +o n +Ġt he +e r +Ġ s +a t +Ġ w +Ġ o +e n +Ġ c +i t +i s +a n +o r +e s +Ġ b +e d +Ġ f +in g +Ġ p +o u +Ġa n +a l +a r +Ġt o +Ġ m +Ġo f +Ġ in +Ġ d +Ġ h +Ġan d +i c +a s +l e +Ġt h +i on +o m +l l +en t +Ġ n +Ġ l +s t +Ġ re +v e +Ġ e +r o +l y +Ġb e +Ġ g +Ġ T +c t +Ġ S +i d +o t +Ġ I +u t +e t +Ġ A +Ġ is +Ġ on +i m +a m +o w +a y +a d +s e +Ġth at +Ġ C +i g +Ġf or +a c +Ġ y +v er +u r +Ġ u +l d +Ġs t +Ġ M +' s +Ġ he +Ġ it +at ion +it h +i r +c e +Ġy ou +i l +Ġ B +Ġw h +o l +Ġ P +Ġw ith +Ġ 1 +t er +c h +Ġa s +Ġw e +Ġ ( +n d +i ll +Ġ D +i f +Ġ 2 +a g +er s +k e +Ġ " +Ġ H +e m +Ġc on +Ġ W +Ġ R +he r +Ġw as +Ġ r +o d +Ġ F +u l +at e +Ġa t +r i +p p +o re +ĠT he +Ġs e +u s +Ġp ro +Ġh a +u m +Ġa re +Ġd e +a in +an d +Ġo r +ig h +es t +is t +a b +r om +Ġ N +t h +Ġc om +Ġ G +u n +o p +0 0 +Ġ L +Ġn ot +es s +Ġe x +Ġ v +re s +Ġ E +e w +it y +an t +Ġb y +e l +o s +or t +o c +q u +Ġf rom +Ġha ve +Ġs u +i ve +ou ld +Ġs h +Ġth is +n t +r a +p e +igh t +ar t +m ent +Ġa l +u st +en d +- - 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"rot": 12179, + "ĠSearch": 12180, + "ĠJoyce": 12181, + "ĠPruitt": 12182, + "ĠLevel": 12183, + "ĠBP": 12184, + "ĠLac": 12185, + "had": 12186, + "Ġexpenditure": 12187, + "ĠMadd": 12188, + "Ġpockets": 12189, + "ĠClippers": 12190, + "ĠDear": 12191, + "ĠGive": 12192, + "Ġhal": 12193, + "Ġvertical": 12194, + "Ġwholesale": 12195, + "what": 12196, + "ĠSpringfield": 12197, + "ayed": 12198, + "ĠSom": 12199, + "Ġsecrets": 12200, + "Ġcharts": 12201, + "iar": 12202, + "ibility": 12203, + "LAND": 12204, + "Ġbearing": 12205, + "Ġprom": 12206, + "Ġtab": 12207, + "Ġsheets": 12208, + "ĠGL": 12209, + "Ġendless": 12210, + "opening": 12211, + "ĠOwen": 12212, + "Ġunderneath": 12213, + "ĠErik": 12214, + "ĠDACA": 12215, + "Ġsteering": 12216, + "Ġfootprint": 12217, + "ĠRoma": 12218, + "ĠDucks": 12219, + "ĠEllen": 12220, + "ĠProfessional": 12221, + "ĠGardens": 12222, + "Ġgoalie": 12223, + "Ġshine": 12224, + "Ġturmoil": 12225, + "Ġhunger": 12226, + "ĠâĢĭ": 12227, + "active": 12228, + "hey": 12229, + "Ġblessed": 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12331, + "Ġinterface": 12332, + "Ġdetected": 12333, + "ĠBio": 12334, + "Ġhighlighting": 12335, + "ames": 12336, + "Ġcorresponding": 12337, + "great": 12338, + "Ġgray": 12339, + "Ġadvantages": 12340, + "ĠME": 12341, + "ĠAbbas": 12342, + "Ġnaked": 12343, + "rington": 12344, + ".),": 12345, + "ĠFace": 12346, + "third": 12347, + "Ġtranscript": 12348, + "ples": 12349, + "Good": 12350, + "ĠArctic": 12351, + "Ġtolerance": 12352, + "reat": 12353, + "green": 12354, + "ĠMik": 12355, + "Ġoutreach": 12356, + "Ġrolls": 12357, + "Ġgen": 12358, + "Ġsupplied": 12359, + "Ġguarantees": 12360, + "aug": 12361, + "Ġsemif": 12362, + "ounds": 12363, + "running": 12364, + "Ġfitting": 12365, + "ĠRisk": 12366, + "iveness": 12367, + "family": 12368, + "Ġti": 12369, + "ĠIsaac": 12370, + "Ġdump": 12371, + "ĠPatricia": 12372, + "Ġpassport": 12373, + "ĠRhode": 12374, + "Who": 12375, + "log": 12376, + "Ġstat": 12377, + "Ġrat": 12378, + "ango": 12379, + "SB": 12380, + "ĠMaur": 12381, + "Ġsmiling": 12382, + 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15295, + "Ġimproves": 15296, + "ĠAMD": 15297, + "Ġexcluded": 15298, + "CON": 15299, + "ĠOscars": 15300, + "Ġfixtures": 15301, + "comb": 15302, + "ĠBerg": 15303, + "Ġbother": 15304, + "Ġboring": 15305, + "Ġobservation": 15306, + "ĠCad": 15307, + "Ġrecordings": 15308, + "ĠCultural": 15309, + "Ġweaken": 15310, + "Ġaccuse": 15311, + "ĠAbd": 15312, + "abor": 15313, + "115": 15314, + "uffle": 15315, + "Ġhighways": 15316, + "atham": 15317, + "empt": 15318, + "ĠDeer": 15319, + "ĠEDT": 15320, + "ĠWait": 15321, + "athan": 15322, + "Ġaccumulated": 15323, + "Ġguilt": 15324, + "Ġexempt": 15325, + "Ġdiluted": 15326, + "ĠJamal": 15327, + "Ġshit": 15328, + "cross": 15329, + "Ġeve": 15330, + "Ġshirts": 15331, + "Ġsatisfy": 15332, + "ĠPaulo": 15333, + "AH": 15334, + "sic": 15335, + "ĠChloe": 15336, + "ĠCities": 15337, + "ĠSwansea": 15338, + "Ġprecision": 15339, + "ĠTracy": 15340, + "ping": 15341, + "Ġcontinually": 15342, + "Ġdemographic": 15343, + "Ġcliff": 15344, + "Ġjaw": 15345, + "isted": 15346, + 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18038, + "Ġstaging": 18039, + "rolled": 18040, + "Ġspecially": 18041, + "oop": 18042, + "Ġbaseline": 18043, + "ĠRF": 18044, + "avis": 18045, + "Ġvocals": 18046, + "COL": 18047, + "LD": 18048, + "Ġimpending": 18049, + "ĠCaldwell": 18050, + "Ġaluminium": 18051, + "Ġstra": 18052, + "ĠTayyip": 18053, + "Ġadmissions": 18054, + "falls": 18055, + "Ġrealizing": 18056, + "oen": 18057, + "ĠRV": 18058, + "ĠMog": 18059, + "Ġadvocating": 18060, + "ĠPepper": 18061, + "lived": 18062, + "ĠWick": 18063, + "Facebook": 18064, + "ĠSpect": 18065, + "Ġshout": 18066, + "Ġfractured": 18067, + "vet": 18068, + "Ġ1966": 18069, + "Ġcompensate": 18070, + "ĠVolume": 18071, + "Ġcategor": 18072, + "ĠHuntington": 18073, + "Free": 18074, + "OUGH": 18075, + "local": 18076, + "Sch": 18077, + "uti": 18078, + "Ġburger": 18079, + "Ġbush": 18080, + "Ġimpacting": 18081, + "Ġfrost": 18082, + "tti": 18083, + "ĠFresno": 18084, + "onz": 18085, + "shaw": 18086, + "ĠLibyan": 18087, + "Ġassert": 18088, + "ĠLegacy": 18089, + "ĠIE": 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"Ġlosers": 19113, + "Ġscheduling": 19114, + "esar": 19115, + "Ġtransferring": 19116, + "Ġbankrupt": 19117, + "Ġmethamphetamine": 19118, + "ĠEsk": 19119, + "ĠTreatment": 19120, + "ĠResponse": 19121, + "Ġhomework": 19122, + "ĠBald": 19123, + "Ġembarrassment": 19124, + "Ġpoorest": 19125, + "ĠPlatinum": 19126, + "ĠFac": 19127, + "Ġunleashed": 19128, + "Ġbrighter": 19129, + "002": 19130, + "Ġdisl": 19131, + "ĠLowry": 19132, + "ived": 19133, + "ĠDemon": 19134, + "ĠNonetheless": 19135, + "arro": 19136, + "ĠCONT": 19137, + "ifted": 19138, + "ĠFreder": 19139, + "isson": 19140, + "Ġrout": 19141, + "ARA": 19142, + "Ġswinging": 19143, + "Oct": 19144, + "Ġliable": 19145, + "Ġleaning": 19146, + "Ġlungs": 19147, + "380": 19148, + "ĠProcess": 19149, + "ĠCov": 19150, + "terrorism": 19151, + "Ġresistant": 19152, + "Ġpumped": 19153, + "Ġtripled": 19154, + "Semitism": 19155, + "ĠMia": 19156, + "Ġpenetration": 19157, + "ĠLutheran": 19158, + "BU": 19159, + "odes": 19160, + "Ġspanning": 19161, + "utch": 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"aning": 19266, + "Ġaw": 19267, + "ĠMulti": 19268, + "Ġanimation": 19269, + "850": 19270, + "ĠCounter": 19271, + "ĠNit": 19272, + "alli": 19273, + "Ġcapitalize": 19274, + "Ġexecuting": 19275, + "Ġdescent": 19276, + "ovi": 19277, + "ĠKimberly": 19278, + "headed": 19279, + "Ġmentioning": 19280, + ")-": 19281, + "ĠSpecifically": 19282, + "ayette": 19283, + "ihad": 19284, + "ĠIss": 19285, + "Ġdisagreed": 19286, + "ĠKum": 19287, + "Ġurges": 19288, + "Ġpermitting": 19289, + "Ġpy": 19290, + "isp": 19291, + "Ġhygiene": 19292, + "Ġmourning": 19293, + "Ġcyclists": 19294, + "cats": 19295, + "FER": 19296, + "cycl": 19297, + "Ġnewcomers": 19298, + "Ġplead": 19299, + "Ġmend": 19300, + "secret": 19301, + "fan": 19302, + "Ġtranslates": 19303, + "unit": 19304, + "ĠTank": 19305, + "drive": 19306, + "ĠSite": 19307, + "Ġacceleration": 19308, + "ĠEnrique": 19309, + "ĠElaine": 19310, + "Ġstaring": 19311, + "Ġbackwards": 19312, + "Ġot": 19313, + "Ġvot": 19314, + "ĠHK": 19315, + "Ġfian": 19316, + "ĠLockheed": 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"Ġcongreg": 22880, + "RAL": 22881, + "Ġvolley": 22882, + "Ġestablishments": 22883, + "Ġcite": 22884, + "Miss": 22885, + "Int": 22886, + "iola": 22887, + "ĠBare": 22888, + "KING": 22889, + "ools": 22890, + "private": 22891, + "Ġflaw": 22892, + "Ġwires": 22893, + "Ġideals": 22894, + "oub": 22895, + "Ġ\"'": 22896, + "ĠCompet": 22897, + "ĠStatements": 22898, + "ĠHDR": 22899, + "rm": 22900, + "Ġbegging": 22901, + "uffs": 22902, + "Ġdispatch": 22903, + "Ġskipped": 22904, + "Ġlabs": 22905, + "hawks": 22906, + "Ġexpl": 22907, + "Ġpatriotic": 22908, + "ussions": 22909, + "Ġportrayal": 22910, + "ĠBudapest": 22911, + "ĠCod": 22912, + "Ġextingu": 22913, + "smart": 22914, + "Ġburdens": 22915, + "ĠDrama": 22916, + "Ġaltitude": 22917, + "Ġpursuant": 22918, + "à¥": 22919, + "atari": 22920, + "cot": 22921, + "Ġhotline": 22922, + "ooters": 22923, + "ĠRolls": 22924, + "Ġjeopardy": 22925, + "oids": 22926, + "Ġpageant": 22927, + "149": 22928, + "Ġdistinguish": 22929, + "support": 22930, + "ĠHighlands": 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"issue": 25268, + "Ġlearns": 25269, + "natural": 25270, + "Ġvinegar": 25271, + "ĠSUB": 25272, + "ĠRecon": 25273, + "blers": 25274, + "Ġsniff": 25275, + "Ġsuppression": 25276, + "Ġsaf": 25277, + "urger": 25278, + "Ġbunker": 25279, + "asaki": 25280, + "ĠSpartan": 25281, + "ĠTok": 25282, + "Ġrav": 25283, + "Ġfoc": 25284, + "Sean": 25285, + "etric": 25286, + "Ġballpark": 25287, + "ĠHerb": 25288, + "ĠBM": 25289, + "ĠPublishing": 25290, + "Ġroadmap": 25291, + "pered": 25292, + "Ġpredator": 25293, + "ĠBlockchain": 25294, + "Ġvalidity": 25295, + "ĠGlou": 25296, + "ĠYamaha": 25297, + "Ġadop": 25298, + "Ġswamp": 25299, + "Ġcomplied": 25300, + "Ky": 25301, + "Greg": 25302, + "casts": 25303, + "john": 25304, + "ĠBosnia": 25305, + "Ġcinematic": 25306, + "ĠTavern": 25307, + "Ġfrustrations": 25308, + "eryl": 25309, + "Ġfairy": 25310, + "UNCH": 25311, + "ĠTus": 25312, + "Corp": 25313, + "ĠNug": 25314, + "closed": 25315, + "Ġexercised": 25316, + "urden": 25317, + "Ġdigitally": 25318, + "137": 25319, + 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"bys": 25372, + "erving": 25373, + "antz": 25374, + "Ġwiden": 25375, + "vised": 25376, + "Ev": 25377, + "Ġdiagn": 25378, + "ĠMakes": 25379, + "Ġcer": 25380, + "ĠPats": 25381, + "single": 25382, + "sche": 25383, + "struct": 25384, + "Ġdissolved": 25385, + "Ġtimeout": 25386, + "Ġenhancement": 25387, + "CF": 25388, + "Ġindust": 25389, + "ĠDed": 25390, + "ĠZo": 25391, + "CB": 25392, + "Ġpesticides": 25393, + "ĠRubin": 25394, + "George": 25395, + "opal": 25396, + "Ġmotel": 25397, + "critical": 25398, + "Ġcollapsing": 25399, + "ĠShal": 25400, + "tex": 25401, + "Ġcomplementary": 25402, + "Ġoust": 25403, + "ĠFlu": 25404, + "Ġexporting": 25405, + "Ġdifferential": 25406, + "north": 25407, + "ĠFG": 25408, + "Ġspoon": 25409, + "sha": 25410, + "Ġdismantle": 25411, + "elta": 25412, + "Ġjar": 25413, + "space": 25414, + "Smart": 25415, + "mere": 25416, + "Ð": 25417, + "ĠGillespie": 25418, + "Lo": 25419, + "ĠMead": 25420, + "capacity": 25421, + "ĠIssue": 25422, + "050": 25423, + "ĠVall": 25424, + 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"Ġhydraulic": 29215, + "Ġpredatory": 29216, + "Charles": 29217, + "cond": 29218, + "Ġspawned": 29219, + "Fr": 29220, + "174": 29221, + "Ġtame": 29222, + "Ġaggrav": 29223, + "Ġchrist": 29224, + "true": 29225, + "ivable": 29226, + "Ġhen": 29227, + "ĠKut": 29228, + "Ġskyrocket": 29229, + "Ġeg": 29230, + "Ġveterinarian": 29231, + "ĠStats": 29232, + "Kit": 29233, + "Ġbiologist": 29234, + "Spe": 29235, + "Ġantenna": 29236, + "Ġsust": 29237, + "fill": 29238, + "Ġpayload": 29239, + "227": 29240, + "Ġlivestream": 29241, + "ORN": 29242, + "ĠAbel": 29243, + "Ġdeception": 29244, + "ussen": 29245, + "Britain": 29246, + "partisan": 29247, + "Ġbrowse": 29248, + "Ġmelan": 29249, + "172": 29250, + "ĠNumerous": 29251, + "ĠMansion": 29252, + "Ġassailants": 29253, + "£": 29254, + "olerance": 29255, + "Ġdirectives": 29256, + "ĠInteg": 29257, + "zers": 29258, + "Ġduct": 29259, + "ĠHonestly": 29260, + "ĠImmediately": 29261, + "ixty": 29262, + "Ġdiagnose": 29263, + "Ġimplication": 29264, + "ĠiPads": 29265, + 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"Ġtul": 30892, + "Ġfacilit": 30893, + "Ġgrandma": 30894, + "ĠFeeling": 30895, + "Ġobst": 30896, + "ĠFranz": 30897, + "ĠPalin": 30898, + "ĠIncrease": 30899, + "gets": 30900, + "ĠImam": 30901, + "âĢİ": 30902, + "Ġcoincides": 30903, + "urrence": 30904, + "Ġlifes": 30905, + "Lab": 30906, + "Ham": 30907, + "angelo": 30908, + "Wild": 30909, + "Ġvetoed": 30910, + "Ġventilation": 30911, + "olid": 30912, + "Summer": 30913, + "Ġfacade": 30914, + "neys": 30915, + "ĠWOM": 30916, + "ĠBenny": 30917, + "ĠMarried": 30918, + "squ": 30919, + "ĠReflect": 30920, + "return": 30921, + "elia": 30922, + "olding": 30923, + "Ġrefine": 30924, + "ĠMadness": 30925, + "innacle": 30926, + "posts": 30927, + "287": 30928, + "fruit": 30929, + "274": 30930, + "icator": 30931, + "ĠVoy": 30932, + "Ġunsett": 30933, + "Ġfant": 30934, + "Ġtreaties": 30935, + "Ġcrystals": 30936, + "Ġhijacked": 30937, + "words": 30938, + "ĠReleased": 30939, + "Save": 30940, + "Ġcannon": 30941, + "Ġanomaly": 30942, + "Ġbeacon": 30943, + "Ġcrippled": 30944, + "Ġbundles": 30945, + "Ġuntreated": 30946, + "Ġhappiest": 30947, + "Ġgalaxies": 30948, + "Ġoccupational": 30949, + "416": 30950, + "Dar": 30951, + "Ġcrank": 30952, + "Ġappropriation": 30953, + "asking": 30954, + "mens": 30955, + "Ġdetector": 30956, + "Ġskewed": 30957, + "Ġpoke": 30958, + "254": 30959, + "Ġhypertension": 30960, + "apolog": 30961, + "Ġevaluations": 30962, + "blocks": 30963, + "Ġpow": 30964, + "GEN": 30965, + "Ġscalp": 30966, + "Ġarrogant": 30967, + "AIDS": 30968, + "ority": 30969, + "Ġredirect": 30970, + "Ġderogatory": 30971, + "Ġlateral": 30972, + "495": 30973, + "rolley": 30974, + "brew": 30975, + "Ġbabys": 30976, + "Ġmuff": 30977, + "ĠRequ": 30978, + "Ġdime": 30979, + "Ġwonderfully": 30980, + "Ġtreasures": 30981, + "ĠNES": 30982, + "Ġponds": 30983, + "Ġimpulse": 30984, + "Ġdetecting": 30985, + "Ġgrin": 30986, + "Ġbrid": 30987, + "Ġshoved": 30988, + "Ġpurge": 30989, + "irteen": 30990, + "OTHER": 30991, + "ÙĦ": 30992, + "irsch": 30993, + "ĠOcc": 30994, + "193": 30995, + "Ġfodder": 30996, + "wrote": 30997, + "meric": 30998, + "posal": 30999, + "Ġwinters": 31000, + "ĠJuice": 31001, + "hub": 31002, + "Ġcontrasting": 31003, + "Brazil": 31004, + "Ġflashy": 31005, + "uffer": 31006, + "technology": 31007, + "Children": 31008, + "Ġcatapult": 31009, + "owsky": 31010, + "ĠEclipse": 31011, + "abeth": 31012, + "ĠParticip": 31013, + "Ġlaud": 31014, + "ĠQuiet": 31015, + "Ġsimulations": 31016, + "Ġsacrificing": 31017, + "Ġpreaching": 31018, + "Ġvoicing": 31019, + "itizen": 31020, + "Ġgn": 31021, + "Ġsans": 31022, + "Ġ285": 31023, + "ĠRobot": 31024, + "Ġ1936": 31025, + "Ġsham": 31026, + "ĠKislyak": 31027, + "ĠGCC": 31028, + "tale": 31029, + "ĠShades": 31030, + "Ġsediment": 31031, + "Ġconveniently": 31032, + "Give": 31033, + "mounted": 31034, + "Ġpeel": 31035, + "Jun": 31036, + "ĠEisenhower": 31037, + "Ġdiplom": 31038, + "ĠPreservation": 31039, + "Ġaffirm": 31040, + "Ġtaboo": 31041, + "ĠGarr": 31042, + "ĠApply": 31043, + "prim": 31044, + "Ġausp": 31045, + "Ġtextbook": 31046, + "Ġforfeit": 31047, + "icides": 31048, + "Ġundis": 31049, + "DJ": 31050, + "Ġ\"...": 31051, + "ĠXperia": 31052, + "Ġfurry": 31053, + "Australian": 31054, + "Ġpreach": 31055, + "Ġparamed": 31056, + "Ġ196": 31057, + "agos": 31058, + "ĠRIP": 31059, + "Ġ408": 31060, + "ĠQuarterly": 31061, + "ĠQuentin": 31062, + "Ġdeft": 31063, + "ĠVlad": 31064, + "massive": 31065, + "apore": 31066, + "Ġquestionnaire": 31067, + "secution": 31068, + "ĠTunnel": 31069, + "ĠAssist": 31070, + "BILITY": 31071, + "everything": 31072, + "vich": 31073, + "Ġcomparatively": 31074, + "heng": 31075, + "ETH": 31076, + "ĠiPod": 31077, + "Ġinsurgent": 31078, + "Ġtestosterone": 31079, + "191": 31080, + "Ġmoons": 31081, + "Ġgripped": 31082, + "Ġstrang": 31083, + "pects": 31084, + "ĠSERVICE": 31085, + "Ġnumb": 31086, + "Ġmeasurable": 31087, + "Ġdismantled": 31088, + "Ġdepict": 31089, + "Ġretake": 31090, + "Light": 31091, + "Ġaquatic": 31092, + "useum": 31093, + "judicial": 31094, + "Ġ****": 31095, + "Ġrosters": 31096, + "certain": 31097, + "Ġhypothesis": 31098, + "2002": 31099, + "Snow": 31100, + "Ġpounded": 31101, + "ĠZel": 31102, + "ĠTrem": 31103, + "iversity": 31104, + "219": 31105, + "Jen": 31106, + "ĠAdventures": 31107, + "Ġcylinder": 31108, + "Ġbanging": 31109, + "Ġbalk": 31110, + "analy": 31111, + "ĠHust": 31112, + "ookie": 31113, + "ĠReturning": 31114, + "Ġpods": 31115, + "analysis": 31116, + "ĠTruman": 31117, + "Ġorg": 31118, + "Ġsar": 31119, + "Ġdred": 31120, + "ĠTelecommunications": 31121, + "ĠSven": 31122, + "carry": 31123, + "ĠLOVE": 31124, + "Ġparting": 31125, + "asar": 31126, + "utations": 31127, + "itic": 31128, + "Ġactu": 31129, + "Ġbananas": 31130, + "ĠNights": 31131, + "410": 31132, + "Still": 31133, + "Ġtweaked": 31134, + "went": 31135, + "Ġtoddlers": 31136, + "irted": 31137, + "Ġpaed": 31138, + "ĠWink": 31139, + "Ġviewpoint": 31140, + "ĠHelic": 31141, + "Ġhandshake": 31142, + "Ġpoaching": 31143, + "Ġrounding": 31144, + "268": 31145, + "ĠNVIDIA": 31146, + "Ġsquat": 31147, + "Ġtowed": 31148, + "Ġhandler": 31149, + "Ġconspir": 31150, + "Ġadditionally": 31151, + "CENT": 31152, + "ĠÃľ": 31153, + "article": 31154, + "ĠTough": 31155, + "NM": 31156, + "Rem": 31157, + "Ġstunts": 31158, + "ILS": 31159, + "ĠLM": 31160, + "Connect": 31161, + "ĠParagu": 31162, + "Ġcomplexities": 31163, + "Ġhugging": 31164, + "Ġabolish": 31165, + "ricting": 31166, + "ĠItems": 31167, + "Ġtemples": 31168, + "ĠSeat": 31169, + "ĠRubber": 31170, + "Ġindic": 31171, + "ĠVitamin": 31172, + "Ġcitations": 31173, + "Ġarmored": 31174, + "---------------": 31175, + "ĠNeo": 31176, + "ippy": 31177, + "Que": 31178, + "Ġrag": 31179, + "Ġlov": 31180, + "630": 31181, + "Ġadept": 31182, + "orbit": 31183, + "253": 31184, + "412": 31185, + "Ġbutterflies": 31186, + "Ġoutl": 31187, + "ĠCycle": 31188, + "Ġaesthetics": 31189, + "ĠTwitch": 31190, + "405": 31191, + "factor": 31192, + "ðŁij": 31193, + "ĠCircus": 31194, + "Posted": 31195, + "Ġintroductory": 31196, + "ĠStack": 31197, + "atoes": 31198, + "Ġfurn": 31199, + "ĠHond": 31200, + "Ġbipolar": 31201, + "ĠAging": 31202, + "inches": 31203, + "Ġincompetence": 31204, + "Ġaloud": 31205, + "Imagine": 31206, + "Ġsepar": 31207, + "Ġmanip": 31208, + "ophobic": 31209, + "inion": 31210, + "bek": 31211, + "Ġquer": 31212, + "ĠArmen": 31213, + "Ġhumorous": 31214, + "Ġmundane": 31215, + "Ġapologizing": 31216, + "Ġpioneered": 31217, + "Ġ303": 31218, + "282": 31219, + "Ġcalming": 31220, + "orious": 31221, + "760": 31222, + "Ġstitches": 31223, + "Ġthrottle": 31224, + "Ġspinach": 31225, + "urities": 31226, + "ĠCologne": 31227, + "Ġripple": 31228, + "Cs": 31229, + "Cent": 31230, + "Should": 31231, + "Ġaffinity": 31232, + "amount": 31233, + "ĠMISS": 31234, + "Ġsage": 31235, + "Ġamusing": 31236, + "Ġsnatch": 31237, + "clair": 31238, + "ĠGuess": 31239, + "bench": 31240, + "ĠMoj": 31241, + "nuclear": 31242, + "Ġfid": 31243, + "ĠVM": 31244, + "ĠGN": 31245, + "brainer": 31246, + "Ġcurled": 31247, + "Ġbushes": 31248, + "icably": 31249, + "Ġcreeping": 31250, + "Ġveil": 31251, + "ĠALS": 31252, + "ESPN": 31253, + "ulsion": 31254, + "ĠGTX": 31255, + "ĠANN": 31256, + "Ġcomplicit": 31257, + "assault": 31258, + "IOR": 31259, + "Ġpolymer": 31260, + "Ġestimating": 31261, + "277": 31262, + "alog": 31263, + "Ġglimps": 31264, + "Ġreinforces": 31265, + "Ġtextbooks": 31266, + "Ġdictated": 31267, + "ĠReyn": 31268, + "latable": 31269, + "ĠOrth": 31270, + "520": 31271, + "Ġtrickle": 31272, + "ĠWrong": 31273, + ".[": 31274, + "ĠDesigner": 31275, + "304": 31276, + "ĠInner": 31277, + "Ġrave": 31278, + "ppa": 31279, + "ĠGim": 31280, + "Ġswath": 31281, + "Ġcarts": 31282, + "atlantic": 31283, + "Ġpersists": 31284, + "ĠDeveloper": 31285, + "Ġgoodies": 31286, + "isive": 31287, + "Inf": 31288, + "ĠSaving": 31289, + "loop": 31290, + "tions": 31291, + "Ġabusers": 31292, + "Ġclot": 31293, + "Ġmesmer": 31294, + "Ġdeg": 31295, + "Ġskirts": 31296, + "257": 31297, + "Ġunreliable": 31298, + "ĠCOMM": 31299, + "Ġ194": 31300, + "Ġfledgling": 31301, + "administ": 31302, + "Israeli": 31303, + "ĠBarbie": 31304, + "ĠJeanne": 31305, + "Ġgenerously": 31306, + "ĠStruct": 31307, + "ĠZap": 31308, + "Ġvetted": 31309, + "ĠViolet": 31310, + "Ġ),": 31311, + "Ġembarrass": 31312, + "bang": 31313, + "ĠProvider": 31314, + "getting": 31315, + "alg": 31316, + "Ġunconditional": 31317, + "ĠHulk": 31318, + "ĠWad": 31319, + "utation": 31320, + "Ġpointless": 31321, + "Ġdeprivation": 31322, + "Ġstarving": 31323, + "ĠImpossible": 31324, + "ĠStir": 31325, + "Ġknack": 31326, + "anse": 31327, + "Ġsecurely": 31328, + "Ġply": 31329, + "395": 31330, + "Pack": 31331, + "liv": 31332, + "Ġridden": 31333, + "alks": 31334, + "308": 31335, + "male": 31336, + "Ġbitterly": 31337, + "Ġirrational": 31338, + "Members": 31339, + "ported": 31340, + "qq": 31341, + "ractor": 31342, + "Ġinflict": 31343, + "ĠBoehner": 31344, + "Ġthickness": 31345, + "Ġdome": 31346, + "ĠInflu": 31347, + "Ġheap": 31348, + "Ġmirrored": 31349, + "Ġconstituent": 31350, + "Ġfertile": 31351, + "Ġvaping": 31352, + "266": 31353, + "riages": 31354, + "Ġembassies": 31355, + "Ġpersu": 31356, + "ĠMacArthur": 31357, + "issions": 31358, + "Main": 31359, + "aths": 31360, + "onne": 31361, + "circ": 31362, + "Ġsweating": 31363, + "quartered": 31364, + "Ġsax": 31365, + "Ġ540": 31366, + "Ġreputable": 31367, + "Ġsatire": 31368, + "Ġpastors": 31369, + "ventional": 31370, + "Mic": 31371, + "female": 31372, + "Ġpity": 31373, + "appropri": 31374, + "voc": 31375, + "hei": 31376, + "Ġimperial": 31377, + "Ġcorrective": 31378, + "Ġresent": 31379, + "Ġtempered": 31380, + "Ġdiffers": 31381, + "Hamilton": 31382, + "Ġsaddle": 31383, + "Ġgrenades": 31384, + "ĠQuart": 31385, + "onymous": 31386, + "til": 31387, + "Ġdepiction": 31388, + "Ġdisreg": 31389, + "Ġpetitioner": 31390, + "Ġfret": 31391, + "ĠEns": 31392, + "Emer": 31393, + "540": 31394, + "opathy": 31395, + "vertisements": 31396, + "Ġsketches": 31397, + "venth": 31398, + "Ġautomate": 31399, + "Ġjihad": 31400, + "iping": 31401, + "Ġtert": 31402, + "ĠSop": 31403, + "ships": 31404, + "Ġdeceptive": 31405, + "ĠPryor": 31406, + "ĠGorge": 31407, + "ĠMeridian": 31408, + "rero": 31409, + "affected": 31410, + "Ġlame": 31411, + "660": 31412, + "rub": 31413, + "Hello": 31414, + "ĠNumbers": 31415, + "269": 31416, + "Ġmarg": 31417, + "Fran": 31418, + "640": 31419, + "Ġcath": 31420, + "winter": 31421, + "ĠMosque": 31422, + "Ġreckoning": 31423, + "ĠImaging": 31424, + "Ġmutation": 31425, + "ĠMild": 31426, + "Ġkidnap": 31427, + "Ġnav": 31428, + "Ġferocious": 31429, + "Ġdusty": 31430, + "Cele": 31431, + "ĠFoss": 31432, + "Ġregrett": 31433, + "lymp": 31434, + "Ġcoli": 31435, + "Ġstereo": 31436, + "Ġforesee": 31437, + "alties": 31438, + "Ġresusc": 31439, + "Full": 31440, + "wash": 31441, + "ĠINST": 31442, + "ĠPars": 31443, + "Ġcoated": 31444, + "ĠHT": 31445, + "Ġdiscord": 31446, + "Ġreforming": 31447, + "CAN": 31448, + "Ġblink": 31449, + "Ġlubric": 31450, + "Ġmishand": 31451, + "ensible": 31452, + "existent": 31453, + "secondary": 31454, + "ĠDoesn": 31455, + "terrorist": 31456, + "Ġriff": 31457, + "custom": 31458, + "ĠDET": 31459, + "Ġreusable": 31460, + "ĠCRA": 31461, + "ĠScalia": 31462, + "Ġaccelerator": 31463, + "Ġpropag": 31464, + "ĠMID": 31465, + "ework": 31466, + "Ġlooted": 31467, + "oscope": 31468, + "eners": 31469, + "ruction": 31470, + "Ġbarr": 31471, + "Ġviewership": 31472, + "Ġlends": 31473, + "obil": 31474, + "ĠRoots": 31475, + "ĠCame": 31476, + "ibel": 31477, + "Ġglobalization": 31478, + "lab": 31479, + "information": 31480, + "Ġcoordin": 31481, + "Ġglitch": 31482, + "Ġworms": 31483, + "Ġslurs": 31484, + "Ġcontemplated": 31485, + "ĠPenal": 31486, + "Ġ191": 31487, + "Ġ221": 31488, + "Ġexposes": 31489, + "Ġ248": 31490, + "ĠASP": 31491, + "Ġdependency": 31492, + "urga": 31493, + "pdf": 31494, + "Ġvibr": 31495, + "clone": 31496, + "ossible": 31497, + "ĠUtt": 31498, + "serv": 31499, + "ĠLevant": 31500, + "maybe": 31501, + "MU": 31502, + "ĠLunar": 31503, + "Ġbystanders": 31504, + "Ġcapitals": 31505, + "Ġpreacher": 31506, + "thin": 31507, + "Ġunderscore": 31508, + "Ġ('": 31509, + "Ġmedd": 31510, + "Ġautobiography": 31511, + "Ġpersistence": 31512, + "Ġarming": 31513, + "Ġappalled": 31514, + "Ġcontradictory": 31515, + "Ġreciproc": 31516, + "Ġtakedown": 31517, + "tan": 31518, + "Ġnecessities": 31519, + "itans": 31520, + "ĠAlas": 31521, + "Ġsegregated": 31522, + "ĠResponsibility": 31523, + "ĠSHOW": 31524, + "ISIS": 31525, + "Ġpengu": 31526, + "Ġumb": 31527, + "ĠHO": 31528, + "HB": 31529, + "ĠChou": 31530, + "Ġalluded": 31531, + "Ġharms": 31532, + "bara": 31533, + "ĠWOR": 31534, + "Sorry": 31535, + "Ġstarvation": 31536, + "Ġspilling": 31537, + "Ġcarb": 31538, + "annis": 31539, + "ĠGarrison": 31540, + "Ġmillionaire": 31541, + "ifling": 31542, + "ĠCancel": 31543, + "Ġimprint": 31544, + "Ġborrower": 31545, + "455": 31546, + "ĠCic": 31547, + "Ġexposures": 31548, + "dest": 31549, + "Ġunn": 31550, + "Ġ802": 31551, + "Ġadherence": 31552, + "prints": 31553, + "Ġweary": 31554, + 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34723, + "ĠWhale": 34724, + "Ġshorten": 34725, + "Ġcorrectness": 34726, + "evil": 34727, + "network": 34728, + "Ġreactive": 34729, + "assuming": 34730, + "ĠLaksh": 34731, + "games": 34732, + "Ġruining": 34733, + "excluding": 34734, + "annels": 34735, + "º": 34736, + "Ġrubbed": 34737, + "aleb": 34738, + "flex": 34739, + "iped": 34740, + "ĠLimit": 34741, + "allowed": 34742, + "ĠDMV": 34743, + "ĠLD": 34744, + "Ġstamina": 34745, + "conduct": 34746, + "Ġmislead": 34747, + "lib": 34748, + "ĠEminem": 34749, + "Ġpayoff": 34750, + "Ġkernel": 34751, + "Ġsweeps": 34752, + "Ġsonic": 34753, + "ĠKodi": 34754, + "unique": 34755, + "Ġsurrog": 34756, + "Michigan": 34757, + "Ġattest": 34758, + "Ġdummy": 34759, + "ĠStellar": 34760, + "ĠSquadron": 34761, + "ĠHait": 34762, + "ĠSpirits": 34763, + "605": 34764, + "ĠHemisphere": 34765, + "legram": 34766, + "ĠRack": 34767, + "opol": 34768, + "Ġfreshwater": 34769, + "cession": 34770, + "Ġabort": 34771, + "ĠLOG": 34772, + "Ġfuzzy": 34773, + "Ġcrystall": 34774, + 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34930, + "bands": 34931, + "Ġavatar": 34932, + "ĠBarrier": 34933, + "Ġcharacterization": 34934, + "student": 34935, + "Ġgays": 34936, + "Ġspecialize": 34937, + "ĠJudging": 34938, + "Ġinitiation": 34939, + "Ġshove": 34940, + "Ġpirates": 34941, + "Ġfictitious": 34942, + "ĠPoker": 34943, + "ĠElsa": 34944, + "ĠTECH": 34945, + "handedly": 34946, + "Ġglued": 34947, + "Ġclinically": 34948, + "Ġinaccessible": 34949, + "Ġderegulation": 34950, + "Ġprohib": 34951, + "Ġdangling": 34952, + "Ġnoses": 34953, + "Ġstash": 34954, + "اØ": 34955, + "ESH": 34956, + "Ġmonstrous": 34957, + "Ġcrept": 34958, + "ĠCharm": 34959, + "Ġbeh": 34960, + "Ġshuts": 34961, + "Ġ236": 34962, + "imedia": 34963, + "445": 34964, + "Du": 34965, + "Ġafar": 34966, + "ĠRout": 34967, + "Ġflares": 34968, + "Utah": 34969, + "Ġ808": 34970, + "Ġjewels": 34971, + "2004": 34972, + "Ġrecal": 34973, + "Gas": 34974, + "ĠExcellent": 34975, + "Ġpitfalls": 34976, + "ĠDrawing": 34977, + "viously": 34978, + "angered": 34979, + "changes": 34980, 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"ĠVIEW": 35086, + "ovation": 35087, + "Ġneedless": 35088, + "ĠIU": 35089, + "ignant": 35090, + "ĠConfeder": 35091, + "316": 35092, + "fine": 35093, + "Ġjars": 35094, + "gotten": 35095, + "Bron": 35096, + "Ġmindfulness": 35097, + "imating": 35098, + "Ġhysteria": 35099, + "Ġhurried": 35100, + "Ġinfantry": 35101, + "ĠNYU": 35102, + "tags": 35103, + "Penn": 35104, + "Ġtracing": 35105, + "ĠSwing": 35106, + "ĠIo": 35107, + "Ġreckoned": 35108, + "ĠRecall": 35109, + "ĠVersion": 35110, + "314": 35111, + "Ġecology": 35112, + "Ġarmoured": 35113, + "Ġresonance": 35114, + "970": 35115, + "Ġvigilance": 35116, + "Ġrede": 35117, + "ĠBohem": 35118, + "Ġchau": 35119, + "ĠDevi": 35120, + "Ġtru": 35121, + "))": 35122, + "Put": 35123, + "Ġflavored": 35124, + "ĠClown": 35125, + "Senate": 35126, + "ĠScandinavian": 35127, + "mable": 35128, + "Residents": 35129, + "ĠFranchise": 35130, + "Ġprecincts": 35131, + "Prem": 35132, + "ĠNeutral": 35133, + "coal": 35134, + "Ġdelinqu": 35135, + "Mus": 35136, + "UME": 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35188, + "ĠVerd": 35189, + "cook": 35190, + "Ġsimulator": 35191, + "players": 35192, + "Early": 35193, + "infeld": 35194, + "Ġmaximizing": 35195, + "Philipp": 35196, + "ĠPhotoshop": 35197, + "Ġdestroys": 35198, + "Ġbefriend": 35199, + "Ġfilthy": 35200, + "ĠIncident": 35201, + "gha": 35202, + "Ġcomplicity": 35203, + "Ġmessing": 35204, + "YA": 35205, + "ĠNegro": 35206, + "adows": 35207, + "374": 35208, + "Ġpip": 35209, + "cean": 35210, + "Ġ1924": 35211, + "Sent": 35212, + "represent": 35213, + "Ġdeems": 35214, + "ĠRue": 35215, + "Ġtitanium": 35216, + "Ġmanners": 35217, + "âĢ¦âĢ¦": 35218, + "bare": 35219, + "Ġusur": 35220, + "mma": 35221, + "ĠPanda": 35222, + "ulus": 35223, + "ĠSlav": 35224, + "324": 35225, + "ĠMole": 35226, + "^": 35227, + "micro": 35228, + "foreign": 35229, + "lest": 35230, + "ocular": 35231, + "ĠUniv": 35232, + "ĠFrag": 35233, + "Ġshepherd": 35234, + "Ġelectron": 35235, + "ĠFSA": 35236, + "Ġunl": 35237, + "dose": 35238, + "Ġimmersion": 35239, + "ĠDeL": 35240, + 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"Ġkitten": 35292, + "Ġconstit": 35293, + "iration": 35294, + "Speed": 35295, + "ecycle": 35296, + "ĠFalse": 35297, + "ĠDealer": 35298, + "Could": 35299, + "655": 35300, + "outside": 35301, + "Ġworldview": 35302, + "Ġ246": 35303, + "Ġspitting": 35304, + "595": 35305, + "MN": 35306, + "ĠComes": 35307, + "ingu": 35308, + "Ġenzymes": 35309, + "Ġcompass": 35310, + "Ġexclaimed": 35311, + "ĠMalays": 35312, + "Ġ1916": 35313, + "Ġcoloring": 35314, + "Ġrepeats": 35315, + "Ġsoils": 35316, + "Ġtrivia": 35317, + "ĠIsles": 35318, + "Const": 35319, + "ĠFiction": 35320, + "665": 35321, + "Ġcriminality": 35322, + "ĠZi": 35323, + "384": 35324, + "ĠWilderness": 35325, + "ĠCanary": 35326, + "ĠVs": 35327, + "и": 35328, + "ĠAPIs": 35329, + "Ġbehest": 35330, + "Ġeb": 35331, + "ĠHipp": 35332, + "Ġpreempt": 35333, + "Ġevoke": 35334, + "Ġinept": 35335, + "tele": 35336, + "447": 35337, + "ĠGarmin": 35338, + "Ġpursuits": 35339, + "351": 35340, + "Ġcliché": 35341, + "ĠJihad": 35342, + "Ġ308": 35343, + "ĠSnake": 35344, + "ĠAnnounce": 35345, + "Nearly": 35346, + "!'\"": 35347, + "Ġ1927": 35348, + "saw": 35349, + "Ġabhor": 35350, + "Plan": 35351, + "rawled": 35352, + "ĠRiy": 35353, + "ensor": 35354, + "Fal": 35355, + "quick": 35356, + "odynamic": 35357, + "Ġsubstitution": 35358, + "Ġprovoking": 35359, + "Operation": 35360, + "rupulous": 35361, + "Ġsweetness": 35362, + "folk": 35363, + "ĠDefault": 35364, + "Ġstarved": 35365, + "ĠPrinting": 35366, + "urious": 35367, + "ĠTracker": 35368, + "them": 35369, + "Ġleth": 35370, + "Ġemptied": 35371, + "Ġfootprints": 35372, + "ilian": 35373, + "Ġbattalion": 35374, + "Ġprophet": 35375, + "Ġrailing": 35376, + "Ġhect": 35377, + "rouch": 35378, + "lees": 35379, + "Ġideologies": 35380, + "Ġ254": 35381, + "ĠGods": 35382, + "ĠAvalon": 35383, + "Ġfrontrunner": 35384, + "ĠPork": 35385, + "ĠPipe": 35386, + "Ġscaven": 35387, + "Ġming": 35388, + "Ġerg": 35389, + "Ġ520": 35390, + "Ġhatched": 35391, + "asant": 35392, + "ĠHI": 35393, + "Ġpend": 35394, + "Ġ288": 35395, + 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40924, + "apsed": 40925, + "Fortunately": 40926, + "gered": 40927, + "Pict": 40928, + "Ġmastering": 40929, + "ĠHIM": 40930, + "ĠObesity": 40931, + "Ġornament": 40932, + "advant": 40933, + "ĠCous": 40934, + "032": 40935, + "cells": 40936, + "Ġpreclude": 40937, + "Ġanecdote": 40938, + "Ġpatriarchy": 40939, + "ĠSending": 40940, + "Pie": 40941, + "Ġdepressive": 40942, + "ĠEnds": 40943, + "712": 40944, + "zos": 40945, + "icka": 40946, + "Ġ1906": 40947, + "Anti": 40948, + "vana": 40949, + "ĠRestrict": 40950, + "Ġprotr": 40951, + "Ġusername": 40952, + "Ġparach": 40953, + "1997": 40954, + "imental": 40955, + "rower": 40956, + "carb": 40957, + "033": 40958, + "Ġobligatory": 40959, + "Ġwillful": 40960, + "Ġsnail": 40961, + "json": 40962, + "izarre": 40963, + "Ġmiscar": 40964, + "Ġdopamine": 40965, + "л": 40966, + "Ġapplic": 40967, + "Ġnervously": 40968, + "YY": 40969, + "alez": 40970, + "ĠSoviets": 40971, + "ĠMister": 40972, + "Ġcrates": 40973, + "Ġheavenly": 40974, + "Ġdoct": 40975, + "048": 40976, + "Ġ2400": 40977, + "ivia": 40978, + "adies": 40979, + "Phone": 40980, + "asks": 40981, + "Ġperenn": 40982, + "Ġcomposing": 40983, + "Ġraiding": 40984, + "requent": 40985, + "ibli": 40986, + "ĠFeedback": 40987, + "cellaneous": 40988, + "ĠContracts": 40989, + "ĠCasting": 40990, + "vim": 40991, + "Cut": 40992, + "Ġabbrevi": 40993, + "Ġintest": 40994, + "ricted": 40995, + "969": 40996, + "nostic": 40997, + "Ġinverted": 40998, + "ĠEG": 40999, + "aiden": 41000, + "ĠClaud": 41001, + "ĠiP": 41002, + "urized": 41003, + "Emily": 41004, + "Ġ353": 41005, + "Ġ((": 41006, + "ammad": 41007, + "Reb": 41008, + "plom": 41009, + "YES": 41010, + "connection": 41011, + "ĠWra": 41012, + "ĠMerch": 41013, + "Ġether": 41014, + "Elizabeth": 41015, + "Chip": 41016, + "relevant": 41017, + "URA": 41018, + "Ġantioxidant": 41019, + "ĠChron": 41020, + "Ġtheological": 41021, + "HCR": 41022, + "ruits": 41023, + "Body": 41024, + "enezuel": 41025, + "Few": 41026, + "adder": 41027, + "Ġinducing": 41028, + "ĠDarth": 41029, + "Ġimplicitly": 41030, + "Ġoverfl": 41031, + "Ġrelics": 41032, + "Must": 41033, + "ĠAnswers": 41034, + "Ġretina": 41035, + "ĠSlowly": 41036, + "ĠShib": 41037, + "software": 41038, + "Ġ\"\"": 41039, + "hack": 41040, + "Apart": 41041, + "told": 41042, + "Ger": 41043, + "Civil": 41044, + "problem": 41045, + "Ġslang": 41046, + "Ġtactile": 41047, + "Ġtabl": 41048, + "ĠAscension": 41049, + "Ġhumankind": 41050, + "Howard": 41051, + "rescent": 41052, + "ĠReleases": 41053, + "arijuana": 41054, + "Christopher": 41055, + "ĠWarden": 41056, + "blogspot": 41057, + "ĠVari": 41058, + "idency": 41059, + "ĠHandler": 41060, + "Round": 41061, + "MJ": 41062, + "Ġrhyth": 41063, + "Tai": 41064, + "terson": 41065, + "Ġ,\"": 41066, + "portation": 41067, + "ĠOrbital": 41068, + "Ġfantas": 41069, + "Ġattribut": 41070, + "Ġdiagram": 41071, + "atech": 41072, + "1992": 41073, + "ibl": 41074, + "Woman": 41075, + "ternally": 41076, + "Days": 41077, + "Ġdebunk": 41078, + "ĠPhant": 41079, + "ĠOath": 41080, + "sharp": 41081, + "Ġclaws": 41082, + "Lots": 41083, + "Incre": 41084, + "Aff": 41085, + "hooting": 41086, + "rect": 41087, + "Ġaltru": 41088, + "Ġwors": 41089, + "Ġtho": 41090, + "Ġ349": 41091, + "clusions": 41092, + "Ġpseudonym": 41093, + "Bec": 41094, + "Ġphosphorus": 41095, + "ivic": 41096, + "Ġ348": 41097, + "otent": 41098, + "Ġub": 41099, + "Ġcoales": 41100, + "regate": 41101, + "Ġ1870": 41102, + "Ġglide": 41103, + "treated": 41104, + "ĠSymb": 41105, + "Ġenchant": 41106, + "Besides": 41107, + "stocks": 41108, + "Ġ388": 41109, + "--------------": 41110, + "interpret": 41111, + "ouple": 41112, + "Ġdrawback": 41113, + "ĠRevised": 41114, + "Ġanat": 41115, + "Ġpsychosis": 41116, + "ب": 41117, + "Ġdiffuse": 41118, + "Ġaffidav": 41119, + "elve": 41120, + "amination": 41121, + "ĠTackle": 41122, + "hunter": 41123, + "env": 41124, + "Ġchests": 41125, + "Ġsubter": 41126, + "Ġconquest": 41127, + "Ġfidelity": 41128, + "Ġinfringing": 41129, + "opathic": 41130, + "ĠGrip": 41131, + "ĠKeyboard": 41132, + "Ġobjectionable": 41133, + "Ġmetabol": 41134, + "ĠGö": 41135, + "Room": 41136, + "...)": 41137, + "KEN": 41138, + "assic": 41139, + "Ġgeop": 41140, + "Tro": 41141, + "Ġcursing": 41142, + "Ġdile": 41143, + "Ġultraviolet": 41144, + "inarily": 41145, + "Ġdistilled": 41146, + "sect": 41147, + "ĠShooter": 41148, + "uckles": 41149, + "Ġdistortions": 41150, + "Map": 41151, + "Doctor": 41152, + "Ġinstalls": 41153, + "oire": 41154, + "Ġstarch": 41155, + "ociation": 41156, + "Lev": 41157, + "Ġscripture": 41158, + "Ġsalient": 41159, + "ilitating": 41160, + "wb": 41161, + "ĠSov": 41162, + "ĠDamn": 41163, + "Grey": 41164, + "Ġ980": 41165, + "Ġjung": 41166, + "Ġlicking": 41167, + "029": 41168, + "ĠDian": 41169, + "ĠBabylon": 41170, + "к": 41171, + "ĠRomantic": 41172, + "Ġguesses": 41173, + "ĠFren": 41174, + "Generally": 41175, + "ultural": 41176, + "istence": 41177, + "Ġiniti": 41178, + "Ġ341": 41179, + "ĠSlave": 41180, + "ultan": 41181, + "ĠTrash": 41182, + "ĠEmpty": 41183, + "ĠHundred": 41184, + "ĠDirective": 41185, + "Anderson": 41186, + "Advertisement": 41187, + "RH": 41188, + "ĠOo": 41189, + "ĠHik": 41190, + "peg": 41191, + "Sup": 41192, + "ĠXT": 41193, + "Ġencrypt": 41194, + "selage": 41195, + "ĠThrone": 41196, + "Ġconsecut": 41197, + "Li": 41198, + "ĠVirus": 41199, + "ĠCookies": 41200, + "SHIP": 41201, + "Ġflavorful": 41202, + "odynamics": 41203, + "animal": 41204, + "spread": 41205, + "ĠIPCC": 41206, + "jobs": 41207, + "ernand": 41208, + "ĠHaunted": 41209, + "Ġintolerable": 41210, + "ĠLAR": 41211, + "ixtape": 41212, + "Ġneur": 41213, + "Ġcausal": 41214, + "ĠPsychiatry": 41215, + "ĠVim": 41216, + "Ġgenomic": 41217, + "duration": 41218, + "ĠUsername": 41219, + "ategy": 41220, + "Ġunic": 41221, + "ĠKILL": 41222, + "blooded": 41223, + "Ġcaucuses": 41224, + "ĠPOLITICO": 41225, + "Spanish": 41226, + "Ġobedience": 41227, + "Ġinconven": 41228, + "MAT": 41229, + "Ġbends": 41230, + "ĠImprovements": 41231, + "Ġrelig": 41232, + "ĠForth": 41233, + "ĠLumia": 41234, + "uces": 41235, + "Ġunim": 41236, + "ĠStatistical": 41237, + "kb": 41238, + "auntlet": 41239, + "ĠDisco": 41240, + "ĠInstruction": 41241, + "ooo": 41242, + "ĠDictionary": 41243, + "culated": 41244, + "Adv": 41245, + "ĠAvatar": 41246, + "ictional": 41247, + "Ġcentr": 41248, + "ifles": 41249, + "orks": 41250, + "skill": 41251, + "Ġlatex": 41252, + "ĠPagan": 41253, + "Ġdevast": 41254, + "Ġprol": 41255, + "896": 41256, + "Product": 41257, + "968": 41258, + "Ġfrench": 41259, + "083": 41260, + "ĠCluster": 41261, + "cloth": 41262, + "ĠFilter": 41263, + "ĠDisorders": 41264, + "etimes": 41265, + "Ġinstinctively": 41266, + "ĠBritann": 41267, + "Ġaft": 41268, + "ĠVict": 41269, + "Ġâĺħ": 41270, + "Ġperverse": 41271, + "Ġcontraceptives": 41272, + "ĠHannibal": 41273, + "escap": 41274, + "ĠApostle": 41275, + "ĠXiao": 41276, + "ĠMagnum": 41277, + "Ġphosphate": 41278, + "Ġ399": 41279, + "utable": 41280, + "Ġsten": 41281, + "Ġwearer": 41282, + "Ġsmug": 41283, + "ĠInfluence": 41284, + "Ġ384": 41285, + "Truth": 41286, + "struction": 41287, + "Ġmaniac": 41288, + "ĠMagnetic": 41289, + "ousands": 41290, + "Ġsemen": 41291, + "dir": 41292, + "ĠTornado": 41293, + "Ġexplos": 41294, + "1995": 41295, + "Xi": 41296, + "Steel": 41297, + "057": 41298, + "Barn": 41299, + "Fan": 41300, + "ĠChatt": 41301, + "Chem": 41302, + "ĠFold": 41303, + "bees": 41304, + "1080": 41305, + "ĠMaze": 41306, + "ierre": 41307, + "oeuv": 41308, + "Cand": 41309, + "odium": 41310, + "mmm": 41311, + "ereo": 41312, + "Ġreactionary": 41313, + "Ġacidic": 41314, + "ĠRemoval": 41315, + "Ġnont": 41316, + "031": 41317, + "ĠTerminator": 41318, + "ĠVendor": 41319, + "enemy": 41320, + "Ġreconstructed": 41321, + "ĠGalileo": 41322, + "Ġtesters": 41323, + "albeit": 41324, + "uminium": 41325, + "Ġrite": 41326, + "ĠInput": 41327, + "committee": 41328, + "Ġjour": 41329, + "gements": 41330, + "Ġgerm": 41331, + "Dick": 41332, + "ĠRequirements": 41333, + "omsday": 41334, + "Î": 41335, + "ISSION": 41336, + "Ġmolded": 41337, + "Ġrye": 41338, + "Attorney": 41339, + "population": 41340, + "Ġrepet": 41341, + "Sync": 41342, + "breaks": 41343, + "Ġbanished": 41344, + "Ġraspberry": 41345, + "Ġammo": 41346, + "Ġorthodox": 41347, + "Ġwebcam": 41348, + "ĠAsc": 41349, + "vl": 41350, + "1989": 41351, + "Ġdiscipl": 41352, + "Ġmoreover": 41353, + "Ġexplodes": 41354, + "1960": 41355, + "Ġpropositions": 41356, + "Protect": 41357, + "Ġsexes": 41358, + "physical": 41359, + "ĠAthena": 41360, + "ocent": 41361, + "ĠGothic": 41362, + "ĠRacial": 41363, + "istani": 41364, + "Ġhelium": 41365, + "ĠPresumably": 41366, + "Ġperman": 41367, + "becue": 41368, + "ĠHW": 41369, + "rued": 41370, + "ĠCNS": 41371, + "DEP": 41372, + "ĠManifest": 41373, + "2500": 41374, + "ĠMyst": 41375, + "Economic": 41376, + "Prot": 41377, + "Ġledge": 41378, + "Ġimitate": 41379, + "ĠTotally": 41380, + "ĠBeaut": 41381, + "OIL": 41382, + "Ġ1440": 41383, + "Moscow": 41384, + "ĠSets": 41385, + "merga": 41386, + "Ġlesbians": 41387, + "Walker": 41388, + "Move": 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41441, + "Ġvec": 41442, + "ĠActions": 41443, + "Ġdistinguishing": 41444, + "ĠTsarnaev": 41445, + "ĠEndurance": 41446, + "ederation": 41447, + "itant": 41448, + "Ġstreetcar": 41449, + "041": 41450, + "ĠAval": 41451, + "ĠCompanion": 41452, + "ĠCartoon": 41453, + "Ġcalculus": 41454, + "993": 41455, + "eq": 41456, + "ĠVanilla": 41457, + "MAC": 41458, + "wolves": 41459, + "fg": 41460, + "Ġfermentation": 41461, + "Ġinformants": 41462, + "Ġsudo": 41463, + "Ġperipher": 41464, + "Ġindign": 41465, + "parts": 41466, + "detail": 41467, + "femin": 41468, + "blade": 41469, + "Ġinserts": 41470, + "Ġoffsets": 41471, + "Ġantidepressants": 41472, + "Ġphr": 41473, + "Ġresultant": 41474, + "biology": 41475, + "Ġacquies": 41476, + "UFF": 41477, + "****************": 41478, + "ĠPenalty": 41479, + "Ġrever": 41480, + "heric": 41481, + "ĠShadows": 41482, + "command": 41483, + "Ġreprint": 41484, + "089": 41485, + "empty": 41486, + "ĠTAG": 41487, + "stim": 41488, + "FK": 41489, + "Ġkins": 41490, + "uggle": 41491, + "imura": 41492, + "wit": 41493, + "Kill": 41494, + "Beck": 41495, + "Ocean": 41496, + "Ġlabyrinth": 41497, + "ĠNorse": 41498, + "IENCE": 41499, + "Ġ+++": 41500, + "DoS": 41501, + "gm": 41502, + "Ġbarbar": 41503, + "ĠCeres": 41504, + "Ġhashing": 41505, + "eworthy": 41506, + "Ġrecite": 41507, + "Ġelectrodes": 41508, + "Ġconformity": 41509, + "response": 41510, + "olate": 41511, + "Ġ357": 41512, + "Snap": 41513, + "Crime": 41514, + "Ġpointer": 41515, + "ĠTIT": 41516, + "Ġdistinctions": 41517, + "Ġ427": 41518, + "ĠÙĪ": 41519, + "abases": 41520, + "Mars": 41521, + "ĠSpiritual": 41522, + "Ġimpuls": 41523, + "Philadelphia": 41524, + "1994": 41525, + "Ġcunning": 41526, + "Ġfram": 41527, + "Ġinco": 41528, + "Ġomnip": 41529, + "imize": 41530, + "ervative": 41531, + "Gy": 41532, + "Drug": 41533, + "Ġcarniv": 41534, + "ĠSailor": 41535, + "download": 41536, + "ĠBeetle": 41537, + "ĠEarthqu": 41538, + "izontal": 41539, + "Alan": 41540, + "Nice": 41541, + "Prior": 41542, + "MAG": 41543, + "Ġautobi": 41544, + "ĠBrill": 41545, + "Ġpredominant": 41546, + "ĠMessiah": 41547, + "REM": 41548, + "ĠSlip": 41549, + "ĠWebs": 41550, + "ademic": 41551, + "<": 41552, + "ĠVessel": 41553, + "vari": 41554, + "Code": 41555, + "Ġbeetle": 41556, + "projects": 41557, + "BAT": 41558, + "Ġpsychotic": 41559, + "Ġunderside": 41560, + "Ġrefute": 41561, + "Considering": 41562, + "kees": 41563, + "wd": 41564, + "priority": 41565, + "Ġtwentieth": 41566, + "Ġatheist": 41567, + "amina": 41568, + "Ġeuphem": 41569, + "Ġtripod": 41570, + "ĠTrayvon": 41571, + "ĠNON": 41572, + "2200": 41573, + "ĠNPC": 41574, + "ependence": 41575, + "ĠMHz": 41576, + "ĠBung": 41577, + "Ġpane": 41578, + "Ġaboriginal": 41579, + "ĠPLUS": 41580, + "igers": 41581, + "ĠSexy": 41582, + "MF": 41583, + "Chall": 41584, + "Ay": 41585, + "ilingual": 41586, + "adj": 41587, + "Ġfrown": 41588, + "successful": 41589, + "stack": 41590, + "Ġic": 41591, + "ĠSeah": 41592, + "Ġconsequ": 41593, + "bugs": 41594, + "ĠScand": 41595, + "ĠCurve": 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42163, + "ĠPHP": 42164, + "Ġclaimant": 42165, + "uitive": 42166, + "Ġpussy": 42167, + "Ġcorpus": 42168, + "ĠAo": 42169, + "Qual": 42170, + "ĠXVI": 42171, + "requ": 42172, + "Ġsympt": 42173, + "mination": 42174, + "Ġhairy": 42175, + "ĠBattles": 42176, + "owntown": 42177, + "Roberts": 42178, + "Ġnec": 42179, + "ablo": 42180, + "AMD": 42181, + "internet": 42182, + "Tar": 42183, + "direction": 42184, + "ouston": 42185, + "ĠGlock": 42186, + "ĠYanukovych": 42187, + "ogens": 42188, + "rogram": 42189, + "otype": 42190, + "ĠPt": 42191, + "tenance": 42192, + "Ġaromatic": 42193, + "oxin": 42194, + "Vert": 42195, + "Ġsociop": 42196, + "cible": 42197, + "Db": 42198, + "________________": 42199, + "Third": 42200, + "ĠShips": 42201, + "!.": 42202, + "expensive": 42203, + "WOR": 42204, + "primary": 42205, + "Ġ666": 42206, + "Ġdecaying": 42207, + "Ġclustered": 42208, + "Ġbeetles": 42209, + "ĠHogwarts": 42210, + "Ġheaders": 42211, + "ĠJudah": 42212, + "Ġscen": 42213, + "Ġcosmos": 42214, + "ĠGenetic": 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42633, + "LIST": 42634, + "IDES": 42635, + "ĠThick": 42636, + "Ġdisks": 42637, + "Kin": 42638, + "Ġghetto": 42639, + "ĠObjects": 42640, + "Ġprism": 42641, + "ĠNether": 42642, + "Ġvul": 42643, + "iky": 42644, + "]:": 42645, + "ĠDetail": 42646, + "Ġfucked": 42647, + "!?": 42648, + "anium": 42649, + "Ġlords": 42650, + "ilities": 42651, + "ĠEthnic": 42652, + "static": 42653, + "$$": 42654, + "evidence": 42655, + "Ġmainline": 42656, + "Ġpeasant": 42657, + "ĠEnhance": 42658, + "ĠForced": 42659, + "virt": 42660, + "Ġii": 42661, + "Ġsymm": 42662, + "Ġconverter": 42663, + "ularity": 42664, + "Ġrepent": 42665, + "num": 42666, + "ĠScrew": 42667, + "ĠFTA": 42668, + "Ġmarines": 42669, + "hetto": 42670, + "blow": 42671, + "Ġado": 42672, + "ĠTypical": 42673, + "Ġoverw": 42674, + "ĠBerm": 42675, + "keley": 42676, + "Song": 42677, + "hao": 42678, + "valid": 42679, + "EXT": 42680, + "ĠProvides": 42681, + "âĺħâĺħ": 42682, + "ĠOdin": 42683, + "Shot": 42684, + "Ġgamma": 42685, + "Princ": 42686, + "asonry": 42687, + "ĠAccuracy": 42688, + "Ġcriterion": 42689, + "Ġdescriptive": 42690, + "Gall": 42691, + "gray": 42692, + "ĠCalcul": 42693, + "Ġaxes": 42694, + "ĠCommunists": 42695, + "ĠRebellion": 42696, + "Success": 42697, + "tg": 42698, + "Ġâĺ": 42699, + "Ġmultiplier": 42700, + "ravity": 42701, + "Thus": 42702, + "URL": 42703, + "Ġalternatively": 42704, + "duction": 42705, + "Ġsarcast": 42706, + "ĠCarth": 42707, + "ĠUSL": 42708, + "ĠInvisible": 42709, + "larg": 42710, + "pleted": 42711, + "pathic": 42712, + "Additionally": 42713, + "ĠCao": 42714, + "Ġlatent": 42715, + "ĠSurge": 42716, + "MEN": 42717, + "communications": 42718, + "ĠArray": 42719, + "Pink": 42720, + "commit": 42721, + "isodes": 42722, + "earcher": 42723, + "Ukraine": 42724, + "ĠAnthrop": 42725, + "incial": 42726, + "Ġquotations": 42727, + "adena": 42728, + "Ġwhining": 42729, + "Ġretri": 42730, + "ĠAssass": 42731, + "elligent": 42732, + "ĠPERSON": 42733, + "Py": 42734, + "Send": 42735, + "ĠâĪĴ": 42736, + "DON": 42737, + "Ġwatt": 42738, + "description": 42739, + "POS": 42740, + "Ġrepro": 42741, + "destroy": 42742, + "icidal": 42743, + "Ġmidrange": 42744, + "Ġinfographic": 42745, + "interesting": 42746, + "category": 42747, + "Flash": 42748, + "ĠInvasion": 42749, + "ĠExodus": 42750, + "restricted": 42751, + "Ġinference": 42752, + "dding": 42753, + "mingham": 42754, + "Ġcircumst": 42755, + "Wi": 42756, + "ĠHast": 42757, + "Ġsubjug": 42758, + "Ġwhispering": 42759, + "-.": 42760, + "Ġadren": 42761, + "ĠPattern": 42762, + "BOX": 42763, + "ĠEnhancement": 42764, + "Exc": 42765, + "ĠBucket": 42766, + "ĠGUN": 42767, + "deen": 42768, + "ĠHomo": 42769, + "1985": 42770, + "Ġclo": 42771, + "Ġsnippet": 42772, + "Ġ1896": 42773, + "TPP": 42774, + "Seg": 42775, + "success": 42776, + ";\"": 42777, + "ĠMUCH": 42778, + "Author": 42779, + "Ġreplication": 42780, + "Ġhallucinations": 42781, + "Inv": 42782, + "ĠAware": 42783, + "ĠViper": 42784, + "kai": 42785, + "frames": 42786, + "ĠTHANK": 42787, + "ĠSHA": 42788, + "wordpress": 42789, + "Ġbc": 42790, + "CIA": 42791, + "arrison": 42792, + "Ġalloc": 42793, + "ĠAlz": 42794, + "letcher": 42795, + "ĠDaredevil": 42796, + "iversary": 42797, + "Ġmanuals": 42798, + "Catholic": 42799, + "feat": 42800, + "Ġkinetic": 42801, + "JB": 42802, + "yeah": 42803, + "ĠLDS": 42804, + "Ġppm": 42805, + "ĠADC": 42806, + "pring": 42807, + "cence": 42808, + "Ġclasp": 42809, + "Ġsetups": 42810, + "Ġdeity": 42811, + "ĠIndra": 42812, + "ĠWander": 42813, + "Ġantib": 42814, + "Otherwise": 42815, + "ombie": 42816, + "Bitcoin": 42817, + "ipop": 42818, + "expression": 42819, + "Animal": 42820, + "ĠResurrection": 42821, + "ĠMoral": 42822, + "ĠSDK": 42823, + "Ġwretched": 42824, + "ogenous": 42825, + "species": 42826, + "Ġchuckled": 42827, + "Thor": 42828, + "Ġ428": 42829, + "avery": 42830, + "ĠPry": 42831, + "asures": 42832, + "ĠErn": 42833, + "apor": 42834, + "Ġinnumerable": 42835, + "Ġbaptized": 42836, + "ĠExplosive": 42837, + "Ġelves": 42838, + "idges": 42839, + "ĠParadox": 42840, + "Close": 42841, + "aldehyde": 42842, + "construct": 42843, + "Ġvirginity": 42844, + "Poll": 42845, + "assin": 42846, + "Doctors": 42847, + "Pos": 42848, + "NECT": 42849, + "Moreover": 42850, + "Commercial": 42851, + "cknowled": 42852, + "1988": 42853, + "Ġquotation": 42854, + "marriage": 42855, + "ĠBapt": 42856, + "ĠSina": 42857, + "ĠGloves": 42858, + "gian": 42859, + "Ġconfounding": 42860, + "URRENT": 42861, + "Dean": 42862, + "Brew": 42863, + "thur": 42864, + "pty": 42865, + "immune": 42866, + "ĠSQU": 42867, + "Ġcounterfe": 42868, + "rider": 42869, + "Ġinferred": 42870, + "ĠDimension": 42871, + "ĠToad": 42872, + "Ġafterlife": 42873, + "ĠHERO": 42874, + "Indiana": 42875, + "seek": 42876, + "Ġdistinguishes": 42877, + "ĠQur": 42878, + "ĠMethods": 42879, + "combat": 42880, + "Ġcateg": 42881, + "ĠStruggle": 42882, + "teness": 42883, + "liquid": 42884, + "Ġblinking": 42885, + "ĠCONTIN": 42886, + "iae": 42887, + "Ġaerobic": 42888, + "Ġstrugg": 42889, + "Ġegalitarian": 42890, + "hello": 42891, + "orrect": 42892, + "ĠAbandon": 42893, + "Ġferment": 42894, + "Area": 42895, + "idem": 42896, + "ĠMania": 42897, + "Ġjs": 42898, + "ĠBALL": 42899, + "Running": 42900, + "Ġregenerate": 42901, + "iquid": 42902, + "Uh": 42903, + "Crystal": 42904, + "ĠItal": 42905, + "ĠHeavenly": 42906, + "в": 42907, + "CRIPTION": 42908, + "Consumer": 42909, + "dust": 42910, + "amiliar": 42911, + "ĠRhino": 42912, + "Rocket": 42913, + "Ġreversible": 42914, + "kok": 42915, + "ĠSketch": 42916, + "Ġshotguns": 42917, + "apses": 42918, + "Ġdetach": 42919, + "ĠCells": 42920, + "artist": 42921, + "rily": 42922, + "ĠRestore": 42923, + "Scar": 42924, + "Ġevid": 42925, + "Ġspaced": 42926, + "ĠContributions": 42927, + "Ġ418": 42928, + "ĠMystic": 42929, + "Ġobfusc": 42930, + "Russ": 42931, + "wings": 42932, + "Pear": 42933, + "osite": 42934, + "Nusra": 42935, + "urations": 42936, + "ovie": 42937, + "icago": 42938, + "ĠConcepts": 42939, + "Ġstimuli": 42940, + "Ġaroused": 42941, + "aughty": 42942, + 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42993, + "Ġconquering": 42994, + "ĠFinder": 42995, + "window": 42996, + "Gra": 42997, + "Ġfonts": 42998, + "laughter": 42999, + "Ġcolonization": 43000, + "ĠDOD": 43001, + ")!": 43002, + ",)": 43003, + "ĠGeral": 43004, + "ĠSpoiler": 43005, + "ĠComponent": 43006, + "Ġgist": 43007, + "hiro": 43008, + "Ġlicens": 43009, + "nesses": 43010, + "Ġkarma": 43011, + "?\".": 43012, + "OPA": 43013, + "Ġsquats": 43014, + "ĠRAND": 43015, + "Ġorally": 43016, + "document": 43017, + "olars": 43018, + "Ġpresumptive": 43019, + "Pers": 43020, + "OAD": 43021, + "ufficient": 43022, + "LESS": 43023, + "Hidden": 43024, + "ORK": 43025, + "xs": 43026, + "Ġmathematician": 43027, + "ĠGloss": 43028, + "Ġannihilation": 43029, + "Ġmanifold": 43030, + "Ry": 43031, + "Thunder": 43032, + "Yan": 43033, + "Activ": 43034, + "Ġworldly": 43035, + "TED": 43036, + "marg": 43037, + "ĠStun": 43038, + "ryce": 43039, + "ĠVG": 43040, + "Isn": 43041, + "ĠCyn": 43042, + "Expl": 43043, + "IRED": 43044, + "Ġcompr": 43045, + "Ġindisc": 43046, + "Boss": 43047, + "()": 43048, + "berman": 43049, + "ĠBegins": 43050, + "ujah": 43051, + "ornia": 43052, + "hetical": 43053, + "Ġcivilizations": 43054, + "Ġfundamentalist": 43055, + "strap": 43056, + "Forward": 43057, + "ettlement": 43058, + "Ġprophetic": 43059, + "glers": 43060, + "bending": 43061, + "Terry": 43062, + "Ġidi": 43063, + "Ġtrunc": 43064, + "Ġcreeps": 43065, + "intel": 43066, + "switch": 43067, + "ailand": 43068, + "Ġinstaller": 43069, + "GOP": 43070, + "Ġ499": 43071, + "ĠParallel": 43072, + "Cru": 43073, + "Ġ\"@": 43074, + "Ġ396": 43075, + "ĠUnlock": 43076, + "Raven": 43077, + "Corn": 43078, + "Ġcircadian": 43079, + "Ġ********************************": 43080, + "iliate": 43081, + "ĠFunctional": 43082, + "Ġpronouns": 43083, + "ĠSatoshi": 43084, + "Ġstim": 43085, + "Gay": 43086, + "Iss": 43087, + "ĠThief": 43088, + "atellite": 43089, + "Ġshards": 43090, + "Ġphil": 43091, + "protein": 43092, + "Ġalters": 43093, + "Poor": 43094, + "Typically": 43095, + "KER": 43096, + "ociate": 43097, + "Ġemits": 43098, + "recy": 43099, + "Ġmechanically": 43100, + "Ġ...\"": 43101, + "nature": 43102, + "sys": 43103, + "ysc": 43104, + "Ġwavelengths": 43105, + "pattern": 43106, + "insured": 43107, + "Ġparasitic": 43108, + "ĠLCS": 43109, + "ĠPACs": 43110, + "Ġheals": 43111, + "ĠCCP": 43112, + "ĠHacker": 43113, + "Ġpsy": 43114, + "ĠBeans": 43115, + "Ġdemonic": 43116, + "JV": 43117, + "Ġatmosp": 43118, + "equality": 43119, + "Ġairst": 43120, + "Ġincarn": 43121, + "ynthesis": 43122, + "Ġequations": 43123, + "tch": 43124, + "ĠHUGE": 43125, + "ĠChanged": 43126, + "itatively": 43127, + "Job": 43128, + "gaming": 43129, + "Ġ1899": 43130, + "ĠMorsi": 43131, + "Ġconjecture": 43132, + "riad": 43133, + "Ġprimates": 43134, + "ĠArtemis": 43135, + "ĠThro": 43136, + "Ġbiologically": 43137, + "Church": 43138, + "topia": 43139, + "recomm": 43140, + "Ġgradient": 43141, + "Ġful": 43142, + "Ġbastard": 43143, + "CHO": 43144, + "IUM": 43145, + "sleep": 43146, + "Construction": 43147, + "raints": 43148, + "vable": 43149, + "ionage": 43150, + "Ġcomrade": 43151, + "Ġpopulate": 43152, + "Ġnerds": 43153, + "ĠXie": 43154, + "result": 43155, + "ĠImper": 43156, + "Ġpamphlet": 43157, + "Ku": 43158, + "Ġbackend": 43159, + "ificent": 43160, + "etus": 43161, + "Ġdisson": 43162, + "config": 43163, + "Ġsuc": 43164, + "Ġwavelength": 43165, + "external": 43166, + "owder": 43167, + "Ġpredis": 43168, + "eenth": 43169, + "Det": 43170, + "andem": 43171, + "Ġ1865": 43172, + "ĠDefeat": 43173, + "Individual": 43174, + "Ġretrieving": 43175, + "stories": 43176, + "Ġdesolate": 43177, + "Ġlett": 43178, + "Ġunpublished": 43179, + "Ġpassively": 43180, + "Ġdissertation": 43181, + "raits": 43182, + "abee": 43183, + "ĠResist": 43184, + "Robin": 43185, + "Ġbenevolent": 43186, + "blast": 43187, + "Offic": 43188, + "snap": 43189, + "vernment": 43190, + "Ġextermin": 43191, + "wt": 43192, + "bitious": 43193, + "hibited": 43194, + "Insp": 43195, + "posted": 43196, + "ĠYugoslav": 43197, + "rational": 43198, + "adapt": 43199, + "ĠAtari": 43200, + "Ġplugin": 43201, + "oglobin": 43202, + "efeated": 43203, + "ĠHRC": 43204, + "cko": 43205, + "ilver": 43206, + "ĠDestruction": 43207, + "gewater": 43208, + "ĠRadiation": 43209, + "Ġimprison": 43210, + "origin": 43211, + "antine": 43212, + "ĠPublication": 43213, + "Ġhealer": 43214, + "istered": 43215, + "ĠTHEIR": 43216, + "hazard": 43217, + "Contract": 43218, + "Ġmediated": 43219, + "Ġindexed": 43220, + "ĠSYSTEM": 43221, + "Labor": 43222, + "Blade": 43223, + "Ġyog": 43224, + "Champ": 43225, + "Gordon": 43226, + "IAS": 43227, + "Ġnineteenth": 43228, + "animous": 43229, + "begin": 43230, + "ĠHolo": 43231, + "Planet": 43232, + "udding": 43233, + "default": 43234, + "ĠOMG": 43235, + "Ġwond": 43236, + "wm": 43237, + "pend": 43238, + "Extreme": 43239, + "Ġinterstellar": 43240, + "ASED": 43241, + "ĠBerks": 43242, + "Ġprimal": 43243, + "Foot": 43244, + "Ġinadvert": 43245, + "amboo": 43246, + "ĠLeica": 43247, + "Events": 43248, + "ĠPigs": 43249, + "RAFT": 43250, + "ï": 43251, + "ĠGentleman": 43252, + "Multiple": 43253, + "ĠPsychiatric": 43254, + "Ġdespise": 43255, + "ĠZionism": 43256, + "ĠSSL": 43257, + "shit": 43258, + "Ġthreaded": 43259, + "Ġartifact": 43260, + "Ġmitochondrial": 43261, + "ĠLayer": 43262, + "inus": 43263, + "podcast": 43264, + "Ġawaken": 43265, + "Management": 43266, + "Ġdelusions": 43267, + "grey": 43268, + "Ġpseud": 43269, + "agonal": 43270, + "ĠHirosh": 43271, + "Georg": 43272, + "Dragon": 43273, + "Stack": 43274, + "ohm": 43275, + "Ġvener": 43276, + "Row": 43277, + "Ġsandbox": 43278, + "Ġblinding": 43279, + "razen": 43280, + "Ġ389": 43281, + "Ġcrappy": 43282, + "Ġlith": 43283, + "antha": 43284, + "Ġplurality": 43285, + "ĠDAC": 43286, + "inently": 43287, + "intage": 43288, + "Ġ1902": 43289, + "ĠDepend": 43290, + "Ġelapsed": 43291, + "==": 43292, + "ĠGenie": 43293, + "Bush": 43294, + "ĠPlanetary": 43295, + "Bah": 43296, + "ĠKira": 43297, + "emn": 43298, + "Month": 43299, + "allic": 43300, + "coded": 43301, + 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+ +#### 📌 Pinned + +* [2024.08.17] 🚀🚀🚀 MiniCPM-V 2.6 is now fully supported by [official](https://github.com/ggerganov/llama.cpp) llama.cpp! GGUF models of various sizes are available [here](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf). +* [2024.08.15] We now also support multi-image SFT. For more details, please refer to the [document](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune). +* [2024.08.14] MiniCPM-V 2.6 now also supports [fine-tuning](https://github.com/modelscope/ms-swift/issues/1613) with the SWIFT framework! +* [2024.08.10] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 is now fully supported by [official](https://github.com/ggerganov/llama.cpp) llama.cpp! GGUF models of various sizes are available [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf). +* [2024.08.06] 🔥🔥🔥 We open-source MiniCPM-V 2.6, which outperforms GPT-4V on single image, multi-image and video understanding. It advances popular features of MiniCPM-Llama3-V 2.5, and can support real-time video understanding on iPad. Try it now! +* [2024.08.03] MiniCPM-Llama3-V 2.5 technical report is released! See [here](https://arxiv.org/abs/2408.01800). +* [2024.07.19] MiniCPM-Llama3-V 2.5 supports vLLM now! See [here](#inference-with-vllm). +* [2024.05.28] 💫 We now support LoRA fine-tuning for MiniCPM-Llama3-V 2.5, using only 2 V100 GPUs! See more statistics [here](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#model-fine-tuning-memory-usage-statistics). +* [2024.05.23] 🔍 We've released a comprehensive comparison between Phi-3-vision-128k-instruct and MiniCPM-Llama3-V 2.5, including benchmarks evaluations, multilingual capabilities, and inference efficiency 🌟📊🌍🚀. Click [here](./docs/compare_with_phi-3_vision.md) to view more details. +* [2024.05.23] 🔥🔥🔥 MiniCPM-V tops GitHub Trending and Hugging Face Trending! Our demo, recommended by Hugging Face Gradio’s official account, is available [here](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). Come and try it out! + +
+ +
+Click to view more news. + +* [2024.06.03] Now, you can run MiniCPM-Llama3-V 2.5 on multiple low VRAM GPUs(12 GB or 16 GB) by distributing the model's layers across multiple GPUs. For more details, Check this [link](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/inference_on_multiple_gpus.md). +* [2024.05.28] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 now fully supports its feature in llama.cpp and ollama! Please pull the latest code **of our provided forks** ([llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md), [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5)). GGUF models in various sizes are available [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main). MiniCPM-Llama3-V 2.5 series is **not supported by the official repositories yet**, and we are working hard to merge PRs. Please stay tuned! +* [2024.05.25] MiniCPM-Llama3-V 2.5 now supports streaming outputs and customized system prompts. Try it [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage)! +* [2024.05.24] We release the MiniCPM-Llama3-V 2.5 [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf), which supports [llama.cpp](#inference-with-llamacpp) inference and provides a 6~8 token/s smooth decoding on mobile phones. Try it now! +* [2024.05.20] We open-soure MiniCPM-Llama3-V 2.5, it has improved OCR capability and supports 30+ languages, representing the first end-side MLLM achieving GPT-4V level performance! We provide [efficient inference](#deployment-on-mobile-phone) and [simple fine-tuning](./finetune/readme.md). Try it now! +* [2024.04.23] MiniCPM-V-2.0 supports vLLM now! Click [here](#inference-with-vllm) to view more details. +* [2024.04.18] We create a HuggingFace Space to host the demo of MiniCPM-V 2.0 at [here](https://huggingface.co/spaces/openbmb/MiniCPM-V-2)! +* [2024.04.17] MiniCPM-V-2.0 supports deploying [WebUI Demo](#webui-demo) now! +* [2024.04.15] MiniCPM-V-2.0 now also supports [fine-tuning](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v-2最佳实践.md) with the SWIFT framework! +* [2024.04.12] We open-source MiniCPM-V 2.0, which achieves comparable performance with Gemini Pro in understanding scene text and outperforms strong Qwen-VL-Chat 9.6B and Yi-VL 34B on OpenCompass, a comprehensive evaluation over 11 popular benchmarks. Click here to view the MiniCPM-V 2.0 technical blog. +* [2024.03.14] MiniCPM-V now supports [fine-tuning](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v最佳实践.md) with the SWIFT framework. Thanks to [Jintao](https://github.com/Jintao-Huang) for the contribution！ +* [2024.03.01] MiniCPM-V now can be deployed on Mac! +* [2024.02.01] We open-source MiniCPM-V and OmniLMM-12B, which support efficient end-side deployment and powerful multimodal capabilities correspondingly. +
+ + +## Contents + + +- [MiniCPM-V 2.6](#minicpm-v-26) +- [MiniCPM-Llama3-V 2.5](#minicpm-llama3-v-25) +- [MiniCPM-V 2.0](#minicpm-v-20) +- [Chat with Our Demo on Gradio 🤗](#chat-with-our-demo-on-gradio-) +- [Install](#install) +- [Inference](#inference) + - [Model Zoo](#model-zoo) + - [Multi-turn Conversation](#multi-turn-conversation) + - [Chat with multiple images](#chat-with-multiple-images) + - [In-context few-shot learning](#in-context-few-shot-learning) + - [Chat with video](#chat-with-video) + - [Inference on Multiple GPUs](#inference-on-multiple-gpus) + - [Inference on Mac](#inference-on-mac) + - [Deployment on Mobile Phone](#deployment-on-mobile-phone) + - [Inference with llama.cpp](#inference-with-llamacpp) + - [Inference with ollama](#inference-with-ollama) + - [Inference with vLLM](#inference-with-vllm) +- [Fine-tuning](#fine-tuning) +- [FAQs](#faqs) + + +## MiniCPM-V 2.6 + +**MiniCPM-V 2.6** is the latest and most capable model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-Llama3-V 2.5, and introduces new features for multi-image and video understanding. Notable features of MiniCPM-V 2.6 include: + +- 🔥 **Leading Performance.** + MiniCPM-V 2.6 achieves an average score of 65.2 on the latest version of OpenCompass, a comprehensive evaluation over 8 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4o mini, GPT-4V, Gemini 1.5 Pro, and Claude 3.5 Sonnet** for single image understanding. + +- 🖼️ **Multi Image Understanding and In-context Learning.** MiniCPM-V 2.6 can also perform **conversation and reasoning over multiple images**. It achieves **state-of-the-art performance** on popular multi-image benchmarks such as Mantis-Eval, BLINK, Mathverse mv and Sciverse mv, and also shows promising in-context learning capability. + +- 🎬 **Video Understanding.** MiniCPM-V 2.6 can also **accept video inputs**, performing conversation and providing dense captions for spatial-temporal information. It outperforms **GPT-4V, Claude 3.5 Sonnet and LLaVA-NeXT-Video-34B** on Video-MME with/without subtitles. + +- 💪 **Strong OCR Capability and Others.** + MiniCPM-V 2.6 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344). It achieves **state-of-the-art performance on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V, and Gemini 1.5 Pro**. + Based on the the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) and [VisCPM](https://github.com/OpenBMB/VisCPM) techniques, it features **trustworthy behaviors**, with significantly lower hallucination rates than GPT-4o and GPT-4V on Object HalBench, and supports **multilingual capabilities** on English, Chinese, German, French, Italian, Korean, etc. + + +- 🚀 **Superior Efficiency.** + In addition to its friendly size, MiniCPM-V 2.6 also shows **state-of-the-art token density** (i.e., number of pixels encoded into each visual token). **It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models**. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-V 2.6 can efficiently support **real-time video understanding** on end-side devices such as iPad. + +- 💫 **Easy Usage.** +MiniCPM-V 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) and [ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#inference-with-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks, (5) quick local WebUI demo setup with [Gradio](#chat-with-our-demo-on-gradio), and (6) online web [demo](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6). + +### Evaluation +
+ +
+ +
+Click to view single image results on OpenCompass, MME, MMVet, OCRBench, MMMU, MathVista, MMB, AI2D, TextVQA, DocVQA, HallusionBench, Object HalBench. +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeToken Density+OpenCompassMMEMMVetOCRBenchMMMU valMathVista miniMMB1.1 testAI2DTextVQA valDocVQA testHallusionBenchObject HalBench
Proprietary
GPT-4o-108869.92328.769.173669.261.382.284.6-92.855.017.6
Claude 3.5 Sonnet-75067.91920.066.078865.961.678.580.2-95.249.913.8
Gemini 1.5 Pro--64.42110.664.075460.657.773.979.173.586.545.6-
GPT-4o mini-108864.12003.466.978560.052.476.077.8--46.112.4
GPT-4V-108863.52070.267.565661.754.779.878.678.087.243.914.2
Step-1V--59.52206.463.362549.944.878.079.271.6-48.4-
Qwen-VL-Max-78458.32281.761.868452.043.474.675.779.593.141.213.4
Open-source
LLaVA-NeXT-Yi-34B34B15755.02006.550.757448.840.477.878.969.3-34.812.6
Mini-Gemini-HD-34B34B157-2141.059.351848.043.3-80.574.178.9--
Cambrian-34B34B182058.32049.953.259150.450.377.879.576.775.541.614.7
GLM-4V-9B13B78459.12018.858.077646.951.167.971.2--45.0-
InternVL2-8B8B70664.12215.154.379451.258.379.483.677.491.645.021.3
MiniCPM-Llama-V 2.58B188258.82024.652.872545.854.372.078.476.684.842.410.3
MiniCPM-V 2.68B282265.22348.4*60.0852*49.8*60.678.082.180.190.848.1*8.2
+ +
+* We evaluate this benchmark using chain-of-thought prompting. Specifically, for MME, we used this technique only for the Cognition set. + ++ Token Density: number of pixels encoded into each visual token at maximum resolution, i.e., # pixels at maximum resolution / # visual tokens. + +Note: For proprietary models, we calculate token density based on the image encoding charging strategy defined in the official API documentation, which provides an upper-bound estimation. + +
+ + +
+Click to view multi-image results on Mantis Eval, BLINK, Mathverse mv, Sciverse mv, MIRB. +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeMantis EvalBLINK valMathverse mvSciverse mvMIRB
Proprietary
GPT-4V-62.754.660.366.953.1
LLaVA-NeXT-Interleave-14B14B66.452.632.730.2-
Open-source
Emu2-Chat37B37.836.2-27.2-
CogVLM17B45.241.1---
VPG-C7B52.443.124.323.1-
VILA 8B8B51.239.3-36.5-
InternLM-XComposer-2.58B53.1*48.932.1*-42.5
InternVL2-8B8B59.0*50.930.5*34.4*56.9*
MiniCPM-V 2.68B69.153.084.974.953.8
+ +
+* We evaluate the officially released checkpoint by ourselves. +
+ +
+Click to view video results on Video-MME and Video-ChatGPT. +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeVideo-MMEVideo-ChatGPT
w/o subsw subsCorrectnessDetailContextTemporalConsistency
Proprietary
Claude 3.5 Sonnet-60.062.9-----
GPT-4V-59.963.3-----
Open-source
LLaVA-NeXT-7B7B--3.393.293.922.603.12
LLaVA-NeXT-34B34B--3.293.233.832.513.47
CogVLM2-Video12B--3.493.463.232.983.64
LongVA7B52.454.33.053.093.772.443.64
InternVL2-8B8B54.056.9-----
InternLM-XComposer-2.58B55.8------
LLaVA-NeXT-Video32B60.263.03.483.373.952.643.28
MiniCPM-V 2.68B60.963.63.593.283.932.733.62
+
+
+ + +
+Click to view few-shot results on TextVQA, VizWiz, VQAv2, OK-VQA. +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeShotTextVQA valVizWiz test-devVQAv2 test-devOK-VQA val
Flamingo80B0*35.031.656.340.6
436.539.663.157.4
837.344.865.657.5
IDEFICS80B0*30.936.060.045.2
434.340.463.652.4
835.746.164.855.1
OmniCorpus7B0*43.049.863.245.5
445.451.364.546.5
845.652.264.746.6
Emu237B026.440.433.526.7
448.254.667.053.2
849.354.767.854.1
MM130B026.240.448.926.7
849.354.770.954.1
MiniCPM-V 2.6+8B043.933.845.423.9
463.660.565.550.1
864.663.468.251.4
+ + +
+* denotes zero image shot and two additional text shots following Flamingo. + ++ We evaluate the pretraining ckpt without SFT. +
+ +### Examples + +
+ Bike + Menu + Code + Mem + medal +
+
+ Click to view more cases. +
+ elec + Menu +
+
+ +We deploy MiniCPM-V 2.6 on end devices. The demo video is the raw screen recording on a iPad Pro without edition. + + +

+ +      + +

+
+ + +

+ +      + +

+
+ + +

+ + +

+
+ +## MiniCPM-Llama3-V 2.5 + +
+Click to view more details of MiniCPM-Llama3-V 2.5 + +**MiniCPM-Llama3-V 2.5** is the latest model in the MiniCPM-V series. The model is built on SigLip-400M and Llama3-8B-Instruct with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.0. Notable features of MiniCPM-Llama3-V 2.5 include: + +- 🔥 **Leading Performance.** + MiniCPM-Llama3-V 2.5 has achieved an average score of 65.1 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4V-1106, Gemini Pro, Claude 3 and Qwen-VL-Max** and greatly outperforms other Llama 3-based MLLMs. + +- 💪 **Strong OCR Capabilities.** + MiniCPM-Llama3-V 2.5 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), achieving a **700+ score on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V-0409, Qwen-VL-Max and Gemini Pro**. Based on recent user feedback, MiniCPM-Llama3-V 2.5 has now enhanced full-text OCR extraction, table-to-markdown conversion, and other high-utility capabilities, and has further strengthened its instruction-following and complex reasoning abilities, enhancing multimodal interaction experiences. + +- 🏆 **Trustworthy Behavior.** + Leveraging the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) method (the newest technique in the [RLHF-V](https://github.com/RLHF-V) [CVPR'24] series), MiniCPM-Llama3-V 2.5 exhibits more trustworthy behavior. It achieves a **10.3%** hallucination rate on Object HalBench, lower than GPT-4V-1106 (13.6%), achieving the best-level performance within the open-source community. [Data released](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset). + +- 🌏 **Multilingual Support.** + Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from [VisCPM](https://github.com/OpenBMB/VisCPM), MiniCPM-Llama3-V 2.5 extends its bilingual (Chinese-English) multimodal capabilities to **over 30 languages including German, French, Spanish, Italian, Korean etc.** [All Supported Languages](./assets/minicpm-llama-v-2-5_languages.md). + +- 🚀 **Efficient Deployment.** + MiniCPM-Llama3-V 2.5 systematically employs **model quantization, CPU optimizations, NPU optimizations and compilation optimizations**, achieving high-efficiency deployment on end-side devices. For mobile phones with Qualcomm chips, we have integrated the NPU acceleration framework QNN into llama.cpp for the first time. After systematic optimization, MiniCPM-Llama3-V 2.5 has realized a **150x acceleration in end-side MLLM image encoding** and a **3x speedup in language decoding**. + +- 💫 **Easy Usage.** +MiniCPM-Llama3-V 2.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) support for efficient CPU inference on local devices, (2) [GGUF](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) format quantized models in 16 sizes, (3) efficient [LoRA](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning) fine-tuning with only 2 V100 GPUs, (4) [streaming output](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage), (5) quick local WebUI demo setup with [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) and [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py), and (6) interactive demos on [HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). + +### Evaluation + +
+ +
+
+Click to view results on TextVQA, DocVQA, OCRBench, OpenCompass, MME, MMBench, MMMU, MathVista, LLaVA Bench, RealWorld QA, Object HalBench. +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeOCRBenchTextVQA valDocVQA testOpen-CompassMMEMMB test (en)MMB test (cn)MMMU valMath-VistaLLaVA BenchRealWorld QAObject HalBench
Proprietary
Gemini Pro-68074.688.162.92148.973.674.348.945.879.960.4-
GPT-4V (2023.11.06)-64578.088.463.51771.577.074.453.847.893.163.086.4
Open-source
Mini-Gemini2.2B-56.234.2*-1653.0--31.7----
Qwen-VL-Chat9.6B48861.562.651.61860.061.856.337.033.867.749.356.2
DeepSeek-VL-7B7.3B43564.7*47.0*54.61765.473.871.438.336.877.854.2-
Yi-VL-34B34B29043.4*16.9*52.22050.272.470.745.130.762.354.879.3
CogVLM-Chat17.4B59070.433.3*54.21736.665.855.937.334.773.960.373.6
TextMonkey9.7B55864.366.7---------
Idefics28.0B-73.074.057.21847.675.768.645.252.249.160.7-
Bunny-LLama-3-8B8.4B---54.31920.377.073.941.331.561.258.8-
LLaVA-NeXT Llama-3-8B8.4B--78.2-1971.5--41.737.580.160.0-
Phi-3-vision-128k-instruct4.2B639*70.9--1537.5*--40.444.564.2*58.8*-
MiniCPM-V 1.02.8B36660.638.247.51650.264.162.638.328.951.351.278.4
MiniCPM-V 2.02.8B60574.171.954.51808.669.166.538.238.769.255.885.5
MiniCPM-Llama3-V 2.58.5B72576.684.865.12024.677.274.245.854.386.763.589.7
+ + +
+* We evaluate the officially released checkpoint by ourselves. + +
+ +
+ +
+ Evaluation results of multilingual LLaVA Bench +
+ +### Examples + + +

+ +

+
+ +
+ + +## MiniCPM-V 2.0 + +
+Click to view more details of MiniCPM-V 2.0 + + +**MiniCPM-V 2.0** is an efficient version with promising performance for deployment. The model is built based on SigLip-400M and [MiniCPM-2.4B](https://github.com/OpenBMB/MiniCPM/), connected by a perceiver resampler. Our latest version, MiniCPM-V 2.0 has several notable features. + +- 🔥 **State-of-the-art Performance.** + + MiniCPM-V 2.0 achieves **state-of-the-art performance** on multiple benchmarks (including OCRBench, TextVQA, MME, MMB, MathVista, etc) among models under 7B parameters. It even **outperforms strong Qwen-VL-Chat 9.6B, CogVLM-Chat 17.4B, and Yi-VL 34B on OpenCompass, a comprehensive evaluation over 11 popular benchmarks**. Notably, MiniCPM-V 2.0 shows **strong OCR capability**, achieving **comparable performance to Gemini Pro in scene-text understanding**, and **state-of-the-art performance on OCRBench** among open-source models. + +- 🏆 **Trustworthy Behavior.** + + LMMs are known for suffering from hallucination, often generating text not factually grounded in images. MiniCPM-V 2.0 is **the first end-side LMM aligned via multimodal RLHF for trustworthy behavior** (using the recent [RLHF-V](https://rlhf-v.github.io/) [CVPR'24] series technique). This allows the model to **match GPT-4V in preventing hallucinations** on Object HalBench. + +- 🌟 **High-Resolution Images at Any Aspect Raito.** + + MiniCPM-V 2.0 can accept **1.8 million pixels (e.g., 1344x1344) images at any aspect ratio**. This enables better perception of fine-grained visual information such as small objects and optical characters, which is achieved via a recent technique from [LLaVA-UHD](https://arxiv.org/pdf/2403.11703.pdf). + +- ⚡️ **High Efficiency.** + + MiniCPM-V 2.0 can be **efficiently deployed on most GPU cards and personal computers**, and **even on end devices such as mobile phones**. For visual encoding, we compress the image representations into much fewer tokens via a perceiver resampler. This allows MiniCPM-V 2.0 to operate with **favorable memory cost and speed during inference even when dealing with high-resolution images**. + +- 🙌 **Bilingual Support.** + + MiniCPM-V 2.0 **supports strong bilingual multimodal capabilities in both English and Chinese**. This is enabled by generalizing multimodal capabilities across languages, a technique from [VisCPM](https://arxiv.org/abs/2308.12038) [ICLR'24]. + +### Examples + + +

+ +

+
+ +We deploy MiniCPM-V 2.0 on end devices. The demo video is the raw screen recording on a Xiaomi 14 Pro without edition. + + +

+ + +

+
+ +
+ +## Legacy Models + +| Model | Introduction and Guidance | +|:----------------------|:-------------------:| +| MiniCPM-V 1.0 | [Document](./minicpm_v1.md) | +| OmniLMM-12B | [Document](./omnilmm_en.md) | + + +## Chat with Our Demo on Gradio 🤗 + +We provide online and local demos powered by Hugging Face Gradio , the most popular model deployment framework nowadays. It supports streaming outputs, progress bars, queuing, alerts, and other useful features. + + +### Online Demo + +Click here to try out the online demo of [MiniCPM-V 2.6](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6) | [MiniCPM-Llama3-V 2.5](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) | [MiniCPM-V 2.0](https://huggingface.co/spaces/openbmb/MiniCPM-V-2). + +### Local WebUI Demo + +You can easily build your own local WebUI demo with Gradio using the following commands. + +```shell +pip install -r requirements.txt +``` + +```shell +# For NVIDIA GPUs, run: +python web_demo_2.6.py --device cuda + +``` + + +## Install + +1. Clone this repository and navigate to the source folder + +```bash +git clone https://github.com/OpenBMB/MiniCPM-V.git +cd MiniCPM-V +``` + +2. Create conda environment + +```Shell +conda create -n MiniCPM-V python=3.10 -y +conda activate MiniCPM-V +``` + +3. Install dependencies + +```shell +pip install -r requirements.txt +``` + +## Inference + + +### Model Zoo + +| Model | Device | Memory |          Description | Download | +|:-----------|:--:|:-----------:|:-------------------|:---------------:| +| MiniCPM-V 2.6| GPU | 17 GB | The latest version, achieving state-of-the-art end-side performance for single image, multi-image and video understanding. | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2_6)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6) | +| MiniCPM-V 2.6 gguf | CPU | 6 GB | The gguf version, lower memory usage and faster inference. | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6-gguf) | +| MiniCPM-V 2.6 int4 | GPU | 7 GB | The int4 quantized version, lower GPU memory usage. | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6-int4) | +| MiniCPM-Llama3-V 2.5 | GPU | 19 GB | Strong end-side multimodal performance. | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5) | +| MiniCPM-Llama3-V 2.5 gguf | CPU | 6 GB | The gguf version, lower memory usage and faster inference. | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf)   [](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-gguf) | +| MiniCPM-Llama3-V 2.5 int4 | GPU | 8 GB | The int4 quantized version, lower GPU memory usage. | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-int4/)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-int4) | +| MiniCPM-V 2.0 | GPU | 8 GB | Light version, balance the performance the computation cost. | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2) | +| MiniCPM-V 1.0 | GPU | 7 GB | Lightest version, achieving the fastest inference. | [🤗](https://huggingface.co/openbmb/MiniCPM-V)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V) | + +### Multi-turn Conversation + +Please refer to the following codes to run. + +
+ +
+ + +```python +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +torch.manual_seed(0) + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager +model = model.eval().cuda() +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) + +image = Image.open('./assets/airplane.jpeg').convert('RGB') + +# First round chat +question = "Tell me the model of this aircraft." +msgs = [{'role': 'user', 'content': [image, question]}] + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer +) +print(answer) + +# Second round chat +# pass history context of multi-turn conversation +msgs.append({"role": "assistant", "content": [answer]}) +msgs.append({"role": "user", "content": ["Introduce something about Airbus A380."]}) + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer +) +print(answer) +``` + +You will get the following output: + +``` +"The aircraft in the image is an Airbus A380, which can be identified by its large size, double-deck structure, and the distinctive shape of its wings and engines. The A380 is a wide-body aircraft known for being the world's largest passenger airliner, designed for long-haul flights. It has four engines, which are characteristic of large commercial aircraft. The registration number on the aircraft can also provide specific information about the model if looked up in an aviation database." + +"The Airbus A380 is a double-deck, wide-body, four-engine jet airliner made by Airbus. It is the world's largest passenger airliner and is known for its long-haul capabilities. The aircraft was developed to improve efficiency and comfort for passengers traveling over long distances. It has two full-length passenger decks, which can accommodate more passengers than a typical single-aisle airplane. The A380 has been operated by airlines such as Lufthansa, Singapore Airlines, and Emirates, among others. It is widely recognized for its unique design and significant impact on the aviation industry." +``` + +#### Chat with multiple images +
+ Click to view Python code running MiniCPM-V 2.6 with multiple images input. + +```python +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager +model = model.eval().cuda() +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) + +image1 = Image.open('image1.jpg').convert('RGB') +image2 = Image.open('image2.jpg').convert('RGB') +question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.' + +msgs = [{'role': 'user', 'content': [image1, image2, question]}] + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer +) +print(answer) +``` +
+ +#### In-context few-shot learning +
+ Click to view Python code running MiniCPM-V 2.6 with few-shot input. + +```python +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager +model = model.eval().cuda() +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) + +question = "production date" +image1 = Image.open('example1.jpg').convert('RGB') +answer1 = "2023.08.04" +image2 = Image.open('example2.jpg').convert('RGB') +answer2 = "2007.04.24" +image_test = Image.open('test.jpg').convert('RGB') + +msgs = [ + {'role': 'user', 'content': [image1, question]}, {'role': 'assistant', 'content': [answer1]}, + {'role': 'user', 'content': [image2, question]}, {'role': 'assistant', 'content': [answer2]}, + {'role': 'user', 'content': [image_test, question]} +] + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer +) +print(answer) +``` +
+ +#### Chat with video +
+ Click to view Python code running MiniCPM-V 2.6 with video input. + +```python +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer +from decord import VideoReader, cpu # pip install decord + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager +model = model.eval().cuda() +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) + +MAX_NUM_FRAMES=64 # if cuda OOM set a smaller number + +def encode_video(video_path): + def uniform_sample(l, n): + gap = len(l) / n + idxs = [int(i * gap + gap / 2) for i in range(n)] + return [l[i] for i in idxs] + + vr = VideoReader(video_path, ctx=cpu(0)) + sample_fps = round(vr.get_avg_fps() / 1) # FPS + frame_idx = [i for i in range(0, len(vr), sample_fps)] + if len(frame_idx) > MAX_NUM_FRAMES: + frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES) + frames = vr.get_batch(frame_idx).asnumpy() + frames = [Image.fromarray(v.astype('uint8')) for v in frames] + print('num frames:', len(frames)) + return frames + +video_path="video_test.mp4" +frames = encode_video(video_path) +question = "Describe the video" +msgs = [ + {'role': 'user', 'content': frames + [question]}, +] + +# Set decode params for video +params = {} +params["use_image_id"] = False +params["max_slice_nums"] = 2 # use 1 if cuda OOM and video resolution > 448*448 + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer, + **params +) +print(answer) +``` +
+ + +### Inference on Multiple GPUs +You can run MiniCPM-Llama3-V 2.5 on multiple low VRAM GPUs (12 GB or 16 GB) by distributing the model's layers across multiple GPUs. Please refer to this [tutorial](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/inference_on_multiple_gpus.md) for detailed instructions on how to load the model and inference using multiple low VRAM GPUs. + + +### Inference on Mac +
+Click to view an example, to run MiniCPM-Llama3-V 2.5 on 💻 Mac with MPS (Apple silicon or AMD GPUs). + +```python +# test.py Need more than 16GB memory. +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True, low_cpu_mem_usage=True) +model = model.to(device='mps') + +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True) +model.eval() + +image = Image.open('./assets/hk_OCR.jpg').convert('RGB') +question = 'Where is this photo taken?' +msgs = [{'role': 'user', 'content': question}] + +answer, context, _ = model.chat( + image=image, + msgs=msgs, + context=None, + tokenizer=tokenizer, + sampling=True +) +print(answer) +``` +Run with command: +```shell +PYTORCH_ENABLE_MPS_FALLBACK=1 python test.py +``` +
+ +### Deployment on Mobile Phone +MiniCPM-V 2.0 can be deployed on mobile phones with Android operating systems. 🚀 Click [MiniCPM-V 2.0](https://github.com/OpenBMB/mlc-MiniCPM) to install apk. + +### Inference with llama.cpp +MiniCPM-V 2.6 can run with llama.cpp now! See [our fork of llama.cpp](https://github.com/OpenBMB/llama.cpp/tree/minicpmv-main/examples/llava/README-minicpmv2.6.md) for more detail. This implementation supports smooth inference of 16~18 token/s on iPad (test environment：iPad Pro + M4). + +### Inference with ollama +MiniCPM-V 2.6 can run with ollama now! See [our fork of ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) for more detail. This implementation supports smooth inference of 16~18 token/s on iPad (test environment：iPad Pro + M4). + +### Inference with vLLM + +
+ vLLM now officially supports MiniCPM-V 2.6, MiniCPM-Llama3-V 2.5 and MiniCPM-V 2.0, Click to see. + +1. Install vLLM(>=0.5.4): +```shell +pip install vllm +``` +2. Install timm: (optional, MiniCPM-V 2.0 need timm) +```shell +pip install timm==0.9.10 +``` +3. Run the example(for image): +```python +from transformers import AutoTokenizer +from PIL import Image +from vllm import LLM, SamplingParams + +MODEL_NAME = "openbmb/MiniCPM-V-2_6" +# Also available for previous models +# MODEL_NAME = "openbmb/MiniCPM-Llama3-V-2_5" +# MODEL_NAME = "HwwwH/MiniCPM-V-2" + +image = Image.open("xxx.png").convert("RGB") +tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True) +llm = LLM( + model=MODEL_NAME, + trust_remote_code=True, + gpu_memory_utilization=1, + max_model_len=2048 +) + +messages = [{ + "role": + "user", + "content": + # Number of images + "(./)" + \ + "\nWhat is the content of this image?" +}] +prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True +) + +# Single Inference +inputs = { + "prompt": prompt, + "multi_modal_data": { + "image": image + # Multi images, the number of images should be equal to that of `(./)` + # "image": [image, image] + }, +} +# Batch Inference +# inputs = [{ +# "prompt": prompt, +# "multi_modal_data": { +# "image": image +# }, +# } for _ in 2] + + +# 2.6 +stop_tokens = ['<|im_end|>', '<|endoftext|>'] +stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens] +# 2.0 +# stop_token_ids = [tokenizer.eos_id] +# 2.5 +# stop_token_ids = [tokenizer.eos_id, tokenizer.eot_id] + +sampling_params = SamplingParams( + stop_token_ids=stop_token_ids, + use_beam_search=True, + temperature=0, + best_of=3, + max_tokens=1024 +) + +outputs = llm.generate(inputs, sampling_params=sampling_params) + +print(outputs[0].outputs[0].text) +``` +4. click [here](https://modelbest.feishu.cn/wiki/C2BWw4ZP0iCDy7kkCPCcX2BHnOf?from=from_copylink) if you want to use it with *video*, or get more details about `vLLM`. +
+ +## Fine-tuning + +### Simple Fine-tuning + +We support simple fine-tuning with Hugging Face for MiniCPM-V 2.0 and MiniCPM-Llama3-V 2.5. + +[Reference Document](./finetune/readme.md) + +### With the SWIFT Framework + +We now support MiniCPM-V series fine-tuning with the SWIFT framework. SWIFT supports training, inference, evaluation and deployment of nearly 200 LLMs and MLLMs . It supports the lightweight training solutions provided by PEFT and a complete Adapters Library including techniques such as NEFTune, LoRA+ and LLaMA-PRO. + +Best Practices：[MiniCPM-V 1.0](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v最佳实践.md), [MiniCPM-V 2.0](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v-2最佳实践.md), [MiniCPM-V 2.6](https://github.com/modelscope/ms-swift/issues/1613). + +## FAQs +Click here to view the [FAQs](./docs/faqs.md) + +## Model License + +* This repository is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. + +* The usage of MiniCPM-V model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md). + +* The models and weights of MiniCPM are completely free for academic research. after filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, are also available for free commercial use. + + +## Statement + +As LMMs, MiniCPM-V models (including OmniLMM) generate contents by learning a large amount of multimodal corpora, but they cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-V models does not represent the views and positions of the model developers + +We will not be liable for any problems arising from the use of MiniCPM-V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model. + + +## Institutions + +This project is developed by the following institutions: + +- [THUNLP](https://nlp.csai.tsinghua.edu.cn/) +- [ModelBest](https://modelbest.cn/) +- [Zhihu](https://www.zhihu.com/ ) + +## 🌟 Star History + + + +

+ +

+
+ + + +## Key Techniques and Other Multimodal Projects + +👏 Welcome to explore key techniques of MiniCPM-V and other multimodal projects of our team: + +[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD) | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V) + + +## Citation + +If you find our model/code/paper helpful, please consider cite our papers 📝 and star us ⭐️！ + +```bib +@article{yao2024minicpm, + title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone}, + author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others}, + journal={arXiv preprint arXiv:2408.01800}, + year={2024} +} +``` diff --git a/MiniCPM-V/README_en.md b/MiniCPM-V/README_en.md new file mode 100644 index 0000000000000000000000000000000000000000..e126cf4f5943261fb743528788d713423115b1a8 --- /dev/null +++ b/MiniCPM-V/README_en.md @@ -0,0 +1,1695 @@ +
+ + + +**A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone** + + [中文](./README_zh.md) | + English + +Join our 💬 WeChat | View MiniCPM-V 📖 best practices + + +

+ MiniCPM-V 2.6 🤗 🤖 | MiniCPM-Llama3-V 2.5 🤗 🤖 | + MiniCPM-Llama3-V 2.5 Technical Report +

+ +
+ + +**MiniCPM-V** is a series of end-side multimodal LLMs (MLLMs) designed for vision-language understanding. The models take image, video and text as inputs and provide high-quality text outputs. Since February 2024, we have released 5 versions of the model, aiming to achieve **strong performance and efficient deployment**. The most notable models in this series currently include: + +- **MiniCPM-V 2.6**: 🔥🔥🔥 The latest and most capable model in the MiniCPM-V series. With a total of 8B parameters, the model **surpasses GPT-4V in single image, multi-image and video understanding**. It outperforms **GPT-4o mini, Gemini 1.5 Pro and Claude 3.5 Sonnet** in single image understanding, and advances MiniCPM-Llama3-V 2.5's features such as strong OCR capability, trustworthy behavior, multilingual support, and end-side deployment. Due to its superior token density, MiniCPM-V 2.6 can for the first time support real-time video understanding on end-side devices such as iPad. + +- **MiniCPM-V 2.0**: The lightest model in the MiniCPM-V series. With 2B parameters, it surpasses larger models such as Yi-VL 34B, CogVLM-Chat 17B, and Qwen-VL-Chat 10B in overall performance. It can accept image inputs of any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), achieving comparable performance with Gemini Pro in understanding scene-text and matches GPT-4V in low hallucination rates. + + +## News + +#### 📌 Pinned + +* [2024.08.17] 🚀🚀🚀 MiniCPM-V 2.6 is now fully supported by [official](https://github.com/ggerganov/llama.cpp) llama.cpp! GGUF models of various sizes are available [here](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf). +* [2024.08.15] We now also support multi-image SFT. For more details, please refer to the [document](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune). +* [2024.08.14] MiniCPM-V 2.6 now also supports [fine-tuning](https://github.com/modelscope/ms-swift/issues/1613) with the SWIFT framework! +* [2024.08.10] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 is now fully supported by [official](https://github.com/ggerganov/llama.cpp) llama.cpp! GGUF models of various sizes are available [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf). +* [2024.08.06] 🔥🔥🔥 We open-source MiniCPM-V 2.6, which outperforms GPT-4V on single image, multi-image and video understanding. It advances popular features of MiniCPM-Llama3-V 2.5, and can support real-time video understanding on iPad. Try it now! +* [2024.08.03] MiniCPM-Llama3-V 2.5 technical report is released! See [here](https://arxiv.org/abs/2408.01800). +* [2024.07.19] MiniCPM-Llama3-V 2.5 supports vLLM now! See [here](#inference-with-vllm). +* [2024.05.28] 💫 We now support LoRA fine-tuning for MiniCPM-Llama3-V 2.5, using only 2 V100 GPUs! See more statistics [here](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#model-fine-tuning-memory-usage-statistics). +* [2024.05.23] 🔍 We've released a comprehensive comparison between Phi-3-vision-128k-instruct and MiniCPM-Llama3-V 2.5, including benchmarks evaluations, multilingual capabilities, and inference efficiency 🌟📊🌍🚀. Click [here](./docs/compare_with_phi-3_vision.md) to view more details. +* [2024.05.23] 🔥🔥🔥 MiniCPM-V tops GitHub Trending and Hugging Face Trending! Our demo, recommended by Hugging Face Gradio’s official account, is available [here](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). Come and try it out! + +
+ +
+Click to view more news. + +* [2024.06.03] Now, you can run MiniCPM-Llama3-V 2.5 on multiple low VRAM GPUs(12 GB or 16 GB) by distributing the model's layers across multiple GPUs. For more details, Check this [link](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/inference_on_multiple_gpus.md). +* [2024.05.28] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 now fully supports its feature in llama.cpp and ollama! Please pull the latest code **of our provided forks** ([llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md), [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5)). GGUF models in various sizes are available [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main). MiniCPM-Llama3-V 2.5 series is **not supported by the official repositories yet**, and we are working hard to merge PRs. Please stay tuned! +* [2024.05.25] MiniCPM-Llama3-V 2.5 now supports streaming outputs and customized system prompts. Try it [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage)! +* [2024.05.24] We release the MiniCPM-Llama3-V 2.5 [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf), which supports [llama.cpp](#inference-with-llamacpp) inference and provides a 6~8 token/s smooth decoding on mobile phones. Try it now! +* [2024.05.20] We open-soure MiniCPM-Llama3-V 2.5, it has improved OCR capability and supports 30+ languages, representing the first end-side MLLM achieving GPT-4V level performance! We provide [efficient inference](#deployment-on-mobile-phone) and [simple fine-tuning](./finetune/readme.md). Try it now! +* [2024.04.23] MiniCPM-V-2.0 supports vLLM now! Click [here](#inference-with-vllm) to view more details. +* [2024.04.18] We create a HuggingFace Space to host the demo of MiniCPM-V 2.0 at [here](https://huggingface.co/spaces/openbmb/MiniCPM-V-2)! +* [2024.04.17] MiniCPM-V-2.0 supports deploying [WebUI Demo](#webui-demo) now! +* [2024.04.15] MiniCPM-V-2.0 now also supports [fine-tuning](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v-2最佳实践.md) with the SWIFT framework! +* [2024.04.12] We open-source MiniCPM-V 2.0, which achieves comparable performance with Gemini Pro in understanding scene text and outperforms strong Qwen-VL-Chat 9.6B and Yi-VL 34B on OpenCompass, a comprehensive evaluation over 11 popular benchmarks. Click here to view the MiniCPM-V 2.0 technical blog. +* [2024.03.14] MiniCPM-V now supports [fine-tuning](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v最佳实践.md) with the SWIFT framework. Thanks to [Jintao](https://github.com/Jintao-Huang) for the contribution！ +* [2024.03.01] MiniCPM-V now can be deployed on Mac! +* [2024.02.01] We open-source MiniCPM-V and OmniLMM-12B, which support efficient end-side deployment and powerful multimodal capabilities correspondingly. +
+ + +## Contents + + +- [MiniCPM-V 2.6](#minicpm-v-26) +- [MiniCPM-Llama3-V 2.5](#minicpm-llama3-v-25) +- [MiniCPM-V 2.0](#minicpm-v-20) +- [Chat with Our Demo on Gradio 🤗](#chat-with-our-demo-on-gradio-) +- [Install](#install) +- [Inference](#inference) + - [Model Zoo](#model-zoo) + - [Multi-turn Conversation](#multi-turn-conversation) + - [Chat with multiple images](#chat-with-multiple-images) + - [In-context few-shot learning](#in-context-few-shot-learning) + - [Chat with video](#chat-with-video) + - [Inference on Multiple GPUs](#inference-on-multiple-gpus) + - [Inference on Mac](#inference-on-mac) + - [Deployment on Mobile Phone](#deployment-on-mobile-phone) + - [Inference with llama.cpp](#inference-with-llamacpp) + - [Inference with ollama](#inference-with-ollama) + - [Inference with vLLM](#inference-with-vllm) +- [Fine-tuning](#fine-tuning) +- [FAQs](#faqs) + + +## MiniCPM-V 2.6 + +**MiniCPM-V 2.6** is the latest and most capable model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-Llama3-V 2.5, and introduces new features for multi-image and video understanding. Notable features of MiniCPM-V 2.6 include: + +- 🔥 **Leading Performance.** + MiniCPM-V 2.6 achieves an average score of 65.2 on the latest version of OpenCompass, a comprehensive evaluation over 8 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4o mini, GPT-4V, Gemini 1.5 Pro, and Claude 3.5 Sonnet** for single image understanding. + +- 🖼️ **Multi Image Understanding and In-context Learning.** MiniCPM-V 2.6 can also perform **conversation and reasoning over multiple images**. It achieves **state-of-the-art performance** on popular multi-image benchmarks such as Mantis-Eval, BLINK, Mathverse mv and Sciverse mv, and also shows promising in-context learning capability. + +- 🎬 **Video Understanding.** MiniCPM-V 2.6 can also **accept video inputs**, performing conversation and providing dense captions for spatial-temporal information. It outperforms **GPT-4V, Claude 3.5 Sonnet and LLaVA-NeXT-Video-34B** on Video-MME with/without subtitles. + +- 💪 **Strong OCR Capability and Others.** + MiniCPM-V 2.6 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344). It achieves **state-of-the-art performance on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V, and Gemini 1.5 Pro**. + Based on the the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) and [VisCPM](https://github.com/OpenBMB/VisCPM) techniques, it features **trustworthy behaviors**, with significantly lower hallucination rates than GPT-4o and GPT-4V on Object HalBench, and supports **multilingual capabilities** on English, Chinese, German, French, Italian, Korean, etc. + + +- 🚀 **Superior Efficiency.** + In addition to its friendly size, MiniCPM-V 2.6 also shows **state-of-the-art token density** (i.e., number of pixels encoded into each visual token). **It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models**. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-V 2.6 can efficiently support **real-time video understanding** on end-side devices such as iPad. + +- 💫 **Easy Usage.** +MiniCPM-V 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) and [ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#inference-with-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks, (5) quick local WebUI demo setup with [Gradio](#chat-with-our-demo-on-gradio), and (6) online web [demo](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6). + +### Evaluation +
+ +
+ +
+Click to view single image results on OpenCompass, MME, MMVet, OCRBench, MMMU, MathVista, MMB, AI2D, TextVQA, DocVQA, HallusionBench, Object HalBench. +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeToken Density+OpenCompassMMEMMVetOCRBenchMMMU valMathVista miniMMB1.1 testAI2DTextVQA valDocVQA testHallusionBenchObject HalBench
Proprietary
GPT-4o-108869.92328.769.173669.261.382.284.6-92.855.017.6
Claude 3.5 Sonnet-75067.91920.066.078865.961.678.580.2-95.249.913.8
Gemini 1.5 Pro--64.42110.664.075460.657.773.979.173.586.545.6-
GPT-4o mini-108864.12003.466.978560.052.476.077.8--46.112.4
GPT-4V-108863.52070.267.565661.754.779.878.678.087.243.914.2
Step-1V--59.52206.463.362549.944.878.079.271.6-48.4-
Qwen-VL-Max-78458.32281.761.868452.043.474.675.779.593.141.213.4
Open-source
LLaVA-NeXT-Yi-34B34B15755.02006.550.757448.840.477.878.969.3-34.812.6
Mini-Gemini-HD-34B34B157-2141.059.351848.043.3-80.574.178.9--
Cambrian-34B34B182058.32049.953.259150.450.377.879.576.775.541.614.7
GLM-4V-9B13B78459.12018.858.077646.951.167.971.2--45.0-
InternVL2-8B8B70664.12215.154.379451.258.379.483.677.491.645.021.3
MiniCPM-Llama-V 2.58B188258.82024.652.872545.854.372.078.476.684.842.410.3
MiniCPM-V 2.68B282265.22348.4*60.0852*49.8*60.678.082.180.190.848.1*8.2
+ +
+* We evaluate this benchmark using chain-of-thought prompting. Specifically, for MME, we used this technique only for the Cognition set. + ++ Token Density: number of pixels encoded into each visual token at maximum resolution, i.e., # pixels at maximum resolution / # visual tokens. + +Note: For proprietary models, we calculate token density based on the image encoding charging strategy defined in the official API documentation, which provides an upper-bound estimation. + +
+ + +
+Click to view multi-image results on Mantis Eval, BLINK, Mathverse mv, Sciverse mv, MIRB. +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeMantis EvalBLINK valMathverse mvSciverse mvMIRB
Proprietary
GPT-4V-62.754.660.366.953.1
LLaVA-NeXT-Interleave-14B14B66.452.632.730.2-
Open-source
Emu2-Chat37B37.836.2-27.2-
CogVLM17B45.241.1---
VPG-C7B52.443.124.323.1-
VILA 8B8B51.239.3-36.5-
InternLM-XComposer-2.58B53.1*48.932.1*-42.5
InternVL2-8B8B59.0*50.930.5*34.4*56.9*
MiniCPM-V 2.68B69.153.084.974.953.8
+ +
+* We evaluate the officially released checkpoint by ourselves. +
+ +
+Click to view video results on Video-MME and Video-ChatGPT. +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeVideo-MMEVideo-ChatGPT
w/o subsw subsCorrectnessDetailContextTemporalConsistency
Proprietary
Claude 3.5 Sonnet-60.062.9-----
GPT-4V-59.963.3-----
Open-source
LLaVA-NeXT-7B7B--3.393.293.922.603.12
LLaVA-NeXT-34B34B--3.293.233.832.513.47
CogVLM2-Video12B--3.493.463.232.983.64
LongVA7B52.454.33.053.093.772.443.64
InternVL2-8B8B54.056.9-----
InternLM-XComposer-2.58B55.8------
LLaVA-NeXT-Video32B60.263.03.483.373.952.643.28
MiniCPM-V 2.68B60.963.63.593.283.932.733.62
+
+
+ + +
+Click to view few-shot results on TextVQA, VizWiz, VQAv2, OK-VQA. +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeShotTextVQA valVizWiz test-devVQAv2 test-devOK-VQA val
Flamingo80B0*35.031.656.340.6
436.539.663.157.4
837.344.865.657.5
IDEFICS80B0*30.936.060.045.2
434.340.463.652.4
835.746.164.855.1
OmniCorpus7B0*43.049.863.245.5
445.451.364.546.5
845.652.264.746.6
Emu237B026.440.433.526.7
448.254.667.053.2
849.354.767.854.1
MM130B026.240.448.926.7
849.354.770.954.1
MiniCPM-V 2.6+8B043.933.845.423.9
463.660.565.550.1
864.663.468.251.4
+ + +
+* denotes zero image shot and two additional text shots following Flamingo. + ++ We evaluate the pretraining ckpt without SFT. +
+ +### Examples + +
+ Bike + Menu + Code + Mem + medal +
+
+ Click to view more cases. +
+ elec + Menu +
+
+ +We deploy MiniCPM-V 2.6 on end devices. The demo video is the raw screen recording on a iPad Pro without edition. + + +

+ +      + +

+
+ + +

+ +      + +

+
+ + +

+ + +

+
+ +## MiniCPM-Llama3-V 2.5 + +
+Click to view more details of MiniCPM-Llama3-V 2.5 + +**MiniCPM-Llama3-V 2.5** is the latest model in the MiniCPM-V series. The model is built on SigLip-400M and Llama3-8B-Instruct with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.0. Notable features of MiniCPM-Llama3-V 2.5 include: + +- 🔥 **Leading Performance.** + MiniCPM-Llama3-V 2.5 has achieved an average score of 65.1 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4V-1106, Gemini Pro, Claude 3 and Qwen-VL-Max** and greatly outperforms other Llama 3-based MLLMs. + +- 💪 **Strong OCR Capabilities.** + MiniCPM-Llama3-V 2.5 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), achieving a **700+ score on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V-0409, Qwen-VL-Max and Gemini Pro**. Based on recent user feedback, MiniCPM-Llama3-V 2.5 has now enhanced full-text OCR extraction, table-to-markdown conversion, and other high-utility capabilities, and has further strengthened its instruction-following and complex reasoning abilities, enhancing multimodal interaction experiences. + +- 🏆 **Trustworthy Behavior.** + Leveraging the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) method (the newest technique in the [RLHF-V](https://github.com/RLHF-V) [CVPR'24] series), MiniCPM-Llama3-V 2.5 exhibits more trustworthy behavior. It achieves a **10.3%** hallucination rate on Object HalBench, lower than GPT-4V-1106 (13.6%), achieving the best-level performance within the open-source community. [Data released](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset). + +- 🌏 **Multilingual Support.** + Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from [VisCPM](https://github.com/OpenBMB/VisCPM), MiniCPM-Llama3-V 2.5 extends its bilingual (Chinese-English) multimodal capabilities to **over 30 languages including German, French, Spanish, Italian, Korean etc.** [All Supported Languages](./assets/minicpm-llama-v-2-5_languages.md). + +- 🚀 **Efficient Deployment.** + MiniCPM-Llama3-V 2.5 systematically employs **model quantization, CPU optimizations, NPU optimizations and compilation optimizations**, achieving high-efficiency deployment on end-side devices. For mobile phones with Qualcomm chips, we have integrated the NPU acceleration framework QNN into llama.cpp for the first time. After systematic optimization, MiniCPM-Llama3-V 2.5 has realized a **150x acceleration in end-side MLLM image encoding** and a **3x speedup in language decoding**. + +- 💫 **Easy Usage.** +MiniCPM-Llama3-V 2.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) support for efficient CPU inference on local devices, (2) [GGUF](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) format quantized models in 16 sizes, (3) efficient [LoRA](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning) fine-tuning with only 2 V100 GPUs, (4) [streaming output](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage), (5) quick local WebUI demo setup with [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) and [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py), and (6) interactive demos on [HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). + +### Evaluation + +
+ +
+
+Click to view results on TextVQA, DocVQA, OCRBench, OpenCompass, MME, MMBench, MMMU, MathVista, LLaVA Bench, RealWorld QA, Object HalBench. +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeOCRBenchTextVQA valDocVQA testOpen-CompassMMEMMB test (en)MMB test (cn)MMMU valMath-VistaLLaVA BenchRealWorld QAObject HalBench
Proprietary
Gemini Pro-68074.688.162.92148.973.674.348.945.879.960.4-
GPT-4V (2023.11.06)-64578.088.463.51771.577.074.453.847.893.163.086.4
Open-source
Mini-Gemini2.2B-56.234.2*-1653.0--31.7----
Qwen-VL-Chat9.6B48861.562.651.61860.061.856.337.033.867.749.356.2
DeepSeek-VL-7B7.3B43564.7*47.0*54.61765.473.871.438.336.877.854.2-
Yi-VL-34B34B29043.4*16.9*52.22050.272.470.745.130.762.354.879.3
CogVLM-Chat17.4B59070.433.3*54.21736.665.855.937.334.773.960.373.6
TextMonkey9.7B55864.366.7---------
Idefics28.0B-73.074.057.21847.675.768.645.252.249.160.7-
Bunny-LLama-3-8B8.4B---54.31920.377.073.941.331.561.258.8-
LLaVA-NeXT Llama-3-8B8.4B--78.2-1971.5--41.737.580.160.0-
Phi-3-vision-128k-instruct4.2B639*70.9--1537.5*--40.444.564.2*58.8*-
MiniCPM-V 1.02.8B36660.638.247.51650.264.162.638.328.951.351.278.4
MiniCPM-V 2.02.8B60574.171.954.51808.669.166.538.238.769.255.885.5
MiniCPM-Llama3-V 2.58.5B72576.684.865.12024.677.274.245.854.386.763.589.7
+ + +
+* We evaluate the officially released checkpoint by ourselves. + +
+ +
+ +
+ Evaluation results of multilingual LLaVA Bench +
+ +### Examples + + +

+ +

+
+ +
+ + +## MiniCPM-V 2.0 + +
+Click to view more details of MiniCPM-V 2.0 + + +**MiniCPM-V 2.0** is an efficient version with promising performance for deployment. The model is built based on SigLip-400M and [MiniCPM-2.4B](https://github.com/OpenBMB/MiniCPM/), connected by a perceiver resampler. Our latest version, MiniCPM-V 2.0 has several notable features. + +- 🔥 **State-of-the-art Performance.** + + MiniCPM-V 2.0 achieves **state-of-the-art performance** on multiple benchmarks (including OCRBench, TextVQA, MME, MMB, MathVista, etc) among models under 7B parameters. It even **outperforms strong Qwen-VL-Chat 9.6B, CogVLM-Chat 17.4B, and Yi-VL 34B on OpenCompass, a comprehensive evaluation over 11 popular benchmarks**. Notably, MiniCPM-V 2.0 shows **strong OCR capability**, achieving **comparable performance to Gemini Pro in scene-text understanding**, and **state-of-the-art performance on OCRBench** among open-source models. + +- 🏆 **Trustworthy Behavior.** + + LMMs are known for suffering from hallucination, often generating text not factually grounded in images. MiniCPM-V 2.0 is **the first end-side LMM aligned via multimodal RLHF for trustworthy behavior** (using the recent [RLHF-V](https://rlhf-v.github.io/) [CVPR'24] series technique). This allows the model to **match GPT-4V in preventing hallucinations** on Object HalBench. + +- 🌟 **High-Resolution Images at Any Aspect Raito.** + + MiniCPM-V 2.0 can accept **1.8 million pixels (e.g., 1344x1344) images at any aspect ratio**. This enables better perception of fine-grained visual information such as small objects and optical characters, which is achieved via a recent technique from [LLaVA-UHD](https://arxiv.org/pdf/2403.11703.pdf). + +- ⚡️ **High Efficiency.** + + MiniCPM-V 2.0 can be **efficiently deployed on most GPU cards and personal computers**, and **even on end devices such as mobile phones**. For visual encoding, we compress the image representations into much fewer tokens via a perceiver resampler. This allows MiniCPM-V 2.0 to operate with **favorable memory cost and speed during inference even when dealing with high-resolution images**. + +- 🙌 **Bilingual Support.** + + MiniCPM-V 2.0 **supports strong bilingual multimodal capabilities in both English and Chinese**. This is enabled by generalizing multimodal capabilities across languages, a technique from [VisCPM](https://arxiv.org/abs/2308.12038) [ICLR'24]. + +### Examples + + +

+ +

+
+ +We deploy MiniCPM-V 2.0 on end devices. The demo video is the raw screen recording on a Xiaomi 14 Pro without edition. + + +

+ + +

+
+ +
+ +## Legacy Models + +| Model | Introduction and Guidance | +|:----------------------|:-------------------:| +| MiniCPM-V 1.0 | [Document](./minicpm_v1.md) | +| OmniLMM-12B | [Document](./omnilmm_en.md) | + + +## Chat with Our Demo on Gradio 🤗 + +We provide online and local demos powered by Hugging Face Gradio , the most popular model deployment framework nowadays. It supports streaming outputs, progress bars, queuing, alerts, and other useful features. + + +### Online Demo + +Click here to try out the online demo of [MiniCPM-V 2.6](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6) | [MiniCPM-Llama3-V 2.5](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) | [MiniCPM-V 2.0](https://huggingface.co/spaces/openbmb/MiniCPM-V-2). + +### Local WebUI Demo + +You can easily build your own local WebUI demo with Gradio using the following commands. + +```shell +pip install -r requirements.txt +``` + +```shell +# For NVIDIA GPUs, run: +python web_demo_2.6.py --device cuda + +``` + + +## Install + +1. Clone this repository and navigate to the source folder + +```bash +git clone https://github.com/OpenBMB/MiniCPM-V.git +cd MiniCPM-V +``` + +2. Create conda environment + +```Shell +conda create -n MiniCPM-V python=3.10 -y +conda activate MiniCPM-V +``` + +3. Install dependencies + +```shell +pip install -r requirements.txt +``` + +## Inference + + +### Model Zoo + +| Model | Device | Memory |          Description | Download | +|:-----------|:--:|:-----------:|:-------------------|:---------------:| +| MiniCPM-V 2.6| GPU | 17 GB | The latest version, achieving state-of-the-art end-side performance for single image, multi-image and video understanding. | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2_6)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6) | +| MiniCPM-V 2.6 gguf | CPU | 6 GB | The gguf version, lower memory usage and faster inference. | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6-gguf) | +| MiniCPM-V 2.6 int4 | GPU | 7 GB | The int4 quantized version, lower GPU memory usage. | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6-int4) | +| MiniCPM-Llama3-V 2.5 | GPU | 19 GB | Strong end-side multimodal performance. | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5) | +| MiniCPM-Llama3-V 2.5 gguf | CPU | 6 GB | The gguf version, lower memory usage and faster inference. | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf)   [](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-gguf) | +| MiniCPM-Llama3-V 2.5 int4 | GPU | 8 GB | The int4 quantized version, lower GPU memory usage. | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-int4/)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-int4) | +| MiniCPM-V 2.0 | GPU | 8 GB | Light version, balance the performance the computation cost. | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2) | +| MiniCPM-V 1.0 | GPU | 7 GB | Lightest version, achieving the fastest inference. | [🤗](https://huggingface.co/openbmb/MiniCPM-V)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V) | + +### Multi-turn Conversation + +Please refer to the following codes to run. + +
+ +
+ + +```python +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +torch.manual_seed(0) + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager +model = model.eval().cuda() +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) + +image = Image.open('./assets/airplane.jpeg').convert('RGB') + +# First round chat +question = "Tell me the model of this aircraft." +msgs = [{'role': 'user', 'content': [image, question]}] + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer +) +print(answer) + +# Second round chat +# pass history context of multi-turn conversation +msgs.append({"role": "assistant", "content": [answer]}) +msgs.append({"role": "user", "content": ["Introduce something about Airbus A380."]}) + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer +) +print(answer) +``` + +You will get the following output: + +``` +"The aircraft in the image is an Airbus A380, which can be identified by its large size, double-deck structure, and the distinctive shape of its wings and engines. The A380 is a wide-body aircraft known for being the world's largest passenger airliner, designed for long-haul flights. It has four engines, which are characteristic of large commercial aircraft. The registration number on the aircraft can also provide specific information about the model if looked up in an aviation database." + +"The Airbus A380 is a double-deck, wide-body, four-engine jet airliner made by Airbus. It is the world's largest passenger airliner and is known for its long-haul capabilities. The aircraft was developed to improve efficiency and comfort for passengers traveling over long distances. It has two full-length passenger decks, which can accommodate more passengers than a typical single-aisle airplane. The A380 has been operated by airlines such as Lufthansa, Singapore Airlines, and Emirates, among others. It is widely recognized for its unique design and significant impact on the aviation industry." +``` + +#### Chat with multiple images +
+ Click to view Python code running MiniCPM-V 2.6 with multiple images input. + +```python +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager +model = model.eval().cuda() +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) + +image1 = Image.open('image1.jpg').convert('RGB') +image2 = Image.open('image2.jpg').convert('RGB') +question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.' + +msgs = [{'role': 'user', 'content': [image1, image2, question]}] + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer +) +print(answer) +``` +
+ +#### In-context few-shot learning +
+ Click to view Python code running MiniCPM-V 2.6 with few-shot input. + +```python +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager +model = model.eval().cuda() +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) + +question = "production date" +image1 = Image.open('example1.jpg').convert('RGB') +answer1 = "2023.08.04" +image2 = Image.open('example2.jpg').convert('RGB') +answer2 = "2007.04.24" +image_test = Image.open('test.jpg').convert('RGB') + +msgs = [ + {'role': 'user', 'content': [image1, question]}, {'role': 'assistant', 'content': [answer1]}, + {'role': 'user', 'content': [image2, question]}, {'role': 'assistant', 'content': [answer2]}, + {'role': 'user', 'content': [image_test, question]} +] + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer +) +print(answer) +``` +
+ +#### Chat with video +
+ Click to view Python code running MiniCPM-V 2.6 with video input. + +```python +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer +from decord import VideoReader, cpu # pip install decord + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager +model = model.eval().cuda() +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) + +MAX_NUM_FRAMES=64 # if cuda OOM set a smaller number + +def encode_video(video_path): + def uniform_sample(l, n): + gap = len(l) / n + idxs = [int(i * gap + gap / 2) for i in range(n)] + return [l[i] for i in idxs] + + vr = VideoReader(video_path, ctx=cpu(0)) + sample_fps = round(vr.get_avg_fps() / 1) # FPS + frame_idx = [i for i in range(0, len(vr), sample_fps)] + if len(frame_idx) > MAX_NUM_FRAMES: + frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES) + frames = vr.get_batch(frame_idx).asnumpy() + frames = [Image.fromarray(v.astype('uint8')) for v in frames] + print('num frames:', len(frames)) + return frames + +video_path="video_test.mp4" +frames = encode_video(video_path) +question = "Describe the video" +msgs = [ + {'role': 'user', 'content': frames + [question]}, +] + +# Set decode params for video +params = {} +params["use_image_id"] = False +params["max_slice_nums"] = 2 # use 1 if cuda OOM and video resolution > 448*448 + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer, + **params +) +print(answer) +``` +
+ + +### Inference on Multiple GPUs +You can run MiniCPM-Llama3-V 2.5 on multiple low VRAM GPUs (12 GB or 16 GB) by distributing the model's layers across multiple GPUs. Please refer to this [tutorial](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/inference_on_multiple_gpus.md) for detailed instructions on how to load the model and inference using multiple low VRAM GPUs. + + +### Inference on Mac +
+Click to view an example, to run MiniCPM-Llama3-V 2.5 on 💻 Mac with MPS (Apple silicon or AMD GPUs). + +```python +# test.py Need more than 16GB memory. +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True, low_cpu_mem_usage=True) +model = model.to(device='mps') + +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True) +model.eval() + +image = Image.open('./assets/hk_OCR.jpg').convert('RGB') +question = 'Where is this photo taken?' +msgs = [{'role': 'user', 'content': question}] + +answer, context, _ = model.chat( + image=image, + msgs=msgs, + context=None, + tokenizer=tokenizer, + sampling=True +) +print(answer) +``` +Run with command: +```shell +PYTORCH_ENABLE_MPS_FALLBACK=1 python test.py +``` +
+ +### Deployment on Mobile Phone +MiniCPM-V 2.0 can be deployed on mobile phones with Android operating systems. 🚀 Click [MiniCPM-V 2.0](https://github.com/OpenBMB/mlc-MiniCPM) to install apk. + +### Inference with llama.cpp +MiniCPM-V 2.6 can run with llama.cpp now! See [our fork of llama.cpp](https://github.com/OpenBMB/llama.cpp/tree/minicpmv-main/examples/llava/README-minicpmv2.6.md) for more detail. This implementation supports smooth inference of 16~18 token/s on iPad (test environment：iPad Pro + M4). + +### Inference with ollama +MiniCPM-V 2.6 can run with ollama now! See [our fork of ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) for more detail. This implementation supports smooth inference of 16~18 token/s on iPad (test environment：iPad Pro + M4). + +### Inference with vLLM + +
+ vLLM now officially supports MiniCPM-V 2.6, MiniCPM-Llama3-V 2.5 and MiniCPM-V 2.0, Click to see. + +1. Install vLLM(>=0.5.4): +```shell +pip install vllm +``` +2. Install timm: (optional, MiniCPM-V 2.0 need timm) +```shell +pip install timm==0.9.10 +``` +3. Run the example(for image): +```python +from transformers import AutoTokenizer +from PIL import Image +from vllm import LLM, SamplingParams + +MODEL_NAME = "openbmb/MiniCPM-V-2_6" +# Also available for previous models +# MODEL_NAME = "openbmb/MiniCPM-Llama3-V-2_5" +# MODEL_NAME = "HwwwH/MiniCPM-V-2" + +image = Image.open("xxx.png").convert("RGB") +tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True) +llm = LLM( + model=MODEL_NAME, + trust_remote_code=True, + gpu_memory_utilization=1, + max_model_len=2048 +) + +messages = [{ + "role": + "user", + "content": + # Number of images + "(./)" + \ + "\nWhat is the content of this image?" +}] +prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True +) + +# Single Inference +inputs = { + "prompt": prompt, + "multi_modal_data": { + "image": image + # Multi images, the number of images should be equal to that of `(./)` + # "image": [image, image] + }, +} +# Batch Inference +# inputs = [{ +# "prompt": prompt, +# "multi_modal_data": { +# "image": image +# }, +# } for _ in 2] + + +# 2.6 +stop_tokens = ['<|im_end|>', '<|endoftext|>'] +stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens] +# 2.0 +# stop_token_ids = [tokenizer.eos_id] +# 2.5 +# stop_token_ids = [tokenizer.eos_id, tokenizer.eot_id] + +sampling_params = SamplingParams( + stop_token_ids=stop_token_ids, + use_beam_search=True, + temperature=0, + best_of=3, + max_tokens=1024 +) + +outputs = llm.generate(inputs, sampling_params=sampling_params) + +print(outputs[0].outputs[0].text) +``` +4. click [here](https://modelbest.feishu.cn/wiki/C2BWw4ZP0iCDy7kkCPCcX2BHnOf?from=from_copylink) if you want to use it with *video*, or get more details about `vLLM`. +
+ +## Fine-tuning + +### Simple Fine-tuning + +We support simple fine-tuning with Hugging Face for MiniCPM-V 2.0 and MiniCPM-Llama3-V 2.5. + +[Reference Document](./finetune/readme.md) + +### With the SWIFT Framework + +We now support MiniCPM-V series fine-tuning with the SWIFT framework. SWIFT supports training, inference, evaluation and deployment of nearly 200 LLMs and MLLMs . It supports the lightweight training solutions provided by PEFT and a complete Adapters Library including techniques such as NEFTune, LoRA+ and LLaMA-PRO. + +Best Practices：[MiniCPM-V 1.0](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v最佳实践.md), [MiniCPM-V 2.0](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v-2最佳实践.md), [MiniCPM-V 2.6](https://github.com/modelscope/ms-swift/issues/1613). + +## FAQs +Click here to view the [FAQs](./docs/faqs.md) + +## Model License + +* This repository is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. + +* The usage of MiniCPM-V model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md). + +* The models and weights of MiniCPM are completely free for academic research. after filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, are also available for free commercial use. + + +## Statement + +As LMMs, MiniCPM-V models (including OmniLMM) generate contents by learning a large amount of multimodal corpora, but they cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-V models does not represent the views and positions of the model developers + +We will not be liable for any problems arising from the use of MiniCPM-V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model. + + +## Institutions + +This project is developed by the following institutions: + +- [THUNLP](https://nlp.csai.tsinghua.edu.cn/) +- [ModelBest](https://modelbest.cn/) +- [Zhihu](https://www.zhihu.com/ ) + +## 🌟 Star History + + + +

+ +

+
+ + + +## Key Techniques and Other Multimodal Projects + +👏 Welcome to explore key techniques of MiniCPM-V and other multimodal projects of our team: + +[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD) | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V) + + +## Citation + +If you find our model/code/paper helpful, please consider cite our papers 📝 and star us ⭐️！ + +```bib +@article{yao2024minicpm, + title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone}, + author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others}, + journal={arXiv preprint arXiv:2408.01800}, + year={2024} +} +``` diff --git a/MiniCPM-V/README_zh.md b/MiniCPM-V/README_zh.md new file mode 100644 index 0000000000000000000000000000000000000000..57867dfc42a9190120d36b0be879ece28117388d --- /dev/null +++ b/MiniCPM-V/README_zh.md @@ -0,0 +1,1720 @@ +
+ + + + + +**端侧可用的 GPT-4V 级单图、多图、视频多模态大模型** + + 中文 | + [English](./README_en.md) + + 加入我们的 💬 微信社区 +｜ 了解 MiniCPM-V 📖 最佳实践 + + +

+ MiniCPM-V 2.6 🤗 🤖 | MiniCPM-Llama3-V 2.5 🤗 🤖 | + MiniCPM-Llama3-V 2.5 技术报告 +

+ + +
+ + +**MiniCPM-V**是面向图文理解的端侧多模态大模型系列。该系列模型接受图像和文本输入，并提供高质量的文本输出。自2024年2月以来，我们共发布了5个版本模型，旨在实现**领先的性能和高效的部署**，目前该系列最值得关注的模型包括： + + +- **MiniCPM-V 2.6**: 🔥🔥🔥 MiniCPM-V系列的最新、性能最佳模型。总参数量 8B，单图、多图和视频理解性能**超越了 GPT-4V**。在单图理解上，它取得了优于 **GPT-4o mini、Gemini 1.5 Pro 和 Claude 3.5 Sonnet**等商用闭源模型的表现，并进一步优化了 MiniCPM-Llama3-V 2.5 的 OCR、可信行为、多语言支持以及端侧部署等诸多特性。基于其领先的视觉 token 密度，MiniCPM-V 2.6 成为了首个支持在 iPad 等端侧设备上进行实时视频理解的多模态大模型。 + +- **MiniCPM-V 2.0**：MiniCPM-V系列的最轻量级模型。总参数量2B，多模态综合性能超越 Yi-VL 34B、CogVLM-Chat 17B、Qwen-VL-Chat 10B 等更大参数规模的模型，可接受 180 万像素的任意长宽比图像输入，实现了和 Gemini Pro 相近的场景文字识别能力以及和 GPT-4V 相匹的低幻觉率。 + + + +## 更新日志 + +#### 📌 置顶 + + +* [2024.08.17] 🚀🚀🚀 llama.cpp [官方仓库](https://github.com/ggerganov/llama.cpp)正式支持 MiniCPM-V 2.6 啦！点击[这里](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf)查看各种大小的 GGUF 版本。 +* [2024.08.15] MiniCPM-V 2.6 现在支持多图像 SFT。有关更多详细信息，请参阅[微调文档](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune) +* [2024.08.14] MiniCPM-V 2.6 现在可以通过 SWIFT 框架 [微调](https://github.com/modelscope/ms-swift/issues/1613) 了！ +* [2024.08.10] 🚀🚀🚀 llama.cpp [官方仓库](https://github.com/ggerganov/llama.cpp)正式支持 MiniCPM-Llama3-V 2.5 啦！点击[这里](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main)查看各种大小的 GGUF 版本。 +* [2024.08.06] 🔥🔥🔥 我们开源了 MiniCPM-V 2.6，该模型在单图、多图和视频理解方面取得了优于 GPT-4V 的表现。我们还进一步提升了 MiniCPM-Llama3-V 2.5 的多项亮点能力，并首次支持了 iPad 上的实时视频理解。欢迎试用！ +* [2024.08.03] MiniCPM-Llama3-V 2.5 技术报告已发布！欢迎点击[这里](https://arxiv.org/abs/2408.01800)查看。 +* [2024.07.19] MiniCPM-Llama3-V 2.5 现已支持[vLLM](#vllm-部署-) ！ +* [2024.05.28] 💫 我们现在支持 MiniCPM-Llama3-V 2.5 的 LoRA 微调，更多内存使用统计信息可以在[这里](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#model-fine-tuning-memory-usage-statistics)找到。 +* [2024.05.23] 🔍 我们添加了Phi-3-vision-128k-instruct 与 MiniCPM-Llama3-V 2.5的全面对比，包括基准测试评估、多语言能力和推理效率 🌟📊🌍🚀。点击[这里](./docs/compare_with_phi-3_vision.md)查看详细信息。 +* [2024.05.23] 🔥🔥🔥 MiniCPM-V 在 GitHub Trending 和 Hugging Face Trending 上登顶！MiniCPM-Llama3-V 2.5 Demo 被 Hugging Face 的 Gradio 官方账户推荐，欢迎点击[这里](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5)体验！ + + +
+ +
+点击查看完整更新日志。 + +* [2024.06.03] 现在，你可以利用多张低显存显卡（12G/16G）进行GPU串行推理。详情请参见该[文档](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/inference_on_multiple_gpus.md)配置。 +* [2024.05.28] 💥 MiniCPM-Llama3-V 2.5 现在在 llama.cpp 和 ollama 中完全支持其功能！**请拉取我们最新的 fork 来使用**：[llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) & [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5)。我们还发布了各种大小的 GGUF 版本，请点击[这里](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main)查看。请注意，**目前官方仓库尚未支持 MiniCPM-Llama3-V 2.5**，我们也正积极推进将这些功能合并到 llama.cpp & ollama 官方仓库，敬请关注！ +* [2024.05.25] MiniCPM-Llama3-V 2.5 [支持流式输出和自定义系统提示词](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage)了，欢迎试用! +* [2024.05.24] 我们开源了 MiniCPM-Llama3-V 2.5 [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf)，支持 [llama.cpp](#llamacpp-部署) 推理！实现端侧 6-8 tokens/s 的流畅解码，欢迎试用！ +* [2024.05.20] 我们开源了 MiniCPM-Llama3-V 2.5，增强了 OCR 能力，支持 30 多种语言，并首次在端侧实现了 GPT-4V 级的多模态能力！我们提供了[高效推理](#手机端部署)和[简易微调](./finetune/readme.md)的支持，欢迎试用！ +* [2024.04.23] 我们增加了MiniCPM-V 2.0对 [vLLM](#vllm-部署-) 的支持，欢迎体验！ +* [2024.04.18] 我们在 HuggingFace Space 新增了 MiniCPM-V 2.0 的 [demo](https://huggingface.co/spaces/openbmb/MiniCPM-V-2)，欢迎体验！ +* [2024.04.17] MiniCPM-V 2.0 现在支持用户部署本地 [WebUI Demo](#本地webui-demo部署) 了，欢迎试用! +* [2024.04.15] MiniCPM-V 2.0 现在可以通过 SWIFT 框架 [微调](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v-2最佳实践.md) 了，支持流式输出! +* [2024.04.12] 我们开源了 MiniCPM-V 2.0，该模型刷新了 OCRBench 开源模型最佳成绩，在场景文字识别能力上比肩 Gemini Pro，同时还在综合了 11 个主流多模态大模型评测基准的 OpenCompass 榜单上超过了 Qwen-VL-Chat 10B、CogVLM-Chat 17B 和 Yi-VL 34B 等更大参数规模的模型！点击这里查看 MiniCPM-V 2.0 技术博客。 +* [2024.03.14] MiniCPM-V 现在支持 SWIFT 框架下的[微调](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v最佳实践.md)了，感谢 [Jintao](https://github.com/Jintao-Huang) 的贡献！ +* [2024.03.01] MiniCPM-V 现在支持在 Mac 电脑上进行部署！ +* [2024.02.01] 我们开源了 MiniCPM-V 和 OmniLMM-12B，分别可以支持高效的端侧部署和同规模领先的多模态能力！ +
+ + +## 目录 + +- [MiniCPM-V 2.6](#minicpm-v-26) +- [MiniCPM-Llama3-V 2.5](#minicpm-llama3-v-25) +- [MiniCPM-V 2.0](#minicpm-v-20) +- [Gradio Demo 🤗](#gradio-demo-) +- [安装](#安装) +- [推理](#推理) + - [模型库](#模型库) + - [多轮对话](#多轮对话) + - [多图理解](#多图理解) + - [少样本上下文学习](#少样本上下文学习) + - [视频理解](#视频理解) + - [多卡推理](#多卡推理) + - [Mac 推理](#mac-推理) + - [手机端部署](#手机端部署) + - [本地WebUI Demo部署](#本地webui-demo部署) + - [llama.cpp 部署](#llamacpp-部署) + - [ollama 部署](#ollama-部署) + - [vLLM 部署 ](#vllm-部署-) +- [微调](#微调) +- [FAQs](#faqs) + +## MiniCPM-V 2.6 + +**MiniCPM-V 2.6** 是 MiniCPM-V 系列中最新、性能最佳的模型。该模型基于 SigLip-400M 和 Qwen2-7B 构建，共 8B 参数。与 MiniCPM-Llama3-V 2.5 相比，MiniCPM-V 2.6 性能提升显著，并引入了多图和视频理解的新功能。MiniCPM-V 2.6 的主要特点包括： + + +- 🔥 **领先的性能。** + MiniCPM-V 2.6 在最新版本 OpenCompass 榜单上（综合 8 个主流多模态评测基准）平均得分 65.2，**以8B量级的大小在单图理解方面超越了 GPT-4o mini、GPT-4V、Gemini 1.5 Pro 和 Claude 3.5 Sonnet 等主流商用闭源多模态大模型**。 + +- 🖼️ **多图理解和上下文学习。** + MiniCPM-V 2.6 还支持**多图对话和推理**。它在 Mantis-Eval、BLINK、Mathverse mv 和 Sciverse mv 等主流多图评测基准中取得了**最佳水平**，并展现出了优秀的上下文学习能力。 + +- 🎬 **视频理解。** + MiniCPM-V 2.6 还可以**接受视频输入**，进行对话和提供涵盖时序和空间信息的详细视频描述。模型在 有/无字幕 评测场景下的 Video-MME 表现均超过了 **GPT-4V、Claude 3.5 Sonnet 和 LLaVA-NeXT-Video-34B**等商用闭源模型。 + +- 💪 **强大的 OCR 能力及其他功能。** + MiniCPM-V 2.6 可以处理任意长宽比的图像，像素数可达 180 万（如 1344x1344）。在 OCRBench 上取得**最佳水平，超过 GPT-4o、GPT-4V 和 Gemini 1.5 Pro 等商用闭源模型**。基于最新的 [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) 和 [VisCPM](https://github.com/OpenBMB/VisCPM) 技术，其具备了**可信的多模态行为**，在 Object HalBench 上的幻觉率显著低于 GPT-4o 和 GPT-4V，并支持英语、中文、德语、法语、意大利语、韩语等**多种语言**。 + +- 🚀 **卓越的效率。** + 除了对个人用户友好的模型大小，MiniCPM-V 2.6 还表现出**最先进的视觉 token 密度**（即每个视觉 token 编码的像素数量）。它**仅需 640 个 token 即可处理 180 万像素图像，比大多数模型少 75%**。这一特性优化了模型的推理速度、首 token 延迟、内存占用和功耗。因此，MiniCPM-V 2.6 可以支持 iPad 等终端设备上的高效**实时视频理解**。 + +- 💫 **易于使用。** + MiniCPM-V 2.6 可以通过多种方式轻松使用：(1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) 和 [ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) 支持在本地设备上进行高效的 CPU 推理，(2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) 和 [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) 格式的量化模型，有 16 种尺寸，(3) [vLLM](#vllm-部署-) 支持高吞吐量和内存高效的推理，(4) 针对新领域和任务进行微调，(5) 使用 [Gradio](#本地-webui-demo-) 快速设置本地 WebUI 演示，(6) 在线[demo](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6)即可体验。 + +### 性能评估 +
+ +
+ +
+点击查看 OpenCompass, MME, MMVet, OCRBench, MMMU, MathVista, MMB, AI2D, TextVQA, DocVQA, HallusionBench, Object HalBench 上的单图评测结果详情。 +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeToken Density+OpenCompassMMEMMVetOCRBenchMMMU valMathVista miniMMB1.1 testAI2DTextVQA valDocVQA testHallusionBenchObject HalBench
Proprietary
GPT-4o-108869.92328.769.173669.261.382.284.6-92.855.017.6
Claude 3.5 Sonnet-75067.91920.066.078865.961.678.580.2-95.249.913.8
Gemini 1.5 Pro--64.42110.664.075460.657.773.979.173.586.545.6-
GPT-4o mini-108864.12003.466.978560.052.476.077.8--46.112.4
GPT-4V-108863.52070.267.565661.754.779.878.678.087.243.914.2
Step-1V--59.52206.463.362549.944.878.079.271.6-48.4-
Qwen-VL-Max-78458.32281.761.868452.043.474.675.779.593.141.213.4
Open-source
LLaVA-NeXT-Yi-34B34B15755.02006.550.757448.840.477.878.969.3-34.812.6
Mini-Gemini-HD-34B34B157-214159.351848.043.3-80.574.178.9--
Cambrian-34B34B182058.32049.953.259150.450.377.879.576.775.541.614.7
GLM-4V-9B13B78459.12018.858.077646.951.167.971.2--45.0-
InternVL2-8B8B70664.12215.154.379451.258.379.483.677.491.645.021.3
MiniCPM-Llama-V 2.58B188258.82024.652.872545.854.372.078.476.684.842.410.3
MiniCPM-V 2.68B282265.22348.4*60.0852*49.8*60.678.082.180.190.848.1*8.2
+ +
+* 我们使用思维链提示词来评估这些基准。 + ++ Token Density：每个视觉 token 在最大分辨率下编码的像素数，即最大分辨率下的像素数 / 视觉 token 数。 + +注意：闭源模型的 Token Density 由 API 收费方式估算得到。 +
+ + +
+点击查看 Mantis Eval, BLINK, Mathverse mv, Sciverse mv, MIRB 上的多图评测结果详情。 +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeMantis EvalBLINK valMathverse mvSciverse mvMIRB
Proprietary
GPT-4V-62.754.660.366.953.1
LLaVA-NeXT-Interleave-14B14B66.452.632.730.2-
Open-source
Emu2-Chat37B37.836.2-27.2-
CogVLM17B45.241.1---
VPG-C7B52.443.124.323.1-
VILA 8B8B51.239.3-36.5-
InternLM-XComposer-2.58B53.1*48.932.1*-42.5
InternVL2-8B8B59.0*50.930.5*34.4*56.9*
MiniCPM-V 2.68B69.153.084.974.953.8
+ + +
+* 正式开源模型权重的评测结果。 +
+ +
+点击查看 Video-MME 和 Video-ChatGPT 上的视频评测结果详情。 +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeVideo-MMEVideo-ChatGPT
w/o subsw subsCorrectnessDetailContextTemporalConsistency
Proprietary
Claude 3.5 Sonnet-60.062.9-----
GPT-4V-59.963.3-----
Open-source
LLaVA-NeXT-7B7B--3.393.293.922.603.12
LLaVA-NeXT-34B34B--3.293.233.832.513.47
CogVLM2-Video12B--3.493.463.232.983.64
LongVA7B52.454.33.053.093.772.443.64
InternVL2-8B8B54.056.9-----
InternLM-XComposer-2.58B55.8------
LLaVA-NeXT-Video32B60.263.03.483.373.952.643.28
MiniCPM-V 2.68B60.963.63.593.283.932.733.62
+
+
+ + +
+点击查看 TextVQA, VizWiz, VQAv2, OK-VQA上的少样本评测结果详情。 +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeShotTextVQA valVizWiz test-devVQAv2 test-devOK-VQA val
Flamingo80B0*35.031.656.340.6
436.539.663.157.4
837.344.865.657.5
IDEFICS80B0*30.936.060.045.2
434.340.463.652.4
835.746.164.855.1
OmniCorpus7B0*43.049.863.245.5
445.451.364.546.5
845.652.264.746.6
Emu237B026.440.433.526.7
448.254.667.053.2
849.354.767.854.1
MM130B026.240.448.926.7
849.354.770.954.1
MiniCPM-V 2.6+8B043.933.845.423.9
463.660.565.550.1
864.663.468.251.4
+ + +
+* 使用 Flamingo 方式 zero image shot 和 two additional text shots 评估零样本性能。 + ++ 我们在没有进行监督微调 (SFT) 的情况下评估预训练的模型权重 (ckpt)。 +
+ +### 典型示例 + +
+ Bike + Menu + Code + Mem + medal +
+
+ 点击查看更多示例。 +
+ elec + Menu +
+
+ +我们将 MiniCPM-V 2.6 部署在iPad Pro上，并录制了以下演示视频。 + + +

+ +      + +

+
+ + +

+ + +

+
+ +## MiniCPM-Llama3-V 2.5 + +
+查看 MiniCPM-Llama3-V 2.5 的详细信息 + +**MiniCPM-Llama3-V 2.5** 是 MiniCPM-V 系列的最新版本模型，基于 SigLip-400M 和 Llama3-8B-Instruct 构建，共 8B 参数量，相较于 MiniCPM-V 2.0 性能取得较大幅度提升。MiniCPM-Llama3-V 2.5 值得关注的特点包括： + +- 🔥 **领先的性能。** + MiniCPM-Llama3-V 2.5 在综合了 11 个主流多模态大模型评测基准的 OpenCompass 榜单上平均得分 65.1，**以 8B 量级的大小超过了 GPT-4V-1106、Gemini Pro、Claude 3、Qwen-VL-Max 等主流商用闭源多模态大模型**，大幅超越基于Llama 3构建的其他多模态大模型。 + +- 💪 **优秀的 OCR 能力。** + MiniCPM-Llama3-V 2.5 可接受 180 万像素的任意宽高比图像输入，**OCRBench 得分达到 725，超越 GPT-4o、GPT-4V、Gemini Pro、Qwen-VL-Max 等商用闭源模型**，达到最佳水平。基于近期用户反馈建议，MiniCPM-Llama3-V 2.5 增强了全文 OCR 信息提取、表格图像转 markdown 等高频实用能力，并且进一步加强了指令跟随、复杂推理能力，带来更好的多模态交互体感。 + +- 🏆 **可信行为。** + 借助最新的 [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) 对齐技术（[RLHF-V](https://github.com/RLHF-V/) [CVPR'24]系列的最新技术），MiniCPM-Llama3-V 2.5 具有更加可信的多模态行为，在 Object HalBench 的幻觉率降低到了 **10.3%**，显著低于 GPT-4V-1106 (13.6%)，达到开源社区最佳水平。[数据集已发布](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset)。 + +- 🌏 **多语言支持。** + 得益于 Llama 3 强大的多语言能力和 VisCPM 的跨语言泛化技术，MiniCPM-Llama3-V 2.5 在中英双语多模态能力的基础上，仅通过少量翻译的多模态数据的指令微调，高效泛化支持了**德语、法语、西班牙语、意大利语、韩语等 30+ 种语言**的多模态能力，并表现出了良好的多语言多模态对话性能。[查看所有支持语言](./assets/minicpm-llama-v-2-5_languages.md) + +- 🚀 **高效部署。** + MiniCPM-Llama3-V 2.5 较为系统地通过**模型量化、CPU、NPU、编译优化**等高效加速技术，实现高效的终端设备部署。对于高通芯片的移动手机，我们首次将 NPU 加速框架 QNN 整合进了 llama.cpp。经过系统优化后，MiniCPM-Llama3-V 2.5 实现了多模态大模型端侧**语言解码速度 3 倍加速**、**图像编码 150 倍加速**的巨大提升。 + +- 💫 **易于使用。** + MiniCPM-Llama3-V 2.5 可以通过多种方式轻松使用：（1）[llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) 和 [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) 支持在本地设备上进行高效的 CPU 推理；（2）提供 16 种尺寸的 [GGUF](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) 格式量化模型；（3）仅需 2 张 V100 GPU 即可进行高效的 [LoRA](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning) 微调；（ 4）支持[流式输出](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage)；（5）快速搭建 [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) 和 [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py) 本地 WebUI demo；（ 6.）[HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) 交互式 demo。 + +### 性能评估 + +
+ +
+
+TextVQA, DocVQA, OCRBench, OpenCompass MultiModal Avg Score, MME, MMBench, MMMU, MathVista, LLaVA Bench, RealWorld QA, Object HalBench上的详细评测结果。 +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeOCRBenchTextVQA valDocVQA testOpen-CompassMMEMMB test (en)MMB test (cn)MMMU valMath-VistaLLaVA BenchRealWorld QAObject HalBench
Proprietary
Gemini Pro-68074.688.162.92148.973.674.348.945.879.960.4-
GPT-4V (2023.11.06)-64578.088.463.51771.577.074.453.847.893.163.086.4
Open-source
Mini-Gemini2.2B-56.234.2*-1653.0--31.7----
Qwen-VL-Chat9.6B48861.562.651.61860.061.856.337.033.867.749.356.2
DeepSeek-VL-7B7.3B43564.7*47.0*54.61765.473.871.438.336.877.854.2-
Yi-VL-34B34B29043.4*16.9*52.22050.272.470.745.130.762.354.879.3
CogVLM-Chat17.4B59070.433.3*54.21736.665.855.937.334.773.960.373.6
TextMonkey9.7B55864.366.7---------
Idefics28.0B-73.074.057.21847.675.768.645.252.249.160.7-
Bunny-LLama-3-8B8.4B---54.31920.377.073.941.331.561.258.8-
LLaVA-NeXT Llama-3-8B8.4B----1971.5--41.7-80.160.0-
Phi-3-vision-128k-instruct4.2B639*70.9--1537.5*--40.444.564.2*58.8*-
MiniCPM-V 1.02.8B36660.638.247.51650.264.162.638.328.951.351.278.4
MiniCPM-V 2.02.8B60574.171.954.51808.669.166.538.238.769.255.885.5
MiniCPM-Llama3-V 2.58.5B72576.684.865.12024.677.274.245.854.386.763.589.7
+ +
+* 正式开源模型权重的评测结果。 +
+ +
+ +
+ 多语言LLaVA Bench评测结果 +
+ + +### 典型示例 + +

+ +

+
+ + +
+ +## MiniCPM-V 2.0 + +
+查看 MiniCPM-V 2.0 的详细信息 + +**MiniCPM-V 2.0**可以高效部署到终端设备。该模型基于 SigLip-400M 和 [MiniCPM-2.4B](https://github.com/OpenBMB/MiniCPM/)构建，通过perceiver resampler连接。其特点包括： + +- 🔥 **优秀的性能。** + + MiniCPM-V 2.0 在多个测试基准（如 OCRBench, TextVQA, MME, MMB, MathVista 等）中实现了 7B 以下模型的**最佳性能**。**在综合了 11 个主流多模态大模型评测基准的 OpenCompass 榜单上超过了 Qwen-VL-Chat 9.6B、CogVLM-Chat 17.4B 和 Yi-VL 34B 等更大参数规模的模型**。MiniCPM-V 2.0 还展现出**领先的 OCR 能力**，在场景文字识别能力上**接近 Gemini Pro**，OCRBench 得分达到**开源模型第一**。 + + +- 🏆 **可信行为。** + + 多模态大模型深受幻觉问题困扰，模型经常生成和图像中的事实不符的文本。MiniCPM-V 2.0 是 **第一个通过多模态 RLHF 对齐的端侧多模态大模型**（借助 [RLHF-V](https://rlhf-v.github.io/) [CVPR'24] 系列技术）。该模型在 [Object HalBench](https://arxiv.org/abs/2312.00849) 达到**和 GPT-4V 相仿**的性能。 + + +- 🌟 **高清图像高效编码。** + + MiniCPM-V 2.0 可以接受 **180 万像素的任意长宽比图像输入**（基于最新的[LLaVA-UHD](https://arxiv.org/pdf/2403.11703.pdf) 技术），这使得模型可以感知到小物体、密集文字等更加细粒度的视觉信息。 + + +- ⚡️ **高效部署。** + + MiniCPM-V 2.0 可以**高效部署在大多数消费级显卡和个人电脑上**，包括**移动手机等终端设备**。在视觉编码方面，我们通过perceiver resampler将图像表示压缩为更少的 token。这使得 MiniCPM-V 2.0 即便是**面对高分辨率图像，也能占用较低的存储并展现优秀的推理速度**。 + +- 🙌 **双语支持。** + + MiniCPM-V 2.0 **提供领先的中英双语多模态能力支持**。 + 该能力通过 [VisCPM](https://arxiv.org/abs/2308.12038) [ICLR'24] 论文中提出的多模态能力的跨语言泛化技术实现。 + +### 典型示例 + + + +

+ +

+
+ +我们将 MiniCPM-V 2.0 部署在小米 14 Pro 上，并录制了以下演示视频，未经任何视频剪辑。 + + +

+ + +

+
+ +
+ + + + +## 历史版本模型 + + +| 模型 | 介绍信息和使用教程 | +|:----------------------|:-------------------:| +| MiniCPM-V 1.0 | [文档](./minicpm_v1.md) | +| OmniLMM-12B | [文档](./omnilmm.md) | + + +## Gradio Demo 🤗 + +我们提供由 Hugging Face Gradio 支持的在线和本地 Demo。Gradio 是目前最流行的模型部署框架，支持流式输出、进度条、process bars 和其他常用功能。 + +### Online Demo + +欢迎试用 Online Demo: [MiniCPM-V 2.6](https://huggingface.co/spaces/openbmb/MiniCPM-V-2_6) | [MiniCPM-Llama3-V 2.5](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) | [MiniCPM-V 2.0](https://huggingface.co/spaces/openbmb/MiniCPM-V-2) 。 + +### 本地 WebUI Demo + +您可以使用以下命令轻松构建自己的本地 WebUI Demo。 + +```shell +pip install -r requirements.txt +``` + +```shell +# 对于 NVIDIA GPU，请运行： +python web_demo_2.6.py --device cuda + +``` + + + +## 安装 + +1. 克隆我们的仓库并跳转到相应目录 + +```bash +git clone https://github.com/OpenBMB/MiniCPM-V.git +cd MiniCPM-V +``` + +1. 创建 conda 环境 + +```Shell +conda create -n MiniCPMV python=3.10 -y +conda activate MiniCPMV +``` + +3. 安装依赖 + +```shell +pip install -r requirements.txt +``` + +## 推理 + +### 模型库 + +| 模型 | 设备 | 资源 |          简介 | 下载链接 | +|:--------------|:-:|:----------:|:-------------------|:---------------:| +| MiniCPM-V 2.6| GPU | 17 GB | 最新版本，提供最佳的端侧单图、多图、视频理解能力。 | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2_6)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6) | +| MiniCPM-V 2.6 gguf | CPU | 6 GB | gguf 版本，更低的内存占用和更高的推理效率。 | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6-gguf) | +| MiniCPM-V 2.6 int4 | GPU | 7 GB | int4量化版，更低显存占用。 | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2_6-int4) | +| MiniCPM-Llama3-V 2.5| GPU | 19 GB | 提供出色的端侧多模态理解能力。 | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5) | +| MiniCPM-Llama3-V 2.5 gguf | CPU | 6 GB | gguf 版本，更低的内存占用和更高的推理效率。 | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-gguf) | +| MiniCPM-Llama3-V 2.5 int4 | GPU | 8 GB | int4量化版，更低显存占用。 | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-int4/)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-int4) | +| MiniCPM-V 2.0 | GPU | 8 GB | 轻量级版本，平衡计算开销和多模态理解能力。 | [🤗](https://huggingface.co/openbmb/MiniCPM-V-2)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V-2) | +| MiniCPM-V 1.0 | GPU | 7 GB | 最轻量版本， 提供最快的推理速度。 | [🤗](https://huggingface.co/openbmb/MiniCPM-V)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V) | + +更多[历史版本模型](#legacy-models) + +### 多轮对话 + +请参考以下代码进行推理。 + +
+ +
+ + +```python +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +torch.manual_seed(0) + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager +model = model.eval().cuda() +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) + +image = Image.open('./assets/airplane.jpeg').convert('RGB') + +# First round chat +question = "Tell me the model of this aircraft." +msgs = [{'role': 'user', 'content': [image, question]}] + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer +) +print(answer) + +# Second round chat +# pass history context of multi-turn conversation +msgs.append({"role": "assistant", "content": [answer]}) +msgs.append({"role": "user", "content": ["Introduce something about Airbus A380."]}) + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer +) +print(answer) +``` + +可以得到以下输出: + +``` +"The aircraft in the image is an Airbus A380, which can be identified by its large size, double-deck structure, and the distinctive shape of its wings and engines. The A380 is a wide-body aircraft known for being the world's largest passenger airliner, designed for long-haul flights. It has four engines, which are characteristic of large commercial aircraft. The registration number on the aircraft can also provide specific information about the model if looked up in an aviation database." + +"The Airbus A380 is a double-deck, wide-body, four-engine jet airliner made by Airbus. It is the world's largest passenger airliner and is known for its long-haul capabilities. The aircraft was developed to improve efficiency and comfort for passengers traveling over long distances. It has two full-length passenger decks, which can accommodate more passengers than a typical single-aisle airplane. The A380 has been operated by airlines such as Lufthansa, Singapore Airlines, and Emirates, among others. It is widely recognized for its unique design and significant impact on the aviation industry." +``` + +#### 多图理解 +
+ 点击查看使用 MiniCPM-V 2.6 进行多图理解的Python示例 + +```python +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager +model = model.eval().cuda() +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) + +image1 = Image.open('image1.jpg').convert('RGB') +image2 = Image.open('image2.jpg').convert('RGB') +question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.' + +msgs = [{'role': 'user', 'content': [image1, image2, question]}] + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer +) +print(answer) +``` +
+ +#### 少样本上下文学习 + +
+ 点击查看使用 MiniCPM-V 2.6 进行few-shot推理的Python示例 + +```python +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager +model = model.eval().cuda() +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) + +question = "production date" +image1 = Image.open('example1.jpg').convert('RGB') +answer1 = "2023.08.04" +image2 = Image.open('example2.jpg').convert('RGB') +answer2 = "2007.04.24" +image_test = Image.open('test.jpg').convert('RGB') + +msgs = [ + {'role': 'user', 'content': [image1, question]}, {'role': 'assistant', 'content': [answer1]}, + {'role': 'user', 'content': [image2, question]}, {'role': 'assistant', 'content': [answer2]}, + {'role': 'user', 'content': [image_test, question]} +] + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer +) +print(answer) +``` +
+ +#### 视频理解 +
+ 点击查看使用 MiniCPM-V 2.6 进行视频理解的Python示例 + +```python +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer +from decord import VideoReader, cpu # pip install decord + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager +model = model.eval().cuda() +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) + +MAX_NUM_FRAMES=64 # if cuda OOM set a smaller number + +def encode_video(video_path): + def uniform_sample(l, n): + gap = len(l) / n + idxs = [int(i * gap + gap / 2) for i in range(n)] + return [l[i] for i in idxs] + + vr = VideoReader(video_path, ctx=cpu(0)) + sample_fps = round(vr.get_avg_fps() / 1) # FPS + frame_idx = [i for i in range(0, len(vr), sample_fps)] + if len(frame_idx) > MAX_NUM_FRAMES: + frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES) + frames = vr.get_batch(frame_idx).asnumpy() + frames = [Image.fromarray(v.astype('uint8')) for v in frames] + print('num frames:', len(frames)) + return frames + +video_path="video_test.mp4" +frames = encode_video(video_path) +question = "Describe the video" +msgs = [ + {'role': 'user', 'content': frames + [question]}, +] + +# Set decode params for video +params = {} +params["use_image_id"] = False +params["max_slice_nums"] = 2 # 如果cuda OOM且视频分辨率大于448*448可设为1 + +answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer, + **params +) +print(answer) +``` +
+ + +### 多卡推理 +您可以通过将模型的层分布在多个低显存显卡（12 GB 或 16 GB）上，运行 MiniCPM-Llama3-V 2.5。请查看该[教程](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/inference_on_multiple_gpus.md)，详细了解如何使用多张低显存显卡载入模型并进行推理。 + + +### Mac 推理 +
+点击查看 MiniCPM-Llama3-V 2.5 / MiniCPM-V 2.0 基于Mac MPS运行 (Apple silicon 或 AMD GPUs)的示例。 + +```python +# test.py Need more than 16GB memory to run. +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True, low_cpu_mem_usage=True) +model = model.to(device='mps') + +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True) +model.eval() + +image = Image.open('./assets/hk_OCR.jpg').convert('RGB') +question = 'Where is this photo taken?' +msgs = [{'role': 'user', 'content': question}] + +answer, context, _ = model.chat( + image=image, + msgs=msgs, + context=None, + tokenizer=tokenizer, + sampling=True +) +print(answer) +``` +运行: +```shell +PYTORCH_ENABLE_MPS_FALLBACK=1 python test.py +``` +
+ + +### 手机端部署 +MiniCPM-V 2.0 可运行在Android手机上，点击[MiniCPM-V 2.0](https://github.com/OpenBMB/mlc-MiniCPM)安装apk使用; + +### 本地WebUI Demo部署 +
+点击查看本地WebUI demo 在 NVIDIA GPU、Mac等不同设备部署方法 + +```shell +pip install -r requirements.txt +``` + +```shell +# For NVIDIA GPUs, run: +python web_demo_2.6.py --device cuda +``` +
+ +### llama.cpp 部署 +MiniCPM-V 2.6 现在支持llama.cpp啦! 用法请参考[我们的fork llama.cpp](https://github.com/OpenBMB/llama.cpp/tree/minicpmv-main/examples/llava/README-minicpmv2.6.md)， 在iPad上可以支持 16~18 token/s 的流畅推理（测试环境：iPad Pro + M4）。 + +### ollama 部署 +MiniCPM-V 2.6 现在支持ollama啦! 用法请参考[我们的fork ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md)， 在iPad上可以支持 16~18 token/s 的流畅推理（测试环境：iPad Pro + M4）。 + +### vLLM 部署 +
+点击查看, vLLM 现已官方支持MiniCPM-V 2.6、MiniCPM-Llama3-V 2.5 和 MiniCPM-V 2.0 + +1. 安装 vLLM(>=0.5.4): +```shell +pip install vllm +``` +3. 安装 timm 库: （可选，MiniCPM-V 2.0需安装） +```shell +pip install timm=0.9.10 +``` +4. 运行示例代码:（注意：如果使用本地路径的模型，请确保模型代码已更新到Hugging Face上的最新版) +```python +from transformers import AutoTokenizer +from PIL import Image +from vllm import LLM, SamplingParams + +MODEL_NAME = "openbmb/MiniCPM-V-2_6" +# Also available for previous models +# MODEL_NAME = "openbmb/MiniCPM-Llama3-V-2_5" +# MODEL_NAME = "HwwwH/MiniCPM-V-2" + +image = Image.open("xxx.png").convert("RGB") +tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True) +llm = LLM( + model=MODEL_NAME, + trust_remote_code=True, + gpu_memory_utilization=1, + max_model_len=2048 +) + +messages = [{ + "role": + "user", + "content": + # Number of images + "(./)" + \ + "\nWhat is the content of this image?" +}] +prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True +) + +# Single Inference +inputs = { + "prompt": prompt, + "multi_modal_data": { + "image": image + # Multi images, the number of images should be equal to that of `(./)` + # "image": [image, image] + }, +} +# Batch Inference +# inputs = [{ +# "prompt": prompt, +# "multi_modal_data": { +# "image": image +# }, +# } for _ in 2] + + +# 2.6 +stop_tokens = ['<|im_end|>', '<|endoftext|>'] +stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens] +# 2.0 +# stop_token_ids = [tokenizer.eos_id] +# 2.5 +# stop_token_ids = [tokenizer.eos_id, tokenizer.eot_id] + +sampling_params = SamplingParams( + stop_token_ids=stop_token_ids, + use_beam_search=True, + temperature=0, + best_of=3, + max_tokens=1024 +) + +outputs = llm.generate(inputs, sampling_params=sampling_params) + +print(outputs[0].outputs[0].text) +``` +4. [点击此处](https://modelbest.feishu.cn/wiki/C2BWw4ZP0iCDy7kkCPCcX2BHnOf?from=from_copylink)查看带视频推理和其他有关 `vLLM` 的信息。 + +
+ + +## 微调 + +### 简易微调 + +我们支持使用 Huggingface Transformers 库简易地微调 MiniCPM-V 2.0 和 MiniCPM-Llama3-V 2.5 模型。 + +[参考文档](./finetune/readme.md) + +### 使用 SWIFT 框架 + +我们支持使用 SWIFT 框架微调 MiniCPM-V 系列模型。SWIFT 支持近 200 种大语言模型和多模态大模型的训练、推理、评测和部署。支持 PEFT 提供的轻量训练方案和完整的 Adapters 库支持的最新训练技术如 NEFTune、LoRA+、LLaMA-PRO 等。 + +参考文档：[MiniCPM-V 1.0](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v最佳实践.md)，[MiniCPM-V 2.0](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v-2最佳实践.md) [MiniCPM-V 2.6](https://github.com/modelscope/ms-swift/issues/1613). + +## FAQs +点击查看 [FAQs](./docs/faqs.md) + + +## 模型协议 + +* 本仓库中代码依照 [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) 协议开源 +* MiniCPM-V 模型权重的使用则需要遵循 [“MiniCPM模型商用许可协议.md”](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%E6%A8%A1%E5%9E%8B%E5%95%86%E7%94%A8%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.md)。 +* MiniCPM 模型权重对学术研究完全开放，在填写[“问卷”](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g)进行登记后亦允许免费商业使用。 + +## 声明 + +作为多模态大模型，MiniCPM-V 系列模型（包括 OmniLMM）通过学习大量的多模态数据来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。 + +因此用户在使用本项目的系列模型生成的内容时，应自行负责对其进行评估和验证。如果由于使用本项目的系列开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 + + +## 机构 + +本项目由以下机构共同开发： + +- [清华大学自然语言处理实验室](https://nlp.csai.tsinghua.edu.cn/) +- [面壁智能](https://modelbest.cn/) +- [知乎](https://www.zhihu.com/ ) + +## 🌟 Star History + + + +

+ +

+
+ + + +## 支持技术和其他多模态项目 + +👏 欢迎了解 MiniCPM-V 背后的支持技术和更多我们的多模态项目！ + +[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD) | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V) + + + +## 引用 + +如果您觉得我们模型/代码/论文有帮助，请给我们 ⭐ 和 引用 📝，感谢！ + +```bib +@article{yao2024minicpm, + title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone}, + author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others}, + journal={arXiv preprint arXiv:2408.01800}, + year={2024} +} +``` diff --git a/MiniCPM-V/assets/.DS_Store b/MiniCPM-V/assets/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..f80deb17eb12c9b19d462a94fb75398681c18551 Binary files /dev/null and b/MiniCPM-V/assets/.DS_Store differ diff --git a/MiniCPM-V/assets/MiniCPM-Llama3-V-2.5-peformance.png 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0000000000000000000000000000000000000000..f01b68876dafc774da5db881d47de01646888dec Binary files /dev/null and b/MiniCPM-V/assets/xinferenc_demo_image/xinference_webui_button.png differ diff --git a/MiniCPM-V/assets/zhihu.webp b/MiniCPM-V/assets/zhihu.webp new file mode 100644 index 0000000000000000000000000000000000000000..33d3dffc86a04a4f97250223d57fe88fc367317c Binary files /dev/null and b/MiniCPM-V/assets/zhihu.webp differ diff --git a/MiniCPM-V/chat.py b/MiniCPM-V/chat.py new file mode 100644 index 0000000000000000000000000000000000000000..8d36e562e2d15156a3f80780c4e5844ff66ab092 --- /dev/null +++ b/MiniCPM-V/chat.py @@ -0,0 +1,293 @@ +import os +import torch +import json +from PIL import Image +import base64 +import io +from accelerate import load_checkpoint_and_dispatch, init_empty_weights +from transformers import AutoTokenizer, AutoModel + +from omnilmm.utils import disable_torch_init +from omnilmm.model.omnilmm import OmniLMMForCausalLM +from omnilmm.model.utils import build_transform +from omnilmm.train.train_utils import omni_preprocess + +DEFAULT_IMAGE_TOKEN = "" +DEFAULT_IMAGE_PATCH_TOKEN = "" +DEFAULT_IM_START_TOKEN = "" +DEFAULT_IM_END_TOKEN = "" + + + +def init_omni_lmm(model_path): + torch.backends.cuda.matmul.allow_tf32 = True + disable_torch_init() + model_name = os.path.expanduser(model_path) + print(f'Load omni_lmm model and tokenizer from {model_name}') + tokenizer = AutoTokenizer.from_pretrained( + model_name, model_max_length=2048) + + if False: + # model on multiple devices for small size gpu memory (Nvidia 3090 24G x2) + with init_empty_weights(): + model = OmniLMMForCausalLM.from_pretrained(model_name, tune_clip=True, torch_dtype=torch.bfloat16) + model = load_checkpoint_and_dispatch(model, model_name, dtype=torch.bfloat16, + device_map="auto", no_split_module_classes=['Eva','MistralDecoderLayer', 'ModuleList', 'Resampler'] + ) + else: + model = OmniLMMForCausalLM.from_pretrained( + model_name, tune_clip=True, torch_dtype=torch.bfloat16 + ).to(device='cuda', dtype=torch.bfloat16) + + image_processor = build_transform( + is_train=False, input_size=model.model.config.image_size, std_mode='OPENAI_CLIP') + + mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False) + assert mm_use_im_start_end + + tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, + DEFAULT_IM_END_TOKEN], special_tokens=True) + + + vision_config = model.model.vision_config + vision_config.im_patch_token = tokenizer.convert_tokens_to_ids( + [DEFAULT_IMAGE_PATCH_TOKEN])[0] + vision_config.use_im_start_end = mm_use_im_start_end + vision_config.im_start_token, vision_config.im_end_token = tokenizer.convert_tokens_to_ids( + [DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN]) + image_token_len = model.model.config.num_query + + return model, image_processor, image_token_len, tokenizer + +def expand_question_into_multimodal(question_text, image_token_len, im_st_token, im_ed_token, im_patch_token): + if '' in question_text[0]['content']: + question_text[0]['content'] = question_text[0]['content'].replace( + '', im_st_token + im_patch_token * image_token_len + im_ed_token) + else: + question_text[0]['content'] = im_st_token + im_patch_token * \ + image_token_len + im_ed_token + '\n' + question_text[0]['content'] + return question_text + +def wrap_question_for_omni_lmm(question, image_token_len, tokenizer): + question = expand_question_into_multimodal( + question, image_token_len, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_IMAGE_PATCH_TOKEN) + + conversation = question + data_dict = omni_preprocess(sources=[conversation], + tokenizer=tokenizer, + generation=True) + + data_dict = dict(input_ids=data_dict["input_ids"][0], + labels=data_dict["labels"][0]) + return data_dict + + + +class OmniLMM12B: + def __init__(self, model_path) -> None: + model, img_processor, image_token_len, tokenizer = init_omni_lmm(model_path) + self.model = model + self.image_token_len = image_token_len + self.image_transform = img_processor + self.tokenizer = tokenizer + self.model.eval() + + def decode(self, image, input_ids): + with torch.inference_mode(): + output = self.model.generate_vllm( + input_ids=input_ids.unsqueeze(0).cuda(), + images=image.unsqueeze(0).half().cuda(), + temperature=0.6, + max_new_tokens=1024, + # num_beams=num_beams, + do_sample=True, + output_scores=True, + return_dict_in_generate=True, + repetition_penalty=1.1, + top_k=30, + top_p=0.9, + ) + + response = self.tokenizer.decode( + output.sequences[0], skip_special_tokens=True) + response = response.strip() + return response + + def chat(self, input): + try: + image = Image.open(io.BytesIO(base64.b64decode(input['image']))).convert('RGB') + except Exception as e: + return "Image decode error" + + msgs = json.loads(input['question']) + input_ids = wrap_question_for_omni_lmm( + msgs, self.image_token_len, self.tokenizer)['input_ids'] + input_ids = torch.as_tensor(input_ids) + #print('input_ids', input_ids) + image = self.image_transform(image) + + out = self.decode(image, input_ids) + + return out + + +def img2base64(file_name): + with open(file_name, 'rb') as f: + encoded_string = base64.b64encode(f.read()) + return encoded_string + +class MiniCPMV: + def __init__(self, model_path) -> None: + self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(dtype=torch.bfloat16) + self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + self.model.eval().cuda() + + def chat(self, input): + try: + image = Image.open(io.BytesIO(base64.b64decode(input['image']))).convert('RGB') + except Exception as e: + return "Image decode error" + + msgs = json.loads(input['question']) + + answer, context, _ = self.model.chat( + image=image, + msgs=msgs, + context=None, + tokenizer=self.tokenizer, + sampling=True, + temperature=0.7 + ) + return answer + +class MiniCPMV2_5: + def __init__(self, model_path) -> None: + self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(dtype=torch.float16) + self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + self.model.eval().cuda() + + def chat(self, input): + try: + image = Image.open(io.BytesIO(base64.b64decode(input['image']))).convert('RGB') + except Exception as e: + return "Image decode error" + + msgs = json.loads(input['question']) + + answer = self.model.chat( + image=image, + msgs=msgs, + tokenizer=self.tokenizer, + sampling=True, + temperature=0.7 + ) + return answer + +class MiniCPMV2_6: + def __init__(self, model_path, multi_gpus=False) -> None: + + print('torch_version:', torch.__version__) + if multi_gpus: # inference on multi-gpus + from accelerate import load_checkpoint_and_dispatch, init_empty_weights, infer_auto_device_map + with init_empty_weights(): + model = AutoModel.from_pretrained(model_path, trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) + + device_map = infer_auto_device_map(model, max_memory={0: "10GB", 1: "10GB"}, + no_split_module_classes=['SiglipVisionTransformer', 'Qwen2DecoderLayer']) + device_id = device_map["llm.model.embed_tokens"] + device_map["llm.lm_head"] = device_id # first and last layer of llm should be in the same device + device_map["vpm"] = device_id + device_map["resampler"] = device_id + device_id2 = device_map["llm.model.layers.26"] + device_map["llm.model.layers.8"] = device_id2 + device_map["llm.model.layers.9"] = device_id2 + device_map["llm.model.layers.10"] = device_id2 + device_map["llm.model.layers.11"] = device_id2 + device_map["llm.model.layers.12"] = device_id2 + device_map["llm.model.layers.13"] = device_id2 + device_map["llm.model.layers.14"] = device_id2 + device_map["llm.model.layers.15"] = device_id2 + device_map["llm.model.layers.16"] = device_id2 + print(device_map) + + self.model = load_checkpoint_and_dispatch(model, model_path, dtype=torch.bfloat16, device_map=device_map) + self.model.eval() + else: + self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True, + attn_implementation='sdpa', torch_dtype=torch.bfloat16) + self.model.eval().cuda() + + self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + + def chat(self, input): + image = None + if "image" in input and len(input["image"]) > 10: # legacy API + try: + image = Image.open(io.BytesIO(base64.b64decode(input['image']))).convert('RGB') + except Exception as e: + return "Image decode error" + + msgs = json.loads(input["question"]) + + for msg in msgs: + contents = msg.pop('content') # support str or List[Dict] + if isinstance(contents, str): + contents = [contents] + + new_cnts = [] + for c in contents: + if isinstance(c, dict): + if c['type'] == 'text': + c = c['pairs'] + elif c['type'] == 'image': + c = Image.open(io.BytesIO(base64.b64decode(c["pairs"]))).convert('RGB') + else: + raise ValueError("content type only support text and image.") + new_cnts.append(c) + msg['content'] = new_cnts + print(f'msgs: {str(msgs)}') + + answer = self.model.chat( + image=image, + msgs=msgs, + tokenizer=self.tokenizer, + ) + return answer + + +class MiniCPMVChat: + def __init__(self, model_path, multi_gpus=False) -> None: + if '12B' in model_path: + self.model = OmniLMM12B(model_path) + elif 'MiniCPM-Llama3-V' in model_path: + self.model = MiniCPMV2_5(model_path) + elif 'MiniCPM-V-2_6' in model_path: + self.model = MiniCPMV2_6(model_path, multi_gpus) + else: + self.model = MiniCPMV(model_path) + + def chat(self, input): + return self.model.chat(input) + + +if __name__ == '__main__': + + model_path = 'openbmb/OmniLMM-12B' + chat_model = MiniCPMVChat(model_path) + + im_64 = img2base64('./assets/worldmap_ck.jpg') + + # first round chat + msgs = [{"role": "user", "content": "What is interesting about this image?"}] + input = {"image": im_64, "question": json.dumps(msgs, ensure_ascii=True)} + answer = chat_model.chat(input) + print(msgs[-1]["content"]+'\n', answer) + + # second round chat + msgs.append({"role": "assistant", "content": answer}) + msgs.append({"role": "user", "content": "Where is China in the image"}) + input = {"image": im_64,"question": json.dumps(msgs, ensure_ascii=True)} + answer = chat_model.chat(input) + print(msgs[-1]["content"]+'\n', answer) + diff --git a/MiniCPM-V/docs/MiniCPM_Llama3_V_25_technical_report.pdf b/MiniCPM-V/docs/MiniCPM_Llama3_V_25_technical_report.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e9bccf5b9013ec139fd366871dbfdc10859b0daf --- /dev/null +++ b/MiniCPM-V/docs/MiniCPM_Llama3_V_25_technical_report.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:884ea8d6c957abe660fe7986ea319404042d3045e642d15c3575b2c3591858aa +size 11083166 diff --git a/MiniCPM-V/docs/best_practice_summary.md b/MiniCPM-V/docs/best_practice_summary.md new file mode 100644 index 0000000000000000000000000000000000000000..a3bd6faff984770adc38fa3415d3932e32facf90 --- /dev/null +++ b/MiniCPM-V/docs/best_practice_summary.md @@ -0,0 +1,23 @@ +# MiniCPM-V Best Practices + +**MiniCPM-V** is a series of end-side multimodal LLMs (MLLMs) designed for vision-language understanding. The models take image, video and text as inputs and provide high-quality text output, aiming to achieve **strong performance and efficient deployment**. The most notable models in this series currently include MiniCPM-Llama3-V 2.5 and MiniCPM-V 2.6. The following sections provide detailed tutorials and guidance for each version of the MiniCPM-V models. + + +## MiniCPM-V 2.6 + +MiniCPM-V 2.6 is the latest and most capable model in the MiniCPM-V series. With a total of 8B parameters, the model **surpasses GPT-4V in single image, multi-image and video understanding**. It outperforms **GPT-4o mini, Gemini 1.5 Pro and Claude 3.5 Sonnet** in single image understanding, and advances MiniCPM-Llama3-V 2.5's features such as strong OCR capability, trustworthy behavior, multilingual support, and end-side deployment. Due to its superior token density, MiniCPM-V 2.6 can for the first time support real-time video understanding on end-side devices such as iPad. + +* [Deployment Tutorial](https://modelbest.feishu.cn/wiki/C2BWw4ZP0iCDy7kkCPCcX2BHnOf) +* [Training Tutorial](https://modelbest.feishu.cn/wiki/GeHMwLMa0i2FhUkV0f6cz3HWnV1) +* [Quantization Tutorial](https://modelbest.feishu.cn/wiki/YvsPwnPwWiqUjlkmW0scQ76TnBb) + +## MiniCPM-Llama3-V 2.5 + +MiniCPM-Llama3-V 2.5 is built on SigLip-400M and Llama3-8B-Instruct with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.0. + +* [Quantization Tutorial](https://modelbest.feishu.cn/wiki/Kc7ywV4X1ipSaAkuPFOc9SFun8b) +* [Training Tutorial](https://modelbest.feishu.cn/wiki/UpSiw63o9iGDhIklmwScX4a6nhW) +* [End-side Deployment](https://modelbest.feishu.cn/wiki/Lwr9wpOQdinr6AkLzHrc9LlgnJD) +* [Deployment Tutorial](https://modelbest.feishu.cn/wiki/LTOKw3Hz7il9kGkCLX9czsennKe) +* [HD Decoding Tutorial](https://modelbest.feishu.cn/wiki/Ug8iwdXfhiHVsDk2gGEco6xnnVg) +* [Model Structure](https://modelbest.feishu.cn/wiki/ACtAw9bOgiBQ9lkWyafcvtVEnQf) \ No newline at end of file diff --git a/MiniCPM-V/docs/best_practice_summary_zh.md b/MiniCPM-V/docs/best_practice_summary_zh.md new file mode 100644 index 0000000000000000000000000000000000000000..9aafe11b2ac6f27e569638a6ab5ae7d65fd433db --- /dev/null +++ b/MiniCPM-V/docs/best_practice_summary_zh.md @@ -0,0 +1,22 @@ +# MiniCPM-V 最佳实践 + +**MiniCPM-V**是面向图文理解的端侧多模态大模型系列。该系列模型接受图像和文本输入，并提供高质量的文本输出。自2024年2月以来，我们共发布了5个版本模型，旨在实现**领先的性能和高效的部署**，目前该系列最值得关注的模型包括： + +## MiniCPM-V 2.6 + +MiniCPM-V系列的最新、性能最佳模型。总参数量 8B，单图、多图和视频理解性能**超越了 GPT-4V**。在单图理解上，它取得了优于 **GPT-4o mini、Gemini 1.5 Pro 和 Claude 3.5 Sonnet** 等商用闭源模型的表现，并进一步优化了 MiniCPM-Llama3-V 2.5 的 OCR、可信行为、多语言支持以及端侧部署等诸多特性。基于其领先的视觉 token 密度，MiniCPM-V 2.6 成为了首个支持在 iPad 等端侧设备上进行实时视频理解的多模态大模型。 + +* [部署教程](https://modelbest.feishu.cn/wiki/LZxLwp4Lzi29vXklYLFchwN5nCf) +* [训练教程](https://modelbest.feishu.cn/wiki/HvfLwYzlIihqzXkmeCdczs6onmd) +* [量化教程](https://modelbest.feishu.cn/wiki/PAsHw6N6xiEy0DkJWpJcIocRnz9) + +## MiniCPM-Llama3-V 2.5 + +MiniCPM-Llama3-V 2.5 基于 SigLip-400M 和 Llama3-8B-Instruct 构建，总共有 80 亿参数。其性能相比 MiniCPM-V 2.0 有了显著提升。 + +* [量化教程](https://modelbest.feishu.cn/wiki/O0KTwQV5piUPzTkRXl9cSFyHnQb) +* [训练教程](https://modelbest.feishu.cn/wiki/MPkPwvONEiZm3BkWMnyc83Tin4d) +* [端侧部署](https://modelbest.feishu.cn/wiki/CZZJw1EDGitSSZka664cZwbWnrb) +* [部署教程](https://modelbest.feishu.cn/wiki/BcHIwjOLGihJXCkkSdMc2WhbnZf) +* [高清解码教程](https://modelbest.feishu.cn/wiki/L0ajwm8VAiiPY6kDZfJce3B7nRg) +* [模型结构](https://modelbest.feishu.cn/wiki/X15nwGzqpioxlikbi2RcXDpJnjd) \ No newline at end of file diff --git a/MiniCPM-V/docs/compare_with_phi-3_vision.md b/MiniCPM-V/docs/compare_with_phi-3_vision.md new file mode 100644 index 0000000000000000000000000000000000000000..1edab3e49f6996a0b34e5997e5df35c4ffc069ab --- /dev/null +++ b/MiniCPM-V/docs/compare_with_phi-3_vision.md @@ -0,0 +1,27 @@ +## Phi-3-vision-128K-Instruct vs MiniCPM-Llama3-V 2.5 + +Comparison results of Phi-3-vision-128K-Instruct and MiniCPM-Llama3-V 2.5, regarding the model size, hardware requirements, and performances. +With int4 quantization, MiniCPM-Llama3-V 2.5 delivers **smooth inference with only 8GB of GPU memory**. In most benchmarks, MiniCPM-Llama3-V 2.5 achieves **better performance** compared with Phi-3-vision-128K-Instruct. Moreover, MiniCPM-Llama3-V 2.5 also exhibits **lower latency and better throughtput even without quantization**. + +我们提供了从模型参数、硬件需求、性能指标等方面对比 Phi-3-vision-128K-Instruct 和 MiniCPM-Llama3-V 2.5 的结果。通过 int4 量化，MiniCPM-Llama3-V 2.5 **仅需 8GB 显存即可推理**。在大多数评测集上， MiniCPM-Llama3-V 2.5 相比于 Phi-3-vision-128K-Instruct 都展现出了**更优的性能表现**。 即使未经量化，MiniCPM-Llama3-V 2.5 的**推理延迟和吞吐率也都更具优势**。 + +
+ +
+ + + +### Multilingual Capabilities（多语言能力对比） + + +MiniCPM-Llama3-V 2.5 exhibits **stronger multilingual** capabilities compared with Phi-3-vision-128K-Instruct on LLaVA Bench. + +MiniCPM-Llama3-V 2.5 在对话和推理评测榜单 LLaVA Bench 上展现出了比 Phi-3-vision-128K-Instruct **更强的多语言的性能**。 + +
+ +
+ Evaluation results of multilingual LLaVA Bench +
+ 多语言LLaVA Bench评测结果 +
diff --git a/MiniCPM-V/docs/faqs.md b/MiniCPM-V/docs/faqs.md new file mode 100644 index 0000000000000000000000000000000000000000..4cae555848d621f4d84a8cc76571767d34a95622 --- /dev/null +++ b/MiniCPM-V/docs/faqs.md @@ -0,0 +1,30 @@ +### FAQs + +
+Q: How to choose between sampling or beam search for inference + +In various scenarios, the quality of results obtained from beam search and sampling decoding strategies can vary. You can determine your decoding strategy based on the following aspects: + +If you have the following needs, consider using sampling decoding: + +1. You require faster inference speed. +2. You wish for a streaming generation approach. +3. Your task necessitates some open-ended responses. + +If your task is about providing deterministic answers, you might want to experiment with beam search to see if it can achieve better outcomes. +
+ + +
+Q: How to ensure that the model generates results of sufficient length + +We've observed that during multi-language inference on MiniCPM-V 2.6, the generation sometimes ends prematurely. You can improve the results by passing a `min_new_tokens` parameter. +```python +res = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer, + min_new_tokens=100 +) +``` +
diff --git a/MiniCPM-V/docs/inference_on_multiple_gpus.md b/MiniCPM-V/docs/inference_on_multiple_gpus.md new file mode 100644 index 0000000000000000000000000000000000000000..acd0dbffdb5b7a13aa0ddd41bfe458914882b2ec --- /dev/null +++ b/MiniCPM-V/docs/inference_on_multiple_gpus.md @@ -0,0 +1,159 @@ +## Using MiniCPM-Llama3-V-2_5 with Multiple GPUs + +Due to the limited memory capacity of a single GPU, it may be impossible to load the entire MiniCPMV model (the model weights account for 18 GiB) onto one device for inference (assume one gpu only has 12 GiB or 16 GiB GPU memory). To address this limitation, multi-GPU inference can be employed, where the model's layers are distributed across multiple GPUs. + +A minimal modification method can be used to achieve this distribution, ensuring that the layers are assigned to different GPUs with minimal changes to the original model structure. + +To implement this, we utilize features provided by `accelerate` library. + +Install all requirements of MiniCPM-Llama3-V-2_5, additionally, you also need to install `accelerate`. + +```bash +pip install accelerate +``` + +
+ +### Example Usage for `2x16GiB` GPUs + +Now we consider a demo for two GPUs, where each GPU has 16 GiB GPU memory. + +1. Import necessary libraries. + +```python +from PIL import Image +import torch +from transformers import AutoConfig, AutoModel, AutoTokenizer +from accelerate import init_empty_weights, infer_auto_device_map, load_checkpoint_in_model, dispatch_model +``` + +2. Download model weights. + +```python +MODEL_PATH = '/local/path/to/MiniCPM-Llama3-V-2_5' # you can download in advance or use `openbmb/MiniCPM-Llama3-V-2_5` +``` + +3. Determine the distribution of layers on multiple GPUs. + +```python +max_memory_each_gpu = '10GiB' # Define the maximum memory to use on each gpu, here we suggest using a balanced value, because the weight is not everything, the intermediate activation value also uses GPU memory (10GiB < 16GiB) + +gpu_device_ids = [0, 1] # Define which gpu to use (now we have two GPUs, each has 16GiB memory) + +no_split_module_classes = ["LlamaDecoderLayer"] + +max_memory = { + device_id: max_memory_each_gpu for device_id in gpu_device_ids +} + +config = AutoConfig.from_pretrained( + MODEL_PATH, + trust_remote_code=True +) + +tokenizer = AutoTokenizer.from_pretrained( + MODEL_PATH, + trust_remote_code=True +) + +with init_empty_weights(): + model = AutoModel.from_config( + config, + torch_dtype=torch.float16, + trust_remote_code=True + ) + +device_map = infer_auto_device_map( + model, + max_memory=max_memory, no_split_module_classes=no_split_module_classes +) + +print("auto determined device_map", device_map) + +# Here we want to make sure the input and output layer are all on the first gpu to avoid any modifications to original inference script. + +device_map["llm.model.embed_tokens"] = 0 +device_map["llm.model.layers.0"] = 0 +device_map["llm.lm_head"] = 0 +device_map["vpm"] = 0 +device_map["resampler"] = 0 + +print("modified device_map", device_map) + +``` + +You may see this output: + +``` +modified device_map OrderedDict([('llm.model.embed_tokens', 0), ('llm.model.layers.0', 0), ('llm.model.layers.1', 0), ('llm.model.layers.2', 0), ('llm.model.layers.3', 0), ('llm.model.layers.4', 0), ('llm.model.layers.5', 0), ('llm.model.layers.6', 0), ('llm.model.layers.7', 0), ('llm.model.layers.8', 0), ('llm.model.layers.9', 0), ('llm.model.layers.10', 0), ('llm.model.layers.11', 0), ('llm.model.layers.12', 0), ('llm.model.layers.13', 0), ('llm.model.layers.14', 0), ('llm.model.layers.15', 0), ('llm.model.layers.16', 1), ('llm.model.layers.17', 1), ('llm.model.layers.18', 1), ('llm.model.layers.19', 1), ('llm.model.layers.20', 1), ('llm.model.layers.21', 1), ('llm.model.layers.22', 1), ('llm.model.layers.23', 1), ('llm.model.layers.24', 1), ('llm.model.layers.25', 1), ('llm.model.layers.26', 1), ('llm.model.layers.27', 1), ('llm.model.layers.28', 1), ('llm.model.layers.29', 1), ('llm.model.layers.30', 1), ('llm.model.layers.31', 1), ('llm.model.norm', 1), ('llm.lm_head', 0), ('vpm', 0), ('resampler', 0)]) +``` + +4. Next, use the `device_map` to dispatch the model layers to corresponding gpus. + +```python +load_checkpoint_in_model( + model, + MODEL_PATH, + device_map=device_map) + +model = dispatch_model( + model, + device_map=device_map +) + +torch.set_grad_enabled(False) + +model.eval() +``` + + + +5. Chat! + +```python +image_path = '/local/path/to/test.png' + +response = model.chat( + image=Image.open(image_path).convert("RGB"), + msgs=[ + { + "role": "user", + "content": "guess what I am doing?" + } + ], + tokenizer=tokenizer +) + +print(response) +``` + +In this case the OOM (CUDA out of memory) problem may be eliminated. We have tested that: + +- it works well for `3000` text input tokens and `1000` text output tokens. +- it works well for a high-resolution input image. + +
+ +### Usage for general cases + +It is similar to the previous example, but you may consider modifying these two variables. + +```python +max_memory_each_gpu = '10GiB' # Define the maximum memory to use on each gpu, here we suggest using a balanced value, because the weight is not everything, the intermediate activation value also uses GPU memory (10GiB < 16GiB) + +gpu_device_ids = [0, 1, ...] # Define which gpu to use (now we have two GPUs, each has 16GiB memory) +``` + +You may use the following shell script to monitor the memory usage during inference, if there is OOM, try to reduce `max_memory_each_gpu`, to make memory pressure more balanced across all gpus. + +```bash +watch -n1 nvidia-smi +``` + +
+ + +### References + +[Ref 1](https://zhuanlan.zhihu.com/p/639850033) + diff --git a/MiniCPM-V/docs/swift_train_and_infer.md b/MiniCPM-V/docs/swift_train_and_infer.md new file mode 100644 index 0000000000000000000000000000000000000000..693efe622bdd0d2e5961d01d6291a5fb4dac887e --- /dev/null +++ b/MiniCPM-V/docs/swift_train_and_infer.md @@ -0,0 +1,135 @@ +## SWIFT install +You can quickly install SWIFT using bash commands. + +``` bash +git clone https://github.com/modelscope/swift.git +cd swift +pip install -r requirements.txt +pip install -e '.[llm]' +``` + +## SWIFT Infer +Inference using SWIFT can be carried out in two ways: through a command line interface and via Python code. + +### Quick start +Here are the steps to launch SWIFT from the Bash command line: + +1. Run the bash code will download the model of MiniCPM-Llama3-V-2_5 and run the inference +``` shell +CUDA_VISIBLE_DEVICES=0 swift infer --model_type minicpm-v-v2_5-chat +``` + +2. You can also run the code with more arguments below to run the inference: +``` +model_id_or_path # Can be the model ID from Hugging Face or the local path to the model +infer_backend ['AUTO', 'vllm', 'pt'] # Backend for inference, default is auto +dtype ['bf16', 'fp16', 'fp32', 'AUTO'] # Computational precision +max_length # Maximum length +max_new_tokens: int = 2048 # Maximum number of tokens to generate +do_sample: bool = True # Whether to sample during generation +temperature: float = 0.3 # Temperature coefficient during generation +top_k: int = 20 +top_p: float = 0.7 +repetition_penalty: float = 1. # Penalty for repetition +num_beams: int = 1 # Number of beams for beam search +stop_words: List[str] = None # List of stop words +quant_method ['bnb', 'hqq', 'eetq', 'awq', 'gptq', 'aqlm'] # Quantization method for the model +quantization_bit [0, 1, 2, 3, 4, 8] # Default is 0, which means no quantization is used +``` +3. Example: +``` shell +CUDA_VISIBLE_DEVICES=0，1 swift infer \ +--model_type minicpm-v-v2_5-chat \ +--model_id_or_path /root/ld/ld_model_pretrain/MiniCPM-Llama3-V-2_5 \ +--dtype bf16 +``` +### Python code with SWIFT infer +The following demonstrates using Python code to initiate inference with the MiniCPM-Llama3-V-2_5 model through SWIFT. + +```python +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '0,1' # Set the number of GPUs to use + +from swift.llm import ( + get_model_tokenizer, get_template, inference, ModelType, + get_default_template_type, inference_stream +) # Import necessary modules + +from swift.utils import seed_everything # Set random seed +import torch + +model_type = ModelType.minicpm_v_v2_5_chat +template_type = get_default_template_type(model_type) # Obtain the template type, primarily used for constructing special tokens and image processing workflow +print(f'template_type: {template_type}') + +model, tokenizer = get_model_tokenizer(model_type, torch.bfloat16, + model_id_or_path='/root/ld/ld_model_pretrain/MiniCPM-Llama3-V-2_5', + model_kwargs={'device_map': 'auto'}) # Load the model, set model type, model path, model parameters, device allocation, etc., computation precision, etc. +model.generation_config.max_new_tokens = 256 +template = get_template(template_type, tokenizer) # Construct the template based on the template type +seed_everything(42) + +images = ['http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/road.png'] # Image URL +query = '距离各城市多远？' # Note: Query is still in Chinese, consider translating if needed +response, history = inference(model, template, query, images=images) # Obtain results through inference +print(f'query: {query}') +print(f'response: {response}') + +# Streaming output +query = '距离最远的城市是哪？' # Note: Query is still in Chinese, consider translating if needed +gen = inference_stream(model, template, query, history, images=images) # Call the streaming output interface +print_idx = 0 +print(f'query: {query}\nresponse: ', end='') +for response, history in gen: + delta = response[print_idx:] + print(delta, end='', flush=True) + print_idx = len(response) +print() +print(f'history: {history}') +``` + +## SWIFT train +SWIFT supports training on the local dataset,the training steps are as follows: +1. Make the train data like this: +```jsonl +{"query": "What does this picture describe?", "response": "This picture has a giant panda.", "images": ["local_image_path"]} +{"query": "What does this picture describe?", "response": "This picture has a giant panda.", "history": [], "images": ["image_path"]} +{"query": "Is bamboo tasty?", "response": "It seems pretty tasty judging by the panda's expression.", "history": [["What's in this picture?", "There's a giant panda in this picture."], ["What is the panda doing?", "Eating bamboo."]], "images": ["image_url"]} +``` +2. LoRA Tuning: + +The LoRA target model are k and v weight in LLM you should pay attention to the eval_steps,maybe you should set the eval_steps to a large value, like 200000,beacuase in the eval time , SWIFT will return a memory bug so you should set the eval_steps to a very large value. +```shell +# Experimental environment: A100 +# 32GB GPU memory +CUDA_VISIBLE_DEVICES=0 swift sft \ +--model_type minicpm-v-v2_5-chat \ +--dataset coco-en-2-mini \ +``` +3. All parameters finetune: + +When the argument of lora_target_modules is ALL, the model will finetune all the parameters. +```shell +CUDA_VISIBLE_DEVICES=0,1 swift sft \ +--model_type minicpm-v-v2_5-chat \ +--dataset coco-en-2-mini \ +--lora_target_modules ALL \ +--eval_steps 200000 +``` + +## LoRA Merge and Infer +The LoRA weight can be merge to the base model and then load to infer. + +1. Load the LoRA weight to infer run the follow code: +```shell +CUDA_VISIBLE_DEVICES=0 swift infer \ +--ckpt_dir /your/lora/save/checkpoint +``` +2. Merge the LoRA weight to the base model: + +The code will load and merge the LoRA weight to the base model, save the merge model to the LoRA save path and load the merge model to infer +```shell +CUDA_VISIBLE_DEVICES=0 swift infer \ +--ckpt_dir your/lora/save/checkpoint \ +--merge_lora true +``` \ No newline at end of file diff --git a/MiniCPM-V/docs/wechat.md b/MiniCPM-V/docs/wechat.md new file mode 100644 index 0000000000000000000000000000000000000000..156f584877aa740a49c689ce120aa2f8fbc4f36f --- /dev/null +++ b/MiniCPM-V/docs/wechat.md @@ -0,0 +1,6 @@ +
+ + +

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diff --git a/MiniCPM-V/docs/xinference_infer.md b/MiniCPM-V/docs/xinference_infer.md new file mode 100644 index 0000000000000000000000000000000000000000..ab06a2b7b822635606c977a0d9766df065310c05 --- /dev/null +++ b/MiniCPM-V/docs/xinference_infer.md @@ -0,0 +1,67 @@ +## Xinference Infer +Xinference is a unified inference platform that provides a unified interface for different inference engines. It supports LLM, text generation, image generation, and more.but it's not bigger than Swift too much. + + +### Xinference install +Xinference can be installed simply by using the following easy bash code: +```shell +pip install "xinference[all]" +``` + +### Quick start +The initial steps for conducting inference with Xinference involve downloading the model during the first launch. +1. Start Xinference in the terminal: +```shell +xinference +``` +2. Start the web ui. +3. Search for "MiniCPM-Llama3-V-2_5" in the search box. + +![alt text](../assets/xinferenc_demo_image/xinference_search_box.png) + +4. Find and click the MiniCPM-Llama3-V-2_5 button. +5. Follow the config and launch the model. +```plaintext +Model engine : Transformers +model format : pytorch +Model size : 8 +quantization : none +N-GPU : auto +Replica : 1 +``` +6. After first click the launch button,xinference will download the model from huggingface. We should click the webui button. + +![alt text](../assets/xinferenc_demo_image/xinference_webui_button.png) + +7. Upload the image and chatting with the MiniCPM-Llama3-V-2_5 + +### Local MiniCPM-Llama3-V-2_5 Launch +If you have already downloaded the MiniCPM-Llama3-V-2_5 model locally, you can proceed with Xinference inference following these steps: +1. Start Xinference +```shell +xinference +``` +2. Start the web ui. +3. To register a new model, follow these steps: the settings highlighted in red are fixed and cannot be changed, whereas others are customizable according to your needs. Complete the process by clicking the 'Register Model' button. + +![alt text](../assets/xinferenc_demo_image/xinference_register_model1.png) +![alt text](../assets/xinferenc_demo_image/xinference_register_model2.png) + +4. After completing the model registration, proceed to 'Custom Models' and locate the model you just registered. +5. Follow the config and launch the model. +```plaintext +Model engine : Transformers +model format : pytorch +Model size : 8 +quantization : none +N-GPU : auto +Replica : 1 +``` +6. After first click the launch button,Xinference will download the model from Huggingface. we should click the chat button. +![alt text](../assets/xinferenc_demo_image/xinference_webui_button.png) +7. Upload the image and chatting with the MiniCPM-Llama3-V-2_5 + +### FAQ +1. Why can't the sixth step open the WebUI? + +Maybe your firewall or mac os to prevent the web to open. \ No newline at end of file diff --git a/MiniCPM-V/eval_mm/.DS_Store b/MiniCPM-V/eval_mm/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..f32d6711e0a08929b66cc7d3fcdc339bee4f83c5 Binary files /dev/null and b/MiniCPM-V/eval_mm/.DS_Store differ diff --git a/MiniCPM-V/eval_mm/README.md b/MiniCPM-V/eval_mm/README.md new file mode 100644 index 0000000000000000000000000000000000000000..92d875dea5c441a315876d81ee1e37558f65122b --- /dev/null +++ b/MiniCPM-V/eval_mm/README.md @@ -0,0 +1,362 @@ +# Evaluation + +## MiniCPM-V 2.6 + +### opencompass +First, enter the `vlmevalkit` directory and install all dependencies: +```bash +cd vlmevalkit +pip install --upgrade pip +pip install -e . +wget https://download.pytorch.org/whl/cu118/torch-2.2.0%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=4377e0a7fe8ff8ffc4f7c9c6130c1dcd3874050ae4fc28b7ff1d35234fbca423 +wget https://download.pytorch.org/whl/cu118/torchvision-0.17.0%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=2e63d62e09d9b48b407d3e1b30eb8ae4e3abad6968e8d33093b60d0657542428 +wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.6.3/flash_attn-2.6.3+cu118torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl +pip install torch-2.2.0%2Bcu118-cp310-cp310-linux_x86_64.whl +pip install torchvision-0.17.0%2Bcu118-cp310-cp310-linux_x86_64.whl +pip install flash_attn-2.6.3+cu118torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl +``` +
+ +Then, run `scripts/run_inference.sh`, which receives three input parameters in sequence: `MODELNAME`, `DATALIST`, and `MODE`. `MODELNAME` represents the name of the model, `DATALIST` represents the datasets used for inference, and `MODE` represents evaluation mode: +```bash +chmod +x ./scripts/run_inference.sh +./scripts/run_inference.sh $MODELNAME $DATALIST $MODE +``` +
+ +The four available choices for `MODELNAME` are listed in `vlmeval/config.py`: +```bash +minicpm_series = { + 'MiniCPM-V': partial(MiniCPM_V, model_path='openbmb/MiniCPM-V'), + 'MiniCPM-V-2': partial(MiniCPM_V, model_path='openbmb/MiniCPM-V-2'), + 'MiniCPM-Llama3-V-2_5': partial(MiniCPM_Llama3_V, model_path='openbmb/MiniCPM-Llama3-V-2_5'), + 'MiniCPM-V-2_6': partial(MiniCPM_V_2_6, model_path='openbmb/MiniCPM-V-2_6'), +} +``` +
+ +All available choices for `DATALIST` are listed in `vlmeval/utils/dataset_config.py`. Separate the names of different datasets with spaces and add quotation marks at both ends: +```bash +$DATALIST="MMMU_DEV_VAL MathVista_MINI MMVet MMBench_DEV_EN_V11 MMBench_DEV_CN_V11 MMStar HallusionBench AI2D_TEST" +``` +
+ +While scoring on each benchmark directly, set `MODE=all`. If only inference results are required, set `MODE=infer`. In order to reproduce the results in the table displayed on the homepage (columns between MME and HallusionBench), you need to run the script according to the following settings: +```bash +# without CoT +./scripts/run_inference.sh MiniCPM-V-2_6 "MMMU_DEV_VAL MathVista_MINI MMVet MMBench_DEV_EN_V11 MMBench_DEV_CN_V11 MMStar HallusionBench AI2D_TEST" all +./scripts/run_inference.sh MiniCPM-V-2_6 MME all +# with CoT +# While running the CoT version of MME, you need to modify the 'use_cot' function in vlmeval/vlm/minicpm_v.py and add MME to the branch that returns True. +./scripts/run_inference/sh MiniCPM-V-2_6 "MMMU_DEV_VAL MMVet MMStar HallusionBench OCRBench" all +./scripts/run_inference.sh MiniCPM-V-2_6 MME all +``` +
+ +### vqadataset +First, enter the `vqaeval` directory and install all dependencies. Then, create `downloads` subdirectory to store the downloaded dataset for all tasks: +```bash +cd vqaeval +pip install -r requirements.txt +mkdir downloads +``` +
+ +Download the datasets from the following links and place it in the specified directories: +###### TextVQA +```bash +cd downloads +mkdir TextVQA && cd TextVQA +wget https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip +unzip train_val_images.zip && rm train_val_images.zip +mv train_val_images/train_images . && rm -rf train_val_images +wget https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_val.json +cd ../.. +``` + +###### DocVQA / DocVQATest + +```bash +cd downloads +mkdir DocVQA && cd DocVQA && mkdir spdocvqa_images +# Download Images and Annotations from Task 1 - Single Page Document Visual Question Answering at https://rrc.cvc.uab.es/?ch=17&com=downloads +# Move the spdocvqa_images.tar.gz and spdocvqa_qas.zip to DocVQA directory +tar -zxvf spdocvqa_images.tar.gz -C spdocvqa_images && rm spdocvqa_images.tar.gz +unzip spdocvqa_qas.zip && rm spdocvqa_qas.zip +cp spdocvqa_qas/val_v1.0_withQT.json . && cp spdocvqa_qas/test_v1.0.json . && rm -rf spdocvqa_qas +cd ../.. +``` +
+ +The `downloads` directory should be organized according to the following structure: +```bash +downloads +├── TextVQA +│ ├── train_images +│ │ ├── ... +│ ├── TextVQA_0.5.1_val.json +├── DocVQA +│ ├── spdocvqa_images +│ │ ├── ... +│ ├── val_v1.0_withQT.json +│ ├── test_v1.0.json +``` +
+ +Modify the parameters in `shell/run_inference.sh` and run inference: + +```bash +chmod +x ./shell/run_inference.sh +./shell/run_inference.sh +``` +
+ +All optional parameters are listed in `eval_utils/getargs.py`. The meanings of some major parameters are listed as follows. +For `MiniCPM-V-2_6`, set `model_name` to `minicpmv26`: +```bash +# path to images and their corresponding questions +# TextVQA +--textVQA_image_dir +--textVQA_ann_path +# DocVQA +--docVQA_image_dir +--docVQA_ann_path +# DocVQATest +--docVQATest_image_dir +--docVQATest_ann_path + +# whether to eval on certain task +--eval_textVQA +--eval_docVQA +--eval_docVQATest +--eval_all + +# model name and model path +--model_name +--model_path +# load model from ckpt +--ckpt +# the way the model processes input data, "interleave" represents interleaved image-text form, while "old" represents non-interleaved. +--generate_method + +--batchsize + +# path to save the outputs +--answer_path +``` +
+ +While evaluating on different tasks, parameters need to be set as follows: +###### TextVQA +```bash +--eval_textVQA +--textVQA_image_dir ./downloads/TextVQA/train_images +--textVQA_ann_path ./downloads/TextVQA/TextVQA_0.5.1_val.json +``` + +###### DocVQA +```bash +--eval_docVQA +--docVQA_image_dir ./downloads/DocVQA/spdocvqa_images +--docVQA_ann_path ./downloads/DocVQA/val_v1.0_withQT.json +``` + +###### DocVQATest +```bash +--eval_docVQATest +--docVQATest_image_dir ./downloads/DocVQA/spdocvqa_images +--docVQATest_ann_path ./downloads/DocVQA/test_v1.0.json +``` + +
+ +For the DocVQATest task, in order to upload the inference results to the [official website](https://rrc.cvc.uab.es/?ch=17) for evaluation, run `shell/run_transform.sh` for format transformation after inference. `input_file_path` represents the path to the original output json, `output_file_path` represents the path to the transformed json: +```bash +chmod +x ./shell/run_transform.sh +./shell/run_transform.sh +``` +
+ +## MiniCPM-Llama3-V-2_5 + +
+Expand + +### opencompass +First, enter the `vlmevalkit` directory and install all dependencies: +```bash +cd vlmevalkit +pip install -r requirements.txt +``` +
+ +Then, run `scripts/run_inference.sh`, which receives three input parameters in sequence: `MODELNAME`, `DATALIST`, and `MODE`. `MODELNAME` represents the name of the model, `DATALIST` represents the datasets used for inference, and `MODE` represents evaluation mode: +```bash +chmod +x ./scripts/run_inference.sh +./scripts/run_inference.sh $MODELNAME $DATALIST $MODE +``` +
+ +The three available choices for `MODELNAME` are listed in `vlmeval/config.py`: +```bash +ungrouped = { + 'MiniCPM-V':partial(MiniCPM_V, model_path='openbmb/MiniCPM-V'), + 'MiniCPM-V-2':partial(MiniCPM_V, model_path='openbmb/MiniCPM-V-2'), + 'MiniCPM-Llama3-V-2_5':partial(MiniCPM_Llama3_V, model_path='openbmb/MiniCPM-Llama3-V-2_5'), +} +``` +
+ +All available choices for `DATALIST` are listed in `vlmeval/utils/dataset_config.py`. While evaluating on a single dataset, call the dataset name directly without quotation marks; while evaluating on multiple datasets, separate the names of different datasets with spaces and add quotation marks at both ends: +```bash +$DATALIST="POPE ScienceQA_TEST ChartQA_TEST" +``` +
+ +While scoring on each benchmark directly, set `MODE=all`. If only inference results are required, set `MODE=infer`. In order to reproduce the results in the table displayed on the homepage (columns between MME and RealWorldQA), you need to run the script according to the following settings: +```bash +# run on all 7 datasets +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 "MME MMBench_TEST_EN MMBench_TEST_CN MMMU_DEV_VAL MathVista_MINI LLaVABench RealWorldQA" all + +# The following are instructions for running on a single dataset +# MME +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MME all +# MMBench_TEST_EN +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MMBench_TEST_EN all +# MMBench_TEST_CN +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MMBench_TEST_CN all +# MMMU_DEV_VAL +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MMMU_DEV_VAL all +# MathVista_MINI +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MathVista_MINI all +# LLaVABench +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 LLaVABench all +# RealWorldQA +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 RealWorldQA all +``` +
+ +### vqadataset +First, enter the `vqaeval` directory and install all dependencies. Then, create `downloads` subdirectory to store the downloaded dataset for all tasks: +```bash +cd vqaeval +pip install -r requirements.txt +mkdir downloads +``` +
+ +Download the datasets from the following links and place it in the specified directories: +###### TextVQA +```bash +cd downloads +mkdir TextVQA && cd TextVQA +wget https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip +unzip train_val_images.zip && rm train_val_images.zip +mv train_val_images/train_images . && rm -rf train_val_images +wget https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_val.json +cd ../.. +``` + +###### DocVQA / DocVQATest + +```bash +cd downloads +mkdir DocVQA && cd DocVQA && mkdir spdocvqa_images +# Download Images and Annotations from Task 1 - Single Page Document Visual Question Answering at https://rrc.cvc.uab.es/?ch=17&com=downloads +# Move the spdocvqa_images.tar.gz and spdocvqa_qas.zip to DocVQA directory +tar -zxvf spdocvqa_images.tar.gz -C spdocvqa_images && rm spdocvqa_images.tar.gz +unzip spdocvqa_qas.zip && rm spdocvqa_qas.zip +cp spdocvqa_qas/val_v1.0_withQT.json . && cp spdocvqa_qas/test_v1.0.json . && rm -rf spdocvqa_qas +cd ../.. +``` +
+ +The `downloads` directory should be organized according to the following structure: +```bash +downloads +├── TextVQA +│ ├── train_images +│ │ ├── ... +│ ├── TextVQA_0.5.1_val.json +├── DocVQA +│ ├── spdocvqa_images +│ │ ├── ... +│ ├── val_v1.0_withQT.json +│ ├── test_v1.0.json +``` +
+ +Modify the parameters in `shell/run_inference.sh` and run inference: + +```bash +chmod +x ./shell/run_inference.sh +./shell/run_inference.sh +``` +
+ +All optional parameters are listed in `eval_utils/getargs.py`. The meanings of some major parameters are listed as follows. +For `MiniCPM-Llama3-V-2_5`, set `model_name` to `minicpmv`: +```bash +# path to images and their corresponding questions +# TextVQA +--textVQA_image_dir +--textVQA_ann_path +# DocVQA +--docVQA_image_dir +--docVQA_ann_path +# DocVQATest +--docVQATest_image_dir +--docVQATest_ann_path + +# whether to eval on certain task +--eval_textVQA +--eval_docVQA +--eval_docVQATest +--eval_all + +# model name and model path +--model_name +--model_path +# load model from ckpt +--ckpt +# the way the model processes input data, "interleave" represents interleaved image-text form, while "old" represents non-interleaved. +--generate_method + +--batchsize + +# path to save the outputs +--answer_path +``` +
+ +While evaluating on different tasks, parameters need to be set as follows: +###### TextVQA +```bash +--eval_textVQA +--textVQA_image_dir ./downloads/TextVQA/train_images +--textVQA_ann_path ./downloads/TextVQA/TextVQA_0.5.1_val.json +``` + +###### DocVQA +```bash +--eval_docVQA +--docVQA_image_dir ./downloads/DocVQA/spdocvqa_images +--docVQA_ann_path ./downloads/DocVQA/val_v1.0_withQT.json +``` + +###### DocVQATest +```bash +--eval_docVQATest +--docVQATest_image_dir ./downloads/DocVQA/spdocvqa_images +--docVQATest_ann_path ./downloads/DocVQA/test_v1.0.json +``` + +
+ +For the DocVQATest task, in order to upload the inference results to the [official website](https://rrc.cvc.uab.es/?ch=17) for evaluation, run `shell/run_transform.sh` for format transformation after inference. `input_file_path` represents the path to the original output json, `output_file_path` represents the path to the transformed json: +```bash +chmod +x ./shell/run_transform.sh +./shell/run_transform.sh +``` + +
\ No newline at end of file diff --git a/MiniCPM-V/eval_mm/README_zh.md b/MiniCPM-V/eval_mm/README_zh.md new file mode 100644 index 0000000000000000000000000000000000000000..3eef51c23be5d527f2b2f47b4476d0f44e888010 --- /dev/null +++ b/MiniCPM-V/eval_mm/README_zh.md @@ -0,0 +1,358 @@ +# Evaluation + +## MiniCPM-V 2.6 + +### opencompass +首先，进入 `vlmevalkit` 目录下，安装必要的依赖： +```bash +cd vlmevalkit +pip install --upgrade pip +pip install -e . +wget https://download.pytorch.org/whl/cu118/torch-2.2.0%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=4377e0a7fe8ff8ffc4f7c9c6130c1dcd3874050ae4fc28b7ff1d35234fbca423 +wget https://download.pytorch.org/whl/cu118/torchvision-0.17.0%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=2e63d62e09d9b48b407d3e1b30eb8ae4e3abad6968e8d33093b60d0657542428 +wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.6.3/flash_attn-2.6.3+cu118torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl +pip install torch-2.2.0%2Bcu118-cp310-cp310-linux_x86_64.whl +pip install torchvision-0.17.0%2Bcu118-cp310-cp310-linux_x86_64.whl +pip install flash_attn-2.6.3+cu118torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl +rm *.whl +``` +
+ +然后，运行 `scripts/run_inference.sh`，该脚本依次接收三个输入参数：`MODELNAME`, `DATALIST`, `MODE`。`MODELNAME` 为模型名称，`DATALIST` 为目标数据集，`MODE` 为评测模式。 +```bash +chmod +x ./scripts/run_inference.sh +./scripts/run_inference.sh $MODELNAME $DATALIST $MODE +``` +
+ +`MODELNAME` 有四种选择，位于 `vlmeval/config.py` 中： +```bash +minicpm_series = { + 'MiniCPM-V': partial(MiniCPM_V, model_path='openbmb/MiniCPM-V'), + 'MiniCPM-V-2': partial(MiniCPM_V, model_path='openbmb/MiniCPM-V-2'), + 'MiniCPM-Llama3-V-2_5': partial(MiniCPM_Llama3_V, model_path='openbmb/MiniCPM-Llama3-V-2_5'), + 'MiniCPM-V-2_6': partial(MiniCPM_V_2_6, model_path='openbmb/MiniCPM-V-2_6'), +} +``` +
+ +可选的所有 `DATALIST` 位于 `vlmeval/utils/dataset_config.py` 中。将不同数据集名称以空格隔开，两端加引号： +```bash +$DATALIST="MMMU_DEV_VAL MathVista_MINI MMVet MMBench_DEV_EN_V11 MMBench_DEV_CN_V11 MMStar HallusionBench AI2D_TEST" +``` +
+ +直接对各 benchmark 进行评分时，设置 `MODE=all`。如果仅需要推理结果，则设置 `MODE=infer`。 +为了复现出首页展示的表格中的各项结果（MME 到 HallusionBench 之间的列），需要按照如下设置运行： +```bash +# without CoT +./scripts/run_inference.sh MiniCPM-V-2_6 "MMMU_DEV_VAL MathVista_MINI MMVet MMBench_DEV_EN_V11 MMBench_DEV_CN_V11 MMStar HallusionBench AI2D_TEST" all +./scripts/run_inference.sh MiniCPM-V-2_6 MME all +# with CoT，运行 CoT 版本的 MME 时，需要改写 vlmeval/vlm/minicpm_v.py 中的 'use_cot' 函数，将 MME 添加到 return True 的分支中 +./scripts/run_inference/sh MiniCPM-V-2_6 "MMMU_DEV_VAL MMVet MMStar HallusionBench OCRBench" all +./scripts/run_inference.sh MiniCPM-V-2_6 MME all +``` +
+ +### vqadataset +首先，进入 `vqaeval` 目录下，安装必要的依赖，并创建 `downloads` 子目录，用于存储下载的数据集： +```bash +cd vqaeval +pip install -r requirements.txt +mkdir downloads +``` +
+ +然后，从下列各地址下载数据集并置于指定目录下： +###### TextVQA +```bash +cd downloads +mkdir TextVQA && cd TextVQA +wget https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip +unzip train_val_images.zip && rm train_val_images.zip +mv train_val_images/train_images . && rm -rf train_val_images +wget https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_val.json +cd ../.. +``` + +###### DocVQA / DocVQATest +```bash +cd downloads +mkdir DocVQA && cd DocVQA && mkdir spdocvqa_images +# 在 https://rrc.cvc.uab.es/?ch=17&com=downloads 下载 Task 1 - Single Page Document Visual Question Answering 下的 Images 和 Annotations +# 将下载得到的 spdocvqa_images.tar.gz 以及 spdocvqa_qas.zip 置于 DocVQA 目录下 +tar -zxvf spdocvqa_images.tar.gz -C spdocvqa_images && rm spdocvqa_images.tar.gz +unzip spdocvqa_qas.zip && rm spdocvqa_qas.zip +cp spdocvqa_qas/val_v1.0_withQT.json . && cp spdocvqa_qas/test_v1.0.json . && rm -rf spdocvqa_qas +cd ../.. +``` +
+ +`downloads` 目录应当按照下列结构组织： +```bash +downloads +├── TextVQA +│ ├── train_images +│ │ ├── ... +│ ├── TextVQA_0.5.1_val.json +├── DocVQA +│ ├── spdocvqa_images +│ │ ├── ... +│ ├── val_v1.0_withQT.json +│ ├── test_v1.0.json +``` +
+ +准备好相应的数据集之后，修改 `shell/run_inference.sh` 的参数，运行推理： + +```bash +chmod +x ./shell/run_inference.sh +./shell/run_inference.sh +``` +
+ +可以传入的参数位于 `eval_utils/getargs.py` 中，各主要参数的含义如下。 +对于 `MiniCPM-V-2_6`，需要将 `model_name`设置为 `minicpmv26`： +```bash +# 指定 TextVQA 评测所有图片和问题的路径 +--textVQA_image_dir +--textVQA_ann_path +# 指定 DocVQA 评测所有图片和问题的路径 +--docVQA_image_dir +--docVQA_ann_path +# 指定 DocVQATest 评测所有图片和问题的路径 +--docVQATest_image_dir +--docVQATest_ann_path + +# 决定是否评测某个任务，eval_all 设置为 True 表示所有任务都评测 +--eval_textVQA +--eval_docVQA +--eval_docVQATest +--eval_all + +# 模型名称、模型路径（从指定路径加载模型） +--model_name +--model_path +# 从 checkpoint 加载模型 +--ckpt +# 模型处理输入数据的方式，interleave 表示图文交错式，old 表示非交错式 +--generate_method +# 推理时的批处理规模，建议推理时设置为 1 +--batchsize + +# 输出内容保存的路径 +--answer_path +``` +
+ +评测三个任务需要设置的参数如下： +###### TextVQA +```bash +--eval_textVQA +--textVQA_image_dir ./downloads/TextVQA/train_images +--textVQA_ann_path ./downloads/TextVQA/TextVQA_0.5.1_val.json +``` + +###### DocVQA +```bash +--eval_docVQA +--docVQA_image_dir ./downloads/DocVQA/spdocvqa_images +--docVQA_ann_path ./downloads/DocVQA/val_v1.0_withQT.json +``` + +###### DocVQATest +```bash +--eval_docVQATest +--docVQATest_image_dir ./downloads/DocVQA/spdocvqa_images +--docVQATest_ann_path ./downloads/DocVQA/test_v1.0.json +``` +
+ +对于 DocVQATest 任务，为了将推理结果上传到[官方网站](https://rrc.cvc.uab.es/?ch=17)进行评测，还需要运行 `shell/run_transform.sh` 进行格式转换。其中，`input_file_path` 对应原始输出的 json 的路径，`output_file_path` 为自定义的转换后的 json 的路径： +```bash +chmod +x ./shell/run_transform.sh +./shell/run_transform.sh +``` +
+ +## MiniCPM-Llama3-V-2_5 + +
+展开 + +### opencompass +首先，进入 `vlmevalkit` 目录下，安装必要的依赖： +```bash +cd vlmevalkit +pip install -r requirements.txt +``` +
+ +然后，运行 `scripts/run_inference.sh`，该脚本依次接收三个输入参数：`MODELNAME`, `DATALIST`, `MODE`。`MODELNAME` 为模型名称，`DATALIST` 为目标数据集，`MODE` 为评测模式。 +```bash +chmod +x ./scripts/run_inference.sh +./scripts/run_inference.sh $MODELNAME $DATALIST $MODE +``` +
+ +`MODELNAME` 有三种选择，位于 `vlmeval/config.py` 中： +```bash +ungrouped = { + 'MiniCPM-V':partial(MiniCPM_V, model_path='openbmb/MiniCPM-V'), + 'MiniCPM-V-2':partial(MiniCPM_V, model_path='openbmb/MiniCPM-V-2'), + 'MiniCPM-Llama3-V-2_5':partial(MiniCPM_Llama3_V, model_path='openbmb/MiniCPM-Llama3-V-2_5'), +} +``` +
+ +可选的所有 `DATALIST` 位于 `vlmeval/utils/dataset_config.py` 中，评测单个数据集时，直接调用数据集名称，不加引号；评测多个数据集时，将不同数据集名称以空格隔开，两端加引号： +```bash +$DATALIST="POPE ScienceQA_TEST ChartQA_TEST" +``` +
+ +直接对各 benchmark 进行评分时，设置 `MODE=all`。如果仅需要推理结果，则设置 `MODE=infer` +为了复现出首页展示的表格中的各项结果（MME 到 RealWorldQA 之间的列），需要按照如下设置运行： +```bash +# 一次性运行 7 个数据集 +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 "MME MMBench_TEST_EN MMBench_TEST_CN MMMU_DEV_VAL MathVista_MINI LLaVABench RealWorldQA" all + +# 以下是单独运行 1 个数据集的指令 +# MME +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MME all +# MMBench_TEST_EN +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MMBench_TEST_EN all +# MMBench_TEST_CN +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MMBench_TEST_CN all +# MMMU_DEV_VAL +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MMMU_DEV_VAL all +# MathVista_MINI +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 MathVista_MINI all +# LLaVABench +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 LLaVABench all +# RealWorldQA +./scripts/run_inference.sh MiniCPM-Llama3-V-2_5 RealWorldQA all +``` +
+ +### vqadataset +首先，进入 `vqaeval` 目录下，安装必要的依赖，并创建 `downloads` 子目录，用于存储下载的数据集： +```bash +cd vqaeval +pip install -r requirements.txt +mkdir downloads +``` +
+ +然后，从下列各地址下载数据集并置于指定目录下： +###### TextVQA +```bash +cd downloads +mkdir TextVQA && cd TextVQA +wget https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip +unzip train_val_images.zip && rm train_val_images.zip +mv train_val_images/train_images . && rm -rf train_val_images +wget https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_val.json +cd ../.. +``` + +###### DocVQA / DocVQATest +```bash +cd downloads +mkdir DocVQA && cd DocVQA && mkdir spdocvqa_images +# 在 https://rrc.cvc.uab.es/?ch=17&com=downloads 下载 Task 1 - Single Page Document Visual Question Answering 下的 Images 和 Annotations +# 将下载得到的 spdocvqa_images.tar.gz 以及 spdocvqa_qas.zip 置于 DocVQA 目录下 +tar -zxvf spdocvqa_images.tar.gz -C spdocvqa_images && rm spdocvqa_images.tar.gz +unzip spdocvqa_qas.zip && rm spdocvqa_qas.zip +cp spdocvqa_qas/val_v1.0_withQT.json . && cp spdocvqa_qas/test_v1.0.json . && rm -rf spdocvqa_qas +cd ../.. +``` +
+ +`downloads` 目录应当按照下列结构组织： +```bash +downloads +├── TextVQA +│ ├── train_images +│ │ ├── ... +│ ├── TextVQA_0.5.1_val.json +├── DocVQA +│ ├── spdocvqa_images +│ │ ├── ... +│ ├── val_v1.0_withQT.json +│ ├── test_v1.0.json +``` +
+ +准备好相应的数据集之后，修改 `shell/run_inference.sh` 的参数，运行推理： + +```bash +chmod +x ./shell/run_inference.sh +./shell/run_inference.sh +``` +
+ +可以传入的参数位于 `eval_utils/getargs.py` 中，各主要参数的含义如下。 +对于 `MiniCPM-Llama3-V-2_5`，需要将 `model_name` 设置为 `minicpmv`： +```bash +# 指定 TextVQA 评测所有图片和问题的路径 +--textVQA_image_dir +--textVQA_ann_path +# 指定 DocVQA 评测所有图片和问题的路径 +--docVQA_image_dir +--docVQA_ann_path +# 指定 DocVQATest 评测所有图片和问题的路径 +--docVQATest_image_dir +--docVQATest_ann_path + +# 决定是否评测某个任务，eval_all 设置为 True 表示所有任务都评测 +--eval_textVQA +--eval_docVQA +--eval_docVQATest +--eval_all + +# 模型名称、模型路径（从指定路径加载模型） +--model_name +--model_path +# 从 checkpoint 加载模型 +--ckpt +# 模型处理输入数据的方式，interleave 表示图文交错式，old 表示非交错式 +--generate_method +# 推理时的批处理规模，建议推理时设置为 1 +--batchsize + +# 输出内容保存的路径 +--answer_path +``` +
+ +评测三个任务需要设置的参数如下： +###### TextVQA +```bash +--eval_textVQA +--textVQA_image_dir ./downloads/TextVQA/train_images +--textVQA_ann_path ./downloads/TextVQA/TextVQA_0.5.1_val.json +``` + +###### DocVQA +```bash +--eval_docVQA +--docVQA_image_dir ./downloads/DocVQA/spdocvqa_images +--docVQA_ann_path ./downloads/DocVQA/val_v1.0_withQT.json +``` + +###### DocVQATest +```bash +--eval_docVQATest +--docVQATest_image_dir ./downloads/DocVQA/spdocvqa_images +--docVQATest_ann_path ./downloads/DocVQA/test_v1.0.json +``` +
+ +对于 DocVQATest 任务，为了将推理结果上传到[官方网站](https://rrc.cvc.uab.es/?ch=17)进行评测，还需要运行 `shell/run_transform.sh` 进行格式转换。其中，`input_file_path` 对应原始输出的 json 的路径，`output_file_path` 为自定义的转换后的 json 的路径： +```bash +chmod +x ./shell/run_transform.sh +./shell/run_transform.sh +``` + +
\ No newline at end of file diff --git a/MiniCPM-V/eval_mm/vlmevalkit/.DS_Store b/MiniCPM-V/eval_mm/vlmevalkit/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..e08cf98697005178645e101b1feb1e4fb1181a8a Binary files /dev/null and b/MiniCPM-V/eval_mm/vlmevalkit/.DS_Store differ diff --git a/MiniCPM-V/eval_mm/vlmevalkit/requirements.txt b/MiniCPM-V/eval_mm/vlmevalkit/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..b45133ce07f8e38e0b34bd94f5027ecf52d86398 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/requirements.txt @@ -0,0 +1,30 @@ +decord +gradio +huggingface_hub +imageio +matplotlib +moviepy +numpy>=1.23.4 +omegaconf +openai==1.3.5 +opencv-python>=4.4.0.46 +openpyxl +pandas +peft +pillow +portalocker +python-dotenv +requests +rich +sentencepiece +setuptools +sty +tabulate +tiktoken +timeout-decorator +torch>=2.0.1 +tqdm +transformers +typing_extensions==4.7.1 +validators +xlsxwriter diff --git a/MiniCPM-V/eval_mm/vlmevalkit/requirements/docs.txt b/MiniCPM-V/eval_mm/vlmevalkit/requirements/docs.txt new file mode 100644 index 0000000000000000000000000000000000000000..02587e64881bbf53f7e29ee780a75a728cc8ac2f --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/requirements/docs.txt @@ -0,0 +1,11 @@ +docutils==0.18.1 +modelindex +myst-parser +-e git+https://github.com/open-compass/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme +sphinx==6.1.3 +sphinx-copybutton +sphinx-design +sphinx-notfound-page +sphinx-tabs +sphinxcontrib-jquery +tabulate diff --git a/MiniCPM-V/eval_mm/vlmevalkit/run.py b/MiniCPM-V/eval_mm/vlmevalkit/run.py new file mode 100644 index 0000000000000000000000000000000000000000..278caf8f79014180e98246d5f32c960893295bf7 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/run.py @@ -0,0 +1,223 @@ +import torch +import torch.distributed as dist + +from vlmeval.config import supported_VLM +from vlmeval.dataset import build_dataset +from vlmeval.inference import infer_data_job +from vlmeval.inference_video import infer_data_job_video +from vlmeval.inference_mt import infer_data_job_mt +from vlmeval.smp import * +from vlmeval.utils.result_transfer import MMMU_result_transfer, MMTBench_result_transfer + + +def parse_args(): + parser = argparse.ArgumentParser() + # Essential Args + parser.add_argument('--data', type=str, nargs='+', required=True) + parser.add_argument('--model', type=str, nargs='+', required=True) + # Args that only apply to Video Dataset + parser.add_argument('--nframe', type=int, default=8) + parser.add_argument('--pack', action='store_true') + parser.add_argument('--use-subtitle', action='store_true') + # Work Dir + parser.add_argument('--work-dir', type=str, default='./outputs', help='select the output directory') + # Infer + Eval or Infer Only + parser.add_argument('--mode', type=str, default='all', choices=['all', 'infer']) + # API Kwargs, Apply to API VLMs and Judge API LLMs + parser.add_argument('--nproc', type=int, default=4, help='Parallel API calling') + parser.add_argument('--retry', type=int, default=None, help='retry numbers for API VLMs') + # Explicitly Set the Judge Model + parser.add_argument('--judge', type=str, default=None) + # Logging Utils + parser.add_argument('--verbose', action='store_true') + # Configuration for Resume + # Ignore: will not rerun failed VLM inference + parser.add_argument('--ignore', action='store_true', help='Ignore failed indices. ') + # Rerun: will remove all evaluation temp files + parser.add_argument('--rerun', action='store_true') + args = parser.parse_args() + return args + + +def main(): + logger = get_logger('RUN') + + args = parse_args() + assert len(args.data), '--data should be a list of data files' + + if args.retry is not None: + for k, v in supported_VLM.items(): + if hasattr(v, 'keywords') and 'retry' in v.keywords: + v.keywords['retry'] = args.retry + supported_VLM[k] = v + if hasattr(v, 'keywords') and 'verbose' in v.keywords: + v.keywords['verbose'] = args.verbose + supported_VLM[k] = v + + rank, world_size = get_rank_and_world_size() + if world_size > 1: + local_rank = os.environ.get('LOCAL_RANK', 0) + torch.cuda.set_device(int(local_rank)) + dist.init_process_group(backend='nccl', timeout=datetime.timedelta(seconds=10800)) + + for _, model_name in enumerate(args.model): + model = None + + pred_root = osp.join(args.work_dir, model_name) + os.makedirs(pred_root, exist_ok=True) + + for _, dataset_name in enumerate(args.data): + dataset_kwargs = {} + if dataset_name in ['MMLongBench_DOC', 'DUDE', 'DUDE_MINI', 'SLIDEVQA', 'SLIDEVQA_MINI']: + dataset_kwargs['model'] = model_name + if dataset_name == 'MMBench-Video': + dataset_kwargs['pack'] = args.pack + if dataset_name == 'Video-MME': + dataset_kwargs['use_subtitle'] = args.use_subtitle + + # If distributed, first build the dataset on the main process for doing preparation works + if world_size > 1: + dataset = build_dataset(dataset_name, **dataset_kwargs) if rank == 0 else None + dist.barrier() + dataset_list = [dataset] + dist.broadcast_object_list(dataset_list, src=0) + dataset = dataset_list[0] + else: + dataset = build_dataset(dataset_name, **dataset_kwargs) + if dataset is None: + logger.error(f'Dataset {dataset_name} is not valid, will be skipped. ') + continue + + result_file = f'{pred_root}/{model_name}_{dataset_name}.xlsx' + if dataset_name in ['MMBench-Video']: + packstr = 'pack' if args.pack else 'nopack' + result_file = f'{pred_root}/{model_name}_{dataset_name}_{args.nframe}frame_{packstr}.xlsx' + elif dataset.MODALITY == 'VIDEO': + if args.pack: + logger.info(f'{dataset_name} not support Pack Mode, directly change to unpack') + args.pack = False + packstr = 'pack' if args.pack else 'nopack' + result_file = f'{pred_root}/{model_name}_{dataset_name}_{args.nframe}frame_{packstr}.xlsx' + if dataset_name in ['Video-MME']: + subtitlestr = 'subs' if args.use_subtitle else 'nosubs' + result_file = result_file.replace('.xlsx', f'_{subtitlestr}.xlsx') + + if dataset.TYPE == 'MT': + result_file = result_file.replace('.xlsx', '.tsv') + + if osp.exists(result_file) and args.rerun: + for keyword in ['openai', 'gpt', 'auxmatch']: + os.system(f'rm {pred_root}/{model_name}_{dataset_name}_{keyword}*') + + if model is None: + model = model_name # which is only a name + + # Perform the Inference + if dataset.MODALITY == 'VIDEO': + model = infer_data_job_video( + model, + work_dir=pred_root, + model_name=model_name, + dataset=dataset, + nframe=args.nframe, + pack=args.pack, + verbose=args.verbose, + subtitle=args.use_subtitle, + api_nproc=args.nproc) + elif dataset.TYPE == 'MT': + model = infer_data_job_mt( + model, + work_dir=pred_root, + model_name=model_name, + dataset=dataset, + verbose=args.verbose, + api_nproc=args.nproc, + ignore_failed=args.ignore) + else: + model = infer_data_job( + model, + work_dir=pred_root, + model_name=model_name, + dataset=dataset, + verbose=args.verbose, + api_nproc=args.nproc, + ignore_failed=args.ignore) + + # Set the judge kwargs first before evaluation or dumping + judge_kwargs = { + 'nproc': args.nproc, + 'verbose': args.verbose, + } + if args.retry is not None: + judge_kwargs['retry'] = args.retry + if args.judge is not None: + judge_kwargs['model'] = args.judge + else: + if dataset.TYPE in ['MCQ', 'Y/N']: + judge_kwargs['model'] = 'chatgpt-0125' + elif listinstr(['MMVet', 'MathVista', 'LLaVABench', 'MMBench-Video', 'MathVision'], dataset_name): + judge_kwargs['model'] = 'gpt-4-turbo' + elif listinstr(['MMLongBench', 'MMDU', 'DUDE', 'DUDE_MINI', 'SLIDEVQA', 'SLIDEVQA_MINI'], dataset_name): + judge_kwargs['model'] = 'gpt-4o' + if 'OPENAI_API_KEY_JUDGE' in os.environ and len(os.environ['OPENAI_API_KEY_JUDGE']): + judge_kwargs['key'] = os.environ['OPENAI_API_KEY_JUDGE'] + if 'OPENAI_API_BASE_JUDGE' in os.environ and len(os.environ['OPENAI_API_BASE_JUDGE']): + judge_kwargs['api_base'] = os.environ['OPENAI_API_BASE_JUDGE'] + + if rank == 0: + if dataset_name in ['MMMU_TEST']: + result_json = MMMU_result_transfer(result_file) + logger.info(f'Transfer MMMU_TEST result to json for official evaluation, ' + f'json file saved in {result_json}') # noqa: E501 + continue + elif 'MMT-Bench_ALL' in dataset_name: + submission_file = MMTBench_result_transfer(result_file, **judge_kwargs) + logger.info(f'Extract options from prediction of MMT-Bench FULL split for official evaluation ' + f'(https://eval.ai/web/challenges/challenge-page/2328/overview), ' + f'submission file saved in {submission_file}') # noqa: E501 + continue + elif 'MLLMGuard_DS' in dataset_name: + logger.info('The evaluation of MLLMGuard_DS is not supported yet. ') # noqa: E501 + continue + elif 'AesBench_TEST' == dataset_name: + logger.info(f'The results are saved in {result_file}. ' + f'Please send it to the AesBench Team via huangyipo@hotmail.com.') # noqa: E501 + continue + + if dataset_name in [ + 'MMBench_TEST_CN', 'MMBench_TEST_EN', 'MMBench', 'MMBench_CN', + 'MMBench_TEST_CN_V11', 'MMBench_TEST_EN_V11', 'MMBench_V11', 'MMBench_CN_V11' + ]: + if not MMBenchOfficialServer(dataset_name): + logger.error( + f'Can not evaluate {dataset_name} on non-official servers, ' + 'will skip the evaluation. ' + ) + continue + + eval_proxy = os.environ.get('EVAL_PROXY', None) + old_proxy = os.environ.get('HTTP_PROXY', '') + + if rank == 0 and args.mode == 'all': + if eval_proxy is not None: + proxy_set(eval_proxy) + + eval_results = dataset.evaluate(result_file, **judge_kwargs) + if eval_results is not None: + assert isinstance(eval_results, dict) or isinstance(eval_results, pd.DataFrame) + logger.info(f'The evaluation of model {model_name} x dataset {dataset_name} has finished! ') + logger.info('Evaluation Results:') + if isinstance(eval_results, dict): + logger.info('\n' + json.dumps(eval_results, indent=4)) + elif isinstance(eval_results, pd.DataFrame): + if len(eval_results) < len(eval_results.columns): + eval_results = eval_results.T + logger.info('\n' + tabulate(eval_results)) + + if eval_proxy is not None: + proxy_set(old_proxy) + + +if __name__ == '__main__': + load_env() + main() diff --git a/MiniCPM-V/eval_mm/vlmevalkit/scripts/run_inference.sh b/MiniCPM-V/eval_mm/vlmevalkit/scripts/run_inference.sh new file mode 100644 index 0000000000000000000000000000000000000000..3952194cd30b444f2fc9a21402ff6efb7da8aefc --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/scripts/run_inference.sh @@ -0,0 +1,31 @@ +export PATH=/usr/local/cuda/bin:$PATH + +export HF_ENDPOINT=https://hf-mirror.com +export OMP_NUM_THREADS=1 +export timestamp=`date +"%Y%m%d%H%M%S"` +export OLD_VERSION='False' +export PYTHONPATH=$(dirname $SELF_DIR):$PYTHONPATH + +# gpu consumed +# fp16 17-18G +# int4 7-8G + +# model to be used +# Example: MODELNAME=MiniCPM_V_2_6 +MODELNAME=$1 +# datasets to be tested +# Example: DATALIST="MMMU_DEV_VAL MathVista_MINI MMVet MMBench_DEV_EN_V11 MMBench_DEV_CN_V11 MMStar HallusionBench AI2D_TEST" +DATALIST=$2 +# test mode, all or infer +MODE=$3 + +echo "Starting inference with model $MODELNAME on datasets $DATALIST" +# run on multi gpus with torchrun command +# remember to run twice, the first run may fail +torchrun --nproc_per_node=8 run.py --data $DATALIST --model $MODELNAME --mode $MODE +torchrun --nproc_per_node=8 run.py --data $DATALIST --model $MODELNAME --mode $MODE +# run on single gpu with python command +# python run.py --data $DATALIST --model $MODELNAME --verbose --mode $MODE +# python run.py --data $DATALIST --model $MODELNAME --verbose --mode $MODE + +ls diff --git a/MiniCPM-V/eval_mm/vlmevalkit/setup.py b/MiniCPM-V/eval_mm/vlmevalkit/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..8b7a894e968c5f87c8563ecb6b9045f8296051c6 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/setup.py @@ -0,0 +1,122 @@ +import re +import sys +from os.path import exists +from setuptools import find_packages, setup + + +def parse_requirements(fname='requirements.txt', with_version=True): + """Parse the package dependencies listed in a requirements file but strips + specific versioning information. + + Args: + fname (str): path to requirements file + with_version (bool, default=False): if True include version specs + + Returns: + List[str]: list of requirements items + + CommandLine: + python -c "import setup; print(setup.parse_requirements())" + """ + + require_fpath = fname + + def parse_line(line): + """Parse information from a line in a requirements text file.""" + if line.startswith('-r '): + # Allow specifying requirements in other files + target = line.split(' ')[1] + for info in parse_require_file(target): + yield info + else: + info = {'line': line} + if line.startswith('-e '): + info['package'] = line.split('#egg=')[1] + elif '@git+' in line: + info['package'] = line + else: + # Remove versioning from the package + pat = '(' + '|'.join(['>=', '==', '>']) + ')' + parts = re.split(pat, line, maxsplit=1) + parts = [p.strip() for p in parts] + + info['package'] = parts[0] + if len(parts) > 1: + op, rest = parts[1:] + if ';' in rest: + # Handle platform specific dependencies + # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies + version, platform_deps = map(str.strip, + rest.split(';')) + info['platform_deps'] = platform_deps + else: + version = rest # NOQA + info['version'] = (op, version) + yield info + + def parse_require_file(fpath): + with open(fpath, 'r') as f: + for line in f.readlines(): + line = line.strip() + if line and not line.startswith('#'): + for info in parse_line(line): + yield info + + def gen_packages_items(): + if exists(require_fpath): + for info in parse_require_file(require_fpath): + parts = [info['package']] + if with_version and 'version' in info: + parts.extend(info['version']) + if not sys.version.startswith('3.4'): + # apparently package_deps are broken in 3.4 + platform_deps = info.get('platform_deps') + if platform_deps is not None: + parts.append(';' + platform_deps) + item = ''.join(parts) + yield item + + packages = list(gen_packages_items()) + return packages + + +with open('README.md') as f: + readme = f.read() + + +def do_setup(): + setup( + name='vlmeval', + version='0.1.0', + description='OpenCompass VLM Evaluation Kit', + author='Haodong Duan', + author_email='dhd.efz@gmail.com', + maintainer='Haodong Duan', + maintainer_email='dhd.efz@gmail.com', + long_description=readme, + long_description_content_type='text/markdown', + cmdclass={}, + install_requires=parse_requirements('requirements.txt'), + setup_requires=[], + python_requires='>=3.7.0', + packages=find_packages(exclude=[ + 'test*', + 'paper_test*', + ]), + keywords=['AI', 'NLP', 'in-context learning'], + entry_points={ + 'console_scripts': ['vlmutil = vlmeval:cli'] + }, + classifiers=[ + 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3.8', + 'Programming Language :: Python :: 3.9', + 'Programming Language :: Python :: 3.10', + 'Intended Audience :: Developers', + 'Intended Audience :: Education', + 'Intended Audience :: Science/Research', + ]) + + +if __name__ == '__main__': + do_setup() diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/.DS_Store b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..41cb35598c8e9b79252e3a54559dddb5425f4d39 Binary files /dev/null and b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/.DS_Store differ diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/__init__.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f14739fd39bd1c8c3dce45b8e609a00df9a1c91d --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/__init__.py @@ -0,0 +1,16 @@ +try: + import torch +except ImportError: + pass + +from .smp import * +from .api import * +from .dataset import * +from .utils import * +from .vlm import * +from .config import * +from .tools import cli + +load_env() + +__version__ = '0.2rc1' diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/api/__init__.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b2573c5dc9f01f4eee349056f50856ef5f1da8bd --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/api/__init__.py @@ -0,0 +1,5 @@ +from .gpt import OpenAIWrapper, GPT4V + +__all__ = [ + 'OpenAIWrapper', 'GPT4V' +] diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/api/base.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/api/base.py new file mode 100644 index 0000000000000000000000000000000000000000..8df5a5c6abe5e7c6dbe45cadfcfd715fc9c67b94 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/api/base.py @@ -0,0 +1,265 @@ +import time +import random as rd +from abc import abstractmethod +import os.path as osp +import copy as cp +from ..smp import get_logger, parse_file, concat_images_vlmeval + + +class BaseAPI: + + allowed_types = ['text', 'image'] + INTERLEAVE = True + INSTALL_REQ = False + + def __init__(self, + retry=10, + wait=3, + system_prompt=None, + verbose=True, + fail_msg='Failed to obtain answer via API.', + **kwargs): + """Base Class for all APIs. + + Args: + retry (int, optional): The retry times for `generate_inner`. Defaults to 10. + wait (int, optional): The wait time after each failed retry of `generate_inner`. Defaults to 3. + system_prompt (str, optional): Defaults to None. + verbose (bool, optional): Defaults to True. + fail_msg (str, optional): The message to return when failed to obtain answer. + Defaults to 'Failed to obtain answer via API.'. + **kwargs: Other kwargs for `generate_inner`. + """ + + self.wait = wait + self.retry = retry + self.system_prompt = system_prompt + self.verbose = verbose + self.fail_msg = fail_msg + self.logger = get_logger('ChatAPI') + + if len(kwargs): + self.logger.info(f'BaseAPI received the following kwargs: {kwargs}') + self.logger.info('Will try to use them as kwargs for `generate`. ') + self.default_kwargs = kwargs + + @abstractmethod + def generate_inner(self, inputs, **kwargs): + """The inner function to generate the answer. + + Returns: + tuple(int, str, str): ret_code, response, log + """ + self.logger.warning('For APIBase, generate_inner is an abstract method. ') + assert 0, 'generate_inner not defined' + ret_code, answer, log = None, None, None + # if ret_code is 0, means succeed + return ret_code, answer, log + + def working(self): + """If the API model is working, return True, else return False. + + Returns: + bool: If the API model is working, return True, else return False. + """ + self.old_timeout = None + if hasattr(self, 'timeout'): + self.old_timeout = self.timeout + self.timeout = 120 + + retry = 5 + while retry > 0: + ret = self.generate('hello') + if ret is not None and ret != '' and self.fail_msg not in ret: + if self.old_timeout is not None: + self.timeout = self.old_timeout + return True + retry -= 1 + + if self.old_timeout is not None: + self.timeout = self.old_timeout + return False + + def check_content(self, msgs): + """Check the content type of the input. Four types are allowed: str, dict, liststr, listdict. + + Args: + msgs: Raw input messages. + + Returns: + str: The message type. + """ + if isinstance(msgs, str): + return 'str' + if isinstance(msgs, dict): + return 'dict' + if isinstance(msgs, list): + types = [self.check_content(m) for m in msgs] + if all(t == 'str' for t in types): + return 'liststr' + if all(t == 'dict' for t in types): + return 'listdict' + return 'unknown' + + def preproc_content(self, inputs): + """Convert the raw input messages to a list of dicts. + + Args: + inputs: raw input messages. + + Returns: + list(dict): The preprocessed input messages. Will return None if failed to preprocess the input. + """ + if self.check_content(inputs) == 'str': + return [dict(type='text', value=inputs)] + elif self.check_content(inputs) == 'dict': + assert 'type' in inputs and 'value' in inputs + return [inputs] + elif self.check_content(inputs) == 'liststr': + res = [] + for s in inputs: + mime, pth = parse_file(s) + if mime is None or mime == 'unknown': + res.append(dict(type='text', value=s)) + else: + res.append(dict(type=mime.split('/')[0], value=pth)) + return res + elif self.check_content(inputs) == 'listdict': + for item in inputs: + assert 'type' in item and 'value' in item + mime, s = parse_file(item['value']) + if mime is None: + assert item['type'] == 'text', item['value'] + else: + assert mime.split('/')[0] == item['type'] + item['value'] = s + return inputs + else: + return None + + # May exceed the context windows size, so try with different turn numbers. + def chat_inner(self, inputs, **kwargs): + _ = kwargs.pop('dataset', None) + while len(inputs): + try: + return self.generate_inner(inputs, **kwargs) + except: + inputs = inputs[1:] + while len(inputs) and inputs[0]['role'] != 'user': + inputs = inputs[1:] + continue + return -1, self.fail_msg + ': ' + 'Failed with all possible conversation turns.', None + + def chat(self, messages, **kwargs1): + """The main function for multi-turn chatting. Will call `chat_inner` with the preprocessed input messages.""" + assert hasattr(self, 'chat_inner'), 'The API model should has the `chat_inner` method. ' + for msg in messages: + assert isinstance(msg, dict) and 'role' in msg and 'content' in msg, msg + assert self.check_content(msg['content']) in ['str', 'dict', 'liststr', 'listdict'], msg + msg['content'] = self.preproc_content(msg['content']) + # merge kwargs + kwargs = cp.deepcopy(self.default_kwargs) + kwargs.update(kwargs1) + + answer = None + # a very small random delay [0s - 0.5s] + T = rd.random() * 0.5 + time.sleep(T) + + assert messages[-1]['role'] == 'user' + + for i in range(self.retry): + try: + ret_code, answer, log = self.chat_inner(messages, **kwargs) + if ret_code == 0 and self.fail_msg not in answer and answer != '': + if self.verbose: + print(answer) + return answer + elif self.verbose: + if not isinstance(log, str): + try: + log = log.text + except: + self.logger.warning(f'Failed to parse {log} as an http response. ') + self.logger.info(f'RetCode: {ret_code}\nAnswer: {answer}\nLog: {log}') + except Exception as err: + if self.verbose: + self.logger.error(f'An error occured during try {i}:') + self.logger.error(err) + # delay before each retry + T = rd.random() * self.wait * 2 + time.sleep(T) + + return self.fail_msg if answer in ['', None] else answer + + def generate(self, message, **kwargs1): + """The main function to generate the answer. Will call `generate_inner` with the preprocessed input messages. + + Args: + message: raw input messages. + + Returns: + str: The generated answer of the Failed Message if failed to obtain answer. + """ + assert self.check_content(message) in ['str', 'dict', 'liststr', 'listdict'], f'Invalid input type: {message}' + message = self.preproc_content(message) + assert message is not None and self.check_content(message) == 'listdict' + for item in message: + assert item['type'] in self.allowed_types, f'Invalid input type: {item["type"]}' + + # merge kwargs + kwargs = cp.deepcopy(self.default_kwargs) + kwargs.update(kwargs1) + + answer = None + # a very small random delay [0s - 0.5s] + T = rd.random() * 0.5 + time.sleep(T) + + for i in range(self.retry): + try: + ret_code, answer, log = self.generate_inner(message, **kwargs) + if ret_code == 0 and self.fail_msg not in answer and answer != '': + if self.verbose: + print(answer) + return answer + elif self.verbose: + if not isinstance(log, str): + try: + log = log.text + except: + self.logger.warning(f'Failed to parse {log} as an http response. ') + self.logger.info(f'RetCode: {ret_code}\nAnswer: {answer}\nLog: {log}') + except Exception as err: + if self.verbose: + self.logger.error(f'An error occured during try {i}:') + self.logger.error(err) + # delay before each retry + T = rd.random() * self.wait * 2 + time.sleep(T) + + return self.fail_msg if answer in ['', None] else answer + + def message_to_promptimg(self, message, dataset=None): + assert not self.INTERLEAVE + model_name = self.__class__.__name__ + import warnings + warnings.warn( + f'Model {model_name} does not support interleaved input. ' + 'Will use the first image and aggregated texts as prompt. ') + num_images = len([x for x in message if x['type'] == 'image']) + if num_images == 0: + prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text']) + image = None + elif num_images == 1: + prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text']) + image = [x['value'] for x in message if x['type'] == 'image'][0] + else: + prompt = '\n'.join([x['value'] if x['type'] == 'text' else '' for x in message]) + if dataset == 'BLINK': + image = concat_images_vlmeval( + [x['value'] for x in message if x['type'] == 'image'], + target_size=512) + else: + image = [x['value'] for x in message if x['type'] == 'image'][0] + return prompt, image diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/api/gpt.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/api/gpt.py new file mode 100644 index 0000000000000000000000000000000000000000..f308e7c15e278143459e4fee6253333c10cf5385 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/api/gpt.py @@ -0,0 +1,248 @@ +from ..smp import * +import os +import sys +from .base import BaseAPI + +APIBASES = { + 'OFFICIAL': 'https://api.openai.com/v1/chat/completions', +} + + +def GPT_context_window(model): + length_map = { + 'gpt-4': 8192, + 'gpt-4-0613': 8192, + 'gpt-4-turbo-preview': 128000, + 'gpt-4-1106-preview': 128000, + 'gpt-4-0125-preview': 128000, + 'gpt-4-vision-preview': 128000, + 'gpt-4-turbo': 128000, + 'gpt-4-turbo-2024-04-09': 128000, + 'gpt-3.5-turbo': 16385, + 'gpt-3.5-turbo-0125': 16385, + 'gpt-3.5-turbo-1106': 16385, + 'gpt-3.5-turbo-instruct': 4096, + } + if model in length_map: + return length_map[model] + else: + return 128000 + + +class OpenAIWrapper(BaseAPI): + + is_api: bool = True + + def __init__(self, + model: str = 'gpt-3.5-turbo-0613', + retry: int = 5, + wait: int = 5, + key: str = None, + verbose: bool = True, + system_prompt: str = None, + temperature: float = 0, + timeout: int = 60, + api_base: str = None, + max_tokens: int = 1024, + img_size: int = 512, + img_detail: str = 'low', + use_azure: bool = False, + **kwargs): + + self.model = model + self.cur_idx = 0 + self.fail_msg = 'Failed to obtain answer via API. ' + self.max_tokens = max_tokens + self.temperature = temperature + self.use_azure = use_azure + + if 'step-1v' in model: + env_key = os.environ.get('STEPAI_API_KEY', '') + if key is None: + key = env_key + elif 'yi-vision' in model: + env_key = os.environ.get('YI_API_KEY', '') + if key is None: + key = env_key + else: + if use_azure: + env_key = os.environ.get('AZURE_OPENAI_API_KEY', None) + assert env_key is not None, 'Please set the environment variable AZURE_OPENAI_API_KEY. ' + + if key is None: + key = env_key + assert isinstance(key, str), ( + 'Please set the environment variable AZURE_OPENAI_API_KEY to your openai key. ' + ) + else: + env_key = os.environ.get('OPENAI_API_KEY', '') + if key is None: + key = env_key + assert isinstance(key, str) and key.startswith('sk-'), ( + f'Illegal openai_key {key}. ' + 'Please set the environment variable OPENAI_API_KEY to your openai key. ' + ) + + self.key = key + assert img_size > 0 or img_size == -1 + self.img_size = img_size + assert img_detail in ['high', 'low'] + self.img_detail = img_detail + self.timeout = timeout + + super().__init__(wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs) + + if use_azure: + api_base_template = ( + '{endpoint}openai/deployments/{deployment_name}/chat/completions?api-version={api_version}' + ) + endpoint = os.getenv('AZURE_OPENAI_ENDPOINT', None) + assert endpoint is not None, 'Please set the environment variable AZURE_OPENAI_ENDPOINT. ' + deployment_name = os.getenv('AZURE_OPENAI_DEPLOYMENT_NAME', None) + assert deployment_name is not None, 'Please set the environment variable AZURE_OPENAI_DEPLOYMENT_NAME. ' + api_version = os.getenv('OPENAI_API_VERSION', None) + assert api_version is not None, 'Please set the environment variable OPENAI_API_VERSION. ' + + self.api_base = api_base_template.format( + endpoint=os.getenv('AZURE_OPENAI_ENDPOINT'), + deployment_name=os.getenv('AZURE_OPENAI_DEPLOYMENT_NAME'), + api_version=os.getenv('OPENAI_API_VERSION') + ) + else: + if api_base is None: + if 'OPENAI_API_BASE' in os.environ and os.environ['OPENAI_API_BASE'] != '': + self.logger.info('Environment variable OPENAI_API_BASE is set. Will use it as api_base. ') + api_base = os.environ['OPENAI_API_BASE'] + else: + api_base = 'OFFICIAL' + + assert api_base is not None + + if api_base in APIBASES: + self.api_base = APIBASES[api_base] + elif api_base.startswith('http'): + self.api_base = api_base + else: + self.logger.error('Unknown API Base. ') + sys.exit(-1) + + self.logger.info(f'Using API Base: {self.api_base}; API Key: {self.key}') + + # inputs can be a lvl-2 nested list: [content1, content2, content3, ...] + # content can be a string or a list of image & text + def prepare_itlist(self, inputs): + assert np.all([isinstance(x, dict) for x in inputs]) + has_images = np.sum([x['type'] == 'image' for x in inputs]) + if has_images: + content_list = [] + for msg in inputs: + if msg['type'] == 'text': + content_list.append(dict(type='text', text=msg['value'])) + elif msg['type'] == 'image': + from PIL import Image + img = Image.open(msg['value']) + b64 = encode_image_to_base64(img, target_size=self.img_size) + img_struct = dict(url=f'data:image/jpeg;base64,{b64}', detail=self.img_detail) + content_list.append(dict(type='image_url', image_url=img_struct)) + else: + assert all([x['type'] == 'text' for x in inputs]) + text = '\n'.join([x['value'] for x in inputs]) + content_list = [dict(type='text', text=text)] + return content_list + + def prepare_inputs(self, inputs): + input_msgs = [] + if self.system_prompt is not None: + input_msgs.append(dict(role='system', content=self.system_prompt)) + assert isinstance(inputs, list) and isinstance(inputs[0], dict) + assert np.all(['type' in x for x in inputs]) or np.all(['role' in x for x in inputs]), inputs + if 'role' in inputs[0]: + assert inputs[-1]['role'] == 'user', inputs[-1] + for item in inputs: + input_msgs.append(dict(role=item['role'], content=self.prepare_itlist(item['content']))) + else: + input_msgs.append(dict(role='user', content=self.prepare_itlist(inputs))) + return input_msgs + + def generate_inner(self, inputs, **kwargs) -> str: + input_msgs = self.prepare_inputs(inputs) + temperature = kwargs.pop('temperature', self.temperature) + max_tokens = kwargs.pop('max_tokens', self.max_tokens) + + context_window = GPT_context_window(self.model) + max_tokens = min(max_tokens, context_window - self.get_token_len(inputs)) + if 0 < max_tokens <= 100: + self.logger.warning( + 'Less than 100 tokens left, ' + 'may exceed the context window with some additional meta symbols. ' + ) + if max_tokens <= 0: + return 0, self.fail_msg + 'Input string longer than context window. ', 'Length Exceeded. ' + + # Will send request if use Azure, dk how to use openai client for it + if self.use_azure: + headers = {'Content-Type': 'application/json', 'api-key': self.key} + else: + headers = {'Content-Type': 'application/json', 'Authorization': f'Bearer {self.key}'} + payload = dict( + model=self.model, + messages=input_msgs, + max_tokens=max_tokens, + n=1, + temperature=temperature, + **kwargs) + response = requests.post( + self.api_base, + headers=headers, data=json.dumps(payload), timeout=self.timeout * 1.1) + ret_code = response.status_code + ret_code = 0 if (200 <= int(ret_code) < 300) else ret_code + answer = self.fail_msg + try: + resp_struct = json.loads(response.text) + answer = resp_struct['choices'][0]['message']['content'].strip() + except: + pass + return ret_code, answer, response + + def get_image_token_len(self, img_path, detail='low'): + import math + if detail == 'low': + return 85 + + im = Image.open(img_path) + height, width = im.size + if width > 1024 or height > 1024: + if width > height: + height = int(height * 1024 / width) + width = 1024 + else: + width = int(width * 1024 / height) + height = 1024 + + h = math.ceil(height / 512) + w = math.ceil(width / 512) + total = 85 + 170 * h * w + return total + + def get_token_len(self, inputs) -> int: + import tiktoken + try: + enc = tiktoken.encoding_for_model(self.model) + except: + enc = tiktoken.encoding_for_model('gpt-4') + assert isinstance(inputs, list) + tot = 0 + for item in inputs: + if 'role' in item: + tot += self.get_token_len(item['content']) + elif item['type'] == 'text': + tot += len(enc.encode(item['value'])) + elif item['type'] == 'image': + tot += self.get_image_token_len(item['value'], detail=self.img_detail) + return tot + + +class GPT4V(OpenAIWrapper): + + def generate(self, message, dataset=None): + return super(GPT4V, self).generate(message) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/config.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/config.py new file mode 100644 index 0000000000000000000000000000000000000000..be42e9a3cecb608330f447a2716da143d25fc682 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/config.py @@ -0,0 +1,19 @@ +from vlmeval.vlm import * +from vlmeval.api import * +from functools import partial + +minicpm_series = { + 'MiniCPM-V': partial(MiniCPM_V, model_path='openbmb/MiniCPM-V'), + 'MiniCPM-V-2': partial(MiniCPM_V, model_path='openbmb/MiniCPM-V-2'), + 'MiniCPM-Llama3-V-2_5': partial(MiniCPM_Llama3_V, model_path='openbmb/MiniCPM-Llama3-V-2_5'), + 'MiniCPM-V-2_6': partial(MiniCPM_V_2_6, model_path='openbmb/MiniCPM-V-2_6'), +} + +supported_VLM = {} + +model_groups = [ + minicpm_series +] + +for grp in model_groups: + supported_VLM.update(grp) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/.DS_Store b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..d221bb21ff035e2a21239e4a2036f3d999982cfb Binary files /dev/null and b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/.DS_Store differ diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/__init__.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..01e7f7fa4d0ac401708b53c3763fe8723a8a12f0 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/__init__.py @@ -0,0 +1,186 @@ +import warnings + +from .image_base import img_root_map, ImageBaseDataset +from .image_caption import ImageCaptionDataset +from .image_yorn import ImageYORNDataset +from .image_mcq import ImageMCQDataset, MMMUDataset, CustomMCQDataset, MUIRDataset, GMAIMMBenchDataset +from .image_mt import MMDUDataset +from .image_vqa import ( + ImageVQADataset, MathVision, OCRBench, MathVista, LLaVABench, MMVet, MTVQADataset, CustomVQADataset +) + +from .vcr import VCRDataset +from .mmlongbench import MMLongBench +from .dude import DUDE +from .slidevqa import SlideVQA + +from .mmbench_video import MMBenchVideo +from .text_mcq import CustomTextMCQDataset, TextMCQDataset +from .videomme import VideoMME +from .mvbench import MVBench, MVBench_MP4 +from .utils import * +from ..smp import * + + +class ConcatDataset(ImageBaseDataset): + # This dataset takes multiple dataset names as input and aggregate them into a single dataset. + # Each single dataset should not have a field named `SUB_DATASET` + + DATASET_SETS = { + 'MMMB': ['MMMB_ar', 'MMMB_cn', 'MMMB_en', 'MMMB_pt', 'MMMB_ru', 'MMMB_tr'], + 'MTL_MMBench_DEV': [ + 'MMBench_dev_ar', 'MMBench_dev_cn', 'MMBench_dev_en', + 'MMBench_dev_pt', 'MMBench_dev_ru', 'MMBench_dev_tr' + ] + } + + def __init__(self, dataset): + datasets = self.DATASET_SETS[dataset] + self.dataset_map = {} + # The name of the compliation + self.dataset_name = dataset + self.datasets = datasets + for dname in datasets: + dataset = build_dataset(dname) + assert dataset is not None, dataset + self.dataset_map[dname] = dataset + TYPES = [x.TYPE for x in self.dataset_map.values()] + MODALITIES = [x.MODALITY for x in self.dataset_map.values()] + assert np.all([x == TYPES[0] for x in TYPES]), (datasets, TYPES) + assert np.all([x == MODALITIES[0] for x in MODALITIES]), (datasets, MODALITIES) + self.TYPE = TYPES[0] + self.MODALITY = MODALITIES[0] + data_all = [] + for dname in datasets: + data = self.dataset_map[dname].data + data['SUB_DATASET'] = [dname] * len(data) + data_new = localize_df(data, dname, nproc=16) + data_all.append(data_new) + + data = pd.concat(data_all) + data['original_index'] = data.pop('index') + data['index'] = np.arange(len(data)) + self.data = data + + def build_prompt(self, line): + if isinstance(line, int): + line = self.data.iloc[line] + idx = line['original_index'] + dname = line['SUB_DATASET'] + org_data = self.dataset_map[dname].data + org_line = cp.deepcopy(org_data[org_data['index'] == idx]).iloc[0] + return self.dataset_map[dname].build_prompt(org_line) + + def dump_image(self, line): + # Assert all images are pre-dumped + assert 'image' not in line + assert 'image_path' in line + tgt_path = toliststr(line['image_path']) + return tgt_path + + @classmethod + def supported_datasets(cls): + return list(cls.DATASET_SETS) + + def evaluate(self, eval_file, **judge_kwargs): + suffix = eval_file.split('.')[-1] + # First, split the eval_file by dataset + data_all = load(eval_file) + for dname in self.datasets: + tgt = eval_file.replace(self.dataset_name, dname) + data_sub = data_all[data_all['SUB_DATASET'] == dname] + data_sub.pop('index') + data_sub['index'] = data_sub.pop('original_index') + data_sub.pop('SUB_DATASET') + dump(data_sub, tgt) + # Then, evaluate each dataset separately + results_all = [] + for dname in self.datasets: + tgt = eval_file.replace(self.dataset_name, dname) + res = self.dataset_map[dname].evaluate(tgt, **judge_kwargs) + assert isinstance(res, pd.DataFrame) + res['DATASET'] = [dname] * len(res) + results_all.append(res) + result = pd.concat(results_all) + score_file = eval_file.replace(f'.{suffix}', '_acc.csv') + dump(result, score_file) + return result + + +# Add new supported dataset class here +IMAGE_DATASET = [ + ImageCaptionDataset, ImageYORNDataset, ImageMCQDataset, ImageVQADataset, MathVision, + MMMUDataset, OCRBench, MathVista, LLaVABench, MMVet, MTVQADataset, + MMLongBench, VCRDataset, MMDUDataset, DUDE, SlideVQA, MUIRDataset, GMAIMMBenchDataset +] + +VIDEO_DATASET = [ + MMBenchVideo, VideoMME, MVBench, MVBench_MP4 +] + +TEXT_DATASET = [ + TextMCQDataset +] + +CUSTOM_DATASET = [ + CustomMCQDataset, CustomVQADataset, CustomTextMCQDataset +] + +DATASET_COLLECTION = [ConcatDataset] + +DATASET_CLASSES = IMAGE_DATASET + VIDEO_DATASET + TEXT_DATASET + CUSTOM_DATASET + DATASET_COLLECTION +SUPPORTED_DATASETS = [] +for DATASET_CLS in DATASET_CLASSES: + SUPPORTED_DATASETS.extend(DATASET_CLS.supported_datasets()) + + +def DATASET_TYPE(dataset): + for cls in DATASET_CLASSES: + if dataset in cls.supported_datasets(): + if hasattr(cls, 'TYPE'): + return cls.TYPE + # Have to add specific routine to handle ConcatDataset + if dataset in ConcatDataset.DATASET_SETS: + dataset_list = ConcatDataset.DATASET_SETS[dataset] + TYPES = [DATASET_TYPE(dname) for dname in dataset_list] + assert np.all([x == TYPES[0] for x in TYPES]), (dataset_list, TYPES) + return TYPES[0] + + if 'openended' in dataset.lower(): + return 'VQA' + warnings.warn(f'Dataset {dataset} is a custom one and not annotated as `openended`, will treat as MCQ. ') + return 'MCQ' + + +def build_dataset(dataset_name, **kwargs): + for cls in DATASET_CLASSES: + if dataset_name in cls.supported_datasets(): + return cls(dataset=dataset_name, **kwargs) + + warnings.warn(f'Dataset {dataset_name} is not officially supported. ') + + data_file = osp.join(LMUDataRoot(), f'{dataset_name}.tsv') + if not osp.exists(data_file): + warnings.warn(f'Data file {data_file} does not exist. Dataset building failed. ') + return None + + data = load(data_file) + if 'question' not in [x.lower() for x in data.columns]: + warnings.warn(f'Data file {data_file} does not have a `question` column. Dataset building failed. ') + return None + + if 'A' in data and 'B' in data: + if 'image' in data or 'image_path' in data: + warnings.warn(f'Will assume unsupported dataset {dataset_name} as a Custom MCQ dataset. ') + return CustomMCQDataset(dataset=dataset_name, **kwargs) + else: + warnings.warn(f'Will assume unsupported dataset {dataset_name} as a Custom Text MCQ dataset. ') + return CustomTextMCQDataset(dataset=dataset_name, **kwargs) + else: + warnings.warn(f'Will assume unsupported dataset {dataset_name} as a Custom VQA dataset. ') + return CustomVQADataset(dataset=dataset_name, **kwargs) + + +__all__ = [ + 'build_dataset', 'img_root_map', 'build_judge', 'extract_answer_from_item', 'prefetch_answer', 'DEBUG_MESSAGE' +] + [cls.__name__ for cls in DATASET_CLASSES] diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/dude.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/dude.py new file mode 100644 index 0000000000000000000000000000000000000000..5e3c1dab2ddc61dd934c7d20933a7a0cf74f4e10 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/dude.py @@ -0,0 +1,210 @@ +import math +from typing import List + +from .utils.judge_util import build_judge +from .image_base import ImageBaseDataset +from .mmlongbench import concat_images, MMLongBench_auxeval, anls_compute +from ..smp import * + + +FAIL_MSG = 'Failed to obtain answer via API.' + + +def DUDE_acc(result_file): + data = load(result_file) + overall_score = 0.0 + score_list = list() + for i in range(len(data)): + item = data.iloc[i] + if isinstance(item['answer'], float) and math.isnan(item['answer']): + item['answer'] = 'Not answerable' + + item['answer'] = item['answer'].lower() + item['pred'] = item['pred'].lower() + score = anls_compute(item['answer'], item['pred']) + score_list.append(score) + overall_score += score + + data['score'] = score_list + dump(data, result_file) + + res = dict() + res['category'], res['num'], res['avg_score'] = ['anls'], [len(data)], [overall_score / len(data)] + res = pd.DataFrame(res) + return res + + +class DUDE(ImageBaseDataset): + + TYPE = 'VQA' + + DATASET_URL = { + 'DUDE': 'https://opencompass.openxlab.space/utils/VLMEval/DUDE.tsv', + 'DUDE_MINI': 'https://opencompass.openxlab.space/utils/VLMEval/DUDE_MINI.tsv', + } + DATASET_MD5 = { + 'DUDE': '130d860d08206e1e407cd77150c10d88', + 'DUDE_MINI': 'e0c0d998114f0cca7516d12039d2b538', + } + + SUPPORTED_MODELS = { + 'GPT4': (1, 1), + 'GPT4V': (1, 1), + 'GPT4V_HIGH': (1, 1), + 'GPT4o': (1, 1), + 'GPT4o_HIGH': (1, 1), + 'GPT4o_MINI': (1, 1), + 'XComposer2d5': (1, -1), + 'XComposer2_4KHD': (1, -1), + 'MiniCPM-Llama3-V-2_5': (1, 5), + 'InternVL-Chat-V1-5': (5, 2), + } + + def __init__(self, dataset, **kwargs): + self.model_list = list(self.SUPPORTED_MODELS.keys()) + model_name = kwargs['model'] + if not listinstr(self.model_list, model_name): + raise AssertionError("{} doesn't support the evaluation on DUDE.".format(model_name)) + super(DUDE, self).__init__(dataset) + + self.is_api = True if listinstr(['GPT4'], model_name) else False + self.max_pages = 120 + concat_num, column_num = self.SUPPORTED_MODELS.get(model_name) + self.concat_num = concat_num + self.column_num = column_num + + def prepare_tsv(self, url, file_md5=None): + data_root = LMUDataRoot() + os.makedirs(data_root, exist_ok=True) + file_name = url.split('/')[-1] + data_path = osp.join(data_root, file_name) + if osp.exists(data_path) and (file_md5 is None or md5(data_path) == file_md5): + pass + else: + warnings.warn('The dataset tsv is not downloaded') + download_file(url, data_path) + return load(data_path) + + def dump_image(self, origin_line): + os.makedirs(self.img_root, exist_ok=True) + try: + import fitz + except: + warnings.warn('Please use `pip install pymupdf` to parse PDF files.') + + line = origin_line.copy() + if not isinstance(line['image_path'], List): + line['image_path'] = [line['image_path']] + line['image_path'] = line['image_path'][:self.max_pages] + skip_pdf_parse = True + for im_name in line['image_path']: + path = osp.join(self.img_root, im_name) + if not read_ok(path): + skip_pdf_parse = False + break + + # Just for being compatible with the zooped loop: zip(line['image'], line['image_path']) + if skip_pdf_parse: + line['image'] = line['image_path'] + else: + pdf_data = base64.b64decode(line['image']) + pdf_file = io.BytesIO(pdf_data) + encoded_images = [] + with fitz.open(stream=pdf_file, filetype='pdf') as doc: + doc = doc[:self.max_pages] + for page in doc: + image = page.get_pixmap(dpi=144) + image_file = io.BytesIO(image.tobytes(output='png')) + image = Image.open(image_file) + encoded_image = encode_image_to_base64(image) + encoded_images.append(encoded_image) + line['image'] = encoded_images + print('process {}'.format(line['doc_id'])) + + if 'image' in line: + if isinstance(line['image'], list): + tgt_path = [] + assert 'image_path' in line + for img, im_name in zip(line['image'], line['image_path']): + path = osp.join(self.img_root, im_name) + if not read_ok(path): + decode_base64_to_image_file(img, path) + tgt_path.append(path) + else: + tgt_path = osp.join(self.img_root, f"{line['index']}.jpg") + if not read_ok(tgt_path): + decode_base64_to_image_file(line['image'], tgt_path) + tgt_path = [tgt_path] + else: + assert 'image_path' in line + tgt_path = toliststr(line['image_path']) + + if self.concat_num > 0 and not self.is_api: + concatenated_images = concat_images(tgt_path, max_concat=self.concat_num, column_num=self.column_num) + + old_tgt_path = tgt_path + assert isinstance(old_tgt_path, list) + if self.column_num != -1: + tgt_path = [ + '_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat{}_{}.jpg'.format(self.concat_num, i) + for i in range(len(concatenated_images)) + ] + else: + tgt_path = ['_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat_all.jpg'] + + for path, concatenated_image in zip(tgt_path, concatenated_images): + if not read_ok(path): + decode_base64_to_image_file(encode_image_to_base64(concatenated_image), path) + num_images, image_size = len(old_tgt_path), concatenated_image.size + print('concat {} images to a new one with size {}. save at {}'.format(num_images, image_size, path)) + return tgt_path + + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + logger = get_logger('Evaluation') + model = judge_kwargs['model'] + + suffix = eval_file.split('.')[-1] + storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx') + tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl') + + if osp.exists(storage): + logger.warning(f'GPT scoring file {storage} already exists, will reuse it in DUDE_eval. ') + else: + data = load(eval_file) + model = build_judge(max_tokens=128, **judge_kwargs) + lt = len(data) + lines = [data.iloc[i] for i in range(lt)] + tups = [(model, line) for line in lines] + indices = [line['index'] for line in lines] + + ans = {} + if osp.exists(tmp_file): + ans = load(tmp_file) + tups = [x for x, i in zip(tups, indices) if i not in ans] + indices = [i for i in indices if i not in ans] + + if len(indices): + new_results = list() + for model, line in tqdm(tups): + res = MMLongBench_auxeval(model, line) + new_results.append(res) + + log_map, res_map, pred_map = {}, {}, {} + all_inds = [line['index'] for line in lines] + for k, v in zip(all_inds, new_results): + log_map[k] = v['log'] + res_map[k] = v['res'] + pred_map[k] = v['pred'] + data['res'] = [res_map[idx] for idx in data['index']] + data['log'] = [log_map[idx] for idx in data['index']] + data['pred'] = [pred_map[idx] for idx in data['index']] + dump(data, storage) + + score = DUDE_acc(storage) + score_pth = storage.replace('.xlsx', '_score.csv') + + dump(score, score_pth) + logger.info(f'DUDE successfully finished evaluating {eval_file}, results saved in {score_pth}') + logger.info('Score: ') + logger.info(score) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_base.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_base.py new file mode 100644 index 0000000000000000000000000000000000000000..80f7b933b496a5355e796112dbe895cc03f13356 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_base.py @@ -0,0 +1,165 @@ +import pandas as pd +from abc import abstractmethod +from ..smp import * + + +def img_root_map(dataset): + if 'OCRVQA' in dataset: + return 'OCRVQA' + if 'COCO_VAL' == dataset: + return 'COCO' + if 'MMMU' in dataset: + return 'MMMU' + mmbench_root_map = { + 'MMBench_DEV_EN': 'MMBench', 'MMBench_TEST_EN': 'MMBench', + 'MMBench_DEV_CN': 'MMBench', 'MMBench_TEST_CN': 'MMBench', + 'MMBench': 'MMBench', 'MMBench_CN': 'MMBench', + 'MMBench_DEV_EN_V11': 'MMBench_V11', 'MMBench_TEST_EN_V11': 'MMBench_V11', + 'MMBench_DEV_CN_V11': 'MMBench_V11', 'MMBench_TEST_CN_V11': 'MMBench_V11', + 'MMBench_V11': 'MMBench', 'MMBench_CN_V11': 'MMBench', + } + if dataset in mmbench_root_map: + return mmbench_root_map[dataset] + return dataset + + +class ImageBaseDataset: + + MODALITY = 'IMAGE' + DATASET_URL = {} + DATASET_MD5 = {} + + def __init__(self, dataset='MMBench', skip_noimg=True): + ROOT = LMUDataRoot() + # You can override this variable to save image files to a different directory + self.dataset_name = dataset + self.img_root = osp.join(ROOT, 'images', img_root_map(dataset)) + + data = self.load_data(dataset) + self.skip_noimg = skip_noimg + if skip_noimg and 'image' in data: + data = data[~pd.isna(data['image'])] + + data['index'] = [str(x) for x in data['index']] + + self.meta_only = True + + # The image field can store the base64 encoded image or another question index (for saving space) + if 'image' in data: + data['image'] = [str(x) for x in data['image']] + image_map = {x: y for x, y in zip(data['index'], data['image'])} + for k in image_map: + if len(image_map[k]) <= 64: + idx = image_map[k] + assert idx in image_map and len(image_map[idx]) > 64 + image_map[k] = image_map[idx] + + images = [toliststr(image_map[k]) for k in data['index']] + data['image'] = [x[0] if len(x) == 1 else x for x in images] + self.meta_only = False + + if 'image_path' in data: + paths = [toliststr(x) for x in data['image_path']] + data['image_path'] = [x[0] if len(x) == 1 else x for x in paths] + + if np.all([istype(x, int) for x in data['index']]): + data['index'] = [int(x) for x in data['index']] + + self.data = data + self.post_build(dataset) + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return dict(self.data.iloc[idx]) + + def prepare_tsv(self, url, file_md5=None): + data_root = LMUDataRoot() + os.makedirs(data_root, exist_ok=True) + update_flag = False + file_name = url.split('/')[-1] + data_path = osp.join(data_root, file_name) + if osp.exists(data_path) and (file_md5 is None or md5(data_path) == file_md5): + pass + else: + warnings.warn('The dataset tsv is not downloaded') + download_file(url, data_path) + update_flag = True + + if file_size(data_path, 'GB') > 1: + local_path = data_path.replace('.tsv', '_local.tsv') + if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None) or update_flag: + from ..tools import LOCALIZE + LOCALIZE(data_path, local_path) + data_path = local_path + return load(data_path) + + def dump_image(self, line): + os.makedirs(self.img_root, exist_ok=True) + + if 'image' in line: + if isinstance(line['image'], list): + tgt_path = [] + assert 'image_path' in line + for img, im_name in zip(line['image'], line['image_path']): + path = osp.join(self.img_root, im_name) + if not read_ok(path): + decode_base64_to_image_file(img, path) + tgt_path.append(path) + else: + tgt_path = osp.join(self.img_root, f"{line['index']}.jpg") + if not read_ok(tgt_path): + decode_base64_to_image_file(line['image'], tgt_path) + tgt_path = [tgt_path] + else: + assert 'image_path' in line + tgt_path = toliststr(line['image_path']) + + return tgt_path + + def display(self, line): + if isinstance(line, int): + line = self.data.iloc[line] + assert isinstance(line, pd.Series) or isinstance(line, dict) + mmqa_display(line) + + # Return a list of dataset names that are supported by this class, can override + @classmethod + def supported_datasets(cls): + return list(cls.DATASET_URL) + + # Given the dataset name, return the dataset as a pandas dataframe, can override + def load_data(self, dataset): + url = self.DATASET_URL[dataset] + file_md5 = self.DATASET_MD5[dataset] if dataset in self.DATASET_MD5 else None + return self.prepare_tsv(url, file_md5) + + # Post built hook, will be called after the dataset is built, can override + def post_build(self, dataset): + pass + + # Given one data record, return the built prompt (a multi-modal message), can override + def build_prompt(self, line): + if isinstance(line, int): + line = self.data.iloc[line] + + if self.meta_only: + tgt_path = toliststr(line['image_path']) + else: + tgt_path = self.dump_image(line) + + question = line['question'] + + msgs = [] + if isinstance(tgt_path, list): + msgs.extend([dict(type='image', value=p) for p in tgt_path]) + else: + msgs = [dict(type='image', value=tgt_path)] + msgs.append(dict(type='text', value=question)) + return msgs + + # Given the prediction file, return the evaluation results in the format of a dictionary or pandas dataframe + @abstractmethod + def evaluate(self, eval_file, **judge_kwargs): + pass diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_caption.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_caption.py new file mode 100644 index 0000000000000000000000000000000000000000..23282805c8d63047c57d02266cec21b48e191196 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_caption.py @@ -0,0 +1,75 @@ +from .image_base import ImageBaseDataset +from ..smp import * + + +class COCO_Caption_Scorer(): + def __init__(self, ref, gt): + from pycocoevalcap.bleu.bleu import Bleu + from pycocoevalcap.rouge.rouge import Rouge + from pycocoevalcap.cider.cider import Cider + + self.ref = ref + self.gt = gt + print('setting up scorers...') + self.scorers = [ + (Bleu(4), ['Bleu_1', 'Bleu_2', 'Bleu_3', 'Bleu_4']), + (Rouge(), 'ROUGE_L'), + (Cider(), 'CIDEr'), + ] + + def compute_scores(self): + total_scores = {} + for scorer, method in self.scorers: + print('computing %s score...' % (scorer.method())) + score, scores = scorer.compute_score(self.gt, self.ref) + if isinstance(method, list): + for sc, scs, m in zip(score, scores, method): + print('%s: %0.3f' % (m, sc * 100)) + total_scores['Bleu'] = [x * 100 for x in score] + else: + print('%s: %0.3f' % (method, score * 100)) + total_scores[method] = score * 100 + + print('*****DONE*****') + for key, value in total_scores.items(): + print('{}:{}'.format(key, value)) + return total_scores + + +class ImageCaptionDataset(ImageBaseDataset): + + TYPE = 'Caption' + + DATASET_URL = { + 'COCO_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/COCO_VAL.tsv', + } + + DATASET_MD5 = { + 'COCO_VAL': '72a5079dead060269ac222c5aa5128af', + } + + def load_data(self, dataset): + data = super().load_data(dataset) + if 'question' not in data: + data['question'] = [( + 'Please describe this image in general. Directly provide the description, ' + 'do not include prefix like "This image depicts". ' + )] * len(data) + return data + + # It returns a dictionary of scores + @classmethod + def evaluate(self, eval_file, **kwargs): + data = load(eval_file) + lt = len(data) + lines = [data.iloc[i] for i in range(lt)] + ref, gt = {}, {} + for i, line in enumerate(lines): + ref[str(i)] = [str(line['prediction'])] + gt[str(i)] = eval(line['answer']) + + scorer = COCO_Caption_Scorer(ref, gt) + coco_caption_score_dict = scorer.compute_scores() + score_pth = eval_file.replace('.xlsx', '_score.json') + dump(coco_caption_score_dict, score_pth) + return coco_caption_score_dict diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_mcq.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_mcq.py new file mode 100644 index 0000000000000000000000000000000000000000..5fb5ac473b50381671b595047e0337551cf2743d --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_mcq.py @@ -0,0 +1,484 @@ +import warnings + +from .image_base import ImageBaseDataset +from .utils import build_judge, DEBUG_MESSAGE +from ..smp import * + + +MMMB_URLS = { + 'MMMB_ar': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_ar.tsv', + 'MMMB_cn': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_cn.tsv', + 'MMMB_en': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_en.tsv', + 'MMMB_pt': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_pt.tsv', + 'MMMB_ru': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_ru.tsv', + 'MMMB_tr': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmmb/mmmb_tr.tsv', +} + +MTL_MMBench_URLS = { + 'MMBench_dev_ar': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_ar.tsv', + 'MMBench_dev_cn': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_cn.tsv', + 'MMBench_dev_en': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_en.tsv', + 'MMBench_dev_pt': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_pt.tsv', + 'MMBench_dev_tr': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_tr.tsv', + 'MMBench_dev_ru': 'https://huggingface.co/datasets/AIDC-AI/Parrot-dataset/resolve/main/mmbench/mmbench_dev_ru.tsv', +} + +MMMB_MD5 = { + 'MMMB_ar': 'f3a18b6385f1d9701840aa42de27aead', 'MMMB_cn': '13ed82fa89730037292fcaa27f08f430', + 'MMMB_en': '1cd781a71ec5a2983c090b84105d6a01', 'MMMB_pt': '548ea2b3bb2da991790386f0015d30d1', + 'MMMB_ru': 'ce1cc8a0533425ab0d86b326ebfc2984', 'MMMB_tr': '0733739d43090327975294292bc5cd67' +} + +MTL_MMBench_MD5 = { + 'MMBench_dev_ar': '4271b4a0d0200e1a86380a878e0d64a4', 'MMBench_dev_cn': '2ed5135326fed02c8e51ea50dda8222f', + 'MMBench_dev_en': 'd9ab776fc018b3d45785e9a5c23431c2', 'MMBench_dev_pt': '4ddfbcd27ef12444b908c03831cd0295', + 'MMBench_dev_tr': '4fab39d501389d3d6cc90264bb708f11', 'MMBench_dev_ru': '5ba1171ff2e68f80637bf78349e402a5' +} + + +class ImageMCQDataset(ImageBaseDataset): + + TYPE = 'MCQ' + + DATASET_URL = { + # MMBench v1.0 + 'MMBench_DEV_EN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_EN.tsv', + 'MMBench_TEST_EN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_EN.tsv', + 'MMBench_DEV_CN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_CN.tsv', + 'MMBench_TEST_CN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_CN.tsv', + 'MMBench': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench.tsv', # Internal Only + 'MMBench_CN': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_CN.tsv', # Internal Only + # MMBench v1.1 + 'MMBench_DEV_EN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_EN_V11.tsv', + 'MMBench_TEST_EN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_EN_V11.tsv', + 'MMBench_DEV_CN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_DEV_CN_V11.tsv', + 'MMBench_TEST_CN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_TEST_CN_V11.tsv', + 'MMBench_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_V11.tsv', # Internal Only + 'MMBench_CN_V11': 'https://opencompass.openxlab.space/utils/VLMEval/MMBench_CN_V11.tsv', # Internal Only + # SEEDBench Series + 'SEEDBench_IMG': 'https://opencompass.openxlab.space/utils/VLMEval/SEEDBench_IMG.tsv', + 'SEEDBench2': 'https://huggingface.co/datasets/VLMEval/SEEDBench2/resolve/main/SEEDBench2.tsv', + 'SEEDBench2_Plus': 'https://opencompass.openxlab.space/utils/VLMEval/SEEDBench2_Plus.tsv', + # ScienceQA Series + 'ScienceQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/ScienceQA_VAL.tsv', + 'ScienceQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/ScienceQA_TEST.tsv', + # MMT-Bench + 'MMT-Bench_ALL_MI': 'https://opencompass.openxlab.space/utils/VLMEval/MMT-Bench_ALL_MI.tsv', + 'MMT-Bench_ALL': 'https://opencompass.openxlab.space/utils/VLMEval/MMT-Bench_ALL.tsv', + 'MMT-Bench_VAL_MI': 'https://opencompass.openxlab.space/utils/VLMEval/MMT-Bench_VAL_MI.tsv', + 'MMT-Bench_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/MMT-Bench_VAL.tsv', + # AesBench + 'AesBench_VAL': 'https://huggingface.co/datasets/VLMEval/AesBench/resolve/main/AesBench_VAL.tsv', + 'AesBench_TEST': 'https://huggingface.co/datasets/VLMEval/AesBench/resolve/main/AesBench_TEST.tsv', + # Q-Bench1 + 'Q-Bench1_VAL': 'https://huggingface.co/datasets/zhangzicheng/qbench_tsv/resolve/main/Q-Bench1_VAL.tsv', + 'Q-Bench1_TEST': 'https://huggingface.co/datasets/zhangzicheng/qbench_tsv/resolve/main/Q-Bench1_TEST.tsv', + # A-Bench + 'A-Bench_VAL': 'https://huggingface.co/datasets/zhangzicheng/abench_tsv/resolve/main/A-bench_VAL.tsv', + 'A-Bench_TEST': 'https://huggingface.co/datasets/zhangzicheng/abench_tsv/resolve/main/A-bench_TEST.tsv', + # Other Benchmarks + 'CCBench': 'https://opencompass.openxlab.space/utils/VLMEval/CCBench.tsv', + 'AI2D_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/AI2D_TEST.tsv', + 'AI2D_TEST_NO_MASK': 'https://opencompass.openxlab.space/utils/VLMEval/AI2D_TEST_NO_MASK.tsv', + 'MMStar': 'https://opencompass.openxlab.space/utils/VLMEval/MMStar.tsv', + 'RealWorldQA': 'https://opencompass.openxlab.space/utils/VLMEval/RealWorldQA.tsv', + 'MLLMGuard_DS': 'https://opencompass.openxlab.space/utils/VLMEval/MLLMGuard_DS.tsv', + 'BLINK': 'https://opencompass.openxlab.space/utils/VLMEval/BLINK.tsv', + 'TaskMeAnything_v1_imageqa_random': ( + 'https://huggingface.co/datasets/weikaih/TaskMeAnything-v1-imageqa-random/' + 'resolve/main/TaskMeAnything-v1-imageqa-random.tsv' + ), + 'A-OKVQA': 'https://huggingface.co/datasets/Allen8/A-OKVQA/resolve/main/a-okvqa.tsv' + } + + DATASET_MD5 = { + # MMBench v1.0 + 'MMBench_DEV_EN': 'b6caf1133a01c6bb705cf753bb527ed8', + 'MMBench_TEST_EN': '6939fadb0ce626fefc0bdc9c64efc528', + 'MMBench_DEV_CN': '08b8fc3324a5ed74155350f57be69fbd', + 'MMBench_TEST_CN': '7e1239baf0ee4c8b513e19705a0f317e', + 'MMBench': '4115aea3383f3dd0083be6a633e0f820', # Internal Only + 'MMBench_CN': '2e053ffc90ea598b1feae13c36dc13ee', # Internal Only + # MMBench v1.1 + 'MMBench_DEV_EN_V11': '30c05be8f2f347a50be25aa067248184', + 'MMBench_TEST_EN_V11': '26f0f15381a21720255091d3e0316ce6', + 'MMBench_DEV_CN_V11': '593f9b5f6bea453d870a798b34ae4f37', + 'MMBench_TEST_CN_V11': '74bbe4556dac745613c7cbe5ad787050', + 'MMBench_V11': 'b9276414f57af1308dcc4d0cd9b42e7c', # Internal Only + 'MMBench_CN_V11': '95f6980dd1b4de38e3cbffe0305a3f25', # Internal Only + # SEEDBench + 'SEEDBench_IMG': '68017231464752261a2526d6ca3a10c0', + 'SEEDBench2': '4ec15cf864c4f16274112284f531813e', + 'SEEDBench2_Plus': 'e32d3216dc4f452b0fe497a52015d1fd', + # ScienceQA + 'ScienceQA_VAL': '96320d05e142e585e7204e72affd29f3', + 'ScienceQA_TEST': 'e42e9e00f9c59a80d8a5db35bc32b71f', + # MMT-Bench + 'MMT-Bench_ALL_MI': '5272157097e19cdd7cb41e412ab3b7c7', + 'MMT-Bench_ALL': 'b273a2f4c596fe4f2605de0494cd632f', + 'MMT-Bench_VAL_MI': 'c7d7b998eb5cd9aa36c7d4f721472462', + 'MMT-Bench_VAL': '8dd4b730f53dbf9c3aed90ca31c928e0', + # AesBench + 'AesBench_VAL': '3edb0c319e9187aa0b97fe7a11700a8c', + 'AesBench_TEST': '58b1f7ba2cc32e1d68896d6ee716bbf8', + # Q-Bench1 + 'Q-Bench1_VAL': '837bdb6cd2da571713543462815187b7', + 'Q-Bench1_TEST': '15e759bfd58c9d5f30b23a317d347153', + # A-Bench + 'A-Bench_VAL': '218563ec50d34bb336c814143a5bb9c1', + 'A-Bench_TEST': '567013fb033a20cf23f51d8e865bd16c', + # Other Benchmarks + 'CCBench': 'f5dde47f24dc5a6fb6e595b409b466ac', + 'AI2D_TEST': '0f593e0d1c7df9a3d69bf1f947e71975', + 'AI2D_TEST_NO_MASK': 'fd8f463634d4fe9fbd23b876e8eea5be', + 'MMStar': 'e1ecd2140806c1b1bbf54b43372efb9e', + 'RealWorldQA': '92321028d2bc29040284b6674721e48f', + 'MLLMGuard_DS': '975fc0dd7119386e198c37d71e274b3f', + 'BLINK': '3b6649b6a662184ea046908e5506260e', + 'TaskMeAnything_v1_imageqa_random': '023fef69e2ca21827afb77c5ec3bc889' + } + + DATASET_URL.update(MMMB_URLS) + DATASET_URL.update(MTL_MMBench_URLS) + DATASET_MD5.update(MMMB_MD5) + DATASET_MD5.update(MTL_MMBench_MD5) + + def build_prompt(self, line): + + if isinstance(line, int): + line = self.data.iloc[line] + + if self.meta_only: + tgt_path = toliststr(line['image_path']) + else: + tgt_path = self.dump_image(line) + + question = line['question'] + options = { + cand: line[cand] + for cand in string.ascii_uppercase + if cand in line and not pd.isna(line[cand]) + } + options_prompt = 'Options:\n' + for key, item in options.items(): + options_prompt += f'{key}. {item}\n' + hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None + prompt = '' + if hint is not None: + prompt += f'Hint: {hint}\n' + prompt += f'Question: {question}\n' + if len(options): + prompt += options_prompt + prompt += 'Please select the correct answer from the options above. \n' + + msgs = [] + if isinstance(tgt_path, list): + msgs.extend([dict(type='image', value=p) for p in tgt_path]) + else: + msgs = [dict(type='image', value=tgt_path)] + msgs.append(dict(type='text', value=prompt)) + + return msgs + + def evaluate(self, eval_file, **judge_kwargs): + from .utils.multiple_choice import report_acc, report_acc_MMT, mcq_circular_eval, mcq_vanilla_eval + # assert dataset is not None + dataset_map = { + 'MMBench_TEST_EN': 'MMBench', 'MMBench_TEST_EN_V11': 'MMBench_V11', + 'MMBench_TEST_CN': 'MMBench_CN', 'MMBench_TEST_CN_V11': 'MMBench_CN_V11' + } + dataset = self.dataset_name + if dataset in dataset_map: + dataset = dataset_map[dataset] + nproc = judge_kwargs.pop('nproc', 4) + + circular = False + if listinstr(['mmbench', 'ccbench'], dataset.lower()): + data = load(eval_file) + data['index'] = [int(x) for x in data['index']] + dump(data, eval_file) + circular = True + + suffix = eval_file.split('.')[-1] + model = judge_kwargs.get('model', 'exact_matching') + assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125'] + name_str_map = {'chatgpt-0125': 'openai', 'gpt-4-0125': 'gpt4'} + name_str = name_str_map[model] if model in name_str_map else model + + if model == 'exact_matching': + model = None + elif gpt_key_set(): + model = build_judge(**judge_kwargs) + if not model.working(): + warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation') + warnings.warn(DEBUG_MESSAGE) + model = None + else: + warnings.warn('OPENAI_API_KEY is not set properly, will use exact matching for evaluation') + model = None + + result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_result.pkl') + + data = load(eval_file) + data = data.sort_values(by='index') + data['prediction'] = [str(x) for x in data['prediction']] + # If not choice label, then use lower case + for k in data.keys(): + data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k) + + meta = self.data + meta_q_map = {x: y for x, y in zip(meta['index'], meta['question'])} + data_map = {x: y for x, y in zip(data['index'], data['question'])} + for k in data_map: + assert k in meta_q_map, ( + f'eval_file should be the same as or a subset of dataset {self.dataset_name}' + ) + + if circular: + data = mcq_circular_eval(model, data, meta, nproc, result_file, self.dataset_name) + else: + data = mcq_vanilla_eval(model, data, meta, nproc, result_file, self.dataset_name) + + # load split + dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) + data = load(eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) + + # May have different report acc functions for different datasets + if 'MMT' in dataset: + acc = report_acc_MMT(data) + else: + acc = report_acc(data) + + score_file = eval_file.replace(f'.{suffix}', '_acc.csv') + dump(acc, score_file) + + if dataset == 'AesBench_VAL': + warnings.warn('Note that AesBench VAL is just a toy version of AesBench TEST. For full results, \ + please evaluate on AesBench TEST. The AesBench TEST dataset is more than 20 times \ + larger than the VAL dataset and the leaderboard results are based on AesBench TEST.') + return acc + + +class MMMUDataset(ImageMCQDataset): + + DATASET_URL = { + 'MMMU_DEV_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/MMMU_DEV_VAL.tsv', + 'MMMU_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/MMMU_TEST.tsv', + } + + DATASET_MD5 = { + 'MMMU_DEV_VAL': '521afc0f3bf341e6654327792781644d', + 'MMMU_TEST': 'c19875d11a2d348d07e5eb4bdf33166d', + } + + @staticmethod + def split_MMMU(msgs): + text, images = None, [] + for s in msgs: + if s['type'] == 'image': + images.append(s['value']) + elif s['type'] == 'text': + assert text is None + text = s['value'] + text_segs = text.split('' + image_idx = int(seg[0]) - 1 + segs.append(dict(type='image', value=images[image_idx])) + segs.append(dict(type='text', value=seg[2:])) + return segs + + def build_prompt(self, line): + msgs = super().build_prompt(line) + msgs = self.split_MMMU(msgs) + return msgs + + +class MUIRDataset(ImageMCQDataset): + + DATASET_URL = { + 'MUIRBench': 'http://opencompass.openxxlab.com/utils/VLMEval/MUIRBench.tsv' + } + + DATASET_MD5 = { + 'MUIRBench': '2e5e6fd7699761b08a7cb3ab8c0c2ec8' + } + + @staticmethod + def split_MUIR(msgs): + text, images = None, [] + + # Separate images and text from msgs + for s in msgs: + if s['type'] == 'image': + images.append(s['value']) + elif s['type'] == 'text': + assert text is None # Ensure only one text entry is expected + text = s['value'] + + # Split text by tags + text_segs = text.split('') + + # Initialize the segments list + segs = [] + + # Iterate through the text segments and images + for i, seg in enumerate(text_segs): + # Append the image if this is not the first segment and there are still images left + if i > 0 and i - 1 < len(images): + segs.append(dict(type='image', value=images[i - 1])) + # Append the text segment (if it's non-empty) + if len(seg) > 0: + segs.append(dict(type='text', value=seg)) + + return segs + + def build_prompt(self, line): + + if isinstance(line, int): + line = self.data.iloc[line] + + if self.meta_only: + tgt_path = toliststr(line['image_path']) + else: + tgt_path = self.dump_image(line) + + question = line['question'] + options = { + cand: line[cand] + for cand in string.ascii_uppercase + if cand in line and not pd.isna(line[cand]) + } + # options_prompt = '' + options_prompt = '\n'.join([f'{key}. {item}' for key, item in options.items()]) + # for key, item in options.items(): + # options_prompt += f'{key}. {item}\n' + + prompt = '' + + prompt += f'{question}\n' + if len(options): + prompt += options_prompt + prompt += "\nAnswer with the option's letter from the given choices directly." + + msgs = [] + if isinstance(tgt_path, list): + msgs.extend([dict(type='image', value=p) for p in tgt_path]) + else: + msgs = [dict(type='image', value=tgt_path)] + msgs.append(dict(type='text', value=prompt)) + + msgs = self.split_MUIR(msgs) + return msgs + + +class GMAIMMBenchDataset(ImageMCQDataset): + + DATASET_URL = { + 'GMAI-MMBench_VAL': 'https://huggingface.co/datasets/VLMEval/GMAI-MMBench/resolve/main/GMAI-MMBench_VAL.tsv' + } + + DATASET_MD5 = { + 'GMAI-MMBench_VAL': '254bd581627866f1c499d3d6b4422324' + } + + def report_acc_by_groups(self, df, group_column): + res = defaultdict(list) + + # Check for the 'split' column + if 'split' in df: + splits = list(set(df['split'])) + res['split'] = splits + else: + df['split'] = ['none'] * len(df) + res['split'] = ['none'] + + res['Overall'] = [np.mean(df[df['split'] == sp]['hit']) for sp in res['split']] + + if group_column not in df: + raise ValueError(f"Column '{group_column}' not found in dataframe.") + + abilities = list(set(df[group_column])) + abilities = ['None' if isinstance(ab, float) and pd.isna(ab) else ab for ab in abilities] + abilities.sort() + + for ab in abilities: + ab_name = ab + sub_df = df[df[group_column] == ab] + res[ab_name] = [np.mean(sub_df[sub_df['split'] == sp]['hit']) for sp in res['split']] + + return pd.DataFrame(res) + + def evaluate(self, eval_file, **judge_kwargs): + from .utils.multiple_choice import report_acc, mcq_vanilla_eval + nproc = judge_kwargs.pop('nproc', 4) + + suffix = eval_file.split('.')[-1] + model = judge_kwargs.get('model', 'exact_matching') + assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125'] + name_str_map = {'chatgpt-0125': 'openai', 'gpt-4-0125': 'gpt4'} + name_str = name_str_map[model] if model in name_str_map else model + + if model == 'exact_matching': + model = None + elif gpt_key_set(): + model = build_judge(**judge_kwargs) + if not model.working(): + warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation') + warnings.warn(DEBUG_MESSAGE) + model = None + else: + warnings.warn('OPENAI_API_KEY is not set properly, will use exact matching for evaluation') + model = None + + result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_result.pkl') + + data = load(eval_file) + data = data.sort_values(by='index') + data['prediction'] = [str(x) for x in data['prediction']] + # If not choice label, then use lower case + for k in data.keys(): + data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k) + + meta = self.data + meta_q_map = {x: y for x, y in zip(meta['index'], meta['question'])} + data_map = {x: y for x, y in zip(data['index'], data['question'])} + for k in data_map: + assert k in meta_q_map, ( + f'eval_file should be the same as or a subset of dataset {self.dataset_name}' + ) + + data = mcq_vanilla_eval(model, data, meta, nproc, result_file, self.dataset_name) + + # load split + dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) + data = load(eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) + + acc = report_acc(data) + + for group_col in ['clinical vqa task', 'department', 'perceptual granularity']: + acc_grouped = self.report_acc_by_groups(data, group_col) + score_file_grouped = eval_file.replace(f'.{suffix}', f'_{group_col}_acc.csv') + dump(acc_grouped, score_file_grouped) + + return acc + + +class CustomMCQDataset(ImageMCQDataset): + + def load_data(self, dataset): + data_path = osp.join(LMUDataRoot(), f'{dataset}.tsv') + + if file_size(data_path, 'GB') > 1: + local_path = data_path.replace('.tsv', '_local.tsv') + if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None): + from ..tools import LOCALIZE + LOCALIZE(data_path, local_path) + data_path = local_path + return load(data_path) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_mt.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_mt.py new file mode 100644 index 0000000000000000000000000000000000000000..07658948a01c7e65d857794c92ef32e9c4b94f46 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_mt.py @@ -0,0 +1,128 @@ +from .image_base import ImageBaseDataset +from .utils.judge_util import build_judge +from ..smp import * +from ..utils import track_progress_rich + + +class ImageMTDataset(ImageBaseDataset): + + TYPE = 'MT' + + def build_prompt(self, line): + if isinstance(line, int): + line = self.data.iloc[line] + + if self.meta_only: + tgt_path = toliststr(line['image_path']) + else: + tgt_path = self.dump_image(line) + + questions = toliststr(line['question']) + if 'answer' in line: + answers = toliststr(line['answer']) + else: + answers = [''] * len(questions) + assert len(questions) == len(answers) + + dlgs, pics_number = [], 0 + for i in range(len(questions)): + q, a = questions[i], answers[i] + if '' in q: + content = [] + tag_number = q.count('') + images = tgt_path[pics_number: pics_number + tag_number] + pics_number += tag_number + q_split = q.split('') + for i in range(tag_number): + qsp, im = q_split[i], images[i] + if qsp != '': + content.append(dict(type='text', value=qsp)) + content.append(dict(type='image', value=im)) + if q_split[-1] != '': + content.append(dict(type='text', value=q_split[-1])) + else: + content = [dict(type='text', value=q)] + dlgs.append(dict(role='user', content=content)) + assert '' not in a, 'We currently do not support images in the answer. ' + content = [dict(type='text', value=a)] + dlgs.append(dict(role='assistant', content=content)) + return dlgs + + +class MMDUDataset(ImageMTDataset): + + DATASET_URL = {'MMDU': 'https://opencompass.openxlab.space/utils/VLMEval/MMDU.tsv'} + DATASET_MD5 = {'MMDU': '848b635a88a078f49aebcc6e39792061'} + DIMS = [ + 'Creativity', 'Richness', 'Visual Perception', 'Logical Coherence', + 'Answer Accuracy', 'Image Relationship Understanding', 'Overall Score' + ] + + def calculat_metric(self, ans): + all = defaultdict(lambda: 0) + tot = defaultdict(lambda: 0) + valid = defaultdict(lambda: 0) + for k in ans: + res = ans[k]['res'] + assert isinstance(res, pd.DataFrame) + lt = len(res) + for i in range(lt): + line = res.iloc[i] + for k in self.DIMS: + tot[k] += 1 + if k in line and line[k] is not None: + try: + score = int(line[k]) + score = np.clip(score, 0, 10) + all[k] += score + valid[k] += 1 + except Exception as e: + print(f'Failed to parse the score: {str(e)}') + sp1 = {'set': 'all'} + sp1.update({k: all[k] / tot[k] * 10 for k in self.DIMS}) + sp2 = {'set': 'valid'} + sp2.update({k: all[k] / valid[k] * 10 for k in self.DIMS}) + + return pd.DataFrame([sp1, sp2]) + + def evaluate(self, eval_file, **judge_kwargs): + suffix = eval_file.split('.')[-1] + model = judge_kwargs['model'] + + tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl') + score_file = eval_file.replace(f'.{suffix}', f'_{model}_score.csv') + nproc = judge_kwargs.pop('nproc', 4) + + data = load(eval_file) + model = judge_kwargs.pop('model', 'gpt-4o') + judge_model = build_judge(model=model, **judge_kwargs) + + lt = len(data) + lines = [data.iloc[i] for i in range(lt)] + tups = [(judge_model, line) for line in lines] + indices = [line['index'] for line in lines] + + ans = {} + if osp.exists(tmp_file): + ans = load(tmp_file) + + tups = [x for x, i in zip(tups, indices) if i not in ans] + indices = [i for i in indices if i not in ans] + + from .utils.mmdu import mmdu_score + + if len(indices): + new_results = track_progress_rich( + mmdu_score, + tups, + nproc=nproc, + chunksize=nproc, + keys=indices, + save=tmp_file,) + ans = load(tmp_file) + for k, v in zip(indices, new_results): + assert k in ans + + metric = self.calculat_metric(ans) + dump(metric, score_file) + return metric diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_vqa.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_vqa.py new file mode 100644 index 0000000000000000000000000000000000000000..826cf3fc58fa216385661ae89af12b3286767526 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_vqa.py @@ -0,0 +1,433 @@ +from functools import partial + +from .image_base import ImageBaseDataset +from .utils import build_judge, DEBUG_MESSAGE +from ..smp import * +from ..utils import track_progress_rich + + +class ImageVQADataset(ImageBaseDataset): + TYPE = 'VQA' + + DATASET_URL = { + 'OCRVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/OCRVQA_TEST.tsv', + 'OCRVQA_TESTCORE': 'https://opencompass.openxlab.space/utils/VLMEval/OCRVQA_TESTCORE.tsv', + 'TextVQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/TextVQA_VAL.tsv', + 'DocVQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/DocVQA_VAL.tsv', + 'DocVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/DocVQA_TEST.tsv', + 'InfoVQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/InfoVQA_VAL.tsv', + 'InfoVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/InfoVQA_TEST.tsv', + 'ChartQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/ChartQA_TEST.tsv', + } + + DATASET_MD5 = { + 'OCRVQA_TEST': 'ca46a6d74b403e9d6c0b670f6fc00db9', + 'OCRVQA_TESTCORE': 'c5239fe77db8bdc1f2ad8e55e0d1fe97', + 'TextVQA_VAL': 'b233b31f551bbf4056f2f955da3a92cd', + 'DocVQA_VAL': 'd5ee77e1926ff10690d469c56b73eabf', + 'DocVQA_TEST': '6a2f28cac26ef2d3447374e8c6f6c8e9', + 'InfoVQA_VAL': '2342e9c225222f0ef4dec545ebb126fe', + 'InfoVQA_TEST': 'df535bf51b88dc9718252c34131a6227', + 'ChartQA_TEST': 'c902e0aa9be5582a7aad6dcf52734b42', + } + + def build_prompt(self, line): + msgs = super().build_prompt(line) + assert msgs[-1]['type'] == 'text' + msgs[-1]['value'] += '\nAnswer the question using a single word or phrase.' + return msgs + + # It returns a DataFrame + def evaluate(self, eval_file, **judge_kwargs): + from .utils.vqa_eval import hit_calculate, process_line + + data = load(eval_file) + dataset = self.dataset_name + assert 'answer' in data and 'prediction' in data + data['prediction'] = [str(x) for x in data['prediction']] + data['answer'] = [str(x) for x in data['answer']] + lt = len(data) + pool = mp.Pool(16) + lines = [data.iloc[i] for i in range(lt)] + if listinstr(['TextVQA'], dataset): + res = pool.map(partial(process_line, method='vqa_score'), lines) + elif listinstr(['ChartQA'], dataset): + res = pool.map(partial(process_line, method='relaxed_accuracy'), lines) + elif listinstr(['OCRVQA'], dataset): + res = pool.map(partial(process_line, method='accuracy'), lines) + elif listinstr(['DocVQA', 'InfoVQA'], dataset): + res = pool.map(partial(process_line, method='anls'), lines) + else: # default using vqa_score to calculate score + res = pool.map(process_line, lines) + hit = hit_calculate(res, dataset) + ret = dict() + if 'split' in data: + splits = set(data['split']) + for sp in splits: + sub = [r for l, r in zip(lines, res) if l['split'] == sp] + # [np.mean(x['match']) >= full_score_weight for x in sub] + hit = hit_calculate(sub, dataset) + ret[sp] = np.mean(hit) * 100 + sub = [r for l, r in zip(lines, res)] + hit = hit_calculate(sub, dataset) + ret['Overall'] = np.mean(hit) * 100 + else: + ret['Overall'] = np.mean(hit) * 100 + if 'category' in data: + cates = list(set(data['category'])) + cates.sort() + for c in cates: + sub = [r for l, r in zip(lines, res) if l['category'] == c] + # [np.mean(x['match']) >= full_score_weight for x in sub] + hit = hit_calculate(sub, dataset) + ret[c] = np.mean(hit) * 100 + ret = d2df(ret) + ret.round(2) + + suffix = eval_file.split('.')[-1] + result_file = eval_file.replace(f'.{suffix}', '_acc.csv') + dump(ret, result_file) + return ret + + +class OCRBench(ImageBaseDataset): + TYPE = 'VQA' + DATASET_URL = { + 'OCRBench': 'https://opencompass.openxlab.space/utils/VLMEval/OCRBench.tsv' + } + DATASET_MD5 = {'OCRBench': 'e953d98a987cc6e26ef717b61260b778'} + + # It returns a dictionary + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + OCRBench_score = { + 'Regular Text Recognition': 0, + 'Irregular Text Recognition': 0, + 'Artistic Text Recognition': 0, + 'Handwriting Recognition': 0, + 'Digit String Recognition': 0, + 'Non-Semantic Text Recognition': 0, + 'Scene Text-centric VQA': 0, + 'Doc-oriented VQA': 0, + 'Key Information Extraction': 0, + 'Handwritten Mathematical Expression Recognition': 0, + } + + data = load(eval_file) + lt = len(data) + lines = [data.iloc[i] for i in range(lt)] + for i in tqdm(range(len(lines))): + line = lines[i] + predict = str(line['prediction']) + answers = eval(line['answer']) + category = line['category'] + if category == 'Handwritten Mathematical Expression Recognition': + for j in range(len(answers)): + answer = answers[j].strip().replace('\n', ' ').replace(' ', '') + predict = predict.strip().replace('\n', ' ').replace(' ', '') + if answer in predict: + OCRBench_score[category] += 1 + break + else: + for j in range(len(answers)): + answer = answers[j].lower().strip().replace('\n', ' ') + predict = predict.lower().strip().replace('\n', ' ') + if answer in predict: + OCRBench_score[category] += 1 + break + + final_score_dict = {} + final_score_dict['Text Recognition'] = \ + (OCRBench_score['Regular Text Recognition'] + OCRBench_score['Irregular Text Recognition'] + + OCRBench_score['Artistic Text Recognition'] + OCRBench_score['Handwriting Recognition'] + + OCRBench_score['Digit String Recognition'] + OCRBench_score['Non-Semantic Text Recognition']) + final_score_dict['Scene Text-centric VQA'] = OCRBench_score['Scene Text-centric VQA'] + final_score_dict['Doc-oriented VQA'] = OCRBench_score['Doc-oriented VQA'] + final_score_dict['Key Information Extraction'] = OCRBench_score['Key Information Extraction'] + final_score_dict['Handwritten Mathematical Expression Recognition'] = \ + (OCRBench_score['Handwritten Mathematical Expression Recognition']) + final_score_dict['Final Score'] = \ + (final_score_dict['Text Recognition'] + final_score_dict['Scene Text-centric VQA'] + + final_score_dict['Doc-oriented VQA'] + final_score_dict['Key Information Extraction'] + + final_score_dict['Handwritten Mathematical Expression Recognition']) + final_score_dict['Final Score Norm'] = (float(final_score_dict['Final Score']) / 10) + score_pth = eval_file.replace('.xlsx', '_score.json') + dump(final_score_dict, score_pth) + return final_score_dict + + +class MathVista(ImageBaseDataset): + TYPE = 'VQA' + DATASET_URL = { + 'MathVista_MINI': 'https://opencompass.openxlab.space/utils/VLMEval/MathVista_MINI.tsv' + } + DATASET_MD5 = {'MathVista_MINI': 'f199b98e178e5a2a20e7048f5dcb0464'} + + # It returns a DataFrame + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + from .utils.mathvista import MathVista_auxeval, MathVista_acc + + model = judge_kwargs['model'] + suffix = eval_file.split('.')[-1] + storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx') + tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl') + nproc = judge_kwargs.pop('nproc', 4) + + if not osp.exists(storage): + data = load(eval_file) + model = build_judge(max_tokens=128, **judge_kwargs) + assert model.working(), ('MathVista evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE) + lt = len(data) + lines = [data.iloc[i] for i in range(lt)] + tups = [(model, line) for line in lines] + indices = [line['index'] for line in lines] + + ans = {} + if osp.exists(tmp_file): + ans = load(tmp_file) + tups = [x for x, i in zip(tups, indices) if i not in ans] + indices = [i for i in indices if i not in ans] + + if len(indices): + new_results = track_progress_rich( + MathVista_auxeval, + tups, + nproc=nproc, + chunksize=nproc, + keys=indices, + save=tmp_file, + ) + ans = load(tmp_file) + for k, v in zip(indices, new_results): + assert k in ans + assert ans[k]['log'] == v['log'] and ans[k]['res'] == v['res'] + + data['res'] = [ans[idx]['res'] for idx in data['index']] + data['log'] = [ans[idx]['log'] for idx in data['index']] + dump(data, storage) + + score = MathVista_acc(storage) + score_pth = storage.replace('.xlsx', '_score.csv') + dump(score, score_pth) + return score + + +class MathVision(ImageBaseDataset): + TYPE = 'VQA' + DATASET_URL = { + 'MathVision': 'https://opencompass.openxlab.space/utils/VLMEval/MathVision.tsv', + 'MathVision_MINI': 'https://opencompass.openxlab.space/utils/VLMEval/MathVision_MINI.tsv' + } + DATASET_MD5 = { + 'MathVision': '93f6de14f7916e598aa1b7165589831e', + 'MathVision_MINI': '060fe4fa5d868987ce179307bd5f8a33' + } + + # It returns a DataFrame + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + from .utils.mathv import MATH_V_auxeval, MATH_V_acc + + if 'model' in judge_kwargs: + model = judge_kwargs['model'] + else: + model = os.path.basename(os.environ.get('LOCAL_LLM')) + suffix = eval_file.split('.')[-1] + storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx') + tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl') + nproc = judge_kwargs.pop('nproc', 4) + + if not osp.exists(storage): + data = load(eval_file) + model = build_judge(max_tokens=128, **judge_kwargs) + assert model.working(), ('MATH-Vision evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE) + lt = len(data) + lines = [data.iloc[i] for i in range(lt)] + tups = [(model, line) for line in lines] + indices = [line['index'] for line in lines] + + ans = {} + if osp.exists(tmp_file): + ans = load(tmp_file) + tups = [x for x, i in zip(tups, indices) if i not in ans] + indices = [i for i in indices if i not in ans] + + if len(indices): + new_results = track_progress_rich( + MATH_V_auxeval, + tups, + nproc=nproc, + chunksize=nproc, + keys=indices, + save=tmp_file, + ) + ans = load(tmp_file) + for k, v in zip(indices, new_results): + assert k in ans + assert ans[k]['log'] == v['log'] and ans[k]['res'] == v['res'] + + data['res'] = [ans[idx]['res'] for idx in data['index']] + data['log'] = [ans[idx]['log'] for idx in data['index']] + dump(data, storage) + + score = MATH_V_acc(storage) + score_pth = storage.replace('.xlsx', '_score.csv') + dump(score, score_pth) + return score + + +class LLaVABench(ImageBaseDataset): + TYPE = 'VQA' + DATASET_URL = {'LLaVABench': 'https://opencompass.openxlab.space/utils/VLMEval/LLaVABench.tsv'} + DATASET_MD5 = {'LLaVABench': 'd382a093f749a697820d3dadd61c8428'} + + # It returns a DataFrame + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + from .utils.llavabench import ( + build_prompt, + LLaVABench_atomeval, + LLaVABench_score, + ) + + suffix = '.' + eval_file.split('.')[-1] + record_file = eval_file.replace(suffix, '_openai_result' + suffix) + score_file = eval_file.replace(suffix, '_score.csv') + nproc = judge_kwargs.pop('nproc', 4) + system_prompt = 'You are a helpful and precise assistant for checking the quality of the answer.' + + if not osp.exists(record_file): + data = load(eval_file) + lines = [data.iloc[i] for i in range(len(data))] + model = build_judge(temperature=0.2, system_prompt=system_prompt, **judge_kwargs) + assert model.working(), ('LLaVABench evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE) + + prompts = [build_prompt(line) for line in lines] + tups = [(model, prompt) for prompt in prompts] + scores = track_progress_rich(LLaVABench_atomeval, tups, nproc=nproc, chunksize=nproc) + data['gpt4_score'] = [x[0] for x in scores] + data['score'] = [x[1] for x in scores] + dump(data, record_file) + + data = load(record_file) + ret = LLaVABench_score(data).round(1) + dump(ret, score_file) + return ret + + +class MMVet(ImageBaseDataset): + TYPE = 'VQA' + DATASET_URL = { + 'MMVet': 'https://opencompass.openxlab.space/utils/VLMEval/MMVet.tsv' + } + DATASET_MD5 = {'MMVet': '748aa6d4aa9d4de798306a63718455e3'} + + # It returns a DataFrame + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + from .utils.mmvet import MMVet_auxeval, MMVet_acc + + suffix = eval_file.split('.')[-1] + model = judge_kwargs['model'] + storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx') + tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl') + nproc = judge_kwargs.pop('nproc', 4) + if not osp.exists(storage): + data = load(eval_file) + model = build_judge(max_tokens=3, **judge_kwargs) + assert model.working(), ('MMVet evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE) + + lt = len(data) + lines = [data.iloc[i] for i in range(lt)] + tups = [(model, line) for line in lines] + indices = [line['index'] for line in lines] + + ans = load(tmp_file) if osp.exists(tmp_file) else {} + tups = [x for x, i in zip(tups, indices) if i not in ans] + indices = [i for i in indices if i not in ans] + + if len(indices): + new_results = track_progress_rich( + MMVet_auxeval, + tups, + nproc=nproc, + chunksize=nproc, + keys=indices, + save=tmp_file, + ) + ans = load(tmp_file) + for k, v in zip(indices, new_results): + assert k in ans + assert ans[k]['log'] == v['log'] and ans[k]['score'] == v['score'] + data['score'] = [ans[idx]['score'] for idx in data['index']] + data['log'] = [ans[idx]['log'] for idx in data['index']] + dump(data, storage) + + score, score_fine = MMVet_acc(storage) + score_pth = storage.replace('.xlsx', '_score.csv') + score_fine_pth = storage.replace('.xlsx', '_score_fine.csv') + dump(score, score_pth) + dump(score_fine, score_fine_pth) + return score + + +class MTVQADataset(ImageBaseDataset): + TYPE = 'VQA' + DATASET_URL = {'MTVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/MTVQA_TEST.tsv'} + DATASET_MD5 = {'MTVQA_TEST': 'd87c17dbab934b7cd89c0a3c1c5657f4'} + + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + data = load(eval_file) + assert 'answer' in data and 'prediction' in data and 'category' in data + data['prediction'] = [str(x) for x in data['prediction']] + data['answer'] = [str(x) for x in data['answer']] + if 'split' in data: + assert np.all([x.lower() == 'test' for x in data['split']]), 'We only support MTVQA_TEST for now. ' + lt = len(data) + category_scores = defaultdict(list) + for i in range(lt): + line = data.iloc[i] + ans = line['answer'].strip().lower().replace('.', '') + pred = line['prediction'].strip().lower().replace('.', '') + cate = line['category'] + score = 1.0 if ans in pred else 0.0 + category_scores[cate].append(score) + category_scores['Average'].append(score) + # Calculate the average score for each category, the score is normalized to [0, 100] + category_averages = {category: np.mean(scores) * 100 for category, scores in category_scores.items()} + + suffix = eval_file.split('.')[-1] + result_file = eval_file.replace(f'.{suffix}', '_acc.json') + dump(category_averages, result_file) + + return category_averages + + # MT-VQA adopts a custom prompt + def build_prompt(self, line): + msgs = super().build_prompt(line) + assert sum([x['type'] == 'text' for x in msgs]) == 1 + for item in msgs: + if item['type'] == 'text': + item['value'] += '\nAnswer the question using a word or phrase in the language of the question.' + return msgs + + +class CustomVQADataset(ImageBaseDataset): + TYPE = 'VQA' + + def load_data(self, dataset): + data_path = osp.join(LMUDataRoot(), f'{dataset}.tsv') + + if file_size(data_path, 'GB') > 1: + local_path = data_path.replace('.tsv', '_local.tsv') + if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None): + from ..tools import LOCALIZE + + LOCALIZE(data_path, local_path) + data_path = local_path + return load(data_path) + + def evaluate(self, eval_file, **judge_kwargs): + raise NotImplementedError diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_yorn.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_yorn.py new file mode 100644 index 0000000000000000000000000000000000000000..bfdaf949394ebcf381802ae5bcff9340e32c3129 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/image_yorn.py @@ -0,0 +1,88 @@ +from ..smp import * +from ..utils import * +from .image_base import ImageBaseDataset +from .utils import build_judge, DEBUG_MESSAGE + + +class ImageYORNDataset(ImageBaseDataset): + + TYPE = 'Y/N' + + DATASET_URL = { + 'MME': 'https://opencompass.openxlab.space/utils/VLMEval/MME.tsv', + 'HallusionBench': 'https://opencompass.openxlab.space/utils/VLMEval/HallusionBench.tsv', + 'POPE': 'https://opencompass.openxlab.space/utils/VLMEval/POPE.tsv', + } + + DATASET_MD5 = { + 'MME': 'b36b43c3f09801f5d368627fb92187c3', + 'HallusionBench': '0c23ac0dc9ef46832d7a24504f2a0c7c', + 'POPE': 'c12f5acb142f2ef1f85a26ba2fbe41d5', + } + + # It returns a dataframe + def evaluate(self, eval_file, **judge_kwargs): + from .utils.yorn import YOrN_Extraction, YOrN_auxeval + from .utils.yorn import default_rating, MME_rating, Hallusion_rating, POPE_rating + + dataset = self.dataset_name + data = load(eval_file) + data['prediction'] = [str(x) for x in data['prediction']] + storage = eval_file.replace('.xlsx', '_auxmatch.xlsx') + tmp_file = eval_file.replace('.xlsx', '_tmp.pkl') + nproc = judge_kwargs.pop('nproc', 4) + + if not osp.exists(storage): + ans_map = {k: YOrN_Extraction(v) for k, v in zip(data['index'], data['prediction'])} + if osp.exists(tmp_file): + tmp = load(tmp_file) + for k in tmp: + if ans_map[k] == 'Unknown' and tmp[k] != 'Unknown': + ans_map[k] = tmp[k] + + data['extracted'] = [ans_map[x] for x in data['index']] + unknown = data[data['extracted'] == 'Unknown'] + + model = judge_kwargs.get('model', 'exact_matching') + if model == 'exact_matching': + model = None + elif gpt_key_set(): + model = build_judge(**judge_kwargs) + if not model.working(): + warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation') + warnings.warn(DEBUG_MESSAGE) + model = None + else: + model = None + warnings.warn('OPENAI_API_KEY is not working properly, will use exact matching for evaluation') + + if model is not None: + lt = len(unknown) + lines = [unknown.iloc[i] for i in range(lt)] + tups = [(model, line) for line in lines] + indices = list(unknown['index']) + if len(tups): + res = track_progress_rich( + YOrN_auxeval, tups, nproc=nproc, chunksize=nproc, keys=indices, save=tmp_file) + for k, v in zip(indices, res): + ans_map[k] = v + + data['extracted'] = [ans_map[x] for x in data['index']] + dump(data, storage) + + data = load(storage) + data['score'] = (data['answer'] == data['extracted']) + dump(data, storage) + + if dataset is not None and listinstr(['MME'], dataset): + score = MME_rating(storage) + elif dataset is not None and listinstr(['Hallusion'], dataset): + score = Hallusion_rating(storage) + elif dataset is not None and listinstr(['POPE'], dataset): + score = POPE_rating(storage) + else: + score = default_rating(storage) + + score_tgt = eval_file.replace('.xlsx', '_score.csv') + dump(score, score_tgt) + return score diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/mmbench_video.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/mmbench_video.py new file mode 100644 index 0000000000000000000000000000000000000000..cded905c4781d51080405718dca227bb347fca60 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/mmbench_video.py @@ -0,0 +1,252 @@ +from huggingface_hub import snapshot_download +from ..smp import * +from .video_base import VideoBaseDataset +from .utils import build_judge, DEBUG_MESSAGE +from ..utils import track_progress_rich + + +FAIL_MSG = 'Failed to obtain answer via API.' + + +def unwrap_hf_pkl(pth, suffix='.mp4'): + base_dir = os.path.join(pth, 'video_pkl/') + target_dir = os.path.join(pth, 'video/') + pickle_files = [os.path.join(base_dir, file) for file in os.listdir(base_dir)] + pickle_files.sort() + + if not os.path.exists(target_dir): + os.makedirs(target_dir, exist_ok=True) + for pickle_file in pickle_files: + with open(pickle_file, 'rb') as file: + video_data = pickle.load(file) + # For each video file in the pickle file, write its contents to a new mp4 file + for video_name, video_content in video_data.items(): + output_path = os.path.join(target_dir, f'{video_name}{suffix}') + with open(output_path, 'wb') as output_file: + output_file.write(video_content) + print('The video file has been restored and stored from the pickle file.') + else: + print('The video file already exists.') + + +class MMBenchVideo(VideoBaseDataset): + + MD5 = '98f7df3eb1007fc375ea6fe88a98e2ff' + SYS = 'You are an AI assistant responsible for answering questions about videos.' + FRAMES_TMPL_PACK = """ +You will be provided with {} separate frames uniformly sampled from a video, \ +the frames are provided in chronological order of the video. +Please analyze these images and provide the answer / answers to the \ +following question / questions about the video content. +If multiple questions are provided (with indices I1, I2, I3, ...), \ +you should organize your answers in the following json format: +{{ + 'I1': 'Answer to Question I1', + 'I2': 'Answer to Question I2', + ... +}} +Otherwise, please directly reply with your response to the only question. +Even if the information in these separate frames is not enough to give an answer, +PLEASE GIVE A RESPONSE TO EACH OF THE QUESTIONS IN THE FORMAT DESCRIBED ABOVE. +""" + + FRAMES_TMPL_NOPACK = """ +You will be provided with {} separate frames uniformly sampled from a video, \ +the frames are provided in chronological order of the video. +Please analyze these images and provide the answer to the question about the video content. +Please directly reply with your response to the only question. +""" + + TYPE = 'VQA' + + def __init__(self, dataset='MMBench-Video', pack=False): + super().__init__(dataset=dataset, pack=pack) + + @classmethod + def supported_datasets(cls): + return ['MMBench-Video'] + + def prepare_dataset(self, dataset_name='MMBench-Video', repo_id='nebulae09/MMBench-Video'): + def check_integrity(pth): + data_file = osp.join(pth, f'{dataset_name}.tsv') + if md5(data_file) != self.MD5: + return False + data = load(data_file) + for video_pth in data['video_path']: + if not osp.exists(osp.join(pth, video_pth)): + return False + return True + + cache_path = get_cache_path(repo_id) + if cache_path is not None and check_integrity(cache_path): + dataset_path = cache_path + else: + dataset_path = snapshot_download(repo_id=repo_id, repo_type='dataset') + unwrap_hf_pkl(dataset_path) + self.video_path = osp.join(dataset_path, 'video/') + data_file = osp.join(dataset_path, f'{dataset_name}.tsv') + + return dict(data_file=data_file, root=osp.join(dataset_path, 'video')) + + def build_prompt_pack(self, line, num_frames): + if isinstance(line, int): + assert line < len(self) + video = self.videos[line] + elif isinstance(line, pd.Series): + video = line['video'] + elif isinstance(line, str): + video = line + + frames = self.save_video_frames(video, num_frames) + sub = self.data[self.data['video'] == video] + sys_prompt = self.SYS + self.FRAMES_TMPL_PACK.format(num_frames) + message = [dict(type='text', value=sys_prompt)] + for im in frames: + message.append(dict(type='image', value=im)) + nq = len(sub) + prompt = 'Questions: \n{}\nAnswers: \n' + qs = {int(sub.iloc[i]['index']): sub.iloc[i]['question'] for i in range(nq)} + prompt = prompt.format(json.dumps(qs)) + message.append(dict(type='text', value=prompt)) + return message + + def build_prompt_nopack(self, line, num_frames, video_llm): + if isinstance(line, int): + assert line < len(self) + line = self.data.iloc[line] + if video_llm: + question = line['question'] + prefix, video_idx_path = os.path.split(line['video_path']) + message = [dict(type='text', value=question)] + message.append(dict(type='video', value=os.path.join(self.video_path, video_idx_path))) + return message + else: + frames = self.save_video_frames(line['video'], num_frames) + sys_prompt = self.FRAMES_TMPL_NOPACK.format(num_frames) + message = [dict(type='text', value=sys_prompt)] + for im in frames: + message.append(dict(type='image', value=im)) + prompt = 'Question: {}\nAnswer: '.format(line['question']) + message.append(dict(type='text', value=prompt)) + return message + + def build_prompt(self, line, num_frames, video_llm): + if self.pack and not video_llm: + return self.build_prompt_pack(line, num_frames) + else: + return self.build_prompt_nopack(line, num_frames, video_llm) + + @staticmethod + def remove_side_quote(s, syms=[',', '"', "'"]): + if np.all([x in syms for x in s]): + return '' + while s[0] in syms: + s = s[1:] + while s[-1] in syms: + s = s[:-1] + return s + + @staticmethod + def robust_json_load(s): + try: + jsons = list(extract_json_objects(s)) + assert len(jsons) == 1 + return jsons[0] + except: + if '{' in s and s.find('{') == s.rfind('{'): + sub_str = s[s.find('{') + 1:].strip() + lines = sub_str.split('\n') + res = {} + for l in lines: + l = l.strip() + if ': ' in l: + key = l.split(': ')[0].strip() + val = l.split(': ')[1].strip() + key = MMBenchVideo.remove_side_quote(key) + val = MMBenchVideo.remove_side_quote(val) + if len(key) and len(val): + res[key] = val + return res + return None + + def load_pack_answers(self, data_raw): + vstats = defaultdict(lambda: 0) + data = defaultdict(lambda: {}) + + for k in data_raw: + ans = data_raw[k].strip() + if FAIL_MSG in ans: + vstats['GEN_FAIL'] += 1 + continue + res = self.robust_json_load(ans) + if res is not None: + data[k] = res + vstats['PARSE_OK'] += 1 + else: + vstats['PARSE_FAIL'] += 1 + + # return data + meta = cp.deepcopy(self.data) + lt = len(meta) + prediction = [] + for i in range(lt): + line = meta.iloc[i] + vid = line['video'] + idx = str(line['index']) + prediction.append(data[vid][idx] if idx in data[vid] else None) + meta['prediction'] = prediction + vstats['VALIDQ'] = len([x for x in prediction if x is not None]) + vstats['INVALIDQ'] = len([x for x in prediction if x is None]) + return meta, vstats + + # It returns a dictionary + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + from .utils.mmbench_video import get_dimension_rating, system_prompt, build_prompt + + assert eval_file.endswith('.xlsx'), 'data file should be an xlsx file' + judge = judge_kwargs['model'] + nproc = judge_kwargs.pop('nproc', 4) + + tmp_file = eval_file.replace('.xlsx', f'_{judge}_tmp.pkl') + tgt_file = eval_file.replace('.xlsx', f'_{judge}_rating.json') + score_file = eval_file.replace('.xlsx', f'_{judge}_score.xlsx') + + model = build_judge(system_prompt=system_prompt, **judge_kwargs) + assert model.working(), 'MMBench-Video evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE + + if not osp.exists(score_file): + res = {} if not osp.exists(tmp_file) else load(tmp_file) + res = {k: v for k, v in res.items() if model.fail_msg not in v} + + data = load(eval_file) + data_un = data[~data['index'].isin(res)] + data_un = data_un[~pd.isna(data_un['prediction'])] + lt = len(data_un) + prompts = [build_prompt(data_un.iloc[i]) for i in range(lt)] + indices = [data_un.iloc[i]['index'] for i in range(lt)] + + if len(prompts): + _ = track_progress_rich( + model.generate, + prompts, + keys=indices, + save=tmp_file, + nproc=nproc, + chunksize=nproc + ) + score_map = load(tmp_file) + data['score'] = [score_map[idx] if idx in score_map else -1 for idx in data['index']] + rejected = [x for x in score_map.values() if FAIL_MSG in x] + data['score'] = [int(x) if istype(x, int) else -1 for x in data['score']] + print( + f'Among {len(data)} questions, failed to obtain prediction for {len(data) - len(score_map)} questions, ' + f'failed to obtain the score for another {len(rejected)} questions. ' + f'Those questions will be counted as 0 score in ALL rating, and will not be counted in VALID rating.' + ) + + dump(data, score_file) + + rating = get_dimension_rating(score_file) + dump(rating, tgt_file) + return rating diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/mmlongbench.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/mmlongbench.py new file mode 100644 index 0000000000000000000000000000000000000000..be9c1d65b432268beda63a2614587cd43e4e56db --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/mmlongbench.py @@ -0,0 +1,582 @@ +import re +import math +from urllib.request import urlopen +from PIL import Image, ImageDraw, ImageFont +import torchvision.transforms as transforms + +from vlmeval.dataset.utils import build_judge, levenshtein_distance +from vlmeval.smp import * +from .image_base import ImageBaseDataset + +FAIL_MSG = 'Failed to obtain answer via API.' + + +def get_gpt4_ICE(): + example_1 = """ +--- +Question: List the primary questions asked about the services in this report. +Analysis: The primary questions asked about the services in the report for The Limes Residential Home are:\n\n +1. Is the service safe?\n +2. Is the service effective?\n +3. Is the service caring?\n +4. Is the service responsive?\n +5. Is the service well-led? +Extracted answer: [ + 'Is the servife safe?', + 'Is the service effective', + 'Is the serve caring?', + 'Is the service responsive?', + 'Is the service well-led?' +] +Answer format: List\n +""" + + example_2 = """ +--- +Question: How many regulations of the HSCA 2008 are breached in all according to this report? +Analysis: According to the report, the provider breached 10 Health and Social Care Act 2008 (Regulated Activities) +Regulations in total. Here are the specifics:\n\n1. Regulation 13: Safeguarding service users from abuse and +improper treatment\n2. Regulation 12: Safe care and treatment\n3. Regulation 18: Staffing\n4. Regulation 11: +Need for consent\n5. Regulation 10: Dignity and respect\n6. Regulation 9: Person-centred care\n7. Regulation 17: +Good governance\n8. Regulation 18 (CQC Registration Regulations 2009): Notification of other incidents\n9. +Regulation 18: Failure to maintain an accurate and up-to-date care plan\n10. Regulation 11: Failure to implement +the Mental Capacity Act 2005 code of practice effectively\n\nThese breaches involve issues concerning staffing, +safeguarding, medicines management, dignity and respect, consent, care planning, governance, and failure to +notify the CQC of incidents. +Extracted answer: 10 +Answer format: Integer\n +""" + + example_3 = """ +--- +Question: According to the survey that is the percentage of Chinese who are paying more or +about the same attention to politics after Trump's election? +Analysis: The survey provided does not specify the percentage of Chinese individuals specifically who are paying +more or about the same attention to politics after Trump's election. The report focuses primarily on American +demographics and does not include specific details about the Chinese population in relation to this question. If +you need information about a different demographic or a summary of the findings from the American demographic, +I can certainly help with that! +Extracted answer: Not answerable +Answer format: String\n +""" + + example_4 = """ +--- +Question: How many quotations from male respondent over 50 years old are included in this report? +Analysis: The image you've provided appears to be a screenshot of a document with multiple charts. However, the +text is too small and blurry to read accurately. If you can provide a clearer image or more context, I might be +able to help you with your question. +Extracted answer: Fail to answer +Answer format: String\n +""" + + return [example_1, example_2, example_3, example_4] + + +def build_mmlongbench_gpt4_prompt(line): + task_description = """ +Given the question and analysis, you are tasked to extract answers with required formats from the free-form analysis. +- Your extracted answers should be one of the following formats: (1) Integer, (2) Float, (3) String and (4) List. +If you find the analysis the question can not be answered from the given documents, type "Not answerable". +Exception: If the analysis only tells you that it can not read/understand the images or documents, +type "Fail to answer". +- Please make your response as concise as possible. Also note that your response should be formatted as below: +``` +Extracted answer: [answer] +Answer format: [answer format] +``` +Please read the following example, then extract the answer from the model response +and type it at the end of the prompt.\n +""" + question = line['question'] + prediction = str(line['prediction']) + prompt = task_description + examples = get_gpt4_ICE() + for example in examples: + prompt += example + prompt += '---\nQuestion:' + question + '\n' + prompt += 'Analysis: ' + prediction + return prompt + + +def anls_compute(groundtruth, prediction, threshold=0.5): + dist = levenshtein_distance(groundtruth, prediction) + length = max(len(groundtruth.upper()), len(prediction.upper())) + value = 0.0 if length == 0 else float(dist) / float(length) + anls = 1.0 - value + if anls <= threshold: + anls = 0.0 + return anls + + +def is_float_equal(reference, prediction, include_percentage: bool = False, is_close: float = False) -> bool: + def get_precision(gt_ans: float) -> int: + precision = 3 + if '.' in str(gt_ans): + precision = len(str(gt_ans).split('.')[-1]) + return precision + + reference = float(str(reference).strip().rstrip('%').strip()) + try: + prediction = float(str(prediction).strip().rstrip('%').strip()) + except: + return False + + if include_percentage: + gt_result = [reference / 100, reference, reference * 100] + else: + gt_result = [reference] + for item in gt_result: + try: + if is_close: + if math.isclose(item, prediction, rel_tol=0.01): + return True + precision = max(min(get_precision(prediction), get_precision(item)), 2) + if round(prediction, precision) == round(item, precision): + return True + except Exception: + continue + return False + + +def get_clean_string(s): + s = str(s).lower().strip() + if s.endswith('mile'): + s.rstrip('mile').strip() + if s.endswith('miles'): + s.rstrip('miles').strip() + if s.endswith('million'): + s.rstrip('million').strip() + # remove parenthesis + s = re.sub(r'\s*\([^)]*\)', '', s).strip() + # remove quotes + s = re.sub(r"^['\"]|['\"]$", '', s).strip() + s = s.strip().lstrip('$').strip() + s = s.strip().rstrip('%').strip() + return s + + +def is_exact_match(s): + flag = False + # Website + if 'https://' in s: + flag = True + # code file + if s.endswith('.py') or s.endswith('ipynb'): + flag = True + if s.startswith('page'): + flag = True + # telephone number + if re.fullmatch(r'\b\d+(-\d+|\s\d+)?\b', s): + flag = True + # time + if 'a.m.' in s or 'p.m.' in s: + flag = True + # YYYY-MM-DD + if re.fullmatch(r'\b\d{4}[-\s]\d{2}[-\s]\d{2}\b', s): + flag = True + # YYYY-MM + if re.fullmatch(r'\b\d{4}[-\s]\d{2}\b', s): + flag = True + # Email address + if re.fullmatch(r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}', s): + flag = True + return flag + + +def isfloat(num): + try: + float(num) + return True + except ValueError: + return False + + +def get_font(): + try: + truetype_url = 'http://opencompass.openxlab.space/utils/Fonts/SimHei.ttf' + ff = urlopen(truetype_url) + font = ImageFont.truetype(ff, size=40) + except: + print('Fail to download the font. Use the default one.') + font = ImageFont.load_default(size=40) + return font + + +def frame2img(img_path_list, font, save_path=None, idx_start=0): + imgs = [Image.open(img_path) for img_path in img_path_list] + + new_imgs = [] + for img in imgs: + w, h = img.size + scale = w / h + if w > h: + new_w = 560 * 2 + new_h = int(560 * 2 / scale) + else: + new_w = int(560 * 2 * scale) + new_h = 560 * 2 + img = transforms.functional.resize(img, [new_h, new_w],) + new_imgs.append(img) + imgs = new_imgs + new_w = 0 + new_h = 0 + pad = 40 + if w > h: + for im in imgs: + w, h = im.size + new_w = max(new_w, w) + new_h += h + 10 + pad + new_img = Image.new('RGB', (new_w, new_h), 'white') + draw = ImageDraw.Draw(new_img) + curr_h = 0 + for idx, im in enumerate(imgs): + w, h = im.size + new_img.paste(im, (0, pad + curr_h)) + draw.text((0, curr_h), f'', font=font, fill='black') + if idx + 1 < len(imgs): + draw.line([(0, pad + curr_h + h + 5), (new_w, pad + curr_h + h + 5)], fill='black', width=2) + curr_h += h + 10 + pad + else: + for im in imgs: + w, h = im.size + new_w += w + 10 + new_h = max(new_h, h) + new_h += pad + new_img = Image.new('RGB', (new_w, new_h), 'white') + draw = ImageDraw.Draw(new_img) + curr_w = 0 + for idx, im in enumerate(imgs): + w, h = im.size + new_img.paste(im, (curr_w, pad)) + draw.text((curr_w, 0), f'', font=font, fill='black') + if idx + 1 < len(imgs): + draw.line([(curr_w + w + 5, 0), (curr_w + w + 5, new_h)], fill='black', width=2) + curr_w += w + 10 + + if save_path is not None: + new_img.save(save_path) + + return new_img + + +def concat_images(image_list, max_concat=1, column_num=1): + concatenated_images = [] + if column_num == -1: + MAX_COLUMN_NUM = 20 + max_concat = 1 + while len(image_list) / max_concat > MAX_COLUMN_NUM: + max_concat += 1 + interval = max(math.ceil(len(image_list) / max_concat), 1) + for i in range(0, len(image_list), interval): + batch_images = image_list[i:i + interval] + concatenated_image = frame2img(batch_images, font=get_font(), idx_start=i) + concatenated_images.append(concatenated_image) + else: + interval = max(math.ceil(len(image_list) / max_concat), 1) + for i in range(0, len(image_list), interval): + batch_images = [Image.open(filename) for filename in image_list[i:i + interval]] + if column_num == 1: + total_height = batch_images[0].height * len(batch_images) + else: + total_height = batch_images[0].height * ((len(batch_images) - 1) // column_num + 1) + concatenated_image = Image.new('RGB', (batch_images[0].width * column_num, total_height), 'white') + + x_offset, y_offset = 0, 0 + for count, image in enumerate(batch_images): + concatenated_image.paste(image, (x_offset, y_offset)) + x_offset += image.width + if (count + 1) % column_num == 0: + y_offset += image.height + x_offset = 0 + concatenated_images.append(concatenated_image) + return concatenated_images + + +def eval_score(gt, pred, answer_type): + if answer_type == 'Int': + try: + gt, pred = int(gt), int(float(pred)) + except: + pred = '' + score = (gt == pred) + elif answer_type == 'Float': + try: + gt = float(get_clean_string(str(gt))) + pred = float(get_clean_string(str(pred))) + except: + pred = '' + score = is_float_equal(gt, pred, include_percentage=True, is_close=True) + elif answer_type == 'Str': + gt = get_clean_string(gt) + pred = get_clean_string(pred) + if is_exact_match(gt): + score = (gt == pred) + else: + score = anls_compute(gt, pred) + else: + if isinstance(gt, str) and gt.startswith('['): + gt = eval(gt) + if not isinstance(gt, list): + gt = [gt] + if isinstance(pred, str) and pred.startswith('['): + pred = eval(pred) + if not isinstance(pred, list): + pred = [pred] + print(len(gt), len(pred)) + if len(gt) != len(pred): + score = 0.0 + else: + gt = sorted([get_clean_string(a) for a in gt]) + pred = sorted([get_clean_string(a) for a in pred]) + print(gt, pred) + if isfloat(gt[0]) or is_exact_match(gt[0]): + score = ('-'.join(gt) == '-'.join(pred)) + else: + score = min([anls_compute(gt_v, pred_v) for gt_v, pred_v in zip(gt, pred)]) + + return float(score) + + +def MMLongBench_auxeval(model, line): + prompt = build_mmlongbench_gpt4_prompt(line) + log = '' + retry = 5 + + for i in range(retry): + prediction = line['prediction'] + res = model.generate(prompt, temperature=i * 0.5) + + if FAIL_MSG in res: + log += f'Try {i}: output is {prediction}, failed to parse.\n' + else: + log += 'Succeed' + try: + pred = res.split('Answer format:')[0].split('Extracted answer:')[1].strip() + except: + pred = '' + return dict(log=log, res=res, pred=pred) + log += 'All 5 retries failed.\n' + return dict(log=log, res='', pred='') + + +def get_f1(data): + gt_pos_data = data[data.apply(lambda k: k['answer'] != 'Not answerable', axis=1)] + pred_pos_data = data[data.apply(lambda k: k['pred'] != 'Not answerable', axis=1)] + recall = sum(gt_pos_data['score'].tolist()) / len(gt_pos_data) + precision = sum(pred_pos_data['score'].tolist()) / len(pred_pos_data) + return 2 * recall * precision / (recall + precision) + + +def MMLongBench_acc(result_file): + data = load(result_file) + overall_score = 0.0 + score_list = list() + for i in range(len(data)): + item = data.iloc[i] + try: + score = eval_score(item['answer'], item['pred'], item['answer_format']) + except: + score = 0.0 + score_list.append(score) + overall_score += score + + data['score'] = score_list + dump(data, result_file) + + data_chart = data[data.apply(lambda k: 'Chart' in eval(k['evidence_sources']), axis=1)] + data_table = data[data.apply(lambda k: 'Table' in eval(k['evidence_sources']), axis=1)] + data_image = data[data.apply(lambda k: 'Figure' in eval(k['evidence_sources']), axis=1)] + data_text = data[data.apply(lambda k: 'Pure-text (Plain-text)' in eval(k['evidence_sources']), axis=1)] + data_layout = data[data.apply(lambda k: 'Generalized-text (Layout)' in eval(k['evidence_sources']), axis=1)] + + data_single = data[data.apply(lambda k: len(eval(k['evidence_pages'])) == 1, axis=1)] + data_multi = data[data.apply(lambda k: len(eval(k['evidence_pages'])) > 1, axis=1)] + data_unans = data[data.apply(lambda k: len(eval(k['evidence_pages'])) == 0, axis=1)] + + res = dict() + res['category'] = [ + 'overall_f1', 'overall_acc', 'text', 'layout', 'table', 'chart', + 'image', 'single-page', 'multi-page', 'unanswerable' + ] + res['num'] = [ + len(data), len(data), len(data_text), len(data_layout), len(data_table), + len(data_chart), len(data_image), len(data_single), len(data_multi), len(data_unans) + ] + res['avg_score'] = [ + get_f1(data), + overall_score / len(data), + sum(data_text['score'].tolist()) / len(data_text) if len(data_text) > 0 else 0.0, + sum(data_layout['score'].tolist()) / len(data_layout) if len(data_layout) > 0 else 0.0, + sum(data_table['score'].tolist()) / len(data_table) if len(data_table) > 0 else 0.0, + sum(data_chart['score'].tolist()) / len(data_chart) if len(data_chart) > 0 else 0.0, + sum(data_image['score'].tolist()) / len(data_image) if len(data_image) > 0 else 0.0, + sum(data_single['score'].tolist()) / len(data_single) if len(data_single) > 0 else 0.0, + sum(data_multi['score'].tolist()) / len(data_multi) if len(data_multi) > 0 else 0.0, + sum(data_unans['score'].tolist()) / len(data_unans) if len(data_unans) > 0 else 0.0, + ] + res = pd.DataFrame(res) + return res + + +class MMLongBench(ImageBaseDataset): + + TYPE = 'VQA' + + DATASET_URL = { + 'MMLongBench_DOC': 'https://opencompass.openxlab.space/utils/VLMEval/MMLongBench_DOC.tsv', + } + DATASET_MD5 = { + 'MMLongBench_DOC': '9b393e1f4c52718380d50586197eac9b', + } + + SUPPORTED_MODELS = { + 'GPT4': (1, 1), + 'GPT4V': (1, 1), + 'GPT4V_HIGH': (1, 1), + 'GPT4o': (1, 1), + 'GPT4o_HIGH': (1, 1), + 'GPT4o_MINI': (1, 1), + 'MiniCPM-Llama3-V-2_5': (1, 5), + 'InternVL-Chat-V1-5': (5, 2), + 'XComposer2_4KHD': (1, 5), + 'XComposer2d5': (1, -1), + } + + def __init__(self, dataset, **kwargs): + self.model_list = list(self.SUPPORTED_MODELS.keys()) + model_name = kwargs['model'] + if not listinstr(self.model_list, model_name): + raise AssertionError("{} doesn't support the evaluation on MMLongBench_DOC.".format(model_name)) + super(MMLongBench, self).__init__(dataset) + + self.is_api = True if listinstr(['GPT4'], model_name) else False + self.max_pages = 120 + concat_num, column_num = self.SUPPORTED_MODELS.get(model_name) + self.concat_num = concat_num + self.column_num = column_num + + def dump_image(self, origin_line): + os.makedirs(self.img_root, exist_ok=True) + try: + import fitz + except: + warnings.warn('Please use `pip install pymupdf` to parse PDF files.') + + line = origin_line.copy() + line['image_path'] = line['image_path'][:self.max_pages] + skip_pdf_parse = True + for im_name in line['image_path']: + path = osp.join(self.img_root, im_name) + if not read_ok(path): + skip_pdf_parse = False + break + + # Just for being compatible with the zooped loop: zip(line['image'], line['image_path']) + if skip_pdf_parse: + line['image'] = line['image_path'] + else: + pdf_data = base64.b64decode(line['image']) + pdf_file = io.BytesIO(pdf_data) + encoded_images = [] + with fitz.open(stream=pdf_file, filetype='pdf') as doc: + doc = doc[:self.max_pages] + for page in doc: + image = page.get_pixmap(dpi=144) + image_file = io.BytesIO(image.tobytes(output='png')) + image = Image.open(image_file) + encoded_image = encode_image_to_base64(image) + encoded_images.append(encoded_image) + line['image'] = encoded_images + print('process {}'.format(line['doc_id'])) + + if 'image' in line: + if isinstance(line['image'], list): + tgt_path = [] + assert 'image_path' in line + for img, im_name in zip(line['image'], line['image_path']): + path = osp.join(self.img_root, im_name) + if not read_ok(path): + decode_base64_to_image_file(img, path) + tgt_path.append(path) + else: + tgt_path = osp.join(self.img_root, f"{line['index']}.jpg") + if not read_ok(tgt_path): + decode_base64_to_image_file(line['image'], tgt_path) + tgt_path = [tgt_path] + else: + assert 'image_path' in line + tgt_path = toliststr(line['image_path']) + + if self.concat_num > 0 and not self.is_api: + concatenated_images = concat_images(tgt_path, max_concat=self.concat_num, column_num=self.column_num) + + old_tgt_path = tgt_path + assert isinstance(old_tgt_path, list) + if self.column_num != -1: + tgt_path = [ + '_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat{}_{}.jpg'.format(self.concat_num, i) + for i in range(len(concatenated_images)) + ] + else: + tgt_path = [ + '_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat_all_{}.jpg'.format(i) + for i in range(len(concatenated_images)) + ] + + for path, concatenated_image in zip(tgt_path, concatenated_images): + if not read_ok(path): + decode_base64_to_image_file(encode_image_to_base64(concatenated_image), path) + num_images, image_size = len(old_tgt_path), concatenated_image.size + print('concat {} images to a new one with size {}. save at {}'.format(num_images, image_size, path)) + return tgt_path + + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + logger = get_logger('Evaluation') + model = judge_kwargs['model'] + + suffix = eval_file.split('.')[-1] + storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx') + tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl') + + if osp.exists(storage): + logger.warning(f'GPT scoring file {storage} already exists, will reuse it in MMLongBench_eval. ') + else: + data = load(eval_file) + model = build_judge(max_tokens=128, **judge_kwargs) + lt = len(data) + lines = [data.iloc[i] for i in range(lt)] + tups = [(model, line) for line in lines] + indices = [line['index'] for line in lines] + + ans = {} + if osp.exists(tmp_file): + ans = load(tmp_file) + tups = [x for x, i in zip(tups, indices) if i not in ans] + indices = [i for i in indices if i not in ans] + + if len(indices): + new_results = list() + for model, line in tqdm(tups): + res = MMLongBench_auxeval(model, line) + new_results.append(res) + + log_map, res_map, pred_map = {}, {}, {} + all_inds = [line['index'] for line in lines] + for k, v in zip(all_inds, new_results): + log_map[k] = v['log'] + res_map[k] = v['res'] + pred_map[k] = v['pred'] + data['res'] = [res_map[idx] for idx in data['index']] + data['log'] = [log_map[idx] for idx in data['index']] + data['pred'] = [pred_map[idx] for idx in data['index']] + dump(data, storage) + + score = MMLongBench_acc(storage) + score_pth = storage.replace('.xlsx', '_score.csv') + + dump(score, score_pth) + logger.info(f'MMLongBench_eval successfully finished evaluating {eval_file}, results saved in {score_pth}') + logger.info('Score: ') + logger.info(score) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/mvbench.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/mvbench.py new file mode 100644 index 0000000000000000000000000000000000000000..c45c412ddee5b3ea83a073950ad6de4ec9d36dbb --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/mvbench.py @@ -0,0 +1,577 @@ +import huggingface_hub +from huggingface_hub import snapshot_download +from ..smp import * +from .video_base import VideoBaseDataset +from .utils import build_judge, DEBUG_MESSAGE +from ..utils import track_progress_rich +import torchvision.transforms as T +from torchvision import transforms +from torchvision.transforms.functional import InterpolationMode +from decord import VideoReader, cpu +import imageio +import cv2 +import zipfile +import os +import glob +from moviepy.editor import VideoFileClip, ImageSequenceClip +import moviepy.config_defaults +from .utils.mvbench import * + +FAIL_MSG = 'Failed to obtain answer via API.' +moviepy.config_defaults.LOGGER_LEVEL = logging.CRITICAL + 1 + + +class MVBench(VideoBaseDataset): + + MD5 = 'ae2a2607e2f8618155709220c6e927a6' + SYS = """Carefully watch the video and pay attention to the cause and sequence of events, \ +the detail and movement of objects, and the action and pose of persons. \ +Based on your observations, select the best option that accurately addresses the question. +""" + + TYPE = 'MCQ' + + def __init__(self, dataset='MVBench', pack=False): + self.type_data_list = { + 'Action Sequence': ('action_sequence.json', + 'your_data_path/star/Charades_v1_480/', 'video', True), # has start & end + 'Action Prediction': ('action_prediction.json', + 'your_data_path/star/Charades_v1_480/', 'video', True), # has start & end + 'Action Antonym': ('action_antonym.json', + 'your_data_path/ssv2_video/', 'video', False), + 'Fine-grained Action': ('fine_grained_action.json', + 'your_data_path/Moments_in_Time_Raw/videos/', 'video', False), + 'Unexpected Action': ('unexpected_action.json', + 'your_data_path/FunQA_test/test/', 'video', False), + 'Object Existence': ('object_existence.json', + 'your_data_path/clevrer/video_validation/', 'video', False), + 'Object Interaction': ('object_interaction.json', + 'your_data_path/star/Charades_v1_480/', 'video', True), # has start & end + 'Object Shuffle': ('object_shuffle.json', + 'your_data_path/perception/videos/', 'video', False), + 'Moving Direction': ('moving_direction.json', + 'your_data_path/clevrer/video_validation/', 'video', False), + 'Action Localization': ('action_localization.json', + 'your_data_path/sta/sta_video/', 'video', True), # has start & end + 'Scene Transition': ('scene_transition.json', + 'your_data_path/scene_qa/video/', 'video', False), + 'Action Count': ('action_count.json', + 'your_data_path/perception/videos/', 'video', False), + 'Moving Count': ('moving_count.json', + 'your_data_path/clevrer/video_validation/', 'video', False), + 'Moving Attribute': ('moving_attribute.json', + 'your_data_path/clevrer/video_validation/', 'video', False), + 'State Change': ('state_change.json', + 'your_data_path/perception/videos/', 'video', False), + 'Fine-grained Pose': ('fine_grained_pose.json', + 'your_data_path/nturgbd/', 'video', False), + 'Character Order': ('character_order.json', + 'your_data_path/perception/videos/', 'video', False), + 'Egocentric Navigation': ('egocentric_navigation.json', + 'your_data_path/vlnqa/', 'video', False), + 'Episodic Reasoning': ('episodic_reasoning.json', + 'your_data_path/tvqa/frames_fps3_hq/', 'frame', True), # has start & end, read frame + 'Counterfactual Inference': ('counterfactual_inference.json', + 'your_data_path/clevrer/video_validation/', 'video', False), + } + super().__init__(dataset=dataset, pack=pack) + + @classmethod + def supported_datasets(cls): + return ['MVBench'] + + def prepare_dataset(self, dataset_name='MVBench', repo_id='OpenGVLab/MVBench'): + def check_integrity(pth): + data_file = osp.join(pth, f'{dataset_name}.tsv') + + if not os.path.exists(data_file): + return False + + if md5(data_file) != self.MD5: + return False + + data = load(data_file) + for idx, item in data.iterrows(): + if not osp.exists(osp.join(pth, item['prefix'], item['video'])): + return False + return True + + cache_path = get_cache_path(repo_id, branch='main') + if cache_path is not None and check_integrity(cache_path): + dataset_path = cache_path + else: + def unzip_hf_zip(pth): + pth = os.path.join(pth, 'video/') + for filename in os.listdir(pth): + if filename.endswith('.zip'): + # 构建完整的文件路径 + zip_path = os.path.join(pth, filename) + + # 解压 ZIP 文件 + with zipfile.ZipFile(zip_path, 'r') as zip_ref: + zip_ref.extractall(pth) + + def generate_tsv(pth): + data_file = osp.join(pth, f'{dataset_name}.tsv') + if os.path.exists(data_file) and md5(data_file) == self.MD5: + return + json_data_dir = os.path.join(dataset_path, 'json') + self.data_list = [] + for k, v in self.type_data_list.items(): + with open(os.path.join(json_data_dir, v[0]), 'r') as f: + json_data = json.load(f) + for data in json_data: + self.data_list.append({ + 'task_type': k, + 'prefix': v[1].replace('your_data_path', os.path.join(dataset_path, 'video')), + 'data_type': v[2], + 'bound': v[3], + 'start': data['start'] if 'start' in data.keys() else None, + 'end': data['end'] if 'end' in data.keys() else None, + 'video': data['video'], + 'question': data['question'], + 'answer': data['answer'], + 'candidates': data['candidates'] + }) + + data_df = pd.DataFrame(self.data_list) + data_df = data_df.assign(index=range(len(data_df))) + data_df.to_csv(data_file, sep='\t', index=False) + + def move_files(pth): + # special for mvbench + src_folder = os.path.join(pth, 'video/data0613') + for subdir in os.listdir(src_folder): + subdir_path = os.path.join(src_folder, subdir) + if os.path.isdir(subdir_path): + for subsubdir in os.listdir(subdir_path): + subsubdir_path = os.path.join(subdir_path, subsubdir) + if os.path.isdir(subsubdir_path): + for item in os.listdir(subsubdir_path): + item_path = os.path.join(subsubdir_path, item) + target_folder = os.path.join(pth, 'video', subdir, subsubdir, item) + if not os.path.exists(target_folder): + shutil.move(item_path, os.path.join(target_folder, item)) + + hf_token = os.environ.get('HUGGINGFACE_TOKEN') + huggingface_hub.login(hf_token) + dataset_path = snapshot_download(repo_id=repo_id, repo_type='dataset') + move_files(dataset_path) + unzip_hf_zip(dataset_path) + generate_tsv(dataset_path) + + data_file = osp.join(dataset_path, f'{dataset_name}.tsv') + + self.decord_method = { + 'video': self.read_video, + 'gif': self.read_gif, + 'frame': self.read_frame, + } + + self.nframe = 8 + self.resolution = 224 + self.frame_fps = 3 + + # transform + crop_size = self.resolution + scale_size = self.resolution + input_mean = [0.48145466, 0.4578275, 0.40821073] + input_std = [0.26862954, 0.26130258, 0.27577711] + self.transform = T.Compose([ + GroupScale(int(scale_size), interpolation=InterpolationMode.BICUBIC), + GroupCenterCrop(crop_size), + Stack(), + ToTorchFormatTensor(), + GroupNormalize(input_mean, input_std) + ]) + + return dict(root=dataset_path, data_file=data_file) + + def get_index(self, bound, fps, max_frame, first_idx=0): + if bound: + start, end = bound[0], bound[1] + else: + start, end = -100000, 100000 + start_idx = max(first_idx, round(start * fps)) + end_idx = min(round(end * fps), max_frame) + seg_size = float(end_idx - start_idx) / self.num_segments + frame_indices = np.array([ + int(start_idx + (seg_size / 2) + np.round(seg_size * idx)) + for idx in range(self.num_segments) + ]) + return frame_indices + + def read_video(self, video_path, bound=None): + vr = VideoReader(video_path, ctx=cpu(0), num_threads=1) + max_frame = len(vr) - 1 + fps = float(vr.get_avg_fps()) + + images_group = list() + frame_indices = self.get_index(bound, fps, max_frame, first_idx=0) + for frame_index in frame_indices: + img = Image.fromarray(vr[frame_index].asnumpy()) + images_group.append(img) + torch_imgs = self.transform(images_group) + return torch_imgs + + def read_gif(self, video_path, bound=None, fps=25): + gif = imageio.get_reader(video_path) + max_frame = len(gif) - 1 + + images_group = list() + frame_indices = self.get_index(bound, fps, max_frame, first_idx=0) + for index, frame in enumerate(gif): + if index in frame_indices: + img = cv2.cvtColor(frame, cv2.COLOR_RGBA2RGB) + img = Image.fromarray(img) + images_group.append(img) + torch_imgs = self.transform(images_group) + return torch_imgs + + def read_frame(self, video_path, bound=None, fps=3): + max_frame = len(os.listdir(video_path)) + images_group = list() + frame_indices = self.get_index(bound, fps, max_frame, first_idx=1) # frame_idx starts from 1 + for frame_index in frame_indices: + img = Image.open(os.path.join(video_path, f'{frame_index:05d}.jpg')) + images_group.append(img) + torch_imgs = self.transform(images_group) + return torch_imgs + + def save_video_frames(self, imgs, video_name, frames): + + frame_paths = self.frame_paths(video_name, frames) + flag = np.all([osp.exists(p) for p in frame_paths]) + + if not flag: + block_size = imgs.size(0) // frames + split_tensors = torch.split(imgs, block_size) + to_pil = transforms.ToPILImage() + images = [to_pil(arr) for arr in split_tensors] + for im, pth in zip(images, frame_paths): + if not osp.exists(pth): + im.save(pth) + + return frame_paths + + def qa_template(self, data): + question = f"Question: {data['question']}\n" + question += 'Options:\n' + answer = data['answer'] + answer_idx = -1 + for idx, c in enumerate(eval(data['candidates'])): + question += f"({chr(ord('A') + idx)}) {c}\n" + if c == answer: + answer_idx = idx + question = question.rstrip() + answer = f"({chr(ord('A') + answer_idx)}) {answer}" + return question, answer + + def load_into_video_and_process(self, line): + video_path = os.path.join(line['prefix'], line['video']) + + if line['data_type'] in ['gif'] or os.path.splitext(video_path)[1] in ['.webm']: + processed_video_path = video_path.replace(os.path.splitext(video_path)[1], '.mp4') + if not os.path.exists(processed_video_path): + # using MoviePy to transform GIF, webm into mp4 format + gif_clip = VideoFileClip(video_path) + gif_clip.write_videofile(processed_video_path, codec='libx264') + gif_clip.close() + elif line['data_type'] in ['frame']: + input_images = os.path.join(video_path, '*.jpg') + processed_video_path = f'{video_path}.mp4' + if not os.path.exists(processed_video_path): + # using MoviePy to transform images into mp4 + image_files = sorted(glob.glob(input_images)) + image_clip = ImageSequenceClip(image_files, fps=self.frame_fps) + image_clip.write_videofile(processed_video_path, codec='libx264') + image_clip.close() + else: + processed_video_path = video_path + + if line['bound']: + base_name, suffix = os.path.splitext(processed_video_path) + output_video_path = f'{base_name}_processed{suffix}' + if not os.path.exists(output_video_path): + video_clip = VideoFileClip(processed_video_path) + clip = video_clip.subclip(line['start'], min(line['end'], video_clip.duration)) + clip.write_videofile(output_video_path) + clip.close() + else: + output_video_path = processed_video_path + + return output_video_path + + def build_prompt(self, line, num_frames, video_llm): + if isinstance(line, int): + assert line < len(self) + line = self.data.iloc[line] + + question, answer = self.qa_template(line) + message = [dict(type='text', value=self.SYS)] + message.append(dict(type='text', value=question)) + if video_llm: + new_video_path = self.load_into_video_and_process(line) + message.append(dict(type='video', value=new_video_path)) + else: + bound = None + if line['bound']: + bound = ( + line['start'], + line['end'], + ) + video_path = os.path.join(line['prefix'], line['video']) + decord_method = self.decord_method[line['data_type']] + self.num_segments = num_frames if num_frames > 0 else self.nframe + torch_imgs = decord_method(video_path, bound) + img_frame_paths = self.save_video_frames(torch_imgs, line['video'], self.num_segments) + for im in img_frame_paths: + message.append(dict(type='image', value=im)) + message.append(dict(type='text', value='\nOnly give the best option.')) + message.append(dict(type='text', value='Best option:(')) + return message + + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + + assert eval_file.endswith('.xlsx'), 'data file should be an xlsx file' + + tmp_file = eval_file.replace('.xlsx', '_tmp.pkl') + tgt_file = eval_file.replace('.xlsx', '_rating.json') + score_file = eval_file.replace('.xlsx', '_score.xlsx') + + if not osp.exists(score_file): + res = {} if not osp.exists(tmp_file) else load(tmp_file) + res = {k: v for k, v in res.items() if FAIL_MSG not in v} + + data = load(eval_file) + data_un = data[~pd.isna(data['prediction'])] + + for idx in data['index']: + ans = data.loc[data['index'] == idx, 'answer'].values[0] + pred = data.loc[data['index'] == idx, 'prediction'].values[0] + options = eval(data.loc[data['index'] == idx, 'candidates'].values[0]) + answer_idx = -1 + for id, c in enumerate(options): + if c == ans: + answer_idx = id + ans = f"({chr(ord('A') + answer_idx)}) {ans}" + + if FAIL_MSG in pred: + data.loc[idx, 'score'] = -1 + else: + data.loc[idx, 'score'] = int(check_ans(pred, ans)) + + rejected = [x for x in data['score'] if x == -1] + + print( + f'Among {len(data)} questions, failed to obtain prediction for {len(data) - len(data_un)} questions, ' + f'failed to obtain the score for another {len(rejected)} questions. ' + f'Those questions will be counted as -1 score in ALL rating, and will not be counted in VALID rating.' + ) + + dump(data, score_file) + + rating = get_dimension_rating(score_file) + dump(rating, tgt_file) + return rating + + +class MVBench_MP4(VideoBaseDataset): + + MP4_MD5 = '7b4608045347904c28c153015a7a2b6b' + SYS = """Carefully watch the video and pay attention to the cause and sequence of events, \ +the detail and movement of objects, and the action and pose of persons. \ +Based on your observations, select the best option that accurately addresses the question. +""" + TYPE = 'MCQ' + + def __init__(self, dataset='MVBench_MP4', pack=False): + super().__init__(dataset=dataset, pack=pack) + + @classmethod + def supported_datasets(cls): + return ['MVBench_MP4'] + + def prepare_dataset(self, dataset_name='MVBench_MP4', repo_id='OpenGVLab/MVBench'): + def check_integrity(pth): + data_file = osp.join(pth, f'{dataset_name}.tsv') + + if not os.path.exists(data_file): + return False + + if md5(data_file) != self.MP4_MD5: + return False + + data = load(data_file) + for idx, item in data.iterrows(): + if not osp.exists(osp.join(pth, item['prefix'], item['video'])): + return False + return True + + cache_path = get_cache_path(repo_id, branch='video') + if cache_path is not None and check_integrity(cache_path): + dataset_path = cache_path + else: + def generate_tsv(pth): + data_file = osp.join(pth, f'{dataset_name}.tsv') + if os.path.exists(data_file) and md5(data_file) == self.MD5: + return + json_data_path = os.path.join(dataset_path, 'test.json') + json_data = load(json_data_path) + root_data_dict = json_data['root'] + self.data_list = [] + for k, v in json_data['meta'].items(): + for item in v: + self.data_list.append({ + 'task_type': k, + 'prefix': root_data_dict[k], + 'video': item['video'], + 'question': item['question'], + 'answer': item['answer'], + 'candidates': item['candidates'] + }) + data_df = pd.DataFrame(self.data_list) + data_df = data_df.assign(index=range(len(data_df))) + data_df.to_csv(data_file, sep='\t', index=False) + + hf_token = os.environ.get('HUGGINGFACE_TOKEN') + huggingface_hub.login(hf_token) + dataset_path = snapshot_download(repo_id=repo_id, repo_type='dataset', revision='video') + generate_tsv(dataset_path) + + data_file = osp.join(dataset_path, f'{dataset_name}.tsv') + + self.nframe = 8 + self.resolution = 224 + + # transform + crop_size = self.resolution + scale_size = self.resolution + input_mean = [0.48145466, 0.4578275, 0.40821073] + input_std = [0.26862954, 0.26130258, 0.27577711] + self.transform = T.Compose([ + GroupScale(int(scale_size), interpolation=InterpolationMode.BICUBIC), + GroupCenterCrop(crop_size), + Stack(), + ToTorchFormatTensor(), + GroupNormalize(input_mean, input_std) + ]) + + return dict(root=dataset_path, data_file=data_file) + + def qa_template(self, data): + question = f"Question: {data['question']}\n" + question += 'Options:\n' + answer = data['answer'] + answer_idx = -1 + for idx, c in enumerate(eval(data['candidates'])): + question += f"({chr(ord('A') + idx)}) {c}\n" + if c == answer: + answer_idx = idx + question = question.rstrip() + answer = f"({chr(ord('A') + answer_idx)}) {answer}" + return question, answer + + def get_index(self, max_frame): + seg_size = float(max_frame) / self.num_segments + frame_indices = np.array([ + int((seg_size / 2) + np.round(seg_size * idx)) + for idx in range(self.num_segments) + ]) + return frame_indices + + def read_video(self, video_path, bound=None): + vr = VideoReader(video_path, ctx=cpu(0), num_threads=1) + max_frame = len(vr) - 1 + + images_group = list() + frame_indices = self.get_index(max_frame) + for frame_index in frame_indices: + img = Image.fromarray(vr[frame_index].asnumpy()) + images_group.append(img) + torch_imgs = self.transform(images_group) + return torch_imgs + + def save_video_frames(self, imgs, video_name, frames): + + frame_paths = self.frame_paths(video_name, frames) + flag = np.all([osp.exists(p) for p in frame_paths]) + + if not flag: + block_size = imgs.size(0) // frames + split_tensors = torch.split(imgs, block_size) + to_pil = transforms.ToPILImage() + images = [to_pil(arr) for arr in split_tensors] + for im, pth in zip(images, frame_paths): + if not osp.exists(pth): + im.save(pth) + + return frame_paths + + def build_prompt(self, line, num_frames, video_llm): + if isinstance(line, int): + assert line < len(self) + line = self.data.iloc[line] + + question, answer = self.qa_template(line) + message = [dict(type='text', value=self.SYS)] + message.append(dict(type='text', value=question)) + video_path = os.path.join(self.data_root, line['prefix'], line['video']) + if video_llm: + message.append(dict(type='video', value=video_path)) + else: + video_path = os.path.join(self.data_root, line['prefix'], line['video']) + self.num_segments = num_frames if num_frames > 0 else self.nframe + torch_imgs = self.read_video(video_path) + img_frame_paths = self.save_video_frames(torch_imgs, line['video'], self.num_segments) + for im in img_frame_paths: + message.append(dict(type='image', value=im)) + message.append(dict(type='text', value='\nOnly give the best option.')) + message.append(dict(type='text', value='Best option:(')) + return message + + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + + assert eval_file.endswith('.xlsx'), 'data file should be an xlsx file' + + tmp_file = eval_file.replace('.xlsx', '_tmp.pkl') + tgt_file = eval_file.replace('.xlsx', '_rating.json') + score_file = eval_file.replace('.xlsx', '_score.xlsx') + + if not osp.exists(score_file): + res = {} if not osp.exists(tmp_file) else load(tmp_file) + res = {k: v for k, v in res.items() if FAIL_MSG not in v} + + data = load(eval_file) + data_un = data[~pd.isna(data['prediction'])] + + for idx in data['index']: + ans = data.loc[data['index'] == idx, 'answer'].values[0] + pred = data.loc[data['index'] == idx, 'prediction'].values[0] + options = eval(data.loc[data['index'] == idx, 'candidates'].values[0]) + answer_idx = -1 + for id, c in enumerate(options): + if c == ans: + answer_idx = id + ans = f"({chr(ord('A') + answer_idx)}) {ans}" + + if FAIL_MSG in pred: + data.loc[idx, 'score'] = -1 + else: + data.loc[idx, 'score'] = int(check_ans(pred, ans)) + + rejected = [x for x in data['score'] if x == -1] + + print( + f'Among {len(data)} questions, failed to obtain prediction for {len(data) - len(data_un)} questions, ' + f'failed to obtain the score for another {len(rejected)} questions. ' + f'Those questions will be counted as -1 score in ALL rating, and will not be counted in VALID rating.' + ) + + dump(data, score_file) + + rating = get_dimension_rating(score_file) + dump(rating, tgt_file) + return rating diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/slidevqa.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/slidevqa.py new file mode 100644 index 0000000000000000000000000000000000000000..ae7104d43f803a58eb6d6576cb50a0094949c117 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/slidevqa.py @@ -0,0 +1,189 @@ +import re +import math +from typing import List + +from vlmeval.dataset.utils.judge_util import build_judge +from vlmeval.smp import * +from .image_base import ImageBaseDataset +from .mmlongbench import concat_images, MMLongBench_auxeval, anls_compute + + +FAIL_MSG = 'Failed to obtain answer via API.' + + +def get_f1(gt, pred): + gt_bow, pred_bow = gt.strip().split(), pred.strip().split() + if not gt_bow or not pred_bow: + return 0.0 + + recall = len([pred_e for pred_e in pred_bow if pred_e in gt_bow]) / len(gt_bow) + precision = len([pred_e for pred_e in pred_bow if pred_e in gt_bow]) / len(pred_bow) + f1 = 2 * recall * precision / (recall + precision) if (recall + precision) > 1e-4 else 0.0 + return f1 + + +def SlideVQA_acc(result_file): + data = load(result_file) + anls_list, em_list, f1_list = list(), list(), list() + for i in range(len(data)): + item = data.iloc[i] + if isinstance(item['answer'], float) and math.isnan(item['answer']): + item['answer'] = 'Not answerable' + + item['answer'] = re.sub('\n', '', item['answer']).lower() + item['pred'] = str(item['pred']).lower() + anls_score = anls_compute(item['answer'], item['pred']) + em_score = (item['answer'].strip() == item['pred'].strip()) + f1_score = get_f1(item['answer'], item['pred']) + anls_list.append(anls_score) + em_list.append(em_score) + f1_list.append(f1_score) + print('---------------------') + print(item['answer'], item['pred'], anls_score, em_score, f1_score) + + data['anls'] = anls_list + data['em'] = em_list + data['f1'] = f1_list + dump(data, result_file) + + res = dict() + res['category'], res['num'] = ['anls', 'EM', 'F1'], [len(data), len(data), len(data)] + res['avg'] = [sum(anls_list) / len(data), sum(em_list) / len(data), sum(f1_list) / len(data)] + res = pd.DataFrame(res) + return res + + +class SlideVQA(ImageBaseDataset): + + TYPE = 'VQA' + + DATASET_URL = { + 'SLIDEVQA_MINI': 'https://opencompass.openxlab.space/utils/VLMEval/SLIDEVQA_MINI.tsv', + 'SLIDEVQA': 'https://opencompass.openxlab.space/utils/VLMEval/SLIDEVQA.tsv', + } + DATASET_MD5 = { + 'SLIDEVQA_MINI': '6d9a8d8814fa5b7669deb2af3a3208eb', + 'SLIDEVQA': '5e822c2f800e94c1e23badfd478326b6', + } + + SUPPORTED_MODELS = { + 'GPT4': (1, 1), + 'GPT4V': (1, 1), + 'GPT4V_HIGH': (1, 1), + 'GPT4o': (1, 1), + 'GPT4o_HIGH': (1, 1), + 'GPT4o_MINI': (1, 1), + 'XComposer2d5': (1, -1), + 'XComposer2_4KHD': (1, -1), + 'MiniCPM-Llama3-V-2_5': (1, 5), + 'InternVL-Chat-V1-5': (5, 2), + } + + def __init__(self, dataset, **kwargs): + self.model_list = list(self.SUPPORTED_MODELS.keys()) + model_name = kwargs['model'] + if not listinstr(self.model_list, model_name): + raise AssertionError("{} doesn't support the evaluation on SlideVQA.".format(model_name)) + super(SlideVQA, self).__init__(dataset) + + self.is_api = True if listinstr(['GPT4'], model_name) else False + self.max_pages = 120 + concat_num, column_num = self.SUPPORTED_MODELS.get(model_name) + self.concat_num = concat_num + self.column_num = column_num + + def dump_image(self, origin_line): + os.makedirs(self.img_root, exist_ok=True) + + line = origin_line.copy() + if not isinstance(line['image_path'], List): + line['image_path'] = [line['image_path']] + line['image_path'] = line['image_path'][:self.max_pages] + + if 'image' in line: + if isinstance(line['image'], list): + tgt_path = [] + assert 'image_path' in line + for img, im_name in zip(line['image'], line['image_path']): + path = osp.join(self.img_root, im_name) + if not read_ok(path): + decode_base64_to_image_file(img, path) + tgt_path.append(path) + else: + tgt_path = osp.join(self.img_root, f"{line['index']}.jpg") + if not read_ok(tgt_path): + decode_base64_to_image_file(line['image'], tgt_path) + tgt_path = [tgt_path] + else: + assert 'image_path' in line + tgt_path = toliststr(line['image_path']) + + if self.concat_num > 0 and not self.is_api: + concatenated_images = concat_images(tgt_path, max_concat=self.concat_num, column_num=self.column_num) + + old_tgt_path = tgt_path + assert isinstance(old_tgt_path, list) + if self.column_num != -1: + tgt_path = [ + '_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat{}_{}.jpg'.format(self.concat_num, i) + for i in range(len(concatenated_images)) + ] + else: + tgt_path = ['_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat_all.jpg'] + + for path, concatenated_image in zip(tgt_path, concatenated_images): + if not read_ok(path): + decode_base64_to_image_file(encode_image_to_base64(concatenated_image), path) + num_images, image_size = len(old_tgt_path), concatenated_image.size + print('concat {} images to a new one with size {}. save at {}'.format(num_images, image_size, path)) + return tgt_path + + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + logger = get_logger('Evaluation') + model = judge_kwargs['model'] + + suffix = eval_file.split('.')[-1] + storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx') + tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl') + + if osp.exists(storage): + logger.warning(f'GPT scoring file {storage} already exists, will reuse it in SlideVQA_eval. ') + else: + data = load(eval_file) + model = build_judge(max_tokens=128, **judge_kwargs) + lt = len(data) + lines = [data.iloc[i] for i in range(lt)] + tups = [(model, line) for line in lines] + indices = [line['index'] for line in lines] + + ans = {} + if osp.exists(tmp_file): + ans = load(tmp_file) + tups = [x for x, i in zip(tups, indices) if i not in ans] + indices = [i for i in indices if i not in ans] + + if len(indices): + new_results = list() + for model, line in tqdm(tups): + res = MMLongBench_auxeval(model, line) + new_results.append(res) + + log_map, res_map, pred_map = {}, {}, {} + all_inds = [line['index'] for line in lines] + for k, v in zip(all_inds, new_results): + log_map[k] = v['log'] + res_map[k] = v['res'] + pred_map[k] = v['pred'] + data['res'] = [res_map[idx] for idx in data['index']] + data['log'] = [log_map[idx] for idx in data['index']] + data['pred'] = [pred_map[idx] for idx in data['index']] + dump(data, storage) + + score = SlideVQA_acc(storage) + score_pth = storage.replace('.xlsx', '_score.csv') + + dump(score, score_pth) + logger.info(f'SlideVQA successfully finished evaluating {eval_file}, results saved in {score_pth}') + logger.info('Score: ') + logger.info(score) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/text_base.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/text_base.py new file mode 100644 index 0000000000000000000000000000000000000000..67bb9378915f4ca08ddec25ecfa02f27dca06d86 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/text_base.py @@ -0,0 +1,88 @@ +from abc import abstractmethod +from ..smp import * + + +class TextBaseDataset: + MODALITY = 'TEXT' + DATASET_URL = {} + DATASET_MD5 = {} + + def __init__(self, dataset='MMBench', **kwargs): + self.dataset_name = dataset + + data = self.load_data(dataset) + + data['index'] = [str(x) for x in data['index']] + + if np.all([istype(x, int) for x in data['index']]): + data['index'] = [int(x) for x in data['index']] + + self.data = data + self.post_build(dataset) + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return dict(self.data.iloc[idx]) + + def prepare_tsv(self, url, file_md5=None): + data_root = LMUDataRoot() + os.makedirs(data_root, exist_ok=True) + update_flag = False + file_name = url.split('/')[-1] + data_path = osp.join(data_root, file_name) + if osp.exists(data_path) and (file_md5 is None or md5(data_path) == file_md5): + pass + else: + warnings.warn('The dataset tsv is not downloaded') + download_file(url, data_path) + update_flag = True + + if file_size(data_path, 'GB') > 1: + local_path = data_path.replace('.tsv', '_local.tsv') + if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None) or update_flag: + from ..tools import LOCALIZE + LOCALIZE(data_path, local_path) + data_path = local_path + return load(data_path) + + def dump_image(self, line): + return [] + + def display(self, line): + if isinstance(line, int): + line = self.data.iloc[line] + assert isinstance(line, pd.Series) or isinstance(line, dict) + mmqa_display(line) + + # Return a list of dataset names that are supported by this class, can override + @classmethod + def supported_datasets(cls): + return list(cls.DATASET_URL) + + # Given the dataset name, return the dataset as a pandas dataframe, can override + def load_data(self, dataset): + url = self.DATASET_URL[dataset] + file_md5 = self.DATASET_MD5[dataset] + return self.prepare_tsv(url, file_md5) + + # Post built hook, will be called after the dataset is built, can override + def post_build(self, dataset): + pass + + # Given one data record, return the built prompt (a multi-modal message), can override + def build_prompt(self, line): + if isinstance(line, int): + line = self.data.iloc[line] + + question = line['question'] + + msgs = [] + msgs.append(dict(type='text', value=question)) + return msgs + + # Given the prediction file, return the evaluation results in the format of a dictionary or pandas dataframe + @abstractmethod + def evaluate(self, eval_file, **judge_kwargs): + pass diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/text_mcq.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/text_mcq.py new file mode 100644 index 0000000000000000000000000000000000000000..9db53893d69b5c58ed4026e5831b14db0234a6dd --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/text_mcq.py @@ -0,0 +1,123 @@ +from .text_base import TextBaseDataset +from .utils import build_judge, DEBUG_MESSAGE +from ..smp import * + + +class TextMCQDataset(TextBaseDataset): + TYPE = 'MCQ' + + DATASET_URL = {} + + DATASET_MD5 = {} + + def build_prompt(self, line): + + if isinstance(line, int): + line = self.data.iloc[line] + + question = line['question'] + options = { + cand: line[cand] + for cand in string.ascii_uppercase + if cand in line and not pd.isna(line[cand]) + } + options_prompt = 'Options:\n' + for key, item in options.items(): + options_prompt += f'{key}. {item}\n' + hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None + prompt = '' + if hint is not None: + prompt += f'Hint: {hint}\n' + prompt += f'Question: {question}\n' + if len(options): + prompt += options_prompt + prompt += 'Please select the correct answer from the options above. \n' + + msgs = [] + + msgs.append(dict(type='text', value=prompt)) + + return msgs + + def evaluate(self, eval_file, **judge_kwargs): + from .utils.multiple_choice import report_acc, report_acc_MMT, mcq_circular_eval, mcq_vanilla_eval + # assert dataset is not None + dataset_map = { + 'MMBench_TEST_EN': 'MMBench', 'MMBench_TEST_EN_V11': 'MMBench_V11', + 'MMBench_TEST_CN': 'MMBench_CN', 'MMBench_TEST_CN_V11': 'MMBench_CN_V11' + } + dataset = self.dataset_name + if dataset in dataset_map: + dataset = dataset_map[dataset] + nproc = judge_kwargs.pop('nproc', 4) + + circular = False + + suffix = eval_file.split('.')[-1] + model = judge_kwargs.get('model', 'exact_matching') + assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125'] + name_str_map = {'chatgpt-0125': 'openai', 'gpt-4-0125': 'gpt4'} + name_str = name_str_map[model] if model in name_str_map else model + + if model == 'exact_matching': + model = None + elif gpt_key_set(): + model = build_judge(**judge_kwargs) + if not model.working(): + warnings.warn('OPENAI API is not working properly, will use exact matching for evaluation') + warnings.warn(DEBUG_MESSAGE) + model = None + else: + warnings.warn('OPENAI_API_KEY is not set properly, will use exact matching for evaluation') + model = None + + result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_result.pkl') + + data = load(eval_file) + data = data.sort_values(by='index') + data['prediction'] = [str(x) for x in data['prediction']] + # If not choice label, then use lower case + for k in data.keys(): + data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k) + + meta = self.data + meta_q_map = {x: y for x, y in zip(meta['index'], meta['question'])} + data_map = {x: y for x, y in zip(data['index'], data['question'])} + for k in data_map: + assert k in meta_q_map, ( + f'eval_file should be the same as or a subset of dataset {self.dataset_name}' + ) + + if circular: + data = mcq_circular_eval(model, data, meta, nproc, result_file, self.dataset_name) + else: + data = mcq_vanilla_eval(model, data, meta, nproc, result_file, self.dataset_name) + + # load split + dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) + data = load(eval_file.replace(f'.{suffix}', f'_{name_str}_result.{suffix}')) + + # May have different report acc functions for different datasets + if 'MMT' in dataset: + acc = report_acc_MMT(data) + else: + acc = report_acc(data) + + score_file = eval_file.replace(f'.{suffix}', '_acc.csv') + dump(acc, score_file) + + return acc + + +class CustomTextMCQDataset(TextMCQDataset): + + def load_data(self, dataset): + data_path = osp.join(LMUDataRoot(), f'{dataset}.tsv') + + if file_size(data_path, 'GB') > 1: + local_path = data_path.replace('.tsv', '_local.tsv') + if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None): + from ..tools import LOCALIZE + LOCALIZE(data_path, local_path) + data_path = local_path + return load(data_path) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/__init__.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1ade2c430e6c3a14e178ce33ac3a17f9563c9645 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/__init__.py @@ -0,0 +1,9 @@ +from .judge_util import build_judge, DEBUG_MESSAGE +from .multiple_choice import extract_answer_from_item, prefetch_answer +from .vqa_eval import levenshtein_distance + + +__all__ = [ + 'build_judge', 'extract_answer_from_item', 'prefetch_answer', + 'levenshtein_distance', 'DEBUG_MESSAGE' +] diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/judge_util.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/judge_util.py new file mode 100644 index 0000000000000000000000000000000000000000..bc3e9acda9ae7464ad971849fe5307a0429dcf3c --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/judge_util.py @@ -0,0 +1,41 @@ +import os +from ...api import OpenAIWrapper +from ...smp import load_env + +INTERNAL = os.environ.get('INTERNAL', 0) + + +def build_judge(**kwargs): + model = kwargs.pop('model', None) + kwargs.pop('nproc', None) + load_env() + LOCAL_LLM = os.environ.get('LOCAL_LLM', None) + if LOCAL_LLM is None: + model_map = { + 'gpt-4-turbo': 'gpt-4-1106-preview', + 'gpt-4-0613': 'gpt-4-0613', + 'gpt-4-0125': 'gpt-4-0125-preview', + 'gpt-4-0409': 'gpt-4-turbo-2024-04-09', + 'chatgpt-1106': 'gpt-3.5-turbo-1106', + 'chatgpt-0125': 'gpt-3.5-turbo-0125', + 'gpt-4o': 'gpt-4o-2024-05-13', + 'gpt-4o-mini': 'gpt-4o-mini-2024-07-18', + } + model_version = model_map[model] + else: + model_version = LOCAL_LLM + model = OpenAIWrapper(model_version, **kwargs) + return model + + +DEBUG_MESSAGE = """ +To debug the OpenAI API, you can try the following scripts in python: +```python +from vlmeval.api import OpenAIWrapper +model = OpenAIWrapper('gpt-4-1106-preview', verbose=True) +msgs = [dict(type='text', value='Hello!')] +code, answer, resp = model.generate_inner(msgs) +print(code, answer, resp) +``` +You cam see the specific error if the API call fails. +""" diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/llavabench.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/llavabench.py new file mode 100644 index 0000000000000000000000000000000000000000..4e703610084d44e1a70d887c051620fb99ecad2b --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/llavabench.py @@ -0,0 +1,65 @@ +import numpy as np +import pandas as pd +from ...smp import * + +rule_dict = { + 'llava_bench_conv': {'role': 'Assistant', 'prompt': 'We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.'}, # noqa: E501 + 'llava_bench_detail': {'role': 'Assistant', 'prompt': 'We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.'}, # noqa: E501 + 'llava_bench_complex': {'role': 'Assistant', 'prompt': 'We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.'} # noqa: E501 +} + + +def get_eval(judge, content): + return judge.generate(content) + + +def parse_score(review): + logger = get_logger('Evaluation') + try: + score_pair = review.split('\n')[0] + score_pair = score_pair.replace(',', ' ') + sp = score_pair.split(' ') + if len(sp) == 2: + return [float(sp[0]), float(sp[1])] + else: + logger.error('error', review) + return [-1, -1] + except Exception as e: + logger.error(e, 'error', review) + return [-1, -1] + + +def build_prompt(line): + cap_str = line['caption'] + question = line['question'] + ans1 = line['gpt4_ans'] + ans2 = line['prediction'] + category = 'llava_bench_' + line['category'] + rule = rule_dict[category] + role, prompt = rule['role'], rule['prompt'] + + content = (f'[Context]\n{cap_str}\n\n' + f'[Question]\n{question}\n\n' + f'[{role} 1]\n{ans1}\n\n[End of {role} 1]\n\n' + f'[{role} 2]\n{ans2}\n\n[End of {role} 2]\n\n' + f'[System]\n{prompt}\n\n') + return content + + +def LLaVABench_atomeval(model, prompt): + review = get_eval(model, prompt) + scores = parse_score(review) + return scores + + +def LLaVABench_score(data): + cates = ['overall'] + list(set(data['category'])) + ret = defaultdict(list) + + for c in cates: + ret['split'].append(c) + sub = data[data['category'] == c] if c != 'overall' else data + ret['Relative Score (main)'].append(np.mean(sub['score']) / np.mean(sub['gpt4_score']) * 100) + ret['VLM Score'].append(np.mean(sub['score']) * 10) + ret['GPT4 Score'].append(np.mean(sub['gpt4_score']) * 10) + return pd.DataFrame(ret) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mathv.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mathv.py new file mode 100644 index 0000000000000000000000000000000000000000..dd1a69b24c36ca6b55f16ba8d354d6900b7b1e82 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mathv.py @@ -0,0 +1,170 @@ +from ...smp import * +from ...utils import can_infer +try: + from latex2sympy2 import latex2sympy +except ImportError: + print('Please install latex2sympy2 by running "pip install latex2sympy2"') + +FAIL_MSG = 'Failed to obtain answer via API.' + + +def is_equal(asw: str, gt_asw: str) -> bool: + if not isinstance(asw, str) != str or not isinstance(gt_asw, str): + print('Warning: input is not string') + print(asw, gt_asw) + asw = str(asw).lower().strip() + gt_asw = str(gt_asw).lower().strip() + if gt_asw == asw: + return True + try: + a = eval(gt_asw) + b = eval(asw) + if abs(a - b) < 1e-6: + return True + except: + pass + try: + a = latex2sympy(gt_asw) + b = latex2sympy(asw) + if abs(eval(str(a)) - eval(str(b))) < 1e-6: + return True + if abs(a - b) < 1e-6: + return True + except: + pass + return False + + +def get_gpt4_ICE(): + example_1 = """ +Hint: Please answer the question and provide the final answer at the end.\n +Question: Which number is missing?\n +Model response: The number missing in the sequence is 14.\n +Extracted answer: 14 +""" + + example_2 = """ +Hint: Please answer the question and provide the final answer at the end.\n +Question: What is the fraction of females facing the camera?\n +Model response: The fraction of females facing the camera is 0.6, +which means that six out of ten females in the group are facing the camera.\n +Extracted answer: 0.6 +""" + + example_3 = """ +Hint: Please answer the question and provide the final answer at the end.\n +Question: How much money does Luca need to buy a sour apple candy and a butter-scotch candy? (Unit: $)\n +Model response: Luca needs $1.45 to buy a sour apple candy and a butterscotch candy.\n +Extracted answer: 1.45 +""" + + example_4 = """ +Hint: Please answer the question and provide the final answer at the end.\n +Question: Between which two years does the line graph saw its maximum peak?\n +Model response: The line graph saw its maximum peak between 2007 and 2008.\n +Extracted answer: [2007, 2008] +""" + + example_5 = """ +Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end.\n +Question: What fraction of the shape is blue?\n +Choices: (A) 3/11 (B) 8/11 (C) 6/11 (D) 3/5\n +Model response: The correct answer is (B) 8/11.\n +Extracted answer: B +""" + + return [example_1, example_2, example_3, example_4, example_5] + + +def build_mathv_gpt4_prompt(line): + task_description = """ +Please read the following example. +Then extract the answer from the model response and type it at the end of the prompt.\n +""" + question = line['question'] + prediction = str(line['prediction']) + prompt = task_description + examples = get_gpt4_ICE() + for example in examples: + prompt += example + '\n' + prompt += question + '\n' + prompt += 'Model respone: ' + prediction + prompt += 'Extracted answer:' + return prompt + + +def list_to_dict(lst): + return {chr(65 + i): val for i, val in enumerate(lst)} + + +def post_check(line, prefetch=False): + res = None + ans = line['answer'] + response = line['prediction'] if prefetch else line['res'] + try: + if len(eval(line['choices'])) > 0: + ans = line['answer'] + choices = list_to_dict(eval(line['choices'])) + res = can_infer(response, choices) + if prefetch: + return res + else: + res = str(response) + ans = str(ans) + except ValueError: + pass + + if is_equal(res, ans): + return res if prefetch else True + else: + return False + + +def MATH_V_auxeval(model, line): + prompt = build_mathv_gpt4_prompt(line) + log = '' + retry = 5 + if post_check(line, prefetch=True): + res = post_check(line, prefetch=True) + return dict(log='Prefetch succeed', res=res) + for i in range(retry): + prediction = line['prediction'] + res = model.generate(prompt, temperature=i * 0.5) + + if FAIL_MSG in res: + log += f'Try {i}: output is {prediction}, failed to parse.\n' + else: + log += 'Succeed' + return dict(log=log, res=res) + log += 'All 5 retries failed.\n' + return dict(log=log, res='') + + +def MATH_V_acc(result_file): + data = load(result_file) + tot = defaultdict(lambda: 0) + fetch = defaultdict(lambda: 0) + hit = defaultdict(lambda: 0) + lt = len(data) + for i in range(lt): + item = data.iloc[i] + cate = item['category'] + tot['Overall'] += 1 + tot[cate] += 1 + if item['log'] == 'Prefetch succeed': + fetch['Overall'] += 1 + fetch[cate] += 1 + if post_check(item, prefetch=False): + hit['Overall'] += 1 + hit[cate] += 1 + + res = defaultdict(list) + for k in tot.keys(): + res['Subject'].append(k) + res['tot'].append(tot[k]) + res['prefetch'].append(fetch[k]) + res['hit'].append(hit[k]) + res['prefetch_rate'].append(fetch[k] / tot[k] * 100) + res['acc'].append(hit[k] / tot[k] * 100) + res = pd.DataFrame(res).sort_values('Subject', ignore_index=True) + return res diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mathvista.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mathvista.py new file mode 100644 index 0000000000000000000000000000000000000000..52e138508262abd1b93bfa544120d37881d7ff5b --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mathvista.py @@ -0,0 +1,164 @@ +from ...smp import * +from ...utils import can_infer + + +FAIL_MSG = 'Failed to obtain answer via API.' + + +def get_gpt4_ICE(): + example_1 = """ +Hint: Please answer the question requiring an integer answer and provide the final value, +e.g., 1, 2, 3, at the end.\n +Question: Which number is missing?\n +Model response: The number missing in the sequence is 14.\n +Extracted answer: 14 +""" + + example_2 = """ +Hint: Please answer the question requiring a floating-point number with one decimal place and provide the final value, +e.g., 1.2, 1.3, 1.4, at the end.\n +Question: What is the fraction of females facing the camera?\n +Model response: The fraction of females facing the camera is 0.6, +which means that six out of ten females in the group are facing the camera.\n +Extracted answer: 0.6 +""" + + example_3 = """ +Hint: Please answer the question requiring a floating-point number with two decimal places and provide the final value, +e.g., 1.23, 1.34, 1.45, at the end.\n +Question: How much money does Luca need to buy a sour apple candy and a butter-scotch candy? (Unit: $)\n +Model response: Luca needs $1.45 to buy a sour apple candy and a butterscotch candy.\n +Extracted answer: 1.45 +""" + + example_4 = """ +Hint: Please answer the question requiring a Python list as an answer and provide the final list, +e.g., [1, 2, 3], [1.2, 1.3, 1.4], at the end.\n +Question: Between which two years does the line graph saw its maximum peak?\n +Model response: The line graph saw its maximum peak between 2007 and 2008.\n +Extracted answer: [2007, 2008] +""" + + example_5 = """ +Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end.\n +Question: What fraction of the shape is blue?\n +Choices: (A) 3/11 (B) 8/11 (C) 6/11 (D) 3/5\n +Model response: The correct answer is (B) 8/11.\n +Extracted answer: B +""" + + return [example_1, example_2, example_3, example_4, example_5] + + +def build_mathvista_gpt4_prompt(line): + task_description = """ +Please read the following example. +Then extract the answer from the model response and type it at the end of the prompt.\n +""" + question = line['question'] + prediction = str(line['prediction']) + prompt = task_description + examples = get_gpt4_ICE() + for example in examples: + prompt += example + '\n' + prompt += question + '\n' + prompt += 'Model respone: ' + prediction + prompt += 'Extracted answer:' + return prompt + + +def list_to_dict(lst): + return {chr(65 + i): val for i, val in enumerate(lst)} + + +def post_check(line, prefetch=False): + res = None + ans = line['answer'] + response = line['prediction'] if prefetch else line['res'] + try: + if line['question_type'] == 'multi_choice': + ans = line['answer_option'] + choices = list_to_dict(eval(line['choices'])) + res = can_infer(response, choices) + if prefetch: + return res + else: + if line['answer_type'] == 'integer': + res = int(response) + ans = int(line['answer']) + elif line['answer_type'] == 'float': + res = float(response) + ans = float(line['answer']) + else: + res = str(res) + ans = str(ans) + except ValueError: + pass + + if res == ans: + return res if prefetch else True + else: + return False + + +def MathVista_auxeval(model, line): + prompt = build_mathvista_gpt4_prompt(line) + log = '' + retry = 5 + if post_check(line, prefetch=True): + res = post_check(line, prefetch=True) + return dict(log='Prefetch succeed', res=res) + for i in range(retry): + prediction = line['prediction'] + res = model.generate(prompt, temperature=i * 0.5) + + if FAIL_MSG in res: + log += f'Try {i}: output is {prediction}, failed to parse.\n' + else: + log += 'Succeed' + return dict(log=log, res=res) + log += 'All 5 retries failed.\n' + return dict(log=log, res='') + + +def MathVista_acc(result_file): + data = load(result_file) + tot = defaultdict(lambda: 0) + fetch = defaultdict(lambda: 0) + hit = defaultdict(lambda: 0) + lt = len(data) + skill_list = [] + for i in range(lt): + item = data.iloc[i] + cate = item['task'] + tot['Overall'] += 1 + try: + skills = eval(item['skills']) + except SyntaxError: + skills = [item['skills']] + for skill in skills: + if skill not in skill_list: + skill_list.append(skill) + tot[skill] += 1 + tot[cate] += 1 + if item['log'] == 'Prefetch succeed': + fetch['Overall'] += 1 + fetch[cate] += 1 + for skill in skills: + fetch[skill] += 1 + if post_check(item, prefetch=False): + hit['Overall'] += 1 + hit[cate] += 1 + for skill in skills: + hit[skill] += 1 + + res = defaultdict(list) + for k in tot.keys(): + res['Task&Skill'].append(k) + res['tot'].append(tot[k]) + res['prefetch'].append(fetch[k]) + res['hit'].append(hit[k]) + res['prefetch_rate'].append(fetch[k] / tot[k] * 100) + res['acc'].append(hit[k] / tot[k] * 100) + res = pd.DataFrame(res) + return res diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mmbench_video.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mmbench_video.py new file mode 100644 index 0000000000000000000000000000000000000000..75514b09bfd3b459b872059f6f0a7373b0b9c21f --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mmbench_video.py @@ -0,0 +1,70 @@ +from ...smp import * +import numpy as np + +FAIL_MSG = 'Failed to obtain answer via API.' + +system_prompt = """ +As an AI assistant, your task is to evaluate a candidate answer in comparison to a given correct answer. +The question itself, the correct 'groundtruth' answer, and the candidate answer will be provided to you. +Your assessment should range from 0 to 3, \ +based solely on the semantic similarity between the groundtruth and the candidate answer, \ +disregarding any grammatical differences. +A rating of 0 suggests no similarity, implying the candidate answer is entirely incorrect. +A rating of 1 suggests low similarity, meaning the candidate answer is largely incorrect. +A rating of 2 suggests high similarity, meaning the candidate answer is largely correct. +Lastly, a rating of 3 indicates complete similarity, which means the candidate answer is entirely correct. +Your response should be a single integer from 0, 1, 2, or 3. +""" + +MMV_DIMENSIONS = { + 'CP': ['Video Topic', 'Video Emotion', 'Video Scene', 'Video Style'], + 'FP-S': ['OCR', 'Object Recognition', 'Attribute Recognition', 'Event Recognition', 'Human Motion', 'Counting'], + 'FP-C': ['Spatial Relationship', 'Human-object Interaction', 'Human Interaction'], + 'HL': ['Hallucination'], + 'LR': ['Structuralized Image-Text Understanding', 'Mathematical Calculation'], + 'AR': ['Physical Property', 'Function Reasoning', 'Identity Reasoning'], + 'RR': ['Natural Relation', 'Physical Relation', 'Social Relation'], + 'CSR': ['Common Sense Reasoning'], + 'TR': ['Counterfactual Reasoning', 'Causal Reasoning', 'Future Prediction'], +} +L3_DIMS = [] +for k, v in MMV_DIMENSIONS.items(): + L3_DIMS.extend(v) + +MMV_DIMENSIONS['Perception'] = [] +MMV_DIMENSIONS['Reasoning'] = [] +MMV_DIMENSIONS['Overall'] = [] +for k in ['CP', 'FP-C', 'FP-S', 'HL']: + MMV_DIMENSIONS['Perception'].extend(MMV_DIMENSIONS[k]) + MMV_DIMENSIONS['Overall'].extend(MMV_DIMENSIONS[k]) +for k in ['LR', 'AR', 'RR', 'CSR', 'TR']: + MMV_DIMENSIONS['Reasoning'].extend(MMV_DIMENSIONS[k]) + MMV_DIMENSIONS['Overall'].extend(MMV_DIMENSIONS[k]) + + +def get_dimension_rating(data_path): + data = load(data_path) + coarse_rating = {k: [] for k in MMV_DIMENSIONS} + fine_rating = {k: [] for k in L3_DIMS} + + for i in range(len(data)): + cate = data.iloc[i]['dimensions'] + cates = eval(cate) + + for c in cates: + fine_rating[c].append(data.iloc[i]['score']) + + for d in MMV_DIMENSIONS: + if np.any([x in MMV_DIMENSIONS[d] for x in cates]): + coarse_rating[d].append(data.iloc[i]['score']) + + coarse_all = {k: f'{np.mean([max(x, 0) for x in v]):.2f}' for k, v in coarse_rating.items()} + coarse_valid = {k: f'{np.mean([x for x in v if x >= 0]):.2f}' for k, v in coarse_rating.items()} + fine_all = {k: f'{np.mean([max(x, 0) for x in v]):.2f}' for k, v in fine_rating.items()} + fine_valid = {k: f'{np.mean([x for x in v if x >= 0]):.2f}' for k, v in fine_rating.items()} + return dict(coarse_all=coarse_all, coarse_valid=coarse_valid, fine_all=fine_all, fine_valid=fine_valid) + + +def build_prompt(item): + tmpl = 'Question: {}\nGroundtruth answer: {}\nCandidate answer: {}\nYour response: ' + return tmpl.format(item['question'], item['answer'], item['prediction']) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mmdu.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mmdu.py new file mode 100644 index 0000000000000000000000000000000000000000..582b2e0e275e34750a3751b0729f7821c97f8690 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mmdu.py @@ -0,0 +1,126 @@ +from ...smp import * + +meta_prompt = """ +You are an assistant skilled at evaluating the quality of creative text. +Please act as an impartial judge and evaluate the quality of the response provided by an AI assistant to \ +the user question displayed below. You'll need to assess the response on the following dimensions: \ +Creativity, Richness, Visual Perception, Logical Coherence, Answer Accuracy and Image Relationship Understanding. \ +We will provide you with a creative question and the AI model's response and a reference answer for your evaluation. \ +As you begin your assessment, follow this process: +1. Evaluate the AI model's answers on different dimensions, pointing out its strengths or weaknesses \ +in each dimension and assigning a score of 1 to 10 for each. +2. Finally, based on the assessments across dimensions, \ +provide an overall score of 1 to 10 for the AI model's response. +3. Your scoring should be as stringent as possible and follow the scoring rules below: +In general, the higher the quality of the model's response and its strict adherence to user needs, \ +the higher the score. Responses that do not meet user needs will receive lower scores. +Scoring rules: +Creativity: +Scores 1-2 when there is no innovation or uniqueness in the content. +Scores 3-4 when providing partially original content but with low creative quality. +Scores 5-6 when mostly creative but lacks significant novelty, with moderate quality. +Scores 7-8 when having novelty and high-quality content. +Scores 9-10 when highly novel and of exceptional quality compared to the reference answer. +Richness: +Scores 1-2 when lacking depth and breadth, with very limited information. +Scores 3-4 when limited in depth and breadth, with fewer explanations and examples, showing low diversity. +Scores 5-6 when limited in depth and breadth but provides basic necessary information. +Scores 7-8 when providing depth and useful additional information. +Scores 9-10 when providing exceptional depth, breadth, and high diversity compared to the reference answer. +Visual Perception: +Scores 1-2 when the description of the visual information in the image contains errors or \ +is significantly inconsistent with the content of the image. +Scores 3-4 When the description of the visual information in the image reflects only a small amount \ +of the image's information and contains some errors. +Scores 5-6 when the description of the visual information in the image includes the basic information \ +of the image but contains minimal information. +Scores 7-8 when the description of the visual information in the image matches the image well and is rich in content, \ +providing a substantial amount of information about the image. +Scores 9-10 when the description of the visual information in the image not only matches the image \ +but also is more detailed and informative compared to the reference answer, providing more information about the image. +Logical Coherence: +Scores 1-2 when entirely incoherent, lacking any logic, and not matching the question or known information. +Scores 3-4 when somewhat coherent but with many logical errors or inconsistencies. +Scores 5-6 when mostly coherent, with few errors, but may struggle to maintain complete coherence in complex situations. +Scores 7-8 when excellent logical handling, very few errors. +Scores 9-10 when flawless logic, impeccable in handling complexity, \ +and significantly higher logical coherence compared to the reference answer. +Answer Accuracy: +Scores 1-2 when the answer is significantly inconsistent with the question or contains obvious errors. +Scores 3-4 when the answer is partially correct but contains some errors or is incomplete. +Scores 5-6 when the answer is basically correct but lacks details or is not sufficiently detailed. +Scores 7-8 when the answer is accurate and detailed, fully corresponding to the question. +Scores 9-10 when the answer is not only accurate and detailed but also provides additional useful information, \ +exceeding expectations. +Image Relationship Understanding: +Scores 1-2 when there are significant errors or confusion in distinguishing and describing different images, \ +unable to correctly identify and relate the content of the images. +Scores 3-4 when the description of different images reflects only minimal distinguishing information, \ +contains some errors and confusion, and fails to clearly differentiate and relate the images. +Scores 5-6 when the description of different images includes basic distinguishing information, \ +is able to correctly identify and relate the images in a basic manner, \ +but the information provided is minimal and lacks detail. +Scores 7-8 when the description of different images is accurate and detailed, \ +clearly distinguishing and relating the images, \ +with rich content that points out the main commonalities and differences between the images. +Scores 9-10 when the description of different images is not only accurate and detailed but also \ +provides richer information and analysis, clearly distinguishing and relating the images, \ +more comprehensively pointing out the commonalities and differences \ +between the images compared to the reference answer. +Overall Score: +Scores 1-2 when irrelevant to the question, factually incorrect, or generates harmful content. +Scores 3-4 when no serious errors, mostly harmless, but of low quality and does not meet requirements. +Scores 5-6 when basically meeting requirements but performing poorly in some dimensions, with moderate quality. +Scores 7-8 when performing well in all dimensions. +Scores 9-10 when fully addressing user questions and all requirements, significantly surpassing the reference answer. +Please remember, you must evaluate and explain before scoring. After your explanation for each dimension, \ +add the score for that dimension. Finally, at the end of your response, \ +in the format of the dictionary (including brackets), return all your scoring results, \ +ensuring your scores are integers: +{'Dimension One': Score, 'Dimension Two': Score, ..., 'Overall Score': Score}, \ +for example: {'Creativity': 9, 'Richness': 6, ..., 'Overall Score': 7}.\n +""" +question_begin_prompt = '[Question]' +reference_begin_prompt = '[The Start of Reference Answer]' +reference_end_prompt = '[The End of Reference Answer]' +answers_begin_prompt = '[The Start of Assistant’s Answer]' +answers_end_prompt = '[The End of Assistant’s Answer]' + + +def mmdu_score(model, line): + question = eval(line['question']) + gt = eval(line['answer']) + prediction = eval(line['prediction']) + + DIMS = [ + 'Creativity', 'Richness', 'Visual Perception', 'Logical Coherence', + 'Answer Accuracy', 'Image Relationship Understanding', 'Overall Score' + ] + + all_result_dict = [] + logs = [] + for j in range(len(question)): + try: + prompt = meta_prompt + question_begin_prompt + '\n' + question[j] + '\n\n' + \ + reference_begin_prompt + '\n' + gt[j] + '\n' + reference_end_prompt + '\n\n' + \ + answers_begin_prompt + '\n' + prediction[j] + '\n' + answers_end_prompt + response = model.generate(prompt) + start_index = response.find('{') + end_index = response.rfind('}') + 1 + dictionary_str = response[start_index: end_index] + result_dict = eval(dictionary_str) + all_result_dict.append(result_dict) + if all([x in result_dict for x in DIMS]): + logs.append('Succeed') + else: + logs.append( + f'Following Dims are not in results of turn {j}: ' + f'{",".join([x for x in DIMS if x not in result_dict])}' + ) + except Exception as e: + print({e}) + all_result_dict.append({d: None for d in DIMS}) + logs.append(str(e)) + + df = pd.DataFrame(all_result_dict) + return dict(res=df, log='\n'.join(logs)) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mmvet.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mmvet.py new file mode 100644 index 0000000000000000000000000000000000000000..e035eb0b6a29223304309e5624e8f75e328e2b5d --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mmvet.py @@ -0,0 +1,106 @@ +from ...smp import * + + +def build_mmvet_gpt4_prompt(line): + question = line['question'] + gt = str(line['answer']) + prediction = str(line['prediction']) + prompt = """ +Compare the ground truth and prediction from AI models, to give a correctness score for the prediction. + in the ground truth means it is totally right +only when all elements in the ground truth are present in the prediction, +and means it is totally right when any one element in the ground truth is present in the prediction. +The correctness score is 0.0 (totally wrong), 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or 1.0 (totally right). +Just complete the last space of the correctness score. + +Question | Ground truth | Prediction | Correctness +--- | --- | --- | --- +What is x in the equation? | -1 -5 | x = 3 | 0.0 +What is x in the equation? | -1 -5 | x = -1 | 0.5 +What is x in the equation? | -1 -5 | x = -5 | 0.5 +What is x in the equation? | -1 -5 | x = -5 or 5 | 0.5 +What is x in the equation? | -1 -5 | x = -1 or x = -5 | 1.0 +Can you explain this meme? | This meme is poking fun at the fact that the names of the countries +Iceland and Greenland are misleading. Despite its name, Iceland is known for its beautiful green landscapes, +while Greenland is mostly covered in ice and snow. The meme is saying that the person has trust issues +because the names of these countries do not accurately represent their landscapes. | +The meme talks about Iceland and Greenland. It's pointing out that despite their names, +Iceland is not very icy and Greenland isn't very green. | 0.4 +Can you explain this meme? | This meme is poking fun at the fact that the names of the countries +Iceland and Greenland are misleading. Despite its name, Iceland is known for its beautiful green landscapes, +while Greenland is mostly covered in ice and snow. The meme is saying that the person has trust issues +because the names of these countries do not accurately represent their landscapes. | +The meme is using humor to point out the misleading nature of Iceland's and Greenland's names. +Iceland, despite its name, has lush green landscapes while Greenland is mostly covered in ice and snow. +The text 'This is why I have trust issues' is a playful way to suggest +that these contradictions can lead to distrust or confusion. +The humor in this meme is derived from the unexpected contrast between the names of the countries +and their actual physical characteristics. | 1.0 +""" + gpt4_prompt = prompt + '\n' + ' | '.join( + [question, gt.replace('', ' ').replace('', ' '), prediction, '']) + return gpt4_prompt + + +def MMVet_auxeval(model, line): + def float_cvt(s): + try: + return float(s) + except ValueError: + return None + + prompt = build_mmvet_gpt4_prompt(line) + log = '' + retry = 5 + for i in range(retry): + output = model.generate(prompt, temperature=i * 0.5) + score = float_cvt(output) + if score is None: + log += f'Try {i}: output is {output}, failed to parse.\n' + elif score < 0 or score > 1: + log += f'Try {i}: output is {output}, invalid score: {score}.\n' + else: + log += 'Succeed' + return dict(log=log, score=score) + log += 'All 5 retries failed.\n' + return dict(log=log, score=0.0) + + +def MMVet_acc(result_file): + data = load(result_file) + tot = defaultdict(lambda: 0) + score = defaultdict(lambda: 0) + lt = len(data) + cate2_list = [] + for i in range(lt): + item = data.iloc[i] + cate = item['category'] + cate2 = cate.replace(',', '_') + if cate2 not in cate2_list: + cate2_list.append(cate2) + grade = float(item['score']) + cate_list = ['rec', 'ocr', 'know', 'gen', 'spat', 'math'] + for capa in cate_list: + if capa in cate: + tot[capa] += 1 + score[capa] += grade + tot['Overall'] += 1 + tot[cate2] += 1 + score['Overall'] += grade + score[cate2] += grade + + res = defaultdict(list) + res2 = defaultdict(list) + cate_list.append('Overall') + cate2_list.append('Overall') + for k in cate_list: + res['Category'].append(k) + res['tot'].append(tot[k]) + res['acc'].append(score[k] / tot[k] * 100) + for v in cate2_list: + res2['Category'].append(v) + res2['tot'].append(tot[v]) + res2['acc'].append(score[v] / tot[v] * 100) + res = pd.DataFrame(res) + res2 = pd.DataFrame(res2) + return res, res2 diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/multiple_choice.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/multiple_choice.py new file mode 100644 index 0000000000000000000000000000000000000000..bd066011b0aa6d718489c3c99ac75679af4c82b6 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/multiple_choice.py @@ -0,0 +1,442 @@ +import pandas as pd +from ...utils import can_infer, track_progress_rich +from ...smp import * +import numpy as np + +MMB_abbrs = { + 'coarse_perception': 'CP', + 'finegrained_perception (instance-level)': 'FP-S', + 'finegrained_perception (cross-instance)': 'FP-C', + 'logic_reasoning': 'LR', + 'relation_reasoning': 'RR', + 'attribute_reasoning': 'AR' +} + +MMT_abbrs = { + 'visual_recognition': 'VR', + 'localization': 'Loc', + 'ocr': 'OCR', + 'counting': 'Count', + 'hallucination': 'HLN', + 'image_retrieval': 'IR', + 'threed': '3D', + 'visual_captioning': 'VC', + 'visual_grounding': 'VG', + 'doc_understanding': 'DU', + 'action_recognition': 'AR', + 'pixel_level_perception': 'PLP', + 'image-to-image_translation': 'I2IT', + 'relation_reasoning': 'RR', + 'intelligence_quotient_test': 'IQT', + 'emotion': 'Emo', + 'visual_illusion': 'VI', + 'meme_understanding': 'MemU', + 'visual_prompt_understanding': 'VPU', + 'anomaly_detection': 'AND', + 'keypoint_detection': 'KD', + 'visual_commonsense_reasoning': 'VCR', + 'image_evaluation_judgement': 'IEJ', + 'multiple_image_analysis': 'MIA', + 'cross_image_matching': 'CIM', + 'temporal_understanding': 'TU', + 'visual_code': 'VP', + 'medical_understanding': 'MedU', + 'autonomous_driving': 'AUD', + 'discipline_knowledge_reasoning': 'DKR', + 'embodied_ai': 'EA', + 'gui_navigation': 'GN' +} + + +def MMMU_preproc(data): + logger = get_logger('Evaluation') + cnt = 0 + As, Bs, Ans = list(data['A']), list(data['B']), list(data['answer']) + lt = len(data) + for i in range(lt): + if pd.isna(As[i]): + As[i] = Ans[i] + Bs[i] = 'Other Answers' + cnt += 1 + logger.info(f'During MMMU_preproc in Evaluation, {cnt} open questions are re-formulated to multi-choice ones. ') + data['A'] = As + data['B'] = Bs + return data + + +def report_acc(df): + # assert group in [None, 'category', 'l2-category'] + res = defaultdict(list) + + if 'split' in df: + splits = list(set(df['split'])) + res['split'] = splits + else: + df['split'] = ['none'] * len(df) + res['split'] = ['none'] + + for group in [None, 'l2-category', 'category']: + if group is None: + res['Overall'] = [np.mean(df[df['split'] == sp]['hit']) for sp in res['split']] + elif group not in df: + continue + else: + abilities = list(set(df[group])) + abilities.sort() + for ab in abilities: + ab_name = MMB_abbrs[ab] if ab in MMB_abbrs else ab + sub_df = df[df[group] == ab] + res[ab_name] = [np.mean(sub_df[sub_df['split'] == sp]['hit']) for sp in res['split']] + return pd.DataFrame(res) + + +def report_acc_MMT(df): + # assert group in [None, 'category', 'l2-category'] + res = defaultdict(list) + res['split'] = list() + res['Overall'] = list() + for _, name in MMT_abbrs.items(): + res[name] = list() + + if 'split' in df: + splits = list(set(df['split'])) + res['split'] = splits + + else: + df['split'] = ['none'] * len(df) + res['split'] = ['none'] + + for group in [None, 'category', 'l2-category']: + if group is None: + res['Overall'] = [np.mean(df[df['split'] == sp]['hit']) for sp in res['split']] + res['Overall'].extend([np.mean(df['hit'])]) + elif group not in df: + continue + elif group == 'category': + abilities = list(set(df[group])) + abilities.sort() + for ab in abilities: + ab_name = ab + sub_df = df[df[group] == ab] + res[ab_name] = [np.mean(sub_df[sub_df['split'] == sp]['hit']) for sp in res['split']] + res[ab_name].extend([np.mean(sub_df['hit'])]) + else: + abilities = list(set(df[group])) + abilities.sort() + for ab in abilities: + sub_task_name_list = df[df['l2-category'] == ab]['category'].unique() + sub_task_acc = [] + for sub_task_name in sub_task_name_list: + sub_df = df[df['category'] == sub_task_name] + sub_task_acc.append([np.mean(sub_df[sub_df['split'] == sp]['hit']) for sp in res['split']]) + + new_acc = [] + for i in range(len(sub_task_acc[0])): + new_acc.append(sum([_[i] for _ in sub_task_acc]) / len([_ for _ in sub_task_acc])) + ab_name = MMT_abbrs[ab] if ab in MMT_abbrs else ab + res[ab_name] = new_acc + + sub_task_acc = [] + for sub_task_name in sub_task_name_list: + sub_df = df[df['category'] == sub_task_name] + sub_task_acc.append([np.mean(sub_df['hit'])]) + new_acc = [] + for i in range(len(sub_task_acc[0])): + new_acc.append(sum([_[i] for _ in sub_task_acc]) / len([_ for _ in sub_task_acc])) + + res[ab_name].extend(new_acc) + + res['split'].append('ALL') + return pd.DataFrame(res) + + +def build_prompt(question, options, prediction): + tmpl = ( + 'You are an AI assistant who will help me to match ' + 'an answer with several options of a single-choice question. ' + 'You are provided with a question, several options, and an answer, ' + 'and you need to find which option is most similar to the answer. ' + 'If the meaning of all options are significantly different from the answer, output Z. ' + 'Your should output a single uppercase character in A, B, C, D (if they are valid options), and Z. \n' + 'Example 1: \n' + 'Question: What is the main object in image?\nOptions: A. teddy bear B. rabbit C. cat D. dog\n' + 'Answer: a cute teddy bear\nYour output: A\n' + 'Example 2: \n' + 'Question: What is the main object in image?\nOptions: A. teddy bear B. rabbit C. cat D. dog\n' + 'Answer: Spider\nYour output: Z\n' + 'Example 3: \n' + 'Question: {}?\nOptions: {}\nAnswer: {}\nYour output: ' + ) + return tmpl.format(question, options, prediction) + + +def build_prompt_blink(question, options, prediction): + tmpl = ( + 'You are an AI assistant who will help me to match an answer with several options of a single-choice question. ' + 'You are provided with a question, several options, and an answer, ' + 'and you need to find which option is most similar to the answer. ' + "If the answer says things like refuse to answer, I'm sorry cannot help, etc., output Z." + 'If the meaning of all options are significantly different from the answer, ' + 'or the answer does not select any option, output Z. ' + 'Your should output one of the choices, A, B, C, D (if they are valid options), or Z.\n' + 'Example 1: \n' + 'Question: Which point is closer to the camera?\nSelect from the following choices.\n' + 'Options: A. Point A\nB. Point B\n(Z) Failed\n' + 'Answer: Point B, where the child is sitting, is closer to the camera.\nYour output: (B)\n' + 'Example 2: \n' + 'Question: Which point is closer to the camera?\nSelect from the following choices.\n' + 'Options: (A) Point A\n(B) Point B\n(Z) Failed\n' + "Answer: I'm sorry, but I can't assist with that request.\nYour output: (Z)\n" + 'Example 3: \n' + 'Question: Which point is corresponding to the reference point?\nSelect from the following choices.\n' + 'Options: (A) Point A\n(B) Point B\n(Z) Failed\n' + 'Answer:The reference point (REF) on the first image is at the tip of the pot, ' + 'which is the part used to Poke if the pots were used for that action. Looking at the second image, ' + 'we need to find the part of the object that would correspond to poking.\n' + "(A) Point A is at the tip of the spoon's handle, which is not used for poking.\n" + '(B) Point B is at the bottom of the spoon, which is not used for poking.\n' + '(C) Point C is on the side of the pspoonot, which is not used for poking.\n' + '(D) Point D is at the tip of the spoon, which is not used for poking.\n' + '\nTherefore, there is no correct answer in the choices\nYour output: (Z)\n' + 'Example 4: \n' + 'Question: {}?\nOptions: {}\n(Z) Failed\nAnswer: {}\nYour output: ' + ) + return tmpl.format(question, options, prediction) + + +def build_prompt_cn(question, options, prediction): + tmpl = ( + '你是一个帮助我匹配答案与单选题中多个选项的 AI 助手。' + '你会被提供：一个问题，多个选项，一个答案。你的任务是找到与答案意义最相近的选项。' + '如果所有选项的意义都与答案显著不同，则输出 Z。' + '你应该输出一个单个的大写字母，例如 A, B, C, D（如果它们是有效选项），或 Z。' + '例 1:' + '问题: 图中最主要的物体是什么?\n选项: A. 泰迪熊 B. 兔子 C. 猫 D. 狗\n答案: 一只可爱的泰迪熊\n输出: A\n' + '例 2: \n' + '问题: 图中最主要的物体是什么?\n选项: A. 泰迪熊 B. 兔子 C. 猫 D. 狗\n答案: 蜘蛛\n输出: Z\n' + '例 3: \n' + '问题: {}?\n选项: {}\n答案: {}\n输出: ' + ) + return tmpl.format(question, options, prediction) + + +def build_choices(item): + ret = {} + for ch in string.ascii_uppercase: + if ch in item and (not pd.isna(item[ch])): + ret[ch] = item[ch] + return ret + + +def prefetch_answer(item): + choices = build_choices(item) + return can_infer(item['prediction'], choices) + + +def extract_answer_from_item(model, item, dataset_name=None): + logger = get_logger('Evaluation') + # It will return: (pred, raw, llm_time) + choices = build_choices(item) + option_str = build_option_str(choices) + + if dataset_name == 'BLINK': + prompt = build_prompt_blink(item['question'], option_str, item['prediction']) + elif cn_string(item['question']): + prompt = build_prompt_cn(item['question'], option_str, item['prediction']) + else: + prompt = build_prompt(item['question'], option_str, item['prediction']) + retry = 3 + + ret = can_infer(item['prediction'], choices) + if ret: + return dict(opt=ret, log=item['prediction']) + if model is None: + return dict(opt='Z', log='Failed in Prefetch, no GPT-based answer matching under `exact_matching` policy.') + + while retry: + ans = model.generate(prompt) + if 'Failed to obtain answer via API' in ans: + logger.warning('GPT API failed to answer. ') + else: + ret = can_infer(ans, choices) + if ret: + return dict(opt=ret, log=ans) + else: + logger.warning(f'Output includes 0 / > 1 letter among candidates {set(choices)} and Z: {ans}') + retry -= 1 + + if retry == 0: + options = list(choices) + ['Z'] if 'Z' not in choices else [] + return dict(opt=rd.choice(options), log='Failed to predict, thus randomly generate one. ') + + +# For Circular Evaluation +def prefetch_circular_group(sub_data, verbose=False): + lt = len(sub_data) + GT, PRED = [], [] + for i in range(lt): + item = sub_data.iloc[i] + GT.append(item['GT']) + PRED.append(prefetch_answer(item)) + if PRED[-1] and (GT[-1] != PRED[-1]): + log = ( + f'Failed in Prefetching Rolling {i}: Answer is {GT[-1]}, ' + f"Prediction is {item['prediction']}, Pre-fetched is {PRED[-1]}. " + ) + return dict(hit=0, log=log) + flag = True + for g, p in zip(GT, PRED): + if g != p: + flag = False + ret = (dict(hit=1, log='Succeed During Pre-fetching'), ) if flag else (None, ) + ret = ret + (GT, PRED) if verbose else ret + return ret if len(ret) > 1 else ret[0] + + +def eval_vanilla(model, item, dataset_name=None): + res = extract_answer_from_item(model, item, dataset_name=dataset_name) + opt, match_log = res['opt'], res['log'] + if opt == item['GT']: + return dict(hit=1, log=f'Match Log: {match_log}. ') + else: + return dict(hit=0, log=f'Match Log: {match_log}. ') + + +# For Circular Evaluation +def eval_circular_group(model, sub_data, dataset_name=None): + res, GT, PRED = prefetch_circular_group(sub_data, verbose=True) + if res is not None: + return res + + lt = len(sub_data) + log = '' + for i in range(lt): + if PRED[i]: + log += f'Rolling {i} Matched.\n' + else: + res = extract_answer_from_item(model, sub_data.iloc[i], dataset_name=dataset_name) + opt, match_log = res['opt'], res['log'] + PRED[i] = opt + if PRED[i] != GT[i]: + log += ( + f"Failed in Rolling {i}: Answer is {GT[i]}; Prediction is {sub_data.iloc[i]['prediction']}; " + f'Pre-fetched is {PRED[i]}; Match Log is {match_log}.\n' + ) + return dict(hit=0, log=log) + else: + log += ( + f"Rolling {i}: Answer is {GT[i]}, Prediction is {sub_data.iloc[i]['prediction']}, " + f'Pre-fetched is {PRED[i]}.\n' + ) + + return dict(hit=1, log=log) + + +# data, meta are pd.DataFrame, result_file is a path +def mcq_vanilla_eval(model, data, meta, nproc, result_file, dataset_name=None): + result = {} + if osp.exists(result_file): + result = load(result_file) + answer_map = {i: c for i, c in zip(meta['index'], meta['answer'])} + + if 'MMMU' in dataset_name: + data = MMMU_preproc(data) + answer_map = {k: (v if v in list(string.ascii_uppercase) else 'A') for k, v in answer_map.items()} + + data = data[data['index'].isin(answer_map)] + data['GT'] = [answer_map[idx] for idx in data['index']] + items = [] + + for i in range(len(data)): + # Dealing with the normal part + item = data.iloc[i] + if item['index'] not in result: + items.append(item) + + tups = [dict(model=model, item=x, dataset_name=dataset_name) for x in items] + keys = [x['index'] for x in items] + if len(tups): + res = track_progress_rich(eval_vanilla, tups, nproc=nproc, chunksize=nproc, save=result_file, keys=keys) + result = load(result_file) + for k, v in zip(keys, res): + if k in result: + assert result[k]['hit'] == v['hit'] and result[k]['log'] == v['log'] + else: + result[k] = v + data['hit'] = [result[i]['hit'] for i in data['index']] + data['log'] = [result[i]['log'] for i in data['index']] + if 'GT' in data: + data.pop('GT') + return data + + +# data, meta are pd.DataFrame, result_file is a path +def mcq_circular_eval(model, data, meta, nproc, result_file, dataset_name=None): + result = {} + if osp.exists(result_file): + result = load(result_file) + # Build Answer Map + answer_map = {i: c for i, c in zip(meta['index'], meta['answer'])} + + for idx in list(meta['index']) + list(data['index']): + assert istype(idx, int) + + # Only keep those lines in the meta data + data = data[data['index'].isin(answer_map)] + data['GT'] = [answer_map[idx] for idx in data['index']] + data_main = data[data['index'] < int(1e6)] + + data_groups = [] + for i in range(len(data_main)): + # Dealing with the normal part + idx = data_main.iloc[i]['index'] + if idx not in result: + sub_data = data[data['index'] % int(1e6) == idx] + data_groups.append(sub_data) + + if len(data_groups): + prefetched = [prefetch_circular_group(g, verbose=False) for g in data_groups] + remain = [] + for dg, pf in zip(data_groups, prefetched): + if pf is not None: + result[dg.iloc[0]['index'] % 1e6] = pf + else: + remain.append(dg) + dump(result, result_file) + + tups = [dict(model=model, sub_data=x, dataset_name=dataset_name) for x in remain] + keys = [x.iloc[0]['index'] % 1e6 for x in remain] + + if len(tups) == 0: + pass + elif model is None: + logger = get_logger('Evaluation') + logger.warning('Exact Matching mode, will not do GPT-based answer matching. ') + for k in keys: + result[k] = dict( + hit=0, log='Failed in Prefetch, no GPT-based answer matching under `exact_matching` policy.') + else: + res = track_progress_rich( + eval_circular_group, + tups, + nproc=nproc, + chunksize=nproc, + save=result_file, + keys=keys) + result = load(result_file) + for k, v in zip(keys, res): + if k in result: + assert result[k]['hit'] == v['hit'] and result[k]['log'] == v['log'] + else: + result[k] = v + + tmp_pth = f'/tmp/{timestr()}.xlsx' + dump(data_main, tmp_pth) + data_main = load(tmp_pth) + indices = data_main['index'] + data_main['hit'] = [result[i]['hit'] for i in indices] + data_main['log'] = [result[i]['log'] for i in indices] + if 'GT' in data_main: + data_main.pop('GT') + + return data_main diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mvbench.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mvbench.py new file mode 100644 index 0000000000000000000000000000000000000000..2c9a7d4b5c2a5af8227744ec1540422f82617a7b --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/mvbench.py @@ -0,0 +1,450 @@ +from ...smp import * +from PIL import Image, ImageOps +import torchvision +import random +import numbers +import math +import torch + + +def get_dimension_rating(data_path): + data = load(data_path) + result_board = {} + for idx, item in data.iterrows(): + if item['task_type'] not in result_board: + result_board[item['task_type']] = [0, 0] + result_board[item['task_type']][1] += 1 + if item['score']: + result_board[item['task_type']][0] += 1 + + correct = 0 + total = 0 + for key, value in result_board.items(): + correct += value[0] + total += value[1] + result_board[key].append(f'{value[0] / value[1] * 100 :.2f}%') + + result_board['overall'] = [correct, total, f'{correct / total * 100 :.2f}%'] + + return result_board + + +def check_ans(pred, gt): + flag = False + + pred_list = pred.lower().split(' ') + pred_option, _ = pred_list[0], ' '.join(pred_list[1:]) + gt_list = gt.lower().split(' ') + gt_option, gt_content = gt_list[0], ' '.join(gt_list[1:]) + if gt_content[-1] == '.': + gt_content = gt_content[:-1] + + if pred_option.replace('.', '') in gt_option: + flag = True + elif gt_option in pred_option: + flag = True + + return flag + + +class GroupRandomCrop(object): + def __init__(self, size): + if isinstance(size, numbers.Number): + self.size = (int(size), int(size)) + else: + self.size = size + + def __call__(self, img_group): + + w, h = img_group[0].size + th, tw = self.size + + out_images = list() + + x1 = random.randint(0, w - tw) + y1 = random.randint(0, h - th) + + for img in img_group: + assert (img.size[0] == w and img.size[1] == h) + if w == tw and h == th: + out_images.append(img) + else: + out_images.append(img.crop((x1, y1, x1 + tw, y1 + th))) + + return out_images + + +class MultiGroupRandomCrop(object): + def __init__(self, size, groups=1): + if isinstance(size, numbers.Number): + self.size = (int(size), int(size)) + else: + self.size = size + self.groups = groups + + def __call__(self, img_group): + + w, h = img_group[0].size + th, tw = self.size + + out_images = list() + + for i in range(self.groups): + x1 = random.randint(0, w - tw) + y1 = random.randint(0, h - th) + + for img in img_group: + assert (img.size[0] == w and img.size[1] == h) + if w == tw and h == th: + out_images.append(img) + else: + out_images.append(img.crop((x1, y1, x1 + tw, y1 + th))) + + return out_images + + +class GroupCenterCrop(object): + def __init__(self, size): + self.worker = torchvision.transforms.CenterCrop(size) + + def __call__(self, img_group): + return [self.worker(img) for img in img_group] + + +class GroupRandomHorizontalFlip(object): + """Randomly horizontally flips the given PIL.Image with a probability of 0.5 + """ + + def __init__(self, is_flow=False): + self.is_flow = is_flow + + def __call__(self, img_group, is_flow=False): + v = random.random() + if v < 0.5: + ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group] + if self.is_flow: + for i in range(0, len(ret), 2): + # invert flow pixel values when flipping + ret[i] = ImageOps.invert(ret[i]) + return ret + else: + return img_group + + +class GroupNormalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, tensor): + rep_mean = self.mean * (tensor.size()[0] // len(self.mean)) + rep_std = self.std * (tensor.size()[0] // len(self.std)) + + # TODO: make efficient + for t, m, s in zip(tensor, rep_mean, rep_std): + t.sub_(m).div_(s) + + return tensor + + +class GroupScale(object): + """ Rescales the input PIL.Image to the given 'size'. + 'size' will be the size of the smaller edge. + For example, if height > width, then image will be + rescaled to (size * height / width, size) + size: size of the smaller edge + interpolation: Default: PIL.Image.BILINEAR + """ + + def __init__(self, size, interpolation=Image.BILINEAR): + self.worker = torchvision.transforms.Resize(size, interpolation) + + def __call__(self, img_group): + return [self.worker(img) for img in img_group] + + +class GroupOverSample(object): + def __init__(self, crop_size, scale_size=None, flip=True): + self.crop_size = crop_size if not isinstance( + crop_size, int) else (crop_size, crop_size) + + if scale_size is not None: + self.scale_worker = GroupScale(scale_size) + else: + self.scale_worker = None + self.flip = flip + + def __call__(self, img_group): + + if self.scale_worker is not None: + img_group = self.scale_worker(img_group) + + image_w, image_h = img_group[0].size + crop_w, crop_h = self.crop_size + + offsets = GroupMultiScaleCrop.fill_fix_offset( + False, image_w, image_h, crop_w, crop_h) + oversample_group = list() + for o_w, o_h in offsets: + normal_group = list() + flip_group = list() + for i, img in enumerate(img_group): + crop = img.crop((o_w, o_h, o_w + crop_w, o_h + crop_h)) + normal_group.append(crop) + flip_crop = crop.copy().transpose(Image.FLIP_LEFT_RIGHT) + + if img.mode == 'L' and i % 2 == 0: + flip_group.append(ImageOps.invert(flip_crop)) + else: + flip_group.append(flip_crop) + + oversample_group.extend(normal_group) + if self.flip: + oversample_group.extend(flip_group) + return oversample_group + + +class GroupFullResSample(object): + def __init__(self, crop_size, scale_size=None, flip=True): + self.crop_size = crop_size if not isinstance( + crop_size, int) else (crop_size, crop_size) + + if scale_size is not None: + self.scale_worker = GroupScale(scale_size) + else: + self.scale_worker = None + self.flip = flip + + def __call__(self, img_group): + + if self.scale_worker is not None: + img_group = self.scale_worker(img_group) + + image_w, image_h = img_group[0].size + crop_w, crop_h = self.crop_size + + w_step = (image_w - crop_w) // 4 + h_step = (image_h - crop_h) // 4 + + offsets = list() + offsets.append((0 * w_step, 2 * h_step)) # left + offsets.append((4 * w_step, 2 * h_step)) # right + offsets.append((2 * w_step, 2 * h_step)) # center + + oversample_group = list() + for o_w, o_h in offsets: + normal_group = list() + flip_group = list() + for i, img in enumerate(img_group): + crop = img.crop((o_w, o_h, o_w + crop_w, o_h + crop_h)) + normal_group.append(crop) + if self.flip: + flip_crop = crop.copy().transpose(Image.FLIP_LEFT_RIGHT) + + if img.mode == 'L' and i % 2 == 0: + flip_group.append(ImageOps.invert(flip_crop)) + else: + flip_group.append(flip_crop) + + oversample_group.extend(normal_group) + oversample_group.extend(flip_group) + return oversample_group + + +class GroupMultiScaleCrop(object): + + def __init__(self, input_size, scales=None, max_distort=1, + fix_crop=True, more_fix_crop=True): + self.scales = scales if scales is not None else [1, .875, .75, .66] + self.max_distort = max_distort + self.fix_crop = fix_crop + self.more_fix_crop = more_fix_crop + self.input_size = input_size if not isinstance(input_size, int) else [ + input_size, input_size] + self.interpolation = Image.BILINEAR + + def __call__(self, img_group): + + im_size = img_group[0].size + + crop_w, crop_h, offset_w, offset_h = self._sample_crop_size(im_size) + crop_img_group = [ + img.crop( + (offset_w, + offset_h, + offset_w + crop_w, + offset_h + crop_h)) for img in img_group] + ret_img_group = [img.resize((self.input_size[0], self.input_size[1]), self.interpolation) + for img in crop_img_group] + return ret_img_group + + def _sample_crop_size(self, im_size): + image_w, image_h = im_size[0], im_size[1] + + # find a crop size + base_size = min(image_w, image_h) + crop_sizes = [int(base_size * x) for x in self.scales] + crop_h = [ + self.input_size[1] if abs( + x - self.input_size[1]) < 3 else x for x in crop_sizes] + crop_w = [ + self.input_size[0] if abs( + x - self.input_size[0]) < 3 else x for x in crop_sizes] + + pairs = [] + for i, h in enumerate(crop_h): + for j, w in enumerate(crop_w): + if abs(i - j) <= self.max_distort: + pairs.append((w, h)) + + crop_pair = random.choice(pairs) + if not self.fix_crop: + w_offset = random.randint(0, image_w - crop_pair[0]) + h_offset = random.randint(0, image_h - crop_pair[1]) + else: + w_offset, h_offset = self._sample_fix_offset( + image_w, image_h, crop_pair[0], crop_pair[1]) + + return crop_pair[0], crop_pair[1], w_offset, h_offset + + def _sample_fix_offset(self, image_w, image_h, crop_w, crop_h): + offsets = self.fill_fix_offset( + self.more_fix_crop, image_w, image_h, crop_w, crop_h) + return random.choice(offsets) + + @staticmethod + def fill_fix_offset(more_fix_crop, image_w, image_h, crop_w, crop_h): + w_step = (image_w - crop_w) // 4 + h_step = (image_h - crop_h) // 4 + + ret = list() + ret.append((0, 0)) # upper left + ret.append((4 * w_step, 0)) # upper right + ret.append((0, 4 * h_step)) # lower left + ret.append((4 * w_step, 4 * h_step)) # lower right + ret.append((2 * w_step, 2 * h_step)) # center + + if more_fix_crop: + ret.append((0, 2 * h_step)) # center left + ret.append((4 * w_step, 2 * h_step)) # center right + ret.append((2 * w_step, 4 * h_step)) # lower center + ret.append((2 * w_step, 0 * h_step)) # upper center + + ret.append((1 * w_step, 1 * h_step)) # upper left quarter + ret.append((3 * w_step, 1 * h_step)) # upper right quarter + ret.append((1 * w_step, 3 * h_step)) # lower left quarter + ret.append((3 * w_step, 3 * h_step)) # lower righ quarter + + return ret + + +class GroupRandomSizedCrop(object): + """Random crop the given PIL.Image to a random size of (0.08 to 1.0) of the original size + and and a random aspect ratio of 3/4 to 4/3 of the original aspect ratio + This is popularly used to train the Inception networks + size: size of the smaller edge + interpolation: Default: PIL.Image.BILINEAR + """ + + def __init__(self, size, interpolation=Image.BILINEAR): + self.size = size + self.interpolation = interpolation + + def __call__(self, img_group): + for attempt in range(10): + area = img_group[0].size[0] * img_group[0].size[1] + target_area = random.uniform(0.08, 1.0) * area + aspect_ratio = random.uniform(3. / 4, 4. / 3) + + w = int(round(math.sqrt(target_area * aspect_ratio))) + h = int(round(math.sqrt(target_area / aspect_ratio))) + + if random.random() < 0.5: + w, h = h, w + + if w <= img_group[0].size[0] and h <= img_group[0].size[1]: + x1 = random.randint(0, img_group[0].size[0] - w) + y1 = random.randint(0, img_group[0].size[1] - h) + found = True + break + else: + found = False + x1 = 0 + y1 = 0 + + if found: + out_group = list() + for img in img_group: + img = img.crop((x1, y1, x1 + w, y1 + h)) + assert (img.size == (w, h)) + out_group.append( + img.resize( + (self.size, self.size), self.interpolation)) + return out_group + else: + # Fallback + scale = GroupScale(self.size, interpolation=self.interpolation) + crop = GroupRandomCrop(self.size) + return crop(scale(img_group)) + + +class ConvertDataFormat(object): + def __init__(self, model_type): + self.model_type = model_type + + def __call__(self, images): + if self.model_type == '2D': + return images + tc, h, w = images.size() + t = tc // 3 + images = images.view(t, 3, h, w) + images = images.permute(1, 0, 2, 3) + return images + + +class Stack(object): + + def __init__(self, roll=False): + self.roll = roll + + def __call__(self, img_group): + if img_group[0].mode == 'L': + return np.concatenate([np.expand_dims(x, 2) + for x in img_group], axis=2) + elif img_group[0].mode == 'RGB': + if self.roll: + return np.concatenate([np.array(x)[:, :, ::-1] + for x in img_group], axis=2) + else: + # print(np.concatenate(img_group, axis=2).shape) + # print(img_group[0].shape) + return np.concatenate(img_group, axis=2) + + +class ToTorchFormatTensor(object): + """ Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255] + to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """ + + def __init__(self, div=True): + self.div = div + + def __call__(self, pic): + if isinstance(pic, np.ndarray): + # handle numpy array + img = torch.from_numpy(pic).permute(2, 0, 1).contiguous() + else: + # handle PIL Image + img = torch.ByteTensor( + torch.ByteStorage.from_buffer( + pic.tobytes())) + img = img.view(pic.size[1], pic.size[0], len(pic.mode)) + # put it from HWC to CHW format + # yikes, this transpose takes 80% of the loading time/CPU + img = img.transpose(0, 1).transpose(0, 2).contiguous() + return img.float().div(255) if self.div else img.float() + + +class IdentityTransform(object): + + def __call__(self, data): + return data diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/ocrbench.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/ocrbench.py new file mode 100644 index 0000000000000000000000000000000000000000..f88bb246c3a2737dda9d60c335b8b069df8d8b56 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/ocrbench.py @@ -0,0 +1,65 @@ +from ...smp import * + + +def OCRBench_eval(eval_file): + OCRBench_score = { + 'Regular Text Recognition': 0, + 'Irregular Text Recognition': 0, + 'Artistic Text Recognition': 0, + 'Handwriting Recognition': 0, + 'Digit String Recognition': 0, + 'Non-Semantic Text Recognition': 0, + 'Scene Text-centric VQA': 0, + 'Doc-oriented VQA': 0, + 'Key Information Extraction': 0, + 'Handwritten Mathematical Expression Recognition': 0 + } + + logger = get_logger('Evaluation') + + data = load(eval_file) + lt = len(data) + lines = [data.iloc[i] for i in range(lt)] + for i in tqdm(range(len(lines))): + line = lines[i] + predict = str(line['prediction']) + answers = eval(line['answer']) + category = line['category'] + if category == 'Handwritten Mathematical Expression Recognition': + for j in range(len(answers)): + answer = answers[j].strip().replace('\n', ' ').replace(' ', '') + predict = predict.strip().replace('\n', ' ').replace(' ', '') + if answer in predict: + OCRBench_score[category] += 1 + break + else: + for j in range(len(answers)): + answer = answers[j].lower().strip().replace('\n', ' ') + predict = predict.lower().strip().replace('\n', ' ') + if answer in predict: + OCRBench_score[category] += 1 + break + + final_score_dict = {} + final_score_dict['Text Recognition'] = ( + OCRBench_score['Regular Text Recognition'] + OCRBench_score['Irregular Text Recognition'] + + OCRBench_score['Artistic Text Recognition'] + OCRBench_score['Handwriting Recognition'] + + OCRBench_score['Digit String Recognition'] + OCRBench_score['Non-Semantic Text Recognition'] + ) + final_score_dict['Scene Text-centric VQA'] = OCRBench_score['Scene Text-centric VQA'] + final_score_dict['Doc-oriented VQA'] = OCRBench_score['Doc-oriented VQA'] + final_score_dict['Key Information Extraction'] = OCRBench_score['Key Information Extraction'] + final_score_dict['Handwritten Mathematical Expression Recognition'] = \ + OCRBench_score['Handwritten Mathematical Expression Recognition'] + final_score_dict['Final Score'] = ( + final_score_dict['Text Recognition'] + final_score_dict['Scene Text-centric VQA'] + + final_score_dict['Doc-oriented VQA'] + final_score_dict['Key Information Extraction'] + + final_score_dict['Handwritten Mathematical Expression Recognition'] + ) + final_score_dict['Final Score Norm'] = float(final_score_dict['Final Score']) / 10 + score_pth = eval_file.replace('.xlsx', '_score.json') + dump(final_score_dict, score_pth) + logger.info(f'OCRBench_eval successfully finished evaluating {eval_file}, results saved in {score_pth}') + logger.info('Score: ') + for key, value in final_score_dict.items(): + logger.info('{}:{}'.format(key, value)) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/videomme.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/videomme.py new file mode 100644 index 0000000000000000000000000000000000000000..9d3572868407e4e517982cdb2280e7dc51b980d7 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/videomme.py @@ -0,0 +1,140 @@ +from ...smp import * +import numpy as np +import re + +FAIL_MSG = 'Failed to obtain answer via API.' + +DURATIONS = [ + 'short', + 'medium', + 'long', +] + +DOMAINS = [ + 'Knowledge', + 'Film & Television', + 'Sports Competition', + 'Artistic Performance', + 'Life Record', + 'Multilingual' +] + +SUB_CATEGORIES = [ + 'Humanity & History', + 'Literature & Art', + 'Biology & Medicine', + 'Finance & Commerce', + 'Astronomy', + 'Geography', + 'Law', + 'Life Tip', + 'Technology', + 'Animation', + 'Movie & TV Show', + 'Documentary', + 'News Report', + 'Esports', + 'Basketball', + 'Football', + 'Athletics', + 'Other Sports', + 'Stage Play', + 'Magic Show', + 'Variety Show', + 'Acrobatics', + 'Handicraft', + 'Food', + 'Fashion', + 'Daily Life', + 'Travel', + 'Pet & Animal', + 'Exercise', + 'Multilingual' +] + +TASK_CATEGORIES = [ + 'Temporal Perception', + 'Spatial Perception', + 'Attribute Perception', + 'Action Recognition', + 'Object Recognition', + 'OCR Problems', + 'Counting Problem', + 'Temporal Reasoning', + 'Spatial Reasoning', + 'Action Reasoning', + 'Object Reasoning', + 'Information Synopsis', +] + + +def get_dimension_rating(data_path): + data = load(data_path) + + duration_rating = {k: {} for k in DURATIONS} + for duration in DURATIONS + ['overall']: + duration_rating[duration] = { + 'overall': '', + 'domain': {k: [] for k in DOMAINS}, + 'sub_category': {k: [] for k in SUB_CATEGORIES}, + 'task_type': {k: [] for k in TASK_CATEGORIES} + } + + for i in range(len(data)): + + domain = data.iloc[i]['domain'] + sub_ctg = data.iloc[i]['sub_category'] + task_ctg = data.iloc[i]['task_type'] + + duration = data.iloc[i]['duration'] + duration_rating[duration]['domain'][domain].append(data.iloc[i]['score']) + duration_rating[duration]['sub_category'][sub_ctg].append(data.iloc[i]['score']) + duration_rating[duration]['task_type'][task_ctg].append(data.iloc[i]['score']) + + duration_rating['overall']['domain'][domain].append(data.iloc[i]['score']) + duration_rating['overall']['sub_category'][sub_ctg].append(data.iloc[i]['score']) + duration_rating['overall']['task_type'][task_ctg].append(data.iloc[i]['score']) + + for duration in DURATIONS + ['overall']: + + overall_res_dur = f'{np.mean([x for x in sum(duration_rating[duration]["domain"].values(), []) if x >= 0]):.2f}' + duration_rating[duration]['overall'] = overall_res_dur + + for domain in DOMAINS: + domain_res_dur = f'{np.mean([x for x in duration_rating[duration]["domain"][domain] if x >= 0]):.2f}' + duration_rating[duration]['domain'][domain] = domain_res_dur + + for sub_ctg in SUB_CATEGORIES: + sub_res_dur = f'{np.mean([x for x in duration_rating[duration]["sub_category"][sub_ctg] if x >= 0]):.2f}' + duration_rating[duration]['sub_category'][sub_ctg] = sub_res_dur + + for task_ctg in TASK_CATEGORIES: + task_res_dur = f'{np.mean([x for x in duration_rating[duration]["task_type"][task_ctg] if x >= 0]):.2f}' + duration_rating[duration]['task_type'][task_ctg] = task_res_dur + + return duration_rating + + +def extract_characters_regex(s): + s = s.strip() + answer_prefixes = [ + 'The best answer is', + 'The correct answer is', + 'The answer is', + 'The answer', + 'The best option is' + 'The correct option is', + 'Best answer:' + 'Best option:', + 'Answer:', + 'Option:', + ] + for answer_prefix in answer_prefixes: + s = s.replace(answer_prefix, '') + + if len(s.split()) > 10 and not re.search('[ABCD]', s): + return '' + matches = re.search(r'[ABCD]', s) + if matches is None: + return '' + return matches[0] diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/vqa_eval.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/vqa_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..8fcacf1ac62b0f5b12c9ad07ef1e1863dd0f7f61 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/vqa_eval.py @@ -0,0 +1,285 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# Partly adopted from https://github.com/GT-Vision-Lab/VQA +# Copyright (c) 2014, Aishwarya Agrawal + +from ...smp import * +from typing import Optional + + +def _process_digit_article(inText): + outText = [] + tempText = inText.lower().split() + articles = ['a', 'an', 'the'] + manualMap = { + 'none': '0', + 'zero': '0', + 'one': '1', + 'two': '2', + 'three': '3', + 'four': '4', + 'five': '5', + 'six': '6', + 'seven': '7', + 'eight': '8', + 'nine': '9', + 'ten': '10', + } + contractions = { + 'aint': "ain't", + 'arent': "aren't", + 'cant': "can't", + 'couldve': "could've", + 'couldnt': "couldn't", + "couldn'tve": "couldn't've", + "couldnt've": "couldn't've", + 'didnt': "didn't", + 'doesnt': "doesn't", + 'dont': "don't", + 'hadnt': "hadn't", + "hadnt've": "hadn't've", + "hadn'tve": "hadn't've", + 'hasnt': "hasn't", + 'havent': "haven't", + 'hed': "he'd", + "hed've": "he'd've", + "he'dve": "he'd've", + 'hes': "he's", + 'howd': "how'd", + 'howll': "how'll", + 'hows': "how's", + "Id've": "I'd've", + "I'dve": "I'd've", + 'Im': "I'm", + 'Ive': "I've", + 'isnt': "isn't", + 'itd': "it'd", + "itd've": "it'd've", + "it'dve": "it'd've", + 'itll': "it'll", + "let's": "let's", + 'maam': "ma'am", + 'mightnt': "mightn't", + "mightnt've": "mightn't've", + "mightn'tve": "mightn't've", + 'mightve': "might've", + 'mustnt': "mustn't", + 'mustve': "must've", + 'neednt': "needn't", + 'notve': "not've", + 'oclock': "o'clock", + 'oughtnt': "oughtn't", + "ow's'at": "'ow's'at", + "'ows'at": "'ow's'at", + "'ow'sat": "'ow's'at", + 'shant': "shan't", + "shed've": "she'd've", + "she'dve": "she'd've", + "she's": "she's", + 'shouldve': "should've", + 'shouldnt': "shouldn't", + "shouldnt've": "shouldn't've", + "shouldn'tve": "shouldn't've", + "somebody'd": 'somebodyd', + "somebodyd've": "somebody'd've", + "somebody'dve": "somebody'd've", + 'somebodyll': "somebody'll", + 'somebodys': "somebody's", + 'someoned': "someone'd", + "someoned've": "someone'd've", + "someone'dve": "someone'd've", + 'someonell': "someone'll", + 'someones': "someone's", + 'somethingd': "something'd", + "somethingd've": "something'd've", + "something'dve": "something'd've", + 'somethingll': "something'll", + 'thats': "that's", + 'thered': "there'd", + "thered've": "there'd've", + "there'dve": "there'd've", + 'therere': "there're", + 'theres': "there's", + 'theyd': "they'd", + "theyd've": "they'd've", + "they'dve": "they'd've", + 'theyll': "they'll", + 'theyre': "they're", + 'theyve': "they've", + 'twas': "'twas", + 'wasnt': "wasn't", + "wed've": "we'd've", + "we'dve": "we'd've", + 'weve': "we've", + 'werent': "weren't", + 'whatll': "what'll", + 'whatre': "what're", + 'whats': "what's", + 'whatve': "what've", + 'whens': "when's", + 'whered': "where'd", + 'wheres': "where's", + 'whereve': "where've", + 'whod': "who'd", + "whod've": "who'd've", + "who'dve": "who'd've", + 'wholl': "who'll", + 'whos': "who's", + 'whove': "who've", + 'whyll': "why'll", + 'whyre': "why're", + 'whys': "why's", + 'wont': "won't", + 'wouldve': "would've", + 'wouldnt': "wouldn't", + "wouldnt've": "wouldn't've", + "wouldn'tve": "wouldn't've", + 'yall': "y'all", + "yall'll": "y'all'll", + "y'allll": "y'all'll", + "yall'd've": "y'all'd've", + "y'alld've": "y'all'd've", + "y'all'dve": "y'all'd've", + 'youd': "you'd", + "youd've": "you'd've", + "you'dve": "you'd've", + 'youll': "you'll", + 'youre': "you're", + 'youve': "you've", + } + for word in tempText: + word = manualMap.setdefault(word, word) + if word not in articles: + outText.append(word) + for wordId, word in enumerate(outText): + if word in contractions: + outText[wordId] = contractions[word] + outText = ' '.join(outText) + return outText + + +def hit_calculate(result, dataset_name, anls_threshold=0.5): + if listinstr(['TextVQA'], dataset_name): + return [np.mean(x['match']) for x in result] + elif listinstr(['DocVQA', 'InfoVQA'], dataset_name): + return [0.0 if 1 - np.min(x['match']) < anls_threshold else 1 - np.min(x['match']) for x in result] + elif listinstr(['ChartQA', 'OCRVQA'], dataset_name): + return [np.max(x['match']) for x in result] + else: # default using vqa_score to calculate score + return [np.mean(x['match']) for x in result] + + +# https://github.com/google-research/pix2struct/blob/main/pix2struct/metrics.py#L81 +def relaxed_correctness(target: str, + prediction: str, + max_relative_change: float = 0.05) -> bool: + """Calculates relaxed correctness. + + The correctness tolerates certain error ratio defined by max_relative_change. + See https://arxiv.org/pdf/2203.10244.pdf, end of section 5.1: + “Following Methani et al. (2020), we use a relaxed accuracy measure for the + numeric answers to allow a minor inaccuracy that may result from the automatic + data extraction process. We consider an answer to be correct if it is within + 5% of the gold answer. For non-numeric answers, we still need an exact match + to consider an answer to be correct.” + + Args: + target: Target string. + prediction: Predicted string. + max_relative_change: Maximum relative change. + + Returns: + Whether the prediction was correct given the specified tolerance. + """ + + def _to_float(text: str) -> Optional[float]: + try: + if text.endswith('%'): + # Convert percentages to floats. + return float(text.rstrip('%')) / 100.0 + else: + return float(text) + except ValueError: + return None + prediction = str(prediction) + target = str(target) + prediction_float = _to_float(prediction) + target_float = _to_float(target) + if prediction_float is not None and target_float: + relative_change = abs(prediction_float - target_float) / abs(target_float) + return relative_change <= max_relative_change + else: + return prediction.lower() == target.lower() + + +def levenshtein_distance(s1, s2): + if len(s1) > len(s2): + s1, s2 = s2, s1 + + distances = range(len(s1) + 1) + for i2, c2 in enumerate(s2): + distances_ = [i2 + 1] + for i1, c1 in enumerate(s1): + if c1 == c2: + distances_.append(distances[i1]) + else: + distances_.append(1 + min((distances[i1], distances[i1 + 1], distances_[-1]))) + distances = distances_ + return distances[-1] + + +def anls_compute(groundtruth, prediction): + gt_answer = ' '.join(groundtruth.strip().lower().split()) + det_answer = ' '.join(prediction.strip().lower().split()) + dist = levenshtein_distance(gt_answer, det_answer) + length = max(len(groundtruth.upper()), len(prediction.upper())) + values = 0.0 if length == 0 else float(dist) / float(length) + return values + + +def process_answer(answer): + answer = answer.replace('\n', ' ') + answer = answer.replace('\t', ' ') + answer = answer.strip() + answer = process_punctuation(answer) + answer = _process_digit_article(answer) + return answer + + +def process_line(line, method='vqa_score'): + ret = {} + if istype(line['answer'], list): + answers = eval(line['answer']) + else: + answers = [line['answer']] + if method == 'vqa_score': + ret['gt'] = [process_answer(x) for x in answers] + ret['pred'] = process_answer(line['prediction']) + ret['match'] = [] + for current_idx, gtAnsDatum in enumerate(ret['gt']): + otherGTAns = [ + item for ret_gt_idx, item in enumerate(ret['gt']) + if ret_gt_idx != current_idx + ] + matchingAns = [ + item for item in otherGTAns if item == ret['pred'] + ] + acc = min(1, float(len(matchingAns)) / 3) + ret['match'].append(acc) + elif method == 'anls': + ret['gt'] = answers + ret['pred'] = line['prediction'] + ret['match'] = [anls_compute(x, ret['pred']) for x in ret['gt']] + elif method == 'relaxed_accuracy': + ret['gt'] = answers + ret['pred'] = line['prediction'].strip() + ret['match'] = [relaxed_correctness(ret['pred'], x) for x in ret['gt']] + elif method == 'accuracy': + ret['gt'] = answers + ret['pred'] = line['prediction'].strip() + ret['match'] = [(1.0 if (x.strip().lower() == ret['pred'].strip().lower()) else 0.0) for x in ret['gt']] + else: # default using vqa_score to calculate score + ret['gt'] = [process_answer(x) for x in answers] + ret['pred'] = process_answer(line['prediction']) + ret['match'] = [x == ret['pred'] for x in ret['gt']] + + return ret diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/yorn.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/yorn.py new file mode 100644 index 0000000000000000000000000000000000000000..0fb02059668c4ae932a9d7cb3adfd2970cb2b2d9 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/utils/yorn.py @@ -0,0 +1,203 @@ +from ...smp import * + + +def MME_rating(data_file): + data = load(data_file) + stats = defaultdict(dict) + lt = len(data) + for i in range(lt): + item = data.iloc[i] + category = item['category'] + image_path = item['image_path'] + score = item['score'] + if image_path not in stats[category]: + stats[category][image_path] = [] + stats[category][image_path].append(score) + + def acc(key, mode='normal'): + res = stats[key] + values = [] + for val in res.values(): + if mode == 'normal': + values.extend(val) + elif mode == 'plus': + values.append(val[0] * val[1]) + return np.mean(values) * 100 + + scores = {} + for k in stats: + scores[k] = acc(k) + acc(k, 'plus') + + super_cates = dict( + perception=[ + 'OCR', 'artwork', 'celebrity', 'color', 'count', 'existence', + 'landmark', 'position', 'posters', 'scene' + ], + reasoning=['code_reasoning', 'commonsense_reasoning', 'numerical_calculation', 'text_translation'] + ) + + ret = {} + for sc, cate_list in super_cates.items(): + base = 0 + for c in cate_list: + base += scores[c] + ret[sc] = base + ret.update(scores) + ret = d2df(ret) + return ret + + +def Hallusion_rating(data_file): + def calc_fAcc(data): + res = defaultdict(list) + lt = len(data) + for i in range(lt): + line = data.iloc[i] + res[f"{line['l2-category']}_{line['set_id']}_{line['figure_id']}"].append(line['score']) + return np.mean([np.all(x) for x in res.values()]) * 100 + + def calc_qAcc(data): + res = defaultdict(list) + lt = len(data) + for i in range(lt): + line = data.iloc[i] + res[f"{line['l2-category']}_{line['set_id']}_{line['question_id']}"].append(line['score']) + return np.mean([np.all(x) for x in res.values()]) * 100 + + def calc_aAcc(data): + return np.mean(data['score']) * 100 + + data = load(data_file) + data['set_id'] = [x.split('_')[3] for x in data['index']] + data['figure_id'] = [x.split('_')[4] for x in data['index']] + data['question_id'] = [x.split('_')[5] for x in data['index']] + + res = dict(split=[], aAcc=[], fAcc=[], qAcc=[]) + res['split'].append('Overall') + res['aAcc'].append(calc_aAcc(data)) + res['fAcc'].append(calc_fAcc(data)) + res['qAcc'].append(calc_qAcc(data)) + + if 'category' in data: + cates = list(set(data['category'])) + for c in cates: + sub = data[data['category'] == c] + res['split'].append(c) + res['aAcc'].append(calc_aAcc(sub)) + res['fAcc'].append(calc_fAcc(sub)) + res['qAcc'].append(calc_qAcc(sub)) + + if 'l2-category' in data: + cates = list(set(data['l2-category'])) + for c in cates: + sub = data[data['l2-category'] == c] + res['split'].append(c) + res['aAcc'].append(calc_aAcc(sub)) + res['fAcc'].append(calc_fAcc(sub)) + res['qAcc'].append(calc_qAcc(sub)) + ret = pd.DataFrame(res) + return ret + + +def POPE_rating(data_file): + def cal_f1_score(y_true, y_pred): + tp = sum((y_true == 1) & (y_pred == 1)) + fp = sum((y_true == 0) & (y_pred == 1)) + fn = sum((y_true == 1) & (y_pred == 0)) + + precision = tp / (tp + fp) if (tp + fp) != 0 else 0 + recall = tp / (tp + fn) if (tp + fn) != 0 else 0 + f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) != 0 else 0 + return f1_score, precision, recall + + data = load(data_file) + data = data.assign(category=data['category'].str.split(',')).explode('category') + data['index'] = range(len(data)) + res = dict(split=[], Overall=[], acc=[], precision=[], recall=[]) + y_true = np.array([1 if i == 'Yes' else 0 for i in data['answer']]) + y_pred = np.array([1 if i == 'Yes' else 0 for i in data['extracted']]) + f1_score, precision, recall = cal_f1_score(y_true, y_pred) + res['split'].append('Overall') + res['Overall'].append(f1_score * 100) + res['acc'].append(np.mean(data['score']) * 100) + res['precision'].append(precision * 100) + res['recall'].append(recall * 100) + + if 'category' in data: + cates = list(set(data['category'])) + cates = [c for c in cates if not pd.isna(c)] + for c in cates: + sub = data[data['category'] == c] + y_true = np.array([1 if i == 'Yes' else 0 for i in sub['answer']]) + y_pred = np.array([1 if i == 'Yes' else 0 for i in sub['extracted']]) + f1_score, precision, recall = cal_f1_score(y_true, y_pred) + res['split'].append(c) + res['Overall'].append(f1_score * 100) + res['acc'].append(np.mean(sub['score']) * 100) + res['precision'].append(precision * 100) + res['recall'].append(recall * 100) + + ret = pd.DataFrame(res) + return ret + + +def default_rating(data_file): + data = load(data_file) + res = {} + res['Overall'] = np.mean(data['score']) * 100 + if 'category' in data: + cates = list(set(data['category'])) + cates = [c for c in cates if not pd.isna(c)] + cates.sort() + for c in cates: + sub = data[data['category'] == c] + res[c] = np.mean(sub['score']) * 100 + if 'l2-category' in data: + cates = list(set(data['l2-category'])) + cates = [c for c in cates if not pd.isna(c)] + cates.sort() + for c in cates: + sub = data[data['l2-category'] == c] + res[c] = np.mean(sub['score']) * 100 + ret = d2df(res) + return ret + + +def YOrN_match_prompt(line): + tmpl = ( + 'You are an AI assistant who will help me to match an answer with two options of a question. ' + 'The options are only Yes / No. ' + 'You are provided with a question and an answer, ' + 'and you need to find which option (Yes / No) is most similar to the answer. ' + 'If the meaning of all options are significantly different from the answer, output Unknown. ' + 'Your should output a single word among the following 3 choices: Yes, No, Unknown.\n' + 'Example 1: \n' + "Question: Is the word in this image 'Hello'?\nAnswer: The word in this image is 'Hello'.\nYour output: Yes\n" + 'Example 2: \n' + "Question: Is the word in this image 'Hello'?\n" + "Answer: The word in this image is not 'Hello'.\nYour output: No\n" + 'Example 3: \n' + 'Question: {}?\nAnswer: {}\nYour output: ' + ) + return tmpl.format(line['question'], line['prediction']) + + +def YOrN_Extraction(output): + s = output.lower() + words = process_punctuation(s).split() + if 'yes' in words and 'no' not in words: + return 'Yes' + if 'yes' not in words and 'no' in words: + return 'No' + return 'Unknown' + + +def YOrN_auxeval(model, line): + prompt = YOrN_match_prompt(line) + retry = 5 + for i in range(retry): + output = model.generate(prompt, temperature=0.5 * i) + ans = YOrN_Extraction(output) + if ans != 'Unknown': + return ans + return 'Unknown' diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/vcr.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/vcr.py new file mode 100644 index 0000000000000000000000000000000000000000..ba5a432a04776c85601ead8bcabe44aa1b781209 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/vcr.py @@ -0,0 +1,332 @@ +import uuid +from functools import partial +from .image_base import ImageBaseDataset +from ..smp import * + +rouge = None +nlp_en = None +nlp_zh = None +nlp = None + + +def initialize(): + import evaluate + import spacy + + global rouge, nlp_en, nlp_zh, nlp + + try: + rouge = evaluate.load('rouge', experiment_id=str(uuid.uuid4())) + except: + warnings.warn('Please first `pip install rouge_score`.') + + try: + nlp_en = spacy.load('en_core_web_sm') + except: + warnings.warn('Will automatically download en_core_web_sm via spacy.') + spacy.cli.download('en_core_web_sm') + nlp_en = spacy.load('en_core_web_sm') + + try: + nlp_zh = spacy.load('zh_core_web_sm') + except: + warnings.warn('Will automatically download zh_core_web_sm via spacy.') + spacy.cli.download('zh_core_web_sm') + nlp_zh = spacy.load('zh_core_web_sm') + + nlp = {'en': nlp_en, 'zh': nlp_zh} + + +def rough_filter(answer_text): + if "I can't" in answer_text: + return False + elif 'I cannot' in answer_text: + return False + elif 'sorry' in answer_text.lower(): + return False + if '无法' in answer_text: + return False + elif '抱歉' in answer_text: + return False + else: + return True + + +def zero_template(crossed_text): + return { + 'crossed_text': crossed_text, + 'max_sim_val': 0, + 'max_sim_string': '', + 'precision': 0, + 'recall': 0, + 'f1': 0, + 'jaccard': 0, + 'rouge1': 0, + 'exact_match': 0, + } + + +def tokenize(text, language): + """ + Tokenize the text and return the tokens. + + Parameters: + text (str): The text to tokenize. + language (str): The language of the text. + + Returns: + list: The list of tokens. + """ + assert language in ['en', 'zh'] + nlp_language = nlp[language] + processed_text = nlp_language(text) + return [token.text for token in processed_text] + + +def find_best_match(needle, hay, language, rouge): + """ + Finds the best matching n-gram in the haystack for the given needle. + + Parameters: + needle (str): The string to find. + hay (str): The text to search within. + + Returns: + tuple: The highest similarity value and the best matching string. + """ + assert language in ['en', 'zh'] + from nltk.util import ngrams + from difflib import SequenceMatcher as SM + + tokens_hay = tokenize(hay, language) + tokens_needle = tokenize(needle, language) + + splitter = '' if language == 'zh' else ' ' + ngrams_ = ngrams(tokens_hay, len(tokens_needle)) + max_sim_val = 0 + max_sim_string = '' + max_sim_ngram = [] + tokens_needle_set = set(tokens_needle) + ngrams_hasjoint = [ + ngram + for ngram in ngrams_ + if not set(ngram).isdisjoint(tokens_needle_set) + ] + + for ngram in ngrams_hasjoint: + hay_ngram = splitter.join(ngram) + similarity = SM(None, hay_ngram, needle).ratio() + if similarity > max_sim_val: + max_sim_val = similarity + max_sim_string = hay_ngram + max_sim_ngram = ngram + + # Evaluate + if len(max_sim_ngram) == 0: + return { + 'crossed_text': needle, + 'max_sim_val': 0, + 'max_sim_string': '', + 'precision': 0, + 'recall': 0, + 'f1': 0, + 'jaccard': 0, + 'rouge1': 0, + 'exact_match': 0, + } + pred_set = set(max_sim_ngram) + ref_set = set(tokens_needle) + correct_tokens = pred_set.intersection(ref_set) + len_correct_tokens = len(correct_tokens) + + precision = len_correct_tokens / len(pred_set) + recall = len_correct_tokens / len(ref_set) + if (precision + recall) == 0: + f1 = 0 + else: + f1 = 2 * precision * recall / (precision + recall) + union = pred_set.union(ref_set) + jaccard = len_correct_tokens / len(union) if len(union) > 0 else 0 + rouge_1 = rouge.compute( + predictions=[max_sim_string], + references=[needle], + tokenizer=partial(tokenize, language=language), + rouge_types=['rouge1'], + )['rouge1'] + exact_match = float(list(max_sim_ngram) == list(tokens_needle)) + out = { + 'crossed_text': needle, + 'max_sim_string': max_sim_string, + 'max_sim_val': max_sim_val, + 'precision': precision, + 'recall': recall, + 'f1': f1, + 'jaccard': jaccard, + 'rouge1': rouge_1, + 'exact_match': exact_match, + } + return out + + +def process_match_single_new( + image_id, prediction, answer, language, progress +): + """ + process the inference results for a single image and calculate the metrics + + Parameters: + image_id (int): The image id (question id). + prediction (str): The prediction text. + answer (Union[str, List[str]]): The answer text, or a list of answer texts. The masked n-grams in the image. + language (str): The language of the text. Can be "en" or "zh". + rouge (rouge): The rouge metric object. + progress (multiprocessing.Queue): The progress queue. + + Returns: + tuple: The image id (question_id, int) and the result per id (dict of dict of dict). + """ + result_per_id = {image_id: {}} + if isinstance(answer, str): + answer = eval(answer) + assert isinstance(answer, list) + result = prediction.split('Assistant: ')[-1] + for i, crossed_text in enumerate(answer): + if rough_filter(result): + find_best_match_result = find_best_match( + crossed_text, result, language, rouge + ) + if i == 0: + result_per_id[image_id] = {str(i): find_best_match_result} + else: + result_per_id[image_id][str(i)] = find_best_match_result + else: + if i == 0: + result_per_id[image_id] = {str(i): zero_template(crossed_text)} + else: + result_per_id[image_id][str(i)] = zero_template(crossed_text) + progress.put(1) + return image_id, result_per_id + + +class VCRDataset(ImageBaseDataset): + TYPE = 'VQA' + + URL_PREFIX = 'https://huggingface.co/datasets/vcr-org' + + DATASET_URL = { + 'VCR_EN_EASY_500': f'{URL_PREFIX}/VCR-wiki-en-easy-test-500/resolve/main/VCR-wiki-en-easy-test-500.tsv', + 'VCR_EN_EASY_100': f'{URL_PREFIX}/VCR-wiki-en-easy-test-100/resolve/main/VCR-wiki-en-easy-test-100.tsv', + 'VCR_EN_EASY_ALL': f'{URL_PREFIX}/VCR-wiki-en-easy-test/resolve/main/VCR-wiki-en-easy-test.tsv', + 'VCR_EN_HARD_500': f'{URL_PREFIX}/VCR-wiki-en-hard-test-500/resolve/main/VCR-wiki-en-hard-test-500.tsv', + 'VCR_EN_HARD_100': f'{URL_PREFIX}/VCR-wiki-en-hard-test-100/resolve/main/VCR-wiki-en-hard-test-100.tsv', + 'VCR_EN_HARD_ALL': f'{URL_PREFIX}/VCR-wiki-en-hard-test/resolve/main/VCR-wiki-en-hard-test.tsv', + 'VCR_ZH_EASY_500': f'{URL_PREFIX}/VCR-wiki-zh-easy-test-500/resolve/main/VCR-wiki-zh-easy-test-500.tsv', + 'VCR_ZH_EASY_100': f'{URL_PREFIX}/VCR-wiki-zh-easy-test-100/resolve/main/VCR-wiki-zh-easy-test-100.tsv', + 'VCR_ZH_EASY_ALL': f'{URL_PREFIX}/VCR-wiki-zh-easy-test/resolve/main/VCR-wiki-zh-easy-test.tsv', + 'VCR_ZH_HARD_500': f'{URL_PREFIX}/VCR-wiki-zh-hard-test-500/resolve/main/VCR-wiki-zh-hard-test-500.tsv', + 'VCR_ZH_HARD_100': f'{URL_PREFIX}/VCR-wiki-zh-hard-test-100/resolve/main/VCR-wiki-zh-hard-test-100.tsv', + 'VCR_ZH_HARD_ALL': f'{URL_PREFIX}/VCR-wiki-zh-hard-test/resolve/main/VCR-wiki-zh-hard-test.tsv', + } + + DATASET_MD5 = { + 'VCR_EN_EASY_500': 'fd9258db52f8685dc710619a0ea0a261', + 'VCR_EN_EASY_100': '9df5d7266683458621ecbe122beb72f0', + 'VCR_EN_EASY_ALL': '8a9b96885f251d1c85f42f84073327f1', + 'VCR_EN_HARD_500': '0a22a85080b6a1f52b1f95e302d43df4', + 'VCR_EN_HARD_100': '1b20f5cbcbeae0b0bec77f7a36143958', + 'VCR_EN_HARD_ALL': '2d8b8b1ee0eba0e0b618fd3aa7d9710e', + 'VCR_ZH_EASY_500': 'beca5fd54176adf44cf94bd9b50cf048', + 'VCR_ZH_EASY_100': '4a86a5678a79844d6d22ab0629c51cd5', + 'VCR_ZH_EASY_ALL': '5050fe7f0027ad2068fd4c7f220edaea', + 'VCR_ZH_HARD_500': '617e3360f75c54455625cb0a8da5c1e7', + 'VCR_ZH_HARD_100': 'b0e38c85f5d5e63894a3b881c372a62b', + 'VCR_ZH_HARD_ALL': '54bbfef448206518b03127ef8b61404c', + } + + def __init__(self, dataset='VCR_EN_EASY_500', skip_noimg=True): + super().__init__(dataset, skip_noimg) + + initialize() + self.language = 'en' if 'EN' in dataset else 'zh' + self.difficulty = 'easy' if 'EASY' in dataset else 'hard' + + # def build_prompt(self, line): + # msgs = super().build_prompt(line) + # assert msgs[-1]['type'] == 'text' + # if self.language == 'zh': + # msgs[-1]['value'] += '图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。' + # else: + # msgs[-1]['value'] += ('What is the covered texts in the image? ' + # 'Please restore the covered texts without outputting the explanations.') + # return msgs + + def evaluate(self, eval_file, **judge_kwargs): + import multiprocessing + + vcr_score_list = {'Exact_Match': [], 'Jaccard': []} + vcr_score = {'Exact_Match': 0, 'Jaccard': 0} + logger = get_logger('Evaluation') + data = load(eval_file) + + lt = len(data) + lines = [data.iloc[i] for i in range(lt)] + + pool = multiprocessing.Pool() + manager = multiprocessing.Manager() + progress_queue = manager.Queue() + results = [] + + overall_results = {str(image_id): {} for image_id in range(len(lines))} + + for instance_id, instance in enumerate(lines): + results.append( + pool.apply_async( + process_match_single_new, + args=( + str(instance_id), + instance['prediction'], + instance['answer'], + self.language, + progress_queue, + ), + ) + ) + pool.close() + + # Display progress bar + for _ in tqdm(range(len(results))): + progress_queue.get() + + pool.join() + + # Merging results into overall_result + for result in results: + image_id, result_per_id = result.get() + overall_results[str(image_id)].update(result_per_id[image_id]) + for blank_id_str in result_per_id[image_id].keys(): + vcr_score_list['Exact_Match'].append( + result_per_id[image_id][blank_id_str]['exact_match'] + ) + vcr_score_list['Jaccard'].append( + result_per_id[image_id][blank_id_str]['jaccard'] + ) + vcr_score['Exact_Match'] = np.mean(vcr_score_list['Exact_Match']) + vcr_score['Jaccard'] = np.mean(vcr_score_list['Jaccard']) + results_out = { + k: v for i in range(len(results)) for k, v in results[i].get()[1].items() + } + results_with_metrics = { + 'Exact_Match': vcr_score['Exact_Match'], + 'Jaccard': vcr_score['Jaccard'], + 'Predictions': results_out, + } + score_pth = eval_file.replace( + '.xlsx', f'{self.language}_{self.difficulty}_score.json' + ) + dump(results_with_metrics, score_pth) + logger.info( + f'VCR successfully finished evaluating {eval_file}, results saved in {score_pth}' + ) + logger.info('Score: ') + for key, value in vcr_score.items(): + logger.info('{}:{}'.format(key, value)) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/video_base.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/video_base.py new file mode 100644 index 0000000000000000000000000000000000000000..ccda1d87cab1e875eb245604d8f81ad03fda4db1 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/video_base.py @@ -0,0 +1,87 @@ +from abc import abstractmethod +from ..smp import * + + +class VideoBaseDataset: + + MODALITY = 'VIDEO' + + def __init__(self, + dataset='MMBench-Video', + pack=False): + try: + import decord + except: + warnings.warn('Please install decord via `pip install decord`.') + + self.dataset_name = dataset + ret = self.prepare_dataset(dataset) + assert ret is not None + lmu_root = LMUDataRoot() + self.frame_root = osp.join(lmu_root, 'images', dataset) + os.makedirs(self.frame_root, exist_ok=True) + self.frame_tmpl = 'frame-{}-of-{}.jpg' + + self.data_root = ret['root'] + self.data_file = ret['data_file'] + self.data = load(self.data_file) + + assert 'question' in self.data and 'video' in self.data + videos = list(set(self.data['video'])) + videos.sort() + self.videos = videos + self.pack = pack + + def __len__(self): + return len(self.videos) if self.pack else len(self.data) + + def __getitem__(self, idx): + if self.pack: + assert idx < len(self.videos) + sub_data = self.data[self.data['video'] == self.videos[idx]] + return sub_data + else: + assert idx < len(self.data) + return dict(self.data.iloc[idx]) + + def frame_paths(self, video, num_frames=8): + frame_root = osp.join(self.frame_root, video) + os.makedirs(frame_root, exist_ok=True) + return [osp.join(frame_root, self.frame_tmpl.format(i, num_frames)) for i in range(1, num_frames + 1)] + + def save_video_frames(self, video, num_frames=8): + frame_paths = self.frame_paths(video, num_frames) + flag = np.all([osp.exists(p) for p in frame_paths]) + if flag: + return frame_paths + vid_path = osp.join(self.data_root, video + '.mp4') + vid = decord.VideoReader(vid_path) + step_size = len(vid) / (num_frames + 1) + indices = [int(i * step_size) for i in range(1, num_frames + 1)] + images = [vid[i].numpy() for i in indices] + images = [Image.fromarray(arr) for arr in images] + for im, pth in zip(images, frame_paths): + if not osp.exists(pth): + im.save(pth) + return frame_paths + + # Return a list of dataset names that are supported by this class, can override + @classmethod + def supported_datasets(cls): + return ['MMBench-Video', 'Video-MME', 'MVBench'] + + # Given the prediction file, return the evaluation results in the format of a dictionary or pandas dataframe + @abstractmethod + def evaluate(self, eval_file, **judge_kwargs): + pass + + @abstractmethod + def build_prompt(self, idx, num_frames=8): + pass + + @abstractmethod + def prepare_dataset(self, dataset): + # The prepare_dataset function should return a dictionary containing: + # `root` (directory that containing video files) + # `data_file` (the TSV dataset file) + pass diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/videomme.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/videomme.py new file mode 100644 index 0000000000000000000000000000000000000000..0b7f63bd8594a5128968053b4a26b12c98490ad9 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/dataset/videomme.py @@ -0,0 +1,250 @@ +from huggingface_hub import snapshot_download +from ..smp import * +from .video_base import VideoBaseDataset + +FAIL_MSG = 'Failed to obtain answer via API.' + + +def unwrap_hf_pkl(pth, suffix='.mp4'): + base_dir = os.path.join(pth, 'video_pkl/') + target_dir = os.path.join(pth, 'video/') + pickle_files = [os.path.join(base_dir, file) for file in os.listdir(base_dir)] + pickle_files.sort() + + if not os.path.exists(target_dir): + os.makedirs(target_dir, exist_ok=True) + for pickle_file in pickle_files: + with open(pickle_file, 'rb') as file: + video_data = pickle.load(file) + # For each video file in the pickle file, write its contents to a new mp4 file + for video_name, video_content in video_data.items(): + output_path = os.path.join(target_dir, f'{video_name}{suffix}') + with open(output_path, 'wb') as output_file: + output_file.write(video_content) + print('The video file has been restored and stored from the pickle file.') + else: + print('The video file already exists.') + + +class VideoMME(VideoBaseDataset): + + MD5 = '2f16cd40b1c125b67e661e59da2f6cd0' + SYS = '' + + FRAMES_TMPL_NOSUB = """ +These are the frames of a video. \ +Select the best answer to the following multiple-choice question based on the video. \ +Respond with only the letter (A, B, C, or D) of the correct option. +""" + + FRAMES_TMPL_SUB = """ +These are the frames of a video. \ +This video's subtitles are listed below: +{} +Select the best answer to the following multiple-choice question based on the video. \ +Respond with only the letter (A, B, C, or D) of the correct option. +""" + + TYPE = 'MCQ' + + def __init__(self, dataset='Video-MME', use_subtitle=False): + super().__init__(dataset=dataset) + self.use_subtitle = use_subtitle + + @classmethod + def supported_datasets(cls): + return ['Video-MME'] + + def prepare_dataset(self, dataset_name='Video-MME', repo_id='lmms-lab/Video-MME'): + + def check_integrity(pth): + data_file = osp.join(pth, f'{dataset_name}.tsv') + + if not os.path.exists(data_file): + return False + + if md5(data_file) != self.MD5: + return False + data = load(data_file) + for video_pth in data['video_path']: + if not osp.exists(osp.join(pth, video_pth)): + return False + return True + + cache_path = get_cache_path(repo_id) + if cache_path is not None and check_integrity(cache_path): + dataset_path = cache_path + else: + + def unzip_hf_zip(pth): + import zipfile + base_dir = pth + target_dir = os.path.join(pth, 'video/') + zip_files = [ + os.path.join(base_dir, file) for file in os.listdir(base_dir) + if file.endswith('.zip') and file.startswith('video') + ] + zip_files.sort() + + if not os.path.exists(target_dir): + os.makedirs(target_dir, exist_ok=True) + for zip_file in zip_files: + with zipfile.ZipFile(zip_file, 'r') as zip_ref: + for member in zip_ref.namelist(): + # Check if the member is a file (not a directory) + if not member.endswith('/'): + # Extract the file to the specified directory + source = zip_ref.open(member) + target = open(os.path.join(target_dir, os.path.basename(member)), 'wb') + with source, target: + target.write(source.read()) + print('The video file has been restored and stored from the zip file.') + else: + print('The video file already exists.') + + subtitle_zip_file = os.path.join(base_dir, 'subtitle.zip') + subtitle_target_dir = os.path.join(base_dir, 'subtitle') + + if not os.path.exists(subtitle_target_dir): + os.makedirs(subtitle_target_dir, exist_ok=True) + with zipfile.ZipFile(subtitle_zip_file, 'r') as zip_ref: + for member in zip_ref.namelist(): + # Check if the member is a file (not a directory) + if not member.endswith('/'): + # Extract the file to the specified directory + source = zip_ref.open(member) + target = open(os.path.join(subtitle_target_dir, os.path.basename(member)), 'wb') + with source, target: + target.write(source.read()) + print('The subtitle file has been restored and stored from the zip file.') + else: + print('The subtitle file already exists.') + + def generate_tsv(pth): + + data_file = osp.join(pth, f'{dataset_name}.tsv') + if os.path.exists(data_file) and md5(data_file) == self.MD5: + return + + data_file = pd.read_parquet(os.path.join(pth, 'videomme/test-00000-of-00001.parquet')) + data_file = data_file.assign(index=range(len(data_file))) + data_file['video'] = data_file['videoID'] + data_file['video_path'] = data_file['videoID'].apply(lambda x: f'./video/{x}.mp4') + data_file['subtitle_path'] = data_file['videoID'].apply(lambda x: f'./subtitle/{x}.srt') + data_file['question'] += '\n' + data_file['options'].apply(lambda x: '\n'.join(x)) + + data_file = data_file[['index', 'video', 'video_path', 'duration', 'domain', + 'sub_category', 'task_type', 'subtitle_path', 'question', 'answer']] + + data_file.to_csv(osp.join(pth, f'{dataset_name}.tsv'), sep='\t', index=False) + + dataset_path = snapshot_download(repo_id=repo_id, repo_type='dataset') + unzip_hf_zip(dataset_path) + generate_tsv(dataset_path) + + data_file = osp.join(dataset_path, f'{dataset_name}.tsv') + + return dict(data_file=data_file, root=dataset_path) + + def save_video_frames(self, video, num_frames=8): + + vid_path = osp.join(self.data_root, 'video', video + '.mp4') + vid = decord.VideoReader(vid_path) + step_size = len(vid) / (num_frames + 1) + indices = [int(i * step_size) for i in range(1, num_frames + 1)] + + video_info = { + 'fps': vid.get_avg_fps(), + 'n_frames': len(vid), + } + + frame_paths = self.frame_paths(video, num_frames) + flag = np.all([osp.exists(p) for p in frame_paths]) + + if not flag: + images = [vid[i].numpy() for i in indices] + images = [Image.fromarray(arr) for arr in images] + for im, pth in zip(images, frame_paths): + if not osp.exists(pth): + im.save(pth) + + return frame_paths, indices, video_info + + def build_prompt(self, line, num_frames, video_llm): + if isinstance(line, int): + assert line < len(self) + line = self.data.iloc[line] + + frames, indices, video_info = self.save_video_frames(line['video'], num_frames) + + if self.use_subtitle and os.path.exists(osp.join(self.data_root, line['subtitle_path'])): + import pysubs2 + subs = pysubs2.load(osp.join(self.data_root, line['subtitle_path']), encoding='utf-8') + subtitles = [] + + for seleced_frame_id in indices: + sub_text = '' + cur_time = pysubs2.make_time(fps=video_info['fps'], frames=seleced_frame_id) + for sub in subs: + if sub.start < cur_time and sub.end > cur_time: + sub_text = sub.text.replace('\\N', ' ') + break + if sub_text.strip(): + subtitles.append(sub_text) + subtitles = '\n'.join(subtitles) + else: + subtitles = '' + + message = [dict(type='text', value=self.SYS)] + if video_llm: + message.append(dict(type='video', value=osp.join(self.data_root, 'video', line['video'] + '.mp4'))) + else: + for im in frames: + message.append(dict(type='image', value=im)) + + text_prompt = self.FRAMES_TMPL_NOSUB if not self.use_subtitle else self.FRAMES_TMPL_SUB.format(subtitles) + message.append(dict(type='text', value=text_prompt)) + prompt = 'Question: {}\nAnswer: '.format(line['question']) + message.append(dict(type='text', value=prompt)) + return message + + # It returns a dictionary + @classmethod + def evaluate(self, eval_file, **judge_kwargs): + from .utils.videomme import get_dimension_rating, extract_characters_regex + + assert eval_file.endswith('.xlsx'), 'data file should be an xlsx file' + + tmp_file = eval_file.replace('.xlsx', '_tmp.pkl') + tgt_file = eval_file.replace('.xlsx', '_rating.json') + score_file = eval_file.replace('.xlsx', '_score.xlsx') + + if not osp.exists(score_file): + res = {} if not osp.exists(tmp_file) else load(tmp_file) + res = {k: v for k, v in res.items() if FAIL_MSG not in v} + + data = load(eval_file) + data_un = data[~pd.isna(data['prediction'])] + + for idx in data['index']: + ans = data.loc[data['index'] == idx, 'answer'].values[0] + pred = data.loc[data['index'] == idx, 'prediction'].values[0] + + if extract_characters_regex(pred) == '': + data.loc[idx, 'score'] = -1 + else: + data.loc[idx, 'score'] = int(extract_characters_regex(pred) == ans) + + rejected = [x for x in data['score'] if x == -1] + + print( + f'Among {len(data)} questions, failed to obtain prediction for {len(data) - len(data_un)} questions, ' + f'failed to obtain the score for another {len(rejected)} questions. ' + f'Those questions will be counted as -1 score in ALL rating, and will not be counted in VALID rating.' + ) + + dump(data, score_file) + + rating = get_dimension_rating(score_file) + dump(rating, tgt_file) + return rating diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/inference.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..b45fba92c16f24259f4a8c361e6ab72762ff774a --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/inference.py @@ -0,0 +1,171 @@ +import torch +import torch.distributed as dist +from vlmeval.config import supported_VLM +from vlmeval.utils import track_progress_rich +from vlmeval.smp import * + +FAIL_MSG = 'Failed to obtain answer via API.' + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, nargs='+', required=True) + parser.add_argument('--model', type=str, nargs='+', required=True) + parser.add_argument('--nproc', type=int, default=4, required=True) + parser.add_argument('--verbose', action='store_true') + args = parser.parse_args() + return args + + +# Only API model is accepted +def infer_data_api(work_dir, model_name, dataset, index_set=None, api_nproc=4, ignore_failed=False): + rank, world_size = get_rank_and_world_size() + assert rank == 0 and world_size == 1 + dataset_name = dataset.dataset_name + data = dataset.data + if index_set is not None: + data = data[data['index'].isin(index_set)] + + model = supported_VLM[model_name]() if isinstance(model_name, str) else model_name + assert getattr(model, 'is_api', False) + + lt, indices = len(data), list(data['index']) + structs = [dataset.build_prompt(data.iloc[i]) for i in range(lt)] + + out_file = f'{work_dir}/{model_name}_{dataset_name}_supp.pkl' + res = {} + if osp.exists(out_file): + res = load(out_file) + if ignore_failed: + res = {k: v for k, v in res.items() if FAIL_MSG not in v} + + structs = [s for i, s in zip(indices, structs) if i not in res] + indices = [i for i in indices if i not in res] + + gen_func = model.generate + structs = [dict(message=struct, dataset=dataset_name) for struct in structs] + + if len(structs): + track_progress_rich(gen_func, structs, nproc=api_nproc, chunksize=api_nproc, save=out_file, keys=indices) + + res = load(out_file) + if index_set is not None: + res = {k: v for k, v in res.items() if k in index_set} + os.remove(out_file) + return res + + +def infer_data(model_name, work_dir, dataset, out_file, verbose=False, api_nproc=4): + dataset_name = dataset.dataset_name + prev_file = f'{work_dir}/{model_name}_{dataset_name}_PREV.pkl' + res = load(prev_file) if osp.exists(prev_file) else {} + if osp.exists(out_file): + res.update(load(out_file)) + + rank, world_size = get_rank_and_world_size() + sheet_indices = list(range(rank, len(dataset), world_size)) + lt = len(sheet_indices) + data = dataset.data.iloc[sheet_indices] + data_indices = [i for i in data['index']] + + # If finished, will exit without building the model + all_finished = True + for i in range(lt): + idx = data.iloc[i]['index'] + if idx not in res: + all_finished = False + if all_finished: + res = {k: res[k] for k in data_indices} + dump(res, out_file) + return + + # Data need to be inferred + data = data[~data['index'].isin(res)] + lt = len(data) + + model = supported_VLM[model_name]() if isinstance(model_name, str) else model_name + + is_api = getattr(model, 'is_api', False) + if is_api: + lt, indices = len(data), list(data['index']) + supp = infer_data_api( + work_dir=work_dir, + model_name=model_name, + dataset=dataset, + index_set=set(indices), + api_nproc=api_nproc) + for idx in indices: + assert idx in supp + res.update(supp) + res = {k: res[k] for k in data_indices} + dump(res, out_file) + return model_name + else: + model.set_dump_image(dataset.dump_image) + + for i in tqdm(range(lt)): + idx = data.iloc[i]['index'] + if idx in res: + continue + + if hasattr(model, 'use_custom_prompt') and model.use_custom_prompt(dataset_name): + struct = model.build_prompt(data.iloc[i], dataset=dataset_name) + else: + struct = dataset.build_prompt(data.iloc[i]) + + response = model.generate(message=struct, dataset=dataset_name) + torch.cuda.empty_cache() + + if verbose: + print(response, flush=True) + + res[idx] = response + if (i + 1) % 20 == 0: + dump(res, out_file) + + res = {k: res[k] for k in data_indices} + dump(res, out_file) + return model + + +# A wrapper for infer_data, do the pre & post processing +def infer_data_job(model, work_dir, model_name, dataset, verbose=False, api_nproc=4, ignore_failed=False): + rank, world_size = get_rank_and_world_size() + dataset_name = dataset.dataset_name + result_file = osp.join(work_dir, f'{model_name}_{dataset_name}.xlsx') + + prev_file = f'{work_dir}/{model_name}_{dataset_name}_PREV.pkl' + if osp.exists(result_file): + if rank == 0: + data = load(result_file) + results = {k: v for k, v in zip(data['index'], data['prediction'])} + if not ignore_failed: + results = {k: v for k, v in results.items() if FAIL_MSG not in str(v)} + dump(results, prev_file) + if world_size > 1: + dist.barrier() + + tmpl = osp.join(work_dir, '{}' + f'{world_size}_{dataset_name}.pkl') + out_file = tmpl.format(rank) + + model = infer_data( + model, work_dir=work_dir, dataset=dataset, out_file=out_file, verbose=verbose, api_nproc=api_nproc) + if world_size > 1: + dist.barrier() + + if rank == 0: + data_all = {} + for i in range(world_size): + data_all.update(load(tmpl.format(i))) + + data = dataset.data + for x in data['index']: + assert x in data_all + data['prediction'] = [str(data_all[x]) for x in data['index']] + if 'image' in data: + data.pop('image') + + dump(data, result_file) + for i in range(world_size): + os.remove(tmpl.format(i)) + return model diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/inference_mt.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/inference_mt.py new file mode 100644 index 0000000000000000000000000000000000000000..0de9ed61d88cdf9a3b80992a9fdf0addae758ecd --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/inference_mt.py @@ -0,0 +1,180 @@ +import torch +import torch.distributed as dist +from vlmeval.config import supported_VLM +from vlmeval.utils import track_progress_rich +from vlmeval.smp import * + +FAIL_MSG = 'Failed to obtain answer via API.' + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, nargs='+', required=True) + parser.add_argument('--model', type=str, nargs='+', required=True) + parser.add_argument('--nproc', type=int, default=4, required=True) + parser.add_argument('--verbose', action='store_true') + args = parser.parse_args() + return args + + +def chat_mt(model, messages, dataset_name): + assert len(messages) % 2 == 0 + nturn = len(messages) // 2 + utter_stack = [] + predictions = [] + + for i in range(nturn): + utter = messages[2 * i] + utter_stack.append(utter) + try: + resp = model.chat(utter_stack, dataset=dataset_name) + utter_stack.append(dict(role='assistant', content=resp)) + except: + resp = FAIL_MSG + utter_stack.append(dict(role='assistant', content=resp)) + predictions.append(resp) + return predictions + + +# Only API model is accepted +def infer_data_api(work_dir, model_name, dataset, index_set=None, api_nproc=4, ignore_failed=False): + rank, world_size = get_rank_and_world_size() + assert rank == 0 and world_size == 1 + dataset_name = dataset.dataset_name + data = dataset.data + if index_set is not None: + data = data[data['index'].isin(index_set)] + + model = supported_VLM[model_name]() if isinstance(model_name, str) else model_name + assert getattr(model, 'is_api', False) + assert hasattr(model, 'chat_inner') + + lt, indices = len(data), list(data['index']) + structs = [dataset.build_prompt(data.iloc[i]) for i in range(lt)] + + out_file = f'{work_dir}/{model_name}_{dataset_name}_supp.pkl' + res = {} + if osp.exists(out_file): + res = load(out_file) + if ignore_failed: + res = {k: v for k, v in res.items() if FAIL_MSG not in v} + + structs = [s for i, s in zip(indices, structs) if i not in res] + indices = [i for i in indices if i not in res] + + structs = [dict(model=model, messages=struct, dataset_name=dataset_name) for struct in structs] + + if len(structs): + track_progress_rich(chat_mt, structs, nproc=api_nproc, chunksize=api_nproc, save=out_file, keys=indices) + + res = load(out_file) + if index_set is not None: + res = {k: v for k, v in res.items() if k in index_set} + os.remove(out_file) + return res + + +def infer_data(model_name, work_dir, dataset, out_file, verbose=False, api_nproc=4): + dataset_name = dataset.dataset_name + res = {} + if osp.exists(out_file): + res.update(load(out_file)) + + rank, world_size = get_rank_and_world_size() + sheet_indices = list(range(rank, len(dataset), world_size)) + lt = len(sheet_indices) + data = dataset.data.iloc[sheet_indices] + data_indices = [i for i in data['index']] + + # If finished, will exit without building the model + all_finished = True + for i in range(lt): + idx = data.iloc[i]['index'] + if idx not in res: + all_finished = False + if all_finished: + res = {k: res[k] for k in data_indices} + dump(res, out_file) + return + + # Data need to be inferred + data = data[~data['index'].isin(res)] + lt = len(data) + + model = supported_VLM[model_name]() if isinstance(model_name, str) else model_name + assert hasattr(model, 'chat_inner') + + is_api = getattr(model, 'is_api', False) + if is_api: + lt, indices = len(data), list(data['index']) + supp = infer_data_api( + work_dir=work_dir, + model_name=model_name, + dataset=dataset, + index_set=set(indices), + api_nproc=api_nproc) + for idx in indices: + assert idx in supp + res.update(supp) + res = {k: res[k] for k in data_indices} + dump(res, out_file) + return model_name + else: + model.set_dump_image(dataset.dump_image) + + for i in tqdm(range(lt)): + idx = data.iloc[i]['index'] + if idx in res: + continue + + if hasattr(model, 'use_custom_prompt') and model.use_custom_prompt(dataset_name): + struct = model.build_prompt(data.iloc[i], dataset=dataset_name) + else: + struct = dataset.build_prompt(data.iloc[i]) + + response = chat_mt(model, struct, dataset_name) + torch.cuda.empty_cache() + + if verbose: + print(response, flush=True) + + res[idx] = response + if (i + 1) % 20 == 0: + dump(res, out_file) + + res = {k: res[k] for k in data_indices} + dump(res, out_file) + return model + + +# A wrapper for infer_data, do the pre & post processing +def infer_data_job_mt(model, work_dir, model_name, dataset, verbose=False, api_nproc=4, ignore_failed=False): + rank, world_size = get_rank_and_world_size() + dataset_name = dataset.dataset_name + result_file = osp.join(work_dir, f'{model_name}_{dataset_name}.tsv') + + tmpl = osp.join(work_dir, '{}' + f'{world_size}_{dataset_name}.pkl') + out_file = tmpl.format(rank) + + model = infer_data( + model, work_dir=work_dir, dataset=dataset, out_file=out_file, verbose=verbose, api_nproc=api_nproc) + if world_size > 1: + dist.barrier() + + if rank == 0: + data_all = {} + for i in range(world_size): + data_all.update(load(tmpl.format(i))) + + data = dataset.data + for x in data['index']: + assert x in data_all + + data['prediction'] = [data_all[x] for x in data['index']] + if 'image' in data: + data.pop('image') + + dump(data, result_file) + for i in range(world_size): + os.remove(tmpl.format(i)) + return model diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/inference_video.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/inference_video.py new file mode 100644 index 0000000000000000000000000000000000000000..05acb2a6f4bc9283c72acc9ea423f6fa7c00d972 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/inference_video.py @@ -0,0 +1,166 @@ +import torch +import torch.distributed as dist +from vlmeval.config import supported_VLM +from vlmeval.utils import track_progress_rich +from vlmeval.smp import * + +FAIL_MSG = 'Failed to obtain answer via API.' + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, nargs='+', required=True) + parser.add_argument('--model', type=str, nargs='+', required=True) + parser.add_argument('--nproc', type=int, default=4, required=True) + parser.add_argument('--verbose', action='store_true') + args = parser.parse_args() + return args + + +# Only API model is accepted +def infer_data_api(work_dir, model_name, dataset, nframe=8, pack=False, samples_dict={}, api_nproc=4): + rank, world_size = get_rank_and_world_size() + assert rank == 0 and world_size == 1 + dataset_name = dataset.dataset_name + model = supported_VLM[model_name]() if isinstance(model_name, str) else model_name + assert getattr(model, 'is_api', False) + + indices = list(samples_dict.keys()) + structs = [dataset.build_prompt(samples_dict[idx], num_frames=nframe, + video_llm=getattr(model, 'VIDEO_LLM', False)) for idx in indices] + + packstr = 'pack' if pack else 'nopack' + out_file = f'{work_dir}/{model_name}_{dataset_name}_{nframe}frame_{packstr}_supp.pkl' + res = load(out_file) if osp.exists(out_file) else {} + + structs = [s for i, s in zip(indices, structs) if i not in res] + indices = [i for i in indices if i not in res] + + gen_func = model.generate + structs = [dict(message=struct, dataset=dataset_name) for struct in structs] + + if len(structs): + track_progress_rich(gen_func, structs, nproc=api_nproc, chunksize=api_nproc, save=out_file, keys=indices) + + res = load(out_file) + return res + + +def infer_data(model_name, work_dir, dataset, out_file, nframe=8, pack=False, verbose=False, api_nproc=4): + res = load(out_file) if osp.exists(out_file) else {} + rank, world_size = get_rank_and_world_size() + dataset_name = dataset.dataset_name + + sample_indices = list(dataset.videos) if pack else list(dataset.data['index']) + samples = list(dataset.videos) if pack else list(range(len(dataset.data))) + sample_map = {i: s for i, s in zip(sample_indices, samples)} + + sample_indices_sub = sample_indices[rank::world_size] + if np.all([idx in res for idx in sample_indices_sub]): + return model_name + sample_indices_subrem = [x for x in sample_indices_sub if x not in res] + + model = supported_VLM[model_name]() if isinstance(model_name, str) else model_name + + is_api = getattr(model, 'is_api', False) + if is_api: + assert world_size == 1 + supp = infer_data_api( + work_dir=work_dir, + model_name=model_name, + dataset=dataset, + nframe=nframe, + pack=pack, + samples_dict={k: sample_map[k] for k in sample_indices_subrem}, + api_nproc=api_nproc) + for k in sample_indices_subrem: + assert k in supp + res.update(supp) + dump(res, out_file) + return model_name + + for i, idx in tqdm(enumerate(sample_indices_subrem)): + if idx in res: + continue + # adapt to model frame sample number first + nframe = getattr(model, 'nframe', 0) if getattr(model, 'nframe', 0) > 0 else nframe + # when using video-llm, build prompt returns video+question; otherwise, several frames+question + struct = dataset.build_prompt(sample_map[idx], num_frames=nframe, video_llm=getattr(model, 'VIDEO_LLM', False)) + response = model.generate(message=struct, dataset=dataset_name) + torch.cuda.empty_cache() + + if verbose: + print(response, flush=True) + + res[idx] = response + if (i + 1) % 20 == 0: + dump(res, out_file) + + res = {k: res[k] for k in sample_indices_sub} + dump(res, out_file) + return model + + +# A wrapper for infer_data, do the pre & post processing +def infer_data_job_video( + model, + work_dir, + model_name, + dataset, + nframe=8, + pack=False, + verbose=False, + subtitle=False, + api_nproc=4): + + dataset_name = dataset.dataset_name + packstr = 'pack' if pack else 'nopack' + rank, world_size = get_rank_and_world_size() + result_file = osp.join(work_dir, f'{model_name}_{dataset_name}_{nframe}frame_{packstr}.xlsx') + if dataset_name == 'Video-MME': + subtitle_str = 'subs' if subtitle else 'nosubs' + result_file = result_file.replace('.xlsx', f'_{subtitle_str}.xlsx') + + # Dump Predictions to Prev File if result file exists + if osp.exists(result_file): + return model_name + + tmpl = osp.join(work_dir, '{}' + f'{world_size}_{dataset_name}_{nframe}frame_{packstr}.pkl') + if dataset_name == 'Video-MME': + subtitle_str = 'subs' if subtitle else 'nosubs' + tmpl = tmpl.replace('.pkl', f'_{subtitle_str}.pkl') + out_file = tmpl.format(rank) + + model = infer_data( + model, + work_dir=work_dir, + dataset=dataset, + nframe=nframe, + pack=pack, + out_file=out_file, + verbose=verbose, + api_nproc=api_nproc) + + if world_size > 1: + dist.barrier() + + if rank == 0: + data_all = {} + for i in range(world_size): + data_all.update(load(tmpl.format(i))) + + meta = dataset.data + if dataset_name == 'MMBench-Video' and pack: + meta, vstats = dataset.load_pack_answers(data_all) + print(f'Statitics of Pack Video Inference: {vstats}') + else: + for x in meta['index']: + assert x in data_all + meta['prediction'] = [str(data_all[x]) for x in meta['index']] + if 'image' in meta: + meta.pop('image') + + dump(meta, result_file) + for i in range(world_size): + os.remove(tmpl.format(i)) + return model diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/__init__.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..46e89687d469b83ec7dd7e3205841d35087108c7 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/__init__.py @@ -0,0 +1,4 @@ +from .file import * +from .vlm import * +from .misc import * +from .log import * diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/file.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/file.py new file mode 100644 index 0000000000000000000000000000000000000000..cb39d5695966e3d204179eab9f4c32d6444ae3c0 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/file.py @@ -0,0 +1,318 @@ +import json +import pickle +import pandas as pd +import os +import csv +import hashlib +import os.path as osp +import time +import numpy as np +import validators +import mimetypes +import multiprocessing as mp +from .misc import toliststr +from .vlm import decode_base64_to_image_file + + +def decode_img_omni(tup): + root, im, p = tup + images = toliststr(im) + paths = toliststr(p) + if len(images) > 1 and len(paths) == 1: + paths = [osp.splitext(p)[0] + f'_{i}' + osp.splitext(p)[1] for i in range(len(images))] + + assert len(images) == len(paths) + paths = [osp.join(root, p) for p in paths] + for p, im in zip(paths, images): + if osp.exists(p): + continue + if isinstance(im, str) and len(im) > 64: + decode_base64_to_image_file(im, p) + return paths + + +def localize_df(data, dname, nproc=32): + assert 'image' in data + indices = list(data['index']) + indices_str = [str(x) for x in indices] + images = list(data['image']) + image_map = {x: y for x, y in zip(indices_str, images)} + + root = LMUDataRoot() + root = osp.join(root, 'images', dname) + os.makedirs(root, exist_ok=True) + + if 'image_path' in data: + img_paths = list(data['image_path']) + else: + img_paths = [] + for i in indices_str: + if len(image_map[i]) <= 64: + idx = image_map[i] + assert idx in image_map and len(image_map[idx]) > 64 + img_paths.append(f'{idx}.jpg') + else: + img_paths.append(f'{i}.jpg') + + tups = [(root, im, p) for p, im in zip(img_paths, images)] + + pool = mp.Pool(32) + ret = pool.map(decode_img_omni, tups) + pool.close() + data.pop('image') + if 'image_path' not in data: + data['image_path'] = [x[0] if len(x) == 1 else x for x in ret] + return data + + +def LMUDataRoot(): + if 'LMUData' in os.environ and osp.exists(os.environ['LMUData']): + return os.environ['LMUData'] + home = osp.expanduser('~') + root = osp.join(home, 'LMUData') + os.makedirs(root, exist_ok=True) + return root + + +def MMBenchOfficialServer(dataset_name): + root = LMUDataRoot() + + if dataset_name in ['MMBench', 'MMBench_V11', 'MMBench_CN', 'MMBench_CN_V11']: + ans_file = f'{root}/{dataset_name}.tsv' + if osp.exists(ans_file): + data = load(ans_file) + if 'answer' in data and sum([pd.isna(x) for x in data['answer']]) == 0: + return True + + if dataset_name in ['MMBench_TEST_EN', 'MMBench_TEST_CN', 'MMBench_TEST_EN_V11', 'MMBench_TEST_CN_V11']: + ans_file1 = f'{root}/{dataset_name}.tsv' + mapp = { + 'MMBench_TEST_EN': 'MMBench', 'MMBench_TEST_CN': 'MMBench_CN', + 'MMBench_TEST_EN_V11': 'MMBench_V11', 'MMBench_TEST_CN_V11': 'MMBench_CN_V11', + } + ans_file2 = f'{root}/{mapp[dataset_name]}.tsv' + for f in [ans_file1, ans_file2]: + if osp.exists(f): + data = load(f) + if 'answer' in data and sum([pd.isna(x) for x in data['answer']]) == 0: + return True + return False + + +class NumpyEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, (np.int_, np.intc, np.intp, np.int8, + np.int16, np.int32, np.int64, np.uint8, + np.uint16, np.uint32, np.uint64)): + return int(obj) + elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)): + return float(obj) + elif isinstance(obj, (np.complex_, np.complex64, np.complex128)): + return {'real': obj.real, 'imag': obj.imag} + elif isinstance(obj, (np.ndarray,)): + return obj.tolist() + elif isinstance(obj, (np.bool_)): + return bool(obj) + elif isinstance(obj, (np.void)): + return None + return json.JSONEncoder.default(self, obj) + + +# LOAD & DUMP +def dump(data, f, **kwargs): + def dump_pkl(data, pth, **kwargs): + pickle.dump(data, open(pth, 'wb')) + + def dump_json(data, pth, **kwargs): + json.dump(data, open(pth, 'w'), indent=4, ensure_ascii=False, cls=NumpyEncoder) + + def dump_jsonl(data, f, **kwargs): + lines = [json.dumps(x, ensure_ascii=False, cls=NumpyEncoder) for x in data] + with open(f, 'w', encoding='utf8') as fout: + fout.write('\n'.join(lines)) + + def dump_xlsx(data, f, **kwargs): + data.to_excel(f, index=False, engine='xlsxwriter') + + def dump_csv(data, f, quoting=csv.QUOTE_ALL): + data.to_csv(f, index=False, encoding='utf-8', quoting=quoting) + + def dump_tsv(data, f, quoting=csv.QUOTE_ALL): + data.to_csv(f, sep='\t', index=False, encoding='utf-8', quoting=quoting) + + handlers = dict(pkl=dump_pkl, json=dump_json, jsonl=dump_jsonl, xlsx=dump_xlsx, csv=dump_csv, tsv=dump_tsv) + suffix = f.split('.')[-1] + return handlers[suffix](data, f, **kwargs) + + +def load(f, fmt=None): + def load_pkl(pth): + return pickle.load(open(pth, 'rb')) + + def load_json(pth): + return json.load(open(pth, 'r', encoding='utf-8')) + + def load_jsonl(f): + lines = open(f, encoding='utf-8').readlines() + lines = [x.strip() for x in lines] + if lines[-1] == '': + lines = lines[:-1] + data = [json.loads(x) for x in lines] + return data + + def load_xlsx(f): + return pd.read_excel(f) + + def load_csv(f): + return pd.read_csv(f) + + def load_tsv(f): + return pd.read_csv(f, sep='\t') + + handlers = dict(pkl=load_pkl, json=load_json, jsonl=load_jsonl, xlsx=load_xlsx, csv=load_csv, tsv=load_tsv) + if fmt is not None: + return handlers[fmt](f) + + suffix = f.split('.')[-1] + return handlers[suffix](f) + + +def download_file(url, filename=None): + import urllib.request + from tqdm import tqdm + + class DownloadProgressBar(tqdm): + def update_to(self, b=1, bsize=1, tsize=None): + if tsize is not None: + self.total = tsize + self.update(b * bsize - self.n) + + if filename is None: + filename = url.split('/')[-1] + + # If HF_ENDPOINT is set, replace huggingface.co with it + if 'huggingface.co' in url and os.environ.get('HF_ENDPOINT', '') != '': + url = url.replace('huggingface.co', os.environ['HF_ENDPOINT'].split('://')[1]) + + try: + with DownloadProgressBar(unit='B', unit_scale=True, miniters=1, desc=url.split('/')[-1]) as t: + urllib.request.urlretrieve(url, filename=filename, reporthook=t.update_to) + except: + # Handle Failed Downloads from huggingface.co + if 'huggingface.co' in url: + url_new = url.replace('huggingface.co', 'hf-mirror.com') + try: + os.system(f'wget {url_new} -O {filename}') + except: + raise Exception(f'Failed to download {url}') + else: + raise Exception(f'Failed to download {url}') + + return filename + + +def ls(dirname='.', match=[], mode='all', level=1): + if isinstance(level, str): + assert '+' in level + level = int(level[:-1]) + res = [] + for i in range(1, level + 1): + res.extend(ls(dirname, match=match, mode='file', level=i)) + return res + + if dirname == '.': + ans = os.listdir(dirname) + else: + ans = [osp.join(dirname, x) for x in os.listdir(dirname)] + assert mode in ['all', 'dir', 'file'] + assert level >= 1 and isinstance(level, int) + if level == 1: + if isinstance(match, str): + match = [match] + for m in match: + if len(m) == 0: + continue + if m[0] != '!': + ans = [x for x in ans if m in x] + else: + ans = [x for x in ans if m[1:] not in x] + if mode == 'dir': + ans = [x for x in ans if osp.isdir(x)] + elif mode == 'file': + ans = [x for x in ans if not osp.isdir(x)] + return ans + else: + dirs = [x for x in ans if osp.isdir(x)] + res = [] + for d in dirs: + res.extend(ls(d, match=match, mode=mode, level=level - 1)) + return res + + +def mrlines(fname, sp='\n'): + f = open(fname).read().split(sp) + while f != [] and f[-1] == '': + f = f[:-1] + return f + + +def mwlines(lines, fname): + with open(fname, 'w') as fout: + fout.write('\n'.join(lines)) + + +def md5(s): + hash = hashlib.new('md5') + if osp.exists(s): + with open(s, 'rb') as f: + for chunk in iter(lambda: f.read(2**20), b''): + hash.update(chunk) + else: + hash.update(s.encode('utf-8')) + return str(hash.hexdigest()) + + +def last_modified(pth): + stamp = osp.getmtime(pth) + m_ti = time.ctime(stamp) + t_obj = time.strptime(m_ti) + t = time.strftime('%Y%m%d%H%M%S', t_obj)[2:] + return t + + +def parse_file(s): + if osp.exists(s) and s != '.': + assert osp.isfile(s) + suffix = osp.splitext(s)[1].lower() + mime = mimetypes.types_map.get(suffix, 'unknown') + return (mime, s) + elif validators.url(s): + suffix = osp.splitext(s)[1].lower() + if suffix in mimetypes.types_map: + mime = mimetypes.types_map[suffix] + dname = osp.join(LMUDataRoot(), 'files') + os.makedirs(dname, exist_ok=True) + tgt = osp.join(dname, md5(s) + suffix) + download_file(s, tgt) + return (mime, tgt) + else: + return ('url', s) + else: + return (None, s) + + +def file_size(f, unit='GB'): + stats = os.stat(f) + div_map = { + 'GB': 2 ** 30, + 'MB': 2 ** 20, + 'KB': 2 ** 10, + } + return stats.st_size / div_map[unit] + + +def parquet_to_tsv(file_path): + data = pd.read_parquet(file_path) + pth = '/'.join(file_path.split('/')[:-1]) + data_name = file_path.split('/')[-1].split('.')[0] + data.to_csv(osp.join(pth, f'{data_name}.tsv'), sep='\t', index=False) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/log.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/log.py new file mode 100644 index 0000000000000000000000000000000000000000..95804d5ed4048e6aca6535e1e0fde64525c0fac3 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/log.py @@ -0,0 +1,44 @@ +import logging + +logger_initialized = {} + + +def get_logger(name, log_file=None, log_level=logging.INFO, file_mode='w'): + logger = logging.getLogger(name) + if name in logger_initialized: + return logger + + for logger_name in logger_initialized: + if name.startswith(logger_name): + return logger + + stream_handler = logging.StreamHandler() + handlers = [stream_handler] + + try: + import torch.distributed as dist + if dist.is_available() and dist.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 + except ImportError: + rank = 0 + + if rank == 0 and log_file is not None: + file_handler = logging.FileHandler(log_file, file_mode) + handlers.append(file_handler) + + formatter = logging.Formatter( + '%(asctime)s - %(name)s - %(levelname)s - %(message)s') + for handler in handlers: + handler.setFormatter(formatter) + handler.setLevel(log_level) + logger.addHandler(handler) + + if rank == 0: + logger.setLevel(log_level) + else: + logger.setLevel(logging.ERROR) + + logger_initialized[name] = True + return logger diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/misc.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..aad223ddf2b7ed6b8e57b1236d404d2f4ae2ffb4 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/misc.py @@ -0,0 +1,216 @@ +# flake8: noqa: F401, F403 +import abc +import argparse +import csv +import multiprocessing as mp +import os +import os.path as osp +import copy as cp +import random as rd +import requests +import shutil +import subprocess +import warnings +import logging +import pandas as pd +from collections import OrderedDict, defaultdict +from multiprocessing import Pool, current_process +from tqdm import tqdm +import datetime +import matplotlib.pyplot as plt +from tabulate import tabulate +from json import JSONDecoder +from huggingface_hub import scan_cache_dir +from sty import fg, bg, ef, rs + +def process_punctuation(inText): + import re + outText = inText + punct = [ + ';', r'/', '[', ']', '"', '{', '}', '(', ')', '=', '+', '\\', '_', '-', + '>', '<', '@', '`', ',', '?', '!' + ] + commaStrip = re.compile('(\d)(,)(\d)') # noqa: W605 + periodStrip = re.compile('(?!<=\d)(\.)(?!\d)') # noqa: W605 + for p in punct: + if (p + ' ' in inText or ' ' + p in inText) or (re.search( + commaStrip, inText) is not None): + outText = outText.replace(p, '') + else: + outText = outText.replace(p, ' ') + outText = periodStrip.sub('', outText, re.UNICODE) + return outText + +def h2r(value): + if value[0] == '#': + value = value[1:] + assert len(value) == 6 + return tuple(int(value[i:i + 2], 16) for i in range(0, 6, 2)) + +def r2h(rgb): + return '#%02x%02x%02x' % rgb + +def colored(s, color): + if isinstance(color, str): + if hasattr(fg, color): + return getattr(fg, color) + s + fg.rs + color = h2r(color) + return fg(*color) + s + fg.rs + +def istype(s, type): + if isinstance(s, type): + return True + try: + return isinstance(eval(s), type) + except Exception as _: + return False + +def bincount(lst): + bins = defaultdict(lambda: 0) + for item in lst: + bins[item] += 1 + return bins + +def get_cache_path(repo_id, branch=None): + hf_cache_info = scan_cache_dir() + repos = list(hf_cache_info.repos) + repo = None + for r in repos: + if r.repo_id == repo_id: + repo = r + break + if repo is None: + return None + revs = list(repo.revisions) + if branch is not None: + revs = [r for r in revs if r.refs == frozenset({branch})] + rev2keep, last_modified = None, 0 + for rev in revs: + if rev.last_modified > last_modified: + rev2keep, last_modified = rev, rev.last_modified + if rev2keep is None: + return None + return str(rev2keep.snapshot_path) + +def proxy_set(s): + import os + for key in ['http_proxy', 'HTTP_PROXY', 'https_proxy', 'HTTPS_PROXY']: + os.environ[key] = s + +def get_rank_and_world_size(): + rank = int(os.environ.get('RANK', 0)) + world_size = int(os.environ.get('WORLD_SIZE', 1)) + return rank, world_size + +def splitlen(s, sym='/'): + return len(s.split(sym)) + +def listinstr(lst, s): + assert isinstance(lst, list) + for item in lst: + if item in s: + return True + return False + +def d2df(D): + return pd.DataFrame({x: [D[x]] for x in D}) + +def cn_string(s): + import re + if re.search(u'[\u4e00-\u9fff]', s): + return True + return False + +try: + import decord +except ImportError: + pass + +def timestr(second=True, minute=False): + s = datetime.datetime.now().strftime('%Y%m%d%H%M%S')[2:] + if second: + return s + elif minute: + return s[:-2] + else: + return s[:-4] + +def dict_merge(dct, merge_dct): + for k, _ in merge_dct.items(): + if (k in dct and isinstance(dct[k], dict) and isinstance(merge_dct[k], dict)): #noqa + dict_merge(dct[k], merge_dct[k]) + else: + dct[k] = merge_dct[k] + +def youtube_dl(idx): + cmd = f'youtube-dl -f best -f mp4 "{idx}" -o {idx}.mp4' + os.system(cmd) + +def run_command(cmd): + if isinstance(cmd, str): + cmd = cmd.split() + return subprocess.check_output(cmd).decode() + +def load_env(): + logger = logging.getLogger('LOAD_ENV') + try: + import vlmeval + except ImportError: + logger.error('VLMEval is not installed. Failed to import environment variables from .env file. ') + return + pth = osp.realpath(vlmeval.__path__[0]) + pth = osp.join(pth, '../.env') + pth = osp.realpath(pth) + if not osp.exists(pth): + logger.error(f'Did not detect the .env file at {pth}, failed to load. ') + return + + from dotenv import dotenv_values + values = dotenv_values(pth) + for k, v in values.items(): + if v is not None and len(v): + os.environ[k] = v + logger.info(f'API Keys successfully loaded from {pth}') + +def pip_install_robust(package): + import sys + retry = 3 + while retry > 0: + try: + package_base = package.split('=')[0] + module = __import__(package) + return True + except ImportError: + subprocess.check_call([sys.executable, '-m', 'pip', 'install', package]) + retry -= 1 + return False + + +def version_cmp(v1, v2, op='eq'): + from packaging import version + import operator + op_func = getattr(operator, op) + return op_func(version.parse(v1), version.parse(v2)) + + +def toliststr(s): + if isinstance(s, str) and (s[0] == '[') and (s[-1] == ']'): + return [str(x) for x in eval(s)] + elif isinstance(s, str): + return [s] + elif isinstance(s, list): + return [str(x) for x in s] + raise NotImplementedError + + +def extract_json_objects(text, decoder=JSONDecoder()): + pos = 0 + while True: + match = text.find('{', pos) + if match == -1: break + try: + result, index = decoder.raw_decode(text[match:]) + yield result + pos = match + index + except ValueError: + pos = match + 1 diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/vlm.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/vlm.py new file mode 100644 index 0000000000000000000000000000000000000000..efa3821376d0a3ee22a10facd2f51b14c20bd21c --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/smp/vlm.py @@ -0,0 +1,179 @@ +import os +import io +import pandas as pd +import numpy as np +import string +from uuid import uuid4 +import os.path as osp +import base64 +from PIL import Image +import sys + +Image.MAX_IMAGE_PIXELS = 1e9 + + +def rescale_img(img, tgt=None): + assert isinstance(tgt, tuple) and -1 in tgt + w, h = img.size + if tgt[0] != -1: + new_w, new_h = tgt[0], int(tgt[0] / w * h) + elif tgt[1] != -1: + new_w, new_h = int(tgt[1] / h * w), tgt[1] + img = img.resize((new_w, new_h)) + return img + + +def concat_images_vlmeval(images, target_size=-1, mode='h', return_image=False): + from .file import md5 + + ims = [Image.open(im) for im in images] + if target_size != -1: + ims = [ + rescale_img(im, (-1, target_size) if mode == 'h' else (target_size, -1)) + for im in ims + ] + + ws, hs = [x.width for x in ims], [x.height for x in ims] + if mode == 'h': + new_w, new_h = sum(ws), max(hs) + dst = Image.new('RGB', (new_w, new_h)) + for i, im in enumerate(ims): + dst.paste(im, (sum(ws[:i]), 0)) + elif mode == 'v': + new_w, new_h = max(ws), sum(hs) + dst = Image.new('RGB', (new_w, new_h)) + for i, im in enumerate(ims): + dst.paste(im, (sum(ws[:i], 0))) + if return_image: + return dst + else: + _str = '\n'.join(images) + str_md5 = md5(_str) + tgt = osp.join('/tmp', str_md5 + '.jpg') + dst.save(tgt) + return tgt + + +def mmqa_display(question, target_size=512): + question = {k.lower(): v for k, v in question.items()} + keys = list(question.keys()) + keys = [k for k in keys if k not in ['index', 'image']] + + images = question['image'] + if isinstance(images, str): + images = [images] + + idx = question.pop('index', 'XXX') + print(f'INDEX: {idx}') + + for im in images: + image = decode_base64_to_image(im, target_size=target_size) + display(image) # noqa: F821 + + for k in keys: + try: + if not pd.isna(question[k]): + print(f'{k.upper()}. {question[k]}') + except ValueError: + if False in pd.isna(question[k]): + print(f'{k.upper()}. {question[k]}') + + +def encode_image_to_base64(img, target_size=-1): + # if target_size == -1, will not do resizing + # else, will set the max_size ot (target_size, target_size) + if img.mode in ('RGBA', 'P'): + img = img.convert('RGB') + if target_size > 0: + img.thumbnail((target_size, target_size)) + img_buffer = io.BytesIO() + img.save(img_buffer, format='JPEG') + image_data = img_buffer.getvalue() + ret = base64.b64encode(image_data).decode('utf-8') + return ret + + +def encode_image_file_to_base64(image_path, target_size=-1): + image = Image.open(image_path) + return encode_image_to_base64(image, target_size=target_size) + + +def decode_base64_to_image(base64_string, target_size=-1): + image_data = base64.b64decode(base64_string) + image = Image.open(io.BytesIO(image_data)) + if image.mode in ('RGBA', 'P'): + image = image.convert('RGB') + if target_size > 0: + image.thumbnail((target_size, target_size)) + return image + + +def decode_base64_to_image_file(base64_string, image_path, target_size=-1): + image = decode_base64_to_image(base64_string, target_size=target_size) + image.save(image_path) + + +def build_option_str(option_dict): + s = 'There are several options: \n' + for c, content in option_dict.items(): + if not pd.isna(content): + s += f'{c}. {content}\n' + return s + + +def isimg(s): + return osp.exists(s) or s.startswith('http') + + +def read_ok(img_path): + if not osp.exists(img_path): + return False + try: + im = Image.open(img_path) + assert im.size[0] > 0 and im.size[1] > 0 + return True + except: + return False + + +def gpt_key_set(): + openai_key = os.environ.get('OPENAI_API_KEY', None) + return isinstance(openai_key, str) and openai_key.startswith('sk-') + + +def apiok(wrapper): + s = wrapper.generate('Hello!') + return wrapper.fail_msg not in s + + +def circular_pred(df, extract_func=None): + if extract_func is None: + extract_func = lambda x: x # noqa: E731 + df = df.sort_values('index') + from vlmeval.utils import can_infer_option + + shift = int(1e6) + + choices = [extract_func(x) for x in df['prediction']] + pred_map = {i: c for i, c in zip(df['index'], choices)} + flag_map = {i: True for i in pred_map if i < 1e6} + valid_map = {i: True for i in pred_map if i < 1e6} + for i in df['index']: + if i >= shift and pred_map[i] and pred_map[i - shift]: + if pred_map[i] not in list( + string.ascii_uppercase + ) or pred_map[ # noqa: W504 + i - shift + ] not in list( + string.ascii_uppercase + ): + + valid_map[i % shift] = False + continue + if (ord(pred_map[i]) - ord(pred_map[i - shift])) % 4 == 1: + continue + else: + flag_map[i % shift] = False + flag_map = {k: v for k, v in flag_map.items() if valid_map[k]} + flags = list(flag_map.values()) + return np.mean(flags) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/tools.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/tools.py new file mode 100644 index 0000000000000000000000000000000000000000..d665f4ab570d325ea1903ce6d82f1aadee56edab --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/tools.py @@ -0,0 +1,421 @@ +import sys +from vlmeval.config import * +from vlmeval.smp import * + +# Define valid modes +MODES = ('dlist', 'mlist', 'missing', 'circular', 'localize', 'check', 'run', 'eval') + +CLI_HELP_MSG = \ + f""" + Arguments received: {str(['vlmutil'] + sys.argv[1:])}. vlmutil commands use the following syntax: + + vlmutil MODE MODE_ARGS + + Where MODE (required) is one of {MODES} + MODE_ARG (optional) is the argument for specific mode + + Some usages for xtuner commands: (See more by using -h for specific command!) + + 1. List all the dataset by levels: l1, l2, l3, etc.: + vlmutil dlist [l1/l2/l3/...] + 2. List all the models by categories: 4.33.0, 4.37.0, api, etc.: + vlmutil mlist 4.33.0 [all/small/large] + 3. Report missing results: + vlmutil missing [l1/l2/l3/...] + 4. Create circular questions (only for multiple-choice questions with no more than 4 choices): + vlmutil circular input.tsv + 5. Create a localized version of the dataset (for very large tsv files): + vlmutil localize input.tsv + 6. Check the validity of a model: + vlmutil check [model_name/model_series] + 7. Run evaluation for missing results: + vlmutil run l2 hf + 8. Evaluate data file: + vlmutil eval [dataset_name] [prediction_file] + + GitHub: https://github.com/open-compass/VLMEvalKit + """ # noqa: E501 + + +dataset_levels = { + 'l1': [ + ('MMVet', 'gpt-4-turbo_score.csv'), ('MMMU_DEV_VAL', 'acc.csv'), + ('MathVista_MINI', 'gpt-4-turbo_score.csv'), ('HallusionBench', 'score.csv'), + ('OCRBench', 'score.json'), ('AI2D_TEST', 'acc.csv'), ('MMStar', 'acc.csv'), + ('MMBench_V11', 'acc.csv'), ('MMBench_CN_V11', 'acc.csv') + ], + 'l2': [ + ('MME', 'score.csv'), ('LLaVABench', 'score.csv'), ('RealWorldQA', 'acc.csv'), + ('MMBench', 'acc.csv'), ('MMBench_CN', 'acc.csv'), ('CCBench', 'acc.csv'), + ('SEEDBench_IMG', 'acc.csv'), ('COCO_VAL', 'score.json'), ('POPE', 'score.csv'), + ('ScienceQA_VAL', 'acc.csv'), ('ScienceQA_TEST', 'acc.csv'), ('MMT-Bench_VAL', 'acc.csv'), + ('SEEDBench2_Plus', 'acc.csv'), ('BLINK', 'acc.csv'), ('MTVQA_TEST', 'acc.json'), + ('Q-Bench1_VAL', 'acc.csv'), ('A-Bench_VAL', 'acc.csv') + ], + 'l3': [ + ('OCRVQA_TESTCORE', 'acc.csv'), ('TextVQA_VAL', 'acc.csv'), + ('ChartQA_TEST', 'acc.csv'), ('DocVQA_VAL', 'acc.csv'), ('InfoVQA_VAL', 'acc.csv'), + ('SEEDBench2', 'acc.csv') + ] +} + +dataset_levels['l12'] = dataset_levels['l1'] + dataset_levels['l2'] +dataset_levels['l23'] = dataset_levels['l2'] + dataset_levels['l3'] +dataset_levels['l123'] = dataset_levels['l12'] + dataset_levels['l3'] + +models = { + '4.33.0': list(qwen_series) + list(xcomposer_series) + [ + 'mPLUG-Owl2', 'flamingov2', 'VisualGLM_6b', 'MMAlaya', 'PandaGPT_13B', 'VXVERSE' + ] + list(idefics_series) + list(minigpt4_series) + list(instructblip_series), + '4.37.0': [x for x in llava_series if 'next' not in x] + list(internvl_series) + [ + 'TransCore_M', 'emu2_chat', 'MiniCPM-V', 'MiniCPM-V-2', 'OmniLMM_12B', + 'cogvlm-grounding-generalist', 'cogvlm-chat', 'cogvlm2-llama3-chat-19B', + ] + list(xtuner_series) + list(yivl_series) + list(deepseekvl_series) + list(cambrian_series), + '4.40.0': [ + 'idefics2_8b', 'Bunny-llama3-8B', 'MiniCPM-Llama3-V-2_5', '360VL-70B', 'Phi-3-Vision', + ] + list(wemm_series), + 'latest': ['paligemma-3b-mix-448', 'MiniCPM-V-2_6', 'glm-4v-9b'] + [x for x in llava_series if 'next' in x] + + list(chameleon_series) + list(ovis_series) + list(mantis_series), + 'api': list(api_models) +} + +# SKIP_MODELS will be skipped in report_missing and run APIs +SKIP_MODELS = [ + 'MGM_7B', 'GPT4V_HIGH', 'GPT4V', 'flamingov2', 'PandaGPT_13B', + 'GeminiProVision', 'Step1V-0701', 'SenseChat-5-Vision', + 'llava_v1_7b', 'sharegpt4v_7b', 'sharegpt4v_13b', + 'llava-v1.5-7b-xtuner', 'llava-v1.5-13b-xtuner', + 'cogvlm-grounding-generalist', 'InternVL-Chat-V1-1', + 'InternVL-Chat-V1-2', 'InternVL-Chat-V1-2-Plus', 'RekaCore', + 'llava_next_72b', 'llava_next_110b', 'MiniCPM-V', 'sharecaptioner', 'XComposer', + 'VisualGLM_6b', 'idefics_9b_instruct', 'idefics_80b_instruct', + 'mPLUG-Owl2', 'MMAlaya', 'OmniLMM_12B', 'emu2_chat', 'VXVERSE' +] + list(minigpt4_series) + list(instructblip_series) + list(xtuner_series) + list(chameleon_series) + list(vila_series) + +LARGE_MODELS = [ + 'idefics_80b_instruct', '360VL-70B', 'emu2_chat', 'InternVL2-76B', +] + + +def completed(m, d, suf): + score_file = f'outputs/{m}/{m}_{d}_{suf}' + if osp.exists(score_file): + return True + if d == 'MMBench': + s1, s2 = f'outputs/{m}/{m}_MMBench_DEV_EN_{suf}', f'outputs/{m}/{m}_MMBench_TEST_EN_{suf}' + return osp.exists(s1) and osp.exists(s2) + elif d == 'MMBench_CN': + s1, s2 = f'outputs/{m}/{m}_MMBench_DEV_CN_{suf}', f'outputs/{m}/{m}_MMBench_TEST_CN_{suf}' + return osp.exists(s1) and osp.exists(s2) + return False + + +def DLIST(lvl): + lst = [x[0] for x in dataset_levels[lvl]] + return lst + + +def MLIST(lvl, size='all'): + model_list = models[lvl] + if size == 'small': + model_list = [m for m in model_list if m not in LARGE_MODELS] + elif size == 'large': + model_list = [m for m in model_list if m in LARGE_MODELS] + return [x[0] for x in model_list] + + +def MISSING(lvl): + from vlmeval.config import supported_VLM + models = list(supported_VLM) + models = [m for m in models if m not in SKIP_MODELS and osp.exists(osp.join('outputs', m))] + if lvl in dataset_levels.keys(): + data_list = dataset_levels[lvl] + else: + data_list = [(D, suff) for (D, suff) in dataset_levels['l123'] if D == lvl] + missing_list = [] + for f in models: + for D, suff in data_list: + if not completed(f, D, suff): + missing_list.append((f, D)) + return missing_list + + +def CIRCULAR(inp): + assert inp.endswith('.tsv') + data = load(inp) + OFFSET = 1e6 + while max(data['index']) >= OFFSET: + OFFSET *= 10 + + assert 'E' not in data, 'Currently build_circular only works for up to 4-choice questions' + data_2c = data[pd.isna(data['C'])] + data_3c = data[~pd.isna(data['C']) & pd.isna(data['D'])] + data_4c = data[~pd.isna(data['D'])] + map_2c = [('AB', 'BA')] + map_3c = [('ABC', 'BCA'), ('ABC', 'CAB')] + map_4c = [('ABCD', 'BCDA'), ('ABCD', 'CDAB'), ('ABCD', 'DABC')] + + def okn(o, n=4): + ostr = o.replace(',', ' ') + osplits = ostr.split() + if sum([c in osplits for c in string.ascii_uppercase[:n - 1]]) == n - 1: + return False + olower = o.lower() + olower = olower.replace(',', ' ') + olower_splits = olower.split() + if 'all' in olower_splits or 'none' in olower_splits: + return False + return True + + yay4, nay4 = [], [] + lt4 = len(data_4c) + for i in range(lt4): + if okn(data_4c.iloc[i]['D'], 4): + yay4.append(i) + else: + nay4.append(i) + data_4c_y = data_4c.iloc[yay4] + data_4c_n = data_4c.iloc[nay4] + data_3c = pd.concat([data_4c_n, data_3c]) + + yay3, nay3 = [], [] + lt3 = len(data_3c) + for i in range(lt3): + if okn(data_3c.iloc[i]['C'], 3): + yay3.append(i) + else: + nay3.append(i) + data_3c_y = data_3c.iloc[yay3] + data_3c_n = data_3c.iloc[nay3] + data_2c = pd.concat([data_3c_n, data_2c]) + + def remap(data_in, tup, off): + off = int(off) + data = data_in.copy() + char_map = {k: v for k, v in zip(*tup)} + idx = data.pop('index') + answer = data.pop('answer') + answer_new = [char_map[x] if x in char_map else x for x in answer] + data['answer'] = answer_new + options = {} + for c in char_map: + options[char_map[c]] = data.pop(c) + for c in options: + data[c] = options[c] + data.pop('image') + data['image'] = idx + idx = [x + off for x in idx] + data['index'] = idx + return data + + data_all = pd.concat([ + data_2c, + data_3c_y, + data_4c_y, + remap(data_2c, map_2c[0], OFFSET), + remap(data_3c_y, map_3c[0], OFFSET), + remap(data_4c_y, map_4c[0], OFFSET), + remap(data_3c_y, map_3c[1], OFFSET * 2), + remap(data_4c_y, map_4c[1], OFFSET * 2), + remap(data_4c_y, map_4c[2], OFFSET * 3), + ]) + + tgt_file = inp.replace('.tsv', '_CIRC.tsv') + dump(data_all, tgt_file) + print(f'The circularized data is saved to {tgt_file}') + assert osp.exists(tgt_file) + print(f'The MD5 for the circularized data is {md5(tgt_file)}') + + +PTH = osp.realpath(__file__) +IMAGE_PTH = osp.join(osp.dirname(PTH), '../assets/apple.jpg') + +msg1 = [ + IMAGE_PTH, + 'What is in this image?' +] +msg2 = [ + dict(type='image', value=IMAGE_PTH), + dict(type='text', value='What is in this image?') +] +msg3 = [ + IMAGE_PTH, + IMAGE_PTH, + 'How many apples are there in these images?' +] +msg4 = [ + dict(type='image', value=IMAGE_PTH), + dict(type='image', value=IMAGE_PTH), + dict(type='text', value='How many apples are there in these images?') +] + + +def CHECK(val): + if val in supported_VLM: + model = supported_VLM[val]() + print(f'Model: {val}') + for i, msg in enumerate([msg1, msg2, msg3, msg4]): + if i > 1 and not model.INTERLEAVE: + continue + res = model.generate(msg) + print(f'Test {i + 1}: {res}') + elif val in models: + model_list = models[val] + for m in model_list: + CHECK(m) + + +def LOCALIZE(fname, new_fname=None): + if new_fname is None: + new_fname = fname.replace('.tsv', '_local.tsv') + + base_name = osp.basename(fname) + dname = osp.splitext(base_name)[0] + + data = load(fname) + data_new = localize_df(data, dname) + dump(data_new, new_fname) + print(f'The localized version of data file is {new_fname}') + return new_fname + + +def RUN(lvl, model): + import torch + NGPU = torch.cuda.device_count() + SCRIPT = osp.join(osp.dirname(__file__), '../run.py') + logger = get_logger('Run Missing') + + def get_env(name): + assert name in ['433', '437', '440', 'latest'] + load_env() + env_key = f'ENV_{name}' + return os.environ.get(env_key, None) + + missing = MISSING(lvl) + if model == 'all': + pass + elif model == 'api': + missing = [x for x in missing if x[0] in models['api']] + elif model == 'hf': + missing = [x for x in missing if x[0] not in models['api']] + elif model in models: + missing = [x for x in missing if x[0] in models[missing]] + elif model in supported_VLM: + missing = [x for x in missing if x[0] == model] + else: + warnings.warn(f'Invalid model {model}.') + + missing.sort(key=lambda x: x[0]) + groups = defaultdict(list) + for m, D in missing: + groups[m].append(D) + for m in groups: + if m in SKIP_MODELS: + continue + for dataset in groups[m]: + logger.info(f'Running {m} on {dataset}') + exe = 'python' if m in LARGE_MODELS or m in models['api'] else 'torchrun' + if m not in models['api']: + env = None + env = 'latest' if m in models['latest'] else env + env = '433' if m in models['4.33.0'] else env + env = '437' if m in models['4.37.0'] else env + env = '440' if m in models['4.40.0'] else env + if env is None: + # Not found, default to latest + env = 'latest' + logger.warning( + f"Model {m} does not have a specific environment configuration. Defaulting to 'latest'.") + pth = get_env(env) + if pth is not None: + exe = osp.join(pth, 'bin', exe) + else: + logger.warning(f'Cannot find the env path {env} for model {m}') + if exe.endswith('torchrun'): + cmd = f'{exe} --nproc-per-node={NGPU} {SCRIPT} --model {m} --data {dataset}' + elif exe.endswith('python'): + cmd = f'{exe} {SCRIPT} --model {m} --data {dataset}' + os.system(cmd) + + +def EVAL(dataset_name, data_file): + from vlmeval.dataset import build_dataset + logger = get_logger('VLMEvalKit Tool-Eval') + dataset = build_dataset(dataset_name) + # Set the judge kwargs first before evaluation or dumping + judge_kwargs = {'nproc': 4, 'verbose': True} + if dataset.TYPE in ['MCQ', 'Y/N']: + judge_kwargs['model'] = 'chatgpt-0125' + elif listinstr(['MMVet', 'MathVista', 'LLaVABench', 'MMBench-Video', 'MathVision'], dataset_name): + judge_kwargs['model'] = 'gpt-4-turbo' + elif listinstr(['MMLongBench', 'MMDU'], dataset_name): + judge_kwargs['model'] = 'gpt-4o' + eval_results = dataset.evaluate(data_file, **judge_kwargs) + if eval_results is not None: + assert isinstance(eval_results, dict) or isinstance(eval_results, pd.DataFrame) + logger.info('Evaluation Results:') + if isinstance(eval_results, dict): + logger.info('\n' + json.dumps(eval_results, indent=4)) + elif isinstance(eval_results, pd.DataFrame): + if len(eval_results) < len(eval_results.columns): + eval_results = eval_results.T + logger.info('\n' + tabulate(eval_results)) + + +def cli(): + logger = get_logger('VLMEvalKit Tools') + args = sys.argv[1:] + if not args: # no arguments passed + logger.info(CLI_HELP_MSG) + return + if args[0].lower() in MODES: + if args[0].lower() == 'dlist': + assert len(args) >= 2 + lst = DLIST(args[1]) + print(' '.join(lst)) + elif args[0].lower() == 'mlist': + assert len(args) >= 2 + size = 'all' + if len(args) > 2: + size = args[2].lower() + lst = MLIST(args[1], size) + print(' '.join(lst)) + elif args[0].lower() == 'missing': + assert len(args) >= 2 + missing_list = MISSING(args[1]) + logger = get_logger('Find Missing') + logger.info(colored(f'Level {args[1]} Missing Results: ', 'red')) + lines = [] + for m, D in missing_list: + line = f'Model {m}, Dataset {D}' + logger.info(colored(line, 'red')) + lines.append(line) + mwlines(lines, f'{args[1]}_missing.txt') + elif args[0].lower() == 'circular': + assert len(args) >= 2 + CIRCULAR(args[1]) + elif args[0].lower() == 'localize': + assert len(args) >= 2 + LOCALIZE(args[1]) + elif args[0].lower() == 'check': + assert len(args) >= 2 + model_list = args[1:] + for m in model_list: + CHECK(m) + elif args[0].lower() == 'run': + assert len(args) >= 2 + lvl = args[1] + if len(args) == 2: + model = 'all' + RUN(lvl, model) + else: + for model in args[2:]: + RUN(lvl, model) + elif args[0].lower() == 'eval': + assert len(args) == 3 + dataset, data_file = args[1], args[2] + EVAL(dataset, data_file) + else: + logger.error('WARNING: command error!') + logger.info(CLI_HELP_MSG) + return diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/utils/__init__.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cab50b6d5322433d8c109ea297ac7f2abc97f0f2 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/utils/__init__.py @@ -0,0 +1,7 @@ +from .matching_util import can_infer, can_infer_option, can_infer_text +from .mp_util import track_progress_rich + + +__all__ = [ + 'can_infer', 'can_infer_option', 'can_infer_text', 'track_progress_rich', +] diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/utils/matching_util.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/utils/matching_util.py new file mode 100644 index 0000000000000000000000000000000000000000..ccdf1b7d563125a4763724b81cf0164b06b94185 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/utils/matching_util.py @@ -0,0 +1,69 @@ +import string +import copy as cp +import os +from ..smp import * + + +def can_infer_option(answer, choices): + verbose = os.environ.get('VERBOSE', 0) + # Choices is a dictionary + if 'Failed to obtain answer via API' in answer: + return False + + reject_to_answer = [ + "Sorry, I can't help with images of people yet.", + "I can't process this file.", + "I'm sorry, but without the image provided", + 'Cannot determine the answer' + ] + for err in reject_to_answer: + if err in answer: + return 'Z' + + def count_choice(splits, choices, prefix='', suffix=''): + cnt = 0 + for c in choices: + if prefix + c + suffix in splits: + cnt += 1 + return cnt + + answer_mod = cp.copy(answer) + chars = '.()[],:;!*#{}' + for c in chars: + answer_mod = answer_mod.replace(c, ' ') + + splits = [x.strip() for x in answer_mod.split()] + count = count_choice(splits, choices) + + if count == 1: + for ch in choices: + if 'A' in splits and len(splits) > 3 and verbose: + logger = get_logger('Evaluation') + logger.info(f'A might be a quantifier in the string: {answer}.') + return False + if ch in splits: + return ch + elif count == 0 and count_choice(splits, {'Z', ''}) == 1: + return 'Z' + return False + + +def can_infer_text(answer, choices): + answer = answer.lower() + assert isinstance(choices, dict) + for k in choices: + assert k in string.ascii_uppercase + choices[k] = str(choices[k]).lower() + cands = [] + for k in choices: + if choices[k] in answer: + cands.append(k) + if len(cands) == 1: + return cands[0] + return False + + +def can_infer(answer, choices): + answer = str(answer) + copt = can_infer_option(answer, choices) + return copt if copt else can_infer_text(answer, choices) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/utils/mp_util.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/utils/mp_util.py new file mode 100644 index 0000000000000000000000000000000000000000..f8662a89f4b038d30c5c522bcc4734b64ec4b046 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/utils/mp_util.py @@ -0,0 +1,191 @@ +from multiprocessing import Pool +import os +from typing import Callable, Iterable, Sized + +from rich.progress import (BarColumn, MofNCompleteColumn, Progress, Task, + TaskProgressColumn, TextColumn, TimeRemainingColumn) +from rich.text import Text +import os.path as osp +import portalocker +from ..smp import load, dump + + +class _Worker: + """Function wrapper for ``track_progress_rich``""" + + def __init__(self, func) -> None: + self.func = func + + def __call__(self, inputs): + inputs, idx = inputs + if not isinstance(inputs, (tuple, list, dict)): + inputs = (inputs, ) + + if isinstance(inputs, dict): + return self.func(**inputs), idx + else: + return self.func(*inputs), idx + + +class _SkipFirstTimeRemainingColumn(TimeRemainingColumn): + """Skip calculating remaining time for the first few times. + + Args: + skip_times (int): The number of times to skip. Defaults to 0. + """ + + def __init__(self, *args, skip_times=0, **kwargs): + super().__init__(*args, **kwargs) + self.skip_times = skip_times + + def render(self, task: Task) -> Text: + """Show time remaining.""" + if task.completed <= self.skip_times: + return Text('-:--:--', style='progress.remaining') + return super().render(task) + + +def _tasks_with_index(tasks): + """Add index to tasks.""" + for idx, task in enumerate(tasks): + yield task, idx + + +def track_progress_rich(func: Callable, + tasks: Iterable = tuple(), + task_num: int = None, + nproc: int = 1, + chunksize: int = 1, + description: str = 'Processing', + save=None, keys=None, + color: str = 'blue') -> list: + """Track the progress of parallel task execution with a progress bar. The + built-in :mod:`multiprocessing` module is used for process pools and tasks + are done with :func:`Pool.map` or :func:`Pool.imap_unordered`. + + Args: + func (callable): The function to be applied to each task. + tasks (Iterable or Sized): A tuple of tasks. There are several cases + for different format tasks: + - When ``func`` accepts no arguments: tasks should be an empty + tuple, and ``task_num`` must be specified. + - When ``func`` accepts only one argument: tasks should be a tuple + containing the argument. + - When ``func`` accepts multiple arguments: tasks should be a + tuple, with each element representing a set of arguments. + If an element is a ``dict``, it will be parsed as a set of + keyword-only arguments. + Defaults to an empty tuple. + task_num (int, optional): If ``tasks`` is an iterator which does not + have length, the number of tasks can be provided by ``task_num``. + Defaults to None. + nproc (int): Process (worker) number, if nuproc is 1, + use single process. Defaults to 1. + chunksize (int): Refer to :class:`multiprocessing.Pool` for details. + Defaults to 1. + description (str): The description of progress bar. + Defaults to "Process". + color (str): The color of progress bar. Defaults to "blue". + + Examples: + >>> import time + + >>> def func(x): + ... time.sleep(1) + ... return x**2 + >>> track_progress_rich(func, range(10), nproc=2) + + Returns: + list: The task results. + """ + if save is not None: + assert osp.exists(osp.dirname(save)) or osp.dirname(save) == '' + if not osp.exists(save): + dump({}, save) + if keys is not None: + assert len(keys) == len(tasks) + + if not callable(func): + raise TypeError('func must be a callable object') + if not isinstance(tasks, Iterable): + raise TypeError( + f'tasks must be an iterable object, but got {type(tasks)}') + if isinstance(tasks, Sized): + if len(tasks) == 0: + if task_num is None: + raise ValueError('If tasks is an empty iterable, ' + 'task_num must be set') + else: + tasks = tuple(tuple() for _ in range(task_num)) + else: + if task_num is not None and task_num != len(tasks): + raise ValueError('task_num does not match the length of tasks') + task_num = len(tasks) + + if nproc <= 0: + raise ValueError('nproc must be a positive number') + + skip_times = nproc * chunksize if nproc > 1 else 0 + prog_bar = Progress( + TextColumn('{task.description}'), + BarColumn(), + _SkipFirstTimeRemainingColumn(skip_times=skip_times), + MofNCompleteColumn(), + TaskProgressColumn(show_speed=True), + ) + + worker = _Worker(func) + task_id = prog_bar.add_task( + total=task_num, color=color, description=description) + tasks = _tasks_with_index(tasks) + + # Use single process when nproc is 1, else use multiprocess. + with prog_bar: + if nproc == 1: + results = [] + for task in tasks: + result, idx = worker(task) + results.append(result) + if save is not None: + with portalocker.Lock(save, timeout=5) as fh: + ans = load(save) + ans[keys[idx]] = result + + if os.environ.get('VERBOSE', True): + print(keys[idx], result, flush=True) + + dump(ans, save) + fh.flush() + os.fsync(fh.fileno()) + + prog_bar.update(task_id, advance=1, refresh=True) + else: + with Pool(nproc) as pool: + results = [] + unordered_results = [] + gen = pool.imap_unordered(worker, tasks, chunksize) + try: + for result in gen: + result, idx = result + unordered_results.append((result, idx)) + + if save is not None: + with portalocker.Lock(save, timeout=5) as fh: + ans = load(save) + ans[keys[idx]] = result + + if os.environ.get('VERBOSE', False): + print(keys[idx], result, flush=True) + + dump(ans, save) + fh.flush() + os.fsync(fh.fileno()) + + results.append(None) + prog_bar.update(task_id, advance=1, refresh=True) + except Exception as e: + prog_bar.stop() + raise e + for result, idx in unordered_results: + results[idx] = result + return results diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/utils/result_transfer.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/utils/result_transfer.py new file mode 100644 index 0000000000000000000000000000000000000000..649426b87f40cfd67c9308e5c5f4de2f9906660c --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/utils/result_transfer.py @@ -0,0 +1,97 @@ +from ..smp import * +from ..dataset.utils.judge_util import build_judge +from ..dataset.utils.multiple_choice import extract_answer_from_item +from .matching_util import can_infer +from .mp_util import track_progress_rich + + +def MMMU_result_transfer(result_path): + res = {} + result_data = load(result_path) + mcq = result_data['A'].notna() + lt = len(result_data) + for i in range(lt): + line = result_data.iloc[i] + if mcq[i]: + options = { + cand: line[cand] + for cand in string.ascii_uppercase + if cand in line and not pd.isna(line[cand]) + } + prediction = line['prediction'] + infer_prediction = can_infer(prediction, options) + res[line['id']] = infer_prediction + else: + res[line['id']] = line['prediction'] + result_json = result_path.replace('.xlsx', '.json') + dump(res, result_json) + return result_json + + +def MMTBench_result_transfer(eval_file, dataset='default', **judge_kwargs): + logger = get_logger('Evaluation') + nproc = judge_kwargs.pop('nproc', 4) + + rd.seed(2680) + suffix = eval_file.split('.')[-1] + model = judge_kwargs['model'] + assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125'] + name_str_map = { + 'chatgpt-0125': 'openai', + 'gpt-4-0125': 'gpt4' + } + name_str = name_str_map[model] if model in name_str_map else model + + if model == 'exact_matching': + model = None + elif gpt_key_set(): + model = build_judge(**judge_kwargs) + if not model.working(): + logger.error('The OPENAI API is not working properly, will use exact matching for evaluation') + model = None + else: + logger.error('OPENAI_API_KEY is not set properly, will use exact matching for evaluation') + model = None + + logger.info(f'Evaluating {eval_file}') + result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_option.pkl') + result = {} + if osp.exists(result_file): + result = load(result_file) + + data = load(eval_file) + assert 'index' in data, 'Essentail columns missing in the eval_file.' + + data = data.sort_values(by='index') + data['prediction'] = [str(x) for x in data['prediction']] + for k in data.keys(): + data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k) + + idx2lines = {data.iloc[i]['index']: data.iloc[i] for i in range(len(data))} + idx2lines = {k: v for k, v in idx2lines.items() if k not in result} + + indices = list(idx2lines.keys()) + lines = [idx2lines[i] for i in indices] + tups = [(model, line) for line in lines] + res = track_progress_rich( + extract_answer_from_item, + tups, + nproc=nproc, + chunksize=nproc, + save=result_file, + keys=indices) + + for i, r in zip(indices, res): + if i in result: + assert result[i]['opt'] == r['opt'] and result[i]['log'] == r['log'] + else: + result[i] = r + + indices = list(data['index']) + data['opt'] = [result[i]['opt'] for i in data['index']] + data['log'] = [result[i]['log'] for i in data['index']] + + # load split + output_path = eval_file.replace(f'.{suffix}', f'_{name_str}_submission.tsv') + dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_submission.tsv')) + return output_path diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/vlm/__init__.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/vlm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f1e83f9936c2fbab8c24ff55f4d66e8768cc81d6 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/vlm/__init__.py @@ -0,0 +1,6 @@ +import torch + +torch.set_grad_enabled(False) +torch.manual_seed(1234) +from .base import BaseModel +from .minicpm_v import MiniCPM_V, MiniCPM_Llama3_V, MiniCPM_V_2_6 diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/vlm/base.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/vlm/base.py new file mode 100644 index 0000000000000000000000000000000000000000..6212a40d176ab3dd8aa180c1c897ad85741fa5d8 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/vlm/base.py @@ -0,0 +1,167 @@ +from ..smp import * +from ..dataset import img_root_map +from abc import abstractmethod + + +class BaseModel: + + INTERLEAVE = False + allowed_types = ['text', 'image', 'video'] + + def __init__(self): + self.dump_image_func = None + + def use_custom_prompt(self, dataset): + """Whether to use custom prompt for the given dataset. + + Args: + dataset (str): The name of the dataset. + + Returns: + bool: Whether to use custom prompt. If True, will call `build_prompt` of the VLM to build the prompt. + Default to False. + """ + return False + + @abstractmethod + def build_prompt(self, line, dataset): + """Build custom prompts for a specific dataset. Called only if `use_custom_prompt` returns True. + + Args: + line (line of pd.DataFrame): The raw input line. + dataset (str): The name of the dataset. + + Returns: + str: The built message. + """ + raise NotImplementedError + + def set_dump_image(self, dump_image_func): + self.dump_image_func = dump_image_func + + def dump_image(self, line, dataset): + return self.dump_image_func(line) + + @abstractmethod + def generate_inner(self, message, dataset=None): + raise NotImplementedError + + def check_content(self, msgs): + """Check the content type of the input. Four types are allowed: str, dict, liststr, listdict. + """ + if isinstance(msgs, str): + return 'str' + if isinstance(msgs, dict): + return 'dict' + if isinstance(msgs, list): + types = [self.check_content(m) for m in msgs] + if all(t == 'str' for t in types): + return 'liststr' + if all(t == 'dict' for t in types): + return 'listdict' + return 'unknown' + + def preproc_content(self, inputs): + """Convert the raw input messages to a list of dicts. + + Args: + inputs: raw input messages. + + Returns: + list(dict): The preprocessed input messages. Will return None if failed to preprocess the input. + """ + if self.check_content(inputs) == 'str': + return [dict(type='text', value=inputs)] + elif self.check_content(inputs) == 'dict': + assert 'type' in inputs and 'value' in inputs + return [inputs] + elif self.check_content(inputs) == 'liststr': + res = [] + for s in inputs: + mime, pth = parse_file(s) + if mime is None or mime == 'unknown': + res.append(dict(type='text', value=s)) + else: + res.append(dict(type=mime.split('/')[0], value=pth)) + return res + elif self.check_content(inputs) == 'listdict': + for item in inputs: + assert 'type' in item and 'value' in item + mime, s = parse_file(item['value']) + if mime is None: + assert item['type'] == 'text' + else: + assert mime.split('/')[0] == item['type'] + item['value'] = s + return inputs + else: + return None + + def generate(self, message, dataset=None): + """Generate the output message. + + Args: + message (list[dict]): The input message. + dataset (str, optional): The name of the dataset. Defaults to None. + + Returns: + str: The generated message. + """ + assert self.check_content(message) in ['str', 'dict', 'liststr', 'listdict'], f'Invalid input type: {message}' + message = self.preproc_content(message) + assert message is not None and self.check_content(message) == 'listdict' + for item in message: + assert item['type'] in self.allowed_types, f'Invalid input type: {item["type"]}' + return self.generate_inner(message, dataset) + + def chat(self, messages, dataset=None): + """The main function for multi-turn chatting. Will call `chat_inner` with the preprocessed input messages.""" + assert hasattr(self, 'chat_inner'), 'The API model should has the `chat_inner` method. ' + for msg in messages: + assert isinstance(msg, dict) and 'role' in msg and 'content' in msg, msg + assert self.check_content(msg['content']) in ['str', 'dict', 'liststr', 'listdict'], msg + msg['content'] = self.preproc_content(msg['content']) + + while len(messages): + try: + return self.chat_inner(messages, dataset=dataset) + except: + messages = messages[1:] + while len(messages) and messages[0]['role'] != 'user': + messages = messages[1:] + continue + return 'Chat Mode: Failed with all possible conversation turns.' + + def message_to_promptimg(self, message, dataset=None): + assert not self.INTERLEAVE + model_name = self.__class__.__name__ + warnings.warn( + f'Model {model_name} does not support interleaved input. ' + 'Will use the first image and aggregated texts as prompt. ') + num_images = len([x for x in message if x['type'] == 'image']) + if num_images == 0: + prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text']) + image = None + else: + prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text']) + images = [x['value'] for x in message if x['type'] == 'image'] + if 'BLINK' == dataset: + image = concat_images_vlmeval(images, target_size=512) + else: + image = images[0] + return prompt, image + + def message_to_promptvideo(self, message): + if self.VIDEO_LLM: + num_videos = len([x for x in message if x['type'] == 'video']) + if num_videos == 0: + prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text']) + video = None + else: + prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text']) + video = [x['value'] for x in message if x['type'] == 'video'][0] + return prompt, video + else: + import sys + warnings.warn('Model does not support video input.') + sys.exit(-1) diff --git a/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/vlm/minicpm_v.py b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/vlm/minicpm_v.py new file mode 100644 index 0000000000000000000000000000000000000000..0729260514a14b3596430c8015c7b4aacf8a1b63 --- /dev/null +++ b/MiniCPM-V/eval_mm/vlmevalkit/vlmeval/vlm/minicpm_v.py @@ -0,0 +1,459 @@ +import math +import torch +import random +import numpy as np +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +from .base import BaseModel +from ..smp import * +from ..dataset import DATASET_TYPE + + +class MiniCPM_V(BaseModel): + + INSTALL_REQ = False + INTERLEAVE = False + + def __init__(self, model_path='openbmb/MiniCPM-V', **kwargs): + assert model_path is not None + self.model_path = model_path + print(f'load from {self.model_path}') + self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True) + self.model = self.model.to(dtype=torch.bfloat16) + self.model.eval().cuda() + self.kwargs = kwargs + self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True) + torch.cuda.empty_cache() + self.num_beams = 1 if self.model_path == 'openbmb/MiniCPM-V' else 3 + + def use_custom_prompt(self, dataset): + assert dataset is not None + if listinstr(['MMMU'], dataset): + return True + return False + + def build_prompt(self, line, dataset=None): + assert dataset is None or isinstance(dataset, str) + assert self.use_custom_prompt(dataset) + tgt_path = self.dump_image(line, dataset) + + question = line['question'] + options = { + cand: line[cand] + for cand in string.ascii_uppercase + if cand in line and not pd.isna(line[cand]) + } + options_prompt = 'Options:\n' + for key, item in options.items(): + options_prompt += f'{key}. {item}\n' + hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None + prompt = '' + if hint is not None: + prompt += f'Hint: {hint}\n' + prompt += f'{question}\n' + if len(options): + prompt += options_prompt + prompt = 'Study the image carefully and pick the option associated with the correct answer. \ + Focus solely on selecting the option and avoid including any other content.\n' + prompt + message = [dict(type='text', value=prompt)] + message.extend([dict(type='image', value=p) for p in tgt_path]) + + return message + + def generate_inner(self, message, dataset=None): + prompt, image_path = self.message_to_promptimg(message, dataset=dataset) + image = Image.open(image_path).convert('RGB') + msgs = [{'role': 'user', 'content': prompt}] + if DATASET_TYPE(dataset) == 'MCQ': + max_new_tokens = 20 + elif DATASET_TYPE(dataset) == 'Y/N': + max_new_tokens = 100 + else: + max_new_tokens = 1024 + + default_kwargs = dict( + max_new_tokens=max_new_tokens, + sampling=False, + num_beams=self.num_beams + ) + default_kwargs.update(self.kwargs) + res, _, _ = self.model.chat( + image=image, + msgs=msgs, + context=None, + tokenizer=self.tokenizer, + **default_kwargs + ) + return res + + +class MiniCPM_Llama3_V(BaseModel): + + INSTALL_REQ = False + INTERLEAVE = True + + def __init__(self, model_path='openbmb/MiniCPM-Llama3-V-2_5', **kwargs): + assert model_path is not None + self.model_path = model_path + print(f'load from {self.model_path}') + self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True) + self.model = self.model.to(dtype=torch.float16) + self.model.eval().cuda() + self.kwargs = kwargs + self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True) + torch.cuda.empty_cache() + self.num_beams = 1 if self.model_path == 'openbmb/MiniCPM-V' else 3 + self.options_system_prompt = ('Carefully read the following question and select the letter corresponding ' + 'to the correct answer. Highlight the applicable choices without giving ' + 'explanations.') + self.wo_options_system_prompt = 'Carefully read the following question Answer the question directly.' + self.detail_system_prompt = 'Answer this question in detail.' + self.vqa_prompt = 'Answer the question using a single word or phrase.' + + def use_custom_prompt(self, dataset): + if listinstr(['MCQ', 'VQA'], DATASET_TYPE(dataset)): + return True + elif dataset is not None and listinstr(['HallusionBench'], dataset): + return True + return False + + def build_prompt(self, line, dataset=None): + if isinstance(line, int): + line = self.data.iloc[line] + + tgt_path = self.dump_image(line, dataset) + system_prompt = '' + + question = line['question'] + if DATASET_TYPE(dataset) == 'MCQ': + options = { + cand: line[cand] + for cand in string.ascii_uppercase + if cand in line and not pd.isna(line[cand]) + } + options_prompt = 'Options:\n' + for key, item in options.items(): + options_prompt += f'{key}. {item}\n' + hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None + prompt = '' + if hint is not None: + prompt += f'Hint: {hint}\n' + prompt += f'Question: {question}\n' + if len(options): + prompt += options_prompt + system_prompt = self.options_system_prompt + '\nPlease just indicate your choice.' + else: + system_prompt = self.wo_options_system_prompt + if 'MMMU' in dataset: # Corner Case + prompt = system_prompt + '\n' + prompt + system_prompt = '' + elif dataset is not None and listinstr(['HallusionBench'], dataset): + question = line['question'] + ' Yes or No?' + prompt = question + elif dataset is not None and listinstr(['MME'], dataset): + question = line['question'] + ' Yes or No?' + prompt = question + elif dataset is not None and listinstr(['OCRBench'], dataset): + system_prompt = self.vqa_prompt + question = line['question'] + prompt = question + elif DATASET_TYPE(dataset) == 'VQA': + if listinstr(['LLaVABench', 'MMLongBench_DOC'], dataset): + system_prompt = '' + prompt = question + elif listinstr(['MMVet'], dataset): + system_prompt = self.detail_system_prompt + prompt = question + else: + system_prompt = self.vqa_prompt + prompt = question + + msgs = [] + if system_prompt: + msgs.append(dict(type='text', value=system_prompt)) + if isinstance(tgt_path, list): + msgs.extend([dict(type='image', value=p) for p in tgt_path]) + else: + msgs = [dict(type='image', value=tgt_path)] + msgs.append(dict(type='text', value=prompt)) + return msgs + + def generate_inner(self, message, dataset=None): + if DATASET_TYPE(dataset) == 'MCQ': + max_new_tokens = 200 + elif DATASET_TYPE(dataset) == 'Y/N': + max_new_tokens = 3 + else: + max_new_tokens = 1024 + + default_kwargs = dict( + max_new_tokens=max_new_tokens, + sampling=False, + num_beams=self.num_beams, + ) + default_kwargs.update(self.kwargs) + + content = [] + for x in message: + if x['type'] == 'text': + content.append(x['value']) + elif x['type'] == 'image': + image = Image.open(x['value']).convert('RGB') + content.append(image) + msgs = [{'role': 'user', 'content': content}] + + res = self.model.chat( + msgs=msgs, + context=None, + image=None, + tokenizer=self.tokenizer, + **default_kwargs + ) + + if isinstance(res, tuple) and len(res) > 0: + res = res[0] + return res + + def chat_inner(self, message, dataset=None): + max_new_tokens = 1024 + + default_kwargs = dict( + max_new_tokens=max_new_tokens, + sampling=False, + num_beams=self.num_beams, + ) + default_kwargs.update(self.kwargs) + + msgs = [] + for msg in message: + content = [] + if len(msg['content']) == 1 and msg['content'][0]['type'] == 'text': + msg_new = {'role': msg['role'], 'content': msg['content'][0]['value']} + msgs.append(msg_new) + continue + + for x in msg['content']: + if x['type'] == 'text': + content.append(x['value']) + elif x['type'] == 'image': + image = Image.open(x['value']).convert('RGB') + content.append(image) + msg_new = {'role': msg['role'], 'content': content} + msgs.append(msg_new) + + res = self.model.chat( + msgs=msgs, + context=None, + image=None, + tokenizer=self.tokenizer, + **default_kwargs) + + if isinstance(res, tuple) and len(res) > 0: + res = res[0] + return res + + +class MiniCPM_V_2_6(BaseModel): + INSTALL_REQ = False + INTERLEAVE = True + + def __init__(self, model_path='openbmb/MiniCPM-V', **kwargs): + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + torch.cuda.manual_seed_all(0) + + assert model_path is not None + self.model_path = model_path + print(f'load from path {self.model_path}') + self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True) + self.model = self.model.to(dtype=torch.bfloat16) + self.model.eval().cuda() + + self.kwargs = kwargs + self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True) + torch.cuda.empty_cache() + self.num_beams = 1 if self.model_path == 'openbmb/MiniCPM-V' else 3 + + self.options_suffix_prompt = '''\nAnswer with the option's letter from the given choices directly.''' + self.wo_options_system_prompt = 'Carefully read the following question Answer the question directly.' + self.detail_system_prompt = 'Answer this question in detail.' + self.vqa_prompt = 'Answer the question using a single word or phrase.' + + self.multi_choice_cot_prompt = ('''Carefully read the following multichoice question, solve it step ''' + '''by step and finally pick the option associated with the correct ''' + '''answer in the format of "Answer: selected option\n\n''') + self.short_ans_cot_prompt = ('''Read the following question carefully, solve it step by step, and ''' + '''then output the final answer in the format of "Answer: single number ''' + '''or single word or phrase".\n\n''') + + def use_custom_prompt(self, dataset=None): + if dataset is None: + return False + if listinstr(['MCQ', 'VQA', 'Y/N'], DATASET_TYPE(dataset)): + return True + return False + + def use_cot(self, dataset=None): + if dataset is None: + return False + if listinstr(['MMMU', 'HallusionBench', 'OCRBench', 'ChartQA'], dataset): + return True + elif listinstr(['MathVista', 'MMVet', 'MMBench', 'MMStar', 'AI2D', 'RealWorldQA', + 'POPE', 'ScienceQA', 'TextVQA', 'DocVQA'], dataset): + return False + else: + return False + + def use_upsize(self, dataset=None): + if dataset is None: + return False + if listinstr(['MMVet', 'MMBench', 'MMStar', 'AI2D', 'OCRBench'], dataset): + return True + else: + return False + + def build_prompt(self, line, dataset=None): + if isinstance(line, int): + line = self.data.iloc[line] + + tgt_path = self.dump_image(line, dataset) + system_prompt, prompt = '', '' + + question = line['question'] + + if not self.use_cot(dataset): + if DATASET_TYPE(dataset) == 'MCQ': + options = { + cand: line[cand] + for cand in string.ascii_uppercase + if cand in line and not pd.isna(line[cand]) + } + options_prompt = 'Options:\n' + for key, item in options.items(): + options_prompt += f'{key}. {item}\n' + hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None + if hint is not None: + prompt += f'Hint: {hint}\n' + prompt += f'Question: {question}\n' + if len(options): + prompt += options_prompt + prompt += self.options_suffix_prompt + else: + system_prompt = self.wo_options_system_prompt + + if 'MMMU' in dataset: + if len(system_prompt) > 0: + prompt = system_prompt + '\n' + prompt + system_prompt = '' + elif dataset is not None and listinstr(['HallusionBench'], dataset): + question += ' Yes or No?' + prompt = question + elif dataset is not None and listinstr(['OCRBench'], dataset): + system_prompt = self.vqa_prompt + prompt = question + elif DATASET_TYPE(dataset) == 'VQA': + if listinstr(['LLaVABench'], dataset): + system_prompt = '' + elif listinstr(['MMVet'], dataset): + system_prompt = self.detail_system_prompt + else: + system_prompt = self.vqa_prompt + prompt = question + else: + prompt = question + else: + has_options = True + if DATASET_TYPE(dataset) == 'MCQ': + options = { + cand: line[cand] + for cand in string.ascii_uppercase + if cand in line and not pd.isna(line[cand]) + } + options_prompt = '' + for key, item in options.items(): + options_prompt += f'{key}. {item}\n' + hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None + if hint is not None: + prompt += f'Hint: {hint}\n' + prompt += f'{question}\n' + + if len(options): + prompt += options_prompt + else: + has_options = False + + if 'MMMU' in dataset: + if len(system_prompt) > 0: + prompt = system_prompt + '\n' + prompt + system_prompt = '' + else: + prompt = question + + if DATASET_TYPE(dataset) in ['MCQ', 'Y/N', 'VQA']: + if DATASET_TYPE(dataset) == 'MCQ': + if has_options: + prompt = self.multi_choice_cot_prompt + prompt + else: + prompt = self.short_ans_cot_prompt + prompt + elif DATASET_TYPE(dataset) == 'Y/N': + prompt = self.short_ans_cot_prompt + prompt + else: + prompt = self.short_ans_cot_prompt + prompt + + msgs = [] + if system_prompt: + msgs.append(dict(type='text', value=system_prompt)) + if isinstance(tgt_path, list): + msgs.extend([dict(type='image', value=p) for p in tgt_path]) + else: + msgs = [dict(type='image', value=tgt_path)] + msgs.append(dict(type='text', value=prompt)) + + return msgs + + def generate_inner(self, message, dataset=None): + max_new_tokens = 2048 + default_kwargs = dict( + max_new_tokens=max_new_tokens, + sampling=False, + num_beams=self.num_beams, + ) + default_kwargs.update(self.kwargs) + + content = [] + + for x in message: + if x['type'] == 'text': + content.append(x['value']) + elif x['type'] == 'image': + image = Image.open(x['value']).convert('RGB') + if not self.use_upsize(dataset): + content.append(image) + else: + img_width, img_height = image.width, image.height + if (img_width * img_height) >= (1344 * 1344): + content.append(image) + else: + ratio = math.sqrt((1344 * 1344) / (img_width * img_height)) + max_img_width = int(img_width * ratio) + new_img_width = random.randint(img_width, max_img_width) + new_img_height = int(new_img_width / img_width * img_height) + resized_image = image.resize((new_img_width, new_img_height)) + content.append(resized_image) + msgs = [{'role': 'user', 'content': content}] + + res = self.model.chat( + image=None, + msgs=msgs, + context=None, + tokenizer=self.tokenizer, + max_inp_length=8192, + **default_kwargs + ) + + if isinstance(res, tuple) and len(res) > 0: + res = res[0] + + return res diff --git a/MiniCPM-V/eval_mm/vqaeval/.DS_Store b/MiniCPM-V/eval_mm/vqaeval/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..9122debd84c0acc1c59872f49d30fd896c174f10 Binary files /dev/null and b/MiniCPM-V/eval_mm/vqaeval/.DS_Store differ diff --git a/MiniCPM-V/eval_mm/vqaeval/README.md b/MiniCPM-V/eval_mm/vqaeval/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b6fd3f4f3886751f838c8708e029e6ff717ab4fe --- /dev/null +++ b/MiniCPM-V/eval_mm/vqaeval/README.md @@ -0,0 +1,3 @@ +# vqa-eval + +contains vqa_eval kit from the server. diff --git a/MiniCPM-V/eval_mm/vqaeval/datasets/__init__.py b/MiniCPM-V/eval_mm/vqaeval/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/MiniCPM-V/eval_mm/vqaeval/datasets/vqa_dataset.py b/MiniCPM-V/eval_mm/vqaeval/datasets/vqa_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..83c9e9aef5359ebed23b85448b382119d8488736 --- /dev/null +++ b/MiniCPM-V/eval_mm/vqaeval/datasets/vqa_dataset.py @@ -0,0 +1,116 @@ +import json +import os +import re +from torch.utils.data import Dataset + +def prompt_processor(prompt): + if prompt.startswith('OCR tokens: '): + pattern = r"Question: (.*?) Short answer:" + match = re.search(pattern, prompt, re.DOTALL) + question = match.group(1) + elif 'Reference OCR token: ' in prompt and len(prompt.split('\n')) == 3: + if prompt.startswith('Reference OCR token:'): + question = prompt.split('\n')[1] + else: + question = prompt.split('\n')[0] + elif len(prompt.split('\n')) == 2: + question = prompt.split('\n')[0] + else: + assert False + + return question.lower() + +class textVQADataset(Dataset): + def __init__( + self, + image_dir="./downloads/TextVQA/train_images", + ann_path="./downloads/TextVQA/TextVQA_0.5.1_val.json", + ): + self.data = json.load(open(ann_path, "r"))["data"] + self.image_dir = image_dir + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + question = self.data[idx]['question'] + answers = self.data[idx]['answers'] + img_id = self.data[idx]['image_id'] + qid = self.data[idx]['question_id'] + img_path = os.path.join(self.image_dir, f"{img_id}.jpg") + + item = { + "question_id": qid, + "image_path": img_path, + "question": question, + "gt_answers": answers + } + + return item + +class docVQADataset(Dataset): + def __init__( + self, + image_dir= "./downloads/DocVQA/spdocvqa_images", + ann_path= "./downloads/DocVQA/val_v1.0_withQT.json", + ocr_token_path=None + ): + + self.data = json.load(open(ann_path, "r"))["data"] + self.image_dir = image_dir + self.ann_path = ann_path + if ocr_token_path: + self.ocr_token_data = {item['image_id']: item for item in json.load(open(ocr_token_path, "r"))["data"]} + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + question_id = self.data[idx]['questionId'] + relative_img_path = self.data[idx]['image'] + corrected_relative_img_path = relative_img_path.replace("documents", "images") + img_path = os.path.join(self.image_dir, corrected_relative_img_path) + question = self.data[idx]['question'] + answers = self.data[idx]['answers'] + + question_type = self.data[idx]['question_types'] + + return { + "question_id": question_id, + "image_path": img_path, + "question": question, + "gt_answers": answers, + 'question_type': question_type, + } + + +class docVQATESTDataset(Dataset): + def __init__( + self, + image_dir= "./downloads/DocVQA/spdocvqa_images", + ann_path= "./downloads/DocVQA/test_v1.0.json", + ocr_token_path=None + ): + + self.data = json.load(open(ann_path, "r"))["data"] + self.image_dir = image_dir + self.ann_path = ann_path + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + question_id = self.data[idx]['questionId'] + relative_img_path = self.data[idx]['image'] + corrected_relative_img_path = relative_img_path.replace("documents", "images") + img_path = os.path.join(self.image_dir, corrected_relative_img_path) + question = self.data[idx]['question'] + + + return { + "question_id": question_id, + "image_path": img_path, + "question": question, + "gt_answers": "", + 'question_type': "", + } diff --git a/MiniCPM-V/eval_mm/vqaeval/eval.py b/MiniCPM-V/eval_mm/vqaeval/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..f5faf9f050874e187d18403dddbfd16169979d1d --- /dev/null +++ b/MiniCPM-V/eval_mm/vqaeval/eval.py @@ -0,0 +1,103 @@ +import sys +import datetime +import json +import os +import torch + +script_dir = os.path.dirname(os.path.realpath(__file__)) + +sys.path.append(os.path.join(script_dir, '..')) + +from datasets.vqa_dataset import docVQADataset, docVQATESTDataset, textVQADataset + + +print(torch.__version__) + +import numpy as np + +from eval_utils.getargs import parse_args +from eval_utils.vqa_evaluate import * + + +def get_model(args): + if args.model_name == '': + raise Exception('Model name cannot be empty str!') + from models.MiniCPM.minicpmv import MiniCPM_V, MiniCPM_V_2_6 + model_path = args.model_path + ckpt = args.ckpt + + if args.model_name == 'minicpmv': + model = MiniCPM_V(model_path=model_path, ckpt=ckpt, device=args.device) + elif args.model_name == 'minicpmv26': + model = MiniCPM_V_2_6(model_path=model_path, ckpt=ckpt, device=args.device) + else: + raise Exception(f"Unexpected Moedel Name {args.model_name}!") + + return model + + +def main(args): + np.random.seed(0) + max_sample_num = None + + torch.distributed.init_process_group( + backend='nccl', + world_size=int(os.getenv('WORLD_SIZE', '1')), + rank=int(os.getenv('RANK', '0')), + ) + torch.cuda.set_device(int(os.getenv('LOCAL_RANK', 0))) + print(f'Init Rank-{torch.distributed.get_rank()}') + if torch.distributed.is_initialized(): + args.device = torch.device(f"cuda:{torch.cuda.current_device()}") + + model = get_model(args) + + result = {} + time = datetime.datetime.now().strftime("%Y%m%d%H%M%S") + + if args.eval_textVQA or args.eval_all: + dataset = textVQADataset(args.textVQA_image_dir, args.textVQA_ann_path) + if max_sample_num is not None: + dataset = torch.utils.data.Subset(dataset, range(max_sample_num)) + acc = evaluate_VQA(model, dataset, args.model_name, 'textVQA', time, \ + batch_size=args.batchsize, generate_method=args.generate_method, answer_path=args.answer_path) + result['textVQA'] = acc + + if args.eval_docVQA or args.eval_all: + dataset = docVQADataset(args.docVQA_image_dir, args.docVQA_ann_path) + if max_sample_num is not None: + dataset = torch.utils.data.Subset(dataset, range(max_sample_num)) + acc = evaluate_VQA(model, dataset, args.model_name, 'docVQA', time, batch_size=args.batchsize, generate_method=args.generate_method, answer_path=args.answer_path) + result['docVQA'] = acc + + if args.eval_docVQATest or args.eval_all: + target_dataset = "docVQATest" + dataset = docVQATESTDataset(args.docVQATest_image_dir, args.docVQATest_ann_path) + if max_sample_num is not None: + dataset = torch.utils.data.Subset(dataset, range(max_sample_num)) + acc = evaluate_VQA(model, dataset, args.model_name, target_dataset, time, batch_size=args.batchsize, generate_method=args.generate_method, answer_path=args.answer_path) + result['docVQATest'] = acc + + if torch.distributed.is_initialized(): + torch.distributed.barrier() + + if torch.distributed.is_initialized() and torch.distributed.get_rank() != 0: + return None + + result_path = os.path.join(os.path.join(args.answer_path, args.model_name), 'result.json') + + output_flag = False + for k, v in result.items(): + if v > 0.0: + output_flag = True + break + + if output_flag: + with open(result_path, "w") as f: + f.write(json.dumps(result, indent=4)) + + +if __name__ == "__main__": + args = parse_args() + + main(args) \ No newline at end of file diff --git a/MiniCPM-V/eval_mm/vqaeval/eval_utils/cal_metric.py b/MiniCPM-V/eval_mm/vqaeval/eval_utils/cal_metric.py new file mode 100644 index 0000000000000000000000000000000000000000..e5fb43986c47c40014eb47275dff927b49f21d44 --- /dev/null +++ b/MiniCPM-V/eval_mm/vqaeval/eval_utils/cal_metric.py @@ -0,0 +1,40 @@ +import json +import glob +import re + +def has_word(sentence, word): + pattern = r"\b" + re.escape(word) + r"\b" + match = re.search(pattern, sentence) + if match: + return True + else: + return False +def remove_special_chars(s): + pattern = r"[^a-zA-Z0-9\s]" + s = re.sub(pattern, "", s) + return s + +for model in glob.glob('./answer_save/*'): + print(model, ':') + result_list = sorted(glob.glob(f'{model}/*.json')) + for task_result_path in result_list: + taskname = task_result_path.split('/')[-1] + taskname = taskname.split('.')[0] + if taskname not in ['IIIT5K', 'svt', 'IC13_857', 'IC15_1811', 'svtp', 'ct80', + 'cocotext', 'ctw', 'totaltext', 'HOST']: + continue + + correct = 0 + num = 0 + with open(task_result_path, 'r') as f: + dict = json.load(f)[:100] + for i in range(len(dict)): + gt_answers = dict[i]['gt_answers'] + answer = dict[i]['answer'] + gt_answers = remove_special_chars(gt_answers).lower() + answer = remove_special_chars(answer).lower() + if has_word(answer, gt_answers): + correct+=1 + num+=1 + print(f'{taskname:10s}:{float(correct)/num*100:.2f}') + print('=' * 32) diff --git a/MiniCPM-V/eval_mm/vqaeval/eval_utils/getargs.py b/MiniCPM-V/eval_mm/vqaeval/eval_utils/getargs.py new file mode 100644 index 0000000000000000000000000000000000000000..07f373db1de90232b08a4a5358638f4e8a9e4a5a --- /dev/null +++ b/MiniCPM-V/eval_mm/vqaeval/eval_utils/getargs.py @@ -0,0 +1,62 @@ +import argparse + + +def parse_args(): + parser = argparse.ArgumentParser(description="Demo") + + parser.add_argument('--local-rank', type=int, default=0, help='Local rank for distributed training.') + + # textVQA + parser.add_argument("--textVQA_image_dir", type=str, default="") + parser.add_argument("--textVQA_ann_path", type=str, default="") + + # docVQA + parser.add_argument("--docVQA_image_dir", type=str, default="") + parser.add_argument("--docVQA_ann_path", type=str, default="") + + # docVQATest + parser.add_argument("--docVQATest_image_dir", type=str, default="") + parser.add_argument("--docVQATest_ann_path", type=str, default="") + + # result path + parser.add_argument("--answer_path", type=str, default="./answers-new") + + # eval + parser.add_argument( + "--eval_textVQA", + action="store_true", + default=False, + help="Whether to evaluate on textVQA." + ) + parser.add_argument( + "--eval_docVQA", + action="store_true", + default=False, + help="Whether to evaluate on docVQA." + ) + parser.add_argument( + "--eval_docVQATest", + action="store_true", + default=False, + help="Whether to evaluate on docVQA." + ) + + parser.add_argument( + "--eval_all", + action="store_true", + default=False, + help="Whether to evaluate all datasets" + ) + + parser.add_argument("--model_name", type=str, default="") + parser.add_argument("--model_path", type=str, default="") + + parser.add_argument("--generate_method", type=str, default="", help="generate with interleave or not.") + + parser.add_argument("--device", type=str, default="cuda:0") + parser.add_argument('--batchsize', type=int, default=1, help='Batch size for processing.') + + parser.add_argument("--ckpt", type=str, default=None) + + args = parser.parse_args() + return args \ No newline at end of file diff --git a/MiniCPM-V/eval_mm/vqaeval/eval_utils/vqa_evaluate.py b/MiniCPM-V/eval_mm/vqaeval/eval_utils/vqa_evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..78888949ce171b972fd459d4e3a8fa37ec173692 --- /dev/null +++ b/MiniCPM-V/eval_mm/vqaeval/eval_utils/vqa_evaluate.py @@ -0,0 +1,446 @@ +import itertools +import json +import os +import re +from collections import namedtuple + +import torch +from tqdm import tqdm + + +class InferenceSampler(torch.utils.data.sampler.Sampler): + + def __init__(self, size): + self._size = int(size) + assert size > 0 + self._rank = torch.distributed.get_rank() + self._world_size = torch.distributed.get_world_size() + self._local_indices = self._get_local_indices(size, self._world_size, + self._rank) + + @staticmethod + def _get_local_indices(total_size, world_size, rank): + shard_size = total_size // world_size + left = total_size % world_size + shard_sizes = [shard_size + int(r < left) for r in range(world_size)] + + begin = sum(shard_sizes[:rank]) + end = min(sum(shard_sizes[:rank + 1]), total_size) + return range(begin, end) + + def __iter__(self): + yield from self._local_indices + + def __len__(self): + return len(self._local_indices) + +def collate_fn_vqa(batches): + ''' + ''' + image_paths = [_['image_path'] for _ in batches] + questions = [_['question'] for _ in batches] + gt_answers = [_['gt_answers'] for _ in batches] + ocr_tokens = [_['ocr_tokens'] if 'ocr_tokens' in _ else None for _ in batches] + question_ids = [_['question_id'] if 'question_id' in _ else None for _ in batches] + question_type = [_['question_type'] if 'question_type' in _ else None for _ in batches] + + return image_paths, questions, gt_answers, ocr_tokens, question_ids, question_type + +def has_word(sentence, word): + if word[0].isalnum(): + start_pattern = r"\b" + else: + start_pattern = r"" + + if word[-1].isalnum(): + end_pattern = r"\b" + else: + end_pattern = r"" + + pattern = start_pattern + re.escape(word) + end_pattern + match = re.search(pattern, sentence) + return bool(match) + +def remove_special_chars(s): + pattern = r"[^a-zA-Z0-9\s]" + s = re.sub(pattern, "", s) + return s + +def levenshtein_distance(s1, s2): + if len(s1) > len(s2): + s1, s2 = s2, s1 + + distances = range(len(s1) + 1) + for i2, c2 in enumerate(s2): + distances_ = [i2+1] + for i1, c1 in enumerate(s1): + if c1 == c2: + distances_.append(distances[i1]) + else: + distances_.append(1 + min((distances[i1], distances[i1 + 1], distances_[-1]))) + distances = distances_ + return distances[-1] + +class VQAEval: + def __init__(self): + self.contractions = { + "aint": "ain't", + "arent": "aren't", + "cant": "can't", + "couldve": "could've", + "couldnt": "couldn't", + "couldn'tve": "couldn't've", + "couldnt've": "couldn't've", + "didnt": "didn't", + "doesnt": "doesn't", + "dont": "don't", + "hadnt": "hadn't", + "hadnt've": "hadn't've", + "hadn'tve": "hadn't've", + "hasnt": "hasn't", + "havent": "haven't", + "hed": "he'd", + "hed've": "he'd've", + "he'dve": "he'd've", + "hes": "he's", + "howd": "how'd", + "howll": "how'll", + "hows": "how's", + "Id've": "I'd've", + "I'dve": "I'd've", + "Im": "I'm", + "Ive": "I've", + "isnt": "isn't", + "itd": "it'd", + "itd've": "it'd've", + "it'dve": "it'd've", + "itll": "it'll", + "let's": "let's", + "maam": "ma'am", + "mightnt": "mightn't", + "mightnt've": "mightn't've", + "mightn'tve": "mightn't've", + "mightve": "might've", + "mustnt": "mustn't", + "mustve": "must've", + "neednt": "needn't", + "notve": "not've", + "oclock": "o'clock", + "oughtnt": "oughtn't", + "ow's'at": "'ow's'at", + "'ows'at": "'ow's'at", + "'ow'sat": "'ow's'at", + "shant": "shan't", + "shed've": "she'd've", + "she'dve": "she'd've", + "she's": "she's", + "shouldve": "should've", + "shouldnt": "shouldn't", + "shouldnt've": "shouldn't've", + "shouldn'tve": "shouldn't've", + "somebody'd": "somebodyd", + "somebodyd've": "somebody'd've", + "somebody'dve": "somebody'd've", + "somebodyll": "somebody'll", + "somebodys": "somebody's", + "someoned": "someone'd", + "someoned've": "someone'd've", + "someone'dve": "someone'd've", + "someonell": "someone'll", + "someones": "someone's", + "somethingd": "something'd", + "somethingd've": "something'd've", + "something'dve": "something'd've", + "somethingll": "something'll", + "thats": "that's", + "thered": "there'd", + "thered've": "there'd've", + "there'dve": "there'd've", + "therere": "there're", + "theres": "there's", + "theyd": "they'd", + "theyd've": "they'd've", + "they'dve": "they'd've", + "theyll": "they'll", + "theyre": "they're", + "theyve": "they've", + "twas": "'twas", + "wasnt": "wasn't", + "wed've": "we'd've", + "we'dve": "we'd've", + "weve": "we've", + "werent": "weren't", + "whatll": "what'll", + "whatre": "what're", + "whats": "what's", + "whatve": "what've", + "whens": "when's", + "whered": "where'd", + "wheres": "where's", + "whereve": "where've", + "whod": "who'd", + "whod've": "who'd've", + "who'dve": "who'd've", + "wholl": "who'll", + "whos": "who's", + "whove": "who've", + "whyll": "why'll", + "whyre": "why're", + "whys": "why's", + "wont": "won't", + "wouldve": "would've", + "wouldnt": "wouldn't", + "wouldnt've": "wouldn't've", + "wouldn'tve": "wouldn't've", + "yall": "y'all", + "yall'll": "y'all'll", + "y'allll": "y'all'll", + "yall'd've": "y'all'd've", + "y'alld've": "y'all'd've", + "y'all'dve": "y'all'd've", + "youd": "you'd", + "youd've": "you'd've", + "you'dve": "you'd've", + "youll": "you'll", + "youre": "you're", + "youve": "you've", + } + self.manualMap = { + "none": "0", + "zero": "0", + "one": "1", + "two": "2", + "three": "3", + "four": "4", + "five": "5", + "six": "6", + "seven": "7", + "eight": "8", + "nine": "9", + "ten": "10", + } + self.articles = ["a", "an", "the"] + + self.periodStrip = re.compile("(?!<=\d)(\.)(?!\d)") + self.commaStrip = re.compile("(\d)(\,)(\d)") + self.punct = [ + ";", + r"/", + "[", + "]", + '"', + "{", + "}", + "(", + ")", + "=", + "+", + "\\", + "_", + "-", + ">", + "<", + "@", + "`", + ",", + "?", + "!", + ] + def clean_text(self, text): + text = text.replace("\n", " ").replace("\t", " ").strip() + text = self.processPunctuation(text) + text = self.processDigitArticle(text) + return text + + def evaluate_vqa_human(self, answer, gt_answers): + '''TextVQA, VQAv2, OKVQA, vizwiz''' + answer = answer.replace("\n", " ").replace("\t", " ").strip() + answer = self.processPunctuation(answer) + answer = self.processDigitArticle(answer) + gt_answers = [self.processPunctuation(ans) for ans in gt_answers] + gt_answers = [self.processDigitArticle(ans) for ans in gt_answers] + + gtAcc = [] + + for idx, gtAnsDatum in enumerate(gt_answers): + otherGTAns = gt_answers[:idx] + gt_answers[idx+1:] + + matchingAns = [item for item in otherGTAns if answer == item] + + acc = min(1, float(len(matchingAns)) / 3) + gtAcc.append(acc) + + avgGTAcc = float(sum(gtAcc)) / len(gtAcc) if gtAcc else 0 + + return avgGTAcc + + def evaluate_anls(self, answer, gt_answers, threshold=0.5): + '''DOcVQA, InfographicsVQA, STVQA''' + answer = ' '.join(answer.strip().lower().split()) + if not isinstance(gt_answers, list): + gt_answers = [gt_answers] + gt_answers = [' '.join(gt_answer.strip().lower().split()) for gt_answer in gt_answers] + + values = [] + for gt_answer in gt_answers: + dist = levenshtein_distance(answer, gt_answer) + length = max(len(answer), len(gt_answer)) + values.append(0.0 if length == 0 else float(dist) / float(length)) + + score = 1 - min(values) + + score = 0 if score < threshold else score + + return score + + def processPunctuation(self, inText): + outText = inText + for p in self.punct: + if (p + " " in inText or " " + p in inText) or ( + re.search(self.commaStrip, inText) != None + ): + outText = outText.replace(p, "") + else: + outText = outText.replace(p, " ") + outText = self.periodStrip.sub("", outText, re.UNICODE) + return outText + + def processDigitArticle(self, inText): + outText = [] + tempText = inText.lower().split() + for word in tempText: + word = self.manualMap.setdefault(word, word) + if word not in self.articles: + outText.append(word) + else: + pass + for wordId, word in enumerate(outText): + if word in self.contractions: + outText[wordId] = self.contractions[word] + outText = " ".join(outText) + return outText + + +def evaluate_dataset(dataset_name, answer_file_path, model_name, method = None): + with open(answer_file_path, 'r', encoding='utf-8') as f: + predictions = json.load(f) + + eval = VQAEval() + total_accuracy = 0 + num = 0 + Entry = namedtuple('Entry', ['text', 'bbox']) + + for item in predictions: + gt_answers = item['gt_answers'] + answer = item['answer'] + if method is not None: + pass + if dataset_name in ["textVQA"]: + if num == 0: + print(f"evaluating vqa...") + accuracy = eval.evaluate_vqa_human(answer, gt_answers) + elif dataset_name in ['docVQA']: + if num == 0: + print(f"evaluating anls...") + accuracy = eval.evaluate_anls(answer, gt_answers) + else: + accuracy = eval.evaluate_has(answer, gt_answers) + item['accuracy'] = accuracy + + total_accuracy += accuracy + num += 1 + + average_accuracy = total_accuracy / num + print(f'{dataset_name}:{average_accuracy}') + + answer_model_method_path = answer_file_path.replace('.json', f'_{model_name}_{method}.json') + with open(answer_model_method_path, "w", encoding='utf-8') as f: + json.dump(predictions, f, indent=4, ensure_ascii=False) + + return average_accuracy + + +def evaluate_VQA( + model, + dataset, + model_name, + dataset_name, + time, + batch_size=1, + generate_method="interleave", + answer_path='./answers', +): + print(f"answer path:{answer_path}") + + sampler = None + if torch.distributed.is_initialized(): + sampler=InferenceSampler(len(dataset)) + + dataloader = torch.utils.data.DataLoader( + dataset=dataset, + batch_size=batch_size, + sampler=sampler, + collate_fn=collate_fn_vqa + ) + + now_rank = torch.distributed.get_rank() + + answer_dir = os.path.join(answer_path, model_name, time) + os.makedirs(answer_dir, exist_ok=True) + + image_list = [] + for item in dataset: + image_list.append(item["image_path"]) + + predictions = [] + + for batch in tqdm(dataloader, desc="Running inference"): + image_paths, questions, gt_answers, ocr_tokens_list, question_ids, question_type = batch + + with torch.no_grad(): + if model_name != "minicpm": + if model_name != "codellama": + outputs = model.generate(images=image_paths, questions=questions, datasetname=dataset_name) + else: + outputs = model.generate() + elif model_name == "minicpm": + if generate_method == "old": + outputs = model.generate(images=image_paths, questions=questions, datasetname=dataset_name) + elif generate_method == "interleave": + outputs = model.generate_with_interleaved(images=image_paths, questions=questions, datasetname=dataset_name) + else: + raise Exception(f"Wrong generate paradigm {generate_method}!") + + for i in range(len(outputs)): + answer_dict = { + 'question_id': question_ids[i], + 'question': questions[i], + 'answer': outputs[i], + 'gt_answers': gt_answers[i], + 'image_path': image_paths[i], + 'model_name': model_name, + 'question_type': question_type[i] + } + predictions.append(answer_dict) + + if torch.distributed.is_initialized(): + torch.distributed.barrier() + if torch.distributed.is_initialized(): + world_size = torch.distributed.get_world_size() + merged_predictions = [None for _ in range(world_size)] + torch.distributed.all_gather_object(merged_predictions, predictions) + predictions = [_ for _ in itertools.chain.from_iterable(merged_predictions)] + + if torch.distributed.is_initialized() and torch.distributed.get_rank() != 0: + return None + + answer_file_path = os.path.join(answer_dir, f"{dataset_name}.json") + print(f"answer_file_path:{answer_file_path}") + + with open(answer_file_path, "w", encoding='utf-8') as f: + json.dump(predictions, f, indent=4, ensure_ascii=False) + + if dataset_name in ["docVQATest"]: + return -1.0 + + return evaluate_dataset(answer_file_path=answer_file_path, dataset_name=dataset_name, model_name=model_name) diff --git a/MiniCPM-V/eval_mm/vqaeval/models/.DS_Store b/MiniCPM-V/eval_mm/vqaeval/models/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..778decc3ae59df8e1a0933504adfafc593ae5738 Binary files /dev/null and b/MiniCPM-V/eval_mm/vqaeval/models/.DS_Store differ diff --git a/MiniCPM-V/eval_mm/vqaeval/models/MiniCPM/minicpmv.py b/MiniCPM-V/eval_mm/vqaeval/models/MiniCPM/minicpmv.py new file mode 100644 index 0000000000000000000000000000000000000000..ea366edca7659e92b107c3c206f4af0341ab6bf1 --- /dev/null +++ b/MiniCPM-V/eval_mm/vqaeval/models/MiniCPM/minicpmv.py @@ -0,0 +1,201 @@ + +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer +import random +import math +import numpy as np + +Image.MAX_IMAGE_PIXELS = 1000000000 + +max_token = { + 'docVQA': 100, + 'textVQA': 100, + "docVQATest": 100 +} + +class MiniCPM_V: + + def __init__(self, model_path, ckpt, device=None)->None: + self.model_path = model_path + self.ckpt = ckpt + self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True).eval() + if self.ckpt is not None: + self.ckpt = ckpt + self.state_dict = torch.load(self.ckpt, map_location=torch.device('cpu')) + self.model.load_state_dict(self.state_dict) + + self.model = self.model.to(dtype=torch.float16) + self.model.to(device) + + self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True) + torch.cuda.empty_cache() + + def generate(self, images, questions, datasetname): + image = Image.open(images[0]).convert('RGB') + try: + max_new_tokens = max_token[datasetname] + except: + max_new_tokens = 1024 + if (datasetname == 'docVQA') or (datasetname == "docVQATest") : + prompt = "Answer the question directly with single word." + "\n" + questions[0] + elif (datasetname == 'textVQA') : + prompt = "Answer the question directly with single word." + '\n'+ questions[0] + + msgs = [{'role': 'user', 'content': prompt}] + default_kwargs = dict( + max_new_tokens=max_new_tokens, + sampling=False, + num_beams=3 + ) + res = self.model.chat( + image=image, + msgs=msgs, + context=None, + tokenizer=self.tokenizer, + **default_kwargs + ) + + return [res] + + def generate_with_interleaved(self, images, questions, datasetname): + try: + max_new_tokens = max_token[datasetname] + except: + max_new_tokens = 1024 + + prompt = "Answer the question directly with single word." + + default_kwargs = dict( + max_new_tokens=max_new_tokens, + sampling=False, + num_beams=3 + ) + + content = [] + message = [ + {'type': 'text', 'value': prompt}, + {'type': 'image', 'value': images[0]}, + {'type': 'text', 'value': questions[0]} + ] + for x in message: + if x['type'] == 'text': + content.append(x['value']) + elif x['type'] == 'image': + image = Image.open(x['value']).convert('RGB') + content.append(image) + msgs = [{'role': 'user', 'content': content}] + + res = self.model.chat( + image=None, + msgs=msgs, + context=None, + tokenizer=self.tokenizer, + **default_kwargs + ) + + if isinstance(res, tuple) and len(res) > 0: + res = res[0] + print(f"Q: {content}, \nA: {res}") + return [res] + + +class MiniCPM_V_2_6: + + def __init__(self, model_path, ckpt, device=None)->None: + seed = 0 + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + self.model_path = model_path + self.ckpt = ckpt + self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True).eval() + if self.ckpt is not None: + self.ckpt = ckpt + self.state_dict = torch.load(self.ckpt, map_location=torch.device('cpu')) + self.model.load_state_dict(self.state_dict) + + self.model = self.model.to(dtype=torch.bfloat16) + self.model.to(device) + + self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True) + torch.cuda.empty_cache() + + def generate(self, images, questions, datasetname): + image = Image.open(images[0]).convert('RGB') + try: + max_new_tokens = max_token[datasetname] + except: + max_new_tokens = 1024 + if (datasetname == 'docVQA') or (datasetname == "docVQATest") : + prompt = "Answer the question directly with single word." + "\n" + questions[0] + elif (datasetname == 'textVQA') : + prompt = "Answer the question directly with single word." + '\n'+ questions[0] + + msgs = [{'role': 'user', 'content': prompt}] + default_kwargs = dict( + max_new_tokens=max_new_tokens, + sampling=False, + num_beams=3 + ) + res = self.model.chat( + image=image, + msgs=msgs, + context=None, + tokenizer=self.tokenizer, + **default_kwargs + ) + + return [res] + + def generate_with_interleaved(self, images, questions, datasetname): + try: + max_new_tokens = max_token[datasetname] + except: + max_new_tokens = 1024 + + prompt = "Answer the question directly with single word." + + default_kwargs = dict( + max_new_tokens=max_new_tokens, + sampling=False, + num_beams=3 + ) + + content = [] + message = [ + {'type': 'text', 'value': prompt}, + {'type': 'image', 'value': images[0]}, + {'type': 'text', 'value': questions[0]} + ] + for x in message: + if x['type'] == 'text': + content.append(x['value']) + elif x['type'] == 'image': + image = Image.open(x['value']).convert('RGB') + img_width, img_height = image.width, image.height + if (img_width * img_height) >= (1344 * 1344): + content.append(image) + else: + ratio = math.sqrt((1344 * 1344) / (img_width * img_height)) + max_img_width = int(img_width * ratio) + new_img_width = random.randint(img_width, max_img_width) + new_img_height = int(new_img_width / img_width * img_height) + resized_image = image.resize((new_img_width, new_img_height)) + content.append(resized_image) + msgs = [{'role': 'user', 'content': content}] + + res = self.model.chat( + image=None, + msgs=msgs, + context=None, + tokenizer=self.tokenizer, + **default_kwargs + ) + + if isinstance(res, tuple) and len(res) > 0: + res = res[0] + print(f"Q: {content}, \nA: {res}") + return [res] diff --git a/MiniCPM-V/eval_mm/vqaeval/requirements.txt b/MiniCPM-V/eval_mm/vqaeval/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5eb942567d8848695989821165de9381d851e34e --- /dev/null +++ b/MiniCPM-V/eval_mm/vqaeval/requirements.txt @@ -0,0 +1,49 @@ +accelerate +aiohttp==3.8.4 +aiosignal==1.3.1 +async-timeout==4.0.2 +attrs==22.2.0 +bitsandbytes==0.37.0 +cchardet==2.1.7 +chardet==5.1.0 +contourpy==1.0.7 +cycler==0.11.0 +filelock==3.9.0 +fonttools==4.38.0 +frozenlist==1.3.3 +huggingface-hub==0.13.4 +importlib-resources==5.12.0 +kiwisolver==1.4.4 +matplotlib==3.7.0 +multidict==6.0.4 +openai==0.27.0 +packaging==23.0 +psutil==5.9.4 +pycocotools==2.0.6 +pyparsing==3.0.9 +python-dateutil==2.8.2 +pyyaml==6.0 +regex==2022.10.31 +tokenizers==0.13.2 +tqdm==4.64.1 +transformers +timm==0.6.13 +spacy==3.5.1 +webdataset==0.2.48 +scikit-learn==1.2.2 +scipy==1.10.1 +yarl==1.8.2 +zipp==3.14.0 +omegaconf==2.3.0 +opencv-python==4.7.0.72 +iopath==0.1.10 +decord==0.6.0 +tenacity==8.2.2 +peft +pycocoevalcap +sentence-transformers +umap-learn +notebook +gradio==3.24.1 +gradio-client==0.0.8 +wandb diff --git a/MiniCPM-V/eval_mm/vqaeval/shell/run_inference.sh b/MiniCPM-V/eval_mm/vqaeval/shell/run_inference.sh new file mode 100644 index 0000000000000000000000000000000000000000..da462bf875493c5edd2b0b64b43ebfb4e55f800d --- /dev/null +++ b/MiniCPM-V/eval_mm/vqaeval/shell/run_inference.sh @@ -0,0 +1,15 @@ +export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" +python -m torch.distributed.launch \ + --nproc_per_node=${NPROC_PER_NODE:-8} \ + --nnodes=${WORLD_SIZE:-1} \ + --node_rank=${RANK:-0} \ + --master_addr=${MASTER_ADDR:-127.0.0.1} \ + --master_port=${MASTER_PORT:-12345} \ + ./eval.py \ + --model_name minicpmv26 \ + --model_path \ + --generate_method interleave \ + --eval_textVQA \ + --eval_docVQA \ + --answer_path ./answers \ + --batchsize 1 \ No newline at end of file diff --git a/MiniCPM-V/eval_mm/vqaeval/shell/run_transform.sh b/MiniCPM-V/eval_mm/vqaeval/shell/run_transform.sh new file mode 100644 index 0000000000000000000000000000000000000000..a19a83fba836a366d4893635aee57852b77f2d8b --- /dev/null +++ b/MiniCPM-V/eval_mm/vqaeval/shell/run_transform.sh @@ -0,0 +1,3 @@ +python ./transform_docvqatest_for_submission.py \ + --input_file_path \ + --output_file_path \ No newline at end of file diff --git a/MiniCPM-V/eval_mm/vqaeval/transform_docvqatest_for_submission.py b/MiniCPM-V/eval_mm/vqaeval/transform_docvqatest_for_submission.py new file mode 100644 index 0000000000000000000000000000000000000000..44048fef9b2c8bc673d449096504fbc708a24762 --- /dev/null +++ b/MiniCPM-V/eval_mm/vqaeval/transform_docvqatest_for_submission.py @@ -0,0 +1,16 @@ +import argparse +import json + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--input_file_path", type=str, default="", help="path to the originial output json.") + parser.add_argument("--output_file_path", type=str, default="", help="path to where you want to save the processed json.") + args = parser.parse_args() + + with open(args.input_file_path , 'r') as f: + data = json.load(f) + + transformed_data = [{"questionId": item["question_id"], "answer": item["answer"].replace("", "")} for item in data] + + with open(args.output_file_path, 'w') as f: + json.dump(transformed_data, f) diff --git a/MiniCPM-V/finetune/__init__.py b/MiniCPM-V/finetune/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/MiniCPM-V/finetune/dataset.py b/MiniCPM-V/finetune/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..dbf6bbca68a7aea71b4989e398f46d0dad51beff --- /dev/null +++ b/MiniCPM-V/finetune/dataset.py @@ -0,0 +1,581 @@ +import copy +import json +import logging +import math +import os +import re +import random +from dataclasses import dataclass, field +from typing import Dict, List, Optional + +import numpy as np +import torch +from PIL import Image +from torch.nn.utils.rnn import pad_sequence +from torch.utils.data import Dataset +from transformers import AutoProcessor, AutoTokenizer +import logging + +logger = logging.getLogger(__name__) + +llama3_chat_template = "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}" + +class SupervisedDataset(Dataset): + """Dataset for supervised fine-tuning.""" + + def __init__( + self, + raw_data, + transform, + tokenizer, + slice_config, + llm_type="minicpm", + patch_size=14, + query_nums=64, + batch_vision=False, + max_length=2048, + ): + super(SupervisedDataset, self).__init__() + self.raw_data = raw_data + self.tokenizer = tokenizer + self.transform = transform + self.slice_config = slice_config + self.llm_type = llm_type + self.patch_size = patch_size + self.query_nums=query_nums + self.batch_vision = batch_vision + self.max_length = max_length + + def __len__(self): + return len(self.raw_data) + + def __getitem__(self, i) -> Dict[str, torch.Tensor]: + try: + if isinstance(self.raw_data[i]["image"], str): + images_dict = { "" : Image.open(self.raw_data[i]["image"]).convert("RGB") } + elif isinstance(self.raw_data[i]["image"], Dict): + ### for multi-images input, the template for every image is , such as , + images_dict = {img_name : Image.open(img_path).convert("RGB") for img_name, img_path in self.raw_data[i]["image"].items()} + + ret = preprocess( + images_dict, + self.raw_data[i]["conversations"], + self.tokenizer, + self.transform, + query_nums=self.query_nums, + slice_config=self.slice_config, + llm_type=self.llm_type, + patch_size=self.patch_size, + batch_vision=self.batch_vision, + max_length=self.max_length + ) + ret = dict( + input_ids=ret["input_ids"], + position_ids=ret["position_ids"], + labels=ret["target"], + attention_mask=torch.ones_like(ret["input_ids"], dtype=torch.bool), + pixel_values=ret["pixel_values"], + tgt_sizes=ret["tgt_sizes"], + image_bound=ret["image_bound"], + ) + except: + logger.error(f"data fetch error") + return self.__getitem__(random.randint(0, len(self))) + return ret + + +def data_collator(examples, padding_value=0, max_length=2048): + def trim_and_pad(seq, batch_first, padding_value): + return pad_sequence([s[:max_length] for s in seq], batch_first=True, padding_value=padding_value) + + input_ids = trim_and_pad( + [example["input_ids"] for example in examples], + batch_first=True, + padding_value=padding_value, + ) + position_ids = trim_and_pad( + [example["position_ids"] for example in examples], + batch_first=True, + padding_value=padding_value, + ) + targets = trim_and_pad( + [example["labels"] for example in examples], + batch_first=True, + padding_value=-100, + ) + attention_mask = trim_and_pad( + [example["attention_mask"] for example in examples], + batch_first=True, + padding_value=padding_value, + ) + pixel_values = [example["pixel_values"] for example in examples] + image_bound = [example["image_bound"] for example in examples] + tgt_sizes = [example["tgt_sizes"] for example in examples] + return { + "input_ids": input_ids, + "position_ids": position_ids, + "labels": targets, + "attention_mask": attention_mask, + "image_bound": image_bound, + "tgt_sizes": tgt_sizes, + "pixel_values": pixel_values, + } + + +def conversation_to_ids(conversation, tokenizer, llm_type=None, new_schema=False, max_length=2048): + """ + for single image multi-turn conversation + conversation: [{'role': 'user', 'content': 'Describe this image'}, + {'role': 'assistant', 'content': 'This is a cat.'}] + """ + if llm_type == "llama3": + input_ids, context, raw_msg = conversation_to_ids_llama3( + conversation, tokenizer + ) + elif llm_type == "qwen2": + input_ids, context, raw_msg = conversation_to_ids_qwen2( + conversation, tokenizer + ) + else: + input_ids, context, raw_msg = conversation_to_ids_minicpm( + conversation, tokenizer + ) + + ids = torch.from_numpy(np.hstack(input_ids, dtype=np.int32)) + context = torch.from_numpy(np.hstack(context, dtype=np.int8)) + if input_ids.shape[-1] > max_length: + ids =ids[:max_length] + context = context[:max_length] + logger.warning(f"The input length ({input_ids.shape[-1]}) exceeds the model's maximum length ({max_length}), so it has been truncated") + + if torch.all(context): + logger.error("No tokens available to compute loss.") + raise Exception("No tokens available to compute loss.") + + # build target + target = torch.full_like(ids, -100, dtype=torch.int32) + + for i in range(1, len(ids)): + if context[i] == 0: + target[i - 1] = ids[i] + if context[i] == 1 and context[i - 1] == 0: + if hasattr(tokenizer, "eot_id"): + target[i - 1] = tokenizer.eot_id + else: + target[i - 1] = tokenizer.eos_id + + # build image bound + if new_schema: + start_cond = (ids == tokenizer.im_start_id) | (ids == tokenizer.slice_start_id) + end_cond = (ids == tokenizer.im_end_id) | (ids == tokenizer.slice_end_id) + image_start_tokens = torch.where(start_cond)[0] + image_start_tokens += 1 + image_end_tokens = torch.where(end_cond)[0] + else: + image_start_tokens = torch.where(ids == tokenizer.im_start_id)[0] + image_start_tokens += 1 + image_end_tokens = torch.where(ids == tokenizer.im_end_id)[0] + if len(image_start_tokens) != len(image_end_tokens): + logger.error("image start token != image end tokens") + raise Exception("image start token != image end tokens") + + if len(image_start_tokens) > 0: + image_bound = torch.hstack( + [image_start_tokens.unsqueeze(-1), image_end_tokens.unsqueeze(-1)] + ) + else: + image_bound = [] + + position_ids = torch.arange(ids.size(0)).long() + return { + "input_ids": ids, + "target": target, + "image_bound": image_bound, + "raw_msg": raw_msg, + "position_ids": position_ids + } + + +def conversation_to_ids_minicpm(conversation, tokenizer): + raw_msg = "" + input_ids = [] + context = [] + for idx, msg in enumerate(conversation): + role = msg["role"] + message = msg["content"] + assert role in ["user", "assistant"] + if role == "user": + prefix = "<用户>" + else: + prefix = "" + # append eos + if idx == len(conversation) - 1: + message = message + tokenizer.eos_token + prefix_ids = tokenizer.encode(prefix)[1:] # remove bos + message_ids = tokenizer.encode(message)[1:] + + input_ids.append(prefix_ids) + input_ids.append(message_ids) + + context.append(np.ones((len(prefix_ids),), dtype=np.int8)) + if role == "assistant": + context.append(np.zeros((len(message_ids),), dtype=np.int8)) + else: + context.append(np.ones((len(message_ids),), dtype=np.int8)) + + raw_msg += prefix + message + + return input_ids, context, raw_msg + + +def conversation_to_ids_llama3(conversation, tokenizer): + raw_msg = "" + input_ids = [] + context = [] + raw_msg = tokenizer.apply_chat_template( + conversation, tokenize=False, add_generation_prompt=False, chat_template=llama3_chat_template, + ) + input_ids = tokenizer.apply_chat_template( + conversation, tokenize=True, add_generation_prompt=False, chat_template=llama3_chat_template, + ) + input_ids = np.array(input_ids) + + start_header_idxs = np.where( + input_ids == tokenizer.convert_tokens_to_ids("<|start_header_id|>") + )[0] + assistant_idxs = np.where( + input_ids == tokenizer.convert_tokens_to_ids("assistant") + )[0] + end_header_idxs = np.where( + input_ids == tokenizer.convert_tokens_to_ids("<|end_header_id|>") + )[0] + eot_idxs = np.where( + input_ids == tokenizer.convert_tokens_to_ids("<|eot_id|>"))[0] + + context = np.ones_like(input_ids, dtype=np.int8) + + for assistant_idx in assistant_idxs: + if assistant_idx in set((start_header_idxs + end_header_idxs) / 2): + st = assistant_idx + 3 # assistant<|end_header_id|>\n\n + for eot_idx in eot_idxs: + if eot_idx > st: + context[st: eot_idx + 1] = 0 + break + + input_ids = np.hstack(input_ids) + context = np.hstack(context) + + return input_ids, context, raw_msg + + +def conversation_to_ids_qwen2(conversation, tokenizer): + raw_msg = "" + chat = [] + context = [] + for idx, msg in enumerate(conversation): + role = msg["role"] + message = msg["content"] + assert role in ["user", "assistant"] + if role == "user": + prefix = "user" + else: + prefix = "assistant" + chat.append({"role":prefix, "content":message}) + raw_msg += prefix + message + assert set([i['role'] for i in chat]) & set(['assistant']) + + ret = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False) + input_ids = tokenizer.apply_chat_template(chat, tokenize=True, add_generation_prompt=False) + input_ids = np.array(input_ids) + + start_idxs = np.where(input_ids == tokenizer.convert_tokens_to_ids('<|im_start|>'))[0] + assistant_idxs = np.where(input_ids == tokenizer.convert_tokens_to_ids('assistant'))[0] + end_idxs = np.where(input_ids == tokenizer.convert_tokens_to_ids('<|im_end|>'))[0] + + context = np.ones_like(input_ids, dtype=np.int8) + + for assistant_idx in assistant_idxs: + if assistant_idx-1 in set(start_idxs): + st = assistant_idx + 1 + for end_idx in end_idxs: + if end_idx > st: + context[st: end_idx + 1] = 0 + break + + input_ids = np.hstack(input_ids) + context = np.hstack(context) + return input_ids, context, raw_msg + + +def preprocess( + images_dict, + conversations, + tokenizer, + transform, + query_nums=64, + slice_config=None, + llm_type=None, + patch_size=14, + batch_vision=False, + max_length=2048, +): + """ + single(multi) image(s) preprocess, the image(s) will be placed at the top of the conversation + """ + conversations = copy.deepcopy(conversations) + assert len(conversations) > 1, "conversations length must large than 2" + assert conversations[0]["role"] == "user", "the first role must be user" + + if slice_config is not None: + assert isinstance(slice_config, Dict) + assert "patch_size" in slice_config + assert "max_slice_nums" in slice_config + assert "scale_resolution" in slice_config + default_image_placeholder = ( + tokenizer.im_start + tokenizer.unk_token * query_nums + tokenizer.im_end + ) + new_schema = False + use_image_id = False + if llm_type=='qwen2': + new_schema = True + use_image_id = True + image_placeholder_dict = {} + images = [] + image_id_cnt = 0 + for img_name, image in images_dict.items(): + if slice_config: + source_image, patches, best_grid = slice_image( + image, + slice_config["max_slice_nums"], + slice_config["scale_resolution"], + slice_config["patch_size"], + ) + images.append(source_image) + image_placeholder = default_image_placeholder + if len(patches) > 0: + for i in range(len(patches)): + for j in range(len(patches[0])): + images.append(patches[i][j]) + if use_image_id: + image_placeholder = f'{tokenizer.im_id_start}{image_id_cnt}{tokenizer.im_id_end}' + image_placeholder + image_id_cnt += 1 + image_placeholder += get_grid_placeholder( + tokenizer, best_grid, query_nums, new_schema = new_schema) + image_placeholder_dict[img_name] = image_placeholder + else: + images.append(image) + if use_image_id: + image_placeholder = f'{tokenizer.im_id_start}{image_id_cnt}{tokenizer.im_id_end}' + image_placeholder + image_id_cnt += 1 + else: + image_placeholder = default_image_placeholder + image_placeholder_dict[img_name] = image_placeholder + + images = [transform(i) for i in images] + + if len(images_dict) == 1 and "" in images_dict: + if "" in conversations[0]["content"]: + conversations[0]["content"] = conversations[0]["content"].replace( + "", image_placeholder + ) + else: + conversations[0]["content"] = ( + image_placeholder + "\n" + conversation[0]["content"] + ) + input_dict = conversation_to_ids(conversations, tokenizer, llm_type, new_schema, max_length) + else: + pattern = r'' + new_conversations = [] + for conversation in conversations: + content = conversation['content'] + parts = re.split(f'({pattern})', content) + for i, part in enumerate(parts): + if not part.strip(): + continue + if re.match(pattern, part): + if part in image_placeholder_dict: + parts[i] = image_placeholder_dict[part] + else: + raise Exception(f"not found {part} in image dict") + conversation['content'] = '\n'.join(parts) + new_conversations.append(conversation) + conversations = new_conversations + + input_dict = conversation_to_ids(conversations, tokenizer, llm_type, new_schema, max_length) + + if batch_vision: + tgt_sizes = [] + reshape_images = [] + for image in images: + H, W = image.shape[1:] + reshape_image = reshape_by_patch(image, patch_size) + reshape_images.append(reshape_image) + tgt_sizes.append([H // patch_size, W // patch_size]) + if tgt_sizes: + tgt_sizes = torch.Tensor(tgt_sizes).type(torch.int32) + + input_dict["pixel_values"] = reshape_images + input_dict["tgt_sizes"] = tgt_sizes + + else: + input_dict["pixel_values"] = images + input_dict["tgt_sizes"] = [] + + return input_dict + + +def slice_image( + image, max_slice_nums=9, scale_resolution=448, patch_size=14, never_split=False +): + original_size = image.size + original_width, original_height = original_size + log_ratio = math.log(original_width / original_height) + ratio = original_width * original_height / \ + (scale_resolution * scale_resolution) + multiple = min(math.ceil(ratio), max_slice_nums) + + source_image = None + best_grid = None + patches = [] + + if multiple <= 1 or never_split: + # dont need to slice, upsample + best_size = find_best_resize( + original_size, scale_resolution, patch_size, allow_upscale=True + ) + source_image = image.resize(best_size, Image.Resampling.BICUBIC) + else: + candidate_split_grids_nums = [] + for i in [multiple - 1, multiple, multiple + 1]: + if i == 1 or i > max_slice_nums: + continue + candidate_split_grids_nums.append(i) + + # source image, down-sampling and ensure divided by patch_size + best_resize = find_best_resize( + original_size, scale_resolution, patch_size) + source_image = image.copy().resize(best_resize, Image.Resampling.BICUBIC) + candidate_grids = [] + + # find best grid + for split_grids_nums in candidate_split_grids_nums: + m = 1 + while m <= split_grids_nums: + if split_grids_nums % m == 0: + candidate_grids.append([m, split_grids_nums // m]) + m += 1 + + best_grid = [1, 1] + min_error = float("inf") + for grid in candidate_grids: + error = abs(log_ratio - math.log(grid[0] / grid[1])) + if error < min_error: + best_grid = grid + min_error = error + + refine_size = get_refine_size( + original_size, best_grid, scale_resolution, patch_size, allow_upscale=True + ) + + refine_image = image.resize(refine_size, Image.Resampling.BICUBIC) + patches = split_to_patches(refine_image, best_grid) + + return source_image, patches, best_grid + + +def ensure_divide(length, patch_size): + return max(round(length / patch_size) * patch_size, patch_size) + + +def find_best_resize(original_size, scale_resolution, patch_size, allow_upscale=False): + width, height = original_size + if (width * height > scale_resolution * scale_resolution) or allow_upscale: + r = width / height + height = int(scale_resolution / math.sqrt(r)) + width = int(height * r) + best_width = ensure_divide(width, patch_size) + best_height = ensure_divide(height, patch_size) + return (best_width, best_height) + + +def get_refine_size( + original_size, grid, scale_resolution, patch_size, allow_upscale=False +): + width, height = original_size + grid_x, grid_y = grid + + refine_width = ensure_divide(width, grid_x) + refine_height = ensure_divide(height, grid_y) + + grid_width = refine_width / grid_x + grid_height = refine_height / grid_y + + best_grid_size = find_best_resize( + (grid_width, grid_height), + scale_resolution, + patch_size, + allow_upscale=allow_upscale, + ) + + refine_size = (best_grid_size[0] * grid_x, best_grid_size[1] * grid_y) + + return refine_size + + +def split_to_patches(image, grid): + patches = [] + width, height = image.size + grid_x = int(width / grid[0]) + grid_y = int(height / grid[1]) + + for i in range(0, height, grid_y): + images = [] + for j in range(0, width, grid_x): + box = (j, i, j + grid_x, i + grid_y) + patch = image.crop(box) + images.append(patch) + patches.append(images) + + return patches + + +def get_grid_placeholder(tokenizer, grid, query_num, new_schema=False): + if new_schema: + image_placeholder = ( + tokenizer.slice_start + tokenizer.unk_token * query_num + tokenizer.slice_end + ) + else: + image_placeholder = ( + tokenizer.im_start + tokenizer.unk_token * query_num + tokenizer.im_end + ) + + cols = grid[0] + rows = grid[1] + slices = [] + for i in range(rows): + lines = [] + for j in range(cols): + lines.append(image_placeholder) + slices.append("".join(lines)) + if new_schema: + slice_placeholder = '\n'.join(slices) + else: + slice_placeholder = tokenizer.slice_start + \ + "\n".join(slices) + tokenizer.slice_end + return slice_placeholder + + +def reshape_by_patch(image_tensor, patch_size): + """ + :param image_tensor: shape [3, H, W] + :param patch_size: + :return: [3, patch_size, HW/patch_size] + """ + patches = torch.nn.functional.unfold( + image_tensor, (patch_size, patch_size), stride=(patch_size, patch_size) + ) + + patches = patches.reshape(image_tensor.size(0), patch_size, patch_size, -1) + patches = patches.permute(0, 1, 3, 2).reshape( + image_tensor.size(0), patch_size, -1) + return patches diff --git a/MiniCPM-V/finetune/ds_config_zero2.json b/MiniCPM-V/finetune/ds_config_zero2.json new file mode 100644 index 0000000000000000000000000000000000000000..4d42d440b4b2b55dba2e54863109773b1ee3fee9 --- /dev/null +++ b/MiniCPM-V/finetune/ds_config_zero2.json @@ -0,0 +1,54 @@ +{ + "fp16": { + "enabled": "auto", + "loss_scale": 0, + "loss_scale_window": 1000, + "initial_scale_power": 16, + "hysteresis": 2, + "min_loss_scale": 1 + }, + + "bf16": { + "enabled": "auto" + }, + + "optimizer": { + "type": "AdamW", + "params": { + "lr": "auto", + "betas": "auto", + "eps": "auto", + "weight_decay": "auto" + } + }, + + "scheduler": { + "type": "WarmupLR", + "params": { + "warmup_min_lr": "auto", + "warmup_max_lr": "auto", + "warmup_num_steps": "auto" + } + }, + + "zero_optimization": { + "stage": 2, + "offload_optimizer": { + "device": "none", + "pin_memory": true + }, + "allgather_partitions": true, + "allgather_bucket_size": 2e8, + "overlap_comm": true, + "reduce_scatter": true, + "reduce_bucket_size": 2e8, + "contiguous_gradients": true + }, + + "gradient_accumulation_steps": "auto", + "gradient_clipping": "auto", + "steps_per_print": 100, + "train_batch_size": "auto", + "train_micro_batch_size_per_gpu": "auto", + "wall_clock_breakdown": false +} diff --git a/MiniCPM-V/finetune/ds_config_zero3.json b/MiniCPM-V/finetune/ds_config_zero3.json new file mode 100644 index 0000000000000000000000000000000000000000..5c3cd9c9274dee4f57449196edbc07920b3b94ec --- /dev/null +++ b/MiniCPM-V/finetune/ds_config_zero3.json @@ -0,0 +1,61 @@ + +{ + "fp16": { + "enabled": "auto", + "loss_scale": 0, + "loss_scale_window": 1000, + "initial_scale_power": 16, + "hysteresis": 2, + "min_loss_scale": 1 + }, + "bf16": { + "enabled": "auto" + }, + "optimizer": { + "type": "AdamW", + "params": { + "lr": "auto", + "betas": "auto", + "eps": "auto", + "weight_decay": "auto" + } + }, + + "scheduler": { + "type": "WarmupLR", + "params": { + "warmup_min_lr": "auto", + "warmup_max_lr": "auto", + "warmup_num_steps": "auto" + } + }, + + "zero_optimization": { + "stage": 3, + "offload_optimizer": { + "device": "none", + "pin_memory": true + }, + "offload_param": { + "device": "none", + "pin_memory": true + }, + "overlap_comm": true, + "contiguous_gradients": true, + "sub_group_size": 1e9, + "reduce_bucket_size": "auto", + "stage3_prefetch_bucket_size": "auto", + "stage3_param_persistence_threshold": "auto", + "stage3_max_live_parameters": 1e9, + "stage3_max_reuse_distance": 1e9, + "stage3_gather_16bit_weights_on_model_save": true + }, + + "gradient_accumulation_steps": "auto", + "gradient_clipping": "auto", + "steps_per_print": 100, + "train_batch_size": "auto", + "train_micro_batch_size_per_gpu": "auto", + "wall_clock_breakdown": false +} + diff --git a/MiniCPM-V/finetune/finetune.py b/MiniCPM-V/finetune/finetune.py new file mode 100644 index 0000000000000000000000000000000000000000..0c596d7b2385d6ea29c674119b2d1df4c3bd9e29 --- /dev/null +++ b/MiniCPM-V/finetune/finetune.py @@ -0,0 +1,299 @@ +import glob +import json +import logging +import os +from dataclasses import dataclass, field +from functools import partial +from typing import Dict, List, Optional, Union, Literal, Tuple +from types import MethodType +from torchvision import transforms + +import torch +import transformers +from accelerate.utils import DistributedType +from deepspeed import zero +from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus + +from transformers import AutoModel, AutoTokenizer +from transformers.integrations import deepspeed +from transformers import AutoModel, AutoTokenizer + +from dataset import SupervisedDataset, data_collator +from trainer import CPMTrainer + +from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training + +@dataclass +class ModelArguments: + model_name_or_path: Optional[str] = field(default="openbmb/MiniCPM-V-2") + + +@dataclass +class DataArguments: + data_path: str = field( + default=None, metadata={"help": "Path to the training data."} + ) + eval_data_path: str = field( + default=None, metadata={"help": "Path to the evaluation data."} + ) + + +@dataclass +class TrainingArguments(transformers.TrainingArguments): + cache_dir: Optional[str] = field(default=None) + optim: str = field(default="adamw_torch") + model_max_length: int = field( + default=2048, + metadata={ + "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)." + }, + ) + tune_vision: Optional[bool] = field(default=True) + tune_llm: Optional[bool] = field(default=True) + llm_type: str = field(default="minicpm") + use_lora: Optional[bool] = field(default=False) + max_slice_nums: Optional[int] = field(default=9) + + +@dataclass +class LoraArguments: + lora_r: int = 64 + lora_alpha: int = 64 + lora_dropout: float = 0.05 + lora_target_modules: str = r"llm\..*layers\.\d+\.self_attn\.(q_proj|k_proj|v_proj)" + lora_weight_path: str = "" + lora_bias: str = "none" + q_lora: bool = False + lora_modules_to_save: str = "" + lora_layer_replication: Optional[List[Tuple[int, int]]] = None + lora_layers_to_transform: Optional[List[int]] = None + lora_layers_pattern: Optional[str] = None + +local_rank = None +def rank0_print(*args): + if local_rank == 0: + print(*args) + + +def safe_save_model_for_hf_trainer(trainer, output_dir: str, bias="none"): + """Collects the state dict and dump to disk.""" + if trainer.args.should_save and trainer.args.local_rank == 0: + trainer.save_model(output_dir,) + + +def make_supervised_data_module( + tokenizer: transformers.PreTrainedTokenizer, + data_args, + transform, + data_collator=None, + llm_type="minicpm", + slice_config=None, + patch_size=14, + query_nums=64, + batch_vision=False, + max_length=2048, +) -> Dict: + """Make dataset and collator for supervised fine-tuning.""" + dataset_cls = SupervisedDataset + + rank0_print("Loading data...") + + train_json = json.load(open(data_args.data_path, "r")) + train_dataset = dataset_cls( + train_json, + transform, + tokenizer, + slice_config=slice_config, + llm_type=llm_type, + patch_size=patch_size, + query_nums=query_nums, + batch_vision=batch_vision, + max_length=max_length, + ) + + if data_args.eval_data_path: + eval_json = json.load(open(data_args.eval_data_path, "r")) + eval_dataset = dataset_cls( + eval_json, + transform, + tokenizer, + slice_config=slice_config, + llm_type=llm_type, + patch_size=patch_size, + query_nums=query_nums, + batch_vision=batch_vision, + max_length=max_length, + ) + else: + eval_dataset = None + + return dict( + train_dataset=train_dataset, + eval_dataset=eval_dataset, + data_collator= partial(data_collator, max_length=max_length), + ) + + +def build_transform(): + IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5) # timm.data.IMAGENET_INCEPTION_MEAN + IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5) # timm.data.IMAGENET_INCEPTION_STD + return transforms.Compose( + [ + transforms.ToTensor(), + transforms.Normalize( + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD + ), + ] + ) + +def get_parameter_number(model): + trainable_params, all_param = 0, 0 + for param in model.parameters(): + num_params = param.numel() + # if using DS Zero 3 and the weights are initialized empty + if num_params == 0 and hasattr(param, "ds_numel"): + num_params = param.ds_numel + + all_param += num_params + if param.requires_grad: + trainable_params += num_params + + return {'Total': all_param, 'Trainable': trainable_params} + + +local_rank = 0 + + +def train(): + global local_rank + parser = transformers.HfArgumentParser( + (ModelArguments, DataArguments, TrainingArguments, LoraArguments) + ) + + ( + model_args, + data_args, + training_args, + lora_args, + ) = parser.parse_args_into_dataclasses() + + if getattr(training_args, "deepspeed", None) : + training_args.distributed_state.distributed_type = DistributedType.DEEPSPEED + + compute_dtype = ( + torch.float16 + if training_args.fp16 + else (torch.bfloat16 if training_args.bf16 else torch.float32) + ) + + local_rank = training_args.local_rank + world_size = int(os.environ.get("WORLD_SIZE", 1)) + ddp = world_size != 1 + device_map = None + if lora_args.q_lora: + device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} if ddp else None + if len(training_args.fsdp) > 0 or deepspeed.is_deepspeed_zero3_enabled(): + logging.warning( + "FSDP or ZeRO3 are not incompatible with QLoRA." + ) + + model = AutoModel.from_pretrained( + model_args.model_name_or_path, + trust_remote_code=True, + torch_dtype=compute_dtype, + device_map=device_map, + ) + + tokenizer = AutoTokenizer.from_pretrained( + model_args.model_name_or_path, trust_remote_code=True + ) + + if not training_args.tune_vision: + model.vpm.requires_grad_(False) + if not training_args.tune_llm: + model.llm.requires_grad_(False) + + if training_args.use_lora: + if training_args.use_lora and training_args.tune_llm: + raise ValueError("The model cannot simultaneously adjust LLM parameters and apply LoRA.") + + rank0_print("Currently using LoRA for fine-tuning the MiniCPM-V model.") + for name, param in model.llm.named_parameters(): + param.requires_grad = False + modules_to_save = ['embed_tokens','resampler'] + if training_args.tune_vision: + modules_to_save.append('vpm') + lora_config = LoraConfig( + r=lora_args.lora_r, + lora_alpha=lora_args.lora_alpha, + target_modules=lora_args.lora_target_modules, + lora_dropout=lora_args.lora_dropout, + bias=lora_args.lora_bias, + layers_to_transform=lora_args.lora_layers_to_transform, + modules_to_save=modules_to_save, + ) + if not hasattr(model, 'get_input_embeddings'): + def get_input_embeddings(self): + return self.llm.get_input_embeddings() + model.get_input_embeddings = MethodType(get_input_embeddings, model) + if lora_args.q_lora: + model = prepare_model_for_kbit_training( + model, use_gradient_checkpointing=training_args.gradient_checkpointing + ) + model = get_peft_model(model, lora_config) + if training_args.gradient_checkpointing: + model.enable_input_require_grads() + + rank0_print(get_parameter_number(model)) + + llm_type = training_args.llm_type + + rank0_print(f'llm_type={llm_type}') + + + # Load data + if hasattr(model.config, "slice_config"): + model.config.slice_config.max_slice_nums = training_args.max_slice_nums + slice_config = model.config.slice_config.to_dict() + else: + model.config.max_slice_nums = training_args.max_slice_nums + slice_config = model.config.to_dict() + + if hasattr(model.config, "batch_vision_input"): + batch_vision = model.config.batch_vision_input + else: + batch_vision = False + + transform_func = build_transform() + data_module = make_supervised_data_module( + tokenizer=tokenizer, + data_args=data_args, + transform=transform_func, + data_collator=data_collator, + slice_config=slice_config, + llm_type=llm_type, + patch_size=model.config.patch_size, + query_nums=model.config.query_num, + batch_vision=batch_vision, + max_length=training_args.model_max_length, + ) + + training_args.gradient_checkpointing_kwargs={"use_reentrant":False} + trainer = CPMTrainer( + model=model, + tokenizer=tokenizer, + args=training_args, + **data_module, + ) + + trainer.train() + trainer.save_state() + + safe_save_model_for_hf_trainer( + trainer=trainer, + output_dir=training_args.output_dir, + bias=lora_args.lora_bias) + + +if __name__ == "__main__": + train() diff --git a/MiniCPM-V/finetune/finetune_ds.sh b/MiniCPM-V/finetune/finetune_ds.sh new file mode 100644 index 0000000000000000000000000000000000000000..c04947156e9b2053794b2103fb20c5c6fda2fc0e --- /dev/null +++ b/MiniCPM-V/finetune/finetune_ds.sh @@ -0,0 +1,64 @@ +#!/bin/bash + +GPUS_PER_NODE=8 +NNODES=1 +NODE_RANK=0 +MASTER_ADDR=localhost +MASTER_PORT=6001 + +MODEL="openbmb/MiniCPM-V-2_6" +# or openbmb/MiniCPM-V-2, openbmb/MiniCPM-Llama3-V-2_5 +# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations. +# See the section for finetuning in README for more information. +DATA="path/to/trainging_data" +EVAL_DATA="path/to/test_data" +LLM_TYPE="qwen2" # if use openbmb/MiniCPM-V-2, please set LLM_TYPE=minicpm, if use openbmb/MiniCPM-Llama3-V-2_5, please set LLM_TYPE="llama3" +MODEL_MAX_Length=2048 # if conduct multi-images sft, please set MODEL_MAX_Length=4096 + + +DISTRIBUTED_ARGS=" + --nproc_per_node $GPUS_PER_NODE \ + --nnodes $NNODES \ + --node_rank $NODE_RANK \ + --master_addr $MASTER_ADDR \ + --master_port $MASTER_PORT +" +torchrun $DISTRIBUTED_ARGS finetune.py \ + --model_name_or_path $MODEL \ + --llm_type $LLM_TYPE \ + --data_path $DATA \ + --eval_data_path $EVAL_DATA \ + --remove_unused_columns false \ + --label_names "labels" \ + --prediction_loss_only false \ + --bf16 true \ + --bf16_full_eval true \ + --fp16 false \ + --fp16_full_eval false \ + --do_train \ + --do_eval \ + --tune_vision true \ + --tune_llm true \ + --model_max_length $MODEL_MAX_Length \ + --max_slice_nums 9 \ + --max_steps 10000 \ + --eval_steps 1000 \ + --output_dir output/output_minicpmv26 \ + --logging_dir output/output_minicpmv26 \ + --logging_strategy "steps" \ + --per_device_train_batch_size 1 \ + --per_device_eval_batch_size 1 \ + --gradient_accumulation_steps 1 \ + --evaluation_strategy "steps" \ + --save_strategy "steps" \ + --save_steps 1000 \ + --save_total_limit 10 \ + --learning_rate 1e-6 \ + --weight_decay 0.1 \ + --adam_beta2 0.95 \ + --warmup_ratio 0.01 \ + --lr_scheduler_type "cosine" \ + --logging_steps 1 \ + --gradient_checkpointing true \ + --deepspeed ds_config_zero2.json \ + --report_to "tensorboard" diff --git a/MiniCPM-V/finetune/finetune_lora.sh b/MiniCPM-V/finetune/finetune_lora.sh new file mode 100644 index 0000000000000000000000000000000000000000..19437b1a9c37daf75b7b10f00f19801d7711f4b6 --- /dev/null +++ b/MiniCPM-V/finetune/finetune_lora.sh @@ -0,0 +1,67 @@ +#!/bin/bash + +GPUS_PER_NODE=8 +NNODES=1 +NODE_RANK=0 +MASTER_ADDR=localhost +MASTER_PORT=6001 + +MODEL="openbmb/MiniCPM-V-2_6" # or openbmb/MiniCPM-V-2, openbmb/MiniCPM-Llama3-V-2_5 +# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations. +# See the section for finetuning in README for more information. +DATA="path/to/trainging_data" +EVAL_DATA="path/to/test_data" +LLM_TYPE="qwen2" +# if use openbmb/MiniCPM-V-2, please set LLM_TYPE=minicpm +#if use openbmb/MiniCPM-Llama3-V-2_5, please set LLM_TYPE=llama3 + +MODEL_MAX_Length=2048 # if conduct multi-images sft, please set MODEL_MAX_Length=4096 + +DISTRIBUTED_ARGS=" + --nproc_per_node $GPUS_PER_NODE \ + --nnodes $NNODES \ + --node_rank $NODE_RANK \ + --master_addr $MASTER_ADDR \ + --master_port $MASTER_PORT +" +torchrun $DISTRIBUTED_ARGS finetune.py \ + --model_name_or_path $MODEL \ + --llm_type $LLM_TYPE \ + --data_path $DATA \ + --eval_data_path $EVAL_DATA \ + --remove_unused_columns false \ + --label_names "labels" \ + --prediction_loss_only false \ + --bf16 false \ + --bf16_full_eval false \ + --fp16 true \ + --fp16_full_eval true \ + --do_train \ + --do_eval \ + --tune_vision true \ + --tune_llm false \ + --use_lora true \ + --lora_target_modules "llm\..*layers\.\d+\.self_attn\.(q_proj|k_proj|v_proj|o_proj)" \ + --model_max_length $MODEL_MAX_Length \ + --max_slice_nums 9 \ + --max_steps 10000 \ + --eval_steps 1000 \ + --output_dir output/output__lora \ + --logging_dir output/output_lora \ + --logging_strategy "steps" \ + --per_device_train_batch_size 1 \ + --per_device_eval_batch_size 1 \ + --gradient_accumulation_steps 1 \ + --evaluation_strategy "steps" \ + --save_strategy "steps" \ + --save_steps 1000 \ + --save_total_limit 10 \ + --learning_rate 1e-6 \ + --weight_decay 0.1 \ + --adam_beta2 0.95 \ + --warmup_ratio 0.01 \ + --lr_scheduler_type "cosine" \ + --logging_steps 1 \ + --gradient_checkpointing true \ + --deepspeed ds_config_zero2.json \ + --report_to "tensorboard" # wandb diff --git a/MiniCPM-V/finetune/readme.md b/MiniCPM-V/finetune/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..9ba8c960fde2fe7c87dfbde1ad911cde472aeec8 --- /dev/null +++ b/MiniCPM-V/finetune/readme.md @@ -0,0 +1,276 @@ +# MiniCPM-V Finetuning + + +We offer the official scripts for easy finetuning of the pretrained **MiniCPM-V-2_6**, **MiniCPM-Llama3-V 2.5** and **MiniCPM-V 2.0** on downstream tasks. Our finetune scripts use transformers Trainer and DeepSpeed by default. + +### Data preparation + +To prepare your fine-tuning data, you should formulate each sample as a dictionary consisting of an id, an image path (or list of images), and a list of conversations. Then, save the data samples in JSON files. + +For vision-language tasks, you must provide placeholders like **\** or **\** to define where to insert the image embeddings within the conversation. If no placeholder is provided, the image will be placed at the front of the conversation by default. + +#### Single Image Example +If your input consists of a single image, you can use a single placeholder **\** to indicate where the image should be inserted in the conversation. +
+ + Single image example (vl_finetune_data.json) with 1 samples. + + +``` + [ + { + "id": "0", + "image": 'path/to/image_0.jpg', + "conversations": [ + { + 'role': 'user', + 'content': '\nHow many desserts are on the white plate?' + }, + { + 'role': 'assistant', + 'content': 'There are three desserts on the white plate.' + }, + { + 'role': 'user', + 'content': 'What type of desserts are they?' + }, + { + 'role': 'assistant', + 'content': 'The desserts are cakes with bananas and pecans on top. They share similarities with donuts, but the presence of bananas and pecans differentiates them.' + }, + { + 'role': 'user', + 'content': 'What is the setting of the image?'}, + { + 'role': 'assistant', + 'content': 'The image is set on a table top with a plate containing the three desserts.' + }, + ] + }, + ] +``` + +
+ +#### Multiple Images Example +For inputs containing multiple images, utilize a dictionary where each key represents a unique placeholder (e.g., **\**, **\ + + Multiple images example (vl_finetune_data.json) with 1 samples. + + +``` + [ + { + "id": "0", + "image": { + "": "path/to/image_0.jpg", + "": "path/to/image_1.jpg", + "": "path/to/image_2.jpg", + "": "path/to/image_3.jpg" + }, + "conversations": [ + { + "role": "user", + "content": "How to create such text-only videos using CapCut?\n\n\n\n\n" + }, + { + "role": "assistant", + "content": "To create a text-only video as shown in the images, follow these steps in CapCut..." + } + ] + } + ] +``` + + +### Full-parameter finetuning + +Full-parameter parameter finetuning requires updating all parameters of LLM in the whole training process. Please specify the correct MODEL path, DATA path and LLM_TYPE in the shell scripts. + +```shell +MODEL="openbmb/MiniCPM-V-2_6" # or openbmb/MiniCPM-Llama3-V-2_5, openbmb/MiniCPM-V-2 +DATA="path/to/trainging_data" # json file +EVAL_DATA="path/to/test_data" # json file +LLM_TYPE="qwen2" # if use openbmb/MiniCPM-V-2, please set LLM_TYPE=minicpm, if use openbmb/MiniCPM-Llama3-V-2_5, please set LLM_TYPE="llama3" +``` + +To launch your training, run the following script: + +``` +sh finetune_ds.sh +``` + + +### LoRA finetuning + +The LoRA allows light-weight model tuning with only a small subset of parameters updated. We provide the LoRA implementation based on `peft`. To launch your training, run the following script: + +``` +sh finetune_lora.sh +``` + +After training, you could load the model with the path to the adapter. We advise you to use absolute path for your pretrained model. This is because LoRA only saves the adapter and the absolute path in the adapter configuration json file is used for finding out the pretrained model to load. + +``` +from peft import PeftModel +from transformers import AutoModel +model_type= "openbmb/MiniCPM-V-2_6" # or openbmb/MiniCPM-Llama3-V-2_5 , openbmb/MiniCPM-V-2 +path_to_adapter="path_to_your_fine_tuned_checkpoint" + +model = AutoModel.from_pretrained( + model_type, + trust_remote_code=True + ) + +lora_model = PeftModel.from_pretrained( + model, + path_to_adapter, + device_map="auto", + trust_remote_code=True +).eval().cuda() +``` + + +### Model Fine-tuning Memory Usage Statistics + +The following table presents the memory usage of the model when fine-tuning using NVIDIA A100 (80GiB) GPUs under different numbers of GPUs. The fine-tuning was performed with the DeepSpeed Zero-3 optimization, Gradient Checkpointing techniques and offloading optimizer as well as parameters memory to cpu, with a maximum length set to 2048 and batch size set to 1. You refer to [deepspeed zero stage](https://huggingface.co/docs/transformers/v4.41.2/en/deepspeed#select-a-zero-stage) to reduce memory cost. + +| Fine-tuning Method | GPUs: 2 | GPUs: 4 | GPUs: 8 | +|--------------------|---------|---------|---------| +| LoRA Fine-tuning | 14.4 GiB| 13.6 GiB| 13.1 GiB | +| Full Parameters Fine-tuning | 16.0 GiB | 15.8 GiB | 15.63GiB | + +### Notes +- **Fine-tuning Method**: Displays two different fine-tuning strategies, LoRA fine-tuning and Full parameters fine-tuning. +- **Number of GPUs**: The table lists the memory usage for configurations with 2, 4, and 8 GPUs. +- **Memory Usage**: Expressed in GiB, this shows the required memory for each fine-tuning method under corresponding GPU configurations. +- **Out of memory**: Indicates that the memory was insufficient for full parameters fine-tuning under the current GPU configurations. + +### Finetuning FAQs + +
+Q:When you encounter Out of Memory (OOM) issues during training large models, you can try the following methods to resolve or mitigate the issue: + +A：When you face Out of Memory (OOM) issues during training large models, the following strategies may help resolve or mitigate the problem: +#### Adjust Model Hyperparameters +- **Reduce `max_model_length`**: Decreasing the maximum sequence length the model processes can significantly reduce the memory required for each operation. For example, reducing the maximum length from 2048 to 1200 or another value suitable for your dataset. +``` +--model_max_length 1200 + +``` +- **Lower `batch_size`**: Reducing the amount of data processed in each batch helps decrease memory consumption. +``` +--batch_size 1 + ``` +- **Reduce the number of slices (`slice`)**: When handling large datasets such as large images files, reducing the number of slices processed each time can lower memory requirements. +``` +--max_slice_nums 9 +``` + +#### Reduce Training Model Parameters +- **Do not train VPM (Visual Processing Module)**: You can adjust hyperparameters in the finetune script to opt out of training the visual processing module to save memory. +``` +--tune_vision false +``` +- **Use LoRA finetuning**: Refer to the [LoRA finetuning](#LoRA-finetuning) section. + +#### Optimize with DeepSpeed +- **Configure DeepSpeed Zero Stage 2**: Use the following configuration to offload optimizer parameters to the CPU, reducing memory pressure on the GPU: + ```json + "zero_optimization": { + "stage": 2, + "offload_optimizer": { + "device": "cpu", + "pin_memory": true + } + } +- **Configure DeepSpeed Zero Stage 3**：Further offload model parameters and optimizer parameters to the CPU, further reducing GPU memory usage: +```json +"zero_optimization": { + "stage": 3, + "offload_optimizer": { + "device": "cpu", + "pin_memory": true + }, + "offload_param": { + "device": "cpu", + "pin_memory": true + } +} +``` +You can visit [huggingface deepspeed](https://huggingface.co/docs/transformers/deepspeed) to find out more about how to use DeepSpeed. +
+
+Q: Encounter an error while using the AutoPeftModelForCausalLM to load a checkpoint that has undergone lora fine-tuning + +A: The error as described in [issues 168](https://github.com/OpenBMB/MiniCPM-V/issues/168) occurs because the model lacks `get_input_embeddings` and `set_input_embeddings` methods. Follow these steps to resolve this issue: + +1.**Reload the Fine-Tuned Model:** Make sure you correctly load the checkpoint that has been fine-tuned using lora techniques. Use the following code example to guide you: + ```python + from peft import AutoPeftModel + +path_to_adapter="path_to_your_fine_tuned_checkpoint" + +model = AutoPeftModel.from_pretrained( + # path to the output directory + path_to_adapter, + device_map="auto", + trust_remote_code=True +).eval().cuda() + ``` + 2.**Update the `model_minicpmv.py` File:** + - **Verification:** Make sure you verify and update your `model_minicpmv.py` file to ensure it is the latest version. + - **Update Hugging Face Library Code:** If the issue persists after updating the file, consider updating the related code in the Hugging Face library. + - **Direct File Copy:** For a quick resolution, directly download and copy the latest `model_minicpmv.py` file into your project. This file is available from the following sources: + - [MiniCPM-Llama3-V-2_5 on Hugging Face](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/tree/main) + - [MiniCPM-V-2 on Hugging Face](https://huggingface.co/openbmb/MiniCPM-V-2) +
+ +
+Q: How do I use the `flash_attention_2` implementation when loading a pretrained model? + +A: If your environment supports `flash_attn2`, you can add an argument `_attn_implementation="flash_attention_2"` when using the `AutoModel.from_pretrained` method to load a model. For example: + +```python +model = AutoModel.from_pretrained('model_name', _attn_implementation="flash_attention_2") +``` +
+ +
+Q: What if our data is resized to 512? Can we use the original image size instead? + +A: Our model supports up to 1344x1344 lossless encoding. If you are currently resizing your images to 512, you might want to try using the original image sizes instead. Our system automatically includes a high-definition image encoding scheme by default. + +
+ +
+Q: What should we do if we encounter out-of-memory (OOM) errors? + +A: If you experience OOM issues, consider reducing the batch size (`bs`). To maintain an equivalent total batch size, you can adjust the `gradient_accumulation_steps` setting. This approach allows you to manage memory usage effectively while still processing the desired amount of data per training step. +
+ +
+Q: How can we determine the maximum length for our training data, and what if we do not want to train the vision encoder? + +A: I recommend using this function [here](https://github.com/OpenBMB/MiniCPM-V/blob/main/finetune/dataset.py#L220) to sample the length of your training data. Note that the `input_ids` length includes the image portion. Once you determine the maximum length, you can specify it in the startup command using `--model_max_length xxx`. + +Additionally, if you prefer not to train the vision encoder, you can add `--tune_vision false` to your command. + +
+ +
+Q: How can we adjust training hyperparameters when using LoRA to train our model? + +A: You can refer to the [LoRA documentation](https://huggingface.co/docs/peft/en/package_reference/lora#peft.LoraConfig) for guidance on adjusting your training hyperparameters when using LoRA. This documentation provides detailed information on configuring various parameters specific to the LoRA adaptation technique. +
+ +#### Customizing Hyperparameters +To tailor the training process according to your specific requirements, you can adjust various hyperparameters. For comprehensive documentation on available hyperparameters and their functionalities, you can refer to the [official Transformers documentation](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) and [Lora documentation](https://huggingface.co/docs/peft/en/package_reference/lora#peft.LoraConfig). Experimentation and fine-tuning of these parameters are essential for achieving optimal model performance tailored to your specific task and dataset. diff --git a/MiniCPM-V/finetune/trainer.py b/MiniCPM-V/finetune/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..7da95ed874449e36f16153d076a46bf1788234c8 --- /dev/null +++ b/MiniCPM-V/finetune/trainer.py @@ -0,0 +1,250 @@ + +import torch +import torch.nn as nn +import deepspeed +from transformers import Trainer +from transformers.trainer_pt_utils import nested_detach +from transformers.utils import is_sagemaker_mp_enabled +from transformers.trainer import * +from transformers.integrations import is_deepspeed_zero3_enabled + + +class CPMTrainer(Trainer): + def compute_loss(self, model, inputs, return_outputs=False): + if "labels" in inputs: + labels = inputs.pop("labels") + else: + labels = None + + if not self.args.use_lora: + outputs = self.model(data = inputs, use_cache=False) + else: + with self.model._enable_peft_forward_hooks(**inputs): + outputs = self.model.base_model(data = inputs, use_cache=False) + + if labels is not None: + # Flatten the tokens + loss_fct = nn.CrossEntropyLoss() + logits = outputs.logits.view(-1, + self.model.config.vocab_size).contiguous() + labels = labels.view(-1).long().contiguous() + # Enable model parallelism + labels = labels.to(logits.device) + loss = loss_fct(logits, labels) + else: + if isinstance(outputs, dict) and "loss" not in outputs: + raise ValueError( + "The model did not return a loss from the inputs, only the following keys: " + f"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}." + ) + # We don't use .loss here since the model may return tuples instead of ModelOutput. + loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0] + + return (loss, outputs) if return_outputs else loss + + def prediction_step( + self, + model: nn.Module, + inputs: Dict[str, Union[torch.Tensor, Any]], + prediction_loss_only: bool, + ignore_keys: Optional[List[str]] = None, + ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]: + """ + Perform an evaluation step on `model` using `inputs`. + + Subclass and override to inject custom behavior. + + Args: + model (`nn.Module`): + The model to evaluate. + inputs (`Dict[str, Union[torch.Tensor, Any]]`): + The inputs and targets of the model. + + The dictionary will be unpacked before being fed to the model. Most models expect the targets under the + argument `labels`. Check your model's documentation for all accepted arguments. + prediction_loss_only (`bool`): + Whether or not to return the loss only. + ignore_keys (`List[str]`, *optional*): + A list of keys in the output of your model (if it is a dictionary) that should be ignored when + gathering predictions. + + Return: + Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, + logits and labels (each being optional). + """ + has_labels = ( + False + if len(self.label_names) == 0 + else all(inputs.get(k) is not None for k in self.label_names) + ) + # For CLIP-like models capable of returning loss values. + # If `return_loss` is not specified or being `None` in `inputs`, we check if the default value of `return_loss` + # is `True` in `model.forward`. + return_loss = inputs.get("return_loss", None) + if return_loss is None: + return_loss = self.can_return_loss + loss_without_labels = ( + True if len(self.label_names) == 0 and return_loss else False + ) + + inputs = self._prepare_inputs(inputs) + if ignore_keys is None: + if hasattr(self.model, "config"): + ignore_keys = getattr( + self.model.config, "keys_to_ignore_at_inference", [] + ) + else: + ignore_keys = [] + + # labels may be popped when computing the loss (label smoothing for instance) so we grab them first. + if has_labels or loss_without_labels: + labels = nested_detach(tuple(inputs.get(name) + for name in self.label_names)) + if len(labels) == 1: + labels = labels[0] + else: + labels = None + + with torch.no_grad(): + if is_sagemaker_mp_enabled(): + raw_outputs = smp_forward_only(model, inputs) + if has_labels or loss_without_labels: + if isinstance(raw_outputs, dict): + loss_mb = raw_outputs["loss"] + logits_mb = tuple( + v + for k, v in raw_outputs.items() + if k not in ignore_keys + ["loss"] + ) + else: + loss_mb = raw_outputs[0] + logits_mb = raw_outputs[1:] + + loss = loss_mb.reduce_mean().detach().cpu() + logits = smp_nested_concat(logits_mb) + else: + loss = None + if isinstance(raw_outputs, dict): + logits_mb = tuple( + v for k, v in raw_outputs.items() if k not in ignore_keys + ) + else: + logits_mb = raw_outputs + logits = smp_nested_concat(logits_mb) + else: + if has_labels or loss_without_labels: + with self.compute_loss_context_manager(): + loss, outputs = self.compute_loss( + model, inputs, return_outputs=True + ) + loss = loss.mean().detach() + + if isinstance(outputs, dict): + logits = tuple( + v + for k, v in outputs.items() + if k not in ignore_keys + ["loss"] + ) + else: + logits = outputs[1:] + else: + loss = None + with self.compute_loss_context_manager(): + outputs = model(**inputs) + if isinstance(outputs, dict): + logits = tuple( + v for k, v in outputs.items() if k not in ignore_keys + ) + else: + logits = outputs + # TODO: this needs to be fixed and made cleaner later. + if self.args.past_index >= 0: + self._past = outputs[self.args.past_index - 1] + + if prediction_loss_only: + return (loss, None, None) + + logits = nested_detach(logits) + if len(logits) == 1: + logits = logits[0] + + return (loss, logits, labels) + + def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor: + """ + Perform a training step on a batch of inputs. + + Subclass and override to inject custom behavior. + + Args: + model (`nn.Module`): + The model to train. + inputs (`Dict[str, Union[torch.Tensor, Any]]`): + The inputs and targets of the model. + + The dictionary will be unpacked before being fed to the model. Most models expect the targets under the + argument `labels`. Check your model's documentation for all accepted arguments. + + Return: + `torch.Tensor`: The tensor with training loss on this batch. + """ + model.train() + inputs = self._prepare_inputs(inputs) + + if is_sagemaker_mp_enabled(): + loss_mb = smp_forward_backward(model, inputs, self.args.gradient_accumulation_steps) + return loss_mb.reduce_mean().detach().to(self.args.device) + + with self.compute_loss_context_manager(): + loss = self.compute_loss(model, inputs) + + del inputs + torch.cuda.empty_cache() + + if self.args.n_gpu > 1: + loss = loss.mean() # mean() to average on multi-gpu parallel training + + if self.use_apex: + with amp.scale_loss(loss, self.optimizer) as scaled_loss: + scaled_loss.backward() + else: + self.accelerator.backward(loss) + + return loss.detach() / self.args.gradient_accumulation_steps + + def _save(self, output_dir: Optional[str] = None, state_dict=None): + # If we are executing this function, we are the process zero, so we don't check for that. + output_dir = output_dir if output_dir is not None else self.args.output_dir + os.makedirs(output_dir, exist_ok=True) + logger.info(f"Saving model checkpoint to {output_dir}") + + supported_classes = (PreTrainedModel,) if not is_peft_available() else (PreTrainedModel, PeftModel) + # Save a trained model and configuration using `save_pretrained()`. + # They can then be reloaded using `from_pretrained()` + if not isinstance(self.model, supported_classes): + if state_dict is None: + state_dict = self.model.state_dict() + + if isinstance(unwrap_model(self.model), supported_classes): + unwrap_model(self.model).save_pretrained( + output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors + ) + else: + logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.") + if self.args.save_safetensors: + safetensors.torch.save_file( + state_dict, os.path.join(output_dir, SAFE_WEIGHTS_NAME), metadata={"format": "pt"} + ) + else: + torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME)) + else: + + self.model.save_pretrained( + output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors + ) + + if self.tokenizer is not None: + self.tokenizer.save_pretrained(output_dir) + + # Good practice: save your training arguments together with the trained model + torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME)) diff --git a/MiniCPM-V/minicpm_v1.md b/MiniCPM-V/minicpm_v1.md new file mode 100644 index 0000000000000000000000000000000000000000..af345c2fb780343f37aeea03fd4c1e7ecb80a16a --- /dev/null +++ b/MiniCPM-V/minicpm_v1.md @@ -0,0 +1,214 @@ +## MiniCPM-V 1.0 + + +> Archive at：2024-05-19 + +MiniCPM-V 1.0 is an efficient version with promising performance for deployment. The model is built based on SigLip-400M and [MiniCPM-2.4B](https://github.com/OpenBMB/MiniCPM/), connected by a perceiver resampler. Notable features of MiniCPM-V 1.0 include: + +- ⚡️ **High Efficiency.** + + MiniCPM-V 1.0 can be **efficiently deployed on most GPU cards and personal computers**, and **even on end devices such as mobile phones**. In terms of visual encoding, we compress the image representations into 64 tokens via a perceiver resampler, which is significantly fewer than other LMMs based on MLP architecture (typically > 512 tokens). This allows MiniCPM-V 1.0 to operate with **much less memory cost and higher speed during inference**. + +- 🔥 **Promising Performance.** + + MiniCPM-V 1.0 achieves **state-of-the-art performance** on multiple benchmarks (including MMMU, MME, and MMbech, etc) among models with comparable sizes, surpassing existing LMMs built on Phi-2. It even **achieves comparable or better performance than the 9.6B Qwen-VL-Chat**. + +- 🙌 **Bilingual Support.** + + MiniCPM-V 1.0 is **the first end-deployable LMM supporting bilingual multimodal interaction in English and Chinese**. This is achieved by generalizing multimodal capabilities across languages, a technique from the ICLR 2024 spotlight [paper](https://arxiv.org/abs/2308.12038). + +### Evaluation + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeVisual TokensMMEMMB dev (en)MMB dev (zh)MMMU valCMMMU val
LLaVA-Phi3B576133559.8- - -
MobileVLM3B144128959.6- - -
Imp-v13B576143466.5- - -
Qwen-VL-Chat9.6B256148760.6 56.7 35.9 30.7
CogVLM17.4B 12251438 63.7 53.8 32.1 -
MiniCPM-V 1.03B 641452 67.9 65.3 37.2 32.1
+ +
+ +### Examples + +We deploy MiniCPM-V 1.0 on end devices. The demo video is the raw screen recording on a OnePlus 9R without edition. + + +

+ + +

+
+ +## Install + +1. Clone this repository and navigate to the source folder + +```bash +git clone https://github.com/OpenBMB/OmniLMM.git +cd OmniLMM +``` + +2. Create conda environment + +```Shell +conda create -n OmniLMM python=3.10 -y +conda activate OmniLMM +``` + +3. Install dependencies + +```shell +pip install -r requirements.txt +``` + +## Inference + +### Model Zoo +| Model | Description | Download Link | +|:----------------------|:-------------------|:---------------:| +| MiniCPM-V 1.0 | The efficient version for end device deployment. | [🤗](https://huggingface.co/openbmb/MiniCPM-V)    [](https://modelscope.cn/models/OpenBMB/MiniCPM-V/files) | + + +### Multi-turn Conversation +Please refer to the following codes to run `MiniCPM-V 1.0`. + +
+ +
+ + +```python +from chat import OmniLMMChat, img2base64 + +chat_model = OmniLMMChat('openbmb/MiniCPM-V') + +im_64 = img2base64('./assets/worldmap_ck.jpg') + +# First round chat +msgs = [{"role": "user", "content": "What is interesting about this image?"}] + +inputs = {"image": im_64, "question": json.dumps(msgs)} +answer = chat_model.chat(inputs) +print(answer) + +# Second round chat +# pass history context of multi-turn conversation +msgs.append({"role": "assistant", "content": answer}) +msgs.append({"role": "user", "content": "Where is China in the image"}) + +inputs = {"image": im_64, "question": json.dumps(msgs)} +answer = chat_model.chat(inputs) +print(answer) +``` + + +### Inference on Mac +
+Click to view example, MiniCPM-V 1.0 can run on Mac with MPS (Apple silicon or AMD GPUs). + +```python +# test.py +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer + +model = AutoModel.from_pretrained('openbmb/MiniCPM-V', trust_remote_code=True, torch_dtype=torch.bfloat16) +model = model.to(device='mps', dtype=torch.float16) + +tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V', trust_remote_code=True) +model.eval() + +image = Image.open('./assets/worldmap_ck.jpg').convert('RGB') +question = 'What is interesting about this image?' +msgs = [{'role': 'user', 'content': question}] + +answer, context, _ = model.chat( + image=image, + msgs=msgs, + context=None, + tokenizer=tokenizer, + sampling=True +) +print(answer) +``` +Run with command: +```shell +PYTORCH_ENABLE_MPS_FALLBACK=1 python test.py +``` +
+ +### Deployment on Mobile Phone + +Currently MiniCPM-V 1.0 can be deployed on mobile phones with Android and Harmony operating systems. 🚀 Try it out [here](https://github.com/OpenBMB/mlc-MiniCPM). diff --git a/MiniCPM-V/omnilmm.md b/MiniCPM-V/omnilmm.md new file mode 100644 index 0000000000000000000000000000000000000000..3c1753c0b669f514a398fa28056f530f979d21f4 --- /dev/null +++ b/MiniCPM-V/omnilmm.md @@ -0,0 +1,183 @@ +## OmniLMM-12B + +> OmniLMM-12B 发布于本项目早期。推荐您使用我们[最新发布的模型](./README_zh.md)，以获得更高效的推理和更强大的性能体验。 + +> 归档时间：2024-05-19 + +**OmniLMM-12B** 是当前系列中性能最佳的版本。该模型基于EVA02-5B和Zephyr-7B-β初始化构建，并使用perceiver resampler连接，采用了课程学习的方法在多模态数据上进行训练。该模型具有三个特点： + +- 🔥 **性能领先。** + + OmniLMM-12B 相比其他同规模模型在多个基准测试中取得**领先的性能**（包括 MME、MMBench、SEED-Bench 等），模型掌握了较为丰富的多模态世界知识。 + +- 🏆 **行为可信。** + + 多模态大模型的幻觉问题备受关注，模型经常生成和图像中的事实不符的文本（例如，确信地描述图片中并不存在的物体）。OmniLMM-12B是 **第一个通过多模态 RLHF 对齐的综合能力优秀的开源多模态大模型**（借助 [RLHF-V](https://rlhf-v.github.io/) [CVPR'24] 系列技术）。该模型在 [MMHal-Bench](https://huggingface.co/datasets/Shengcao1006/MMHal-Bench) 幻觉评测基准上达到**开源模型最佳水平**，并在 [Object HalBench](https://arxiv.org/abs/2312.00849) 中**优于GPT-4V**。 + +- 🕹 **实时多模态交互。** + + 我们尝试结合OmniLMM-12B和GPT-3.5 (纯文本模型) ，实现**实时多模态交互助手**。该模型接受来自摄像头的视频流，并借助工具处理语音输入输出。虽然还很初步，我们发现该模型无需视频编辑可以**复现Gemini演示视频中的一些有趣例子**。 + +### 评测结果 + +
+ +
+
+ MME, MMBench, MMMU, MMBench, MMHal-Bench, Object HalBench, SeedBench, LLaVA Bench W, MathVista 上的详细评测结果。 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeMMEMMB dev (en)MMMU valMMHal-BenchObject HalBenchSeedBench-IMathVistaLLaVA Bench
GPT-4V†-1771.575.1 56.83.53 / 70.886.4 / 92.771.6 47.8 93.1
Qwen-VL-Plus†-2183.466.2 45.2- - 65.7 36.0 73.7
Yi-VL 6B6.7B 1915.1 68.6 40.3 - - 67.5 28.8 51.9
Qwen-VL-Chat9.6B1860.060.6 35.92.93 / 59.456.2 / 80.064.8 33.8 67.7
CogVLM-Chat17.4B1736.663.7 32.1 2.68 / 52.1 73.6 / 87.4 68.8 34.7 73.9
LLaVA 1.513.6B 1808.4 68.2 36.4 2.71 / 51.0 53.7 / 77.4 68.1 26.4 64.6
OmniLMM-12B11.6B 1935.8 71.6 40.7 3.45 / 68.8 90.3 / 95.5 71.1 34.9 72.0
+†: 闭源模型 +
+
+ +### 典型示例 + + +

+ +

+
+ + +我们结合 OmniLMM-12B 和 ChatGPT-3.5 (纯文本模型) 尝试构建 **实时多模态交互助手**. OmniLMM-12B 将视频帧转为对应的图像描述并输入给ChatGPT-3.5来生成对用户指令的响应。演示视频未经编辑。 + +
+
+ +## Online Demo + +欢迎通过以下链接使用我们的网页端推理服务： [OmniLMM-12B](http://120.92.209.146:8081) ｜ [MiniCPM-V 2.0](http://120.92.209.146:80). + +## 安装 + +1. 克隆我们的仓库并跳转到相应目录 + +```bash +git clone https://github.com/OpenBMB/MiniCPM-V.git +cd MiniCPM-V +``` + +1. 创建 conda 环境 + +```Shell +conda create -n MiniCPMV python=3.10 -y +conda activate MiniCPMV +``` + +3. 安装依赖 + +```shell +pip install -r requirements.txt +``` + +## 推理 + +### 模型库 + +| 模型 | 简介 | 下载链接 | +|:----------------------|:-------------------|:---------------:| +| OmniLMM-12B | 性能最强的版本 | [🤗](https://huggingface.co/openbmb/OmniLMM-12B)    [](https://modelscope.cn/models/OpenBMB/OmniLMM-12B/files) | + diff --git a/MiniCPM-V/omnilmm/.DS_Store b/MiniCPM-V/omnilmm/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..099fc34e6c70d4273f9b43e7a6104296138f8f56 Binary files /dev/null and b/MiniCPM-V/omnilmm/.DS_Store differ diff --git a/MiniCPM-V/omnilmm/__init__.py b/MiniCPM-V/omnilmm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/MiniCPM-V/omnilmm/constants.py b/MiniCPM-V/omnilmm/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..a1ac41d363a634f45a8ba6ef681d4844a2824f5e --- /dev/null +++ b/MiniCPM-V/omnilmm/constants.py @@ -0,0 +1,4 @@ +CONTROLLER_HEART_BEAT_EXPIRATION = 30 +WORKER_HEART_BEAT_INTERVAL = 15 + +LOGDIR = "." diff --git a/MiniCPM-V/omnilmm/conversation.py b/MiniCPM-V/omnilmm/conversation.py new file mode 100644 index 0000000000000000000000000000000000000000..8e997f01f0cbe5caab5c48112189947f5392b9d5 --- /dev/null +++ b/MiniCPM-V/omnilmm/conversation.py @@ -0,0 +1,320 @@ +import dataclasses +from enum import auto, Enum +from typing import List, Tuple + + +class SeparatorStyle(Enum): + """Different separator style.""" + SINGLE = auto() + TWO = auto() + + +@dataclasses.dataclass +class Conversation: + """A class that keeps all conversation history.""" + system: str + roles: List[str] + messages: List[List[str]] + offset: int + sep_style: SeparatorStyle = SeparatorStyle.SINGLE + sep: str = "###" + sep2: str = None + version: str = "Unknown" + + skip_next: bool = False + + def get_prompt(self): + if self.sep_style == SeparatorStyle.SINGLE: + ret = self.system + self.sep + for role, message in self.messages: + if message: + if type(message) is tuple: + message, _, _ = message + ret += role + ": " + message + self.sep + else: + ret += role + ":" + return ret + elif self.sep_style == SeparatorStyle.TWO: + seps = [self.sep, self.sep2] + ret = self.system + seps[0] + for i, (role, message) in enumerate(self.messages): + if message: + if type(message) is tuple: + message, _, _ = message + ret += role + ": " + message + seps[i % 2] + else: + ret += role + ":" + return ret + else: + raise ValueError(f"Invalid style: {self.sep_style}") + + def append_message(self, role, message): + self.messages.append([role, message]) + + def get_images(self, return_pil=False): + images = [] + for i, (role, msg) in enumerate(self.messages[self.offset:]): + if i % 2 == 0: + if type(msg) is tuple: + import base64 + from io import BytesIO + from PIL import Image + msg, image, image_process_mode = msg + if image_process_mode == "Pad": + def expand2square(pil_img, background_color=(122, 116, 104)): + width, height = pil_img.size + if width == height: + return pil_img + elif width > height: + result = Image.new( + pil_img.mode, (width, width), background_color) + result.paste( + pil_img, (0, (width - height) // 2)) + return result + else: + result = Image.new( + pil_img.mode, (height, height), background_color) + result.paste( + pil_img, ((height - width) // 2, 0)) + return result + image = expand2square(image) + elif image_process_mode == "Crop": + pass + elif image_process_mode == "Resize": + image = image.resize((224, 224)) + else: + raise ValueError( + f"Invalid image_process_mode: {image_process_mode}") + max_hw, min_hw = max(image.size), min(image.size) + aspect_ratio = max_hw / min_hw + max_len, min_len = 800, 400 + shortest_edge = int( + min(max_len / aspect_ratio, min_len, min_hw)) + longest_edge = int(shortest_edge * aspect_ratio) + W, H = image.size + if H > W: + H, W = longest_edge, shortest_edge + else: + H, W = shortest_edge, longest_edge + image = image.resize((W, H)) + if return_pil: + images.append(image) + else: + buffered = BytesIO() + image.save(buffered, format="JPEG") + img_b64_str = base64.b64encode( + buffered.getvalue()).decode() + images.append(img_b64_str) + return images + + def to_gradio_chatbot(self): + ret = [] + for i, (role, msg) in enumerate(self.messages[self.offset:]): + if i % 2 == 0: + if type(msg) is tuple: + import base64 + from io import BytesIO + msg, image, image_process_mode = msg + max_hw, min_hw = max(image.size), min(image.size) + aspect_ratio = max_hw / min_hw + max_len, min_len = 800, 400 + shortest_edge = int( + min(max_len / aspect_ratio, min_len, min_hw)) + longest_edge = int(shortest_edge * aspect_ratio) + W, H = image.size + if H > W: + H, W = longest_edge, shortest_edge + else: + H, W = shortest_edge, longest_edge + image = image.resize((W, H)) + # image = image.resize((224, 224)) + buffered = BytesIO() + image.save(buffered, format="JPEG") + img_b64_str = base64.b64encode( + buffered.getvalue()).decode() + img_str = f'user upload image' + msg = msg.replace('', img_str) + ret.append([msg, None]) + else: + ret[-1][-1] = msg + return ret + + def copy(self): + return Conversation( + system=self.system, + roles=self.roles, + messages=[[x, y] for x, y in self.messages], + offset=self.offset, + sep_style=self.sep_style, + sep=self.sep, + sep2=self.sep2) + + def dict(self): + if len(self.get_images()) > 0: + return { + "system": self.system, + "roles": self.roles, + "messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages], + "offset": self.offset, + "sep": self.sep, + "sep2": self.sep2, + } + return { + "system": self.system, + "roles": self.roles, + "messages": self.messages, + "offset": self.offset, + "sep": self.sep, + "sep2": self.sep2, + } + + +conv_v1 = Conversation( + system="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("Human", "Assistant"), + messages=( + ("Human", "Give three tips for staying healthy."), + ("Assistant", + "Sure, here are three tips for staying healthy:\n" + "1. Exercise regularly: Regular physical activity can help improve your overall health and wellbeing. " + "It can also help reduce your risk of chronic conditions such as obesity, diabetes, heart disease, " + "and certain cancers. Aim for at least 150 minutes of moderate-intensity aerobic exercise or " + "75 minutes of vigorous-intensity aerobic exercise per week, along with muscle-strengthening " + "activities at least two days per week.\n" + "2. Eat a balanced diet: Eating a balanced diet that is rich in fruits, " + "vegetables, whole grains, lean proteins, and healthy fats can help support " + "your overall health. Try to limit your intake of processed and high-sugar foods, " + "and aim to drink plenty of water throughout the day.\n" + "3. Get enough sleep: Getting enough quality sleep is essential for your physical " + "and mental health. Adults should aim for seven to nine hours of sleep per night. " + "Establish a regular sleep schedule and try to create a relaxing bedtime routine to " + "help improve the quality of your sleep.") + ), + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +conv_v1_2 = Conversation( + system="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("Human", "Assistant"), + messages=( + ("Human", "What are the key differences between renewable and non-renewable energy sources?"), + ("Assistant", + "Renewable energy sources are those that can be replenished naturally in a relatively " + "short amount of time, such as solar, wind, hydro, geothermal, and biomass. " + "Non-renewable energy sources, on the other hand, are finite and will eventually be " + "depleted, such as coal, oil, and natural gas. Here are some key differences between " + "renewable and non-renewable energy sources:\n" + "1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable " + "energy sources are finite and will eventually run out.\n" + "2. Environmental impact: Renewable energy sources have a much lower environmental impact " + "than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, " + "and other negative effects.\n" + "3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically " + "have lower operational costs than non-renewable sources.\n" + "4. Reliability: Renewable energy sources are often more reliable and can be used in more remote " + "locations than non-renewable sources.\n" + "5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different " + "situations and needs, while non-renewable sources are more rigid and inflexible.\n" + "6. Sustainability: Renewable energy sources are more sustainable over the long term, while " + "non-renewable sources are not, and their depletion can lead to economic and social instability.\n") + ), + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +conv_vicuna_v1_1 = Conversation( + system="A chat between a curious user and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the user's questions.", + roles=("USER", "ASSISTANT"), + version="v1", + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2="", +) + +conv_bair_v1 = Conversation( + system="BEGINNING OF CONVERSATION:", + roles=("USER", "GPT"), + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2="", +) + +simple_conv = Conversation( + system="You are LLaVA, a large language model trained by UW Madison WAIV Lab, based on LLaMA architecture." + "You are designed to assist human with a variety of tasks using natural language." + "Follow the instructions carefully.", + roles=("Human", "Assistant"), + messages=( + ("Human", "Hi!"), + ("Assistant", "Hi there! How can I help you today?\n") + ), + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +simple_conv_multimodal = Conversation( + system="A chat between a curious user and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the user's questions.", + roles=("Human", "Assistant"), + messages=( + ), + offset=0, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +simple_conv_legacy = Conversation( + system="You are LLaVA, a large language model trained by UW Madison WAIV Lab." + "You are designed to assist human with a variety of tasks using natural language." + "Follow the instructions carefully.", + roles=("Human", "Assistant"), + messages=( + ("Human", "Hi!\n\n### Response:"), + ("Assistant", "Hi there! How can I help you today?\n") + ), + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +conv_llava_v1 = Conversation( + system="You are LLaVA, a large language and vision assistant trained by UW Madison WAIV Lab." + "You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language." + "Follow the instructions carefully and explain your answers in detail.", + roles=("USER", "ASSISTANT"), + version="v1", + messages=(), + offset=0, + sep_style=SeparatorStyle.TWO, + sep=" ", + sep2="", +) + +default_conversation = conv_v1_2 +conv_templates = { + "default": conv_v1_2, + "simple": simple_conv, + "simple_legacy": simple_conv_legacy, + "multimodal": simple_conv_multimodal, + "llava_v1": conv_llava_v1, + + # fastchat + "v1": conv_v1_2, + "bair_v1": conv_bair_v1, + "vicuna_v1_1": conv_vicuna_v1_1, +} + + +if __name__ == "__main__": + print(default_conversation.get_prompt()) diff --git a/MiniCPM-V/omnilmm/model/__init__.py b/MiniCPM-V/omnilmm/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..35b9845c8726de2a6b82fb05ab11071115f6b09c --- /dev/null +++ b/MiniCPM-V/omnilmm/model/__init__.py @@ -0,0 +1 @@ +from .omnilmm import OmniLMMForCausalLM \ No newline at end of file diff --git a/MiniCPM-V/omnilmm/model/omnilmm.py b/MiniCPM-V/omnilmm/model/omnilmm.py new file mode 100644 index 0000000000000000000000000000000000000000..e720675292e9d8e14e25a057624ae5c290f82d5d --- /dev/null +++ b/MiniCPM-V/omnilmm/model/omnilmm.py @@ -0,0 +1,457 @@ + +import gc +import math +import timm +import torch +from torch import Tensor +import torch.nn as nn +from torch.nn import CrossEntropyLoss +from typing import List, Optional, Tuple, Union + +from transformers import AutoConfig, AutoModelForCausalLM +from transformers import MistralForCausalLM, MistralModel, MistralConfig +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast + +from omnilmm.model.utils import build_transform +from omnilmm.model.resampler import Resampler + +DEFAULT_IMAGE_PATCH_TOKEN = "" +DEFAULT_IM_START_TOKEN = "" +DEFAULT_IM_END_TOKEN = "" + + +class OmniLMMConfig(MistralConfig): + model_type = "omnilmm" + + +class Identity(torch.nn.Identity): + def forward(self, input: Tensor, **kwargs) -> Tensor: + return super().forward(input) + + +def create_vision_module(config): + vision_tower = timm.create_model('eva02_enormous_patch14_clip_224.laion2b_plus', + pretrained=False, + num_classes=0, + dynamic_img_size=True, + dynamic_img_pad=True) + + if isinstance(vision_tower, timm.models.VisionTransformer): + if vision_tower.attn_pool is not None: + vision_tower.attn_pool = Identity() + + # use 2nd last layer's output + vision_tower.blocks[-1] = Identity() + + embed_dim = config.hidden_size + resampler = Resampler( + grid_size=int(math.sqrt(config.num_query)), + embed_dim=embed_dim, + num_heads=embed_dim // 128, + kv_dim=vision_tower.embed_dim, + ) + return vision_tower, resampler + + +class OmniLMMModel(MistralModel): + config_class = OmniLMMConfig + + def __init__(self, config: OmniLMMConfig, mm_vision_tower=None, mm_hidden_size=None, tune_clip=True): + super(OmniLMMModel, self).__init__(config) + + if hasattr(config, "mm_vision_tower"): + vision_tower, resampler = create_vision_module(config) + + # print(__file__, 'skip loading vision tower weights') + + # HACK: for FSDP + self.vision_tower = [vision_tower] + self.resampler = resampler + if tune_clip: + self.vision_tower = self.vision_tower[0] + + self.vision_config = lambda x: None + + def initialize_vision_modules(self, vision_tower, no_randaug, num_query, image_size, tune_clip=False): + self.config.mm_vision_tower = vision_tower + self.config.use_mm_proj = True + self.config.num_query = num_query + self.config.image_size = image_size + + if not hasattr(self, 'vision_tower'): + vision_tower, resampler = create_vision_module(self.config) + state_dict = torch.load( + '/tt/data/public/multimodal/multimodal_model_ckpts/timm/eva02_enormous_patch14_clip_224.laion2b_plus.pt') + vision_tower.load_state_dict(state_dict, strict=False) + del state_dict + gc.collect() + else: + if isinstance(self.vision_tower, list): + vision_tower = self.vision_tower[0] + else: + vision_tower = self.vision_tower + resampler = self.resampler + self.vision_tower = vision_tower if tune_clip else [vision_tower] + self.resampler = resampler + + train_img_transform = build_transform( + is_train=True, randaug=not no_randaug, input_size=self.config.image_size, std_mode='OPENAI_CLIP') + eval_img_transform = build_transform( + is_train=False, input_size=self.config.image_size, std_mode='OPENAI_CLIP') + + return dict( + image_processor=(train_img_transform, eval_img_transform), + image_token_len=num_query, + vision_config=self.vision_config + ) + + def get_vision_embedding(self, pixel_values): + if isinstance(self.vision_tower, list): + vision_tower = self.vision_tower[0] # HACK: for FSDP + else: + vision_tower = self.vision_tower + + dtype = vision_tower.pos_embed.data.dtype + vision_embedding = vision_tower.forward_features( + pixel_values.type(dtype)) + if hasattr(vision_tower, 'num_prefix_tokens') and vision_tower.num_prefix_tokens > 0: + vision_embedding = vision_embedding[:, + vision_tower.num_prefix_tokens:] + res = self.resampler(vision_embedding) + return res + + def get_vllm_embedding(self, data): + + if 'vision_hidden_states' not in data: + pixel_values_list = data['pixel_values'] + vision_hidden_states = [] + for pixel_values in pixel_values_list: + if len(pixel_values) > 0: + vision_hidden_states.append(self.get_vision_embedding(pixel_values.unsqueeze(0))[0]) + else: + vision_hidden_states.append([]) + else: + vision_hidden_states = data['vision_hidden_states'] + + #vllm_embedding = self.llm.model.embed_tokens(data['input_ids']) * self.llm.config.scale_emb + inputs_embeds = self.embed_tokens(data['input_ids']) + vision_hidden_states = [i.type(inputs_embeds.dtype) + if isinstance(i, torch.Tensor) else i for i in vision_hidden_states + ] + + + # HACK: replace back original embeddings for LLaVA pretraining + orig_embeds_params = getattr(self, 'orig_embeds_params', None) + + new_input_embeds = [] + cur_image_idx = 0 + for cur_input_ids, cur_input_embeds in zip(data['input_ids'], inputs_embeds): + if (cur_input_ids == self.vision_config.im_patch_token).sum() == 0: + # multimodal LLM, but the current sample is not multimodal + cur_input_embeds = cur_input_embeds + (0. * dummy_image_features).sum() + new_input_embeds.append(cur_input_embeds) + continue + + if self.vision_config.use_im_start_end: + cur_image_features = vision_hidden_states[cur_image_idx] + num_patches = cur_image_features.shape[0] + if (cur_input_ids == self.vision_config.im_start_token).sum() != (cur_input_ids == self.vision_config.im_end_token).sum(): + raise ValueError( + "The number of image start tokens and image end tokens should be the same.") + image_start_tokens = torch.where( + cur_input_ids == self.vision_config.im_start_token)[0] + for image_start_token_pos in image_start_tokens: + cur_image_features = vision_hidden_states[cur_image_idx].to( + device=cur_input_embeds.device) + num_patches = cur_image_features.shape[0] + if cur_input_ids[image_start_token_pos + num_patches + 1] != self.vision_config.im_end_token: + raise ValueError( + "The image end token should follow the image start token.") + if orig_embeds_params is not None: + cur_new_input_embeds = torch.cat((cur_input_embeds[:image_start_token_pos].detach(), cur_input_embeds[image_start_token_pos:image_start_token_pos+1], cur_image_features, + cur_input_embeds[image_start_token_pos + num_patches + 1:image_start_token_pos + num_patches + 2], cur_input_embeds[image_start_token_pos + num_patches + 2:].detach()), dim=0) + else: + cur_new_input_embeds = torch.cat( + (cur_input_embeds[:image_start_token_pos+1], cur_image_features, cur_input_embeds[image_start_token_pos + num_patches + 1:]), dim=0) + cur_image_idx += 1 + new_input_embeds.append(cur_new_input_embeds) + else: + raise NotImplementedError + inputs_embeds = torch.stack(new_input_embeds, dim=0) + + return inputs_embeds, vision_hidden_states + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + images: Optional[torch.FloatTensor] = None, + return_dict: Optional[bool] = None, + **kwargs + ) -> Union[Tuple, BaseModelOutputWithPast]: + + # HACK: replace back original embeddings for LLaVA pretraining + orig_embeds_params = getattr(self, 'orig_embeds_params', None) + + if inputs_embeds is None and past_key_values is None: + inputs_embeds = self.embed_tokens(input_ids) + + vision_tower = getattr(self, 'vision_tower', None) + if vision_tower is not None and (input_ids.shape[1] != 1 or self.training) and images is not None: + + if type(images) is list: + image_features = [] + for image in images: + image_forward_out = self.get_vision_embedding(image.unsqueeze(0))[ + 0] + image_features.append(image_forward_out) + else: + image_features = self.get_vision_embedding(images) + + dummy_image_features = torch.zeros( + self.config.num_query, + self.config.hidden_size, + device=inputs_embeds.device, + dtype=inputs_embeds.dtype) + + new_input_embeds = [] + cur_image_idx = 0 + for cur_input_ids, cur_input_embeds in zip(input_ids, inputs_embeds): + if (cur_input_ids == self.vision_config.im_patch_token).sum() == 0: + # multimodal LLM, but the current sample is not multimodal + cur_input_embeds = cur_input_embeds + \ + (0. * dummy_image_features).sum() + new_input_embeds.append(cur_input_embeds) + continue + + if self.vision_config.use_im_start_end: + cur_image_features = image_features[cur_image_idx] + num_patches = cur_image_features.shape[0] + if (cur_input_ids == self.vision_config.im_start_token).sum() != (cur_input_ids == self.vision_config.im_end_token).sum(): + raise ValueError( + "The number of image start tokens and image end tokens should be the same.") + image_start_tokens = torch.where( + cur_input_ids == self.vision_config.im_start_token)[0] + for image_start_token_pos in image_start_tokens: + cur_image_features = image_features[cur_image_idx].to( + device=cur_input_embeds.device) + num_patches = cur_image_features.shape[0] + if cur_input_ids[image_start_token_pos + num_patches + 1] != self.vision_config.im_end_token: + raise ValueError( + "The image end token should follow the image start token.") + if orig_embeds_params is not None: + cur_new_input_embeds = torch.cat((cur_input_embeds[:image_start_token_pos].detach(), cur_input_embeds[image_start_token_pos:image_start_token_pos+1], cur_image_features, + cur_input_embeds[image_start_token_pos + num_patches + 1:image_start_token_pos + num_patches + 2], cur_input_embeds[image_start_token_pos + num_patches + 2:].detach()), dim=0) + else: + cur_new_input_embeds = torch.cat( + (cur_input_embeds[:image_start_token_pos+1], cur_image_features, cur_input_embeds[image_start_token_pos + num_patches + 1:]), dim=0) + cur_image_idx += 1 + new_input_embeds.append(cur_new_input_embeds) + else: + raise NotImplementedError + inputs_embeds = torch.stack(new_input_embeds, dim=0) + input_ids = None + + return super(OmniLMMModel, self).forward( + input_ids=input_ids, attention_mask=attention_mask, past_key_values=past_key_values, + inputs_embeds=inputs_embeds, use_cache=use_cache, + output_attentions=output_attentions, output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs + ) + + +class OmniLMMForCausalLM(MistralForCausalLM): + config_class = OmniLMMConfig + + def __init__(self, config, mm_vision_tower=None, tune_clip=True): + super(MistralForCausalLM, self).__init__(config) + self.model = OmniLMMModel( + config, mm_vision_tower=mm_vision_tower, tune_clip=tune_clip) + + self.lm_head = nn.Linear( + config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + images: Optional[torch.FloatTensor] = None, + return_dict: Optional[bool] = None, + **kwargs + ) -> Union[Tuple, CausalLMOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # print(f'@@@ At forward, labels: {labels.shape}-{labels}', flush=True) + # print(f'@@@ At forward, input_ids: {input_ids.shape}-{input_ids}', flush=True) + # print(f'@@@ At forward, input_ids: {attention_mask.shape}-{attention_mask}', flush=True) + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + images=images, + **kwargs + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model/pipeline parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + # TODO could be removed for generate_vllm() + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + if past_key_values: + input_ids = input_ids[:, -1:] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + "images": kwargs.get("images", None), + } + ) + return model_inputs + + def generate_vllm( + self, + input_ids: torch.LongTensor = None, + images: Optional[torch.FloatTensor] = None, + vision_hidden_states=None, + return_vision_hidden_states=False, + **kwargs + ): + model_inputs = {'input_ids': input_ids} + if vision_hidden_states is None: + model_inputs['pixel_values'] = images + else: + model_inputs['vision_hidden_states'] = vision_hidden_states + + with torch.inference_mode(): + inputs_embeds, vision_hidden_states = self.model.get_vllm_embedding(model_inputs) + + result = self.generate( + inputs_embeds=inputs_embeds, + **kwargs + ) + + if return_vision_hidden_states: + return result, vision_hidden_states + + return result + + + def initialize_vision_tokenizer(self, mm_use_im_start_end, tokenizer, device, + tune_mm_mlp_adapter=False): + self.model.vision_config.use_im_start_end = mm_use_im_start_end + tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) + self.resize_token_embeddings(len(tokenizer)) + + if mm_use_im_start_end: + num_new_tokens = tokenizer.add_tokens( + [DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True) + self.resize_token_embeddings(len(tokenizer)) + self.model.vision_config.im_start_token, self.model.vision_config.im_end_token = tokenizer.convert_tokens_to_ids( + [DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN]) + + if num_new_tokens > 0: + input_embeddings = self.get_input_embeddings().weight.data + output_embeddings = self.get_output_embeddings().weight.data + + input_embeddings_avg = input_embeddings[:-num_new_tokens].mean( + dim=0, keepdim=True) + output_embeddings_avg = output_embeddings[:-num_new_tokens].mean( + dim=0, keepdim=True) + + input_embeddings[-num_new_tokens:] = input_embeddings_avg + output_embeddings[-num_new_tokens:] = output_embeddings_avg + + # for new sft data + num_new_tokens = tokenizer.add_tokens( + ['', '', '', '', '', ''], special_tokens=True) + self.resize_token_embeddings(len(tokenizer)) + + if num_new_tokens > 0: + input_embeddings = self.get_input_embeddings().weight.data + output_embeddings = self.get_output_embeddings().weight.data + + input_embeddings_avg = input_embeddings[:-num_new_tokens].mean( + dim=0, keepdim=True) + output_embeddings_avg = output_embeddings[:-num_new_tokens].mean( + dim=0, keepdim=True) + + input_embeddings[-num_new_tokens:] = input_embeddings_avg + output_embeddings[-num_new_tokens:] = output_embeddings_avg + + if tune_mm_mlp_adapter: + self.model.orig_embeds_params = [ + self.get_input_embeddings().weight.data.clone().to(device=device)] + for p in self.get_input_embeddings().parameters(): + p.requires_grad = True + for p in self.get_output_embeddings().parameters(): + p.requires_grad = False + + self.model.vision_config.im_patch_token = tokenizer.convert_tokens_to_ids( + [DEFAULT_IMAGE_PATCH_TOKEN])[0] + print(f'Tokenizer: {tokenizer}\n patch_token_id: {self.model.vision_config.im_patch_token}, visoin_config: {self.model.vision_config}', flush=True) + # exit() + + +AutoConfig.register("omnilmm", OmniLMMConfig) +AutoModelForCausalLM.register(OmniLMMConfig, OmniLMMForCausalLM) diff --git a/MiniCPM-V/omnilmm/model/resampler.py b/MiniCPM-V/omnilmm/model/resampler.py new file mode 100644 index 0000000000000000000000000000000000000000..0d46f55f460384ab23d0b4ff00768417281110dc --- /dev/null +++ b/MiniCPM-V/omnilmm/model/resampler.py @@ -0,0 +1,171 @@ +# Copyright (c) Alibaba Cloud. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from collections import OrderedDict +import math +import requests +from io import BytesIO +from functools import partial +from PIL import Image +from typing import Callable, Optional, Sequence, Tuple, List, Union +import numpy as np + +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn.init import trunc_normal_ +from torchvision import transforms +from torchvision.transforms import InterpolationMode + + +def get_abs_pos(abs_pos, tgt_size): + # abs_pos: L, C + # tgt_size: M + # return: M, C + src_size = int(math.sqrt(abs_pos.size(0))) + tgt_size = int(math.sqrt(tgt_size)) + dtype = abs_pos.dtype + + if src_size != tgt_size: + return F.interpolate( + abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2), + size=(tgt_size, tgt_size), + mode="bicubic", + align_corners=False, + ).permute(0, 2, 3, 1).flatten(0, 2).to(dtype=dtype) + else: + return abs_pos + + +# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20 +def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + grid_h = np.arange(grid_size, dtype=np.float32) + grid_w = np.arange(grid_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + + grid = grid.reshape([2, 1, grid_size, grid_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token: + pos_embed = np.concatenate( + [np.zeros([1, embed_dim]), pos_embed], axis=0) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid( + embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid( + embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float32) + omega /= embed_dim / 2. + omega = 1. / 10000 ** omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + + +class Resampler(nn.Module): + """ + A 2D perceiver-resampler network with one cross attention layers by + (grid_size**2) learnable queries and 2d sincos pos_emb + Outputs: + A tensor with the shape of (grid_size**2, embed_dim) + """ + + def __init__( + self, + grid_size, + embed_dim, + num_heads, + kv_dim=None, + norm_layer=partial(nn.LayerNorm, eps=1e-6) + ): + super().__init__() + self.num_queries = grid_size ** 2 + self.embed_dim = embed_dim + self.num_heads = num_heads + + self.pos_embed = nn.Parameter( + torch.from_numpy(get_2d_sincos_pos_embed( + embed_dim, grid_size)).float() + ).requires_grad_(False) + + self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim)) + trunc_normal_(self.query, std=.02) + + if kv_dim is not None and kv_dim != embed_dim: + self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False) + else: + self.kv_proj = nn.Identity() + + self.attn = nn.MultiheadAttention(embed_dim, num_heads) + self.ln_q = norm_layer(embed_dim) + self.ln_kv = norm_layer(embed_dim) + + self.ln_post = norm_layer(embed_dim) + self.proj = nn.Parameter( + (embed_dim ** -0.5) * torch.randn(embed_dim, embed_dim)) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x, attn_mask=None): + + pos_embed = get_abs_pos(self.pos_embed, x.size(1)) + + x = self.kv_proj(x) + x = self.ln_kv(x).permute(1, 0, 2) + + N = x.shape[1] + q = self.ln_q(self.query) + # print((self._repeat(q, N) + self.pos_embed.unsqueeze(1)).dtype, (x + pos_embed.unsqueeze(1)).dtype, x.dtype) + out = self.attn( + self._repeat(q, N) + self.pos_embed.unsqueeze(1), + x + pos_embed.unsqueeze(1), + x, + attn_mask=attn_mask)[0] + x = out.permute(1, 0, 2) + + x = self.ln_post(x) + x = x @ self.proj + return x + + def _repeat(self, query, N: int): + return query.unsqueeze(1).repeat(1, N, 1) diff --git a/MiniCPM-V/omnilmm/model/utils.py b/MiniCPM-V/omnilmm/model/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fcb8eba1f74b111aa68be3aa0211c831cf243cb9 --- /dev/null +++ b/MiniCPM-V/omnilmm/model/utils.py @@ -0,0 +1,555 @@ +from torchvision import transforms +from timm.data.transforms import RandomResizedCropAndInterpolation +from timm.data.constants import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD +from transformers import AutoConfig +from PIL import Image +from io import BytesIO +import torch.distributed as dist +import numpy as np +import pickle +import base64 +import cv2 +import os +import torch +from transformers import AutoConfig, StoppingCriteria + +try: + from timm.data.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD +except ImportError: + OPENAI_CLIP_MEAN = (0.48145466, 0.4578275, 0.40821073) + OPENAI_CLIP_STD = (0.26862954, 0.26130258, 0.27577711) + + +def auto_upgrade(config): + cfg = AutoConfig.from_pretrained(config) + if 'llava' in config and cfg.model_type != 'llava': + print("You are using newer LLaVA code base, while the checkpoint of v0 is from older code base.") + print("You must upgrade the checkpoint to the new code base (this can be done automatically).") + confirm = input( + "Please confirm that you want to upgrade the checkpoint. [Y/N]") + if confirm.lower() in ["y", "yes"]: + print("Upgrading checkpoint...") + assert len(cfg.architectures) == 1 + setattr(cfg.__class__, "model_type", "llava") + cfg.architectures[0] = 'LlavaLlamaForCausalLM' + cfg.save_pretrained(config) + print("Checkpoint upgraded.") + else: + print("Checkpoint upgrade aborted.") + exit(1) + + +class KeywordsStoppingCriteria(StoppingCriteria): + def __init__(self, keywords, tokenizer, input_ids): + self.keywords = keywords + self.tokenizer = tokenizer + self.start_len = None + self.input_ids = input_ids + + def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: + if self.start_len is None: + self.start_len = self.input_ids.shape[1] + else: + outputs = self.tokenizer.batch_decode( + output_ids[:, self.start_len:], skip_special_tokens=True)[0] + for keyword in self.keywords: + if keyword in outputs: + return True + return False + + +def auto_upgrade(config): + cfg = AutoConfig.from_pretrained(config) + if 'llava' in config and cfg.model_type != 'llava': + print("You are using newer LLaVA code base, while the checkpoint of v0 is from older code base.") + print("You must upgrade the checkpoint to the new code base (this can be done automatically).") + confirm = input( + "Please confirm that you want to upgrade the checkpoint. [Y/N]") + if confirm.lower() in ["y", "yes"]: + print("Upgrading checkpoint...") + assert len(cfg.architectures) == 1 + setattr(cfg.__class__, "model_type", "llava") + cfg.architectures[0] = 'LlavaLlamaForCausalLM' + cfg.save_pretrained(config) + print("Checkpoint upgraded.") + else: + print("Checkpoint upgrade aborted.") + exit(1) + +# aug functions + + +def identity_func(img): + return img + + +def autocontrast_func(img, cutoff=0): + ''' + same output as PIL.ImageOps.autocontrast + ''' + n_bins = 256 + + def tune_channel(ch): + n = ch.size + cut = cutoff * n // 100 + if cut == 0: + high, low = ch.max(), ch.min() + else: + hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins]) + low = np.argwhere(np.cumsum(hist) > cut) + low = 0 if low.shape[0] == 0 else low[0] + high = np.argwhere(np.cumsum(hist[::-1]) > cut) + high = n_bins - 1 if high.shape[0] == 0 else n_bins - 1 - high[0] + if high <= low: + table = np.arange(n_bins) + else: + scale = (n_bins - 1) / (high - low) + table = np.arange(n_bins) * scale - low * scale + table[table < 0] = 0 + table[table > n_bins - 1] = n_bins - 1 + table = table.clip(0, 255).astype(np.uint8) + return table[ch] + + channels = [tune_channel(ch) for ch in cv2.split(img)] + out = cv2.merge(channels) + return out + + +def equalize_func(img): + ''' + same output as PIL.ImageOps.equalize + PIL's implementation is different from cv2.equalize + ''' + n_bins = 256 + + def tune_channel(ch): + hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins]) + non_zero_hist = hist[hist != 0].reshape(-1) + step = np.sum(non_zero_hist[:-1]) // (n_bins - 1) + if step == 0: + return ch + n = np.empty_like(hist) + n[0] = step // 2 + n[1:] = hist[:-1] + table = (np.cumsum(n) // step).clip(0, 255).astype(np.uint8) + return table[ch] + + channels = [tune_channel(ch) for ch in cv2.split(img)] + out = cv2.merge(channels) + return out + + +def rotate_func(img, degree, fill=(0, 0, 0)): + ''' + like PIL, rotate by degree, not radians + ''' + H, W = img.shape[0], img.shape[1] + center = W / 2, H / 2 + M = cv2.getRotationMatrix2D(center, degree, 1) + out = cv2.warpAffine(img, M, (W, H), borderValue=fill) + return out + + +def solarize_func(img, thresh=128): + ''' + same output as PIL.ImageOps.posterize + ''' + table = np.array([el if el < thresh else 255 - el for el in range(256)]) + table = table.clip(0, 255).astype(np.uint8) + out = table[img] + return out + + +def color_func(img, factor): + ''' + same output as PIL.ImageEnhance.Color + ''' + # implementation according to PIL definition, quite slow + # degenerate = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[:, :, np.newaxis] + # out = blend(degenerate, img, factor) + # M = ( + # np.eye(3) * factor + # + np.float32([0.114, 0.587, 0.299]).reshape(3, 1) * (1. - factor) + # )[np.newaxis, np.newaxis, :] + M = ( + np.float32([ + [0.886, -0.114, -0.114], + [-0.587, 0.413, -0.587], + [-0.299, -0.299, 0.701]]) * factor + + np.float32([[0.114], [0.587], [0.299]]) + ) + out = np.matmul(img, M).clip(0, 255).astype(np.uint8) + return out + + +def contrast_func(img, factor): + """ + same output as PIL.ImageEnhance.Contrast + """ + mean = np.sum(np.mean(img, axis=(0, 1)) * np.array([0.114, 0.587, 0.299])) + table = np.array([( + el - mean) * factor + mean + for el in range(256) + ]).clip(0, 255).astype(np.uint8) + out = table[img] + return out + + +def brightness_func(img, factor): + ''' + same output as PIL.ImageEnhance.Contrast + ''' + table = (np.arange(256, dtype=np.float32) * + factor).clip(0, 255).astype(np.uint8) + out = table[img] + return out + + +def sharpness_func(img, factor): + ''' + The differences the this result and PIL are all on the 4 boundaries, the center + areas are same + ''' + kernel = np.ones((3, 3), dtype=np.float32) + kernel[1][1] = 5 + kernel /= 13 + degenerate = cv2.filter2D(img, -1, kernel) + if factor == 0.0: + out = degenerate + elif factor == 1.0: + out = img + else: + out = img.astype(np.float32) + degenerate = degenerate.astype(np.float32)[1:-1, 1:-1, :] + out[1:-1, 1:-1, :] = degenerate + factor * \ + (out[1:-1, 1:-1, :] - degenerate) + out = out.astype(np.uint8) + return out + + +def shear_x_func(img, factor, fill=(0, 0, 0)): + H, W = img.shape[0], img.shape[1] + M = np.float32([[1, factor, 0], [0, 1, 0]]) + out = cv2.warpAffine(img, M, (W, H), borderValue=fill, + flags=cv2.INTER_LINEAR).astype(np.uint8) + return out + + +def translate_x_func(img, offset, fill=(0, 0, 0)): + ''' + same output as PIL.Image.transform + ''' + H, W = img.shape[0], img.shape[1] + M = np.float32([[1, 0, -offset], [0, 1, 0]]) + out = cv2.warpAffine(img, M, (W, H), borderValue=fill, + flags=cv2.INTER_LINEAR).astype(np.uint8) + return out + + +def translate_y_func(img, offset, fill=(0, 0, 0)): + ''' + same output as PIL.Image.transform + ''' + H, W = img.shape[0], img.shape[1] + M = np.float32([[1, 0, 0], [0, 1, -offset]]) + out = cv2.warpAffine(img, M, (W, H), borderValue=fill, + flags=cv2.INTER_LINEAR).astype(np.uint8) + return out + + +def posterize_func(img, bits): + ''' + same output as PIL.ImageOps.posterize + ''' + out = np.bitwise_and(img, np.uint8(255 << (8 - bits))) + return out + + +def shear_y_func(img, factor, fill=(0, 0, 0)): + H, W = img.shape[0], img.shape[1] + M = np.float32([[1, 0, 0], [factor, 1, 0]]) + out = cv2.warpAffine(img, M, (W, H), borderValue=fill, + flags=cv2.INTER_LINEAR).astype(np.uint8) + return out + + +def cutout_func(img, pad_size, replace=(0, 0, 0)): + replace = np.array(replace, dtype=np.uint8) + H, W = img.shape[0], img.shape[1] + rh, rw = np.random.random(2) + pad_size = pad_size // 2 + ch, cw = int(rh * H), int(rw * W) + x1, x2 = max(ch - pad_size, 0), min(ch + pad_size, H) + y1, y2 = max(cw - pad_size, 0), min(cw + pad_size, W) + out = img.copy() + out[x1:x2, y1:y2, :] = replace + return out + + +# level to args +def enhance_level_to_args(MAX_LEVEL): + def level_to_args(level): + return ((level / MAX_LEVEL) * 1.8 + 0.1,) + return level_to_args + + +def shear_level_to_args(MAX_LEVEL, replace_value): + def level_to_args(level): + level = (level / MAX_LEVEL) * 0.3 + if np.random.random() > 0.5: + level = -level + return (level, replace_value) + + return level_to_args + + +def translate_level_to_args(translate_const, MAX_LEVEL, replace_value): + def level_to_args(level): + level = (level / MAX_LEVEL) * float(translate_const) + if np.random.random() > 0.5: + level = -level + return (level, replace_value) + + return level_to_args + + +def cutout_level_to_args(cutout_const, MAX_LEVEL, replace_value): + def level_to_args(level): + level = int((level / MAX_LEVEL) * cutout_const) + return (level, replace_value) + + return level_to_args + + +def solarize_level_to_args(MAX_LEVEL): + def level_to_args(level): + level = int((level / MAX_LEVEL) * 256) + return (level, ) + return level_to_args + + +def none_level_to_args(level): + return () + + +def posterize_level_to_args(MAX_LEVEL): + def level_to_args(level): + level = int((level / MAX_LEVEL) * 4) + return (level, ) + return level_to_args + + +def rotate_level_to_args(MAX_LEVEL, replace_value): + def level_to_args(level): + level = (level / MAX_LEVEL) * 30 + if np.random.random() < 0.5: + level = -level + return (level, replace_value) + + return level_to_args + + +func_dict = { + 'Identity': identity_func, + 'AutoContrast': autocontrast_func, + 'Equalize': equalize_func, + 'Rotate': rotate_func, + 'Solarize': solarize_func, + 'Color': color_func, + 'Contrast': contrast_func, + 'Brightness': brightness_func, + 'Sharpness': sharpness_func, + 'ShearX': shear_x_func, + 'TranslateX': translate_x_func, + 'TranslateY': translate_y_func, + 'Posterize': posterize_func, + 'ShearY': shear_y_func, +} + +translate_const = 10 +MAX_LEVEL = 10 +replace_value = (128, 128, 128) +arg_dict = { + 'Identity': none_level_to_args, + 'AutoContrast': none_level_to_args, + 'Equalize': none_level_to_args, + 'Rotate': rotate_level_to_args(MAX_LEVEL, replace_value), + 'Solarize': solarize_level_to_args(MAX_LEVEL), + 'Color': enhance_level_to_args(MAX_LEVEL), + 'Contrast': enhance_level_to_args(MAX_LEVEL), + 'Brightness': enhance_level_to_args(MAX_LEVEL), + 'Sharpness': enhance_level_to_args(MAX_LEVEL), + 'ShearX': shear_level_to_args(MAX_LEVEL, replace_value), + 'TranslateX': translate_level_to_args( + translate_const, MAX_LEVEL, replace_value + ), + 'TranslateY': translate_level_to_args( + translate_const, MAX_LEVEL, replace_value + ), + 'Posterize': posterize_level_to_args(MAX_LEVEL), + 'ShearY': shear_level_to_args(MAX_LEVEL, replace_value), +} + + +class RandomAugment(object): + + def __init__(self, N=2, M=10, isPIL=False, augs=[]): + self.N = N + self.M = M + self.isPIL = isPIL + if augs: + self.augs = augs + else: + self.augs = list(arg_dict.keys()) + + def get_random_ops(self): + sampled_ops = np.random.choice(self.augs, self.N) + return [(op, 0.5, self.M) for op in sampled_ops] + + def __call__(self, img): + if self.isPIL: + img = np.array(img) + ops = self.get_random_ops() + for name, prob, level in ops: + if np.random.random() > prob: + continue + args = arg_dict[name](level) + img = func_dict[name](img, *args) + return img + + +def build_transform(is_train, randaug=True, input_size=224, interpolation='bicubic', std_mode='IMAGENET_INCEPTION'): + if std_mode == 'IMAGENET_INCEPTION': + mean = IMAGENET_INCEPTION_MEAN + std = IMAGENET_INCEPTION_STD + elif std_mode == 'OPENAI_CLIP': + mean = OPENAI_CLIP_MEAN + std = OPENAI_CLIP_STD + else: + raise NotImplementedError + + if is_train: + crop_scale = float(os.environ.get('TRAIN_CROP_SCALE', 0.9999)) + t = [ + RandomResizedCropAndInterpolation( + input_size, scale=(crop_scale, 1.0), interpolation='bicubic'), + # transforms.RandomHorizontalFlip(), + ] + if randaug and os.environ.get('TRAIN_DO_AUG', 'False') == 'True': + print(f'@@@@@ Do random aug during training', flush=True) + t.append( + RandomAugment( + 2, 7, isPIL=True, + augs=[ + 'Identity', 'AutoContrast', 'Equalize', 'Brightness', 'Sharpness', + 'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate', + ])) + else: + print(f'@@@@@ Skip random aug during training', flush=True) + t += [ + transforms.ToTensor(), + transforms.Normalize(mean=mean, std=std), + ] + t = transforms.Compose(t) + else: + t = transforms.Compose([ + transforms.Resize((input_size, input_size), + interpolation=transforms.InterpolationMode.BICUBIC), + transforms.ToTensor(), + transforms.Normalize(mean=mean, std=std) + ]) + + return t + + +def img2b64(img_path): + img = Image.open(img_path) # path to file + img_buffer = BytesIO() + img.save(img_buffer, format=img.format) + byte_data = img_buffer.getvalue() + base64_str = base64.b64encode(byte_data) # bytes + base64_str = base64_str.decode("utf-8") # str + return base64_str + + +def str2b64(str): + return base64.b64encode(str.encode('utf-8')).decode('utf-8') + + +def b642str(b64): + return base64.b64decode(b64).decode('utf-8') + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + world_size = get_world_size() + if world_size == 1: + return [data] + + # serialized to a Tensor + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to("cuda") + + # obtain Tensor size of each rank + local_size = torch.LongTensor([tensor.numel()]).to("cuda") + size_list = [torch.LongTensor([0]).to("cuda") for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.ByteTensor(size=(max_size,)).to("cuda")) + if local_size != max_size: + padding = torch.ByteTensor(size=(max_size - local_size,)).to("cuda") + tensor = torch.cat((tensor, padding), dim=0) + dist.all_gather(tensor_list, tensor) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def mean(lst): + return sum(lst) / len(lst) + + +def stop_gradient_by_name(name: str): + def apply_fn(module): + if hasattr(module, name): + getattr(module, name).requires_grad_(False) + + return apply_fn diff --git a/MiniCPM-V/omnilmm/train/train_utils.py b/MiniCPM-V/omnilmm/train/train_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..48af610d369071f7ec28784ed436d0f4a4e7b1c0 --- /dev/null +++ b/MiniCPM-V/omnilmm/train/train_utils.py @@ -0,0 +1,153 @@ +import os +import gc +import copy +import time + +import torch +import warnings +import transformers + +import numpy as np + +from typing import Dict, Optional, Sequence +from omnilmm import conversation as conversation_lib + +IGNORE_INDEX = -100 +DEFAULT_IMAGE_TOKEN = "" +DEFAULT_IMAGE_PATCH_TOKEN = "" +DEFAULT_IM_START_TOKEN = "" +DEFAULT_IM_END_TOKEN = "" + + +def _tokenize_fn(strings: Sequence[str], + tokenizer: transformers.PreTrainedTokenizer) -> Dict: + """Tokenize a list of strings.""" + tokenized_list = [ + tokenizer( + text, + return_tensors="pt", + padding="longest", + max_length=tokenizer.model_max_length, + truncation=True, + ) for text in strings + ] + input_ids = labels = [ + tokenized.input_ids[0] for tokenized in tokenized_list + ] + input_ids_lens = labels_lens = [ + tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() + for tokenized in tokenized_list + ] + return dict( + input_ids=input_ids, + labels=labels, + input_ids_lens=input_ids_lens, + labels_lens=labels_lens, + ) + + + +def omni_preprocess(sources, + tokenizer: transformers.PreTrainedTokenizer, + generation=False): + system_content = 'You are an artificial intelligence assistant, which gives helpful, detailed, and polite answers to the human\'s questions.' + ignore_index = -100 + + response_template = '\n<|assistant|>\n' + instruction_template = '\n<|user|>\n' + response_token_ids = tokenizer.encode( + response_template, add_special_tokens=False) + instruction_token_ids = tokenizer.encode( + instruction_template, add_special_tokens=False) + + batch_input_ids = [] + batch_labels = [] + for i in range(len(sources)): + new_source = [] + prev_role = 'unexpect' + for conv_turn in sources[i]: + role = conv_turn['from'] if 'from' in conv_turn else conv_turn['role'] + content = conv_turn['value'] if 'value' in conv_turn else conv_turn['content'] + + role = 'user' if role == 'human' else role + role = 'assistant' if role == 'gpt' else role + + assert role in ['user', 'assistant'] + assert role != prev_role, f'role={role}, prev_role={prev_role}' + prev_role = role + + new_turn = { + 'role': role, + 'content': content + } + new_source.append(new_turn) + if new_source[0]['role'] != 'system': + new_source.insert(0, {'role': 'system', 'content': system_content}) + + # TODO: this automatically add '\n' to the end + res_text = tokenizer.apply_chat_template( + new_source, tokenize=False, add_generation_prompt=generation) + if not generation: + res_text = res_text.strip() + + conversations_tokenized = _tokenize_fn([res_text], tokenizer) + res_input_ids = conversations_tokenized["input_ids"][0] + + # since labels and input_ids are reference towards the same object + res_labels = copy.deepcopy(conversations_tokenized["labels"][0]) + + response_token_ids_idxs = [] + human_token_ids_idxs = [] + + for assistant_idx in np.where(res_labels == response_token_ids[0])[0]: + # find the indexes of the start of a response. + if (response_token_ids == res_labels[assistant_idx: assistant_idx + len( + response_token_ids)].tolist() + ): + response_token_ids_idxs.append( + assistant_idx + len(response_token_ids)) + + if len(response_token_ids_idxs) == 0: + warnings.warn( + f"Could not find response key `{response_template}` in the " + f'following instance: @===>{tokenizer.decode(res_input_ids)}<===@ ' + f'Raw text is @===>{res_text}<===@' + f'Raw source is @===>{new_source}<===@' + f"This instance will be ignored in loss calculation. " + f"Note, if this happens often, consider increasing the `max_seq_length`." + ) + res_labels[:] = ignore_index + + human_token_ids = instruction_token_ids + for human_idx in np.where(res_labels == human_token_ids[0])[0]: + # find the indexes of the start of a human answer. + if human_token_ids == res_labels[human_idx: human_idx + len(human_token_ids)].tolist(): + human_token_ids_idxs.append(human_idx) + + if len(human_token_ids_idxs) == 0: + warnings.warn( + f"Could not find instruction key `{instruction_template}` in the " + f'following instance: @===>{tokenizer.decode(res_input_ids)}<===@ ' + f'Raw text is @===>{res_text}<===@' + f'Raw source is @===>{new_source}<===@' + f"This instance will be ignored in loss calculation. " + f"Note, if this happens often, consider increasing the `max_seq_length`." + ) + res_labels[:] = ignore_index + + for idx, (start, end) in enumerate(zip(human_token_ids_idxs, response_token_ids_idxs)): + # Make pytorch loss function ignore all non response tokens + if idx != 0: + res_labels[start:end] = ignore_index + else: + res_labels[:end] = ignore_index + + if len(response_token_ids_idxs) < len(human_token_ids_idxs): + res_labels[human_token_ids_idxs[-1]:] = ignore_index + + batch_input_ids.append(res_input_ids) + batch_labels.append(res_labels) + + return dict(input_ids=batch_input_ids, labels=batch_labels) + + diff --git a/MiniCPM-V/omnilmm/utils.py b/MiniCPM-V/omnilmm/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..784dabe80320119b6998d79e2840303bdee3ed04 --- /dev/null +++ b/MiniCPM-V/omnilmm/utils.py @@ -0,0 +1,127 @@ +import datetime +import logging +import logging.handlers +import os +import sys + +import requests + +from omnilmm.constants import LOGDIR + +server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**" +moderation_msg = "YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN." + +handler = None + + +def build_logger(logger_name, logger_filename): + global handler + + formatter = logging.Formatter( + fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + + # Set the format of root handlers + if not logging.getLogger().handlers: + logging.basicConfig(level=logging.INFO) + logging.getLogger().handlers[0].setFormatter(formatter) + + # Redirect stdout and stderr to loggers + stdout_logger = logging.getLogger("stdout") + stdout_logger.setLevel(logging.INFO) + sl = StreamToLogger(stdout_logger, logging.INFO) + sys.stdout = sl + + stderr_logger = logging.getLogger("stderr") + stderr_logger.setLevel(logging.ERROR) + sl = StreamToLogger(stderr_logger, logging.ERROR) + sys.stderr = sl + + # Get logger + logger = logging.getLogger(logger_name) + logger.setLevel(logging.INFO) + + # Add a file handler for all loggers + if handler is None: + os.makedirs(LOGDIR, exist_ok=True) + filename = os.path.join(LOGDIR, logger_filename) + handler = logging.handlers.TimedRotatingFileHandler( + filename, when='D', utc=True) + handler.setFormatter(formatter) + + for name, item in logging.root.manager.loggerDict.items(): + if isinstance(item, logging.Logger): + item.addHandler(handler) + + return logger + + +class StreamToLogger(object): + """ + Fake file-like stream object that redirects writes to a logger instance. + """ + + def __init__(self, logger, log_level=logging.INFO): + self.terminal = sys.stdout + self.logger = logger + self.log_level = log_level + self.linebuf = '' + + def __getattr__(self, attr): + return getattr(self.terminal, attr) + + def write(self, buf): + temp_linebuf = self.linebuf + buf + self.linebuf = '' + for line in temp_linebuf.splitlines(True): + # From the io.TextIOWrapper docs: + # On output, if newline is None, any '\n' characters written + # are translated to the system default line separator. + # By default sys.stdout.write() expects '\n' newlines and then + # translates them so this is still cross platform. + if line[-1] == '\n': + self.logger.log(self.log_level, line.rstrip()) + else: + self.linebuf += line + + def flush(self): + if self.linebuf != '': + self.logger.log(self.log_level, self.linebuf.rstrip()) + self.linebuf = '' + + +def disable_torch_init(): + """ + Disable the redundant torch default initialization to accelerate model creation. + """ + import torch + setattr(torch.nn.Linear, "reset_parameters", lambda self: None) + setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None) + + +def violates_moderation(text): + """ + Check whether the text violates OpenAI moderation API. + """ + url = "https://api.openai.com/v1/moderations" + headers = {"Content-Type": "application/json", + "Authorization": "Bearer " + os.environ["OPENAI_API_KEY"]} + text = text.replace("\n", "") + data = "{" + '"input": ' + f'"{text}"' + "}" + data = data.encode("utf-8") + try: + ret = requests.post(url, headers=headers, data=data, timeout=5) + flagged = ret.json()["results"][0]["flagged"] + except requests.exceptions.RequestException as e: + flagged = False + except KeyError as e: + flagged = False + + return flagged + + +def pretty_print_semaphore(semaphore): + if semaphore is None: + return "None" + return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})" diff --git a/MiniCPM-V/omnilmm_en.md b/MiniCPM-V/omnilmm_en.md new file mode 100644 index 0000000000000000000000000000000000000000..62f799222df555fb8d36d46649813280383a1048 --- /dev/null +++ b/MiniCPM-V/omnilmm_en.md @@ -0,0 +1,155 @@ +## OmniLMM-12B + +> OmniLMM-12B is released at early time of this project. We recommond you to use our [recently released models](./README_en.md), for better performance and efficiency. + +> Archieve at: 2024-05-19 + + +**OmniLMM-12B** is the most capable version. The model is built based on EVA02-5B and Zephyr-7B-β, connected with a perceiver resampler layer, and trained on multimodal data in a curriculum fashion. The model has three notable features: + +- 🔥 **Strong Performance.** + + OmniLMM-12B achieves **leading performance** among models with comparable sizes, surpassing established LMMs on multiple benchmarks (including MME, MMBench, SEED-Bench, etc). The model also endows rich multi-modal world knowledge. + +- 🏆 **Trustworthy Behavior.** + + LMMs are known for suffering from hallucination, often generating text that is not factually grounded in images (e.g., faithfully describing non-existing objects in images). OmniLMM-12B is **the first state-of-the-art open-source LMM aligned via multimodal RLHF for trustworthy behavior** (using the recent [RLHF-V](https://rlhf-v.github.io/) technique). It **ranks #1** among open-source models on [MMHal-Bench](https://huggingface.co/datasets/Shengcao1006/MMHal-Bench), and **outperforms GPT-4V** on [Object HalBench](https://arxiv.org/abs/2312.00849). + +- 🕹 **Real-time Multimodal Interaction.** + + We combine the OmniLMM-12B and GPT-3.5 (text-only) into a **real-time multimodal interactive assistant**. The assistant accepts video streams from the camera and speech streams from the microphone and emits speech output. While still primary, we find the model can **replicate some of the fun cases shown in the Gemini Demo video, without any video edition**. + + +### Evaluation +
+ +
+
+Click to view results on MME, MMBench, MMMU, MMBench, MMHal-Bench, Object HalBench, SeedBench, LLaVA Bench, MathVista. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSizeMMEMMB dev (en)MMMU valMMHal-BenchObject HalBenchSeedBench-IMathVistaLLaVA Bench
GPT-4V†-1771.575.1 56.83.53 / 70.886.4 / 92.771.6 47.8 93.1
Qwen-VL-Plus†-2183.466.2 45.2- - 65.7 36.0 73.7
Yi-VL 6B6.7B 1915.1 68.6 40.3 - - 67.5 28.8 51.9
Qwen-VL-Chat9.6B1860.060.6 35.92.93 / 59.456.2 / 80.064.8 33.8 67.7
CogVLM-Chat17.4B1736.663.7 32.1 2.68 / 52.1 73.6 / 87.4 68.8 34.7 73.9
LLaVA 1.513.6B 1808.4 68.2 36.4 2.71 / 51.0 53.7 / 77.4 68.1 26.4 64.6
OmniLMM-12B11.6B 1935.8 71.6 40.7 3.45 / 68.8 90.3 / 95.5 71.1 34.9 72.0
+†: Proprietary models +
+
+ +### Examples + + +

+ +

+
+ + +We combine the OmniLMM-12B and GPT-3.5 (text-only) into a **real-time multimodal interactive assistant**. Video frames are described in text using OmniLMM-12B, and ChatGPT 3.5 (text-only) is employed to generate response according to the descriptions and user prompts. The demo video is a raw recording without edition. + +
+
+ +### Model Zoo + +| Model | Description | Download Link | +|:----------------------|:-------------------|:---------------:| +| OmniLMM-12B | The most capable version with leading performance. | [🤗](https://huggingface.co/openbmb/OmniLMM-12B)    [](https://modelscope.cn/models/OpenBMB/OmniLMM-12B/files) | diff --git a/MiniCPM-V/quantize/bnb_quantize.py b/MiniCPM-V/quantize/bnb_quantize.py new file mode 100644 index 0000000000000000000000000000000000000000..7aa7b46c37f685443d38fcf7506e42712b0d62fe --- /dev/null +++ b/MiniCPM-V/quantize/bnb_quantize.py @@ -0,0 +1,81 @@ +""" +the script will use bitandbytes to quantize the MiniCPM-Llama3-V-2_5 model. +the be quantized model can be finetuned by MiniCPM-Llama3-V-2_5 or not. +you only need to set the model_path 、save_path and run bash code + +cd MiniCPM-V +python quantize/bnb_quantize.py + +you will get the quantized model in save_path、quantized_model test time and gpu usage +""" + + +import torch +from transformers import AutoModel, AutoTokenizer, BitsAndBytesConfig +from PIL import Image +import time +import torch +import GPUtil +import os + +assert torch.cuda.is_available(),"CUDA is not available, but this code requires a GPU." + +device = 'cuda' # Select GPU to use +model_path = '/root/ld/ld_model_pretrained/MiniCPM-Llama3-V-2_5' # Model download path +save_path = '/root/ld/ld_model_pretrain/MiniCPM-Llama3-V-2_5_int4' # Quantized model save path +image_path = './assets/airplane.jpeg' + + +# Create a configuration object to specify quantization parameters +quantization_config = BitsAndBytesConfig( + load_in_4bit=True, # Whether to perform 4-bit quantization + load_in_8bit=False, # Whether to perform 8-bit quantization + bnb_4bit_compute_dtype=torch.float16, # Computation precision setting + bnb_4bit_quant_storage=torch.uint8, # Storage format for quantized weights + bnb_4bit_quant_type="nf4", # Quantization format, here using normally distributed int4 + bnb_4bit_use_double_quant=True, # Whether to use double quantization, i.e., quantizing zeropoint and scaling parameters + llm_int8_enable_fp32_cpu_offload=False, # Whether LLM uses int8, with fp32 parameters stored on the CPU + llm_int8_has_fp16_weight=False, # Whether mixed precision is enabled + llm_int8_skip_modules=["out_proj", "kv_proj", "lm_head"], # Modules not to be quantized + llm_int8_threshold=6.0 # Outlier value in the llm.int8() algorithm, distinguishing whether to perform quantization based on this value +) + +tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) +model = AutoModel.from_pretrained( + model_path, + device_map=device, # Allocate model to device + quantization_config=quantization_config, + trust_remote_code=True +) + +gpu_usage = GPUtil.getGPUs()[0].memoryUsed +start=time.time() +response = model.chat( + image=Image.open(image_path).convert("RGB"), + msgs=[ + { + "role": "user", + "content": "What is in this picture?" + } + ], + tokenizer=tokenizer +) # 模型推理 +print('Output after quantization:',response) +print('Inference time after quantization:',time.time()-start) +print(f"GPU memory usage after quantization: {round(gpu_usage/1024,2)}GB") + +""" +Expected output: + + Output after quantization: This picture contains specific parts of an airplane, including wings, engines, and tail sections. These components are key parts of large commercial aircraft. + The wings support lift during flight, while the engines provide thrust to move the plane forward. The tail section is typically used for stabilizing flight and plays a role in airline branding. + The design and color of the airplane indicate that it belongs to Air China, likely a passenger aircraft due to its large size and twin-engine configuration. + There are no markings or insignia on the airplane indicating the specific model or registration number; such information may require additional context or a clearer perspective to discern. + Inference time after quantization: 8.583992719650269 seconds + GPU memory usage after quantization: 6.41 GB +""" + +# Save the model and tokenizer +os.makedirs(save_path, exist_ok=True) +model.save_pretrained(save_path, safe_serialization=True) +tokenizer.save_pretrained(save_path) \ No newline at end of file diff --git a/MiniCPM-V/requirements.txt b/MiniCPM-V/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..86188689bf4dd6e6a1b9b82439d0cb9e02cce77a --- /dev/null +++ b/MiniCPM-V/requirements.txt @@ -0,0 +1,34 @@ +packaging==23.2 +addict==2.4.0 +editdistance==0.6.2 +einops==0.7.0 +fairscale==0.4.0 +jsonlines==4.0.0 +markdown2==2.4.10 +matplotlib==3.7.4 +more_itertools==10.1.0 +nltk==3.8.1 +numpy==1.24.4 +opencv_python_headless==4.5.5.64 +openpyxl==3.1.2 +Pillow==10.1.0 +sacrebleu==2.3.2 +seaborn==0.13.0 +shortuuid==1.0.11 +#spacy==3.7.2 +timm==0.9.10 +torch==2.1.2 +torchvision==0.16.2 +tqdm==4.66.1 +protobuf==4.25.0 +transformers==4.40.0 +typing_extensions==4.8.0 +uvicorn==0.24.0.post1 +#xformers==0.0.22.post7 +#flash_attn==2.3.4 +sentencepiece==0.1.99 +accelerate==0.30.1 +socksio==1.0.0 +gradio==4.41.0 +gradio_client +http://thunlp.oss-cn-qingdao.aliyuncs.com/multi_modal/never_delete/modelscope_studio-0.4.0.9-py3-none-any.whl diff --git a/MiniCPM-V/web_demo.py b/MiniCPM-V/web_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..668dcf4ddd9ac201d2bbe93cefcaffee1be123c1 --- /dev/null +++ b/MiniCPM-V/web_demo.py @@ -0,0 +1,264 @@ +#!/usr/bin/env python +# encoding: utf-8 +import gradio as gr +from PIL import Image +import traceback +import re +import torch +import argparse +from transformers import AutoModel, AutoTokenizer + +# README, How to run demo on different devices +# For Nvidia GPUs support BF16 (like A100, H100, RTX3090) +# python web_demo.py --device cuda --dtype bf16 + +# For Nvidia GPUs do NOT support BF16 (like V100, T4, RTX2080) +# python web_demo.py --device cuda --dtype fp16 + +# For Mac with MPS (Apple silicon or AMD GPUs). +# PYTORCH_ENABLE_MPS_FALLBACK=1 python web_demo.py --device mps --dtype fp16 + +# Argparser +parser = argparse.ArgumentParser(description='demo') +parser.add_argument('--device', type=str, default='cuda', help='cuda or mps') +parser.add_argument('--dtype', type=str, default='bf16', help='bf16 or fp16') +args = parser.parse_args() +device = args.device +assert device in ['cuda', 'mps'] +if args.dtype == 'bf16': + if device == 'mps': + print('Warning: MPS does not support bf16, will use fp16 instead') + dtype = torch.float16 + else: + dtype = torch.bfloat16 +else: + dtype = torch.float16 + +# Load model +model_path = 'openbmb/MiniCPM-V-2' +model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(dtype=torch.bfloat16) +tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + +model = model.to(device=device, dtype=dtype) +model.eval() + + + +ERROR_MSG = "Error, please retry" +model_name = 'MiniCPM-V 2.0' + +form_radio = { + 'choices': ['Beam Search', 'Sampling'], + #'value': 'Beam Search', + 'value': 'Sampling', + 'interactive': True, + 'label': 'Decode Type' +} +# Beam Form +num_beams_slider = { + 'minimum': 0, + 'maximum': 5, + 'value': 3, + 'step': 1, + 'interactive': True, + 'label': 'Num Beams' +} +repetition_penalty_slider = { + 'minimum': 0, + 'maximum': 3, + 'value': 1.2, + 'step': 0.01, + 'interactive': True, + 'label': 'Repetition Penalty' +} +repetition_penalty_slider2 = { + 'minimum': 0, + 'maximum': 3, + 'value': 1.05, + 'step': 0.01, + 'interactive': True, + 'label': 'Repetition Penalty' +} +max_new_tokens_slider = { + 'minimum': 1, + 'maximum': 4096, + 'value': 1024, + 'step': 1, + 'interactive': True, + 'label': 'Max New Tokens' +} + +top_p_slider = { + 'minimum': 0, + 'maximum': 1, + 'value': 0.8, + 'step': 0.05, + 'interactive': True, + 'label': 'Top P' +} +top_k_slider = { + 'minimum': 0, + 'maximum': 200, + 'value': 100, + 'step': 1, + 'interactive': True, + 'label': 'Top K' +} +temperature_slider = { + 'minimum': 0, + 'maximum': 2, + 'value': 0.7, + 'step': 0.05, + 'interactive': True, + 'label': 'Temperature' +} + + +def create_component(params, comp='Slider'): + if comp == 'Slider': + return gr.Slider( + minimum=params['minimum'], + maximum=params['maximum'], + value=params['value'], + step=params['step'], + interactive=params['interactive'], + label=params['label'] + ) + elif comp == 'Radio': + return gr.Radio( + choices=params['choices'], + value=params['value'], + interactive=params['interactive'], + label=params['label'] + ) + elif comp == 'Button': + return gr.Button( + value=params['value'], + interactive=True + ) + + +def chat(img, msgs, ctx, params=None, vision_hidden_states=None): + default_params = {"num_beams":3, "repetition_penalty": 1.2, "max_new_tokens": 1024} + if params is None: + params = default_params + if img is None: + return -1, "Error, invalid image, please upload a new image", None, None + try: + image = img.convert('RGB') + answer, context, _ = model.chat( + image=image, + msgs=msgs, + context=None, + tokenizer=tokenizer, + **params + ) + res = re.sub(r'(.*)', '', answer) + res = res.replace('', '') + res = res.replace('', '') + res = res.replace('', '') + answer = res.replace('', '') + return 0, answer, None, None + except Exception as err: + print(err) + traceback.print_exc() + return -1, ERROR_MSG, None, None + + +def upload_img(image, _chatbot, _app_session): + image = Image.fromarray(image) + + _app_session['sts']=None + _app_session['ctx']=[] + _app_session['img']=image + _chatbot.append(('', 'Image uploaded successfully, you can talk to me now')) + return _chatbot, _app_session + + +def respond(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature): + if _app_cfg.get('ctx', None) is None: + _chat_bot.append((_question, 'Please upload an image to start')) + return '', _chat_bot, _app_cfg + + _context = _app_cfg['ctx'].copy() + if _context: + _context.append({"role": "user", "content": _question}) + else: + _context = [{"role": "user", "content": _question}] + print(':', _question) + + if params_form == 'Beam Search': + params = { + 'sampling': False, + 'num_beams': num_beams, + 'repetition_penalty': repetition_penalty, + "max_new_tokens": 896 + } + else: + params = { + 'sampling': True, + 'top_p': top_p, + 'top_k': top_k, + 'temperature': temperature, + 'repetition_penalty': repetition_penalty_2, + "max_new_tokens": 896 + } + code, _answer, _, sts = chat(_app_cfg['img'], _context, None, params) + print(':', _answer) + + _context.append({"role": "assistant", "content": _answer}) + _chat_bot.append((_question, _answer)) + if code == 0: + _app_cfg['ctx']=_context + _app_cfg['sts']=sts + return '', _chat_bot, _app_cfg + + +def regenerate_button_clicked(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature): + if len(_chat_bot) <= 1: + _chat_bot.append(('Regenerate', 'No question for regeneration.')) + return '', _chat_bot, _app_cfg + elif _chat_bot[-1][0] == 'Regenerate': + return '', _chat_bot, _app_cfg + else: + _question = _chat_bot[-1][0] + _chat_bot = _chat_bot[:-1] + _app_cfg['ctx'] = _app_cfg['ctx'][:-2] + return respond(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature) + + + +with gr.Blocks() as demo: + with gr.Row(): + with gr.Column(scale=1, min_width=300): + params_form = create_component(form_radio, comp='Radio') + with gr.Accordion("Beam Search") as beams_according: + num_beams = create_component(num_beams_slider) + repetition_penalty = create_component(repetition_penalty_slider) + with gr.Accordion("Sampling") as sampling_according: + top_p = create_component(top_p_slider) + top_k = create_component(top_k_slider) + temperature = create_component(temperature_slider) + repetition_penalty_2 = create_component(repetition_penalty_slider2) + regenerate = create_component({'value': 'Regenerate'}, comp='Button') + with gr.Column(scale=3, min_width=500): + app_session = gr.State({'sts':None,'ctx':None,'img':None}) + bt_pic = gr.Image(label="Upload an image to start") + chat_bot = gr.Chatbot(label=f"Chat with {model_name}") + txt_message = gr.Textbox(label="Input text") + + regenerate.click( + regenerate_button_clicked, + [txt_message, chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature], + [txt_message, chat_bot, app_session] + ) + txt_message.submit( + respond, + [txt_message, chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature], + [txt_message, chat_bot, app_session] + ) + bt_pic.upload(lambda: None, None, chat_bot, queue=False).then(upload_img, inputs=[bt_pic,chat_bot,app_session], outputs=[chat_bot,app_session]) + +# launch +demo.launch(share=False, debug=True, show_api=False, server_port=8080, server_name="0.0.0.0") + diff --git a/MiniCPM-V/web_demo_2.5.py b/MiniCPM-V/web_demo_2.5.py new file mode 100644 index 0000000000000000000000000000000000000000..6f6b81af37f1063a2f34d13484211713728f80b6 --- /dev/null +++ b/MiniCPM-V/web_demo_2.5.py @@ -0,0 +1,256 @@ +#!/usr/bin/env python +# encoding: utf-8 +import gradio as gr +from PIL import Image +import traceback +import re +import torch +import argparse +from transformers import AutoModel, AutoTokenizer + +# README, How to run demo on different devices + +# For Nvidia GPUs. +# python web_demo_2.5.py --device cuda + +# For Mac with MPS (Apple silicon or AMD GPUs). +# PYTORCH_ENABLE_MPS_FALLBACK=1 python web_demo_2.5.py --device mps + +# Argparser +parser = argparse.ArgumentParser(description='demo') +parser.add_argument('--device', type=str, default='cuda', help='cuda or mps') +args = parser.parse_args() +device = args.device +assert device in ['cuda', 'mps'] + +# Load model +model_path = 'openbmb/MiniCPM-Llama3-V-2_5' +if 'int4' in model_path: + if device == 'mps': + print('Error: running int4 model with bitsandbytes on Mac is not supported right now.') + exit() + model = AutoModel.from_pretrained(model_path, trust_remote_code=True) +else: + model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.float16, device_map=device) +tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) +model.eval() + + + +ERROR_MSG = "Error, please retry" +model_name = 'MiniCPM-V 2.5' + +form_radio = { + 'choices': ['Beam Search', 'Sampling'], + #'value': 'Beam Search', + 'value': 'Sampling', + 'interactive': True, + 'label': 'Decode Type' +} +# Beam Form +num_beams_slider = { + 'minimum': 0, + 'maximum': 5, + 'value': 3, + 'step': 1, + 'interactive': True, + 'label': 'Num Beams' +} +repetition_penalty_slider = { + 'minimum': 0, + 'maximum': 3, + 'value': 1.2, + 'step': 0.01, + 'interactive': True, + 'label': 'Repetition Penalty' +} +repetition_penalty_slider2 = { + 'minimum': 0, + 'maximum': 3, + 'value': 1.05, + 'step': 0.01, + 'interactive': True, + 'label': 'Repetition Penalty' +} +max_new_tokens_slider = { + 'minimum': 1, + 'maximum': 4096, + 'value': 1024, + 'step': 1, + 'interactive': True, + 'label': 'Max New Tokens' +} + +top_p_slider = { + 'minimum': 0, + 'maximum': 1, + 'value': 0.8, + 'step': 0.05, + 'interactive': True, + 'label': 'Top P' +} +top_k_slider = { + 'minimum': 0, + 'maximum': 200, + 'value': 100, + 'step': 1, + 'interactive': True, + 'label': 'Top K' +} +temperature_slider = { + 'minimum': 0, + 'maximum': 2, + 'value': 0.7, + 'step': 0.05, + 'interactive': True, + 'label': 'Temperature' +} + + +def create_component(params, comp='Slider'): + if comp == 'Slider': + return gr.Slider( + minimum=params['minimum'], + maximum=params['maximum'], + value=params['value'], + step=params['step'], + interactive=params['interactive'], + label=params['label'] + ) + elif comp == 'Radio': + return gr.Radio( + choices=params['choices'], + value=params['value'], + interactive=params['interactive'], + label=params['label'] + ) + elif comp == 'Button': + return gr.Button( + value=params['value'], + interactive=True + ) + + +def chat(img, msgs, ctx, params=None, vision_hidden_states=None): + default_params = {"num_beams":3, "repetition_penalty": 1.2, "max_new_tokens": 1024} + if params is None: + params = default_params + if img is None: + return -1, "Error, invalid image, please upload a new image", None, None + try: + image = img.convert('RGB') + answer = model.chat( + image=image, + msgs=msgs, + tokenizer=tokenizer, + **params + ) + res = re.sub(r'(.*)', '', answer) + res = res.replace('', '') + res = res.replace('', '') + res = res.replace('', '') + answer = res.replace('', '') + return 0, answer, None, None + except Exception as err: + print(err) + traceback.print_exc() + return -1, ERROR_MSG, None, None + + +def upload_img(image, _chatbot, _app_session): + image = Image.fromarray(image) + + _app_session['sts']=None + _app_session['ctx']=[] + _app_session['img']=image + _chatbot.append(('', 'Image uploaded successfully, you can talk to me now')) + return _chatbot, _app_session + + +def respond(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature): + if _app_cfg.get('ctx', None) is None: + _chat_bot.append((_question, 'Please upload an image to start')) + return '', _chat_bot, _app_cfg + + _context = _app_cfg['ctx'].copy() + if _context: + _context.append({"role": "user", "content": _question}) + else: + _context = [{"role": "user", "content": _question}] + print(':', _question) + + if params_form == 'Beam Search': + params = { + 'sampling': False, + 'num_beams': num_beams, + 'repetition_penalty': repetition_penalty, + "max_new_tokens": 896 + } + else: + params = { + 'sampling': True, + 'top_p': top_p, + 'top_k': top_k, + 'temperature': temperature, + 'repetition_penalty': repetition_penalty_2, + "max_new_tokens": 896 + } + code, _answer, _, sts = chat(_app_cfg['img'], _context, None, params) + print(':', _answer) + + _context.append({"role": "assistant", "content": _answer}) + _chat_bot.append((_question, _answer)) + if code == 0: + _app_cfg['ctx']=_context + _app_cfg['sts']=sts + return '', _chat_bot, _app_cfg + + +def regenerate_button_clicked(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature): + if len(_chat_bot) <= 1: + _chat_bot.append(('Regenerate', 'No question for regeneration.')) + return '', _chat_bot, _app_cfg + elif _chat_bot[-1][0] == 'Regenerate': + return '', _chat_bot, _app_cfg + else: + _question = _chat_bot[-1][0] + _chat_bot = _chat_bot[:-1] + _app_cfg['ctx'] = _app_cfg['ctx'][:-2] + return respond(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature) + + + +with gr.Blocks() as demo: + with gr.Row(): + with gr.Column(scale=1, min_width=300): + params_form = create_component(form_radio, comp='Radio') + with gr.Accordion("Beam Search") as beams_according: + num_beams = create_component(num_beams_slider) + repetition_penalty = create_component(repetition_penalty_slider) + with gr.Accordion("Sampling") as sampling_according: + top_p = create_component(top_p_slider) + top_k = create_component(top_k_slider) + temperature = create_component(temperature_slider) + repetition_penalty_2 = create_component(repetition_penalty_slider2) + regenerate = create_component({'value': 'Regenerate'}, comp='Button') + with gr.Column(scale=3, min_width=500): + app_session = gr.State({'sts':None,'ctx':None,'img':None}) + bt_pic = gr.Image(label="Upload an image to start") + chat_bot = gr.Chatbot(label=f"Chat with {model_name}") + txt_message = gr.Textbox(label="Input text") + + regenerate.click( + regenerate_button_clicked, + [txt_message, chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature], + [txt_message, chat_bot, app_session] + ) + txt_message.submit( + respond, + [txt_message, chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature], + [txt_message, chat_bot, app_session] + ) + bt_pic.upload(lambda: None, None, chat_bot, queue=False).then(upload_img, inputs=[bt_pic,chat_bot,app_session], outputs=[chat_bot,app_session]) + +# launch +demo.launch(share=False, debug=True, show_api=False, server_port=8080, server_name="0.0.0.0") + diff --git a/MiniCPM-V/web_demo_2.6.py b/MiniCPM-V/web_demo_2.6.py new file mode 100644 index 0000000000000000000000000000000000000000..2bebc19d10c829f20bd27d6aa2d7ae36caf5afec --- /dev/null +++ b/MiniCPM-V/web_demo_2.6.py @@ -0,0 +1,557 @@ +#!/usr/bin/env python +# encoding: utf-8 +import torch +import argparse +from transformers import AutoModel, AutoTokenizer +import gradio as gr +from PIL import Image +from decord import VideoReader, cpu +import io +import os +import copy +import requests +import base64 +import json +import traceback +import re +import modelscope_studio as mgr + + +# README, How to run demo on different devices + +# For Nvidia GPUs. +# python web_demo_2.6.py --device cuda + +# For Mac with MPS (Apple silicon or AMD GPUs). +# PYTORCH_ENABLE_MPS_FALLBACK=1 python web_demo_2.6.py --device mps + +# Argparser +parser = argparse.ArgumentParser(description='demo') +parser.add_argument('--device', type=str, default='cuda', help='cuda or mps') +parser.add_argument('--multi-gpus', action='store_true', default=False, help='use multi-gpus') +args = parser.parse_args() +device = args.device +assert device in ['cuda', 'mps'] + +# Load model +model_path = 'openbmb/MiniCPM-V-2_6' +if 'int4' in model_path: + if device == 'mps': + print('Error: running int4 model with bitsandbytes on Mac is not supported right now.') + exit() + model = AutoModel.from_pretrained(model_path, trust_remote_code=True) +else: + if args.multi_gpus: + from accelerate import load_checkpoint_and_dispatch, init_empty_weights, infer_auto_device_map + with init_empty_weights(): + model = AutoModel.from_pretrained(model_path, trust_remote_code=True, attn_implementation='sdpa', torch_dtype=torch.bfloat16) + device_map = infer_auto_device_map(model, max_memory={0: "10GB", 1: "10GB"}, + no_split_module_classes=['SiglipVisionTransformer', 'Qwen2DecoderLayer']) + device_id = device_map["llm.model.embed_tokens"] + device_map["llm.lm_head"] = device_id # firtt and last layer should be in same device + device_map["vpm"] = device_id + device_map["resampler"] = device_id + device_id2 = device_map["llm.model.layers.26"] + device_map["llm.model.layers.8"] = device_id2 + device_map["llm.model.layers.9"] = device_id2 + device_map["llm.model.layers.10"] = device_id2 + device_map["llm.model.layers.11"] = device_id2 + device_map["llm.model.layers.12"] = device_id2 + device_map["llm.model.layers.13"] = device_id2 + device_map["llm.model.layers.14"] = device_id2 + device_map["llm.model.layers.15"] = device_id2 + device_map["llm.model.layers.16"] = device_id2 + #print(device_map) + + model = load_checkpoint_and_dispatch(model, model_path, dtype=torch.bfloat16, device_map=device_map) + else: + model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16) + model = model.to(device=device) +tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) +model.eval() + + + + +ERROR_MSG = "Error, please retry" +model_name = 'MiniCPM-V 2.6' +MAX_NUM_FRAMES = 64 +IMAGE_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'} +VIDEO_EXTENSIONS = {'.mp4', '.mkv', '.mov', '.avi', '.flv', '.wmv', '.webm', '.m4v'} + +def get_file_extension(filename): + return os.path.splitext(filename)[1].lower() + +def is_image(filename): + return get_file_extension(filename) in IMAGE_EXTENSIONS + +def is_video(filename): + return get_file_extension(filename) in VIDEO_EXTENSIONS + + +form_radio = { + 'choices': ['Beam Search', 'Sampling'], + #'value': 'Beam Search', + 'value': 'Sampling', + 'interactive': True, + 'label': 'Decode Type' +} + + +def create_component(params, comp='Slider'): + if comp == 'Slider': + return gr.Slider( + minimum=params['minimum'], + maximum=params['maximum'], + value=params['value'], + step=params['step'], + interactive=params['interactive'], + label=params['label'] + ) + elif comp == 'Radio': + return gr.Radio( + choices=params['choices'], + value=params['value'], + interactive=params['interactive'], + label=params['label'] + ) + elif comp == 'Button': + return gr.Button( + value=params['value'], + interactive=True + ) + + +def create_multimodal_input(upload_image_disabled=False, upload_video_disabled=False): + return mgr.MultimodalInput(upload_image_button_props={'label': 'Upload Image', 'disabled': upload_image_disabled, 'file_count': 'multiple'}, + upload_video_button_props={'label': 'Upload Video', 'disabled': upload_video_disabled, 'file_count': 'single'}, + submit_button_props={'label': 'Submit'}) + + +def chat(img, msgs, ctx, params=None, vision_hidden_states=None): + try: + print('msgs:', msgs) + answer = model.chat( + image=None, + msgs=msgs, + tokenizer=tokenizer, + **params + ) + res = re.sub(r'(.*)', '', answer) + res = res.replace('', '') + res = res.replace('', '') + res = res.replace('', '') + answer = res.replace('', '') + print('answer:', answer) + return 0, answer, None, None + except Exception as e: + print(e) + traceback.print_exc() + return -1, ERROR_MSG, None, None + + +def encode_image(image): + if not isinstance(image, Image.Image): + if hasattr(image, 'path'): + image = Image.open(image.path).convert("RGB") + else: + image = Image.open(image.file.path).convert("RGB") + # resize to max_size + max_size = 448*16 + if max(image.size) > max_size: + w,h = image.size + if w > h: + new_w = max_size + new_h = int(h * max_size / w) + else: + new_h = max_size + new_w = int(w * max_size / h) + image = image.resize((new_w, new_h), resample=Image.BICUBIC) + return image + ## save by BytesIO and convert to base64 + #buffered = io.BytesIO() + #image.save(buffered, format="png") + #im_b64 = base64.b64encode(buffered.getvalue()).decode() + #return {"type": "image", "pairs": im_b64} + + +def encode_video(video): + def uniform_sample(l, n): + gap = len(l) / n + idxs = [int(i * gap + gap / 2) for i in range(n)] + return [l[i] for i in idxs] + + if hasattr(video, 'path'): + vr = VideoReader(video.path, ctx=cpu(0)) + else: + vr = VideoReader(video.file.path, ctx=cpu(0)) + sample_fps = round(vr.get_avg_fps() / 1) # FPS + frame_idx = [i for i in range(0, len(vr), sample_fps)] + if len(frame_idx)>MAX_NUM_FRAMES: + frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES) + video = vr.get_batch(frame_idx).asnumpy() + video = [Image.fromarray(v.astype('uint8')) for v in video] + video = [encode_image(v) for v in video] + print('video frames:', len(video)) + return video + + +def check_mm_type(mm_file): + if hasattr(mm_file, 'path'): + path = mm_file.path + else: + path = mm_file.file.path + if is_image(path): + return "image" + if is_video(path): + return "video" + return None + + +def encode_mm_file(mm_file): + if check_mm_type(mm_file) == 'image': + return [encode_image(mm_file)] + if check_mm_type(mm_file) == 'video': + return encode_video(mm_file) + return None + +def make_text(text): + #return {"type": "text", "pairs": text} # # For remote call + return text + +def encode_message(_question): + files = _question.files + question = _question.text + pattern = r"\[mm_media\]\d+\[/mm_media\]" + matches = re.split(pattern, question) + message = [] + if len(matches) != len(files) + 1: + gr.Warning("Number of Images not match the placeholder in text, please refresh the page to restart!") + assert len(matches) == len(files) + 1 + + text = matches[0].strip() + if text: + message.append(make_text(text)) + for i in range(len(files)): + message += encode_mm_file(files[i]) + text = matches[i + 1].strip() + if text: + message.append(make_text(text)) + return message + + +def check_has_videos(_question): + images_cnt = 0 + videos_cnt = 0 + for file in _question.files: + if check_mm_type(file) == "image": + images_cnt += 1 + else: + videos_cnt += 1 + return images_cnt, videos_cnt + + +def count_video_frames(_context): + num_frames = 0 + for message in _context: + for item in message["content"]: + #if item["type"] == "image": # For remote call + if isinstance(item, Image.Image): + num_frames += 1 + return num_frames + + +def respond(_question, _chat_bot, _app_cfg, params_form): + _context = _app_cfg['ctx'].copy() + _context.append({'role': 'user', 'content': encode_message(_question)}) + + images_cnt = _app_cfg['images_cnt'] + videos_cnt = _app_cfg['videos_cnt'] + files_cnts = check_has_videos(_question) + if files_cnts[1] + videos_cnt > 1 or (files_cnts[1] + videos_cnt == 1 and files_cnts[0] + images_cnt > 0): + gr.Warning("Only supports single video file input right now!") + return _question, _chat_bot, _app_cfg + + if params_form == 'Beam Search': + params = { + 'sampling': False, + 'num_beams': 3, + 'repetition_penalty': 1.2, + "max_new_tokens": 2048 + } + else: + params = { + 'sampling': True, + 'top_p': 0.8, + 'top_k': 100, + 'temperature': 0.7, + 'repetition_penalty': 1.05, + "max_new_tokens": 2048 + } + + if files_cnts[1] + videos_cnt > 0: + params["max_inp_length"] = 4352 # 4096+256 + params["use_image_id"] = False + params["max_slice_nums"] = 1 if count_video_frames(_context) > 16 else 2 + + code, _answer, _, sts = chat("", _context, None, params) + + images_cnt += files_cnts[0] + videos_cnt += files_cnts[1] + _context.append({"role": "assistant", "content": [make_text(_answer)]}) + _chat_bot.append((_question, _answer)) + if code == 0: + _app_cfg['ctx']=_context + _app_cfg['sts']=sts + _app_cfg['images_cnt'] = images_cnt + _app_cfg['videos_cnt'] = videos_cnt + + upload_image_disabled = videos_cnt > 0 + upload_video_disabled = videos_cnt > 0 or images_cnt > 0 + return create_multimodal_input(upload_image_disabled, upload_video_disabled), _chat_bot, _app_cfg + + +def fewshot_add_demonstration(_image, _user_message, _assistant_message, _chat_bot, _app_cfg): + ctx = _app_cfg["ctx"] + message_item = [] + if _image is not None: + image = Image.open(_image).convert("RGB") + ctx.append({"role": "user", "content": [encode_image(image), make_text(_user_message)]}) + message_item.append({"text": "[mm_media]1[/mm_media]" + _user_message, "files": [_image]}) + else: + if _user_message: + ctx.append({"role": "user", "content": [make_text(_user_message)]}) + message_item.append({"text": _user_message, "files": []}) + else: + message_item.append(None) + if _assistant_message: + ctx.append({"role": "assistant", "content": [make_text(_assistant_message)]}) + message_item.append({"text": _assistant_message, "files": []}) + else: + message_item.append(None) + + _chat_bot.append(message_item) + return None, "", "", _chat_bot, _app_cfg + + +def fewshot_respond(_image, _user_message, _chat_bot, _app_cfg, params_form): + user_message_contents = [] + _context = _app_cfg["ctx"].copy() + if _image: + image = Image.open(_image).convert("RGB") + user_message_contents += [encode_image(image)] + if _user_message: + user_message_contents += [make_text(_user_message)] + if user_message_contents: + _context.append({"role": "user", "content": user_message_contents}) + + if params_form == 'Beam Search': + params = { + 'sampling': False, + 'num_beams': 3, + 'repetition_penalty': 1.2, + "max_new_tokens": 2048 + } + else: + params = { + 'sampling': True, + 'top_p': 0.8, + 'top_k': 100, + 'temperature': 0.7, + 'repetition_penalty': 1.05, + "max_new_tokens": 2048 + } + + code, _answer, _, sts = chat("", _context, None, params) + + _context.append({"role": "assistant", "content": [make_text(_answer)]}) + + if _image: + _chat_bot.append([ + {"text": "[mm_media]1[/mm_media]" + _user_message, "files": [_image]}, + {"text": _answer, "files": []} + ]) + else: + _chat_bot.append([ + {"text": _user_message, "files": [_image]}, + {"text": _answer, "files": []} + ]) + if code == 0: + _app_cfg['ctx']=_context + _app_cfg['sts']=sts + return None, '', '', _chat_bot, _app_cfg + + +def regenerate_button_clicked(_question, _image, _user_message, _assistant_message, _chat_bot, _app_cfg, params_form): + if len(_chat_bot) <= 1 or not _chat_bot[-1][1]: + gr.Warning('No question for regeneration.') + return '', _image, _user_message, _assistant_message, _chat_bot, _app_cfg + if _app_cfg["chat_type"] == "Chat": + images_cnt = _app_cfg['images_cnt'] + videos_cnt = _app_cfg['videos_cnt'] + _question = _chat_bot[-1][0] + _chat_bot = _chat_bot[:-1] + _app_cfg['ctx'] = _app_cfg['ctx'][:-2] + files_cnts = check_has_videos(_question) + images_cnt -= files_cnts[0] + videos_cnt -= files_cnts[1] + _app_cfg['images_cnt'] = images_cnt + _app_cfg['videos_cnt'] = videos_cnt + upload_image_disabled = videos_cnt > 0 + upload_video_disabled = videos_cnt > 0 or images_cnt > 0 + _question, _chat_bot, _app_cfg = respond(_question, _chat_bot, _app_cfg, params_form) + return _question, _image, _user_message, _assistant_message, _chat_bot, _app_cfg + else: + last_message = _chat_bot[-1][0] + last_image = None + last_user_message = '' + if last_message.text: + last_user_message = last_message.text + if last_message.files: + last_image = last_message.files[0].file.path + _chat_bot = _chat_bot[:-1] + _app_cfg['ctx'] = _app_cfg['ctx'][:-2] + _image, _user_message, _assistant_message, _chat_bot, _app_cfg = fewshot_respond(last_image, last_user_message, _chat_bot, _app_cfg, params_form) + return _question, _image, _user_message, _assistant_message, _chat_bot, _app_cfg + + +def flushed(): + return gr.update(interactive=True) + + +def clear(txt_message, chat_bot, app_session): + txt_message.files.clear() + txt_message.text = '' + chat_bot = copy.deepcopy(init_conversation) + app_session['sts'] = None + app_session['ctx'] = [] + app_session['images_cnt'] = 0 + app_session['videos_cnt'] = 0 + return create_multimodal_input(), chat_bot, app_session, None, '', '' + + +def select_chat_type(_tab, _app_cfg): + _app_cfg["chat_type"] = _tab + return _app_cfg + + +init_conversation = [ + [ + None, + { + # The first message of bot closes the typewriter. + "text": "You can talk to me now", + "flushing": False + } + ], +] + + +css = """ +video { height: auto !important; } +.example label { font-size: 16px;} +""" + +introduction = """ + +## Features: +1. Chat with single image +2. Chat with multiple images +3. Chat with video +4. In-context few-shot learning + +Click `How to use` tab to see examples. +""" + + +with gr.Blocks(css=css) as demo: + with gr.Tab(model_name): + with gr.Row(): + with gr.Column(scale=1, min_width=300): + gr.Markdown(value=introduction) + params_form = create_component(form_radio, comp='Radio') + regenerate = create_component({'value': 'Regenerate'}, comp='Button') + clear_button = create_component({'value': 'Clear History'}, comp='Button') + + with gr.Column(scale=3, min_width=500): + app_session = gr.State({'sts':None,'ctx':[], 'images_cnt': 0, 'videos_cnt': 0, 'chat_type': 'Chat'}) + chat_bot = mgr.Chatbot(label=f"Chat with {model_name}", value=copy.deepcopy(init_conversation), height=600, flushing=False, bubble_full_width=False) + + with gr.Tab("Chat") as chat_tab: + txt_message = create_multimodal_input() + chat_tab_label = gr.Textbox(value="Chat", interactive=False, visible=False) + + txt_message.submit( + respond, + [txt_message, chat_bot, app_session, params_form], + [txt_message, chat_bot, app_session] + ) + + with gr.Tab("Few Shot") as fewshot_tab: + fewshot_tab_label = gr.Textbox(value="Few Shot", interactive=False, visible=False) + with gr.Row(): + with gr.Column(scale=1): + image_input = gr.Image(type="filepath", sources=["upload"]) + with gr.Column(scale=3): + user_message = gr.Textbox(label="User") + assistant_message = gr.Textbox(label="Assistant") + with gr.Row(): + add_demonstration_button = gr.Button("Add Example") + generate_button = gr.Button(value="Generate", variant="primary") + add_demonstration_button.click( + fewshot_add_demonstration, + [image_input, user_message, assistant_message, chat_bot, app_session], + [image_input, user_message, assistant_message, chat_bot, app_session] + ) + generate_button.click( + fewshot_respond, + [image_input, user_message, chat_bot, app_session, params_form], + [image_input, user_message, assistant_message, chat_bot, app_session] + ) + + chat_tab.select( + select_chat_type, + [chat_tab_label, app_session], + [app_session] + ) + chat_tab.select( # do clear + clear, + [txt_message, chat_bot, app_session], + [txt_message, chat_bot, app_session, image_input, user_message, assistant_message] + ) + fewshot_tab.select( + select_chat_type, + [fewshot_tab_label, app_session], + [app_session] + ) + fewshot_tab.select( # do clear + clear, + [txt_message, chat_bot, app_session], + [txt_message, chat_bot, app_session, image_input, user_message, assistant_message] + ) + chat_bot.flushed( + flushed, + outputs=[txt_message] + ) + regenerate.click( + regenerate_button_clicked, + [txt_message, image_input, user_message, assistant_message, chat_bot, app_session, params_form], + [txt_message, image_input, user_message, assistant_message, chat_bot, app_session] + ) + clear_button.click( + clear, + [txt_message, chat_bot, app_session], + [txt_message, chat_bot, app_session, image_input, user_message, assistant_message] + ) + + with gr.Tab("How to use"): + with gr.Column(): + with gr.Row(): + image_example = gr.Image(value="http://thunlp.oss-cn-qingdao.aliyuncs.com/multi_modal/never_delete/m_bear2.gif", label='1. Chat with single or multiple images', interactive=False, width=400, elem_classes="example") + example2 = gr.Image(value="http://thunlp.oss-cn-qingdao.aliyuncs.com/multi_modal/never_delete/video2.gif", label='2. Chat with video', interactive=False, width=400, elem_classes="example") + example3 = gr.Image(value="http://thunlp.oss-cn-qingdao.aliyuncs.com/multi_modal/never_delete/fshot.gif", label='3. Few shot', interactive=False, width=400, elem_classes="example") + + +# launch +demo.launch(share=False, debug=True, show_api=False, server_port=8885, server_name="0.0.0.0") + diff --git a/MiniCPM-V/web_demo_streamlit-2_5.py b/MiniCPM-V/web_demo_streamlit-2_5.py new file mode 100644 index 0000000000000000000000000000000000000000..bf100ce569e87c8934d95e620bd0d92be024caf9 --- /dev/null +++ b/MiniCPM-V/web_demo_streamlit-2_5.py @@ -0,0 +1,108 @@ +import streamlit as st +from PIL import Image +import torch +from transformers import AutoModel, AutoTokenizer + +# Model path +model_path = "openbmb/MiniCPM-Llama3-V-2_5" + +# User and assistant names +U_NAME = "User" +A_NAME = "Assistant" + +# Set page configuration +st.set_page_config( + page_title="MiniCPM-Llama3-V-2_5 Streamlit", + page_icon=":robot:", + layout="wide" +) + + +# Load model and tokenizer +@st.cache_resource +def load_model_and_tokenizer(): + print(f"load_model_and_tokenizer from {model_path}") + model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.float16).to(device="cuda") + tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + return model, tokenizer + + +# Initialize session state +if 'model' not in st.session_state: + st.session_state.model, st.session_state.tokenizer = load_model_and_tokenizer() + st.session_state.model.eval() + print("model and tokenizer had loaded completed!") + +# Initialize session state +if 'chat_history' not in st.session_state: + st.session_state.chat_history = [] + +# Sidebar settings +sidebar_name = st.sidebar.title("MiniCPM-Llama3-V-2_5 Streamlit") +max_length = st.sidebar.slider("max_length", 0, 4096, 2048, step=2) +repetition_penalty = st.sidebar.slider("repetition_penalty", 0.0, 2.0, 1.05, step=0.01) +top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01) +top_k = st.sidebar.slider("top_k", 0, 100, 100, step=1) +temperature = st.sidebar.slider("temperature", 0.0, 1.0, 0.7, step=0.01) + +# Clear chat history button +buttonClean = st.sidebar.button("Clear chat history", key="clean") +if buttonClean: + st.session_state.chat_history = [] + st.session_state.response = "" + if torch.cuda.is_available(): + torch.cuda.empty_cache() + st.rerun() + +# Display chat history +for i, message in enumerate(st.session_state.chat_history): + if message["role"] == "user": + with st.chat_message(name="user", avatar="user"): + if message["image"] is not None: + st.image(message["image"], caption='User uploaded image', width=448, use_column_width=False) + continue + elif message["content"] is not None: + st.markdown(message["content"]) + else: + with st.chat_message(name="model", avatar="assistant"): + st.markdown(message["content"]) + +# Select mode +selected_mode = st.sidebar.selectbox("Select mode", ["Text", "Image"]) +if selected_mode == "Image": + # Image mode + uploaded_image = st.sidebar.file_uploader("Upload image", key=1, type=["jpg", "jpeg", "png"], + accept_multiple_files=False) + if uploaded_image is not None: + st.image(uploaded_image, caption='User uploaded image', width=468, use_column_width=False) + # Add uploaded image to chat history + st.session_state.chat_history.append({"role": "user", "content": None, "image": uploaded_image}) + +# User input box +user_text = st.chat_input("Enter your question") +if user_text: + with st.chat_message(U_NAME, avatar="user"): + st.session_state.chat_history.append({"role": "user", "content": user_text, "image": None}) + st.markdown(f"{U_NAME}: {user_text}") + + # Generate reply using the model + model = st.session_state.model + tokenizer = st.session_state.tokenizer + + with st.chat_message(A_NAME, avatar="assistant"): + # If the previous message contains an image, pass the image to the model + if len(st.session_state.chat_history) > 1 and st.session_state.chat_history[-2]["image"] is not None: + uploaded_image = st.session_state.chat_history[-2]["image"] + imagefile = Image.open(uploaded_image).convert('RGB') + + msgs = [{"role": "user", "content": user_text}] + res = model.chat(image=imagefile, msgs=msgs, context=None, tokenizer=tokenizer, + sampling=True, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty, + temperature=temperature, stream=True) + + # Collect the generated_text str + generated_text = st.write_stream(res) + + st.session_state.chat_history.append({"role": "model", "content": generated_text, "image": None}) + + st.divider() diff --git a/MiniCPM-V/web_demo_streamlit.py b/MiniCPM-V/web_demo_streamlit.py new file mode 100644 index 0000000000000000000000000000000000000000..1b893e6e7579db1cb17caa84a4cc132ff65a3d3a --- /dev/null +++ b/MiniCPM-V/web_demo_streamlit.py @@ -0,0 +1,99 @@ +import streamlit as st +from PIL import Image +import torch +from transformers import AutoModel, AutoTokenizer + +# Model path +model_path = "openbmb/MiniCPM-V-2" + +# User and assistant names +U_NAME = "User" +A_NAME = "Assistant" + +# Set page configuration +st.set_page_config( + page_title="Minicpm-V-2 Streamlit", + page_icon=":robot:", + layout="wide" +) + +# Load model and tokenizer +@st.cache_resource +def load_model_and_tokenizer(): + print(f"load_model_and_tokenizer from {model_path}") + model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16).to( + device="cuda:0", dtype=torch.bfloat16) + tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + return model, tokenizer + +# Initialize session state +if 'model' not in st.session_state: + st.session_state.model, st.session_state.tokenizer = load_model_and_tokenizer() + print("model and tokenizer had loaded completed!") + +# Initialize session state +if 'chat_history' not in st.session_state: + st.session_state.chat_history = [] + +# Sidebar settings +sidebar_name = st.sidebar.title("Minicpm-V-2 Streamlit") +max_length = st.sidebar.slider("max_length", 0, 4096, 2048, step=2) +top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01) +temperature = st.sidebar.slider("temperature", 0.0, 1.0, 0.7, step=0.01) + +# Clear chat history button +buttonClean = st.sidebar.button("Clear chat history", key="clean") +if buttonClean: + st.session_state.chat_history = [] + st.session_state.response = "" + if torch.cuda.is_available(): + torch.cuda.empty_cache() + st.rerun() + +# Display chat history +for i, message in enumerate(st.session_state.chat_history): + if message["role"] == "user": + with st.chat_message(name="user", avatar="user"): + if message["image"] is not None: + st.image(message["image"], caption='User uploaded image', width=468, use_column_width=False) + continue + elif message["content"] is not None: + st.markdown(message["content"]) + else: + with st.chat_message(name="model", avatar="assistant"): + st.markdown(message["content"]) + +# Select mode +selected_mode = st.sidebar.selectbox("Select mode", ["Text", "Image"]) +if selected_mode == "Image": + # Image mode + uploaded_image = st.sidebar.file_uploader("Upload image", key=1, type=["jpg", "jpeg", "png"], accept_multiple_files=False) + if uploaded_image is not None: + st.image(uploaded_image, caption='User uploaded image', width=468, use_column_width=False) + # Add uploaded image to chat history + st.session_state.chat_history.append({"role": "user", "content": None, "image": uploaded_image}) + +# User input box +user_text = st.chat_input("Enter your question") +if user_text: + with st.chat_message(U_NAME, avatar="user"): + st.session_state.chat_history.append({"role": "user", "content": user_text, "image": None}) + st.markdown(f"{U_NAME}: {user_text}") + + # Generate reply using the model + model = st.session_state.model + tokenizer = st.session_state.tokenizer + + with st.chat_message(A_NAME, avatar="assistant"): + # If the previous message contains an image, pass the image to the model + if len(st.session_state.chat_history) > 1 and st.session_state.chat_history[-2]["image"] is not None: + uploaded_image = st.session_state.chat_history[-2]["image"] + imagefile = Image.open(uploaded_image).convert('RGB') + + msgs = [{"role": "user", "content": user_text}] + res, context, _ = model.chat(image=imagefile, msgs=msgs, context=None, tokenizer=tokenizer, + sampling=True,top_p=top_p,temperature=temperature) + st.markdown(f"{A_NAME}: {res}") + st.session_state.chat_history.append({"role": "model", "content": res, "image": None}) + + st.divider() diff --git a/MiniCPM_V_Test.py b/MiniCPM_V_Test.py new file mode 100644 index 0000000000000000000000000000000000000000..99ae6397723bf3cac28d2077c00de97e25b35544 --- /dev/null +++ b/MiniCPM_V_Test.py @@ -0,0 +1,12 @@ +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer +import XGBoost_utils + +ad = Image.open('Demo/004.jpg') +caption = XGBoost_utils.Caption_Generation(ad) +print(caption) + +test_topic = XGBoost_utils.Topic_emb(caption) +print(test_topic.shape) +print(test_topic) \ No newline at end of file diff --git a/Predict.py b/Predict.py new file mode 100644 index 0000000000000000000000000000000000000000..c552b267757096667293636f6225f98ef6e82be5 --- /dev/null +++ b/Predict.py @@ -0,0 +1,270 @@ +import cv2 as cv +import numpy as np +import torch +from gensim import models +import xgboost as xgb +import XGBoost_utils +import sys +import joblib +from DL_models import CustomResNet + +root = '/Users/jianpingye/Desktop/Marketing_Research/XGBoost_Gaze_Prediction_Platform/Gaze-Time-Prediction-for-Advertisement/XGBoost_Prediction_Model' +sys.path.append(root) + +#Ad/Brand Gaze Prediction + +#Now the model is only able to process magazine images or images with full-page counterpages +#Please indicate where is the ad by ad_location parameter: left <- ad_location=0, right <- ad_location=1; otherwise, set it as None +def Ad_Gaze_Prediction(input_ad_path, input_ctpg_path, ad_location, + text_detection_model_path, LDA_model_pth, training_ad_text_dictionary_path, training_lang_preposition_path, + training_language, ad_embeddings, ctpg_embeddings, + surface_sizes=None, Product_Group=None, TextBoxes=None, Obj_and_Topics=None, + obj_detection_model_pth=None, num_topic=20, Gaze_Time_Type='Brand', Info_printing=True): + ##Image Loading + if Info_printing: print('Loading Image ......') + flag_full_page_ad = False + has_ctpg = True + if type(input_ad_path) == str: + ad_img = cv.imread(input_ad_path) + ad_img = cv.cvtColor(ad_img, cv.COLOR_BGR2RGB) + ad_img_dim1, ad_img_dim2 = ad_img.shape[:2] + dim1_scale = int(np.ceil(ad_img_dim1/32)) + dim2_scale = int(np.ceil(ad_img_dim2/32)) + ad_img = cv.resize(ad_img, (32*dim2_scale,32*dim1_scale)) + else: + ad_img = input_ad_path + + if input_ctpg_path is None: + ctpg_img = None #Initialization + flag_full_page_ad = True + has_ctpg = False + else: + if type(input_ctpg_path) == str: + ctpg_img = cv.imread(input_ctpg_path) + ctpg_img = cv.cvtColor(ctpg_img, cv.COLOR_BGR2RGB) + ctpg_img_dim1, ctpg_img_dim2 = ctpg_img.shape[:2] + dim1_scale = int(np.ceil(ctpg_img_dim1/32)) + dim2_scale = int(np.ceil(ctpg_img_dim2/32)) + ctpg_img = cv.resize(ctpg_img, (32*dim2_scale,32*dim1_scale)) + else: + ctpg_img = input_ctpg_path + #ctpg_img_dim1, ctpg_img_dim2 = [None,None] + + # ctpg_img = None #Initialization + # flag_full_page_ad = False + # if has_ctpg: + # img = cv.resize(img, (1280,1024)) + # h, w, _ = img.shape + # page_width = w // 2 + # ctpg_location = 1-ad_location + # ad_img = img[:, (ad_location*page_width):((ad_location+1)*page_width)] + # ctpg_img = img[:, (ctpg_location*page_width):((ctpg_location+1)*page_width)] + # else: + # #if image's width is larger its height, then treat it as a double-page ad + # h, w, _ = img.shape + # if w > h: + # ad_img = cv.resize(img, (1280,1024)) + # flag_full_page_ad = True + # else: + # ad_img = cv.resize(img, (640,1024)) + if Info_printing: print() + + ##File Size + if Info_printing: print('Calculating complexity (filsize) ......') + filesize_ad = XGBoost_utils.filesize_individual(input_ad_path) + if has_ctpg: + filesize_ctpg = XGBoost_utils.filesize_individual(input_ctpg_path) + else: + filesize_ctpg = 0 + if Info_printing: print() + + ##Salience + if Info_printing: print('Processing Salience Information ......') + #Salience Map + S_map_ad = XGBoost_utils.Itti_Saliency(ad_img, scale_final=3) + if has_ctpg: + S_map_ctpg = XGBoost_utils.Itti_Saliency(ctpg_img, scale_final=3) + + #K-Mean + threshold = 0.001 + enhance_rate = 1 + num_clusters = 3 + + if flag_full_page_ad: + width = S_map_ad.shape[1] + + left = S_map_ad[:, :width//2] + vecs_left, km_left = XGBoost_utils.salience_matrix_conv(left,threshold,num_clusters,enhance_rate=enhance_rate) + _,scores_left,widths_left,D_left = XGBoost_utils.img_clusters(num_clusters, left, km_left.labels_, km_left.cluster_centers_, vecs_left) + + right = S_map_ad[:, width//2:] + vecs_right, km_right = XGBoost_utils.salience_matrix_conv(right,threshold,num_clusters,enhance_rate=enhance_rate) + _,scores_right,widths_right,D_right = XGBoost_utils.img_clusters(num_clusters, right, km_right.labels_, km_right.cluster_centers_, vecs_right) + + ad_sal = np.array(scores_left) + np.array(scores_right) + ad_width = np.array(widths_left) + np.array(widths_right); ad_width = np.log(ad_width+1) + ad_sig_obj = D_left + D_right + + ctpg_sal = np.zeros_like(ad_sal) + ctpg_width = np.zeros_like(ad_width) + ctpg_sig_obj = 0 + + else: + vecs, km = XGBoost_utils.salience_matrix_conv(S_map_ad,threshold,num_clusters,enhance_rate=enhance_rate) + _,scores,widths,D = XGBoost_utils.img_clusters(num_clusters, S_map_ad, km.labels_, km.cluster_centers_, vecs) + ad_sal = np.array(scores) + ad_width = np.log(np.array(widths)+1) + ad_sig_obj = D + + if has_ctpg: + vecs, km = XGBoost_utils.salience_matrix_conv(S_map_ctpg,threshold,num_clusters,enhance_rate=enhance_rate) + _,scores,widths,D = XGBoost_utils.img_clusters(num_clusters, S_map_ctpg, km.labels_, km.cluster_centers_, vecs) + ctpg_sal = np.array(scores) + ctpg_width = np.log(np.array(widths)+1) + ctpg_sig_obj = D + else: + ctpg_sal = np.zeros_like(ad_sal) + ctpg_width = np.zeros_like(ad_width) + ctpg_sig_obj = 0 + if Info_printing: print() + + ##Number of Textboxes + if Info_printing: print('Processing Textboxes ......') + if TextBoxes is None: + #Need multiples of 32 in both dimensions + ad_num_textboxes = XGBoost_utils.text_detection_east(ad_img, text_detection_model_path) + if has_ctpg: + ctpg_num_textboxes = XGBoost_utils.text_detection_east(ctpg_img, text_detection_model_path) + else: + ctpg_num_textboxes = 0 + else: + ad_num_textboxes, ctpg_num_textboxes = TextBoxes + if Info_printing: print() + + ##Objects and Topic Difference + if Info_printing: print('Processing Object and Topic Information ......') + if Info_printing: print('Loading Object Detection Model') + if Obj_and_Topics is None: + if obj_detection_model_pth is None: + model_obj = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, trust_repo=True) + else: + model_obj = torch.load(obj_detection_model_pth) + model_lda = models.LdaModel.load(LDA_model_pth) + dictionary = torch.load(training_ad_text_dictionary_path) + dutch_preposition = torch.load(training_lang_preposition_path) + ad_num_objs, ctpg_num_objs, ad_topic_weights, topic_Diff = XGBoost_utils.object_and_topic_variables(ad_img, ctpg_img, has_ctpg, dictionary, + dutch_preposition, training_language, model_obj, + model_lda, num_topic) + else: + ad_num_objs, ctpg_num_objs, ad_topic_soft_weights, ctpg_topic_soft_weights = Obj_and_Topics + indx = np.argmax(ad_topic_soft_weights) + ad_topic_weights = np.zeros(num_topic) + ad_topic_weights[indx] = 1 + topic_Diff = XGBoost_utils.KL_dist(ad_topic_soft_weights, ctpg_topic_soft_weights) + + if Info_printing: print() + + ##Left and Right Indicator + if Info_printing: print('Getting Left/Right Indicator ......') + if flag_full_page_ad: + Left_right_indicator = [1,1] + else: + if has_ctpg: + if ad_location == 0: + Left_right_indicator = [1,0] + elif ad_location == 1: + Left_right_indicator = [0,1] + else: + Left_right_indicator = [1,1] + else: + Left_right_indicator = [1,0] + if Info_printing: print() + + ##Product Category + if Info_printing: print('Getting Product Category Indicator ......') + if Product_Group is None: + group_ind = XGBoost_utils.product_category() + else: + group_ind = Product_Group + if Info_printing: print() + + ##Surface Sizes + if Info_printing: print('Getting Surface Sizes ......') + if surface_sizes is None: + ad_img = cv.cvtColor(ad_img, cv.COLOR_RGB2BGR) + + print('Please select the bounding box for your ad (from top left to bottom right)') + A = XGBoost_utils.Region_Selection(ad_img) + print() + + print('Please select the bounding box for brands (from top left to bottom right)') + B = XGBoost_utils.Region_Selection(ad_img) + print() + + print('Please select the bounding box for texts (from top left to bottom right)') + T = XGBoost_utils.Region_Selection(ad_img) + surface_sizes = [B/A*100,(1-B/A-T/A)*100,T/A*100,sum(Left_right_indicator)*5] + + ##Typicality Measure + # if Info_printing: print('Calculating Typicality Measure ......') + # if Info_printing: print() + + ##Get All things together + if Info_printing: print('Predicting ......') + gaze = 0 + + for i in range(10): + #Var construction + pca_topic_transform = joblib.load('Topic_Embedding_PCAs/pca_model_'+str(i)+'.pkl') + ad_topics_curr = pca_topic_transform.transform(ad_embeddings)[:,:4][0] + ctpg_topics_curr = pca_topic_transform.transform(ctpg_embeddings)[:,:4][0] + ad_topic_weights = ad_topics_curr + topic_Diff = np.linalg.norm(ad_topics_curr-ctpg_topics_curr) + X = surface_sizes+[filesize_ad,filesize_ctpg]+list(ad_sal)+list(ctpg_sal)+list(ad_width)+list(ctpg_width)+[ad_sig_obj,ctpg_sig_obj]+[ad_num_textboxes,ctpg_num_textboxes,ad_num_objs,ctpg_num_objs]+list(group_ind)+list(ad_topic_weights) + X = np.array(X).reshape(1,len(X)) + X_for_typ = list(X[0,[0,1,2,3,4,6,7,8,12,13,14,18,20,22]])+list(group_ind)+list(ad_topic_weights) + X_for_typ = np.array(X_for_typ).reshape(1,len(X_for_typ)) + if Gaze_Time_Type == 'Brand': + med = torch.load('Brand_Gaze_Model/typicality_train_medoid') + elif Gaze_Time_Type == 'Ad': + med = torch.load('Ad_Gaze_Model/typicality_train_medoid') + + typ = XGBoost_utils.typ_cat(med, X_for_typ, group_ind, np.abs) + Var = surface_sizes+[filesize_ad,filesize_ctpg]+list(ad_sal)+list(ctpg_sal)+list(ad_width)+list(ctpg_width)+[ad_sig_obj,ctpg_sig_obj]+[ad_num_textboxes,ctpg_num_textboxes,ad_num_objs,ctpg_num_objs]+Left_right_indicator+list(ad_topic_weights)+list(group_ind)+[topic_Diff.item(),typ.item()] + Var = np.array(Var).reshape(1,len(Var)) + + xgb_model = xgb.XGBRegressor() + if Gaze_Time_Type == 'Brand': + xgb_model.load_model('Brand_Gaze_Model/10_models/Model_'+str(i+1)+'.json') + elif Gaze_Time_Type == 'Ad': + xgb_model.load_model('Ad_Gaze_Model/10_models/Model_'+str(i+1)+'.json') + gaze += xgb_model.predict(Var) + gaze = gaze/10 + if Info_printing: print('The predicted '+Gaze_Time_Type+' gaze time is: ', (np.exp(gaze)-1).item()) + + return (np.exp(gaze)-1).item() + + +def CNN_Prediction(adv_imgs, ctpg_imgs, ad_locations, Gaze_Type='AG'): #Gaze_Type='AG' or 'BG' + gaze = 0 + if torch.cuda.is_available(): + device = 'cuda' + elif torch.backends.mps.is_available(): + device = 'mps' + else: + device = 'cpu' + if Gaze_Type == 'AG': + a_temp = 0.2590; b_temp = 1.1781 #AG + elif Gaze_Type == 'BG': + a_temp = 0.2100; b_temp = 0.3541 #BG + + for i in range(10): + net = CustomResNet() + net.load_state_dict(torch.load('CNN_Gaze_Model/Fine-tune_'+Gaze_Type+'/Model_'+str(i)+'.pth',map_location=torch.device('cpu'))) + net = net.to(device) + with torch.no_grad(): + pred = net.forward(adv_imgs, ctpg_imgs, ad_locations) + pred = torch.exp(pred*a_temp+b_temp) - 1 + gaze += pred/10 + + return gaze \ No newline at end of file diff --git a/README.md b/README.md index 8791341fc823574c9767cbeb3082da3511d79763..f9186e8a885ebd67ca0f10510db57eb048dedb70 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,6 @@ --- -title: XGBoost Gaze -emoji: 🐠 -colorFrom: purple -colorTo: pink +title: XGBoost_Gaze +app_file: Webpage.py sdk: gradio -sdk_version: 5.0.2 -app_file: app.py -pinned: false +sdk_version: 4.43.0 --- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/Topic_Embedding_PCAs/pca_model_0.pkl b/Topic_Embedding_PCAs/pca_model_0.pkl new file mode 100644 index 0000000000000000000000000000000000000000..7a989b0a07bd8f7fbbf22983931394da85fb7171 --- /dev/null +++ b/Topic_Embedding_PCAs/pca_model_0.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:10bb1dbd63b57106021ea6dd38451c059247a0a1a0a2645105b23113a05529ab +size 65711 diff --git a/Topic_Embedding_PCAs/pca_model_1.pkl b/Topic_Embedding_PCAs/pca_model_1.pkl new file mode 100644 index 0000000000000000000000000000000000000000..faeaa0e55a44c5038b1ea57cc20bf9f55a5e247b --- /dev/null +++ b/Topic_Embedding_PCAs/pca_model_1.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:effc5316145f8e51eaa38f42bf48caa94b8b4a4c6f49addca3889ae68bd5d8ee +size 65711 diff --git a/Topic_Embedding_PCAs/pca_model_2.pkl b/Topic_Embedding_PCAs/pca_model_2.pkl new file mode 100644 index 0000000000000000000000000000000000000000..9853e0fc5f2ae4569dc07daf47cff6d0f241b532 --- /dev/null +++ b/Topic_Embedding_PCAs/pca_model_2.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e9accb631be2bb0721d86701488c94d18cf8d72473d7a354954c0d405e73cef8 +size 65711 diff --git a/Topic_Embedding_PCAs/pca_model_3.pkl b/Topic_Embedding_PCAs/pca_model_3.pkl new file mode 100644 index 0000000000000000000000000000000000000000..4cee011c7628263070ffb618ab096742ecc7887c --- /dev/null +++ b/Topic_Embedding_PCAs/pca_model_3.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:834d31de9f5a25a744363b4471084086498e7b020df18906660148d0199cb848 +size 65711 diff --git a/Topic_Embedding_PCAs/pca_model_4.pkl b/Topic_Embedding_PCAs/pca_model_4.pkl new file mode 100644 index 0000000000000000000000000000000000000000..2929bc5c50045c1d8d03efca317f71cbc356b587 --- /dev/null +++ b/Topic_Embedding_PCAs/pca_model_4.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e77d644cc23cbf28bea71a87cf62a5f587737580c1909d428e2257e8c9613441 +size 65711 diff --git a/Topic_Embedding_PCAs/pca_model_5.pkl b/Topic_Embedding_PCAs/pca_model_5.pkl new file mode 100644 index 0000000000000000000000000000000000000000..ff123fd70f8d676b51aba8c956c9c2d9f1e6e32f --- /dev/null +++ b/Topic_Embedding_PCAs/pca_model_5.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5e98172fa6311a550b9e3211fb588e82e3966361bc606ac442d1cb74bbddb9d +size 65711 diff --git a/Topic_Embedding_PCAs/pca_model_6.pkl b/Topic_Embedding_PCAs/pca_model_6.pkl new file mode 100644 index 0000000000000000000000000000000000000000..88a7bf6b4c77131930066c1579bd8fe64cdab729 --- /dev/null +++ b/Topic_Embedding_PCAs/pca_model_6.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ad8193b94f9501822dbdc90afb50cbc3053cb4358dfc6c139583a5628340e8b1 +size 65711 diff --git a/Topic_Embedding_PCAs/pca_model_7.pkl b/Topic_Embedding_PCAs/pca_model_7.pkl new file mode 100644 index 0000000000000000000000000000000000000000..9d08dc9997ec577f562eee634da41b3871dc2740 --- /dev/null +++ b/Topic_Embedding_PCAs/pca_model_7.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:01b22f2606f817cd669ee7f46d29b0238fbef9b5b59bacd019f733f1e91b0e77 +size 65711 diff --git a/Topic_Embedding_PCAs/pca_model_8.pkl b/Topic_Embedding_PCAs/pca_model_8.pkl new file mode 100644 index 0000000000000000000000000000000000000000..77961393cd59d09a4d7405c96300c7229670c825 --- /dev/null +++ b/Topic_Embedding_PCAs/pca_model_8.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:059e8000175a1ed2a1973810e9ded4f578859828bbe252a82336d0cc6e575e2f +size 65711 diff --git a/Topic_Embedding_PCAs/pca_model_9.pkl b/Topic_Embedding_PCAs/pca_model_9.pkl new file mode 100644 index 0000000000000000000000000000000000000000..7a3f98bea87eff4781375db727487d1a66367fb8 --- /dev/null +++ b/Topic_Embedding_PCAs/pca_model_9.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8982417db41477ebfb7fa49f378d6da4a429e16de45e2d87667098712150f85b +size 65711 diff --git a/Webpage.py b/Webpage.py new file mode 100644 index 0000000000000000000000000000000000000000..5b11bd24e0fe716d7876892db840753a0052f291 --- /dev/null +++ b/Webpage.py @@ -0,0 +1,108 @@ +import gradio as gr +from gradio_image_prompter import ImagePrompter +import Predict +import XGBoost_utils +import numpy as np +import cv2 as cv + +GENERAL_CATEGORY = {'Potatoes / Vegetables / Fruit': 0, 'Chemical products': 1, 'Photo / Film / Optical items': 2, 'Catering industry': 3, 'Industrial products other': 4, 'Media': 5, 'Real estate': 6, 'Government': 7, 'Personnel advertisements': 8, 'Cars / Commercial vehicles': 9, 'Cleaning products': 10, 'Retail': 11, 'Fragrances': 12, 'Footwear / Leather goods': 13, 'Software / Automation': 14, 'Telecommunication equipment': 15, 'Tourism': 16, 'Transport/Communication companies': 17, 'Transport services': 18, 'Insurances': 19, 'Meat / Fish / Poultry': 20, 'Detergents': 21, 'Foods General': 22, 'Other services': 23, 'Banks and Financial Services': 24, 'Office Products': 25, 'Household Items': 26, 'Non-alcoholic beverages': 27, 'Hair, Oral and Personal Care': 28, 'Fashion and Clothing': 29, 'Other products and Services': 30, 'Paper products': 31, 'Alcohol and Other Stimulants': 32, 'Medicines': 33, 'Recreation and Leisure': 34, 'Electronics': 35, 'Home Furnishings': 36, 'Products for Business Use': 37} +CATEGORIES = list(GENERAL_CATEGORY.keys()) +CATEGORIES.sort() + +def calculate_areas(prompts, brand_num, pictorial_num, text_num): + points_all = prompts["points"] + brand_surf = 0 + for i in range(brand_num): + x1 = points_all[i][0]; y1 = points_all[i][1] + x2 = points_all[i][3]; y2 = points_all[i][4] + brand_surf += np.abs((x1-x2)*(y1-y2)) + + pictorial_surf = 0 + for i in range(brand_num, brand_num+pictorial_num): + x1 = points_all[i][0]; y1 = points_all[i][1] + x2 = points_all[i][3]; y2 = points_all[i][4] + pictorial_surf += np.abs((x1-x2)*(y1-y2)) + + text_surf = 0 + for i in range(brand_num+pictorial_num, brand_num+pictorial_num+text_num): + x1 = points_all[i][0]; y1 = points_all[i][1] + x2 = points_all[i][3]; y2 = points_all[i][4] + text_surf += np.abs((x1-x2)*(y1-y2)) + + ad_size = 0 + x1 = points_all[-2][0]; y1 = points_all[-2][1] + x2 = points_all[-2][3]; y2 = points_all[-2][4] + ad_size += np.abs((x1-x2)*(y1-y2)) + + whole_size = 0 + x1 = points_all[-1][0]; y1 = points_all[-1][1] + x2 = points_all[-1][3]; y2 = points_all[-1][4] + whole_size += np.abs((x1-x2)*(y1-y2)) + + return (brand_surf/whole_size*100, pictorial_surf/whole_size*100, text_surf/whole_size*100, ad_size/whole_size*100) + + +def attention(whole_display_prompt, ad, context, + brand_num, pictorial_num, text_num, + category, ad_location, gaze_type): + text_detection_model_path = '../XGBoost_Prediction_Model/EAST-Text-Detection/frozen_east_text_detection.pb' + LDA_model_pth = '../XGBoost_Prediction_Model/LDA_Model_trained/lda_model_best_tot.model' + training_ad_text_dictionary_path = '../XGBoost_Prediction_Model/LDA_Model_trained/object_word_dictionary' + training_lang_preposition_path = '../XGBoost_Prediction_Model/LDA_Model_trained/dutch_preposition' + + caption_ad = XGBoost_utils.Caption_Generation(ad) + caption_context = XGBoost_utils.Caption_Generation(context) + ad_topic = XGBoost_utils.Topic_emb(caption_ad) + ctpg_topic = XGBoost_utils.Topic_emb(caption_context) + # ad_topic = np.random.randn(1,768) + # ctpg_topic = np.random.randn(1,768) + + prod_group = np.zeros(38) + prod_group[GENERAL_CATEGORY[category]] = 1 + + if ad_location == 'left': + ad_loc = 0 + elif ad_location == 'right': + ad_loc = 1 + else: + ad_loc = None + + brand_percent, visual_percent, text_percent, adv_size_percent = calculate_areas(whole_display_prompt, brand_num, pictorial_num, text_num) + surfaces = [brand_percent, visual_percent, text_percent, adv_size_percent*10/100] + + ad = ad.convert('RGB') + ad = cv.resize(np.array(ad), (640, 832)) + context = context.convert('RGB') + context = cv.resize(np.array(context), (640, 832)) + + + Gaze = Predict.Ad_Gaze_Prediction(input_ad_path=ad, input_ctpg_path=context, ad_location=ad_loc, + text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, + training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch', + ad_embeddings=ad_topic, ctpg_embeddings=ctpg_topic, + surface_sizes=surfaces, Product_Group=prod_group, + obj_detection_model_pth=None, num_topic=20, Gaze_Time_Type=gaze_type) + return np.round(Gaze,2) + +def greet(name, intensity): + return "Hello " * intensity + name + "!" + +demo = gr.Interface( + fn=attention, + inputs=[ImagePrompter(label="Upload Entire (Ad+Context) Image, and Draw Bounding Boxes"), + gr.Image(label="Ad Image", sources=['upload', 'webcam'], type="pil"), + gr.Image(label="Context Image", sources=['upload', 'webcam'], type="pil"), + gr.Number(label="Number of brand bounding boxes drawn"), + gr.Number(label="Number of pictorial bounding boxes drawn"), + gr.Number(label="Number of text bounding boxes drawn"), + gr.Dropdown(CATEGORIES, label="Product Category"), + gr.Textbox(label="Ad Location", info="Enter left or right or full"), + gr.Textbox(label="Gaze Type", info="Enter Ad or Brand") + ], + outputs=[gr.Number(label="Predicted Gaze (sec)")], + title="Gaze Prediction", + description="In the section right below, please first upload the entire image that contains both ad and context, then draw bounding boxes. Please draw ALL Bounding Boxes in the order of: (1) brand, (2) pictorial, (3) text elements, (4) advertisement and (5) the entire image here. NOTE: Each ad element can have more than 1 boxes.", + theme=gr.themes.Soft() +) + +demo.launch(share=True) \ No newline at end of file diff --git a/XGBoost_utils.py b/XGBoost_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f7126e8ed2fba4bdf6384f02b8c0363017e74d64 --- /dev/null +++ b/XGBoost_utils.py @@ -0,0 +1,630 @@ +import re +import cv2 as cv +import numpy as np +import matplotlib.pyplot as plt +import copy +import pandas as pd +import os +import math +import time +from imutils.object_detection import non_max_suppression +from sklearn.mixture import GaussianMixture +from sklearn.cluster import KMeans +import xgboost as xgb +from sklearn.linear_model import LinearRegression +from sklearn.neural_network import MLPRegressor +from sklearn.datasets import make_regression +from sklearn.model_selection import train_test_split +from scipy.stats import norm +from sklearn.neighbors import KernelDensity +from PIL import Image +from io import BytesIO +from pytesseract import pytesseract +from gensim import corpora, models, similarities +import nltk +from nltk.corpus import stopwords +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer +from sentence_transformers import SentenceTransformer + +General_Category = { + 'Potatoes / Vegetables / Fruit': ['Potatoes / Vegetables / Fruit'], + 'Chemical products': ['Chemical products'], + 'Photo / Film / Optical items': ['Photo / Film / Optical items'], + 'Catering industry': ['Catering industry'], + 'Industrial products other': ['Industrial products other'], + 'Media': ['Media'], + 'Real estate': ['Real estate'], + 'Government': ['Government'], + 'Personnel advertisements': ['Personnel advertisements'], + 'Cars / Commercial vehicles': ['Cars / Commercial vehicles'], + 'Cleaning products': ['Cleaning products'], + 'Retail': ['Retail'], + 'Fragrances': ['Fragrances'], + 'Footwear / Leather goods': ['Footwear / Leather goods'], + 'Software / Automation': ['Software / Automation'], + 'Telecommunication equipment': ['Telecommunication equipment'], + 'Tourism': ['Tourism'], + 'Transport/Communication companies': ['Transport/Communication companies'], + 'Transport services': ['Transport services'], + 'Insurances': ['Insurances'], + 'Meat / Fish / Poultry': ['Meat / Fish / Poultry'], + 'Detergents': ['Detergents'], + 'Foods General': ['Foods general', 'Bread / Banquet', 'Chocolate / Confectionery', 'Soup / Soup products', 'Edible fats', 'Sugar / Herbs / Spices', 'Dairy'], + 'Other services': ['Education', 'Other services'], + 'Banks and Financial Services': ['Banks / Financing', 'Financial services other'], + 'Office Products': ['Office equipment', 'Office automation hardware', 'Office products'], + 'Household Items': ['Household items', 'Small household equipment'], + 'Non-alcoholic beverages': ['Non-alcoholic beverages', 'Coffee/Tea'], + 'Hair, Oral and Personal Care': ['Skin care', 'Hair care', 'Oral care', 'Personal care electric'], + 'Fashion and Clothing': ['Outerwear', 'Underwear / Sleepwear'], + 'Other products and Services': ['Pet foods', 'Other products and services', 'Other advertisements'], + 'Paper products': ['Paper products', 'Paper products body care'], + 'Alcohol and Other Stimulants': ['Weak alcoholic drinks', 'Strong alcoholic drinks', 'Tobacco'], + 'Medicines': ['Medicines', 'Bandages'], + 'Recreation and Leisure': ['Recreation', 'Leisure items / Hobby items'], + 'Electronics': ['Kitchen appliances', 'Brown goods (Sound and video Electronics)'], + 'Home Furnishings': ['Home furnishings', 'Home upholstery', 'Home textiles'], + 'Products for Business Use': ['Products for business use', 'Other business services']} + +#Saliency Map: Itti-Koch +def Itti_Saliency(img, scale_final=4): + + r = copy.copy(img[:,:,0].astype('float64')) + g = copy.copy(img[:,:,1].astype('float64')) + b = copy.copy(img[:,:,2].astype('float64')) + + #Intensity + I = (r+g+b)/3 + dim1_img, dim2_img = np.shape(I) + + #Normalization of r,g,b + mask1 = I >= 0.1*np.max(I) + mask2 = I < 0.1*np.max(I) + r[mask1] = r[mask1]/I[mask1] + r[mask2] = 0 + g[mask1] = g[mask1]/I[mask1] + g[mask2] = 0 + b[mask1] = b[mask1]/I[mask1] + b[mask2] = 0 + + #Fine-tuned Color Channels + R = r-(g+b)/2 + G = g-(r+b)/2 + B = b-(r+g)/2 + Y = (r+g)/2-np.abs(r-g)/2-b + + #Intensity Feature Maps + I_pyr = [I] + R_pyr = [R] + G_pyr = [G] + B_pyr = [B] + Y_pyr = [Y] + + I_maps = [] + RG_maps = [] + BY_maps = [] + + for i in range(8): + I_pyr.append(cv.pyrDown(I_pyr[i])) + R_pyr.append(cv.pyrDown(R_pyr[i])) + G_pyr.append(cv.pyrDown(G_pyr[i])) + B_pyr.append(cv.pyrDown(B_pyr[i])) + Y_pyr.append(cv.pyrDown(Y_pyr[i])) + + for c in (2,3,4): + for d in (3,4): + shape = (np.shape(I_pyr[c])[1],np.shape(I_pyr[c])[0]) + temp = cv.resize(I_pyr[c+d], shape, interpolation=cv.INTER_LINEAR) + temp_G = cv.resize(G_pyr[c+d], shape, interpolation=cv.INTER_LINEAR) + temp_R = cv.resize(R_pyr[c+d], shape, interpolation=cv.INTER_LINEAR) + temp_B = cv.resize(B_pyr[c+d], shape, interpolation=cv.INTER_LINEAR) + temp_Y = cv.resize(Y_pyr[c+d], shape, interpolation=cv.INTER_LINEAR) + + I_maps.append(np.abs(I_pyr[c]-temp)) + RG_maps.append(np.abs((R_pyr[c]-G_pyr[c])-(temp_G-temp_R))) + BY_maps.append(np.abs((B_pyr[c]-Y_pyr[c])-(temp_Y-temp_B))) + + g_kernel = cv.getGaborKernel((5, 5), 2.0, np.pi/4, 10.0, 0.5, 0) + O_maps = [] + for theta in (0, np.pi/4, np.pi/2, 3*np.pi/4): + O_pyr = [I] + for i in range(8): + filtered = cv.filter2D(I_pyr[i], ddepth=-1, kernel=g_kernel) + dim1,dim2 = np.shape(filtered) + O_pyr.append(cv.resize(filtered, (dim1//2,dim2//2), interpolation=cv.INTER_LINEAR)) + for c in (2,3,4): + for d in (3,4): + shape = (np.shape(O_pyr[c])[1],np.shape(O_pyr[c])[0]) + temp = cv.resize(O_pyr[c+d], shape, interpolation=cv.INTER_LINEAR) + + O_maps.append(np.abs(O_pyr[c]-temp)) + + S = 0 + M = 10 + scaling = 2**scale_final + + for I_map in I_maps: + temp = normalization(I_map,M) + temp = cv.resize(temp, (dim1_img//scaling, dim2_img//scaling), interpolation=cv.INTER_LINEAR) + S += temp + + for i in range(len(RG_maps)): + temp = normalization(RG_maps[i],M)+normalization(BY_maps[i],M) + temp = cv.resize(temp, (dim1_img//scaling, dim2_img//scaling), interpolation=cv.INTER_LINEAR) + S += temp + + for O_map in O_maps: + temp = normalization(O_map,M) + temp = cv.resize(temp, (dim1_img//scaling, dim2_img//scaling), interpolation=cv.INTER_LINEAR) + S += temp + + S = 1/3*S + return S + +#Saliency map helper +def normalization(X, M): + max_val = np.max(X) + + #first step + X = X*M/max_val + + #second step + mask = X < M + m_bar = np.mean(X[mask]) + + #third step + return (M-m_bar)**2*X + +#For K-Means and Saliency Features +def salience_matrix_conv(sal_mat,threshold,num_clusters,enhance_rate=2): + norm_sal = sal_mat**enhance_rate/np.max(sal_mat**enhance_rate) + mask = norm_sal < threshold + norm_sal[mask] = 0 + [dim1,dim2] = np.shape(sal_mat) + mask = norm_sal >= threshold + vecs = [] + for i in range(dim1): + for j in range(dim2): + if norm_sal[i,j] == 0: + continue + else: + vecs.append([i/dim1,j/dim2,norm_sal[i,j]]) + vecs = np.array(vecs) + km = KMeans(n_clusters=num_clusters, random_state=0, n_init=10).fit(vecs) + return (vecs, km) + +def Center(X): + ws = X[:,2].reshape(len(X[:,2]),1) + ws = ws/np.sum(ws) + loc = X[:,0:2] + return np.sum(loc*ws,axis=0) + +def Cov_est(X, center): + n = X.shape[0] + loc = X[:,0:2] + return np.matmul((loc-center).T,(loc-center))/n + +def Renyi_Entropy(P): + n = len(P) + Q = np.ones_like(P)/n + return np.sum(P*np.log(P/Q))#-2*np.log(np.sum(np.sqrt(P*Q))) + +def img_clusters(num_clusters, img, kmeans_labels, k_means_centers, vecs, show_cluster=False): + clusters = [] + labels = [] + scores = [] + widths = [] + pred = kmeans_labels + + for i in range(num_clusters): + clusters.append(np.zeros_like(img)) + labels.append(pred == i) + + for k in range(num_clusters): + for item in vecs[labels[k]]: + i,j,val = item + i = int(i); j = int(j) + clusters[k][i,j] = val + scores.append(np.sum(clusters[k])) + widths.append(np.linalg.det(Cov_est(vecs[labels[k]],Center(vecs[labels[k]])))) + ind = np.argsort(-1*np.array(scores)) + scores = np.array(scores)[ind] + widths = np.array(widths)[ind] + perc_S = np.array(scores)/sum(scores) + D = 1/(Renyi_Entropy(perc_S)+0.001) + + if show_cluster: + fig,ax = plt.subplots(1,num_clusters) + for i in range(num_clusters): + ax[i].imshow(clusters[i]) + ax[i].axis('off') + plt.savefig("clusters.png", bbox_inches='tight') + plt.show() + + return (clusters,perc_S,widths,D) + +def weights_pages(ad_size, num_clusters): + if ad_size == '1g': + return np.concatenate((np.ones(num_clusters),0.2*np.ones(num_clusters))) + elif ad_size == '1w': + return np.ones(2*num_clusters) + elif ad_size == 'hw': + return np.concatenate((0.2*np.ones(num_clusters),np.ones(num_clusters))) + else: + return 0.5*np.ones(2*num_clusters) + +def ad_loc_indicator(ad_size): + if ad_size == '1g': + return 0 + elif ad_size == '1w': + return 2 + elif ad_size == 'hw': + return 1 + else: + return 3 + +def full_weights(ad_sizes, num_clusters): + out = [] + for ad_size in ad_sizes: + out.append(weights_pages(ad_size,num_clusters)) + return np.array(out) + +def ad_loc_indicator_full(ad_sizes): + out = [] + for ad_size in ad_sizes: + out.append(ad_loc_indicator(ad_size)) + return np.array(out) + +def filesize_individual(img_path): + if type(img_path) == str: + out = os.path.getsize(img_path)/1000 + else: + img = Image.fromarray(img_path) + img_file = BytesIO() + img.save(img_file, 'jpeg') + out = img_file.tell() + + return out + +def KL_dist(P,Q): + return np.sum(P*(np.log(P)-np.log(Q)),axis=1) + +def KL_score(y_pred,y_test): + kde_pred = KernelDensity(kernel="gaussian", bandwidth=0.75).fit(y_pred.reshape(-1, 1)) + log_dens_pred = kde_pred.score_samples(y_pred.reshape(-1, 1)) + x = np.linspace(-10,10,num=100) + kde_true = KernelDensity(kernel="gaussian", bandwidth=0.75).fit(y_test.reshape(-1, 1)) + log_dens_true = kde_true.score_samples(y_test.reshape(-1, 1)) + plt.plot(x,np.exp(kde_pred.score_samples(x.reshape(-1, 1))),label='pred') + plt.plot(x,np.exp(kde_true.score_samples(x.reshape(-1, 1))),label='true') + plt.legend() + plt.show() + return np.sum(np.exp(log_dens_pred)*(log_dens_pred-log_dens_true)) + +def medoid(X_in, d): + temp = [] + temp_d = [] + for item in X_in: + temp.append(np.sum(d(X_in-item))) + temp_d.append(np.sum(d(X_in-item), axis=1)) + return (np.argmin(temp), temp_d[np.argmin(temp)]) + +def sqr_dist(x): + return np.multiply(x,x) + +def data_normalize(X_train,X_test): + num_train = X_train.shape[0] + m_train = np.sum(X_train,axis=0)/num_train + s_train = np.sqrt(np.sum((X_train-m_train)**2,axis=0)) + X_train_transf = (X_train-m_train)/s_train + X_test_transf = (X_test-m_train)/s_train + return X_train_transf, X_test_transf + +def typ_cat(medoids, X_test, category, d): + ind_temp = np.arange(len(category)) + ind_interest = ind_temp[np.array(category)==1][0] + typ = np.sum(d(X_test-medoids[ind_interest]), axis=1) + return typ + + +#EAST Text Detection +def text_detection_east(image, text_detection_model_path): + orig = image.copy() + (H, W) = image.shape[:2] + (newW, newH) = (W, H) + rW = W / float(newW) + rH = H / float(newH) + # resize the image and grab the new image dimensions + image = cv.resize(image, (newW, newH)) + (H, W) = image.shape[:2] + + # define the two output layer names for the EAST detector model that + # we are interested -- the first is the output probabilities and the + # second can be used to derive the bounding box coordinates of text + layerNames = [ + "feature_fusion/Conv_7/Sigmoid", + "feature_fusion/concat_3"] + + # load the pre-trained EAST text detector + print("[INFO] loading EAST text detector...") + net = cv.dnn.readNet(text_detection_model_path) + # construct a blob from the image and then perform a forward pass of + # the model to obtain the two output layer sets + blob = cv.dnn.blobFromImage(image, 1.0, (W, H), + (123.68, 116.78, 103.94), swapRB=True, crop=False) + start = time.time() + net.setInput(blob) + (scores, geometry) = net.forward(layerNames) + end = time.time() + # show timing information on text prediction + print("[INFO] text detection took {:.6f} seconds".format(end - start)) + + # grab the number of rows and columns from the scores volume, then + # initialize our set of bounding box rectangles and corresponding + # confidence scores + (numRows, numCols) = scores.shape[2:4] + rects = [] + confidences = [] + # loop over the number of rows + for y in range(0, numRows): + # extract the scores (probabilities), followed by the geometrical + # data used to derive potential bounding box coordinates that + # surround text + scoresData = scores[0, 0, y] + xData0 = geometry[0, 0, y] + xData1 = geometry[0, 1, y] + xData2 = geometry[0, 2, y] + xData3 = geometry[0, 3, y] + anglesData = geometry[0, 4, y] + + # loop over the number of columns + for x in range(0, numCols): + # if our score does not have sufficient probability, ignore it + if scoresData[x] < 0.5: + continue + # compute the offset factor as our resulting feature maps will + # be 4x smaller than the input image + (offsetX, offsetY) = (x * 4.0, y * 4.0) + # extract the rotation angle for the prediction and then + # compute the sin and cosine + angle = anglesData[x] + cos = np.cos(angle) + sin = np.sin(angle) + # use the geometry volume to derive the width and height of + # the bounding box + h = xData0[x] + xData2[x] + w = xData1[x] + xData3[x] + # compute both the starting and ending (x, y)-coordinates for + # the text prediction bounding box + endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x])) + endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x])) + startX = int(endX - w) + startY = int(endY - h) + # add the bounding box coordinates and probability score to + # our respective lists + rects.append((startX, startY, endX, endY)) + confidences.append(scoresData[x]) + + # apply non-maxima suppression to suppress weak, overlapping bounding + # boxes + boxes = non_max_suppression(np.array(rects), probs=confidences) + + count = 0 + + for (x1, y1, x2, y2) in boxes: + count += 1 + + return count + + #Texts and Objects +def center_crop(img, dim): + """Returns center cropped image + Args: + img: image to be center cropped + dim: dimensions (width, height) to be cropped + """ + width, height = img.shape[1], img.shape[0] + + # process crop width and height for max available dimension + crop_width = dim[0] if dim[0] 3: + if item not in dutch_preposition and item not in stop_words: + if item in ad_text_dic: + ad_text_dic[item] += 1 + else: + ad_text_dic[item] = 1 + + return ad_text_dic + +def topic_features(ad_objs, ad_text_dic, dictionary, model, num_topics=20): + corpus = [] + + #Object + if len(ad_objs) > 0: + for obj in list(ad_objs.keys()): + corpus.append((dictionary[obj],len(ad_objs[obj][2]))) + + #Words + if len(ad_text_dic) > 0: + for word in list(ad_text_dic.keys()): + if word in dictionary: + corpus.append((dictionary[word],ad_text_dic[word])) + + #Topic weights + ad_topic_weights = np.zeros(num_topics) + aux = np.ones(num_topics) + sum = 0 + count = 0 + for i,w in model[corpus]: + aux[i] = 0 + ad_topic_weights[i] = w + sum += w + count += 1 + if num_topics-count != 0: + ad_topic_weights = ad_topic_weights+(1-sum)/(num_topics-count)*aux + + return ad_topic_weights + +def object_and_topic_variables(ad_image, ctpg_image, has_ctpg, dictionary, dutch_preposition, language, model_obj, model_lda, num_topic=20): + nltk.download('stopwords') + stop_words = stopwords.words(language) + + #Ad + ad_objs, ad_num_objs = ad_object_detection(model_obj, ad_image, crop_dim=600) + ad_text_dic = ad_word_classes(ad_image, dutch_preposition, stop_words) + ad_topic_weights = topic_features(ad_objs, ad_text_dic, dictionary, model_lda, num_topic) + + #Counterpage + if has_ctpg: + ctpg_objs, ctpg_num_objs = ad_object_detection(model_obj, ctpg_image, crop_dim=600) + ctpg_text_dic = ad_word_classes(ctpg_image, dutch_preposition, stop_words) + ctpg_topic_weights = topic_features(ctpg_objs, ctpg_text_dic, dictionary, model_lda, num_topic) + else: + ctpg_num_objs = 0 + ctpg_topic_weights = np.ones(num_topic)/num_topic + + #Topic Difference + Diff = KL_dist(ad_topic_weights.reshape(1,num_topic), ctpg_topic_weights.reshape(1,num_topic)) + + return ad_num_objs, ctpg_num_objs, ad_topic_weights, Diff + +def product_category(): + global General_Category + categories = np.array(list(General_Category.keys())) + five_categories = [] + for i in range(len(categories)//5): + five_categories.append(list(categories[(5*i):(5*i+5)])) + if len(categories)%5 > 0: + i = i+1 + five_categories.append(list(categories[(5*i):(5*i+5)])) + + #Create dictionary + Name_to_Index_dict = {} + for i in range(len(categories)): + Name_to_Index_dict[categories[i]] = i + + #User Questions + flag = True + while flag: + for i, item in enumerate(five_categories): + print("list "+str(i+1)+" out of "+str(len(five_categories))) + for j, cat in enumerate(item): + print(str(j)+": "+cat) + choice = input("Please choose the general category. If no good fit, please type N; otherwise, type the numbering: ") + if choice == "N": + print() + continue + else: + choice = item[int(choice)] + break + confirm = input("If you have chosen successfully, please type Y or y; otherwise, please type any other key: ") + if confirm == 'Y' or confirm == 'y': + flag = False + else: + print('Please choose again.') + + #Output + out = np.zeros(38) + out[Name_to_Index_dict[choice]] = 1 + + return out + + +def Region_Selection(img): + As = [] + while True: + _, _, w, h = cv.selectROI("Select ROI", img, fromCenter=False, showCrosshair=False) + As.append(w*h) + ans = input("Continue? (y/n) ") + if ans == 'n': + break + A = sum(As) + return A + +def RMSRPD(y_pred, y_true): + diff = y_pred - y_true + den = 0.5*(np.abs(y_pred) + np.abs(y_true)) + return np.sqrt(np.mean((diff/den)**2)) + +def Caption_Generation(image): #image is a PIL Image object + with torch.no_grad(): + image = image.convert('RGB') + model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2', trust_remote_code=True, torch_dtype=torch.float16) + model = model.to(device='cpu', dtype=torch.float32).eval() + + # model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True, torch_dtype=torch.float16) + # model = model.to(device=torch.device("mps"), dtype=torch.float16) + + tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2', trust_remote_code=True) #'openbmb/MiniCPM-Llama3-V-2_5' + # tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True) #'openbmb/MiniCPM-Llama3-V-2_5' + + + question = 'Describe the image in a paragraph. Please include details.' + msgs = [{'role': 'user', 'content': question}] + + # curr = [] + + res = model.chat( + image=image, + context=None, + msgs=msgs, + tokenizer=tokenizer, + sampling=True, # if sampling=False, beam_search will be used by default + temperature=0.7, + # system_prompt='' # pass system_prompt if needed + ) + + return res[0] + +def Topic_emb(caption): + with torch.no_grad(): + model = SentenceTransformer("Magazine_Topic_Embedding_sample_size15").eval() + embeddings = model.encode(caption).reshape(1,768) + return embeddings \ No newline at end of file diff --git a/__pycache__/DL_models.cpython-310.pyc b/__pycache__/DL_models.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bb556b7e96776537cc8a141a4cce8148fd6cdb86 Binary files /dev/null and b/__pycache__/DL_models.cpython-310.pyc differ diff --git a/__pycache__/Predict.cpython-310.pyc b/__pycache__/Predict.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b3b3301478818438bc89f75cae90fe3badef4578 Binary files /dev/null and b/__pycache__/Predict.cpython-310.pyc differ diff --git a/__pycache__/XGBoost_utils.cpython-310.pyc b/__pycache__/XGBoost_utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9ff60688f8fb501a700b5d23fb65a017b6eac4a6 Binary files /dev/null and b/__pycache__/XGBoost_utils.cpython-310.pyc differ diff --git a/image_prompt_test.py b/image_prompt_test.py new file mode 100644 index 0000000000000000000000000000000000000000..c1fc1af9df156952dd747b62f136a5bced9e7596 --- /dev/null +++ b/image_prompt_test.py @@ -0,0 +1,66 @@ +import gradio as gr +from gradio_image_prompter import ImagePrompter +import torch +from PIL import Image +from transformers import AutoModel, AutoTokenizer +import XGBoost_utils +import numpy as np + +def calculate_areas(prompts, brand_num, pictorial_num, text_num): + points_all = prompts["points"] + brand_surf = 0 + for i in range(brand_num): + x1 = points_all[i][0]; y1 = points_all[i][1] + x2 = points_all[i][3]; y2 = points_all[i][4] + brand_surf += np.abs((x1-x2)*(y1-y2)) + + pictorial_surf = 0 + for i in range(brand_num, brand_num+pictorial_num): + x1 = points_all[i][0]; y1 = points_all[i][1] + x2 = points_all[i][3]; y2 = points_all[i][4] + pictorial_surf += np.abs((x1-x2)*(y1-y2)) + + text_surf = 0 + for i in range(brand_num+pictorial_num, brand_num+pictorial_num+text_num): + x1 = points_all[i][0]; y1 = points_all[i][1] + x2 = points_all[i][3]; y2 = points_all[i][4] + text_surf += np.abs((x1-x2)*(y1-y2)) + + ad_size = 0 + x1 = points_all[-2][0]; y1 = points_all[-2][1] + x2 = points_all[-2][3]; y2 = points_all[-2][4] + ad_size += np.abs((x1-x2)*(y1-y2)) + + whole_size = 0 + x1 = points_all[-1][0]; y1 = points_all[-1][1] + x2 = points_all[-1][3]; y2 = points_all[-1][4] + whole_size += np.abs((x1-x2)*(y1-y2)) + + return (brand_surf, pictorial_surf/whole_size*100, text_surf/whole_size*100, ad_size/whole_size*100, prompts["points"]) + + +demo = gr.Interface( + # lambda prompts1, prompts2: (prompts1["image"], prompts1["points"]), + fn=calculate_areas, + inputs=[ImagePrompter(label="Draw bounding boxes"), + gr.Number(label="Number of brand bounding boxes"), + gr.Number(label="Number of pictorial bounding boxes"), + gr.Number(label="Number of text bounding boxes")], + outputs=[gr.Number(label="brand surf %"), + gr.Number(label="pictorial surf %"), + gr.Number(label="text surf %"), + gr.Number(label="ad size %"), + gr.Dataframe(label="Points")], + theme=gr.themes.Soft() +) + +# demo2 = gr.Interface( +# lambda prompts: (prompts["image"], prompts["points"]), +# ImagePrompter(show_label=False), +# [gr.Image(show_label=False), gr.Dataframe(label="Points")], +# ) + +# app = gr.TabbedInterface(interface_list=[demo, demo2], +# tab_names = ["Image inference", "Video inference"]) + +demo.launch() diff --git a/my_flask_app/app.py b/my_flask_app/app.py new file mode 100644 index 0000000000000000000000000000000000000000..eee76e2ff23f2d3848d5482da42e5473531e22d6 --- /dev/null +++ b/my_flask_app/app.py @@ -0,0 +1,78 @@ +from flask import Flask, request, render_template, redirect, url_for, send_from_directory, jsonify +import os +import sys +root = '/Users/jianpingye/Desktop/Marketing_Research/XGBoost_Gaze_Prediction_Platform/Gaze-Time-Prediction-for-Advertisement/XGBoost_Prediction_Model' +sys.path.append(root) + +import Predict +import numpy as np + +General_Category = {'Potatoes / Vegetables / Fruit': 0, 'Chemical products': 1, 'Photo / Film / Optical items': 2, 'Catering industry': 3, 'Industrial products other': 4, 'Media': 5, 'Real estate': 6, 'Government': 7, 'Personnel advertisements': 8, 'Cars / Commercial vehicles': 9, 'Cleaning products': 10, 'Retail': 11, 'Fragrances': 12, 'Footwear / Leather goods': 13, 'Software / Automation': 14, 'Telecommunication equipment': 15, 'Tourism': 16, 'Transport/Communication companies': 17, 'Transport services': 18, 'Insurances': 19, 'Meat / Fish / Poultry': 20, 'Detergents': 21, 'Foods General': 22, 'Other services': 23, 'Banks and Financial Services': 24, 'Office Products': 25, 'Household Items': 26, 'Non-alcoholic beverages': 27, 'Hair, Oral and Personal Care': 28, 'Fashion and Clothing': 29, 'Other products and Services': 30, 'Paper products': 31, 'Alcohol and Other Stimulants': 32, 'Medicines': 33, 'Recreation and Leisure': 34, 'Electronics': 35, 'Home Furnishings': 36, 'Products for Business Use': 37} + +app = Flask(__name__) +app.config['UPLOAD_FOLDER'] = os.path.join(os.getcwd(), 'uploads') +app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 # Set max upload size to 16MB +os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True) + +@app.route('/') +def index(): + return render_template('index.html') + +@app.route('/upload', methods=['POST']) +def upload_file(): + if 'image' not in request.files: + return redirect(request.url) + + file = request.files['image'] + if file.filename == '': + return redirect(request.url) + + if file: + filename = file.filename + filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename) + file.save(filepath) + + # Get additional input numbers and option from the form + brand_size = request.form.get('brand_size', type=float) + pictorial_size = request.form.get('pictorial_size', type=float) + text_size = request.form.get('text_size', type=float) + ad_size = request.form.get('ad_size', type=float) + ad_location = request.form.get('ad_location') + gaze_type = request.form.get('gaze_type') + option = request.form.get('option') + + processed_value = process_image_function(filepath, brand_size, pictorial_size, text_size, ad_size, option, ad_location, gaze_type) + formatted_value = f"{processed_value:.2f} sec" + + return render_template('index.html', filename=filename, processed_value=formatted_value) + + +def process_image_function(image_path, brand_size, pictorial_size, text_size, ad_size, option, ad_location, gaze_type): + text_detection_model_path = 'EAST-Text-Detection/frozen_east_text_detection.pb' + LDA_model_pth = 'LDA_Model_trained/lda_model_best_tot.model' + training_ad_text_dictionary_path = 'LDA_Model_trained/object_word_dictionary' + training_lang_preposition_path = 'LDA_Model_trained/dutch_preposition' + global General_Category + prod_group = np.zeros(38) + prod_group[General_Category[option]] = 1 + if ad_location == 'left': + ad_loc = 0 + elif ad_location == 'right': + ad_loc = 1 + else: + ad_loc = None + predicted_gaze = Predict.Ad_Gaze_Prediction(input_ad_path=image_path, input_ctpg_path=None, ad_location=ad_loc, + text_detection_model_path=text_detection_model_path, LDA_model_pth=LDA_model_pth, + training_ad_text_dictionary_path=training_ad_text_dictionary_path, training_lang_preposition_path=training_lang_preposition_path, training_language='dutch', + surface_sizes=[brand_size,pictorial_size,text_size,ad_size], Product_Group=prod_group, + obj_detection_model_pth=None, num_topic=20, Gaze_Time_Type=gaze_type) + return predicted_gaze + + +@app.route('/uploads/') +def uploaded_file(filename): + temp = send_from_directory(app.config['UPLOAD_FOLDER'], filename) + return temp #send_from_directory(app.config['UPLOAD_FOLDER'], filename) + +if __name__ == '__main__': + app.run(port=5000) diff --git a/my_flask_app/static/styles.css b/my_flask_app/static/styles.css new file mode 100644 index 0000000000000000000000000000000000000000..ee4475405d33bd7095e5b0b27f1af5dcf29814f3 --- /dev/null +++ b/my_flask_app/static/styles.css @@ -0,0 +1,43 @@ +body { + font-family: Arial, sans-serif; + display: flex; + flex-direction: column; + align-items: flex-start; + background-color: #f0f0f0; + padding: 20px; +} + +form { + background: #fff; + padding: 20px; + border-radius: 10px; + box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); + margin-bottom: 20px; + width: 100%; + max-width: 600px; +} + +input[type="file"], input[type="number"], input[type="text"], select { + display: block; + margin-bottom: 10px; + width: 100%; +} + +select { + height: 100px; + overflow-y: scroll; +} + +.result { + background: #fff; + padding: 20px; + border-radius: 10px; + box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); + width: 100%; + max-width: 600px; +} + +.result img { + max-width: 100%; + height: auto; +} diff --git a/my_flask_app/templates/index.html b/my_flask_app/templates/index.html new file mode 100644 index 0000000000000000000000000000000000000000..5483e4a862f66bdd33e9ac952f9c15c7209ea688 --- /dev/null +++ b/my_flask_app/templates/index.html @@ -0,0 +1,84 @@ + + + + + + Image Upload + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
+ + {% if filename %} +
+ Uploaded Image +

Predicted Gaze: {{ processed_value }}

+
+ {% endif %} + + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..dd670aaeff299e4a0f5882914b8d2a4f1b077c61 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,51 @@ +accelerate==0.30.1 +decord==0.6.0 +deepspeed==0.15.2 +evaluate==0.4.3 +fitz==0.0.1.dev2 +Flask==3.0.3 +gensim==4.3.0 +GPUtil==1.4.0 +gradio==5.0.2 +gradio_image_prompter==0.1.0 +huggingface_hub==0.24.6 +imageio==2.35.1 +imutils==0.5.4 +joblib==1.4.2 +latex2sympy2==1.9.1 +matplotlib==3.7.4 +modelscope_studio==0.5.0 +moviepy==1.0.3 +nltk==3.8.1 +numpy==1.24.4 +opencv_python==4.7.0.72 +opencv_python_headless==4.5.5.64 +packaging==24.1 +pandas==1.5.3 +peft==0.12.0 +Pillow==10.1.0 +Pillow==10.4.0 +portalocker==2.10.1 +PuLP==2.7.0 +pycocoevalcap==1.2 +pysubs2==1.7.3 +pytesseract==0.3.10 +python-dotenv==1.0.1 +Requests==2.32.3 +rich==13.9.2 +scikit_learn==1.2.2 +scipy==1.14.1 +sentence_transformers==3.1.0 +setuptools==67.7.2 +spacy==3.8.2 +streamlit==1.39.0 +sty==1.0.6 +tabulate==0.9.0 +tiktoken==0.8.0 +timm==0.9.10 +torch==2.1.2 +torchvision==0.16.2 +tqdm==4.66.5 +transformers==4.40.0 +validators==0.34.0 +xgboost==1.7.3 diff --git a/uploads/002.jpg b/uploads/002.jpg new file mode 100644 index 0000000000000000000000000000000000000000..285a69d46ecde8fbbd6928df54bfe515ec2e4b27 Binary files /dev/null and b/uploads/002.jpg differ diff --git a/uploads/004.jpg b/uploads/004.jpg new file mode 100644 index 0000000000000000000000000000000000000000..bf331d711a67e7cd94fcd295936426f24fcb825d Binary files /dev/null and b/uploads/004.jpg differ diff --git a/uploads/008.jpg b/uploads/008.jpg new file mode 100644 index 0000000000000000000000000000000000000000..9e609cd8539cb0c16395966783d74ffdc9d9fd4f Binary files /dev/null and b/uploads/008.jpg differ diff --git a/uploads/012.jpg b/uploads/012.jpg new file mode 100644 index 0000000000000000000000000000000000000000..56962d7dd6bb4393dcc4caea83c88a9fbec38b70 Binary files /dev/null and b/uploads/012.jpg differ diff --git a/yolov5s.pt b/yolov5s.pt new file mode 100644 index 0000000000000000000000000000000000000000..cd69a5e14d396322319b06304274238c0188a522 --- /dev/null +++ b/yolov5s.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b3b748c1e592ddd8868022e8732fde20025197328490623cc16c6f24d0782ee +size 14808437