""" Copyright (c) 2019-present NAVER Corp. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import torch import torch.nn as nn import torch.nn.functional as F from modules.transformation import TPS_SpatialTransformerNetwork from modules.feature_extraction import VGG_FeatureExtractor, RCNN_FeatureExtractor, ResNet_FeatureExtractor from modules.sequence_modeling import BidirectionalLSTM from modules.prediction import Attention from modules.vitstr import create_vitstr import math import sys import settings # singleChar - if -1 then STRScore outputs all char, however if # 0 - N, then it will output the single character confidence of the index 0 to N class STRScore(nn.Module): def __init__(self, opt, converter, device, gtStr="", enableSingleCharAttrAve=False, model=None): super(STRScore, self).__init__() if opt.modelName: settings.MODEL = opt.modelName self.enableSingleCharAttrAve = enableSingleCharAttrAve self.singleChar = -1 self.recentlyPredStr = None self.opt = opt self.converter = converter self.device = device self.gtStr = gtStr self.model = model # Pass here if you want to use it self.blank = torch.tensor([-1], dtype=torch.float).to(self.device) self.separator = torch.tensor([-2], dtype=torch.float).to(self.device) # singleChar - if >=0, then the output of STRScore will only be a single character # instead of a whole. The char index will be equal to the parameter "singleChar". def setSingleCharOutput(self, singleChar): self.singleChar = singleChar def forward(self, preds): bs = preds.shape[0] # text_for_loss, length_for_loss = self.converter.encode(labels, batch_max_length=self.opt.batch_max_length) text_for_loss_length = self.opt.batch_max_length + 1 length_for_pred = torch.IntTensor([self.opt.batch_max_length] * bs).to(self.device) if 'CTC' in self.opt.Prediction: # Calculate evaluation loss for CTC decoder. preds_size = torch.FloatTensor([preds.size(1)] * bs) if self.opt.baiduCTC: _, preds_index = preds.max(2) preds_index = preds_index.view(-1) else: _, preds_index = preds.max(2) # print("preds_index shape: ", preds_index.shape) preds_str = self.converter.decode(preds_index.data, preds_size.data) # preds_str = self.converter.decode(preds_index, length_for_pred) preds = preds.log_softmax(2).permute(1, 0, 2) elif self.opt.Transformer: # preds_index = preds_index.view(-1, self.converter.batch_max_length) # print("preds shape: ", preds.shape) # print("preds_index: ", preds_index) # preds_str = self.converter.decode(preds_index, length_for_pred) if settings.MODEL == 'vitstr': _, preds_index = preds.topk(1, dim=-1, largest=True, sorted=True) preds_str = self.converter.decode(preds_index[:, 1:], length_for_pred) elif settings.MODEL == 'parseq': preds_str, confidence = self.model.tokenizer.decode(preds) self.recentlyPredStr = preds_str[-1] # print("preds_str: ", preds_str) # print("preds_str: ", preds_str) else: preds = preds[:, :text_for_loss_length, :] # select max probabilty (greedy decoding) then decode index to character _, preds_index = preds.max(2) # print("preds shape: ", preds.shape) # print("preds_index: ", preds_index) preds_str = self.converter.decode(preds_index, length_for_pred) # print("preds_str: ", preds_str) # Confidence score # ARGMAX calculation sum = torch.FloatTensor([0]*bs).to(self.device) if self.enableSingleCharAttrAve: sum = torch.zeros((bs, preds.shape[2])).to(self.device) if self.opt.confidence_mode == 0: preds_prob = F.softmax(preds, dim=2) # preds_prob shape: torch.Size([1, 25, 96]) preds_max_prob, preds_max_idx = preds_prob.max(dim=2) # preds_max_prob shape: torch.Size([1, 25]) confidence_score_list = [] count = 0 for one_hot_preds, pred, pred_max_prob in zip(preds_prob, preds_str, preds_max_prob): if self.opt.Transformer: if settings.MODEL == 'vitstr': if self.enableSingleCharAttrAve: one_hot = one_hot_preds[self.singleChar, :] pred = pred[self.singleChar] pred_max_prob = pred_max_prob[self.singleChar] else: pred_EOS = pred.find('[s]') pred = pred[:pred_EOS] pred_max_prob = pred_max_prob[:pred_EOS] # if pred_max_prob.shape[0] == 0: continue if self.enableSingleCharAttrAve: sum[count] = one_hot # sum = one_hot # sum shape: torch.Size([96]) # sum = sum.unsqueeze(0) else: if self.opt.scorer == "cumprod": confidence_score = pred_max_prob.cumprod(dim=0)[-1] ### Maximum is 1 sum[count] += confidence_score elif self.opt.scorer == "mean": confidence_score = torch.mean(pred_max_prob) ### Maximum is 1 sum[count] += confidence_score sum = sum.unsqueeze(1) elif settings.MODEL == 'parseq': if self.enableSingleCharAttrAve: one_hot = one_hot_preds[self.singleChar, :] # pred = pred[self.singleChar] pred_max_prob = pred_max_prob[self.singleChar] else: pred_EOS = len(pred) # Predition string already has no EOS, fully intact pred_max_prob = pred_max_prob[:pred_EOS] # if pred_max_prob.shape[0] == 0: continue if self.enableSingleCharAttrAve: sum[count] = one_hot # sum shape: torch.Size([96]) # sum = sum.unsqueeze(0) else: if self.opt.scorer == "cumprod": confidence_score = pred_max_prob.cumprod(dim=0)[-1] ### Maximum is 1 sum[count] += confidence_score elif self.opt.scorer == "mean": confidence_score = torch.mean(pred_max_prob) ### Maximum is 1 sum[count] += confidence_score sum = sum.unsqueeze(1) elif 'Attn' in self.opt.Prediction: # if pred_max_prob.shape[0] == 0: continue if self.enableSingleCharAttrAve: one_hot = one_hot_preds[self.singleChar, :] sum[count] = one_hot else: pred_EOS = pred.find('[s]') pred = pred[:pred_EOS] pred_max_prob = pred_max_prob[:pred_EOS] ### Use score of all letters # pred_max_prob = pred_max_prob[0:1] ### Use score of first letter only if pred_max_prob.shape[0] == 0: continue confidence_score = pred_max_prob.cumprod(dim=0)[-1] ### Maximum is 1 sum[count] += confidence_score sum = sum.unsqueeze(1) elif 'CTC' in self.opt.Prediction: confidence_score = pred_max_prob.cumprod(dim=0)[-1] sum[count] += confidence_score sum = sum.unsqueeze(1) count += 1 # return sum.detach().cpu().numpy() # print("sumshape: ", sum.shape) elif self.opt.confidence_mode == 1: preds_prob = F.softmax(preds, dim=2) ### Predicted indices preds_max_prob = torch.argmax(preds_prob, 2) # print("preds_max_prob shape: ", preds_max_prob.shape) ### Ground truth indices gtIndices, _ = self.converter.encode([self.gtStr for i in range(0,preds_prob.shape[0])], batch_max_length=self.opt.batch_max_length-1) # print("gtIndices shape: ", gtIndices.shape) ### Acquire levenstein distance m = torch.tensor([preds_prob.shape[1] for i in range(0, gtIndices.shape[0])], dtype=torch.float).to(self.device) n = torch.tensor([preds_prob.shape[1] for i in range(0, gtIndices.shape[0])], dtype=torch.float).to(self.device) # print("m: ", m) # print("preds_max_prob dtype: ", preds_max_prob.dtype) # print("gtIndices dtype: ", gtIndices.dtype) preds_max_prob = preds_max_prob.type(torch.float) gtIndices = gtIndices.type(torch.float) r = levenshtein_distance(preds_max_prob.to(self.device), gtIndices.to(self.device), n, m, torch.cat([self.blank, self.separator]), torch.empty([], dtype=torch.float).to(self.device)) # print("r shape: ", r.shape) # confidence_score_list = [] # count = 0 # for pred, pred_max_prob in zip(preds_str, preds_max_prob): # if 'Attn' in self.opt.Prediction: # pred_EOS = pred.find('[s]') # pred = pred[:pred_EOS] # pred_max_prob = pred_max_prob[:pred_EOS] ### Use score of all letters # # pred_max_prob = pred_max_prob[0:1] ### Use score of first letter only # if pred_max_prob.shape[0] == 0: continue # confidence_score = pred_max_prob.cumprod(dim=0)[-1] # sum[count] += confidence_score # count += 1 # return sum.detach().cpu().numpy() # print("sumshape: ", sum.shape) # sum = sum.unsqueeze(1) rSoft = F.softmax(r[:,2].type(torch.float)) # rSoft = rSoft.contiguous() rNorm = rSoft.max()-rSoft sum = rNorm.unsqueeze(1) return sum class Model(nn.Module): def __init__(self, opt, device=None, converter=None, gt_text=""): super(Model, self).__init__() self.opt = opt self.device = device self.converter = converter self.gt_text = gt_text self.stages = {'Trans': opt.Transformation, 'Feat': opt.FeatureExtraction, 'Seq': opt.SequenceModeling, 'Pred': opt.Prediction, 'ViTSTR': opt.Transformer} """ Transformation """ if opt.Transformation == 'TPS': self.Transformation = TPS_SpatialTransformerNetwork( F=opt.num_fiducial, I_size=(opt.imgH, opt.imgW), I_r_size=(opt.imgH, opt.imgW), I_channel_num=opt.input_channel) else: print('No Transformation module specified') if opt.Transformer: self.vitstr = create_vitstr(num_tokens=opt.num_class, model=opt.TransformerModel) return """ FeatureExtraction """ if opt.FeatureExtraction == 'VGG': self.FeatureExtraction = VGG_FeatureExtractor(opt.input_channel, opt.output_channel) elif opt.FeatureExtraction == 'RCNN': self.FeatureExtraction = RCNN_FeatureExtractor(opt.input_channel, opt.output_channel) elif opt.FeatureExtraction == 'ResNet': self.FeatureExtraction = ResNet_FeatureExtractor(opt.input_channel, opt.output_channel) else: raise Exception('No FeatureExtraction module specified') self.FeatureExtraction_output = opt.output_channel # int(imgH/16-1) * 512 self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None, 1)) # Transform final (imgH/16-1) -> 1 """ Sequence modeling""" if opt.SequenceModeling == 'BiLSTM': self.SequenceModeling = nn.Sequential( BidirectionalLSTM(self.FeatureExtraction_output, opt.hidden_size, opt.hidden_size), BidirectionalLSTM(opt.hidden_size, opt.hidden_size, opt.hidden_size)) self.SequenceModeling_output = opt.hidden_size else: print('No SequenceModeling module specified') self.SequenceModeling_output = self.FeatureExtraction_output """ Prediction """ if opt.Prediction == 'CTC': self.Prediction = nn.Linear(self.SequenceModeling_output, opt.num_class) elif opt.Prediction == 'Attn': self.Prediction = Attention(self.SequenceModeling_output, opt.hidden_size, opt.num_class) else: raise Exception('Prediction is neither CTC or Attn') def set_labels(self, labels): self.labels = labels def patch_embed_func(self): if self.opt.Transformer: return self.vitstr.patch_embed_func() return None def setGTText(self, text): self.gt_text = text def forward(self, input, text="", seqlen=25, is_train=False): # text = torch.FloatTensor(input.shape[0], self.opt.batch_max_length + 1).fill_(0).to(self.device) # text = self.converter.encode(self.labels) if settings.MODEL == 'trba': text = self.gt_text if not self.stages['ViTSTR']: assert(len(text)>0) """ Transformation stage """ if not self.stages['Trans'] == "None": input = self.Transformation(input) if self.stages['ViTSTR']: prediction = self.vitstr(input, seqlen=seqlen) return prediction """ Feature extraction stage """ visual_feature = self.FeatureExtraction(input) visual_feature = self.AdaptiveAvgPool(visual_feature.permute(0, 3, 1, 2)) # [b, c, h, w] -> [b, w, c, h] visual_feature = visual_feature.squeeze(3) """ Sequence modeling stage """ if self.stages['Seq'] == 'BiLSTM': contextual_feature = self.SequenceModeling(visual_feature) else: contextual_feature = visual_feature # for convenience. this is NOT contextually modeled by BiLSTM """ Prediction stage """ if self.stages['Pred'] == 'CTC': prediction = self.Prediction(contextual_feature.contiguous()) else: prediction = self.Prediction(contextual_feature.contiguous(), text, is_train, batch_max_length=self.opt.batch_max_length) return prediction