""" 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_srn.transformation import TPS_SpatialTransformerNetwork from modules_srn.feature_extraction import VGG_FeatureExtractor, RCNN_FeatureExtractor, ResNet_FeatureExtractor from modules_srn.sequence_modeling import BidirectionalLSTM from modules_srn.prediction import Attention from modules_srn.resnet_aster import ResNet_ASTER from modules_srn.bert import Bert_Ocr from modules_srn.bert import Config from modules_srn.SRN_modules import Transforme_Encoder, SRN_Decoder, Torch_transformer_encoder from modules_srn.resnet_fpn import ResNet_FPN import settings import sys # 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__() self.enableSingleCharAttrAve = enableSingleCharAttrAve self.singleChar = -1 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): preds = preds[2] # Access second index 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 # _, preds_index = preds.topk(1, dim=-1, largest=True, sorted=True) # 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_str = self.converter.decode(preds_index[:, 1:], length_for_pred) elif settings.MODEL == 'srn': _, preds_index = preds.max(2) length_for_pred = torch.IntTensor([self.opt.batch_max_length] * bs).to(self.device) preds_str = self.converter.decode(preds_index, length_for_pred) # sys.exit() elif settings.MODEL == 'parseq': preds_str, confidence = self.model.tokenizer.decode(preds) # Confidence score # ARGMAX calculation sum = torch.FloatTensor([0]*bs).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 settings.MODEL == 'vitstr' or settings.MODEL == 'srn': 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 = 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 = 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) 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): super(Model, self).__init__() self.opt = opt self.stages = {'Trans': opt.Transformation, 'Feat': opt.FeatureExtraction, 'Seq': opt.SequenceModeling, 'Pred': opt.Prediction} """ 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') """ 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) self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None, 1)) # Transform final (imgH/16-1) -> 1 elif opt.FeatureExtraction == 'AsterRes': self.FeatureExtraction = ResNet_ASTER(opt.input_channel, opt.output_channel) elif opt.FeatureExtraction == 'ResnetFpn': self.FeatureExtraction = ResNet_FPN() else: raise Exception('No FeatureExtraction module specified') self.FeatureExtraction_output = opt.output_channel # int(imgH/16-1) * 512 """ 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 elif opt.SequenceModeling == 'Bert': cfg = Config() cfg.dim = opt.output_channel; cfg.dim_c = opt.output_channel # 降维减少计算量 cfg.p_dim = opt.position_dim # 一张图片cnn编码之后的特征序列长度 cfg.max_vocab_size = opt.batch_max_length + 1 # 一张图片中最多的文字个数, +1 for EOS cfg.len_alphabet = opt.alphabet_size # 文字的类别个数 self.SequenceModeling = Bert_Ocr(cfg) elif opt.SequenceModeling == 'SRN': self.SequenceModeling = Transforme_Encoder(n_layers=2, n_position=opt.position_dim) # self.SequenceModeling = Torch_transformer_encoder(n_layers=2, n_position=opt.position_dim) self.SequenceModeling_output = 512 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) elif opt.Prediction == 'Bert_pred': pass elif opt.Prediction == 'SRN': self.Prediction = SRN_Decoder(n_position=opt.position_dim, N_max_character=opt.batch_max_character + 1, n_class=opt.alphabet_size) else: raise Exception('Prediction is neither CTC or Attn') def forward(self, input, text=None, is_train=True): """ Transformation stage """ if not self.stages['Trans'] == "None": input = self.Transformation(input) """ Feature extraction stage """ visual_feature = self.FeatureExtraction(input) # if self.stages['Feat'] == 'AsterRes' or self.stages['Feat'] == 'ResnetFpn': if self.stages['Feat'] == 'AsterRes' or self.stages['Feat'] == 'ResnetFpn': b, c, h, w = visual_feature.shape visual_feature = visual_feature.permute(0, 1, 3, 2) visual_feature = visual_feature.contiguous().view(b, c, -1) visual_feature = visual_feature.permute(0, 2, 1) # batch, seq, feature else: 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) elif self.stages['Seq'] == 'Bert': pad_mask = text contextual_feature = self.SequenceModeling(visual_feature, pad_mask) elif self.stages['Seq'] == 'SRN': contextual_feature = self.SequenceModeling(visual_feature, src_mask=None)[0] 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()) elif self.stages['Pred'] == 'Bert_pred': prediction = contextual_feature elif self.stages['Pred'] == 'SRN': prediction = self.Prediction(contextual_feature) else: prediction = self.Prediction(contextual_feature.contiguous(), text, is_train, batch_max_length=self.opt.batch_max_length) return prediction