def main(self): print('Starting Training...') loss_hist = Averager() loss = [] validation_losses = [] precisions = [] itr = 1 for epoch in range(self.num_epochs): self.model.train() loss_hist.reset() for images, targets in self.train_data_loader: images = list(image.to(self.device) for image in images) # targets = [{k: v.to(device) for k, v in t.items()} for t in targets] targets = [{k: v.long().to(self.device) for k, v in t.items()} for t in targets] loss_dict = self.model(images, targets) losses = sum(loss for loss in loss_dict.values()) self.validate() loss_value = losses.item() loss_hist.send(loss_value) loss.append(loss_value) self.optimizer.zero_grad() losses.backward() self.optimizer.step() if math.isnan(loss_value): plot_grad_flow(self.model.named_parameters()) raise ValueError('Loss is nan') if itr % 50 == 0: print(f"Iteration #{itr} loss: {loss_value}") itr += 1 # update the learning rate if self.lr_scheduler is not None: self.lr_scheduler.step() if self.val_dataset: precision, validation_loss = self.validate() precisions.append(precision) validation_losses.append(validation_loss) print(f'Mean Precision for Validation Data: {precision}') print(f'Validation Loss: {validation_loss}') print(f"Epoch #{epoch} loss: {loss_hist.value}") print('Finished!') return loss, precisions, validation_losses
def validate(val_loader, model, device): model.eval() itr = 1 loss_hist = Averager() loss_hist.reset() for images, targets, image_ids in val_loader: images = list(image.to(device) for image in images) targets = [{k: v.to(device) for k, v in t.items()} for t in targets] loss_dict = model(images, targets) losses = sum(loss for loss in loss_dict) loss_value = losses.item() loss_hist.send(loss_value) if itr % 20 == 0: print(f"Iteration: {itr} loss: {loss_hist.value}") itr += 1 return loss_hist.value
def train(self, opt): # src, tar dataloaders src_dataset, tar_dataset, valid_loader = self.dataloader(opt) src_dataset_size = src_dataset.total_data_size tar_dataset_size = tar_dataset.total_data_size train_size = max([src_dataset_size, tar_dataset_size]) iters_per_epoch = int(train_size / opt.batch_size) # Modify train size. Make sure both are of same size. # Modify training loop to continue giving src loss after tar is done. self.model.train() self.global_discriminator.train() self.local_discriminator.train() start_iter = 0 if opt.continue_model != '': self.load(opt.continue_model) print(" [*] Load SUCCESS") # loss averager cls_loss_avg = Averager() sim_loss_avg = Averager() loss_avg = Averager() # training loop print('training start !') start_time = time.time() best_accuracy = -1 best_norm_ED = 1e+6 # i = start_iter gamma = 0 omega = 1 epoch = 0 for step in range(start_iter, opt.num_iter + 1): epoch = step // iters_per_epoch if opt.decay_flag and step > (opt.num_iter // 2): self.d_image_opt.param_groups[0]['lr'] -= (opt.lr / (opt.num_iter // 2)) self.d_inst_opt.param_groups[0]['lr'] -= (opt.lr / (opt.num_iter // 2)) src_image, src_labels = src_dataset.get_batch() src_image = src_image.to(device) src_text, src_length = self.converter.encode( src_labels, batch_max_length=opt.batch_max_length) tar_image, tar_labels = tar_dataset.get_batch() tar_image = tar_image.to(device) tar_text, tar_length = self.converter.encode( tar_labels, batch_max_length=opt.batch_max_length) # Set gradient to zero... self.model.zero_grad() # Domain classifiers self.global_discriminator.zero_grad() self.local_discriminator.zero_grad() # Attention # align with Attention.forward src_preds, src_global_feature, src_local_feature = self.model( src_image, src_text[:, :-1]) # src_global_feature = self.model.visual_feature # src_local_feature = self.model.Prediction.context_history target = src_text[:, 1:] # without [GO] Symbol src_cls_loss = self.criterion( src_preds.view(-1, src_preds.shape[-1]), target.contiguous().view(-1)) src_global_feature = src_global_feature.view( src_global_feature.shape[0], -1) src_local_feature = src_local_feature.view( -1, src_local_feature.shape[-1]) tar_preds, tar_global_feature, tar_local_feature = self.model( tar_image, tar_text[:, :-1], is_train=False) # tar_global_feature = self.model.visual_feature # tar_local_feature = self.model.Prediction.context_history tar_global_feature = tar_global_feature.view( tar_global_feature.shape[0], -1) tar_local_feature = tar_local_feature.view( -1, tar_local_feature.shape[-1]) src_local_feature, tar_local_feature = filter_local_features( opt, src_local_feature, src_preds, tar_local_feature, tar_preds) # Add domain adaption elements # setup hyperparameter if step % 2000 == 0: p = float(step + start_iter) / opt.num_iter gamma = 2. / (1. + np.exp(-10 * p)) - 1 omega = 1 - 1. / (1. + np.exp(-10 * p)) self.global_discriminator.module.set_beta(gamma) self.local_discriminator.module.set_beta(gamma) src_d_img_score = self.global_discriminator(src_global_feature) src_d_inst_score = self.local_discriminator(src_local_feature) tar_d_img_score = self.global_discriminator(tar_global_feature) tar_d_inst_score = self.local_discriminator(tar_local_feature) src_d_img_loss = self.D_criterion( src_d_img_score, torch.zeros_like(src_d_img_score).to(device)) src_d_inst_loss = self.D_criterion( src_d_inst_score, torch.zeros_like(src_d_inst_score).to(device)) tar_d_img_loss = self.D_criterion( tar_d_img_score, torch.ones_like(tar_d_img_score).to(device)) tar_d_inst_loss = self.D_criterion( tar_d_inst_score, torch.ones_like(tar_d_inst_score).to(device)) d_img_loss = src_d_img_loss + tar_d_img_loss d_inst_loss = src_d_inst_loss + tar_d_inst_loss # Add domain loss loss = src_cls_loss.mean() + omega * (d_img_loss.mean() + d_inst_loss.mean()) loss_avg.add(loss) cls_loss_avg.add(src_cls_loss) sim_loss_avg.add(d_img_loss + d_inst_loss) # frcnn backward loss.backward() # clip_gradient(self.model, 10.) torch.nn.utils.clip_grad_norm_( self.model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) # frcnn optimizer update self.optimizer.step() # domain optimizer update self.d_inst_opt.step() self.d_image_opt.step() # validation part if step % opt.valInterval == 0: elapsed_time = time.time() - start_time print( f'[{step}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} CLS_Loss: {cls_loss_avg.val():0.5f} SIMI_Loss: {sim_loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}' ) # for log with open( f'./saved_models/{opt.experiment_name}/log_train.txt', 'a') as log: log.write( f'[{step}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}\n' ) loss_avg.reset() cls_loss_avg.reset() sim_loss_avg.reset() self.model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, labels, infer_time, length_of_data = validation( self.model, self.criterion, valid_loader, self.converter, opt) self.print_prediction_result(preds, labels, log) valid_log = f'[{step}/{opt.num_iter}] valid loss: {valid_loss:0.5f}' valid_log += f' accuracy: {current_accuracy:0.3f}, norm_ED: {current_norm_ED:0.2f}' print(valid_log) log.write(valid_log + '\n') self.model.train() self.global_discriminator.train() self.local_discriminator.train() # keep best accuracy model if current_accuracy > best_accuracy: best_accuracy = current_accuracy save_name = f'./saved_models/{opt.experiment_name}/best_accuracy.pth' self.save(opt, save_name) if current_norm_ED < best_norm_ED: best_norm_ED = current_norm_ED save_name = f'./saved_models/{opt.experiment_name}/best_norm_ED.pth' self.save(opt, save_name) best_model_log = f'best_accuracy: {best_accuracy:0.3f}, best_norm_ED: {best_norm_ED:0.2f}' print(best_model_log) log.write(best_model_log + '\n') # save model per 1e+5 iter. if (step + 1) % 1e+5 == 0: save_name = f'./saved_models/{opt.experiment_name}/iter_{step+1}.pth' self.save(opt, save_name)
def train(opt): """ dataset preparation """ if not opt.data_filtering_off: print('Filtering the images containing characters which are not in opt.character') print('Filtering the images whose label is longer than opt.batch_max_length') # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 # opt.select_data = opt.select_data.split('-')#[MJ,ST] # opt.batch_ratio = opt.batch_ratio.split('-')#[0.5,0.5] # train_dataset = Batch_Balanced_Dataset(opt) # log = open(f'./saved_models/{opt.exp_name}/log_dataset.txt', 'a') AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) # valid_dataset, valid_dataset_log = hierarchical_dataset(root=opt.valid_data, opt=opt) # train_dataset = iiit5k_dataset_builder("/media/ps/hd1/lll/textRecognition/SAR/IIIT5K/train", # "/media/ps/hd1/lll/textRecognition/SAR/IIIT5K/traindata.mat",opt) # train_dataset = PpocrDataset("/home/ldl/桌面/论文/文本识别/data/paddleocr", # "/home/ldl/桌面/论文/文本识别/data/paddleocr/label/train.txt",6625*100) # train_dataset_chinese = TextRecognition(4068*75,opt.charalength,opt.chinesefile) # train_dataset_english = TextRecognition(4068*25,opt.charalength,opt.englishfile) # train_dataset = ConcatDataset([train_dataset_chinese,train_dataset_english]) train_dataset_xunfeieng = mytrdg_cutimg_dataset(total_img_path='/home/ldl/桌面/论文/文本识别/data/finish_data/eng_image/train/img', annotation_path='/home/ldl/桌面/论文/文本识别/data/finish_data/eng_image/train/gt') train_dataset_xunfeichn = mytrdg_cutimg_dataset(total_img_path='/home/ldl/桌面/论文/文本识别/data/finish_data/lan_image/train/img', annotation_path='/home/ldl/桌面/论文/文本识别/data/finish_data/lan_image/train/gt') train_dataset = ConcatDataset([train_dataset_xunfeichn,train_dataset_xunfeieng]) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=opt.batch_size, shuffle=True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid) # valid_dataset = iiit5k_dataset_builder("/media/ps/hd1/lll/textRecognition/SAR/IIIT5K/test", # "/media/ps/hd1/lll/textRecognition/SAR/IIIT5K/testdata.mat",opt) # valid_dataset_chinese = TextRecognition(1001,opt.charalength,opt.chinesefile) # valid_dataset_english = TextRecognition(1001,opt.charalength,opt.englishfile) # valid_dataset = ConcatDataset([valid_dataset_chinese,valid_dataset_english]) valid_dataset_xunfeieng = mytrdg_cutimg_dataset(total_img_path='/home/ldl/桌面/论文/文本识别/data/finish_data/eng_image/test/img', annotation_path='/home/ldl/桌面/论文/文本识别/data/finish_data/eng_image/test/gt') valid_dataset_xunfeichn = mytrdg_cutimg_dataset(total_img_path='/home/ldl/桌面/论文/文本识别/data/finish_data/lan_image/test/img', annotation_path='/home/ldl/桌面/论文/文本识别/data/finish_data/lan_image/test/gt') valid_dataset = ConcatDataset([valid_dataset_xunfeichn,valid_dataset_xunfeieng]) # valid_dataset = PpocrDataset("/home/ldl/桌面/论文/文本识别/data/paddleocr/Synthetic_Chinese_String_Dataset/images", # "/home/ldl/桌面/论文/文本识别/data/paddleocr/Synthetic_Chinese_String_Dataset/test.txt",6625, # split='jpg') valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle=True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid) # log.write(valid_dataset_log) print('-' * 80) # log.write('-' * 80 + '\n') # log.close() """ model configuration """ if 'CTC' in opt.Prediction: if opt.baiduCTC: converter = CTCLabelConverterForBaiduWarpctc(opt.character) else: converter = CTCLabelConverter(opt.character) else: converter = AttnLabelConverter(opt.character) opt.num_class = len(converter.character) if opt.rgb: opt.input_channel = 3 model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) # weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # data parallel for multi-GPU if opt.num_gpu > 1: model = torch.nn.DataParallel(model).to(device) else: model.to(device) model.train() if opt.saved_model != '': print(f'loading pretrained model from {opt.saved_model}') if opt.FT: model.load_state_dict(torch.load(opt.saved_model), strict=False) else: model.load_state_dict(torch.load(opt.saved_model)) print("Model:") # print(model) """ setup loss """ if 'CTC' in opt.Prediction: if opt.baiduCTC: # need to install warpctc. see our guideline. from warpctc_pytorch import CTCLoss criterion = CTCLoss() else: criterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0 # loss averager loss_avg = Averager() # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable params num : ', sum(params_num)) print(f"Train dataset length {len(train_dataset)}") print(f"Train dataset length {len(valid_dataset)}") # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer # if opt.adam: # optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) # else: # optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) optimizer = optim.Adam(filtered_parameters,lr=0.0001) print("Optimizer:") print(optimizer) """ final options """ # print(opt) with open(f'./saved_models/{opt.exp_name}/opt.txt', 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 # if opt.saved_model != '': # try: # start_iter = int(opt.saved_model.split('_')[-1].split('.')[0]) # print(f'continue to train, start_iter: {start_iter}') # except: # pass start_time = time.time() best_accuracy = -1 best_norm_ED = -1 iteration = start_iter epoch = 0 train_iter_loader = iter(train_loader) while(True): # train part try: image_tensors, labels = train_iter_loader.next() # if len(labels)>80: # print(labels) print("{:4}".format(iteration),end='\r') except StopIteration: epoch += 1 print(f"epoch:{epoch}") # if epoch >= 1: # break # train_loader = torch.utils.data.DataLoader( # train_dataset, batch_size=opt.batch_size, # shuffle=True, # 'True' to check training progress with validation function. # num_workers=int(opt.workers), # collate_fn=AlignCollate_valid) train_iter_loader = iter(train_loader) continue image = image_tensors.to(device) text, length = converter.encode(labels, batch_max_length=opt.batch_max_length) batch_size = image.size(0) if 'CTC' in opt.Prediction: preds = model(image, text) preds_size = torch.IntTensor([preds.size(1)] * preds.size(0)) if opt.baiduCTC: preds = preds.permute(1, 0, 2) # to use CTCLoss format cost = criterion(preds, text, preds_size, length) / batch_size else: preds = preds.log_softmax(2).permute(1, 0, 2) try: cost = criterion(preds, text, preds_size, length) except Exception: print(preds.shape,preds_size.shape) raise '' else: preds = model(image, text[:, :-1]) # align with Attention.forward target = text[:, 1:] # without [GO] Symbol cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) model.zero_grad() cost.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) optimizer.step() loss_avg.add(cost) # validation part if (iteration + 1) % opt.valInterval == 0 or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0' elapsed_time = time.time() - start_time # for log with open(f'./saved_models/{opt.exp_name}/log_train.txt', 'a') as log: model.eval() print("validation") with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation( model, criterion, valid_loader, converter, opt) model.train() # training loss and validation loss loss_log = f'[{iteration+1}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}' loss_avg.reset() current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}' # keep best accuracy model (on valid dataset) if current_accuracy >= best_accuracy: best_accuracy = current_accuracy torch.save(model.state_dict(), f'./saved_models/{opt.exp_name}/best_accuracy.pth') if current_norm_ED >= best_norm_ED: best_norm_ED = current_norm_ED torch.save(model.state_dict(), f'./saved_models/{opt.exp_name}/best_norm_ED.pth') best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}' loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}' print(loss_model_log) log.write(loss_model_log + '\n') # show some predicted results dashed_line = '-' * 80 head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n' for gt, pred, confidence in zip(labels[:5], preds[:5], confidence_score[:5]): if 'Attn' in opt.Prediction: gt = gt[:gt.find('[s]')] pred = pred[:pred.find('[s]')] predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n' predicted_result_log += f'{dashed_line}' print(predicted_result_log) log.write(predicted_result_log + '\n') # save model per 1e+5 iter. if (iteration + 1) % 1e+4 == 0: torch.save( model.state_dict(), f'./saved_models/{opt.exp_name}/iter_{iteration+1}.pth') if (iteration + 1) == opt.num_iter: print('end the training') sys.exit() iteration += 1
def train(opt): if opt.use_tb: tb_dir = f'/home_hongdo/{getpass.getuser()}/tb/{opt.experiment_name}' print('tensorboard : ', tb_dir) if not os.path.exists(tb_dir): os.makedirs(tb_dir) writer = SummaryWriter(log_dir=tb_dir) """ dataset preparation """ if not opt.data_filtering_off: print( 'Filtering the images containing characters which are not in opt.character' ) print( 'Filtering the images whose label is longer than opt.batch_max_length' ) # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') train_dataset = Batch_Balanced_Dataset(opt) # log = open(f'./saved_models/{opt.experiment_name}/log_dataset.txt', 'a') log = open(f'{save_dir}/{opt.experiment_name}/log_dataset.txt', 'a') AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) valid_dataset, valid_dataset_log = hierarchical_dataset( root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle= True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() """ model configuration """ converter = AttnLabelConverter(opt.character) opt.num_class = len(converter.character) if opt.rgb: opt.input_channel = 3 # sekim for transfer learning model = Model(opt, 38) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length) # weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # data parallel for multi-GPU model = torch.nn.DataParallel(model).to(device) if opt.saved_model != '': print(f'loading pretrained model from {opt.saved_model}') if opt.FT: model.load_state_dict(torch.load(opt.saved_model), strict=False) else: model.load_state_dict(torch.load(opt.saved_model)) # sekim change last layer in_feature = model.module.Prediction.generator.in_features model.module.Prediction.attention_cell.rnn = nn.LSTMCell( 256 + opt.num_class, 256).to(device) model.module.Prediction.generator = nn.Linear(in_feature, opt.num_class).to(device) print(model.module.Prediction.generator) print("Model:") print(model) model.train() """ setup loss """ criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to( device) # ignore [GO] token = ignore index 0 # loss averager loss_avg = Averager() # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable params num : ', sum(params_num)) # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.adam: optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("Optimizer:") print(optimizer) """ final options """ # with open(f'./saved_models/{opt.experiment_name}/opt.txt', 'a') as opt_file: with open(f'{save_dir}/{opt.experiment_name}/opt.txt', 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_model != '': try: start_iter = int(opt.saved_model.split('_')[-1].split('.')[0]) print("-------------------------------------------------") print(f'continue to train, start_iter: {start_iter}') except: pass start_time = time.time() best_accuracy = -1 best_norm_ED = -1 i = start_iter while (True): # train part image_tensors, labels = train_dataset.get_batch() image = image_tensors.to(device) text, length = converter.encode(labels, batch_max_length=opt.batch_max_length) batch_size = image.size(0) preds = model(image, text[:, :-1]) # align with Attention.forward target = text[:, 1:] # without [GO] Symbol cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) model.zero_grad() cost.backward() torch.nn.utils.clip_grad_norm_( model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) optimizer.step() loss_avg.add(cost) # validation part if i % opt.valInterval == 0: elapsed_time = time.time() - start_time # for log with open(f'{save_dir}/{opt.experiment_name}/log_train.txt', 'a') as log: # with open(f'./saved_models/{opt.experiment_name}/log_train.txt', 'a') as log: model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation( model, criterion, valid_loader, converter, opt) model.train() # training loss and validation loss loss_log = f'[{i}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}' loss_avg.reset() if opt.use_tb: writer.add_scalar('OCR_loss/train_loss', loss_avg.val(), i) writer.add_scalar('OCR_loss/validation_loss', valid_loss, i) current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}' # keep best accuracy model (on valid dataset) if current_accuracy > best_accuracy: best_accuracy = current_accuracy # torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/best_accuracy.pth') torch.save( model.state_dict(), f'{save_dir}/{opt.experiment_name}/best_accuracy.pth') if current_norm_ED > best_norm_ED: best_norm_ED = current_norm_ED # torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/best_norm_ED.pth') torch.save( model.state_dict(), f'{save_dir}/{opt.experiment_name}/best_norm_ED.pth') best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}' loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}' print(loss_model_log) log.write(loss_model_log + '\n') # show some predicted results dashed_line = '-' * 80 head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n' for gt, pred, confidence in zip(labels[:5], preds[:5], confidence_score[:5]): gt = gt[:gt.find('[s]')] pred = pred[:pred.find('[s]')] predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n' predicted_result_log += f'{dashed_line}' print(predicted_result_log) log.write(predicted_result_log + '\n') # save model per 1e+5 iter. if (i + 1) % 1e+5 == 0: # torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/iter_{i+1}.pth') torch.save(model.state_dict(), f'{save_dir}/{opt.experiment_name}/iter_{i + 1}.pth') if i == opt.num_iter: print('end the training') sys.exit() i += 1
def train(opt): plotDir = os.path.join(opt.exp_dir,opt.exp_name,'plots') if not os.path.exists(plotDir): os.makedirs(plotDir) lib.print_model_settings(locals().copy()) """ dataset preparation """ if not opt.data_filtering_off: print('Filtering the images containing characters which are not in opt.character') print('Filtering the images whose label is longer than opt.batch_max_length') # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') #considering the real images for discriminator opt.batch_size = opt.batch_size*2 train_dataset = Batch_Balanced_Dataset(opt) log = open(os.path.join(opt.exp_dir,opt.exp_name,'log_dataset.txt'), 'a') AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) valid_dataset, valid_dataset_log = hierarchical_dataset(root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle=False, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() """ model configuration """ if 'CTC' in opt.Prediction: converter = CTCLabelConverter(opt.character) else: converter = AttnLabelConverter(opt.character) opt.num_class = len(converter.character) if opt.rgb: opt.input_channel = 3 model = AdaINGen(opt) ocrModel = Model(opt) disModel = MsImageDisV1(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) # weight initialization for currModel in [model, ocrModel, disModel]: for name, param in currModel.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # data parallel for multi-GPU ocrModel = torch.nn.DataParallel(ocrModel).to(device) if not opt.ocrFixed: ocrModel.train() else: ocrModel.module.Transformation.eval() ocrModel.module.FeatureExtraction.eval() ocrModel.module.AdaptiveAvgPool.eval() # ocrModel.module.SequenceModeling.eval() ocrModel.module.Prediction.eval() model = torch.nn.DataParallel(model).to(device) model.train() disModel = torch.nn.DataParallel(disModel).to(device) disModel.train() if opt.modelFolderFlag: if len(glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_synth.pth")))>0: opt.saved_synth_model = glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_synth.pth"))[-1] if len(glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_dis.pth")))>0: opt.saved_dis_model = glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_dis.pth"))[-1] #loading pre-trained model if opt.saved_ocr_model != '' and opt.saved_ocr_model != 'None': print(f'loading pretrained ocr model from {opt.saved_ocr_model}') if opt.FT: ocrModel.load_state_dict(torch.load(opt.saved_ocr_model), strict=False) else: ocrModel.load_state_dict(torch.load(opt.saved_ocr_model)) print("OCRModel:") print(ocrModel) if opt.saved_synth_model != '' and opt.saved_synth_model != 'None': print(f'loading pretrained synth model from {opt.saved_synth_model}') if opt.FT: model.load_state_dict(torch.load(opt.saved_synth_model), strict=False) else: model.load_state_dict(torch.load(opt.saved_synth_model)) print("SynthModel:") print(model) if opt.saved_dis_model != '' and opt.saved_dis_model != 'None': print(f'loading pretrained discriminator model from {opt.saved_dis_model}') if opt.FT: disModel.load_state_dict(torch.load(opt.saved_dis_model), strict=False) else: disModel.load_state_dict(torch.load(opt.saved_dis_model)) print("DisModel:") print(disModel) """ setup loss """ if 'CTC' in opt.Prediction: ocrCriterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: ocrCriterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0 recCriterion = torch.nn.L1Loss() styleRecCriterion = torch.nn.L1Loss() # loss averager loss_avg_ocr = Averager() loss_avg = Averager() loss_avg_dis = Averager() loss_avg_ocrRecon_1 = Averager() loss_avg_ocrRecon_2 = Averager() loss_avg_gen = Averager() loss_avg_imgRecon = Averager() loss_avg_styRecon = Averager() ##---------------------------------------## # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable params num : ', sum(params_num)) # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.optim=='adam': optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, opt.beta2), weight_decay=opt.weight_decay) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps, weight_decay=opt.weight_decay) print("SynthOptimizer:") print(optimizer) #filter parameters for OCR training ocr_filtered_parameters = [] ocr_params_num = [] for p in filter(lambda p: p.requires_grad, ocrModel.parameters()): ocr_filtered_parameters.append(p) ocr_params_num.append(np.prod(p.size())) print('OCR Trainable params num : ', sum(ocr_params_num)) # setup optimizer if opt.optim=='adam': ocr_optimizer = optim.Adam(ocr_filtered_parameters, lr=opt.lr, betas=(opt.beta1, opt.beta2), weight_decay=opt.weight_decay) else: ocr_optimizer = optim.Adadelta(ocr_filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps, weight_decay=opt.weight_decay) print("OCROptimizer:") print(ocr_optimizer) #filter parameters for OCR training dis_filtered_parameters = [] dis_params_num = [] for p in filter(lambda p: p.requires_grad, disModel.parameters()): dis_filtered_parameters.append(p) dis_params_num.append(np.prod(p.size())) print('Dis Trainable params num : ', sum(dis_params_num)) # setup optimizer if opt.optim=='adam': dis_optimizer = optim.Adam(dis_filtered_parameters, lr=opt.lr, betas=(opt.beta1, opt.beta2), weight_decay=opt.weight_decay) else: dis_optimizer = optim.Adadelta(dis_filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps, weight_decay=opt.weight_decay) print("DisOptimizer:") print(dis_optimizer) ##---------------------------------------## """ final options """ with open(os.path.join(opt.exp_dir,opt.exp_name,'opt.txt'), 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_synth_model != '' and opt.saved_synth_model != 'None': try: start_iter = int(opt.saved_synth_model.split('_')[-2].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass #get schedulers scheduler = get_scheduler(optimizer,opt) ocr_scheduler = get_scheduler(ocr_optimizer,opt) dis_scheduler = get_scheduler(dis_optimizer,opt) start_time = time.time() best_accuracy = -1 best_norm_ED = -1 best_accuracy_ocr = -1 best_norm_ED_ocr = -1 iteration = start_iter cntr=0 while(True): # train part if opt.lr_policy !="None": scheduler.step() ocr_scheduler.step() dis_scheduler.step() image_tensors_all, labels_1_all, labels_2_all = train_dataset.get_batch() # ## comment # pdb.set_trace() # for imgCntr in range(image_tensors.shape[0]): # save_image(tensor2im(image_tensors[imgCntr]),'temp/'+str(imgCntr)+'.png') # pdb.set_trace() # ### # print(cntr) cntr+=1 disCnt = int(image_tensors_all.size(0)/2) image_tensors, image_tensors_real, labels_gt, labels_2 = image_tensors_all[:disCnt], image_tensors_all[disCnt:disCnt+disCnt], labels_1_all[:disCnt], labels_2_all[:disCnt] image = image_tensors.to(device) image_real = image_tensors_real.to(device) batch_size = image.size(0) ##-----------------------------------## #generate text(labels) from ocr.forward if opt.ocrFixed: # ocrModel.eval() length_for_pred = torch.IntTensor([opt.batch_max_length] * batch_size).to(device) text_for_pred = torch.LongTensor(batch_size, opt.batch_max_length + 1).fill_(0).to(device) if 'CTC' in opt.Prediction: preds = ocrModel(image, text_for_pred) preds = preds[:, :text_for_loss.shape[1] - 1, :] preds_size = torch.IntTensor([preds.size(1)] * batch_size) _, preds_index = preds.max(2) labels_1 = converter.decode(preds_index.data, preds_size.data) else: preds = ocrModel(image, text_for_pred, is_train=False) _, preds_index = preds.max(2) labels_1 = converter.decode(preds_index, length_for_pred) for idx, pred in enumerate(labels_1): pred_EOS = pred.find('[s]') labels_1[idx] = pred[:pred_EOS] # prune after "end of sentence" token ([s]) # ocrModel.train() else: labels_1 = labels_gt ##-----------------------------------## text_1, length_1 = converter.encode(labels_1, batch_max_length=opt.batch_max_length) text_2, length_2 = converter.encode(labels_2, batch_max_length=opt.batch_max_length) #forward pass from style and word generator images_recon_1, images_recon_2, style = model(image, text_1, text_2) if 'CTC' in opt.Prediction: if not opt.ocrFixed: #ocr training with orig image preds_ocr = ocrModel(image, text_1) preds_size_ocr = torch.IntTensor([preds_ocr.size(1)] * batch_size) preds_ocr = preds_ocr.log_softmax(2).permute(1, 0, 2) ocrCost_train = ocrCriterion(preds_ocr, text_1, preds_size_ocr, length_1) #content loss for reconstructed images preds_1 = ocrModel(images_recon_1, text_1) preds_size_1 = torch.IntTensor([preds_1.size(1)] * batch_size) preds_1 = preds_1.log_softmax(2).permute(1, 0, 2) preds_2 = ocrModel(images_recon_2, text_2) preds_size_2 = torch.IntTensor([preds_2.size(1)] * batch_size) preds_2 = preds_2.log_softmax(2).permute(1, 0, 2) ocrCost_1 = ocrCriterion(preds_1, text_1, preds_size_1, length_1) ocrCost_2 = ocrCriterion(preds_2, text_2, preds_size_2, length_2) # ocrCost = 0.5*( ocrCost_1 + ocrCost_2 ) else: if not opt.ocrFixed: #ocr training with orig image preds_ocr = ocrModel(image, text_1[:, :-1]) # align with Attention.forward target_ocr = text_1[:, 1:] # without [GO] Symbol ocrCost_train = ocrCriterion(preds_ocr.view(-1, preds_ocr.shape[-1]), target_ocr.contiguous().view(-1)) #content loss for reconstructed images preds_1 = ocrModel(images_recon_1, text_1[:, :-1], is_train=False) # align with Attention.forward target_1 = text_1[:, 1:] # without [GO] Symbol preds_2 = ocrModel(images_recon_2, text_2[:, :-1], is_train=False) # align with Attention.forward target_2 = text_2[:, 1:] # without [GO] Symbol ocrCost_1 = ocrCriterion(preds_1.view(-1, preds_1.shape[-1]), target_1.contiguous().view(-1)) ocrCost_2 = ocrCriterion(preds_2.view(-1, preds_2.shape[-1]), target_2.contiguous().view(-1)) # ocrCost = 0.5*(ocrCost_1+ocrCost_2) if not opt.ocrFixed: #training OCR ocrModel.zero_grad() ocrCost_train.backward() # torch.nn.utils.clip_grad_norm_(ocrModel.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) ocr_optimizer.step() #if ocr is fixed; ignore this loss loss_avg_ocr.add(ocrCost_train) else: loss_avg_ocr.add(torch.tensor(0.0)) #Domain discriminator: Dis update disModel.zero_grad() disCost = opt.disWeight*0.5*(disModel.module.calc_dis_loss(images_recon_1.detach(), image_real) + disModel.module.calc_dis_loss(images_recon_2.detach(), image)) disCost.backward() # torch.nn.utils.clip_grad_norm_(disModel.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) dis_optimizer.step() loss_avg_dis.add(disCost) # #[Style Encoder] + [Word Generator] update #Adversarial loss disGenCost = 0.5*(disModel.module.calc_gen_loss(images_recon_1)+disModel.module.calc_gen_loss(images_recon_2)) #Input reconstruction loss recCost = recCriterion(images_recon_1,image) #Pair style reconstruction loss if opt.styleReconWeight == 0.0: styleRecCost = torch.tensor(0.0) else: if opt.styleDetach: styleRecCost = styleRecCriterion(model(images_recon_2, None, None, styleFlag=True), style.detach()) else: styleRecCost = styleRecCriterion(model(images_recon_2, None, None, styleFlag=True), style) #OCR Content cost ocrCost = 0.5*(ocrCost_1+ocrCost_2) cost = opt.ocrWeight*ocrCost + opt.reconWeight*recCost + opt.disWeight*disGenCost + opt.styleReconWeight*styleRecCost model.zero_grad() ocrModel.zero_grad() disModel.zero_grad() cost.backward() # torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) optimizer.step() loss_avg.add(cost) #Individual losses loss_avg_ocrRecon_1.add(opt.ocrWeight*0.5*ocrCost_1) loss_avg_ocrRecon_2.add(opt.ocrWeight*0.5*ocrCost_2) loss_avg_gen.add(opt.disWeight*disGenCost) loss_avg_imgRecon.add(opt.reconWeight*recCost) loss_avg_styRecon.add(opt.styleReconWeight*styleRecCost) # validation part if (iteration + 1) % opt.valInterval == 0 or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0' #Save training images os.makedirs(os.path.join(opt.exp_dir,opt.exp_name,'trainImages',str(iteration)), exist_ok=True) for trImgCntr in range(batch_size): try: save_image(tensor2im(image[trImgCntr].detach()),os.path.join(opt.exp_dir,opt.exp_name,'trainImages',str(iteration),str(trImgCntr)+'_input_'+labels_gt[trImgCntr]+'.png')) save_image(tensor2im(images_recon_1[trImgCntr].detach()),os.path.join(opt.exp_dir,opt.exp_name,'trainImages',str(iteration),str(trImgCntr)+'_recon_'+labels_1[trImgCntr]+'.png')) save_image(tensor2im(images_recon_2[trImgCntr].detach()),os.path.join(opt.exp_dir,opt.exp_name,'trainImages',str(iteration),str(trImgCntr)+'_pair_'+labels_2[trImgCntr]+'.png')) except: print('Warning while saving training image') elapsed_time = time.time() - start_time # for log with open(os.path.join(opt.exp_dir,opt.exp_name,'log_train.txt'), 'a') as log: model.eval() ocrModel.module.Transformation.eval() ocrModel.module.FeatureExtraction.eval() ocrModel.module.AdaptiveAvgPool.eval() ocrModel.module.SequenceModeling.eval() ocrModel.module.Prediction.eval() disModel.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation_synth_lrw_res( iteration, model, ocrModel, disModel, recCriterion, styleRecCriterion, ocrCriterion, valid_loader, converter, opt) model.train() if not opt.ocrFixed: ocrModel.train() else: # ocrModel.module.Transformation.eval() # ocrModel.module.FeatureExtraction.eval() # ocrModel.module.AdaptiveAvgPool.eval() ocrModel.module.SequenceModeling.train() # ocrModel.module.Prediction.eval() disModel.train() # training loss and validation loss loss_log = f'[{iteration+1}/{opt.num_iter}] Train OCR loss: {loss_avg_ocr.val():0.5f}, Train Synth loss: {loss_avg.val():0.5f}, Train Dis loss: {loss_avg_dis.val():0.5f}, Valid OCR loss: {valid_loss[0]:0.5f}, Valid Synth loss: {valid_loss[1]:0.5f}, Valid Dis loss: {valid_loss[2]:0.5f}, Elapsed_time: {elapsed_time:0.5f}' current_model_log_ocr = f'{"Current_accuracy_OCR":17s}: {current_accuracy[0]:0.3f}, {"Current_norm_ED_OCR":17s}: {current_norm_ED[0]:0.2f}' current_model_log_1 = f'{"Current_accuracy_recon":17s}: {current_accuracy[1]:0.3f}, {"Current_norm_ED_recon":17s}: {current_norm_ED[1]:0.2f}' current_model_log_2 = f'{"Current_accuracy_pair":17s}: {current_accuracy[2]:0.3f}, {"Current_norm_ED_pair":17s}: {current_norm_ED[2]:0.2f}' #plotting lib.plot.plot(os.path.join(plotDir,'Train-OCR-Loss'), loss_avg_ocr.val().item()) lib.plot.plot(os.path.join(plotDir,'Train-Synth-Loss'), loss_avg.val().item()) lib.plot.plot(os.path.join(plotDir,'Train-Dis-Loss'), loss_avg_dis.val().item()) lib.plot.plot(os.path.join(plotDir,'Train-OCR-Recon1-Loss'), loss_avg_ocrRecon_1.val().item()) lib.plot.plot(os.path.join(plotDir,'Train-OCR-Recon2-Loss'), loss_avg_ocrRecon_2.val().item()) lib.plot.plot(os.path.join(plotDir,'Train-Gen-Loss'), loss_avg_gen.val().item()) lib.plot.plot(os.path.join(plotDir,'Train-ImgRecon1-Loss'), loss_avg_imgRecon.val().item()) lib.plot.plot(os.path.join(plotDir,'Train-StyRecon2-Loss'), loss_avg_styRecon.val().item()) lib.plot.plot(os.path.join(plotDir,'Valid-OCR-Loss'), valid_loss[0].item()) lib.plot.plot(os.path.join(plotDir,'Valid-Synth-Loss'), valid_loss[1].item()) lib.plot.plot(os.path.join(plotDir,'Valid-Dis-Loss'), valid_loss[2].item()) lib.plot.plot(os.path.join(plotDir,'Valid-OCR-Recon1-Loss'), valid_loss[3].item()) lib.plot.plot(os.path.join(plotDir,'Valid-OCR-Recon2-Loss'), valid_loss[4].item()) lib.plot.plot(os.path.join(plotDir,'Valid-Gen-Loss'), valid_loss[5].item()) lib.plot.plot(os.path.join(plotDir,'Valid-ImgRecon1-Loss'), valid_loss[6].item()) lib.plot.plot(os.path.join(plotDir,'Valid-StyRecon2-Loss'), valid_loss[7].item()) lib.plot.plot(os.path.join(plotDir,'Orig-OCR-WordAccuracy'), current_accuracy[0]) lib.plot.plot(os.path.join(plotDir,'Recon-OCR-WordAccuracy'), current_accuracy[1]) lib.plot.plot(os.path.join(plotDir,'Pair-OCR-WordAccuracy'), current_accuracy[2]) lib.plot.plot(os.path.join(plotDir,'Orig-OCR-CharAccuracy'), current_norm_ED[0]) lib.plot.plot(os.path.join(plotDir,'Recon-OCR-CharAccuracy'), current_norm_ED[1]) lib.plot.plot(os.path.join(plotDir,'Pair-OCR-CharAccuracy'), current_norm_ED[2]) # keep best accuracy model (on valid dataset) if current_accuracy[1] > best_accuracy: best_accuracy = current_accuracy[1] torch.save(model.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_accuracy.pth')) torch.save(disModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_accuracy_dis.pth')) if current_norm_ED[1] > best_norm_ED: best_norm_ED = current_norm_ED[1] torch.save(model.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_norm_ED.pth')) torch.save(disModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_norm_ED_dis.pth')) best_model_log = f'{"Best_accuracy_Recon":17s}: {best_accuracy:0.3f}, {"Best_norm_ED_Recon":17s}: {best_norm_ED:0.2f}' # keep best accuracy model (on valid dataset) if current_accuracy[0] > best_accuracy_ocr: best_accuracy_ocr = current_accuracy[0] if not opt.ocrFixed: torch.save(ocrModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_accuracy_ocr.pth')) if current_norm_ED[0] > best_norm_ED_ocr: best_norm_ED_ocr = current_norm_ED[0] if not opt.ocrFixed: torch.save(ocrModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_norm_ED_ocr.pth')) best_model_log_ocr = f'{"Best_accuracy_ocr":17s}: {best_accuracy_ocr:0.3f}, {"Best_norm_ED_ocr":17s}: {best_norm_ED_ocr:0.2f}' loss_model_log = f'{loss_log}\n{current_model_log_ocr}\n{current_model_log_1}\n{current_model_log_2}\n{best_model_log_ocr}\n{best_model_log}' print(loss_model_log) log.write(loss_model_log + '\n') # show some predicted results dashed_line = '-' * 80 head = f'{"Ground Truth":32s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n' for gt_ocr, pred_ocr, confidence_ocr, gt_1, pred_1, confidence_1, gt_2, pred_2, confidence_2 in zip(labels[0][:5], preds[0][:5], confidence_score[0][:5], labels[1][:5], preds[1][:5], confidence_score[1][:5], labels[2][:5], preds[2][:5], confidence_score[2][:5]): if 'Attn' in opt.Prediction: # gt_ocr = gt_ocr[:gt_ocr.find('[s]')] pred_ocr = pred_ocr[:pred_ocr.find('[s]')] # gt_1 = gt_1[:gt_1.find('[s]')] pred_1 = pred_1[:pred_1.find('[s]')] # gt_2 = gt_2[:gt_2.find('[s]')] pred_2 = pred_2[:pred_2.find('[s]')] predicted_result_log += f'{"ocr"}: {gt_ocr:27s} | {pred_ocr:25s} | {confidence_ocr:0.4f}\t{str(pred_ocr == gt_ocr)}\n' predicted_result_log += f'{"recon"}: {gt_1:25s} | {pred_1:25s} | {confidence_1:0.4f}\t{str(pred_1 == gt_1)}\n' predicted_result_log += f'{"pair"}: {gt_2:26s} | {pred_2:25s} | {confidence_2:0.4f}\t{str(pred_2 == gt_2)}\n' predicted_result_log += f'{dashed_line}' print(predicted_result_log) log.write(predicted_result_log + '\n') loss_avg_ocr.reset() loss_avg.reset() loss_avg_dis.reset() loss_avg_ocrRecon_1.reset() loss_avg_ocrRecon_2.reset() loss_avg_gen.reset() loss_avg_imgRecon.reset() loss_avg_styRecon.reset() lib.plot.flush() lib.plot.tick() # save model per 1e+5 iter. if (iteration) % 1e+5 == 0: torch.save( model.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'iter_'+str(iteration+1)+'_synth.pth')) if not opt.ocrFixed: torch.save( ocrModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'iter_'+str(iteration+1)+'_ocr.pth')) torch.save( disModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'iter_'+str(iteration+1)+'_dis.pth')) if (iteration + 1) == opt.num_iter: print('end the training') sys.exit() iteration += 1
def train(opt): """ dataset preparation """ if not opt.data_filtering_off: print( 'Filtering the images containing characters which are not in opt.character' ) print( 'Filtering the images whose label is longer than opt.batch_max_length' ) opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') train_dataset = Batch_Balanced_Dataset(opt) log = open(f'./saved_models/{opt.exp_name}/log_dataset.txt', 'a') AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) valid_dataset, valid_dataset_log = hierarchical_dataset( root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle= True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() """ model configuration """ if 'CTC' in opt.Prediction: if opt.baiduCTC: converter = CTCLabelConverterForBaiduWarpctc(opt.character) else: converter = CTCLabelConverter(opt.character) else: converter = AttnLabelConverter(opt.character) opt.num_class = len(converter.character) if opt.rgb: opt.input_channel = 3 model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) # weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # data parallel for multi-GPU model = torch.nn.DataParallel(model).to(device) model.train() if opt.saved_model != '': print(f'loading pretrained model from {opt.saved_model}') if opt.FT: model.load_state_dict(torch.load(opt.saved_model), strict=False) else: model.load_state_dict(torch.load(opt.saved_model)) print("Model:") print(model) """ setup loss """ if 'CTC' in opt.Prediction: if opt.baiduCTC: # need to install warpctc. see our guideline. from warpctc_pytorch import CTCLoss criterion = CTCLoss() else: criterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to( device) # ignore [GO] token = ignore index 0 # loss averager loss_avg = Averager() # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable params num : ', sum(params_num)) # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.adam: optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("Optimizer:") print(optimizer) """ final options """ # print(opt) with open(f'./saved_models/{opt.exp_name}/opt.txt', 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_model != '': try: start_iter = int(opt.saved_model.split('_')[-1].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass start_time = time.time() best_accuracy = -1 best_norm_ED = -1 iteration = start_iter while (True): # train part image_tensors, labels = train_dataset.get_batch() image = image_tensors.to(device) text, length = converter.encode(labels, batch_max_length=opt.batch_max_length) batch_size = image.size(0) if 'CTC' in opt.Prediction: preds = model(image, text) preds_size = torch.IntTensor([preds.size(1)] * batch_size) if opt.baiduCTC: preds = preds.permute(1, 0, 2) # to use CTCLoss format cost = criterion(preds, text, preds_size, length) / batch_size else: preds = preds.log_softmax(2).permute(1, 0, 2) cost = criterion(preds, text, preds_size, length) else: preds = model(image, text[:, :-1]) # align with Attention.forward target = text[:, 1:] # without [GO] Symbol cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) model.zero_grad() cost.backward() torch.nn.utils.clip_grad_norm_( model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) optimizer.step() loss_avg.add(cost) # validation part if ( iteration + 1 ) % opt.valInterval == 0 or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0' elapsed_time = time.time() - start_time # for log with open(f'./saved_models/{opt.exp_name}/log_train.txt', 'a') as log: model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation( model, criterion, valid_loader, converter, opt) model.train() # training loss and validation loss loss_log = f'[{iteration+1}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}' loss_avg.reset() current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}' # keep best accuracy model (on valid dataset) if current_accuracy > best_accuracy: best_accuracy = current_accuracy torch.save( model.state_dict(), f'./saved_models/{opt.exp_name}/best_accuracy.pth') if current_norm_ED > best_norm_ED: best_norm_ED = current_norm_ED torch.save( model.state_dict(), f'./saved_models/{opt.exp_name}/best_norm_ED.pth') best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}' loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}' print(loss_model_log) log.write(loss_model_log + '\n') # show some predicted results dashed_line = '-' * 80 head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n' for gt, pred, confidence in zip(labels[:5], preds[:5], confidence_score[:5]): if 'Attn' in opt.Prediction: gt = gt[:gt.find('[s]')] pred = pred[:pred.find('[s]')] predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n' predicted_result_log += f'{dashed_line}' print(predicted_result_log) log.write(predicted_result_log + '\n') # save model per 1e+5 iter. if (iteration + 1) % 1e+5 == 0: torch.save( model.state_dict(), f'./saved_models/{opt.exp_name}/iter_{iteration+1}.pth') if (iteration + 1) == opt.num_iter: print('end the training') sys.exit() iteration += 1
def train(self, opt): # Add custom dataset add cfgs from da-faster-rcnn # Make sure you change the imdb_name in factory.py """ Dummy format: args.src_dataset == '$YOUR_DATASET_NAME' args.src_imdb_name = '$YOUR_DATASET_NAME_2007_trainval' args.src_imdbval_name = '$YOUR_DATASET_NAME_2007_test' args.set_cfgs = [...] """ # src, tar dataloaders src_dataset, tar_dataset, valid_loader = self.dataloader(opt) src_dataset_size = src_dataset.total_data_size tar_dataset_size = tar_dataset.total_data_size train_size = max([src_dataset_size, tar_dataset_size]) self.model.train() start_iter = 0 if opt.continue_model != '': self.load(opt.continue_model) print(" [*] Load SUCCESS") # if opt.decay_flag and start_iter > (opt.num_iter // 2): # self.d_image_opt.param_groups[0]['lr'] -= (opt.lr / (opt.num_iter // 2)) * ( # start_iter - opt.num_iter // 2) # self.d_inst_opt.param_groups[0]['lr'] -= (opt.lr / (opt.num_iter // 2)) * ( # start_iter - opt.num_iter // 2) # loss averager cls_loss_avg = Averager() sim_loss_avg = Averager() loss_avg = Averager() # training loop print('training start !') start_time = time.time() best_accuracy = -1 best_norm_ED = 1e+6 for step in range(start_iter, opt.num_iter + 1): src_image, src_labels = src_dataset.get_batch() src_image = src_image.to(device) src_text, src_length = self.converter.encode( src_labels, batch_max_length=opt.batch_max_length) tar_image, tar_labels = tar_dataset.get_batch() tar_image = tar_image.to(device) tar_text, tar_length = self.converter.encode( tar_labels, batch_max_length=opt.batch_max_length) # Set gradient to zero... self.model.zero_grad() # Attention # align with Attention.forward src_preds, src_global_feature, src_local_feature = self.model( src_image, src_text[:, :-1]) target = src_text[:, 1:] # without [GO] Symbol src_cls_loss = self.criterion( src_preds.view(-1, src_preds.shape[-1]), target.contiguous().view(-1)) src_local_feature = src_local_feature.view( -1, src_local_feature.shape[-1]) # TODO tar_preds, tar_global_feature, tar_local_feature = self.model( tar_image, tar_text[:, :-1], is_train=False) tar_local_feature = tar_local_feature.view( -1, tar_local_feature.shape[-1]) d_inst_loss = coral_loss(src_local_feature, src_preds, tar_local_feature, tar_preds) # Add domain loss loss = src_cls_loss.mean() + 0.1 * d_inst_loss.mean() loss_avg.add(loss) cls_loss_avg.add(src_cls_loss) sim_loss_avg.add(d_inst_loss) # frcnn backward loss.backward() torch.nn.utils.clip_grad_norm_( self.model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) # frcnn optimizer update self.optimizer.step() # validation part if step % opt.valInterval == 0: elapsed_time = time.time() - start_time print( f'[{step}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} CLS_Loss: {cls_loss_avg.val():0.5f} SIMI_Loss: {sim_loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}' ) # for log with open( f'./saved_models/{opt.experiment_name}/log_train.txt', 'a') as log: log.write( f'[{step}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}\n' ) loss_avg.reset() cls_loss_avg.reset() sim_loss_avg.reset() self.model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, labels, infer_time, length_of_data = validation( self.model, self.criterion, valid_loader, self.converter, opt) self.print_prediction_result(preds, labels, log) valid_log = f'[{step}/{opt.num_iter}] valid loss: {valid_loss:0.5f}' valid_log += f' accuracy: {current_accuracy:0.3f}, norm_ED: {current_norm_ED:0.2f}' print(valid_log) log.write(valid_log + '\n') self.model.train() # keep best accuracy model if current_accuracy > best_accuracy: best_accuracy = current_accuracy save_name = f'./saved_models/{opt.experiment_name}/best_accuracy.pth' self.save(opt, save_name) if current_norm_ED < best_norm_ED: best_norm_ED = current_norm_ED save_name = f'./saved_models/{opt.experiment_name}/best_norm_ED.pth' self.save(opt, save_name) best_model_log = f'best_accuracy: {best_accuracy:0.3f}, best_norm_ED: {best_norm_ED:0.2f}' print(best_model_log) log.write(best_model_log + '\n') # save model per 1e+5 iter. if (step + 1) % 1e+5 == 0: save_name = f'./saved_models/{opt.experiment_name}/iter_{step+1}.pth' self.save(opt, save_name)
def train(opt): lib.print_model_settings(locals().copy()) # train_transform = transforms.Compose([ # # transforms.RandomResizedCrop(input_size), # transforms.Resize((opt.imgH, opt.imgW)), # # transforms.RandomHorizontalFlip(), # transforms.ToTensor(), # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # ]) # val_transform = transforms.Compose([ # transforms.Resize((opt.imgH, opt.imgW)), # # transforms.CenterCrop(input_size), # transforms.ToTensor(), # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # ]) AlignFontCollateObj = AlignFontCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) train_dataset = fontDataset(imgDir=opt.train_img_dir, annFile=opt.train_ann_file, transform=None, numClasses=opt.numClasses) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=opt.batch_size, shuffle= False, # 'True' to check training progress with validation function. sampler=data_sampler(train_dataset, shuffle=True, distributed=opt.distributed), num_workers=int(opt.workers), collate_fn=AlignFontCollateObj, pin_memory=True, drop_last=False) # numClasses = len(train_dataset.Idx2F) numClasses = np.unique(train_dataset.fontIdx).size train_loader = sample_data(train_loader) print('-' * 80) numTrainSamples = len(train_dataset) # valid_dataset = LmdbStyleDataset(root=opt.valid_data, opt=opt) valid_dataset = fontDataset(imgDir=opt.train_img_dir, annFile=opt.val_ann_file, transform=None, F2Idx=train_dataset.F2Idx, Idx2F=train_dataset.Idx2F, numClasses=opt.numClasses) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle= False, # 'True' to check training progress with validation function. sampler=data_sampler(valid_dataset, shuffle=False, distributed=opt.distributed), num_workers=int(opt.workers), collate_fn=AlignFontCollateObj, pin_memory=True, drop_last=False) numTestSamples = len(valid_dataset) print('numClasses', numClasses) print('numTrainSamples', numTrainSamples) print('numTestSamples', numTestSamples) vggFontModel = VGGFontModel(models.vgg19(pretrained=opt.preTrained), numClasses).to(device) for name, param in vggFontModel.classifier.named_parameters(): try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. print('Exception in weight init' + name) if 'weight' in name: param.data.fill_(1) continue if opt.optim == "sgd": print('SGD optimizer') optimizer = optim.SGD(vggFontModel.parameters(), lr=opt.lr, momentum=0.9) elif opt.optim == "adam": print('Adam optimizer') optimizer = optim.Adam(vggFontModel.parameters(), lr=opt.lr) #get schedulers scheduler = get_scheduler(optimizer, opt) criterion = torch.nn.CrossEntropyLoss() if opt.modelFolderFlag: if len( glob.glob( os.path.join(opt.exp_dir, opt.exp_name, "iter_*_vggFont.pth"))) > 0: opt.saved_font_model = glob.glob( os.path.join(opt.exp_dir, opt.exp_name, "iter_*_vggFont.pth"))[-1] ## Loading pre-trained files if opt.saved_font_model != '' and opt.saved_font_model != 'None': print(f'loading pretrained synth model from {opt.saved_font_model}') checkpoint = torch.load(opt.saved_font_model, map_location=lambda storage, loc: storage) vggFontModel.load_state_dict(checkpoint['vggFontModel']) optimizer.load_state_dict(checkpoint["optimizer"]) scheduler.load_state_dict(checkpoint["scheduler"]) # print('Model Initialization') # # print('Loaded checkpoint') if opt.distributed: vggFontModel = torch.nn.parallel.DistributedDataParallel( vggFontModel, device_ids=[opt.local_rank], output_device=opt.local_rank, broadcast_buffers=False, find_unused_parameters=True) vggFontModel.train() # print('Loaded distributed') if opt.distributed: vggFontModel_module = vggFontModel.module else: vggFontModel_module = vggFontModel # print('Loading module') # loss averager loss_train = Averager() loss_val = Averager() train_acc = Averager() val_acc = Averager() train_acc_5 = Averager() val_acc_5 = Averager() """ final options """ with open(os.path.join(opt.exp_dir, opt.exp_name, 'opt.txt'), 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_font_model != '' and opt.saved_font_model != 'None': try: start_iter = int(opt.saved_font_model.split('_')[-2].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass iteration = start_iter cntr = 0 # trainCorrect=0 # tCntr=0 while (True): # print(cntr) # train part start_time = time.time() if not opt.testFlag: image_input_tensors, labels_gt = next(train_loader) image_input_tensors = image_input_tensors.to(device) labels_gt = labels_gt.view(-1).to(device) preds = vggFontModel(image_input_tensors) loss = criterion(preds, labels_gt) vggFontModel.zero_grad() loss.backward() optimizer.step() # _, preds_max = preds.max(dim=1) # trainCorrect += (preds_max == labels_gt).sum() # tCntr+=preds_max.shape[0] acc1, acc5 = getNumCorrect(preds, labels_gt, topk=(1, min(numClasses, 5))) train_acc.addScalar(acc1, preds.shape[0]) train_acc_5.addScalar(acc5, preds.shape[0]) loss_train.add(loss) if opt.lr_policy != "None": scheduler.step() # print if get_rank() == 0: if ( iteration + 1 ) % opt.valInterval == 0 or iteration == 0 or opt.testFlag: # To see training progress, we also conduct validation when 'iteration == 0' #validation # iCntr=torch.tensor(0.0).to(device) # valCorrect=torch.tensor(0.0).to(device) vggFontModel.eval() print('Inside val', iteration) for vCntr, (image_input_tensors, labels_gt) in enumerate(valid_loader): # print('vCntr--',vCntr) if opt.debugFlag and vCntr > 2: break with torch.no_grad(): image_input_tensors = image_input_tensors.to(device) labels_gt = labels_gt.view(-1).to(device) preds = vggFontModel(image_input_tensors) loss = criterion(preds, labels_gt) loss_val.add(loss) # _, preds_max = preds.max(dim=1) # valCorrect += (preds_max == labels_gt).sum() # iCntr+=preds_max.shape[0] acc1, acc5 = getNumCorrect(preds, labels_gt, topk=(1, min(numClasses, 5))) val_acc.addScalar(acc1, preds.shape[0]) val_acc_5.addScalar(acc5, preds.shape[0]) vggFontModel.train() elapsed_time = time.time() - start_time #DO HERE with open( os.path.join(opt.exp_dir, opt.exp_name, 'log_train.txt'), 'a') as log: # print('COUNT-------',val_acc_5.n_count) # training loss and validation loss loss_log = f'[{iteration+1}/{opt.num_iter}] \ Train loss: {loss_train.val():0.5f}, Val loss: {loss_val.val():0.5f}, \ Train Top-1 Acc: {train_acc.val()*100:0.5f}, Train Top-5 Acc: {train_acc_5.val()*100:0.5f}, \ Val Top-1 Acc: {val_acc.val()*100:0.5f}, Val Top-5 Acc: {val_acc_5.val()*100:0.5f}, \ Elapsed_time: {elapsed_time:0.5f}' #plotting lib.plot.plot(os.path.join(opt.plotDir, 'Train-Loss'), loss_train.val().item()) lib.plot.plot(os.path.join(opt.plotDir, 'Val-Loss'), loss_val.val().item()) lib.plot.plot(os.path.join(opt.plotDir, 'Train-Top-1-Acc'), train_acc.val() * 100) lib.plot.plot(os.path.join(opt.plotDir, 'Train-Top-5-Acc'), train_acc_5.val() * 100) lib.plot.plot(os.path.join(opt.plotDir, 'Val-Top-1-Acc'), val_acc.val() * 100) lib.plot.plot(os.path.join(opt.plotDir, 'Val-Top-5-Acc'), val_acc_5.val() * 100) print(loss_log) log.write(loss_log + "\n") loss_train.reset() loss_val.reset() train_acc.reset() val_acc.reset() train_acc_5.reset() val_acc_5.reset() # trainCorrect=0 # tCntr=0 lib.plot.flush() # save model per 30000 iter. if (iteration) % 15000 == 0: torch.save( { 'vggFontModel': vggFontModel_module.state_dict(), 'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict() }, os.path.join(opt.exp_dir, opt.exp_name, 'iter_' + str(iteration + 1) + '_vggFont.pth')) lib.plot.tick() if (iteration + 1) == opt.num_iter: print('end the training') sys.exit() iteration += 1 cntr += 1
def train(opt): print(opt.local_rank) opt.device = torch.device('cuda:{}'.format(opt.local_rank)) device = opt.device """ dataset preparation """ train_dataset = Batch_Balanced_Dataset(opt) valid_loader = train_dataset.getValDataloader() print('-' * 80) """ model configuration """ if 'CTC' == opt.Prediction: converter = CTCLabelConverter(opt.character, opt) elif 'Attn' == opt.Prediction: converter = AttnLabelConverter(opt.character, opt) elif 'CTC_Attn' == opt.Prediction: converter = CTCLabelConverter(opt.character, opt), AttnLabelConverter( opt.character, opt) opt.num_class = len(opt.character) if opt.rgb: opt.input_channel = 3 model = Model(opt) model.to(opt.device) print(model) print('model input parameters', opt.rgb, opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) # weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue """ setup loss """ if 'CTC' == opt.Prediction: criterion = torch.nn.CTCLoss(zero_infinity=True).to(device) elif 'Attn' == opt.Prediction: criterion = torch.nn.CrossEntropyLoss( ignore_index=0).to(device), torch.nn.MSELoss( reduction="sum").to(device) # ignore [GO] token = ignore index 0 elif 'CTC_Attn' == opt.Prediction: criterion = torch.nn.CTCLoss( zero_infinity=True).to(device), torch.nn.CrossEntropyLoss( ignore_index=0).to(device), torch.nn.MSELoss( reduction='sum').to(device) # loss averager loss_avg = Averager() # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) if opt.local_rank == 0: print('Trainable params num : ', sum(params_num)) # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.sgd: optimizer = optim.SGD(filtered_parameters, lr=opt.lr, momentum=0.9, weight_decay=opt.weight_decay) elif opt.adam: optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) if opt.local_rank == 0: print("Optimizer:") print(optimizer) if opt.sync_bn: model = apex.parallel.convert_syncbn_model(model) if opt.amp > 1: model, optimizer = amp.initialize(model, optimizer, opt_level="O" + str(opt.amp), keep_batchnorm_fp32=True, loss_scale="dynamic") else: model, optimizer = amp.initialize(model, optimizer, opt_level="O" + str(opt.amp)) # data parallel for multi-GPU model = DDP(model) if opt.continue_model != '': print(f'loading pretrained model from {opt.continue_model}') try: model.load_state_dict( torch.load(opt.continue_model, map_location=torch.device( 'cuda', torch.cuda.current_device()))) except: traceback.print_exc() print(f'COPYING pretrained model from {opt.continue_model}') pretrained_dict = torch.load(opt.continue_model, map_location=torch.device( 'cuda', torch.cuda.current_device())) model_dict = model.state_dict() pretrained_dict2 = dict() for k, v in pretrained_dict.items(): if opt.Prediction == 'Attn': if 'module.Prediction_attn.' in k: k = k.replace('module.Prediction_attn.', 'module.Prediction.') if k in model_dict and model_dict[k].shape == v.shape: pretrained_dict2[k] = v model_dict.update(pretrained_dict2) model.load_state_dict(model_dict) model.train() """ final options """ with open(f'./saved_models/{opt.experiment_name}/opt.txt', 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' opt_log += str(model) print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 start_time = time.time() best_accuracy = -1 best_norm_ED = 1e+6 i = start_iter ct = opt.batch_mul model.zero_grad() dist.barrier() while (True): # train part start = time.time() image, labels, pos = train_dataset.sync_get_batch() end = time.time() data_t = end - start start = time.time() batch_size = image.size(0) if 'CTC' == opt.Prediction: text, length = converter.encode( labels, batch_max_length=opt.batch_max_length) preds = model(image, text).log_softmax(2) preds_size = torch.IntTensor([preds.size(1)] * batch_size).to(device) preds = preds.permute(1, 0, 2) # to use CTCLoss format # To avoid ctc_loss issue, disabled cudnn for the computation of the ctc_loss # https://github.com/jpuigcerver/PyLaia/issues/16 torch.backends.cudnn.enabled = False cost = criterion(preds, text, preds_size, length) torch.backends.cudnn.enabled = True elif 'Attn' == opt.Prediction: text, length = converter.encode( labels, batch_max_length=opt.batch_max_length) preds = model(image, text[:, :-1]) # align with Attention.forward preds_attn = preds[0] preds_alpha = preds[1] target = text[:, 1:] # without [GO] Symbol cost = criterion[0](preds_attn.view(-1, preds_attn.shape[-1]), target.contiguous().view(-1)) if opt.posreg_w > 0.001: cost_pos = alpha_loss(preds_alpha, pos, opt, criterion[1]) print('attn_cost = ', cost, 'pos_cost = ', cost_pos * opt.posreg_w) cost += opt.posreg_w * cost_pos else: print('attn_cost = ', cost_attn) elif 'CTC_Attn' == opt.Prediction: text_ctc, length_ctc = converter[0].encode( labels, batch_max_length=opt.batch_max_length) text_attn, length_attn = converter[1].encode( labels, batch_max_length=opt.batch_max_length) """ ctc prediction and loss """ #should input text_attn here preds = model(image, text_attn[:, :-1]) preds_ctc = preds[0].log_softmax(2) preds_ctc_size = torch.IntTensor([preds_ctc.size(1)] * batch_size).to(device) preds_ctc = preds_ctc.permute(1, 0, 2) # to use CTCLoss format # To avoid ctc_loss issue, disabled cudnn for the computation of the ctc_loss # https://github.com/jpuigcerver/PyLaia/issues/16 torch.backends.cudnn.enabled = False cost_ctc = criterion[0](preds_ctc, text_ctc, preds_ctc_size, length_ctc) torch.backends.cudnn.enabled = True """ attention prediction and loss """ preds_attn = preds[1][0] # align with Attention.forward preds_alpha = preds[1][1] target = text_attn[:, 1:] # without [GO] Symbol cost_attn = criterion[1](preds_attn.view(-1, preds_attn.shape[-1]), target.contiguous().view(-1)) cost = opt.ctc_attn_loss_ratio * cost_ctc + ( 1 - opt.ctc_attn_loss_ratio) * cost_attn if opt.posreg_w > 0.001: cost_pos = alpha_loss(preds_alpha, pos, opt, criterion[2]) cost += opt.posreg_w * cost_pos cost_ctc = reduce_tensor(cost_ctc) cost_attn = reduce_tensor(cost_attn) cost_pos = reduce_tensor(cost_pos) if opt.local_rank == 0: print('ctc_cost = ', cost_ctc, 'attn_cost = ', cost_attn, 'pos_cost = ', cost_pos * opt.posreg_w) else: cost_ctc = reduce_tensor(cost_ctc) cost_attn = reduce_tensor(cost_attn) if opt.local_rank == 0: print('ctc_cost = ', cost_ctc, 'attn_cost = ', cost_attn) cost /= opt.batch_mul if opt.amp > 0: with amp.scale_loss(cost, optimizer) as scaled_loss: scaled_loss.backward() else: cost.backward() """ https://github.com/davidlmorton/learning-rate-schedules/blob/master/increasing_batch_size_without_increasing_memory.ipynb """ ct -= 1 if ct == 0: if opt.amp > 0: torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), opt.grad_clip) else: torch.nn.utils.clip_grad_norm_( model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) optimizer.step() model.zero_grad() ct = opt.batch_mul else: continue train_t = time.time() - start cost = reduce_tensor(cost) loss_avg.add(cost) if opt.local_rank == 0: print('iter', i, 'loss =', cost, ', data_t=', data_t, ',train_t=', train_t, ', batchsz=', opt.batch_mul * opt.batch_size) sys.stdout.flush() # validation part if (i > 0 and i % opt.valInterval == 0) or (i == 0 and opt.continue_model != ''): elapsed_time = time.time() - start_time print( f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}' ) # for log with open(f'./saved_models/{opt.experiment_name}/log_train.txt', 'a') as log: log.write( f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}\n' ) loss_avg.reset() model.eval() with torch.no_grad(): if 'CTC_Attn' in opt.Prediction: # we only count for attention accuracy, because ctc is used to help attention valid_loss, current_accuracy_ctc, current_accuracy, current_norm_ED_ctc, current_norm_ED, preds, labels, infer_time, length_of_data = validation( model, criterion[1], valid_loader, converter[1], opt, converter[0]) elif 'Attn' in opt.Prediction: valid_loss, current_accuracy, current_norm_ED, preds, labels, infer_time, length_of_data = validation( model, criterion[0], valid_loader, converter, opt) else: valid_loss, current_accuracy, current_norm_ED, preds, labels, infer_time, length_of_data = validation( model, criterion, valid_loader, converter, opt) model.train() for pred, gt in zip(preds[:10], labels[:10]): if 'Attn' in opt.Prediction: pred = pred[:pred.find('[s]')] gt = gt[:gt.find('[s]')] print(f'{pred:20s}, gt: {gt:20s}, {str(pred == gt)}') log.write( f'{pred:20s}, gt: {gt:20s}, {str(pred == gt)}\n') valid_log = f'[{i}/{opt.num_iter}] valid loss: {valid_loss:0.5f}' valid_log += f' accuracy: {current_accuracy:0.3f}, norm_ED: {current_norm_ED:0.2f}' if 'CTC_Attn' in opt.Prediction: valid_log += f' ctc_accuracy: {current_accuracy_ctc:0.3f}, ctc_norm_ED: {current_norm_ED_ctc:0.2f}' current_accuracy = max(current_accuracy, current_accuracy_ctc) current_norm_ED = min(current_norm_ED, current_norm_ED_ctc) if opt.local_rank == 0: print(valid_log) log.write(valid_log + '\n') # keep best accuracy model if current_accuracy > best_accuracy: best_accuracy = current_accuracy torch.save( model.state_dict(), f'./saved_models/{opt.experiment_name}/best_accuracy.pth' ) torch.save( model, f'./saved_models/{opt.experiment_name}/best_accuracy.model' ) if current_norm_ED < best_norm_ED: best_norm_ED = current_norm_ED torch.save( model.state_dict(), f'./saved_models/{opt.experiment_name}/best_norm_ED.pth' ) torch.save( model, f'./saved_models/{opt.experiment_name}/best_norm_ED.model' ) best_model_log = f'best_accuracy: {best_accuracy:0.3f}, best_norm_ED: {best_norm_ED:0.2f}' print(best_model_log) log.write(best_model_log + '\n') # save model per iter. if (i + 1) % opt.save_interval == 0 and opt.local_rank == 0: torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/iter_{i+1}.pth') if i == opt.num_iter: print('end the training') sys.exit() if opt.prof_iter > 0 and i > opt.prof_iter: sys.exit() i += 1
def train(opt): plotDir = os.path.join(opt.exp_dir, opt.exp_name, 'plots') if not os.path.exists(plotDir): os.makedirs(plotDir) lib.print_model_settings(locals().copy()) """ dataset preparation """ if not opt.data_filtering_off: print( 'Filtering the images containing characters which are not in opt.character' ) print( 'Filtering the images whose label is longer than opt.batch_max_length' ) # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') log = open(os.path.join(opt.exp_dir, opt.exp_name, 'log_dataset.txt'), 'a') AlignCollate_valid = AlignPairCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) train_dataset, train_dataset_log = hierarchical_dataset( root=opt.train_data, opt=opt) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=opt.batch_size, shuffle= True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) log.write(train_dataset_log) print('-' * 80) valid_dataset, valid_dataset_log = hierarchical_dataset( root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle= False, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() converter = CTCLabelConverter(opt.character) opt.num_class = len(converter.character) if opt.rgb: opt.input_channel = 3 styleModel = StyleTensorEncoder(input_dim=opt.input_channel) genModel = AdaIN_Tensor_WordGenerator(opt) disModel = MsImageDisV2(opt) vggRecCriterion = torch.nn.L1Loss() vggModel = VGGPerceptualLossModel(models.vgg19(pretrained=True), vggRecCriterion) print('model input parameters', opt.imgH, opt.imgW, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length) # weight initialization for currModel in [styleModel, genModel, disModel]: for name, param in currModel.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue styleModel = torch.nn.DataParallel(styleModel).to(device) styleModel.train() genModel = torch.nn.DataParallel(genModel).to(device) genModel.train() disModel = torch.nn.DataParallel(disModel).to(device) disModel.train() vggModel = torch.nn.DataParallel(vggModel).to(device) vggModel.eval() if opt.modelFolderFlag: if len( glob.glob( os.path.join(opt.exp_dir, opt.exp_name, "iter_*_synth.pth"))) > 0: opt.saved_synth_model = glob.glob( os.path.join(opt.exp_dir, opt.exp_name, "iter_*_synth.pth"))[-1] if opt.saved_synth_model != '' and opt.saved_synth_model != 'None': print(f'loading pretrained synth model from {opt.saved_synth_model}') checkpoint = torch.load(opt.saved_synth_model) styleModel.load_state_dict(checkpoint['styleModel']) genModel.load_state_dict(checkpoint['genModel']) disModel.load_state_dict(checkpoint['disModel']) if opt.imgReconLoss == 'l1': recCriterion = torch.nn.L1Loss() elif opt.imgReconLoss == 'ssim': recCriterion = ssim elif opt.imgReconLoss == 'ms-ssim': recCriterion = msssim if opt.styleLoss == 'l1': styleRecCriterion = torch.nn.L1Loss() elif opt.styleLoss == 'triplet': styleRecCriterion = torch.nn.TripletMarginLoss( margin=opt.tripletMargin, p=1) #for validation; check only positive pairs styleTestRecCriterion = torch.nn.L1Loss() # loss averager loss_avg = Averager() loss_avg_dis = Averager() loss_avg_gen = Averager() loss_avg_imgRecon = Averager() loss_avg_vgg_per = Averager() loss_avg_vgg_sty = Averager() ##---------------------------------------## # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, styleModel.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) for p in filter(lambda p: p.requires_grad, genModel.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable style and generator params num : ', sum(params_num)) # setup optimizer if opt.optim == 'adam': optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, opt.beta2), weight_decay=opt.weight_decay) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps, weight_decay=opt.weight_decay) print("SynthOptimizer:") print(optimizer) #filter parameters for Dis training dis_filtered_parameters = [] dis_params_num = [] for p in filter(lambda p: p.requires_grad, disModel.parameters()): dis_filtered_parameters.append(p) dis_params_num.append(np.prod(p.size())) print('Dis Trainable params num : ', sum(dis_params_num)) # setup optimizer if opt.optim == 'adam': dis_optimizer = optim.Adam(dis_filtered_parameters, lr=opt.lr, betas=(opt.beta1, opt.beta2), weight_decay=opt.weight_decay) else: dis_optimizer = optim.Adadelta(dis_filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps, weight_decay=opt.weight_decay) print("DisOptimizer:") print(dis_optimizer) ##---------------------------------------## """ final options """ with open(os.path.join(opt.exp_dir, opt.exp_name, 'opt.txt'), 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_synth_model != '' and opt.saved_synth_model != 'None': try: start_iter = int( opt.saved_synth_model.split('_')[-2].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass #get schedulers scheduler = get_scheduler(optimizer, opt) dis_scheduler = get_scheduler(dis_optimizer, opt) start_time = time.time() iteration = start_iter cntr = 0 while (True): # train part if opt.lr_policy != "None": scheduler.step() dis_scheduler.step() image_input_tensors, image_gt_tensors, labels_1, labels_2 = iter( train_loader).next() cntr += 1 image_input_tensors = image_input_tensors.to(device) image_gt_tensors = image_gt_tensors.to(device) batch_size = image_input_tensors.size(0) text_2, length_2 = converter.encode( labels_2, batch_max_length=opt.batch_max_length) #forward pass from style and word generator style = styleModel(image_input_tensors) images_recon_2 = genModel(style, text_2) #Domain discriminator: Dis update disModel.zero_grad() disCost = opt.disWeight * (disModel.module.calc_dis_loss( torch.cat((images_recon_2.detach(), image_input_tensors), dim=1), torch.cat((image_gt_tensors, image_input_tensors), dim=1))) disCost.backward() dis_optimizer.step() loss_avg_dis.add(disCost) # #[Style Encoder] + [Word Generator] update #Adversarial loss disGenCost = disModel.module.calc_gen_loss( torch.cat((images_recon_2, image_input_tensors), dim=1)) #Input reconstruction loss recCost = recCriterion(images_recon_2, image_gt_tensors) #vgg loss vggPerCost, vggStyleCost = vggModel(image_gt_tensors, images_recon_2) cost = opt.reconWeight * recCost + opt.disWeight * disGenCost + opt.vggPerWeight * vggPerCost + opt.vggStyWeight * vggStyleCost styleModel.zero_grad() genModel.zero_grad() disModel.zero_grad() vggModel.zero_grad() cost.backward() optimizer.step() loss_avg.add(cost) #Individual losses loss_avg_gen.add(opt.disWeight * disGenCost) loss_avg_imgRecon.add(opt.reconWeight * recCost) loss_avg_vgg_per.add(opt.vggPerWeight * vggPerCost) loss_avg_vgg_sty.add(opt.vggStyWeight * vggStyleCost) # validation part if ( iteration + 1 ) % opt.valInterval == 0 or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0' #Save training images os.makedirs(os.path.join(opt.exp_dir, opt.exp_name, 'trainImages', str(iteration)), exist_ok=True) for trImgCntr in range(batch_size): try: save_image( tensor2im(image_input_tensors[trImgCntr].detach()), os.path.join( opt.exp_dir, opt.exp_name, 'trainImages', str(iteration), str(trImgCntr) + '_sInput_' + labels_1[trImgCntr] + '.png')) save_image( tensor2im(image_gt_tensors[trImgCntr].detach()), os.path.join( opt.exp_dir, opt.exp_name, 'trainImages', str(iteration), str(trImgCntr) + '_csGT_' + labels_2[trImgCntr] + '.png')) save_image( tensor2im(images_recon_2[trImgCntr].detach()), os.path.join( opt.exp_dir, opt.exp_name, 'trainImages', str(iteration), str(trImgCntr) + '_csRecon_' + labels_2[trImgCntr] + '.png')) except: print('Warning while saving training image') elapsed_time = time.time() - start_time # for log with open(os.path.join(opt.exp_dir, opt.exp_name, 'log_train.txt'), 'a') as log: styleModel.eval() genModel.eval() disModel.eval() with torch.no_grad(): valid_loss, infer_time, length_of_data = validation_synth_v3( iteration, styleModel, genModel, vggModel, disModel, recCriterion, valid_loader, converter, opt) styleModel.train() genModel.train() disModel.train() # training loss and validation loss loss_log = f'[{iteration+1}/{opt.num_iter}] Train Synth loss: {loss_avg.val():0.5f}, \ Train Dis loss: {loss_avg_dis.val():0.5f}, Train Gen loss: {loss_avg_gen.val():0.5f},\ Train ImgRecon loss: {loss_avg_imgRecon.val():0.5f}, Train VGG-Per loss: {loss_avg_vgg_per.val():0.5f},\ Train VGG-Sty loss: {loss_avg_vgg_sty.val():0.5f}, Valid Synth loss: {valid_loss[1]:0.5f}, \ Valid Dis loss: {valid_loss[2]:0.5f}, Elapsed_time: {elapsed_time:0.5f}' #plotting lib.plot.plot(os.path.join(plotDir, 'Train-Synth-Loss'), loss_avg.val().item()) lib.plot.plot(os.path.join(plotDir, 'Train-Dis-Loss'), loss_avg_dis.val().item()) lib.plot.plot(os.path.join(plotDir, 'Train-Gen-Loss'), loss_avg_gen.val().item()) lib.plot.plot(os.path.join(plotDir, 'Train-ImgRecon1-Loss'), loss_avg_imgRecon.val().item()) lib.plot.plot(os.path.join(plotDir, 'Train-VGG-Per-Loss'), loss_avg_vgg_per.val().item()) lib.plot.plot(os.path.join(plotDir, 'Train-VGG-Sty-Loss'), loss_avg_vgg_sty.val().item()) lib.plot.plot(os.path.join(plotDir, 'Valid-Synth-Loss'), valid_loss[0].item()) lib.plot.plot(os.path.join(plotDir, 'Valid-Dis-Loss'), valid_loss[1].item()) lib.plot.plot(os.path.join(plotDir, 'Valid-Gen-Loss'), valid_loss[2].item()) lib.plot.plot(os.path.join(plotDir, 'Valid-ImgRecon1-Loss'), valid_loss[3].item()) lib.plot.plot(os.path.join(plotDir, 'Valid-VGG-Per-Loss'), valid_loss[4].item()) lib.plot.plot(os.path.join(plotDir, 'Valid-VGG-Sty-Loss'), valid_loss[5].item()) print(loss_log) loss_avg.reset() loss_avg_dis.reset() loss_avg_gen.reset() loss_avg_imgRecon.reset() loss_avg_vgg_per.reset() loss_avg_vgg_sty.reset() lib.plot.flush() lib.plot.tick() # save model per 1e+5 iter. if (iteration) % 1e+4 == 0: torch.save( { 'styleModel': styleModel.state_dict(), 'genModel': genModel.state_dict(), 'disModel': disModel.state_dict() }, os.path.join(opt.exp_dir, opt.exp_name, 'iter_' + str(iteration + 1) + '_synth.pth')) if (iteration + 1) == opt.num_iter: print('end the training') sys.exit() iteration += 1
def train(opt, show_number=2, amp=False): """ dataset preparation """ if not opt.data_filtering_off: print( 'Filtering the images containing characters which are not in opt.character' ) print( 'Filtering the images whose label is longer than opt.batch_max_length' ) opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') train_dataset = Batch_Balanced_Dataset(opt) log = open(f'./saved_models/{opt.experiment_name}/log_dataset.txt', 'a', encoding="utf8") AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD, contrast_adjust=opt.contrast_adjust) valid_dataset, valid_dataset_log = hierarchical_dataset( root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=min(32, opt.batch_size), shuffle= True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), prefetch_factor=512, collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() """ model configuration """ if 'CTC' in opt.Prediction: converter = CTCLabelConverter(opt.character) else: converter = AttnLabelConverter(opt.character) opt.num_class = len(converter.character) if opt.rgb: opt.input_channel = 3 model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) if opt.saved_model != '': pretrained_dict = torch.load(opt.saved_model) if opt.new_prediction: model.Prediction = nn.Linear( model.SequenceModeling_output, len(pretrained_dict['module.Prediction.weight'])) model = torch.nn.DataParallel(model).to(device) print(f'loading pretrained model from {opt.saved_model}') if opt.FT: model.load_state_dict(pretrained_dict, strict=False) else: model.load_state_dict(pretrained_dict) if opt.new_prediction: model.module.Prediction = nn.Linear( model.module.SequenceModeling_output, opt.num_class) for name, param in model.module.Prediction.named_parameters(): if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) model = model.to(device) else: # weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue model = torch.nn.DataParallel(model).to(device) model.train() print("Model:") print(model) count_parameters(model) """ setup loss """ if 'CTC' in opt.Prediction: criterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to( device) # ignore [GO] token = ignore index 0 # loss averager loss_avg = Averager() # freeze some layers try: if opt.freeze_FeatureFxtraction: for param in model.module.FeatureExtraction.parameters(): param.requires_grad = False if opt.freeze_SequenceModeling: for param in model.module.SequenceModeling.parameters(): param.requires_grad = False except: pass # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable params num : ', sum(params_num)) # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.optim == 'adam': #optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) optimizer = optim.Adam(filtered_parameters) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("Optimizer:") print(optimizer) """ final options """ # print(opt) with open(f'./saved_models/{opt.experiment_name}/opt.txt', 'a', encoding="utf8") as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_model != '': try: start_iter = int(opt.saved_model.split('_')[-1].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass start_time = time.time() best_accuracy = -1 best_norm_ED = -1 i = start_iter scaler = GradScaler() t1 = time.time() while (True): # train part optimizer.zero_grad(set_to_none=True) if amp: with autocast(): image_tensors, labels = train_dataset.get_batch() image = image_tensors.to(device) text, length = converter.encode( labels, batch_max_length=opt.batch_max_length) batch_size = image.size(0) if 'CTC' in opt.Prediction: preds = model(image, text).log_softmax(2) preds_size = torch.IntTensor([preds.size(1)] * batch_size) preds = preds.permute(1, 0, 2) torch.backends.cudnn.enabled = False cost = criterion(preds, text.to(device), preds_size.to(device), length.to(device)) torch.backends.cudnn.enabled = True else: preds = model(image, text[:, :-1]) # align with Attention.forward target = text[:, 1:] # without [GO] Symbol cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) scaler.scale(cost).backward() scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) scaler.step(optimizer) scaler.update() else: image_tensors, labels = train_dataset.get_batch() image = image_tensors.to(device) text, length = converter.encode( labels, batch_max_length=opt.batch_max_length) batch_size = image.size(0) if 'CTC' in opt.Prediction: preds = model(image, text).log_softmax(2) preds_size = torch.IntTensor([preds.size(1)] * batch_size) preds = preds.permute(1, 0, 2) torch.backends.cudnn.enabled = False cost = criterion(preds, text.to(device), preds_size.to(device), length.to(device)) torch.backends.cudnn.enabled = True else: preds = model(image, text[:, :-1]) # align with Attention.forward target = text[:, 1:] # without [GO] Symbol cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) cost.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) optimizer.step() loss_avg.add(cost) # validation part if (i % opt.valInterval == 0) and (i != 0): print('training time: ', time.time() - t1) t1 = time.time() elapsed_time = time.time() - start_time # for log with open(f'./saved_models/{opt.experiment_name}/log_train.txt', 'a', encoding="utf8") as log: model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels,\ infer_time, length_of_data = validation(model, criterion, valid_loader, converter, opt, device) model.train() # training loss and validation loss loss_log = f'[{i}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}' loss_avg.reset() current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.4f}' # keep best accuracy model (on valid dataset) if current_accuracy > best_accuracy: best_accuracy = current_accuracy torch.save( model.state_dict(), f'./saved_models/{opt.experiment_name}/best_accuracy.pth' ) if current_norm_ED > best_norm_ED: best_norm_ED = current_norm_ED torch.save( model.state_dict(), f'./saved_models/{opt.experiment_name}/best_norm_ED.pth' ) best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.4f}' loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}' print(loss_model_log) log.write(loss_model_log + '\n') # show some predicted results dashed_line = '-' * 80 head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n' #show_number = min(show_number, len(labels)) start = random.randint(0, len(labels) - show_number) for gt, pred, confidence in zip( labels[start:start + show_number], preds[start:start + show_number], confidence_score[start:start + show_number]): if 'Attn' in opt.Prediction: gt = gt[:gt.find('[s]')] pred = pred[:pred.find('[s]')] predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n' predicted_result_log += f'{dashed_line}' print(predicted_result_log) log.write(predicted_result_log + '\n') print('validation time: ', time.time() - t1) t1 = time.time() # save model per 1e+4 iter. if (i + 1) % 1e+4 == 0: torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/iter_{i+1}.pth') if i == opt.num_iter: print('end the training') sys.exit() i += 1
def train(opt): """ dataset preparation """ opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') train_dataset = Batch_Balanced_Dataset(opt) AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) valid_dataset = hierarchical_dataset(root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, # 'True' to check training progress with validation function. shuffle=True, num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) print('-' * 80) """ model configuration """ if 'Transformer' in opt.SequenceModeling: converter = TransformerLabelConverter(opt.character) elif 'CTC' in opt.Prediction: converter = CTCLabelConverter(opt.character) else: converter = AttnLabelConverter(opt.character) opt.num_class = len(converter.character) if opt.rgb: opt.input_channel = 3 model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) # weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue """ setup loss """ if 'Transformer' in opt.SequenceModeling: criterion = transformer_loss elif 'CTC' in opt.Prediction: criterion = torch.nn.CTCLoss(zero_infinity=True).cuda() else: # ignore [GO] token = ignore index 0 criterion = torch.nn.CrossEntropyLoss(ignore_index=0).cuda() # loss averager loss_avg = Averager() # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable params num : ', sum(params_num)) # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.adam: optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) elif 'Transformer' in opt.SequenceModeling and opt.use_scheduled_optim: optimizer = optim.Adam(filtered_parameters, betas=(0.9, 0.98), eps=1e-09) optimizer_schedule = ScheduledOptim(optimizer, opt.d_model, opt.n_warmup_steps) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("Optimizer:") print(optimizer) """ final options """ # print(opt) with open(f'./saved_models/{opt.experiment_name}/opt.txt', 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 start_time = time.time() best_accuracy = -1 best_norm_ED = 1e+6 pickle.load = partial(pickle.load, encoding="latin1") pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1") if opt.load_weights != '' and check_isfile(opt.load_weights): # load pretrained weights but ignore layers that don't match in size checkpoint = torch.load(opt.load_weights, pickle_module=pickle) if type(checkpoint) == dict: pretrain_dict = checkpoint['state_dict'] else: pretrain_dict = checkpoint model_dict = model.state_dict() pretrain_dict = { k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size() } model_dict.update(pretrain_dict) model.load_state_dict(model_dict) print("Loaded pretrained weights from '{}'".format(opt.load_weights)) del checkpoint torch.cuda.empty_cache() if opt.continue_model != '': print(f'loading pretrained model from {opt.continue_model}') checkpoint = torch.load(opt.continue_model) print(checkpoint.keys()) model.load_state_dict(checkpoint['state_dict']) start_iter = checkpoint['step'] + 1 print('continue to train start_iter: ', start_iter) if 'optimizer' in checkpoint.keys(): optimizer.load_state_dict(checkpoint['optimizer']) for state in optimizer.state.values(): for k, v in state.items(): if isinstance(v, torch.Tensor): state[k] = v.cuda() if 'best_accuracy' in checkpoint.keys(): best_accuracy = checkpoint['best_accuracy'] if 'best_norm_ED' in checkpoint.keys(): best_norm_ED = checkpoint['best_norm_ED'] del checkpoint torch.cuda.empty_cache() # data parallel for multi-GPU model = torch.nn.DataParallel(model).cuda() model.train() print("Model size:", count_num_param(model), 'M') if 'Transformer' in opt.SequenceModeling and opt.use_scheduled_optim: optimizer_schedule.n_current_steps = start_iter for i in tqdm(range(start_iter, opt.num_iter)): for p in model.parameters(): p.requires_grad = True cpu_images, cpu_texts = train_dataset.get_batch() image = cpu_images.cuda() if 'Transformer' in opt.SequenceModeling: text, length, text_pos = converter.encode(cpu_texts, opt.batch_max_length) elif 'CTC' in opt.Prediction: text, length = converter.encode(cpu_texts) else: text, length = converter.encode(cpu_texts, opt.batch_max_length) batch_size = image.size(0) if 'Transformer' in opt.SequenceModeling: preds = model(image, text, tgt_pos=text_pos) target = text[:, 1:] # without <s> Symbol cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) elif 'CTC' in opt.Prediction: preds = model(image, text).log_softmax(2) preds_size = torch.IntTensor([preds.size(1)] * batch_size) preds = preds.permute(1, 0, 2) # to use CTCLoss format cost = criterion(preds, text, preds_size, length) else: preds = model(image, text) target = text[:, 1:] # without [GO] Symbol cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) model.zero_grad() cost.backward() if 'Transformer' in opt.SequenceModeling and opt.use_scheduled_optim: optimizer_schedule.step_and_update_lr() elif 'Transformer' in opt.SequenceModeling: optimizer.step() else: # gradient clipping with 5 (Default) torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) optimizer.step() loss_avg.add(cost) # validation part if i > 0 and (i + 1) % opt.valInterval == 0: elapsed_time = time.time() - start_time print( f'[{i+1}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}' ) # for log with open(f'./saved_models/{opt.experiment_name}/log_train.txt', 'a') as log: log.write( f'[{i+1}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}\n' ) loss_avg.reset() model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, gts, infer_time, length_of_data = validation( model, criterion, valid_loader, converter, opt) model.train() for pred, gt in zip(preds[:5], gts[:5]): if 'Transformer' in opt.SequenceModeling: pred = pred[:pred.find('</s>')] gt = gt[:gt.find('</s>')] elif 'Attn' in opt.Prediction: pred = pred[:pred.find('[s]')] gt = gt[:gt.find('[s]')] print(f'{pred:20s}, gt: {gt:20s}, {str(pred == gt)}') log.write( f'{pred:20s}, gt: {gt:20s}, {str(pred == gt)}\n') valid_log = f'[{i+1}/{opt.num_iter}] valid loss: {valid_loss:0.5f}' valid_log += f' accuracy: {current_accuracy:0.3f}, norm_ED: {current_norm_ED:0.2f}' print(valid_log) log.write(valid_log + '\n') # keep best accuracy model if current_accuracy > best_accuracy: best_accuracy = current_accuracy state_dict = model.module.state_dict() save_checkpoint( { 'best_accuracy': best_accuracy, 'state_dict': state_dict, }, False, f'./saved_models/{opt.experiment_name}/best_accuracy.pth' ) if current_norm_ED < best_norm_ED: best_norm_ED = current_norm_ED state_dict = model.module.state_dict() save_checkpoint( { 'best_norm_ED': best_norm_ED, 'state_dict': state_dict, }, False, f'./saved_models/{opt.experiment_name}/best_norm_ED.pth' ) # torch.save( # model.state_dict(), f'./saved_models/{opt.experiment_name}/best_norm_ED.pth') best_model_log = f'best_accuracy: {best_accuracy:0.3f}, best_norm_ED: {best_norm_ED:0.2f}' print(best_model_log) log.write(best_model_log + '\n') # save model per 1000 iter. if (i + 1) % 1000 == 0: state_dict = model.module.state_dict() optimizer_state_dict = optimizer.state_dict() save_checkpoint( { 'state_dict': state_dict, 'optimizer': optimizer_state_dict, 'step': i, 'best_accuracy': best_accuracy, 'best_norm_ED': best_norm_ED, }, False, f'./saved_models/{opt.experiment_name}/iter_{i+1}.pth')
def train(opt, log): if opt.self == "MoCo": opt.batch_size = 256 """ dataset preparation """ if opt.select_data == "unlabel": select_data = ["U1.Book32", "U2.TextVQA", "U3.STVQA"] batch_ratio = [round(1 / len(select_data), 3)] * len(select_data) else: select_data = opt.select_data.split("-") batch_ratio = opt.batch_ratio.split("-") train_loader = Batch_Balanced_Dataset( opt, opt.train_data, select_data, batch_ratio, log, learn_type="self" ) AlignCollate_valid = AlignCollate_SelfSL(opt) valid_dataset, valid_dataset_log = hierarchical_dataset( root=opt.valid_data, opt=opt, data_type="unlabel" ) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle=True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=False, ) log.write(valid_dataset_log) print("-" * 80) log.write("-" * 80 + "\n") """ model configuration """ if opt.self == "RotNet": model = Model(opt, SelfSL_layer=opt.SelfSL_layer) # weight initialization for name, param in model.named_parameters(): if "localization_fc2" in name: print(f"Skip {name} as it is already initialized") continue try: if "bias" in name: init.constant_(param, 0.0) elif "weight" in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if "weight" in name: param.data.fill_(1) continue elif opt.self == "MoCo": model = MoCoLoss( opt, dim=opt.moco_dim, K=opt.moco_k, m=opt.moco_m, T=opt.moco_t ) # data parallel for multi-GPU model = torch.nn.DataParallel(model).to(device) model.train() if opt.saved_model != "": print(f"loading pretrained model from {opt.saved_model}") if opt.FT: model.load_state_dict(torch.load(opt.saved_model), strict=False) else: model.load_state_dict(torch.load(opt.saved_model)) print("Model:") print(model) log.write(repr(model) + "\n") """ setup loss """ criterion = torch.nn.CrossEntropyLoss(ignore_index=-1).to(device) # loss averager train_loss_avg = Averager() valid_loss_avg = Averager() # filter that only require gradient descent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print(f"Trainable params num: {sum(params_num)}") log.write(f"Trainable params num: {sum(params_num)}\n") # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.optimizer == "adam": optimizer = torch.optim.Adam(filtered_parameters, lr=opt.lr) elif opt.self == "MoCo": optimizer = torch.optim.SGD( filtered_parameters, lr=opt.moco_lr, momentum=opt.moco_SGD_m, weight_decay=opt.moco_wd, ) opt.schedule = opt.moco_schedule opt.lr = opt.moco_lr opt.lr_drop_rate = opt.moco_lr_drop_rate else: optimizer = torch.optim.SGD( filtered_parameters, lr=opt.lr, momentum=opt.momentum, weight_decay=opt.weight_decay, ) print("Optimizer:") print(optimizer) log.write(repr(optimizer) + "\n") if "super" in opt.schedule: if opt.optimizer == "sgd": cycle_momentum = True else: cycle_momentum = False scheduler = torch.optim.lr_scheduler.OneCycleLR( optimizer, max_lr=opt.lr, cycle_momentum=cycle_momentum, div_factor=20, final_div_factor=1000, total_steps=opt.num_iter, ) print("Scheduler:") print(scheduler) log.write(repr(scheduler) + "\n") """ final options """ # print(opt) opt_log = "------------ Options -------------\n" args = vars(opt) for k, v in args.items(): opt_log += f"{str(k)}: {str(v)}\n" opt_log += "---------------------------------------\n" print(opt_log) log.write(opt_log) log.close() """ start training """ start_iter = 0 if opt.saved_model != "": try: start_iter = int(opt.saved_model.split("_")[-1].split(".")[0]) print(f"continue to train, start_iter: {start_iter}") except: pass start_time = time.time() iteration = start_iter best_score = -1 # training loop for iteration in tqdm( range(start_iter + 1, opt.num_iter + 1), total=opt.num_iter, position=0, leave=True, ): # train part if opt.self == "RotNet": image, Self_label = train_loader.get_batch() image = image.to(device) preds = model(image, SelfSL_layer=opt.SelfSL_layer) target = torch.LongTensor(Self_label).to(device) elif opt.self == "MoCo": q, k = train_loader.get_batch_two_images() q = q.to(device) k = k.to(device) preds, target = model(im_q=q, im_k=k) loss = criterion(preds, target) train_loss_avg.add(loss) model.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_( model.parameters(), opt.grad_clip ) # gradient clipping with 5 (Default) optimizer.step() if "super" in opt.schedule: scheduler.step() else: adjust_learning_rate(optimizer, iteration, opt) # validation part. # To see training progress, we also conduct validation when 'iteration == 1' if iteration % opt.val_interval == 0 or iteration == 1: # for validation log with open(f"./saved_models/{opt.exp_name}/log_train.txt", "a") as log: model.eval() with torch.no_grad(): length_of_data = 0 infer_time = 0 n_correct = 0 for i, (image_valid, Self_label_valid) in tqdm( enumerate(valid_loader), total=len(valid_loader), position=1, leave=False, ): if opt.self == "RotNet": batch_size = image_valid.size(0) start_infer_time = time.time() preds = model( image_valid.to(device), SelfSL_layer=opt.SelfSL_layer ) forward_time = time.time() - start_infer_time target = torch.LongTensor(Self_label_valid).to(device) elif opt.self == "MoCo": batch_size = image_valid.size(0) q_valid = image_valid.to(device) k_valid = Self_label_valid.to(device) start_infer_time = time.time() preds, target = model(im_q=q_valid, im_k=k_valid) forward_time = time.time() - start_infer_time loss = criterion(preds, target) valid_loss_avg.add(loss) infer_time += forward_time _, preds_index = preds.max(1) n_correct += (preds_index == target).sum().item() length_of_data = length_of_data + batch_size current_score = n_correct / length_of_data * 100 model.train() # keep best score (accuracy) model on valid dataset if current_score > best_score: best_score = current_score torch.save( model.state_dict(), f"./saved_models/{opt.exp_name}/best_score.pth", ) # validation log: loss, lr, score, time. lr = optimizer.param_groups[0]["lr"] elapsed_time = time.time() - start_time valid_log = f"\n[{iteration}/{opt.num_iter}] Train loss: {train_loss_avg.val():0.5f}, Valid loss: {valid_loss_avg.val():0.5f}, lr: {lr:0.7f}\n" valid_log += f"Best_score: {best_score:0.2f}, Current_score: {current_score:0.2f}, " valid_log += ( f"Infer_time: {infer_time:0.1f}, Elapsed_time: {elapsed_time:0.1f}" ) train_loss_avg.reset() valid_loss_avg.reset() # show some predicted results dashed_line = "-" * 80 if opt.self == "RotNet": head = f"GT:0 vs Pred | GT:90 vs Pred | GT:180 vs Pred | GT:270 vs Pred" preds_index = preds_index[:20] gts = Self_label_valid[:20] elif opt.self == "MoCo": head = f"GT:0 vs Pred | GT:0 vs Pred | GT:0 vs Pred | GT:0 vs Pred" preds_index = preds_index[:8] gts = torch.zeros(preds_index.shape[0], dtype=torch.long) predicted_result_log = f"{dashed_line}\n{head}\n{dashed_line}\n" for i, (gt, pred) in enumerate(zip(gts, preds_index)): if opt.self == "RotNet": gt, pred = gt * 90, pred * 90 if i % 4 != 3: predicted_result_log += f"{gt} vs {pred} | " else: predicted_result_log += f"{gt} vs {pred} \n" predicted_result_log += f"{dashed_line}" valid_log = f"{valid_log}\n{predicted_result_log}" print(valid_log) log.write(valid_log + "\n") print( f'finished the experiment: {opt.exp_name}, "CUDA_VISIBLE_DEVICES" was {opt.CUDA_VISIBLE_DEVICES}' )
def train(opt): """ 准备训练和验证的数据集 """ transform = transforms.Compose([ ToTensor(), ]) train_dataset = LmdbDataset(opt.train_data, opt=opt, transform=transform) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=opt.batch_size, shuffle=True, num_workers=int(opt.workers), ) valid_dataset = LmdbDataset(root=opt.valid_data, opt=opt, transform=transform) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle=True, num_workers=int(opt.workers), ) print('-' * 80) """ 模型的配置 """ if 'CTC' in opt.Prediction: converter = CTCLabelConverter(opt.character) else: converter = AttnLabelConverter(opt.character) opt.num_class = len(converter.character) if opt.rgb: opt.input_channel = 3 model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) # 权重初始化 for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue model = model.to(device) model.train() if opt.continue_model != '': print(f'loading pretrained model from {opt.continue_model}') model.load_state_dict(torch.load(opt.continue_model)) print("Model:") print(model) """ setup loss """ if 'CTC' in opt.Prediction: criterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to( device) # ignore [GO] token = ignore index 0 # loss averager loss_avg = Averager() # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable params num : ', sum(params_num)) # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.adam: optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("Optimizer:") print(optimizer) """ final options """ # print(opt) with open(f'./saved_models/{opt.experiment_name}/opt.txt', 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.continue_model != '': start_iter = int(opt.continue_model.split('_')[-1].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') start_time = time.time() best_accuracy = -1 best_norm_ED = 1e+6 i = start_iter while (True): # train part for image_tensors, labels in train_loader: image = image_tensors.to(device) text, length = converter.encode( labels, batch_max_length=opt.batch_max_length ) # text: [index, index, ..., index], length: [10, 8] batch_size = image.size(0) if 'CTC' in opt.Prediction: # set xx = model(image, text) torch.Size([100, 63, 7]), xx.log_softmax(2)[0][0] = xx[0][0].log_softmax(-1) preds = model(image, text).log_softmax(2) # torch.Size([100, 63, 12]) preds_size = torch.IntTensor([preds.size(1)] * batch_size).to(device) preds = preds.permute( 1, 0, 2 ) # to use CTCLoss format # 100 * 63 * 7 -> 63 * 100 * 7 # To avoid ctc_loss issue, disabled cudnn for the computation of the ctc_loss # https://github.com/jpuigcerver/PyLaia/issues/16 torch.backends.cudnn.enabled = False cost = criterion( preds, text, preds_size, length ) # preds.shape: torch.Size([63, 100, 7]), 其中63是序列特征,100是batch_size, 7是输出类别数量; text.shape: torch.Size([1000]), 表示1000个字符 # preds_size:[63, 63, ..., 63] 100,数组中的63表示序列的长度 length: [10, 10, ..., 10] 100,数组中的每个10表示每个标签的长度,意思就是每一张图片有10个字符 torch.backends.cudnn.enabled = True else: preds = model(image, text[:, :-1]) # align with Attention.forward target = text[:, 1:] # without [GO] Symbol cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) model.zero_grad() cost.backward() torch.nn.utils.clip_grad_norm_( model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) optimizer.step() loss_avg.add(cost) # validation part if i % opt.valInterval == 0: elapsed_time = time.time() - start_time print( f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}' ) # for log with open( f'./saved_models/{opt.experiment_name}/log_train.txt', 'a') as log: log.write( f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}\n' ) loss_avg.reset() model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, labels, infer_time, length_of_data = validation( model, criterion, valid_loader, converter, opt) model.train() for pred, gt in zip(preds[:5], labels[:5]): if 'Attn' in opt.Prediction: pred = pred[:pred.find('[s]')] gt = gt[:gt.find('[s]')] print(f'{pred:20s}, gt: {gt:20s}, {str(pred == gt)}') log.write( f'{pred:20s}, gt: {gt:20s}, {str(pred == gt)}\n') valid_log = f'[{i}/{opt.num_iter}] valid loss: {valid_loss:0.5f}' valid_log += f' accuracy: {current_accuracy:0.3f}, norm_ED: {current_norm_ED:0.2f}' print(valid_log) log.write(valid_log + '\n') # keep best accuracy model if current_accuracy > best_accuracy: best_accuracy = current_accuracy torch.save( model.state_dict(), f'./saved_models/{opt.experiment_name}/best_accuracy.pth' ) if current_norm_ED < best_norm_ED: best_norm_ED = current_norm_ED torch.save( model.state_dict(), f'./saved_models/{opt.experiment_name}/best_norm_ED.pth' ) best_model_log = f'best_accuracy: {best_accuracy:0.3f}, best_norm_ED: {best_norm_ED:0.2f}' print(best_model_log) log.write(best_model_log + '\n') # save model per 1e+5 iter. if (i + 1) % 1e+5 == 0: torch.save( model.state_dict(), f'./saved_models/{opt.experiment_name}/iter_{i+1}.pth') if i == opt.num_iter: print('end the training') sys.exit() i += 1
def run(train_path): df = pd.read_csv(train_path) print(df.shape) df['x'] = df['bbox'].apply(lambda x: float(np.array(re.findall("([0-9]+[.]?[0-9]*)", x))[0])) df['y'] = df['bbox'].apply(lambda x: float(np.array(re.findall("([0-9]+[.]?[0-9]*)", x))[1])) df['w'] = df['bbox'].apply(lambda x: float(np.array(re.findall("([0-9]+[.]?[0-9]*)", x))[2])) df['h'] = df['bbox'].apply(lambda x: float(np.array(re.findall("([0-9]+[.]?[0-9]*)", x))[3])) df.drop(['bbox'], inplace=True, axis=1) # split the data image_ids = df['image_id'].unique() valid_ids = image_ids[-665:] train_ids = image_ids[:-665] train_df = df[df['image_id'].isin(train_ids)] valid_df = df[df['image_id'].isin(valid_ids)] train_dataset = WheatDatasetTrain(train_df, config.DIR_TRAIN, get_train_transform()) valid_dataset = WheatDatasetTrain(valid_df, config.DIR_TRAIN, get_valid_transform()) train_data_loader = torch.utils.data.DataLoader( train_dataset, batch_size=config.TRAIN_BS, shuffle=False, num_workers=config.NUM_WORKERS, collate_fn=collate_fn ) valid_data_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=config.VALID_BS, shuffle=False, num_workers=config.NUM_WORKERS, collate_fn=collate_fn ) # Device used is cuda device = torch.device('cuda') model = obtain_model() model.to(device) params = [p for p in model.parameters() if p.requires_grad] optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005) # lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1) lr_scheduler = None loss_hist = Averager() itr = 1 for epoch in range(config.EPOCHS): loss_hist.reset() for images, targets, image_ids in train_data_loader: images = list(image.to(device) for image in images) targets = [{k: v.to(device) for k, v in t.items()} for t in targets] loss_dict = model(images, targets) losses = sum(loss for loss in loss_dict.values()) loss_value = losses.item() loss_hist.send(loss_value) optimizer.zero_grad() losses.backward() optimizer.step() if itr % 50 == 0: print(f"Iteration #{itr} loss: {loss_value}") itr += 1 # update the learning rate if lr_scheduler is not None: lr_scheduler.step() print(f"Epoch #{epoch} loss: {loss_hist.value}")
def train(opt): """ dataset preparation """ if not opt.data_filtering_off: print('Filtering the images containing characters which are not in opt.character') print('Filtering the images whose label is longer than opt.batch_max_length') # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') #considering the real images for discriminator opt.batch_size = opt.batch_size*2 train_dataset = Batch_Balanced_Dataset(opt) log = open(os.path.join(opt.exp_dir,opt.exp_name,'log_dataset.txt'), 'a') AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) valid_dataset, valid_dataset_log = hierarchical_dataset(root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle=True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() """ model configuration """ if 'CTC' in opt.Prediction: converter = CTCLabelConverter(opt.character) else: converter = AttnLabelConverter(opt.character) opt.num_class = len(converter.character) if opt.rgb: opt.input_channel = 3 model = AdaINGen(opt) ocrModel = Model(opt) disModel = MsImageDis() print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) # Synthesizer weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # Recognizer weight initialization for name, param in ocrModel.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # Discriminator weight initialization for name, param in disModel.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # data parallel for multi-GPU model = torch.nn.DataParallel(model).to(device) model.train() ocrModel = torch.nn.DataParallel(ocrModel).to(device) ocrModel.train() disModel = torch.nn.DataParallel(disModel).to(device) disModel.train() if opt.saved_synth_model != '': print(f'loading pretrained synth model from {opt.saved_synth_model}') if opt.FT: model.load_state_dict(torch.load(opt.saved_synth_model), strict=False) else: model.load_state_dict(torch.load(opt.saved_synth_model)) print("Model:") print(model) if opt.saved_ocr_model != '': print(f'loading pretrained ocr model from {opt.saved_ocr_model}') if opt.FT: ocrModel.load_state_dict(torch.load(opt.saved_ocr_model), strict=False) else: ocrModel.load_state_dict(torch.load(opt.saved_ocr_model)) # ocrModel.eval() #as we can't call RNN.backward in eval mode print("OCRModel:") print(ocrModel) if opt.saved_dis_model != '': print(f'loading pretrained discriminator model from {opt.saved_dis_model}') if opt.FT: disModel.load_state_dict(torch.load(opt.saved_dis_model), strict=False) else: disModel.load_state_dict(torch.load(opt.saved_dis_model)) # ocrModel.eval() #as we can't call RNN.backward in eval mode print("DisModel:") print(disModel) """ setup loss """ if 'CTC' in opt.Prediction: ocrCriterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: ocrCriterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0 recCriterion = torch.nn.L1Loss() # loss averager loss_avg = Averager() loss_avg_ocr = Averager() ##---------- loss_avg_dis = Averager() # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable params num : ', sum(params_num)) # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.adam: optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("SynthOptimizer:") print(optimizer) #filter parameters for OCR training # filter that only require gradient decent ocr_filtered_parameters = [] ocr_params_num = [] for p in filter(lambda p: p.requires_grad, ocrModel.parameters()): ocr_filtered_parameters.append(p) ocr_params_num.append(np.prod(p.size())) print('OCR Trainable params num : ', sum(ocr_params_num)) # setup optimizer if opt.adam: ocr_optimizer = optim.Adam(ocr_filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) else: ocr_optimizer = optim.Adadelta(ocr_filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("OCROptimizer:") print(ocr_optimizer) #filter parameters for OCR training # filter that only require gradient decent dis_filtered_parameters = [] dis_params_num = [] for p in filter(lambda p: p.requires_grad, disModel.parameters()): dis_filtered_parameters.append(p) dis_params_num.append(np.prod(p.size())) print('Dis Trainable params num : ', sum(dis_params_num)) # setup optimizer if opt.adam: dis_optimizer = optim.Adam(dis_filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) else: dis_optimizer = optim.Adadelta(dis_filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("DisOptimizer:") print(dis_optimizer) """ final options """ # print(opt) with open(os.path.join(opt.exp_dir,opt.exp_name,'opt.txt'), 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_synth_model != '': try: start_iter = int(opt.saved_synth_model.split('_')[-1].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass start_time = time.time() best_accuracy = -1 best_norm_ED = -1 best_accuracy_ocr = -1 best_norm_ED_ocr = -1 iteration = start_iter while(True): # train part image_tensors_all, labels_1_all, labels_2_all = train_dataset.get_batch() # ## comment # pdb.set_trace() # for imgCntr in range(image_tensors.shape[0]): # save_image(tensor2im(image_tensors[imgCntr]),'temp/'+str(imgCntr)+'.png') # pdb.set_trace() # ### disCnt = int(image_tensors_all.size(0)/2) image_tensors, image_tensors_real, labels_1, labels_2 = image_tensors_all[:disCnt], image_tensors_all[disCnt:disCnt+disCnt], labels_1_all[:disCnt], labels_2_all[:disCnt] image = image_tensors.to(device) image_real = image_tensors_real.to(device) text_1, length_1 = converter.encode(labels_1, batch_max_length=opt.batch_max_length) text_2, length_2 = converter.encode(labels_2, batch_max_length=opt.batch_max_length) batch_size = image.size(0) images_recon_1, images_recon_2, _ = model(image, text_1, text_2) if 'CTC' in opt.Prediction: #ocr training preds_ocr = ocrModel(image, text_1) preds_size_ocr = torch.IntTensor([preds_ocr.size(1)] * batch_size) preds_ocr = preds_ocr.log_softmax(2).permute(1, 0, 2) ocrCost_train = ocrCriterion(preds_ocr, text_1, preds_size_ocr, length_1) #dis training #Check: Using alternate real images disCost = opt.disWeight*0.5*(disModel.module.calc_dis_loss(images_recon_1.detach(), image_real) + disModel.module.calc_dis_loss(images_recon_2.detach(), image)) #synth training preds_1 = ocrModel(images_recon_1, text_1) preds_size_1 = torch.IntTensor([preds_1.size(1)] * batch_size) preds_1 = preds_1.log_softmax(2).permute(1, 0, 2) preds_2 = ocrModel(images_recon_2, text_2) preds_size_2 = torch.IntTensor([preds_2.size(1)] * batch_size) preds_2 = preds_2.log_softmax(2).permute(1, 0, 2) ocrCost = 0.5*(ocrCriterion(preds_1, text_1, preds_size_1, length_1) + ocrCriterion(preds_2, text_2, preds_size_2, length_2)) #gen training disGenCost = 0.5*(disModel.module.calc_gen_loss(images_recon_1)+disModel.module.calc_gen_loss(images_recon_2)) else: preds = model(image, text[:, :-1]) # align with Attention.forward target = text[:, 1:] # without [GO] Symbol ocrCost = ocrCriterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) recCost = recCriterion(images_recon_1,image) cost = opt.ocrWeight*ocrCost + opt.reconWeight*recCost + opt.disWeight*disGenCost disModel.zero_grad() disCost.backward() torch.nn.utils.clip_grad_norm_(disModel.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) dis_optimizer.step() loss_avg_dis.add(disCost) model.zero_grad() ocrModel.zero_grad() disModel.zero_grad() cost.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) optimizer.step() loss_avg.add(cost) #training OCR ocrModel.zero_grad() ocrCost_train.backward() torch.nn.utils.clip_grad_norm_(ocrModel.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) ocr_optimizer.step() loss_avg_ocr.add(ocrCost_train) #START HERE # validation part if (iteration + 1) % opt.valInterval == 0 or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0' #Save training images os.makedirs(os.path.join(opt.exp_dir,opt.exp_name,'trainImages',str(iteration)), exist_ok=True) for trImgCntr in range(batch_size): try: save_image(tensor2im(image[trImgCntr].detach()),os.path.join(opt.exp_dir,opt.exp_name,'trainImages',str(iteration),str(trImgCntr)+'_input_'+labels_1[trImgCntr]+'.png')) save_image(tensor2im(images_recon_1[trImgCntr].detach()),os.path.join(opt.exp_dir,opt.exp_name,'trainImages',str(iteration),str(trImgCntr)+'_recon_'+labels_1[trImgCntr]+'.png')) save_image(tensor2im(images_recon_2[trImgCntr].detach()),os.path.join(opt.exp_dir,opt.exp_name,'trainImages',str(iteration),str(trImgCntr)+'_pair_'+labels_2[trImgCntr]+'.png')) except: print('Warning while saving training image') elapsed_time = time.time() - start_time # for log with open(os.path.join(opt.exp_dir,opt.exp_name,'log_train.txt'), 'a') as log: model.eval() ocrModel.eval() disModel.eval() with torch.no_grad(): # valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation( # model, criterion, valid_loader, converter, opt) valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation_synth_adv( iteration, model, ocrModel, disModel, recCriterion, ocrCriterion, valid_loader, converter, opt) model.train() ocrModel.train() disModel.train() # training loss and validation loss loss_log = f'[{iteration+1}/{opt.num_iter}] Train OCR loss: {loss_avg_ocr.val():0.5f}, Train Synth loss: {loss_avg.val():0.5f}, Train Dis loss: {loss_avg_dis.val():0.5f}, Valid OCR loss: {valid_loss[0]:0.5f}, Valid Synth loss: {valid_loss[1]:0.5f}, Valid Dis loss: {valid_loss[2]:0.5f}, Elapsed_time: {elapsed_time:0.5f}' loss_avg_ocr.reset() loss_avg.reset() loss_avg_dis.reset() current_model_log_ocr = f'{"Current_accuracy_OCR":17s}: {current_accuracy[0]:0.3f}, {"Current_norm_ED_OCR":17s}: {current_norm_ED[0]:0.2f}' current_model_log_1 = f'{"Current_accuracy_recon":17s}: {current_accuracy[1]:0.3f}, {"Current_norm_ED_recon":17s}: {current_norm_ED[1]:0.2f}' current_model_log_2 = f'{"Current_accuracy_pair":17s}: {current_accuracy[2]:0.3f}, {"Current_norm_ED_pair":17s}: {current_norm_ED[2]:0.2f}' # keep best accuracy model (on valid dataset) if current_accuracy[1] > best_accuracy: best_accuracy = current_accuracy[1] torch.save(model.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_accuracy.pth')) torch.save(disModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_accuracy_dis.pth')) if current_norm_ED[1] > best_norm_ED: best_norm_ED = current_norm_ED[1] torch.save(model.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_norm_ED.pth')) torch.save(disModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_norm_ED_dis.pth')) best_model_log = f'{"Best_accuracy_Recon":17s}: {best_accuracy:0.3f}, {"Best_norm_ED_Recon":17s}: {best_norm_ED:0.2f}' # keep best accuracy model (on valid dataset) if current_accuracy[0] > best_accuracy_ocr: best_accuracy_ocr = current_accuracy[0] torch.save(ocrModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_accuracy_ocr.pth')) if current_norm_ED[0] > best_norm_ED_ocr: best_norm_ED_ocr = current_norm_ED[0] torch.save(ocrModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_norm_ED_ocr.pth')) best_model_log_ocr = f'{"Best_accuracy_ocr":17s}: {best_accuracy_ocr:0.3f}, {"Best_norm_ED_ocr":17s}: {best_norm_ED_ocr:0.2f}' loss_model_log = f'{loss_log}\n{current_model_log_ocr}\n{current_model_log_1}\n{current_model_log_2}\n{best_model_log_ocr}\n{best_model_log}' print(loss_model_log) log.write(loss_model_log + '\n') # show some predicted results dashed_line = '-' * 80 head = f'{"Ground Truth":32s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n' for gt_ocr, pred_ocr, confidence_ocr, gt_1, pred_1, confidence_1, gt_2, pred_2, confidence_2 in zip(labels[0][:5], preds[0][:5], confidence_score[0][:5], labels[1][:5], preds[1][:5], confidence_score[1][:5], labels[2][:5], preds[2][:5], confidence_score[2][:5]): if 'Attn' in opt.Prediction: gt = gt[:gt.find('[s]')] pred = pred[:pred.find('[s]')] predicted_result_log += f'{"ocr"}: {gt_ocr:27s} | {pred_ocr:25s} | {confidence_ocr:0.4f}\t{str(pred_ocr == gt_ocr)}\n' predicted_result_log += f'{"recon"}: {gt_1:25s} | {pred_1:25s} | {confidence_1:0.4f}\t{str(pred_1 == gt_1)}\n' predicted_result_log += f'{"pair"}: {gt_2:26s} | {pred_2:25s} | {confidence_2:0.4f}\t{str(pred_2 == gt_2)}\n' predicted_result_log += f'{dashed_line}' print(predicted_result_log) log.write(predicted_result_log + '\n') # save model per 1e+5 iter. if (iteration + 1) % 1e+5 == 0: torch.save( model.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'iter_{iteration+1}.pth')) torch.save( ocrModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'iter_{iteration+1}_ocr.pth')) torch.save( disModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'iter_{iteration+1}_dis.pth')) if (iteration + 1) == opt.num_iter: print('end the training') sys.exit() iteration += 1
def train(): """ dataset preparation """ train_dataset_lmdb = LmdbDataset(cfg.lmdb_trainset_dir_name) val_dataset_lmdb = LmdbDataset(cfg.lmdb_valset_dir_name) train_loader = torch.utils.data.DataLoader( train_dataset_lmdb, batch_size=cfg.batch_size, collate_fn=data_collate, shuffle=True, num_workers=int(cfg.workers), pin_memory=True) valid_loader = torch.utils.data.DataLoader( val_dataset_lmdb, batch_size=cfg.batch_size, collate_fn=data_collate, shuffle=True, # 'True' to check training progress with validation function. num_workers=int(cfg.workers), pin_memory=True) # --------------------训练过程--------------------------------- model = advancedEAST() if int(cfg.train_task_id[-3:]) != 256: id_num = cfg.train_task_id[-3:] idx_dic = {'384': 256, '512': 384, '640': 512, '736': 640} model.load_state_dict(torch.load('./saved_model/3T{}_best_loss.pth'.format(idx_dic[id_num]))) elif os.path.exists('./saved_model/3T{}_best_loss.pth'.format(cfg.train_task_id)): model.load_state_dict(torch.load('./saved_model/3T{}_best_loss.pth'.format(cfg.train_task_id))) model = model.to(device) optimizer = optim.Adam(model.parameters(), lr=cfg.lr, weight_decay=cfg.decay) loss_func = quad_loss train_Loss_list = [] val_Loss_list = [] '''start training''' start_iter = 0 if cfg.saved_model != '': try: start_iter = int(cfg.saved_model.split('_')[-1].split('.')[0]) print('continue to train, start_iter: {}'.format(start_iter)) except Exception as e: print(e) pass start_time = time.time() best_mF1_score = 0 i = start_iter step_num = 0 start_time = time.time() loss_avg = Averager() val_loss_avg = Averager() eval_p_r_f = eval_pre_rec_f1() while(True): model.train() # train part # training----------------------------- for image_tensors, labels, gt_xy_list in train_loader: step_num += 1 batch_x = image_tensors.to(device).float() batch_y = labels.to(device).float() # float64转float32 out = model(batch_x) loss = loss_func(batch_y, out) optimizer.zero_grad() loss.backward() optimizer.step() loss_avg.add(loss) train_Loss_list.append(loss_avg.val()) if i == 5 or (i + 1) % 10 == 0: eval_p_r_f.add(out, gt_xy_list) # 非常耗时!!! # save model per 100 epochs. if (i + 1) % 1e+2 == 0: torch.save(model.state_dict(), './saved_models/{}/{}_iter_{}.pth'.format(cfg.train_task_id, cfg.train_task_id, step_num+1)) print('Epoch:[{}/{}] Training Loss: {:.3f}'.format(i + 1, cfg.epoch_num, train_Loss_list[-1].item())) loss_avg.reset() if i == 5 or (i + 1) % 10 == 0: mPre, mRec, mF1_score = eval_p_r_f.val() print('Training meanPrecision:{:.2f}% meanRecall:{:.2f}% meanF1-score:{:.2f}%'.format(mPre, mRec, mF1_score)) eval_p_r_f.reset() # evaluation-------------------------------- if (i + 1) % cfg.valInterval == 0: elapsed_time = time.time() - start_time print('Elapsed time:{}s'.format(round(elapsed_time))) model.eval() for image_tensors, labels, gt_xy_list in valid_loader: batch_x = image_tensors.to(device) batch_y = labels.to(device).float() # float64转float32 out = model(batch_x) loss = loss_func(batch_y, out) val_loss_avg.add(loss) val_Loss_list.append(val_loss_avg.val()) eval_p_r_f.add(out, gt_xy_list) mPre, mRec, mF1_score = eval_p_r_f.val() print('validation meanPrecision:{:.2f}% meanRecall:{:.2f}% meanF1-score:{:.2f}%'.format(mPre, mRec, mF1_score)) eval_p_r_f.reset() if mF1_score > best_mF1_score: # 记录最佳模型 best_mF1_score = mF1_score torch.save(model.state_dict(), './saved_models/{}/{}_best_mF1_score_{:.3f}.pth'.format(cfg.train_task_id, cfg.train_task_id, mF1_score)) torch.save(model.state_dict(), './saved_model/{}_best_mF1_score.pth'.format(cfg.train_task_id)) print('Validation loss:{:.3f}'.format(val_loss_avg.val().item())) val_loss_avg.reset() if i == cfg.epoch_num: torch.save(model.state_dict(), './saved_models/{}/{}_iter_{}.pth'.format(cfg.train_task_id, cfg.train_task_id, i+1)) print('End the training') break i += 1 sys.exit()
def train(opt, log): """dataset preparation""" # train dataset. for convenience if opt.select_data == "label": select_data = [ "1.SVT", "2.IIIT", "3.IC13", "4.IC15", "5.COCO", "6.RCTW17", "7.Uber", "8.ArT", "9.LSVT", "10.MLT19", "11.ReCTS", ] elif opt.select_data == "synth": select_data = ["MJ", "ST"] elif opt.select_data == "synth_SA": select_data = ["MJ", "ST", "SA"] opt.batch_ratio = "0.4-0.4-0.2" # same ratio with SCATTER paper. elif opt.select_data == "mix": select_data = [ "1.SVT", "2.IIIT", "3.IC13", "4.IC15", "5.COCO", "6.RCTW17", "7.Uber", "8.ArT", "9.LSVT", "10.MLT19", "11.ReCTS", "MJ", "ST", ] elif opt.select_data == "mix_SA": select_data = [ "1.SVT", "2.IIIT", "3.IC13", "4.IC15", "5.COCO", "6.RCTW17", "7.Uber", "8.ArT", "9.LSVT", "10.MLT19", "11.ReCTS", "MJ", "ST", "SA", ] else: select_data = opt.select_data.split("-") # set batch_ratio for each data. if opt.batch_ratio: batch_ratio = opt.batch_ratio.split("-") else: batch_ratio = [round(1 / len(select_data), 3)] * len(select_data) train_loader = Batch_Balanced_Dataset(opt, opt.train_data, select_data, batch_ratio, log) if opt.semi != "None": select_data_unlabel = ["U1.Book32", "U2.TextVQA", "U3.STVQA"] batch_ratio_unlabel = [round(1 / len(select_data_unlabel), 3) ] * len(select_data_unlabel) dataset_root_unlabel = "data_CVPR2021/training/unlabel/" train_loader_unlabel_semi = Batch_Balanced_Dataset( opt, dataset_root_unlabel, select_data_unlabel, batch_ratio_unlabel, log, learn_type="semi", ) AlignCollate_valid = AlignCollate(opt, mode="test") valid_dataset, valid_dataset_log = hierarchical_dataset( root=opt.valid_data, opt=opt, mode="test") valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle= True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=False, ) log.write(valid_dataset_log) print("-" * 80) log.write("-" * 80 + "\n") """ model configuration """ if "CTC" in opt.Prediction: converter = CTCLabelConverter(opt.character) else: converter = AttnLabelConverter(opt.character) opt.sos_token_index = converter.dict["[SOS]"] opt.eos_token_index = converter.dict["[EOS]"] opt.num_class = len(converter.character) model = Model(opt) # weight initialization for name, param in model.named_parameters(): if "localization_fc2" in name: print(f"Skip {name} as it is already initialized") continue try: if "bias" in name: init.constant_(param, 0.0) elif "weight" in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if "weight" in name: param.data.fill_(1) continue # data parallel for multi-GPU model = torch.nn.DataParallel(model).to(device) model.train() if opt.saved_model != "": fine_tuning_log = f"### loading pretrained model from {opt.saved_model}\n" if "MoCo" in opt.saved_model or "MoCo" in opt.self_pre: pretrained_state_dict_qk = torch.load(opt.saved_model) pretrained_state_dict = {} for name in pretrained_state_dict_qk: if "encoder_q" in name: rename = name.replace("encoder_q.", "") pretrained_state_dict[rename] = pretrained_state_dict_qk[ name] else: pretrained_state_dict = torch.load(opt.saved_model) for name, param in model.named_parameters(): try: param.data.copy_(pretrained_state_dict[name].data ) # load from pretrained model if opt.FT == "freeze": param.requires_grad = False # Freeze fine_tuning_log += f"pretrained layer (freezed): {name}\n" else: fine_tuning_log += f"pretrained layer: {name}\n" except: fine_tuning_log += f"non-pretrained layer: {name}\n" print(fine_tuning_log) log.write(fine_tuning_log + "\n") # print("Model:") # print(model) log.write(repr(model) + "\n") """ setup loss """ if "CTC" in opt.Prediction: criterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: # ignore [PAD] token criterion = torch.nn.CrossEntropyLoss( ignore_index=converter.dict["[PAD]"]).to(device) if "Pseudo" in opt.semi: criterion_SemiSL = PseudoLabelLoss(opt, converter, criterion) elif "MeanT" in opt.semi: criterion_SemiSL = MeanTeacherLoss(opt, student_for_init_teacher=model) # loss averager train_loss_avg = Averager() semi_loss_avg = Averager() # semi supervised loss avg # filter that only require gradient descent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print(f"Trainable params num: {sum(params_num)}") log.write(f"Trainable params num: {sum(params_num)}\n") # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.optimizer == "sgd": optimizer = torch.optim.SGD( filtered_parameters, lr=opt.lr, momentum=opt.sgd_momentum, weight_decay=opt.sgd_weight_decay, ) elif opt.optimizer == "adadelta": optimizer = torch.optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) elif opt.optimizer == "adam": optimizer = torch.optim.Adam(filtered_parameters, lr=opt.lr) print("Optimizer:") print(optimizer) log.write(repr(optimizer) + "\n") if "super" in opt.schedule: if opt.optimizer == "sgd": cycle_momentum = True else: cycle_momentum = False scheduler = torch.optim.lr_scheduler.OneCycleLR( optimizer, max_lr=opt.lr, cycle_momentum=cycle_momentum, div_factor=20, final_div_factor=1000, total_steps=opt.num_iter, ) print("Scheduler:") print(scheduler) log.write(repr(scheduler) + "\n") """ final options """ # print(opt) opt_log = "------------ Options -------------\n" args = vars(opt) for k, v in args.items(): if str(k) == "character" and len(str(v)) > 500: opt_log += f"{str(k)}: So many characters to show all: number of characters: {len(str(v))}\n" else: opt_log += f"{str(k)}: {str(v)}\n" opt_log += "---------------------------------------\n" print(opt_log) log.write(opt_log) log.close() """ start training """ start_iter = 0 if opt.saved_model != "": try: start_iter = int(opt.saved_model.split("_")[-1].split(".")[0]) print(f"continue to train, start_iter: {start_iter}") except: pass start_time = time.time() best_score = -1 # training loop for iteration in tqdm( range(start_iter + 1, opt.num_iter + 1), total=opt.num_iter, position=0, leave=True, ): if "MeanT" in opt.semi: image_tensors, image_tensors_ema, labels = train_loader.get_batch_ema( ) else: image_tensors, labels = train_loader.get_batch() image = image_tensors.to(device) labels_index, labels_length = converter.encode( labels, batch_max_length=opt.batch_max_length) batch_size = image.size(0) # default recognition loss part if "CTC" in opt.Prediction: preds = model(image) preds_size = torch.IntTensor([preds.size(1)] * batch_size) preds_log_softmax = preds.log_softmax(2).permute(1, 0, 2) loss = criterion(preds_log_softmax, labels_index, preds_size, labels_length) else: preds = model(image, labels_index[:, :-1]) # align with Attention.forward target = labels_index[:, 1:] # without [SOS] Symbol loss = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) # semi supervised part (SemiSL) if "Pseudo" in opt.semi: image_unlabel, _ = train_loader_unlabel_semi.get_batch_two_images() image_unlabel = image_unlabel.to(device) loss_SemiSL = criterion_SemiSL(image_unlabel, model) loss = loss + loss_SemiSL semi_loss_avg.add(loss_SemiSL) elif "MeanT" in opt.semi: ( image_tensors_unlabel, image_tensors_unlabel_ema, ) = train_loader_unlabel_semi.get_batch_two_images() image_unlabel = image_tensors_unlabel.to(device) student_input = torch.cat([image, image_unlabel], dim=0) image_ema = image_tensors_ema.to(device) image_unlabel_ema = image_tensors_unlabel_ema.to(device) teacher_input = torch.cat([image_ema, image_unlabel_ema], dim=0) loss_SemiSL = criterion_SemiSL( student_input=student_input, student_logit=preds, student=model, teacher_input=teacher_input, iteration=iteration, ) loss = loss + loss_SemiSL semi_loss_avg.add(loss_SemiSL) model.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_( model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) optimizer.step() train_loss_avg.add(loss) if "super" in opt.schedule: scheduler.step() else: adjust_learning_rate(optimizer, iteration, opt) # validation part. # To see training progress, we also conduct validation when 'iteration == 1' if iteration % opt.val_interval == 0 or iteration == 1: # for validation log with open(f"./saved_models/{opt.exp_name}/log_train.txt", "a") as log: model.eval() with torch.no_grad(): ( valid_loss, current_score, preds, confidence_score, labels, infer_time, length_of_data, ) = validation(model, criterion, valid_loader, converter, opt) model.train() # keep best score (accuracy or norm ED) model on valid dataset # Do not use this on test datasets. It would be an unfair comparison # (training should be done without referring test set). if current_score > best_score: best_score = current_score torch.save( model.state_dict(), f"./saved_models/{opt.exp_name}/best_score.pth", ) # validation log: loss, lr, score (accuracy or norm ED), time. lr = optimizer.param_groups[0]["lr"] elapsed_time = time.time() - start_time valid_log = f"\n[{iteration}/{opt.num_iter}] Train_loss: {train_loss_avg.val():0.5f}, Valid_loss: {valid_loss:0.5f}" valid_log += f", Semi_loss: {semi_loss_avg.val():0.5f}\n" valid_log += f'{"Current_score":17s}: {current_score:0.2f}, Current_lr: {lr:0.7f}\n' valid_log += f'{"Best_score":17s}: {best_score:0.2f}, Infer_time: {infer_time:0.1f}, Elapsed_time: {elapsed_time:0.1f}' # show some predicted results dashed_line = "-" * 80 head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f"{dashed_line}\n{head}\n{dashed_line}\n" for gt, pred, confidence in zip(labels[:5], preds[:5], confidence_score[:5]): if "Attn" in opt.Prediction: gt = gt[:gt.find("[EOS]")] pred = pred[:pred.find("[EOS]")] predicted_result_log += f"{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n" predicted_result_log += f"{dashed_line}" valid_log = f"{valid_log}\n{predicted_result_log}" print(valid_log) log.write(valid_log + "\n") opt.writer.add_scalar("train/train_loss", float(f"{train_loss_avg.val():0.5f}"), iteration) opt.writer.add_scalar("train/semi_loss", float(f"{semi_loss_avg.val():0.5f}"), iteration) opt.writer.add_scalar("train/lr", float(f"{lr:0.7f}"), iteration) opt.writer.add_scalar("train/elapsed_time", float(f"{elapsed_time:0.1f}"), iteration) opt.writer.add_scalar("valid/valid_loss", float(f"{valid_loss:0.5f}"), iteration) opt.writer.add_scalar("valid/current_score", float(f"{current_score:0.2f}"), iteration) opt.writer.add_scalar("valid/best_score", float(f"{best_score:0.2f}"), iteration) train_loss_avg.reset() semi_loss_avg.reset() """ Evaluation at the end of training """ print("Start evaluation on benchmark testset") """ keep evaluation model and result logs """ os.makedirs(f"./result/{opt.exp_name}", exist_ok=True) os.makedirs(f"./evaluation_log", exist_ok=True) saved_best_model = f"./saved_models/{opt.exp_name}/best_score.pth" # os.system(f'cp {saved_best_model} ./result/{opt.exp_name}/') model.load_state_dict(torch.load(f"{saved_best_model}")) opt.eval_type = "benchmark" model.eval() with torch.no_grad(): total_accuracy, eval_data_list, accuracy_list = benchmark_all_eval( model, criterion, converter, opt) opt.writer.add_scalar("test/total_accuracy", float(f"{total_accuracy:0.2f}"), iteration) for eval_data, accuracy in zip(eval_data_list, accuracy_list): accuracy = float(accuracy) opt.writer.add_scalar(f"test/{eval_data}", float(f"{accuracy:0.2f}"), iteration) print( f'finished the experiment: {opt.exp_name}, "CUDA_VISIBLE_DEVICES" was {opt.CUDA_VISIBLE_DEVICES}' )
def train(opt): """ dataset preparation """ if not opt.data_filtering_off: print( 'Filtering the images containing characters which are not in opt.character' ) print( 'Filtering the images whose label is longer than opt.batch_max_length' ) # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') train_dataset = Batch_Balanced_Dataset(opt) log = open(f'./saved_models/{opt.exp_name}/log_dataset.txt', 'a') AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) valid_dataset, valid_dataset_log = hierarchical_dataset( root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle= True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() """ model configuration """ # CTCLoss converter_ctc = CTCLabelConverter(opt.character) # Attention converter_atten = AttnLabelConverter(opt.character) opt.num_class_ctc = len(converter_ctc.character) opt.num_class_atten = len(converter_atten.character) if opt.rgb: opt.input_channel = 3 model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class_ctc, opt.num_class_atten, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) # weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p_: p_.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable params num : ', sum(params_num)) # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.adam: optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("Optimizer:") print(optimizer) # use fp16 to train model = model.to(device) if opt.fp16: with open(f'./saved_models/{opt.exp_name}/log_train.txt', 'a') as log: log.write('==> Enable fp16 training' + '\n') print('==> Enable fp16 training') model, optimizer = amp.initialize(model, optimizer, opt_level='O1') # data parallel for multi-GPU if torch.cuda.device_count() > 1: model = torch.nn.DataParallel(model).to(device) model.train() # for i in model.module.Prediction_atten: # i.to(device) # for i in model.module.Feat_Extraction.scr: # i.to(device) if opt.saved_model != '': print(f'loading pretrained model from {opt.saved_model}') if opt.FT: model.load_state_dict(torch.load(opt.saved_model), strict=False) else: model.load_state_dict(torch.load(opt.saved_model)) print("Model:") print(model) """ setup loss """ criterion_ctc = torch.nn.CTCLoss(zero_infinity=True).to(device) criterion_atten = torch.nn.CrossEntropyLoss(ignore_index=0).to( device) # ignore [GO] token = ignore index 0 # loss averager loss_avg = Averager() """ final options """ writer = SummaryWriter(f'./saved_models/{opt.exp_name}') # print(opt) with open(f'./saved_models/{opt.exp_name}/opt.txt', 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_model != '': try: start_iter = int(opt.saved_model.split('_')[-1].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass start_time = time.time() best_accuracy = -1 best_norm_ED = -1 iteration = start_iter # image_tensors, labels = train_dataset.get_batch() while True: # train part image_tensors, labels = train_dataset.get_batch() image = image_tensors.to(device) batch_size = image.size(0) text_ctc, length_ctc = converter_ctc.encode( labels, batch_max_length=opt.batch_max_length) text_atten, length_atten = converter_atten.encode( labels, batch_max_length=opt.batch_max_length) # type tuple; (tensor, list); text_atten[:, :-1]:align with Attention.forward preds_ctc, preds_atten = model(image, text_atten[:, :-1]) # CTC Loss preds_size = torch.IntTensor([preds_ctc.size(1)] * batch_size) # _, preds_index = preds_ctc.max(2) # preds_str_ctc = converter_ctc.decode(preds_index.data, preds_size.data) preds_ctc = preds_ctc.log_softmax(2).permute(1, 0, 2) cost_ctc = 0.1 * criterion_ctc(preds_ctc, text_ctc, preds_size, length_ctc) # Attention Loss # preds_atten = [i[:, :text_atten.shape[1] - 1, :] for i in preds_atten] # # select max probabilty (greedy decoding) then decode index to character # preds_index_atten = [i.max(2)[1] for i in preds_atten] # length_for_pred = torch.IntTensor([opt.batch_max_length] * batch_size).to(device) # preds_str_atten = [converter_atten.decode(i, length_for_pred) for i in preds_index_atten] # preds_str_atten2 = preds_str_atten # preds_str_atten = [] # for i in preds_str_atten2: # prune after "end of sentence" token ([s]) # temp = [] # for j in i: # j = j[:j.find('[s]')] # temp.append(j) # preds_str_atten.append(temp) # preds_str_atten = [j[:j.find('[s]')] for i in preds_str_atten for j in i] target = text_atten[:, 1:] # without [GO] Symbol # cost_atten = 1.0 * criterion_atten(preds_atten.view(-1, preds_atten.shape[-1]), target.contiguous().view(-1)) for index, pred in enumerate(preds_atten): if index == 0: cost_atten = 1.0 * criterion_atten( pred.view(-1, pred.shape[-1]), target.contiguous().view(-1)) else: cost_atten += 1.0 * criterion_atten( pred.view(-1, pred.shape[-1]), target.contiguous().view(-1)) # cost_atten = [1.0 * criterion_atten(pred.view(-1, pred.shape[-1]), target.contiguous().view(-1)) for pred in # preds_atten] # cost_atten = criterion_atten(preds_atten.view(-1, preds_atten.shape[-1]), target.contiguous().view(-1)) cost = cost_ctc + cost_atten writer.add_scalar('loss', cost.item(), global_step=iteration + 1) # cost = cost_ctc # cost = cost_atten if (iteration + 1) % 100 == 0: print('\riter: {:4d}\tloss: {:6.3f}\tavg: {:6.3f}'.format( iteration + 1, cost.item(), loss_avg.val()), end='\n') else: print('\riter: {:4d}\tloss: {:6.3f}\tavg: {:6.3f}'.format( iteration + 1, cost.item(), loss_avg.val()), end='') sys.stdout.flush() if cost < 0.001: print(f'iter: {iteration + 1}\tloss: {cost}') # aaaaaa = 0 # model.zero_grad() optimizer.zero_grad() if torch.isnan(cost): print(f'iter: {iteration + 1}\tloss: {cost}\t==> Loss is NAN') sys.exit() elif torch.isinf(cost): print(f'iter: {iteration + 1}\tloss: {cost}\t==> Loss is INF') sys.exit() else: if opt.fp16: with amp.scale_loss(cost, optimizer) as scaled_loss: scaled_loss.backward() else: cost.backward() torch.nn.utils.clip_grad_norm_( model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) optimizer.step() loss_avg.add(cost) writer.add_scalar('loss_avg', loss_avg.val(), global_step=iteration + 1) # if loss_avg.val() <= 0.6: # opt.grad_clip = 2 # if loss_avg.val() <= 0.3: # opt.grad_clip = 1 # validation part if iteration == 0 or ( iteration + 1 ) % opt.valInterval == 0: # To see training progress, we also conduct validation when 'iteration == 0' elapsed_time = time.time() - start_time # for log with open(f'./saved_models/{opt.exp_name}/log_train.txt', 'a') as log: model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation( model, criterion_atten, valid_loader, converter_atten, opt) model.train() writer.add_scalar('accuracy', current_accuracy, global_step=iteration + 1) # training loss and validation loss loss_log = f'[{iteration + 1}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}' loss_avg.reset() current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}' # keep best accuracy model (on valid dataset) if current_accuracy > best_accuracy: best_accuracy = current_accuracy torch.save( model.state_dict(), f'./saved_models/{opt.exp_name}/best_accuracy.pth') if current_norm_ED > best_norm_ED: best_norm_ED = current_norm_ED torch.save( model.state_dict(), f'./saved_models/{opt.exp_name}/best_norm_ED.pth') best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}' loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}' print(loss_model_log) log.write(loss_model_log + '\n') # show some predicted results dashed_line = '-' * 80 head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n' for gt, pred, confidence in zip(labels[:5], preds[:5], confidence_score[:5]): gt = gt[:gt.find('[s]')] pred = pred[:pred.find('[s]')] predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n' predicted_result_log += f'{dashed_line}' print(predicted_result_log) log.write(predicted_result_log + '\n') # save model per 1e+5 iter. if (iteration + 1) % 1e+5 == 0: torch.save( model.state_dict(), f'./saved_models/{opt.exp_name}/iter_{iteration + 1}.pth') if (iteration + 1) == opt.num_iter: print('end the training') sys.exit() # if (iteration + 1) % opt.valInterval == 0: # print(f'iter: {iteration + 1}\tloss: {cost}') iteration += 1
def train(opt): lib.print_model_settings(locals().copy()) """ dataset preparation """ if not opt.data_filtering_off: print('Filtering the images containing characters which are not in opt.character') print('Filtering the images whose label is longer than opt.batch_max_length') # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 log = open(os.path.join(opt.exp_dir,opt.exp_name,'log_dataset.txt'), 'a') AlignCollate_valid = AlignPairCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) train_dataset, train_dataset_log = hierarchical_dataset(root=opt.train_data, opt=opt) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=opt.batch_size, sampler=data_sampler(train_dataset, shuffle=True, distributed=opt.distributed), num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True, drop_last=True) log.write(train_dataset_log) print('-' * 80) valid_dataset, valid_dataset_log = hierarchical_dataset(root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, sampler=data_sampler(train_dataset, shuffle=False, distributed=opt.distributed), num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True, drop_last=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() if 'Attn' in opt.Prediction: converter = AttnLabelConverter(opt.character) else: converter = CTCLabelConverter(opt.character) opt.num_class = len(converter.character) # styleModel = StyleTensorEncoder(input_dim=opt.input_channel) # genModel = AdaIN_Tensor_WordGenerator(opt) # disModel = MsImageDisV2(opt) # styleModel = StyleLatentEncoder(input_dim=opt.input_channel, norm='none') # mixModel = Mixer(opt,nblk=3, dim=opt.latent) genModel = styleGANGen(opt.size, opt.latent, opt.n_mlp, opt.num_class, channel_multiplier=opt.channel_multiplier).to(device) disModel = styleGANDis(opt.size, channel_multiplier=opt.channel_multiplier, input_dim=opt.input_channel).to(device) g_ema = styleGANGen(opt.size, opt.latent, opt.n_mlp, opt.num_class, channel_multiplier=opt.channel_multiplier).to(device) ocrModel = ModelV1(opt).to(device) accumulate(g_ema, genModel, 0) # # weight initialization # for currModel in [styleModel, mixModel]: # for name, param in currModel.named_parameters(): # if 'localization_fc2' in name: # print(f'Skip {name} as it is already initialized') # continue # try: # if 'bias' in name: # init.constant_(param, 0.0) # elif 'weight' in name: # init.kaiming_normal_(param) # except Exception as e: # for batchnorm. # if 'weight' in name: # param.data.fill_(1) # continue if opt.contentLoss == 'vis' or opt.contentLoss == 'seq': ocrCriterion = torch.nn.L1Loss() else: if 'CTC' in opt.Prediction: ocrCriterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: ocrCriterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0 # vggRecCriterion = torch.nn.L1Loss() # vggModel = VGGPerceptualLossModel(models.vgg19(pretrained=True), vggRecCriterion) print('model input parameters', opt.imgH, opt.imgW, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length) if opt.distributed: genModel = torch.nn.parallel.DistributedDataParallel( genModel, device_ids=[opt.local_rank], output_device=opt.local_rank, broadcast_buffers=False, ) disModel = torch.nn.parallel.DistributedDataParallel( disModel, device_ids=[opt.local_rank], output_device=opt.local_rank, broadcast_buffers=False, ) ocrModel = torch.nn.parallel.DistributedDataParallel( ocrModel, device_ids=[opt.local_rank], output_device=opt.local_rank, broadcast_buffers=False ) # styleModel = torch.nn.DataParallel(styleModel).to(device) # styleModel.train() # mixModel = torch.nn.DataParallel(mixModel).to(device) # mixModel.train() # genModel = torch.nn.DataParallel(genModel).to(device) # g_ema = torch.nn.DataParallel(g_ema).to(device) genModel.train() g_ema.eval() # disModel = torch.nn.DataParallel(disModel).to(device) disModel.train() # vggModel = torch.nn.DataParallel(vggModel).to(device) # vggModel.eval() # ocrModel = torch.nn.DataParallel(ocrModel).to(device) # if opt.distributed: # ocrModel.module.Transformation.eval() # ocrModel.module.FeatureExtraction.eval() # ocrModel.module.AdaptiveAvgPool.eval() # # ocrModel.module.SequenceModeling.eval() # ocrModel.module.Prediction.eval() # else: # ocrModel.Transformation.eval() # ocrModel.FeatureExtraction.eval() # ocrModel.AdaptiveAvgPool.eval() # # ocrModel.SequenceModeling.eval() # ocrModel.Prediction.eval() ocrModel.eval() if opt.distributed: g_module = genModel.module d_module = disModel.module else: g_module = genModel d_module = disModel g_reg_ratio = opt.g_reg_every / (opt.g_reg_every + 1) d_reg_ratio = opt.d_reg_every / (opt.d_reg_every + 1) optimizer = optim.Adam( genModel.parameters(), lr=opt.lr * g_reg_ratio, betas=(0 ** g_reg_ratio, 0.99 ** g_reg_ratio), ) dis_optimizer = optim.Adam( disModel.parameters(), lr=opt.lr * d_reg_ratio, betas=(0 ** d_reg_ratio, 0.99 ** d_reg_ratio), ) ## Loading pre-trained files if opt.modelFolderFlag: if len(glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_synth.pth")))>0: opt.saved_synth_model = glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_synth.pth"))[-1] if opt.saved_ocr_model !='' and opt.saved_ocr_model !='None': if not opt.distributed: ocrModel = torch.nn.DataParallel(ocrModel) print(f'loading pretrained ocr model from {opt.saved_ocr_model}') checkpoint = torch.load(opt.saved_ocr_model) ocrModel.load_state_dict(checkpoint) #temporary fix if not opt.distributed: ocrModel = ocrModel.module if opt.saved_gen_model !='' and opt.saved_gen_model !='None': print(f'loading pretrained gen model from {opt.saved_gen_model}') checkpoint = torch.load(opt.saved_gen_model, map_location=lambda storage, loc: storage) genModel.module.load_state_dict(checkpoint['g']) g_ema.module.load_state_dict(checkpoint['g_ema']) if opt.saved_synth_model != '' and opt.saved_synth_model != 'None': print(f'loading pretrained synth model from {opt.saved_synth_model}') checkpoint = torch.load(opt.saved_synth_model) # styleModel.load_state_dict(checkpoint['styleModel']) # mixModel.load_state_dict(checkpoint['mixModel']) genModel.load_state_dict(checkpoint['genModel']) g_ema.load_state_dict(checkpoint['g_ema']) disModel.load_state_dict(checkpoint['disModel']) optimizer.load_state_dict(checkpoint["optimizer"]) dis_optimizer.load_state_dict(checkpoint["dis_optimizer"]) # if opt.imgReconLoss == 'l1': # recCriterion = torch.nn.L1Loss() # elif opt.imgReconLoss == 'ssim': # recCriterion = ssim # elif opt.imgReconLoss == 'ms-ssim': # recCriterion = msssim # loss averager loss_avg = Averager() loss_avg_dis = Averager() loss_avg_gen = Averager() loss_avg_imgRecon = Averager() loss_avg_vgg_per = Averager() loss_avg_vgg_sty = Averager() loss_avg_ocr = Averager() log_r1_val = Averager() log_avg_path_loss_val = Averager() log_avg_mean_path_length_avg = Averager() log_ada_aug_p = Averager() """ final options """ with open(os.path.join(opt.exp_dir,opt.exp_name,'opt.txt'), 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_synth_model != '' and opt.saved_synth_model != 'None': try: start_iter = int(opt.saved_synth_model.split('_')[-2].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass #get schedulers scheduler = get_scheduler(optimizer,opt) dis_scheduler = get_scheduler(dis_optimizer,opt) start_time = time.time() iteration = start_iter cntr=0 mean_path_length = 0 d_loss_val = 0 r1_loss = torch.tensor(0.0, device=device) g_loss_val = 0 path_loss = torch.tensor(0.0, device=device) path_lengths = torch.tensor(0.0, device=device) mean_path_length_avg = 0 loss_dict = {} accum = 0.5 ** (32 / (10 * 1000)) ada_augment = torch.tensor([0.0, 0.0], device=device) ada_aug_p = opt.augment_p if opt.augment_p > 0 else 0.0 ada_aug_step = opt.ada_target / opt.ada_length r_t_stat = 0 sample_z = torch.randn(opt.n_sample, opt.latent, device=device) while(True): # print(cntr) # train part if opt.lr_policy !="None": scheduler.step() dis_scheduler.step() image_input_tensors, image_gt_tensors, labels_1, labels_2 = iter(train_loader).next() image_input_tensors = image_input_tensors.to(device) image_gt_tensors = image_gt_tensors.to(device) batch_size = image_input_tensors.size(0) requires_grad(genModel, False) # requires_grad(styleModel, False) # requires_grad(mixModel, False) requires_grad(disModel, True) text_1, length_1 = converter.encode(labels_1, batch_max_length=opt.batch_max_length) text_2, length_2 = converter.encode(labels_2, batch_max_length=opt.batch_max_length) #forward pass from style and word generator # style = styleModel(image_input_tensors).squeeze(2).squeeze(2) style = mixing_noise(opt.batch_size, opt.latent, opt.mixing, device) # scInput = mixModel(style,text_2) if 'CTC' in opt.Prediction: images_recon_2,_ = genModel(style, text_2, input_is_latent=opt.input_latent) else: images_recon_2,_ = genModel(style, text_2[:,1:-1], input_is_latent=opt.input_latent) #Domain discriminator: Dis update if opt.augment: image_gt_tensors_aug, _ = augment(image_gt_tensors, ada_aug_p) images_recon_2, _ = augment(images_recon_2, ada_aug_p) else: image_gt_tensors_aug = image_gt_tensors fake_pred = disModel(images_recon_2) real_pred = disModel(image_gt_tensors_aug) disCost = d_logistic_loss(real_pred, fake_pred) loss_dict["d"] = disCost*opt.disWeight loss_dict["real_score"] = real_pred.mean() loss_dict["fake_score"] = fake_pred.mean() loss_avg_dis.add(disCost) disModel.zero_grad() disCost.backward() dis_optimizer.step() if opt.augment and opt.augment_p == 0: ada_augment += torch.tensor( (torch.sign(real_pred).sum().item(), real_pred.shape[0]), device=device ) ada_augment = reduce_sum(ada_augment) if ada_augment[1] > 255: pred_signs, n_pred = ada_augment.tolist() r_t_stat = pred_signs / n_pred if r_t_stat > opt.ada_target: sign = 1 else: sign = -1 ada_aug_p += sign * ada_aug_step * n_pred ada_aug_p = min(1, max(0, ada_aug_p)) ada_augment.mul_(0) d_regularize = cntr % opt.d_reg_every == 0 if d_regularize: image_gt_tensors.requires_grad = True image_input_tensors.requires_grad = True cat_tensor = image_gt_tensors real_pred = disModel(cat_tensor) r1_loss = d_r1_loss(real_pred, cat_tensor) disModel.zero_grad() (opt.r1 / 2 * r1_loss * opt.d_reg_every + 0 * real_pred[0]).backward() dis_optimizer.step() loss_dict["r1"] = r1_loss # #[Style Encoder] + [Word Generator] update image_input_tensors, image_gt_tensors, labels_1, labels_2 = iter(train_loader).next() image_input_tensors = image_input_tensors.to(device) image_gt_tensors = image_gt_tensors.to(device) batch_size = image_input_tensors.size(0) requires_grad(genModel, True) # requires_grad(styleModel, True) # requires_grad(mixModel, True) requires_grad(disModel, False) text_1, length_1 = converter.encode(labels_1, batch_max_length=opt.batch_max_length) text_2, length_2 = converter.encode(labels_2, batch_max_length=opt.batch_max_length) # style = styleModel(image_input_tensors).squeeze(2).squeeze(2) # scInput = mixModel(style,text_2) # images_recon_2,_ = genModel([scInput], input_is_latent=opt.input_latent) style = mixing_noise(batch_size, opt.latent, opt.mixing, device) if 'CTC' in opt.Prediction: images_recon_2, _ = genModel(style, text_2) else: images_recon_2, _ = genModel(style, text_2[:,1:-1]) if opt.augment: images_recon_2, _ = augment(images_recon_2, ada_aug_p) fake_pred = disModel(images_recon_2) disGenCost = g_nonsaturating_loss(fake_pred) loss_dict["g"] = disGenCost # # #Adversarial loss # # disGenCost = disModel.module.calc_gen_loss(torch.cat((images_recon_2,image_input_tensors),dim=1)) # #Input reconstruction loss # recCost = recCriterion(images_recon_2,image_gt_tensors) # #vgg loss # vggPerCost, vggStyleCost = vggModel(image_gt_tensors, images_recon_2) #ocr loss text_for_pred = torch.LongTensor(batch_size, opt.batch_max_length + 1).fill_(0).to(device) length_for_pred = torch.IntTensor([opt.batch_max_length] * batch_size).to(device) if opt.contentLoss == 'vis' or opt.contentLoss == 'seq': preds_recon = ocrModel(images_recon_2, text_for_pred, is_train=False, returnFeat=opt.contentLoss) preds_gt = ocrModel(image_gt_tensors, text_for_pred, is_train=False, returnFeat=opt.contentLoss) ocrCost = ocrCriterion(preds_recon, preds_gt) else: if 'CTC' in opt.Prediction: preds_recon = ocrModel(images_recon_2, text_for_pred, is_train=False) # preds_o = preds_recon[:, :text_1.shape[1], :] preds_size = torch.IntTensor([preds_recon.size(1)] * batch_size) preds_recon_softmax = preds_recon.log_softmax(2).permute(1, 0, 2) ocrCost = ocrCriterion(preds_recon_softmax, text_2, preds_size, length_2) #predict ocr recognition on generated images # preds_recon_size = torch.IntTensor([preds_recon.size(1)] * batch_size) _, preds_recon_index = preds_recon.max(2) labels_o_ocr = converter.decode(preds_recon_index.data, preds_size.data) #predict ocr recognition on gt style images preds_s = ocrModel(image_input_tensors, text_for_pred, is_train=False) # preds_s = preds_s[:, :text_1.shape[1] - 1, :] preds_s_size = torch.IntTensor([preds_s.size(1)] * batch_size) _, preds_s_index = preds_s.max(2) labels_s_ocr = converter.decode(preds_s_index.data, preds_s_size.data) #predict ocr recognition on gt stylecontent images preds_sc = ocrModel(image_gt_tensors, text_for_pred, is_train=False) # preds_sc = preds_sc[:, :text_2.shape[1] - 1, :] preds_sc_size = torch.IntTensor([preds_sc.size(1)] * batch_size) _, preds_sc_index = preds_sc.max(2) labels_sc_ocr = converter.decode(preds_sc_index.data, preds_sc_size.data) else: preds_recon = ocrModel(images_recon_2, text_for_pred[:, :-1], is_train=False) # align with Attention.forward target_2 = text_2[:, 1:] # without [GO] Symbol ocrCost = ocrCriterion(preds_recon.view(-1, preds_recon.shape[-1]), target_2.contiguous().view(-1)) #predict ocr recognition on generated images _, preds_o_index = preds_recon.max(2) labels_o_ocr = converter.decode(preds_o_index, length_for_pred) for idx, pred in enumerate(labels_o_ocr): pred_EOS = pred.find('[s]') labels_o_ocr[idx] = pred[:pred_EOS] # prune after "end of sentence" token ([s]) #predict ocr recognition on gt style images preds_s = ocrModel(image_input_tensors, text_for_pred, is_train=False) _, preds_s_index = preds_s.max(2) labels_s_ocr = converter.decode(preds_s_index, length_for_pred) for idx, pred in enumerate(labels_s_ocr): pred_EOS = pred.find('[s]') labels_s_ocr[idx] = pred[:pred_EOS] # prune after "end of sentence" token ([s]) #predict ocr recognition on gt stylecontent images preds_sc = ocrModel(image_gt_tensors, text_for_pred, is_train=False) _, preds_sc_index = preds_sc.max(2) labels_sc_ocr = converter.decode(preds_sc_index, length_for_pred) for idx, pred in enumerate(labels_sc_ocr): pred_EOS = pred.find('[s]') labels_sc_ocr[idx] = pred[:pred_EOS] # prune after "end of sentence" token ([s]) # cost = opt.reconWeight*recCost + opt.disWeight*disGenCost + opt.vggPerWeight*vggPerCost + opt.vggStyWeight*vggStyleCost + opt.ocrWeight*ocrCost cost = opt.disWeight*disGenCost + opt.ocrWeight*ocrCost # styleModel.zero_grad() genModel.zero_grad() # mixModel.zero_grad() disModel.zero_grad() # vggModel.zero_grad() ocrModel.zero_grad() cost.backward() optimizer.step() loss_avg.add(cost) g_regularize = cntr % opt.g_reg_every == 0 if g_regularize: image_input_tensors, image_gt_tensors, labels_1, labels_2 = iter(train_loader).next() image_input_tensors = image_input_tensors.to(device) image_gt_tensors = image_gt_tensors.to(device) batch_size = image_input_tensors.size(0) text_1, length_1 = converter.encode(labels_1, batch_max_length=opt.batch_max_length) text_2, length_2 = converter.encode(labels_2, batch_max_length=opt.batch_max_length) path_batch_size = max(1, batch_size // opt.path_batch_shrink) # style = styleModel(image_input_tensors).squeeze(2).squeeze(2) # scInput = mixModel(style,text_2) # images_recon_2, latents = genModel([scInput],input_is_latent=opt.input_latent, return_latents=True) style = mixing_noise(path_batch_size, opt.latent, opt.mixing, device) if 'CTC' in opt.Prediction: images_recon_2, latents = genModel(style, text_2[:path_batch_size], return_latents=True) else: images_recon_2, latents = genModel(style, text_2[:path_batch_size,1:-1], return_latents=True) path_loss, mean_path_length, path_lengths = g_path_regularize( images_recon_2, latents, mean_path_length ) genModel.zero_grad() weighted_path_loss = opt.path_regularize * opt.g_reg_every * path_loss if opt.path_batch_shrink: weighted_path_loss += 0 * images_recon_2[0, 0, 0, 0] weighted_path_loss.backward() optimizer.step() mean_path_length_avg = ( reduce_sum(mean_path_length).item() / get_world_size() ) loss_dict["path"] = path_loss loss_dict["path_length"] = path_lengths.mean() accumulate(g_ema, g_module, accum) loss_reduced = reduce_loss_dict(loss_dict) d_loss_val = loss_reduced["d"].mean().item() g_loss_val = loss_reduced["g"].mean().item() r1_val = loss_reduced["r1"].mean().item() path_loss_val = loss_reduced["path"].mean().item() real_score_val = loss_reduced["real_score"].mean().item() fake_score_val = loss_reduced["fake_score"].mean().item() path_length_val = loss_reduced["path_length"].mean().item() #Individual losses loss_avg_gen.add(opt.disWeight*disGenCost) loss_avg_imgRecon.add(torch.tensor(0.0)) loss_avg_vgg_per.add(torch.tensor(0.0)) loss_avg_vgg_sty.add(torch.tensor(0.0)) loss_avg_ocr.add(opt.ocrWeight*ocrCost) log_r1_val.add(loss_reduced["path"]) log_avg_path_loss_val.add(loss_reduced["path"]) log_avg_mean_path_length_avg.add(torch.tensor(mean_path_length_avg)) log_ada_aug_p.add(torch.tensor(ada_aug_p)) if get_rank() == 0: # pbar.set_description( # ( # f"d: {d_loss_val:.4f}; g: {g_loss_val:.4f}; r1: {r1_val:.4f}; " # f"path: {path_loss_val:.4f}; mean path: {mean_path_length_avg:.4f}; " # f"augment: {ada_aug_p:.4f}" # ) # ) if wandb and opt.wandb: wandb.log( { "Generator": g_loss_val, "Discriminator": d_loss_val, "Augment": ada_aug_p, "Rt": r_t_stat, "R1": r1_val, "Path Length Regularization": path_loss_val, "Mean Path Length": mean_path_length, "Real Score": real_score_val, "Fake Score": fake_score_val, "Path Length": path_length_val, } ) # if cntr % 100 == 0: # with torch.no_grad(): # g_ema.eval() # sample, _ = g_ema([scInput[:,:opt.latent],scInput[:,opt.latent:]]) # utils.save_image( # sample, # os.path.join(opt.trainDir, f"sample_{str(cntr).zfill(6)}.png"), # nrow=int(opt.n_sample ** 0.5), # normalize=True, # range=(-1, 1), # ) # validation part if (iteration + 1) % opt.valInterval == 0 or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0' #Save training images curr_batch_size = style[0].shape[0] images_recon_2, _ = g_ema(style, text_2[:curr_batch_size], input_is_latent=opt.input_latent) os.makedirs(os.path.join(opt.trainDir,str(iteration)), exist_ok=True) for trImgCntr in range(batch_size): try: if opt.contentLoss == 'vis' or opt.contentLoss == 'seq': save_image(tensor2im(image_input_tensors[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_sInput_'+labels_1[trImgCntr]+'.png')) save_image(tensor2im(image_gt_tensors[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_csGT_'+labels_2[trImgCntr]+'.png')) save_image(tensor2im(images_recon_2[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_csRecon_'+labels_2[trImgCntr]+'.png')) else: save_image(tensor2im(image_input_tensors[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_sInput_'+labels_1[trImgCntr]+'_'+labels_s_ocr[trImgCntr]+'.png')) save_image(tensor2im(image_gt_tensors[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_csGT_'+labels_2[trImgCntr]+'_'+labels_sc_ocr[trImgCntr]+'.png')) save_image(tensor2im(images_recon_2[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_csRecon_'+labels_2[trImgCntr]+'_'+labels_o_ocr[trImgCntr]+'.png')) except: print('Warning while saving training image') elapsed_time = time.time() - start_time # for log with open(os.path.join(opt.exp_dir,opt.exp_name,'log_train.txt'), 'a') as log: # styleModel.eval() genModel.eval() g_ema.eval() # mixModel.eval() disModel.eval() with torch.no_grad(): valid_loss, infer_time, length_of_data = validation_synth_v6( iteration, g_ema, ocrModel, disModel, ocrCriterion, valid_loader, converter, opt) # styleModel.train() genModel.train() # mixModel.train() disModel.train() # training loss and validation loss loss_log = f'[{iteration+1}/{opt.num_iter}] Train Synth loss: {loss_avg.val():0.5f}, \ Train Dis loss: {loss_avg_dis.val():0.5f}, Train Gen loss: {loss_avg_gen.val():0.5f},\ Train OCR loss: {loss_avg_ocr.val():0.5f}, \ Train R1-val loss: {log_r1_val.val():0.5f}, Train avg-path-loss: {log_avg_path_loss_val.val():0.5f}, \ Train mean-path-length loss: {log_avg_mean_path_length_avg.val():0.5f}, Train ada-aug-p: {log_ada_aug_p.val():0.5f}, \ Valid Synth loss: {valid_loss[0]:0.5f}, \ Valid Dis loss: {valid_loss[1]:0.5f}, Valid Gen loss: {valid_loss[2]:0.5f}, \ Valid OCR loss: {valid_loss[6]:0.5f}, Elapsed_time: {elapsed_time:0.5f}' #plotting lib.plot.plot(os.path.join(opt.plotDir,'Train-Synth-Loss'), loss_avg.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-Dis-Loss'), loss_avg_dis.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-Gen-Loss'), loss_avg_gen.val().item()) # lib.plot.plot(os.path.join(opt.plotDir,'Train-ImgRecon1-Loss'), loss_avg_imgRecon.val().item()) # lib.plot.plot(os.path.join(opt.plotDir,'Train-VGG-Per-Loss'), loss_avg_vgg_per.val().item()) # lib.plot.plot(os.path.join(opt.plotDir,'Train-VGG-Sty-Loss'), loss_avg_vgg_sty.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-OCR-Loss'), loss_avg_ocr.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-r1_val'), log_r1_val.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-path_loss_val'), log_avg_path_loss_val.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-mean_path_length_avg'), log_avg_mean_path_length_avg.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-ada_aug_p'), log_ada_aug_p.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Valid-Synth-Loss'), valid_loss[0].item()) lib.plot.plot(os.path.join(opt.plotDir,'Valid-Dis-Loss'), valid_loss[1].item()) lib.plot.plot(os.path.join(opt.plotDir,'Valid-Gen-Loss'), valid_loss[2].item()) # lib.plot.plot(os.path.join(opt.plotDir,'Valid-ImgRecon1-Loss'), valid_loss[3].item()) # lib.plot.plot(os.path.join(opt.plotDir,'Valid-VGG-Per-Loss'), valid_loss[4].item()) # lib.plot.plot(os.path.join(opt.plotDir,'Valid-VGG-Sty-Loss'), valid_loss[5].item()) lib.plot.plot(os.path.join(opt.plotDir,'Valid-OCR-Loss'), valid_loss[6].item()) print(loss_log) loss_avg.reset() loss_avg_dis.reset() loss_avg_gen.reset() loss_avg_imgRecon.reset() loss_avg_vgg_per.reset() loss_avg_vgg_sty.reset() loss_avg_ocr.reset() log_r1_val.reset() log_avg_path_loss_val.reset() log_avg_mean_path_length_avg.reset() log_ada_aug_p.reset() lib.plot.flush() lib.plot.tick() # save model per 1e+5 iter. if (iteration) % 1e+4 == 0: torch.save({ # 'styleModel':styleModel.state_dict(), # 'mixModel':mixModel.state_dict(), 'genModel':g_module.state_dict(), 'g_ema':g_ema.state_dict(), 'disModel':d_module.state_dict(), 'optimizer':optimizer.state_dict(), 'dis_optimizer':dis_optimizer.state_dict()}, os.path.join(opt.exp_dir,opt.exp_name,'iter_'+str(iteration+1)+'_synth.pth')) if (iteration + 1) == opt.num_iter: print('end the training') sys.exit() iteration += 1 cntr+=1
def train(opt): """ dataset preparation """ if not opt.data_filtering_off: print( 'Filtering the images containing characters which are not in opt.character' ) print( 'Filtering the images whose label is longer than opt.batch_max_length' ) # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') train_dataset = Batch_Balanced_Dataset(opt) log = open(f'./saved_models/{opt.exp_name}/log_dataset.txt', 'a') AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) valid_dataset, valid_dataset_log = hierarchical_dataset( root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle= True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + ' ') log.close() """ model configuration """ # if 'CTC' in opt.Prediction: if opt.baiduCTC: CTC_converter = CTCLabelConverterForBaiduWarpctc(opt.character) else: CTC_converter = CTCLabelConverter(opt.character) # else: Attn_converter = AttnLabelConverter(opt.character) opt.num_class_ctc = len(CTC_converter.character) opt.num_class_attn = len(Attn_converter.character) if opt.rgb: opt.input_channel = 3 model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class_ctc, opt.num_class_attn, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) # weight initialization for name, param in model.named_parameters(): # print(name) if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # data parallel for multi-GPU model = torch.nn.DataParallel(model).to(device) model.train() print("Model:") print(model) # print(summary(model, (1, opt.imgH, opt.imgW,1))) """ setup loss """ if opt.baiduCTC: # need to install warpctc. see our guideline. if opt.label_smooth: criterion_major_path = SmoothCTCLoss(num_classes=opt.num_class_ctc, weight=0.05) else: criterion_major_path = CTCLoss() #criterion_major_path = CTCLoss(average_frames=False, reduction="mean", blank=0) else: criterion_major_path = torch.nn.CTCLoss(zero_infinity=True).to(device) # else: # criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0 # loss averager #criterion_major_path = torch.nn.CTCLoss(zero_infinity=True).to(device) criterion_guide_path = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) loss_avg_major_path = Averager() loss_avg_guide_path = Averager() # filter that only require gradient decent guide_parameters = [] major_parameters = [] guide_model_part_names = [ "Transformation", "FeatureExtraction", "SequenceModeling_Attn", "Attention" ] major_model_part_names = ["SequenceModeling_CTC", "CTC"] for name, param in model.named_parameters(): if param.requires_grad: if name.split(".")[1] in guide_model_part_names: guide_parameters.append(param) elif name.split(".")[1] in major_model_part_names: major_parameters.append(param) # print(name) # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] if opt.continue_training: guide_parameters = [] # setup optimizer if opt.adam: optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) else: optimizer_ctc = AdamW(major_parameters, lr=opt.lr) if not opt.continue_training: optimizer_attn = AdamW(guide_parameters, lr=opt.lr) scheduler_ctc = get_linear_schedule_with_warmup( optimizer_ctc, num_warmup_steps=10000, num_training_steps=opt.num_iter) scheduler_attn = get_linear_schedule_with_warmup( optimizer_attn, num_warmup_steps=10000, num_training_steps=opt.num_iter) start_iter = 0 if opt.saved_model != '' and (not opt.continue_training): print(f'loading pretrained model from {opt.saved_model}') checkpoint = torch.load(opt.saved_model) start_iter = checkpoint['start_iter'] + 1 if not opt.adam: optimizer_ctc.load_state_dict( checkpoint['optimizer_ctc_state_dict']) if not opt.continue_training: optimizer_attn.load_state_dict( checkpoint['optimizer_attn_state_dict']) scheduler_ctc.load_state_dict( checkpoint['scheduler_ctc_state_dict']) scheduler_attn.load_state_dict( checkpoint['scheduler_attn_state_dict']) print(scheduler_ctc.get_lr()) print(scheduler_attn.get_lr()) if opt.FT: model.load_state_dict(checkpoint['model_state_dict'], strict=False) else: model.load_state_dict(checkpoint['model_state_dict']) if opt.continue_training: model.load_state_dict(torch.load(opt.saved_model)) # print("Optimizer:") # print(optimizer) # scheduler_ctc = get_linear_schedule_with_warmup( optimizer_ctc, num_warmup_steps=10000, num_training_steps=opt.num_iter, last_epoch=start_iter - 1) scheduler_attn = get_linear_schedule_with_warmup( optimizer_attn, num_warmup_steps=10000, num_training_steps=opt.num_iter, last_epoch=start_iter - 1) """ final options """ # print(opt) with open(f'./saved_models/{opt.exp_name}/opt.txt', 'a') as opt_file: opt_log = '------------ Options ------------- ' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)} ' opt_log += '--------------------------------------- ' print(opt_log) opt_file.write(opt_log) """ start training """ start_time = time.time() best_accuracy = -1 best_norm_ED = -1 iteration = start_iter - 1 if opt.continue_training: start_iter = 0 while (True): # train part image_tensors, labels = train_dataset.get_batch() iteration += 1 if iteration < start_iter: continue image = image_tensors.to(device) # print(image.size()) text_attn, length_attn = Attn_converter.encode( labels, batch_max_length=opt.batch_max_length) #print("1") text_ctc, length_ctc = CTC_converter.encode( labels, batch_max_length=opt.batch_max_length) #print("2") #if iteration == start_iter : # writer.add_graph(model, (image, text_attn)) batch_size = image.size(0) preds_major, preds_guide = model(image, text_attn[:, :-1]) #print("10") preds_size = torch.IntTensor([preds_major.size(1)] * batch_size) if opt.baiduCTC: preds_major = preds_major.permute(1, 0, 2) # to use CTCLoss format if opt.label_smooth: cost_ctc = criterion_major_path(preds_major, text_ctc, preds_size, length_ctc, batch_size) else: cost_ctc = criterion_major_path( preds_major, text_ctc, preds_size, length_ctc) / batch_size else: preds_major = preds_major.log_softmax(2).permute(1, 0, 2) cost_ctc = criterion_major_path(preds_major, text_ctc, preds_size, length_ctc) #print("3") # preds = model(image, text[:, :-1]) # align with Attention.forward target = text_attn[:, 1:] # without [GO] Symbol if not opt.continue_training: cost_attn = criterion_guide_path( preds_guide.view(-1, preds_guide.shape[-1]), target.contiguous().view(-1)) optimizer_attn.zero_grad() cost_attn.backward(retain_graph=True) torch.nn.utils.clip_grad_norm_( guide_parameters, opt.grad_clip) # gradient clipping with 5 (Default) optimizer_attn.step() optimizer_ctc.zero_grad() cost_ctc.backward() torch.nn.utils.clip_grad_norm_( major_parameters, opt.grad_clip) # gradient clipping with 5 (Default) optimizer_ctc.step() scheduler_ctc.step() scheduler_attn.step() #print("4") loss_avg_major_path.add(cost_ctc) if not opt.continue_training: loss_avg_guide_path.add(cost_attn) if (iteration + 1) % 100 == 0: writer.add_scalar("Loss/train_ctc", loss_avg_major_path.val(), (iteration + 1) // 100) loss_avg_major_path.reset() if not opt.continue_training: writer.add_scalar("Loss/train_attn", loss_avg_guide_path.val(), (iteration + 1) // 100) loss_avg_guide_path.reset() # validation part if ( iteration + 1 ) % opt.valInterval == 0: #or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0' elapsed_time = time.time() - start_time # for log with open(f'./saved_models/{opt.exp_name}/log_train.txt', 'a') as log: model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation( model, criterion_major_path, valid_loader, CTC_converter, opt) model.train() writer.add_scalar("Loss/valid", valid_loss, (iteration + 1) // opt.valInterval) writer.add_scalar("Metrics/accuracy", current_accuracy, (iteration + 1) // opt.valInterval) writer.add_scalar("Metrics/norm_ED", current_norm_ED, (iteration + 1) // opt.valInterval) # loss_log = f'[{iteration+1}/{opt.num_iter}] Train loss: {train_loss:0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}' # loss_avg.reset() current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}' # training loss and validation loss if not opt.continue_training: loss_log = f'[{iteration+1}/{opt.num_iter}] Train loss ctc: {loss_avg_major_path.val():0.5f}, Train loss attn: {loss_avg_guide_path.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}' else: loss_log = f'[{iteration+1}/{opt.num_iter}] Train loss ctc: {loss_avg_major_path.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}' loss_avg_major_path.reset() if not opt.continue_training: loss_avg_guide_path.reset() current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}' # keep best accuracy model (on valid dataset) if current_accuracy > best_accuracy: best_accuracy = current_accuracy torch.save(model.state_dict(), f'{fol_ckpt}/best_accuracy.pth') if current_norm_ED > best_norm_ED: best_norm_ED = current_norm_ED torch.save(model.state_dict(), f'{fol_ckpt}/best_norm_ED.pth') best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}' loss_model_log = f'{loss_log} {current_model_log} {best_model_log}' print(loss_model_log) log.write(loss_model_log + ' ') # show some predicted results dashed_line = '-' * 80 head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f'{dashed_line} {head} {dashed_line} ' for gt, pred, confidence in zip(labels[:5], preds[:5], confidence_score[:5]): # if 'Attn' in opt.Prediction: # gt = gt[:gt.find('[s]')] # pred = pred[:pred.find('[s]')] predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f} {str(pred == gt)} ' predicted_result_log += f'{dashed_line}' print(predicted_result_log) log.write(predicted_result_log + ' ') # save model per 1e+5 iter. if (iteration + 1) % 1e+3 == 0 and (not opt.continue_training): # print(scheduler_ctc.get_lr()) # print(scheduler_attn.get_lr()) torch.save( { 'model_state_dict': model.state_dict(), 'optimizer_attn_state_dict': optimizer_attn.state_dict(), 'optimizer_ctc_state_dict': optimizer_ctc.state_dict(), 'start_iter': iteration, 'scheduler_ctc_state_dict': scheduler_ctc.state_dict(), 'scheduler_attn_state_dict': scheduler_attn.state_dict(), }, f'{fol_ckpt}/current_model.pth') if (iteration + 1) == opt.num_iter: print('end the training') sys.exit()
def train(opt): lib.print_model_settings(locals().copy()) if 'Attn' in opt.Prediction: converter = AttnLabelConverter(opt.character) text_len = opt.batch_max_length+2 else: converter = CTCLabelConverter(opt.character) text_len = opt.batch_max_length opt.classes = converter.character """ dataset preparation """ if not opt.data_filtering_off: print('Filtering the images containing characters which are not in opt.character') print('Filtering the images whose label is longer than opt.batch_max_length') # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 log = open(os.path.join(opt.exp_dir,opt.exp_name,'log_dataset.txt'), 'a') AlignCollate_valid = AlignPairCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) train_dataset = LmdbStyleDataset(root=opt.train_data, opt=opt) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=opt.batch_size*2, #*2 to sample different images from training encoder and discriminator real images shuffle=True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True, drop_last=True) print('-' * 80) valid_dataset = LmdbStyleDataset(root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size*2, #*2 to sample different images from training encoder and discriminator real images shuffle=False, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True, drop_last=True) print('-' * 80) log.write('-' * 80 + '\n') log.close() text_dataset = text_gen(opt) text_loader = torch.utils.data.DataLoader( text_dataset, batch_size=opt.batch_size, shuffle=True, num_workers=int(opt.workers), pin_memory=True, drop_last=True) opt.num_class = len(converter.character) c_code_size = opt.latent cEncoder = GlobalContentEncoder(opt.num_class, text_len, opt.char_embed_size, c_code_size) ocrModel = ModelV1(opt) genModel = styleGANGen(opt.size, opt.latent, opt.latent, opt.n_mlp, channel_multiplier=opt.channel_multiplier) g_ema = styleGANGen(opt.size, opt.latent, opt.latent, opt.n_mlp, channel_multiplier=opt.channel_multiplier) disEncModel = styleGANDis(opt.size, channel_multiplier=opt.channel_multiplier, input_dim=opt.input_channel, code_s_dim=c_code_size) accumulate(g_ema, genModel, 0) # uCriterion = torch.nn.MSELoss() # sCriterion = torch.nn.MSELoss() # if opt.contentLoss == 'vis' or opt.contentLoss == 'seq': # ocrCriterion = torch.nn.L1Loss() # else: if 'CTC' in opt.Prediction: ocrCriterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: print('Not implemented error') sys.exit() # ocrCriterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0 cEncoder= torch.nn.DataParallel(cEncoder).to(device) cEncoder.train() genModel = torch.nn.DataParallel(genModel).to(device) g_ema = torch.nn.DataParallel(g_ema).to(device) genModel.train() g_ema.eval() disEncModel = torch.nn.DataParallel(disEncModel).to(device) disEncModel.train() ocrModel = torch.nn.DataParallel(ocrModel).to(device) if opt.ocrFixed: if opt.Transformation == 'TPS': ocrModel.module.Transformation.eval() ocrModel.module.FeatureExtraction.eval() ocrModel.module.AdaptiveAvgPool.eval() # ocrModel.module.SequenceModeling.eval() ocrModel.module.Prediction.eval() else: ocrModel.train() g_reg_ratio = opt.g_reg_every / (opt.g_reg_every + 1) d_reg_ratio = opt.d_reg_every / (opt.d_reg_every + 1) optimizer = optim.Adam( list(genModel.parameters())+list(cEncoder.parameters()), lr=opt.lr * g_reg_ratio, betas=(0 ** g_reg_ratio, 0.99 ** g_reg_ratio), ) dis_optimizer = optim.Adam( disEncModel.parameters(), lr=opt.lr * d_reg_ratio, betas=(0 ** d_reg_ratio, 0.99 ** d_reg_ratio), ) ocr_optimizer = optim.Adam( ocrModel.parameters(), lr=opt.lr, betas=(0.9, 0.99), ) ## Loading pre-trained files if opt.modelFolderFlag: if len(glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_synth.pth")))>0: opt.saved_synth_model = glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_synth.pth"))[-1] if opt.saved_ocr_model !='' and opt.saved_ocr_model !='None': print(f'loading pretrained ocr model from {opt.saved_ocr_model}') checkpoint = torch.load(opt.saved_ocr_model) ocrModel.load_state_dict(checkpoint) # if opt.saved_gen_model !='' and opt.saved_gen_model !='None': # print(f'loading pretrained gen model from {opt.saved_gen_model}') # checkpoint = torch.load(opt.saved_gen_model, map_location=lambda storage, loc: storage) # genModel.module.load_state_dict(checkpoint['g']) # g_ema.module.load_state_dict(checkpoint['g_ema']) if opt.saved_synth_model != '' and opt.saved_synth_model != 'None': print(f'loading pretrained synth model from {opt.saved_synth_model}') checkpoint = torch.load(opt.saved_synth_model) # styleModel.load_state_dict(checkpoint['styleModel']) # mixModel.load_state_dict(checkpoint['mixModel']) genModel.load_state_dict(checkpoint['genModel']) g_ema.load_state_dict(checkpoint['g_ema']) disEncModel.load_state_dict(checkpoint['disEncModel']) ocrModel.load_state_dict(checkpoint['ocrModel']) optimizer.load_state_dict(checkpoint["optimizer"]) dis_optimizer.load_state_dict(checkpoint["dis_optimizer"]) ocr_optimizer.load_state_dict(checkpoint["ocr_optimizer"]) # if opt.imgReconLoss == 'l1': # recCriterion = torch.nn.L1Loss() # elif opt.imgReconLoss == 'ssim': # recCriterion = ssim # elif opt.imgReconLoss == 'ms-ssim': # recCriterion = msssim # loss averager loss_avg_dis = Averager() loss_avg_gen = Averager() loss_avg_unsup = Averager() loss_avg_sup = Averager() log_r1_val = Averager() log_avg_path_loss_val = Averager() log_avg_mean_path_length_avg = Averager() log_ada_aug_p = Averager() loss_avg_ocr_sup = Averager() loss_avg_ocr_unsup = Averager() """ final options """ with open(os.path.join(opt.exp_dir,opt.exp_name,'opt.txt'), 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_synth_model != '' and opt.saved_synth_model != 'None': try: start_iter = int(opt.saved_synth_model.split('_')[-2].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass #get schedulers scheduler = get_scheduler(optimizer,opt) dis_scheduler = get_scheduler(dis_optimizer,opt) ocr_scheduler = get_scheduler(ocr_optimizer,opt) start_time = time.time() iteration = start_iter cntr=0 mean_path_length = 0 d_loss_val = 0 r1_loss = torch.tensor(0.0, device=device) g_loss_val = 0 path_loss = torch.tensor(0.0, device=device) path_lengths = torch.tensor(0.0, device=device) mean_path_length_avg = 0 # loss_dict = {} accum = 0.5 ** (32 / (10 * 1000)) ada_augment = torch.tensor([0.0, 0.0], device=device) ada_aug_p = opt.augment_p if opt.augment_p > 0 else 0.0 ada_aug_step = opt.ada_target / opt.ada_length r_t_stat = 0 epsilon = 10e-50 # sample_z = torch.randn(opt.n_sample, opt.latent, device=device) while(True): # print(cntr) # train part if opt.lr_policy !="None": scheduler.step() dis_scheduler.step() ocr_scheduler.step() image_input_tensors, _, labels, _ = iter(train_loader).next() labels_z_c = iter(text_loader).next() image_input_tensors = image_input_tensors.to(device) gt_image_tensors = image_input_tensors[:opt.batch_size].detach() real_image_tensors = image_input_tensors[opt.batch_size:].detach() labels_gt = labels[:opt.batch_size] requires_grad(cEncoder, False) requires_grad(genModel, False) requires_grad(disEncModel, True) requires_grad(ocrModel, False) text_z_c, length_z_c = converter.encode(labels_z_c, batch_max_length=opt.batch_max_length) text_gt, length_gt = converter.encode(labels_gt, batch_max_length=opt.batch_max_length) z_c_code = cEncoder(text_z_c) noise_style = mixing_noise_style(opt.batch_size, opt.latent, opt.mixing, device) style=[] style.append(noise_style[0]*z_c_code) if len(noise_style)>1: style.append(noise_style[1]*z_c_code) if opt.zAlone: #to validate orig style gan results newstyle = [] newstyle.append(style[0][:,:opt.latent]) if len(style)>1: newstyle.append(style[1][:,:opt.latent]) style = newstyle fake_img,_ = genModel(style, input_is_latent=opt.input_latent) # #unsupervised code prediction on generated image # u_pred_code = disEncModel(fake_img, mode='enc') # uCost = uCriterion(u_pred_code, z_code) # #supervised code prediction on gt image # s_pred_code = disEncModel(gt_image_tensors, mode='enc') # sCost = uCriterion(s_pred_code, gt_phoc_tensors) #Domain discriminator fake_pred = disEncModel(fake_img) real_pred = disEncModel(real_image_tensors) disCost = d_logistic_loss(real_pred, fake_pred) # dis_cost = disCost + opt.gamma_e*uCost + opt.beta*sCost loss_avg_dis.add(disCost) # loss_avg_sup.add(opt.beta*sCost) # loss_avg_unsup.add(opt.gamma_e * uCost) disEncModel.zero_grad() disCost.backward() dis_optimizer.step() d_regularize = cntr % opt.d_reg_every == 0 if d_regularize: real_image_tensors.requires_grad = True real_pred = disEncModel(real_image_tensors) r1_loss = d_r1_loss(real_pred, real_image_tensors) disEncModel.zero_grad() (opt.r1 / 2 * r1_loss * opt.d_reg_every + 0 * real_pred[0]).backward() dis_optimizer.step() log_r1_val.add(r1_loss) # Recognizer update if not opt.ocrFixed and not opt.zAlone: requires_grad(disEncModel, False) requires_grad(ocrModel, True) if 'CTC' in opt.Prediction: preds_recon = ocrModel(gt_image_tensors, text_gt, is_train=True) preds_size = torch.IntTensor([preds_recon.size(1)] * opt.batch_size) preds_recon_softmax = preds_recon.log_softmax(2).permute(1, 0, 2) ocrCost = ocrCriterion(preds_recon_softmax, text_gt, preds_size, length_gt) else: print("Not implemented error") sys.exit() ocrModel.zero_grad() ocrCost.backward() # torch.nn.utils.clip_grad_norm_(ocrModel.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) ocr_optimizer.step() loss_avg_ocr_sup.add(ocrCost) else: loss_avg_ocr_sup.add(torch.tensor(0.0)) # [Word Generator] update # image_input_tensors, _, labels, _ = iter(train_loader).next() labels_z_c = iter(text_loader).next() # image_input_tensors = image_input_tensors.to(device) # gt_image_tensors = image_input_tensors[:opt.batch_size] # real_image_tensors = image_input_tensors[opt.batch_size:] # labels_gt = labels[:opt.batch_size] requires_grad(cEncoder, True) requires_grad(genModel, True) requires_grad(disEncModel, False) requires_grad(ocrModel, False) text_z_c, length_z_c = converter.encode(labels_z_c, batch_max_length=opt.batch_max_length) z_c_code = cEncoder(text_z_c) noise_style = mixing_noise_style(opt.batch_size, opt.latent, opt.mixing, device) style=[] style.append(noise_style[0]*z_c_code) if len(noise_style)>1: style.append(noise_style[1]*z_c_code) if opt.zAlone: #to validate orig style gan results newstyle = [] newstyle.append(style[0][:,:opt.latent]) if len(style)>1: newstyle.append(style[1][:,:opt.latent]) style = newstyle fake_img,_ = genModel(style, input_is_latent=opt.input_latent) fake_pred = disEncModel(fake_img) disGenCost = g_nonsaturating_loss(fake_pred) if opt.zAlone: ocrCost = torch.tensor(0.0) else: #Compute OCR prediction (Reconstruction of content) # text_for_pred = torch.LongTensor(opt.batch_size, opt.batch_max_length + 1).fill_(0).to(device) # length_for_pred = torch.IntTensor([opt.batch_max_length] * opt.batch_size).to(device) if 'CTC' in opt.Prediction: preds_recon = ocrModel(fake_img, text_z_c, is_train=False) preds_size = torch.IntTensor([preds_recon.size(1)] * opt.batch_size) preds_recon_softmax = preds_recon.log_softmax(2).permute(1, 0, 2) ocrCost = ocrCriterion(preds_recon_softmax, text_z_c, preds_size, length_z_c) else: print("Not implemented error") sys.exit() genModel.zero_grad() cEncoder.zero_grad() gen_enc_cost = disGenCost + opt.ocrWeight * ocrCost grad_fake_OCR = torch.autograd.grad(ocrCost, fake_img, retain_graph=True)[0] loss_grad_fake_OCR = 10**6*torch.mean(grad_fake_OCR**2) grad_fake_adv = torch.autograd.grad(disGenCost, fake_img, retain_graph=True)[0] loss_grad_fake_adv = 10**6*torch.mean(grad_fake_adv**2) if opt.grad_balance: gen_enc_cost.backward(retain_graph=True) grad_fake_OCR = torch.autograd.grad(ocrCost, fake_img, create_graph=True, retain_graph=True)[0] grad_fake_adv = torch.autograd.grad(disGenCost, fake_img, create_graph=True, retain_graph=True)[0] a = opt.ocrWeight * torch.div(torch.std(grad_fake_adv), epsilon+torch.std(grad_fake_OCR)) if a is None: print(ocrCost, disGenCost, torch.std(grad_fake_adv), torch.std(grad_fake_OCR)) if a>1000 or a<0.0001: print(a) ocrCost = a.detach() * ocrCost gen_enc_cost = disGenCost + ocrCost gen_enc_cost.backward(retain_graph=True) grad_fake_OCR = torch.autograd.grad(ocrCost, fake_img, create_graph=False, retain_graph=True)[0] grad_fake_adv = torch.autograd.grad(disGenCost, fake_img, create_graph=False, retain_graph=True)[0] loss_grad_fake_OCR = 10 ** 6 * torch.mean(grad_fake_OCR ** 2) loss_grad_fake_adv = 10 ** 6 * torch.mean(grad_fake_adv ** 2) with torch.no_grad(): gen_enc_cost.backward() else: gen_enc_cost.backward() loss_avg_gen.add(disGenCost) loss_avg_ocr_unsup.add(opt.ocrWeight * ocrCost) optimizer.step() g_regularize = cntr % opt.g_reg_every == 0 if g_regularize: path_batch_size = max(1, opt.batch_size // opt.path_batch_shrink) # image_input_tensors, _, labels, _ = iter(train_loader).next() labels_z_c = iter(text_loader).next() # image_input_tensors = image_input_tensors.to(device) # gt_image_tensors = image_input_tensors[:path_batch_size] # labels_gt = labels[:path_batch_size] text_z_c, length_z_c = converter.encode(labels_z_c[:path_batch_size], batch_max_length=opt.batch_max_length) # text_gt, length_gt = converter.encode(labels_gt, batch_max_length=opt.batch_max_length) z_c_code = cEncoder(text_z_c) noise_style = mixing_noise_style(path_batch_size, opt.latent, opt.mixing, device) style=[] style.append(noise_style[0]*z_c_code) if len(noise_style)>1: style.append(noise_style[1]*z_c_code) if opt.zAlone: #to validate orig style gan results newstyle = [] newstyle.append(style[0][:,:opt.latent]) if len(style)>1: newstyle.append(style[1][:,:opt.latent]) style = newstyle fake_img, grad = genModel(style, return_latents=True, g_path_regularize=True, mean_path_length=mean_path_length) decay = 0.01 path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1)) mean_path_length_orig = mean_path_length + decay * (path_lengths.mean() - mean_path_length) path_loss = (path_lengths - mean_path_length_orig).pow(2).mean() mean_path_length = mean_path_length_orig.detach().item() genModel.zero_grad() cEncoder.zero_grad() weighted_path_loss = opt.path_regularize * opt.g_reg_every * path_loss if opt.path_batch_shrink: weighted_path_loss += 0 * fake_img[0, 0, 0, 0] weighted_path_loss.backward() optimizer.step() # mean_path_length_avg = ( # reduce_sum(mean_path_length).item() / get_world_size() # ) #commented above for multi-gpu , non-distributed setting mean_path_length_avg = mean_path_length accumulate(g_ema, genModel, accum) log_avg_path_loss_val.add(path_loss) log_avg_mean_path_length_avg.add(torch.tensor(mean_path_length_avg)) log_ada_aug_p.add(torch.tensor(ada_aug_p)) if get_rank() == 0: if wandb and opt.wandb: wandb.log( { "Generator": g_loss_val, "Discriminator": d_loss_val, "Augment": ada_aug_p, "Rt": r_t_stat, "R1": r1_val, "Path Length Regularization": path_loss_val, "Mean Path Length": mean_path_length, "Real Score": real_score_val, "Fake Score": fake_score_val, "Path Length": path_length_val, } ) # validation part if (iteration + 1) % opt.valInterval == 0 or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0' #generate paired content with similar style labels_z_c_1 = iter(text_loader).next() labels_z_c_2 = iter(text_loader).next() text_z_c_1, length_z_c_1 = converter.encode(labels_z_c_1, batch_max_length=opt.batch_max_length) text_z_c_2, length_z_c_2 = converter.encode(labels_z_c_2, batch_max_length=opt.batch_max_length) z_c_code_1 = cEncoder(text_z_c_1) z_c_code_2 = cEncoder(text_z_c_2) style_c1_s1 = [] style_c2_s1 = [] style_s1 = mixing_noise_style(opt.batch_size, opt.latent, opt.mixing, device) style_c1_s1.append(style_s1[0]*z_c_code_1) style_c2_s1.append(style_s1[0]*z_c_code_2) if len(style_s1)>1: style_c1_s1.append(style_s1[1]*z_c_code_1) style_c2_s1.append(style_s1[1]*z_c_code_2) noise_style = mixing_noise_style(opt.batch_size, opt.latent, opt.mixing, device) style_c1_s2 = [] style_c1_s2.append(noise_style[0]*z_c_code_1) if len(noise_style)>1: style_c1_s2.append(noise_style[1]*z_c_code_1) if opt.zAlone: #to validate orig style gan results newstyle = [] newstyle.append(style_c1_s1[0][:,:opt.latent]) if len(style_c1_s1)>1: newstyle.append(style_c1_s1[1][:,:opt.latent]) style_c1_s1 = newstyle style_c2_s1 = newstyle style_c1_s2 = newstyle fake_img_c1_s1, _ = g_ema(style_c1_s1, input_is_latent=opt.input_latent) fake_img_c2_s1, _ = g_ema(style_c2_s1, input_is_latent=opt.input_latent) fake_img_c1_s2, _ = g_ema(style_c1_s2, input_is_latent=opt.input_latent) if not opt.zAlone: #Run OCR prediction if 'CTC' in opt.Prediction: preds = ocrModel(fake_img_c1_s1, text_z_c_1, is_train=False) preds_size = torch.IntTensor([preds.size(1)] * opt.batch_size) _, preds_index = preds.max(2) preds_str_fake_img_c1_s1 = converter.decode(preds_index.data, preds_size.data) preds = ocrModel(fake_img_c2_s1, text_z_c_2, is_train=False) preds_size = torch.IntTensor([preds.size(1)] * opt.batch_size) _, preds_index = preds.max(2) preds_str_fake_img_c2_s1 = converter.decode(preds_index.data, preds_size.data) preds = ocrModel(fake_img_c1_s2, text_z_c_1, is_train=False) preds_size = torch.IntTensor([preds.size(1)] * opt.batch_size) _, preds_index = preds.max(2) preds_str_fake_img_c1_s2 = converter.decode(preds_index.data, preds_size.data) preds = ocrModel(gt_image_tensors, text_gt, is_train=False) preds_size = torch.IntTensor([preds.size(1)] * gt_image_tensors.shape[0]) _, preds_index = preds.max(2) preds_str_gt = converter.decode(preds_index.data, preds_size.data) else: print("Not implemented error") sys.exit() else: preds_str_fake_img_c1_s1 = [':None:'] * fake_img_c1_s1.shape[0] preds_str_gt = [':None:'] * fake_img_c1_s1.shape[0] os.makedirs(os.path.join(opt.trainDir,str(iteration)), exist_ok=True) for trImgCntr in range(opt.batch_size): try: save_image(tensor2im(fake_img_c1_s1[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_c1_s1_'+labels_z_c_1[trImgCntr]+'_ocr:'+preds_str_fake_img_c1_s1[trImgCntr]+'.png')) if not opt.zAlone: save_image(tensor2im(fake_img_c2_s1[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_c2_s1_'+labels_z_c_2[trImgCntr]+'_ocr:'+preds_str_fake_img_c2_s1[trImgCntr]+'.png')) save_image(tensor2im(fake_img_c1_s2[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_c1_s2_'+labels_z_c_1[trImgCntr]+'_ocr:'+preds_str_fake_img_c1_s2[trImgCntr]+'.png')) if trImgCntr<gt_image_tensors.shape[0]: save_image(tensor2im(gt_image_tensors[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_gt_act:'+labels_gt[trImgCntr]+'_ocr:'+preds_str_gt[trImgCntr]+'.png')) except: print('Warning while saving training image') elapsed_time = time.time() - start_time # for log with open(os.path.join(opt.exp_dir,opt.exp_name,'log_train.txt'), 'a') as log: # training loss and validation loss loss_log = f'[{iteration+1}/{opt.num_iter}] \ Train Dis loss: {loss_avg_dis.val():0.5f}, Train Gen loss: {loss_avg_gen.val():0.5f},\ Train UnSup OCR loss: {loss_avg_ocr_unsup.val():0.5f}, Train Sup OCR loss: {loss_avg_ocr_sup.val():0.5f}, \ Train R1-val loss: {log_r1_val.val():0.5f}, Train avg-path-loss: {log_avg_path_loss_val.val():0.5f}, \ Train mean-path-length loss: {log_avg_mean_path_length_avg.val():0.5f}, Train ada-aug-p: {log_ada_aug_p.val():0.5f}, \ Elapsed_time: {elapsed_time:0.5f}' #plotting lib.plot.plot(os.path.join(opt.plotDir,'Train-Dis-Loss'), loss_avg_dis.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-Gen-Loss'), loss_avg_gen.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-UnSup-OCR-Loss'), loss_avg_ocr_unsup.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-Sup-OCR-Loss'), loss_avg_ocr_sup.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-r1_val'), log_r1_val.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-path_loss_val'), log_avg_path_loss_val.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-mean_path_length_avg'), log_avg_mean_path_length_avg.val().item()) lib.plot.plot(os.path.join(opt.plotDir,'Train-ada_aug_p'), log_ada_aug_p.val().item()) print(loss_log) loss_avg_dis.reset() loss_avg_gen.reset() loss_avg_ocr_unsup.reset() loss_avg_ocr_sup.reset() log_r1_val.reset() log_avg_path_loss_val.reset() log_avg_mean_path_length_avg.reset() log_ada_aug_p.reset() lib.plot.flush() lib.plot.tick() # save model per 1e+5 iter. if (iteration) % 1e+4 == 0: torch.save({ 'cEncoder':cEncoder.state_dict(), 'genModel':genModel.state_dict(), 'g_ema':g_ema.state_dict(), 'ocrModel':ocrModel.state_dict(), 'disEncModel':disEncModel.state_dict(), 'optimizer':optimizer.state_dict(), 'ocr_optimizer':ocr_optimizer.state_dict(), 'dis_optimizer':dis_optimizer.state_dict()}, os.path.join(opt.exp_dir,opt.exp_name,'iter_'+str(iteration+1)+'_synth.pth')) if (iteration + 1) == opt.num_iter: print('end the training') sys.exit() iteration += 1 cntr+=1
def train(opt): lib.print_model_settings(locals().copy()) if 'Attn' in opt.Prediction: converter = AttnLabelConverter(opt.character) else: converter = CTCLabelConverter(opt.character) opt.classes = converter.character """ dataset preparation """ if not opt.data_filtering_off: print( 'Filtering the images containing characters which are not in opt.character' ) print( 'Filtering the images whose label is longer than opt.batch_max_length' ) # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 log = open(os.path.join(opt.exp_dir, opt.exp_name, 'log_dataset.txt'), 'a') AlignCollate_valid = AlignPHOCCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) train_dataset = LmdbStylePHOCDataset(root=opt.train_data, opt=opt) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=opt.batch_size * 2, #*2 to sample different images from training encoder and discriminator real images shuffle= True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True, drop_last=True) print('-' * 80) valid_dataset = LmdbStylePHOCDataset(root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size * 2, #*2 to sample different images from training encoder and discriminator real images shuffle= False, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True, drop_last=True) print('-' * 80) log.write('-' * 80 + '\n') log.close() phoc_dataset = phoc_gen(opt) phoc_loader = torch.utils.data.DataLoader(phoc_dataset, batch_size=opt.batch_size, shuffle=True, num_workers=int(opt.workers), pin_memory=True, drop_last=True) opt.num_class = len(converter.character) if opt.zAlone: genModel = styleGANGen(opt.size, opt.latent, opt.latent, opt.n_mlp, channel_multiplier=opt.channel_multiplier) g_ema = styleGANGen(opt.size, opt.latent, opt.latent, opt.n_mlp, channel_multiplier=opt.channel_multiplier) else: genModel = styleGANGen(opt.size, opt.latent + phoc_dataset.phoc_size, opt.latent, opt.n_mlp, channel_multiplier=opt.channel_multiplier) g_ema = styleGANGen(opt.size, opt.latent + phoc_dataset.phoc_size, opt.latent, opt.n_mlp, channel_multiplier=opt.channel_multiplier) disEncModel = styleGANDis(opt.size, channel_multiplier=opt.channel_multiplier, input_dim=opt.input_channel, code_s_dim=phoc_dataset.phoc_size) accumulate(g_ema, genModel, 0) uCriterion = torch.nn.MSELoss() sCriterion = torch.nn.MSELoss() genModel = torch.nn.DataParallel(genModel).to(device) g_ema = torch.nn.DataParallel(g_ema).to(device) genModel.train() g_ema.eval() disEncModel = torch.nn.DataParallel(disEncModel).to(device) disEncModel.train() g_reg_ratio = opt.g_reg_every / (opt.g_reg_every + 1) d_reg_ratio = opt.d_reg_every / (opt.d_reg_every + 1) optimizer = optim.Adam( genModel.parameters(), lr=opt.lr * g_reg_ratio, betas=(0**g_reg_ratio, 0.99**g_reg_ratio), ) dis_optimizer = optim.Adam( disEncModel.parameters(), lr=opt.lr * d_reg_ratio, betas=(0**d_reg_ratio, 0.99**d_reg_ratio), ) ## Loading pre-trained files if opt.modelFolderFlag: if len( glob.glob( os.path.join(opt.exp_dir, opt.exp_name, "iter_*_synth.pth"))) > 0: opt.saved_synth_model = glob.glob( os.path.join(opt.exp_dir, opt.exp_name, "iter_*_synth.pth"))[-1] # if opt.saved_ocr_model !='' and opt.saved_ocr_model !='None': # print(f'loading pretrained ocr model from {opt.saved_ocr_model}') # checkpoint = torch.load(opt.saved_ocr_model) # ocrModel.load_state_dict(checkpoint) # if opt.saved_gen_model !='' and opt.saved_gen_model !='None': # print(f'loading pretrained gen model from {opt.saved_gen_model}') # checkpoint = torch.load(opt.saved_gen_model, map_location=lambda storage, loc: storage) # genModel.module.load_state_dict(checkpoint['g']) # g_ema.module.load_state_dict(checkpoint['g_ema']) if opt.saved_synth_model != '' and opt.saved_synth_model != 'None': print(f'loading pretrained synth model from {opt.saved_synth_model}') checkpoint = torch.load(opt.saved_synth_model) # styleModel.load_state_dict(checkpoint['styleModel']) # mixModel.load_state_dict(checkpoint['mixModel']) genModel.load_state_dict(checkpoint['genModel']) g_ema.load_state_dict(checkpoint['g_ema']) disEncModel.load_state_dict(checkpoint['disEncModel']) optimizer.load_state_dict(checkpoint["optimizer"]) dis_optimizer.load_state_dict(checkpoint["dis_optimizer"]) # if opt.imgReconLoss == 'l1': # recCriterion = torch.nn.L1Loss() # elif opt.imgReconLoss == 'ssim': # recCriterion = ssim # elif opt.imgReconLoss == 'ms-ssim': # recCriterion = msssim # loss averager loss_avg_dis = Averager() loss_avg_gen = Averager() loss_avg_unsup = Averager() loss_avg_sup = Averager() log_r1_val = Averager() log_avg_path_loss_val = Averager() log_avg_mean_path_length_avg = Averager() log_ada_aug_p = Averager() """ final options """ with open(os.path.join(opt.exp_dir, opt.exp_name, 'opt.txt'), 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_synth_model != '' and opt.saved_synth_model != 'None': try: start_iter = int( opt.saved_synth_model.split('_')[-2].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass #get schedulers scheduler = get_scheduler(optimizer, opt) dis_scheduler = get_scheduler(dis_optimizer, opt) start_time = time.time() iteration = start_iter cntr = 0 mean_path_length = 0 d_loss_val = 0 r1_loss = torch.tensor(0.0, device=device) g_loss_val = 0 path_loss = torch.tensor(0.0, device=device) path_lengths = torch.tensor(0.0, device=device) mean_path_length_avg = 0 # loss_dict = {} accum = 0.5**(32 / (10 * 1000)) ada_augment = torch.tensor([0.0, 0.0], device=device) ada_aug_p = opt.augment_p if opt.augment_p > 0 else 0.0 ada_aug_step = opt.ada_target / opt.ada_length r_t_stat = 0 # sample_z = torch.randn(opt.n_sample, opt.latent, device=device) while (True): # print(cntr) # train part if opt.lr_policy != "None": scheduler.step() dis_scheduler.step() image_input_tensors, _, labels_1, _, phoc_1, _ = iter( train_loader).next() z_code, z_labels = iter(phoc_loader).next() image_input_tensors = image_input_tensors.to(device) gt_image_tensors = image_input_tensors[:opt.batch_size] real_image_tensors = image_input_tensors[opt.batch_size:] phoc_1 = phoc_1.to(device) gt_phoc_tensors = phoc_1[:opt.batch_size] labels_1 = labels_1[:opt.batch_size] z_code = z_code.to(device) requires_grad(genModel, False) # requires_grad(styleModel, False) # requires_grad(mixModel, False) requires_grad(disEncModel, True) text_1, length_1 = converter.encode( labels_1, batch_max_length=opt.batch_max_length) style = mixing_noise(z_code, opt.batch_size, opt.latent, opt.mixing, device) if opt.zAlone: #to validate orig style gan results newstyle = [] newstyle.append(style[0][:, :opt.latent]) if len(style) > 1: newstyle.append(style[1][:, :opt.latent]) style = newstyle fake_img, _ = genModel(style, input_is_latent=opt.input_latent) #unsupervised code prediction on generated image u_pred_code = disEncModel(fake_img, mode='enc') uCost = uCriterion(u_pred_code, z_code) #supervised code prediction on gt image s_pred_code = disEncModel(gt_image_tensors, mode='enc') sCost = uCriterion(s_pred_code, gt_phoc_tensors) #Domain discriminator fake_pred = disEncModel(fake_img) real_pred = disEncModel(real_image_tensors) disCost = d_logistic_loss(real_pred, fake_pred) dis_enc_cost = disCost + opt.gamma_e * uCost + opt.beta * sCost loss_avg_dis.add(disCost) loss_avg_sup.add(opt.beta * sCost) loss_avg_unsup.add(opt.gamma_e * uCost) disEncModel.zero_grad() dis_enc_cost.backward() dis_optimizer.step() d_regularize = cntr % opt.d_reg_every == 0 if d_regularize: real_image_tensors.requires_grad = True real_pred = disEncModel(real_image_tensors) r1_loss = d_r1_loss(real_pred, real_image_tensors) disEncModel.zero_grad() (opt.r1 / 2 * r1_loss * opt.d_reg_every + 0 * real_pred[0]).backward() dis_optimizer.step() # loss_dict["r1"] = r1_loss # [Word Generator] update image_input_tensors, _, labels_1, _, phoc_1, _ = iter( train_loader).next() z_code, z_labels = iter(phoc_loader).next() image_input_tensors = image_input_tensors.to(device) gt_image_tensors = image_input_tensors[:opt.batch_size] real_image_tensors = image_input_tensors[opt.batch_size:] phoc_1 = phoc_1.to(device) gt_phoc_tensors = phoc_1[:opt.batch_size] labels_1 = labels_1[:opt.batch_size] z_code = z_code.to(device) requires_grad(genModel, True) requires_grad(disEncModel, False) text_1, length_1 = converter.encode( labels_1, batch_max_length=opt.batch_max_length) style = mixing_noise(z_code, opt.batch_size, opt.latent, opt.mixing, device) if opt.zAlone: #to validate orig style gan results newstyle = [] newstyle.append(style[0][:, :opt.latent]) if len(style) > 1: newstyle.append(style[1][:, :opt.latent]) style = newstyle fake_img, _ = genModel(style, input_is_latent=opt.input_latent) #unsupervised code prediction on generated image u_pred_code = disEncModel(fake_img, mode='enc') uCost = uCriterion(u_pred_code, z_code) fake_pred = disEncModel(fake_img) disGenCost = g_nonsaturating_loss(fake_pred) gen_enc_cost = disGenCost + opt.gamma_g * uCost loss_avg_gen.add(disGenCost) loss_avg_unsup.add(opt.gamma_g * uCost) # loss_dict["g"] = disGenCost genModel.zero_grad() disEncModel.zero_grad() gen_enc_cost.backward() optimizer.step() g_regularize = cntr % opt.g_reg_every == 0 if g_regularize: image_input_tensors, _, labels_1, _, phoc_1, _ = iter( train_loader).next() z_code, z_labels = iter(phoc_loader).next() image_input_tensors = image_input_tensors.to(device) path_batch_size = max(1, opt.batch_size // opt.path_batch_shrink) gt_image_tensors = image_input_tensors[:path_batch_size] phoc_1 = phoc_1.to(device) gt_phoc_tensors = phoc_1[:path_batch_size] labels_1 = labels_1[:path_batch_size] z_code = z_code.to(device) z_code = z_code[:path_batch_size] z_labels = z_labels[:path_batch_size] text_1, length_1 = converter.encode( labels_1, batch_max_length=opt.batch_max_length) style = mixing_noise(z_code, path_batch_size, opt.latent, opt.mixing, device) if opt.zAlone: #to validate orig style gan results newstyle = [] newstyle.append(style[0][:, :opt.latent]) if len(style) > 1: newstyle.append(style[1][:, :opt.latent]) style = newstyle fake_img, grad = genModel(style, return_latents=True, g_path_regularize=True, mean_path_length=mean_path_length) decay = 0.01 path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1)) mean_path_length_orig = mean_path_length + decay * ( path_lengths.mean() - mean_path_length) path_loss = (path_lengths - mean_path_length_orig).pow(2).mean() mean_path_length = mean_path_length_orig.detach().item() # path_loss, mean_path_length, path_lengths = g_path_regularize( # images_recon_2, latents, mean_path_length # ) genModel.zero_grad() weighted_path_loss = opt.path_regularize * opt.g_reg_every * path_loss if opt.path_batch_shrink: weighted_path_loss += 0 * fake_img[0, 0, 0, 0] weighted_path_loss.backward() optimizer.step() # mean_path_length_avg = ( # reduce_sum(mean_path_length).item() / get_world_size() # ) #commented above for multi-gpu , non-distributed setting mean_path_length_avg = mean_path_length accumulate(g_ema, genModel, accum) log_r1_val.add(r1_loss) log_avg_path_loss_val.add(path_loss) log_avg_mean_path_length_avg.add(torch.tensor(mean_path_length_avg)) log_ada_aug_p.add(torch.tensor(ada_aug_p)) if get_rank() == 0: if wandb and opt.wandb: wandb.log({ "Generator": g_loss_val, "Discriminator": d_loss_val, "Augment": ada_aug_p, "Rt": r_t_stat, "R1": r1_val, "Path Length Regularization": path_loss_val, "Mean Path Length": mean_path_length, "Real Score": real_score_val, "Fake Score": fake_score_val, "Path Length": path_length_val, }) # validation part if ( iteration + 1 ) % opt.valInterval == 0 or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0' #generate paired content with similar style z_code_1, z_labels_1 = iter(phoc_loader).next() z_code_2, z_labels_2 = iter(phoc_loader).next() z_code_1 = z_code_1.to(device) z_code_2 = z_code_2.to(device) style_1 = mixing_noise(z_code_1, opt.batch_size, opt.latent, opt.mixing, device) style_2 = [] style_2.append( torch.cat((style_1[0][:, :opt.latent], z_code_2), dim=1)) if len(style_1) > 1: style_2.append( torch.cat((style_1[1][:, :opt.latent], z_code_2), dim=1)) if opt.zAlone: #to validate orig style gan results newstyle = [] newstyle.append(style_1[0][:, :opt.latent]) if len(style_1) > 1: newstyle.append(style_1[1][:, :opt.latent]) style_1 = newstyle style_2 = newstyle fake_img_1, _ = g_ema(style_1, input_is_latent=opt.input_latent) fake_img_2, _ = g_ema(style_2, input_is_latent=opt.input_latent) os.makedirs(os.path.join(opt.trainDir, str(iteration)), exist_ok=True) for trImgCntr in range(opt.batch_size): try: save_image( tensor2im(fake_img_1[trImgCntr].detach()), os.path.join( opt.trainDir, str(iteration), str(trImgCntr) + '_pair1_' + z_labels_1[trImgCntr] + '.png')) save_image( tensor2im(fake_img_2[trImgCntr].detach()), os.path.join( opt.trainDir, str(iteration), str(trImgCntr) + '_pair2_' + z_labels_2[trImgCntr] + '.png')) except: print('Warning while saving training image') elapsed_time = time.time() - start_time # for log with open(os.path.join(opt.exp_dir, opt.exp_name, 'log_train.txt'), 'a') as log: # training loss and validation loss loss_log = f'[{iteration+1}/{opt.num_iter}] \ Train Dis loss: {loss_avg_dis.val():0.5f}, Train Gen loss: {loss_avg_gen.val():0.5f},\ Train UnSup loss: {loss_avg_unsup.val():0.5f}, Train Sup loss: {loss_avg_sup.val():0.5f}, \ Train R1-val loss: {log_r1_val.val():0.5f}, Train avg-path-loss: {log_avg_path_loss_val.val():0.5f}, \ Train mean-path-length loss: {log_avg_mean_path_length_avg.val():0.5f}, Train ada-aug-p: {log_ada_aug_p.val():0.5f}, \ Elapsed_time: {elapsed_time:0.5f}' #plotting lib.plot.plot(os.path.join(opt.plotDir, 'Train-Dis-Loss'), loss_avg_dis.val().item()) lib.plot.plot(os.path.join(opt.plotDir, 'Train-Gen-Loss'), loss_avg_gen.val().item()) lib.plot.plot(os.path.join(opt.plotDir, 'Train-UnSup-Loss'), loss_avg_unsup.val().item()) lib.plot.plot(os.path.join(opt.plotDir, 'Train-Sup-Loss'), loss_avg_sup.val().item()) lib.plot.plot(os.path.join(opt.plotDir, 'Train-r1_val'), log_r1_val.val().item()) lib.plot.plot(os.path.join(opt.plotDir, 'Train-path_loss_val'), log_avg_path_loss_val.val().item()) lib.plot.plot( os.path.join(opt.plotDir, 'Train-mean_path_length_avg'), log_avg_mean_path_length_avg.val().item()) lib.plot.plot(os.path.join(opt.plotDir, 'Train-ada_aug_p'), log_ada_aug_p.val().item()) print(loss_log) loss_avg_dis.reset() loss_avg_gen.reset() loss_avg_unsup.reset() loss_avg_sup.reset() log_r1_val.reset() log_avg_path_loss_val.reset() log_avg_mean_path_length_avg.reset() log_ada_aug_p.reset() lib.plot.flush() lib.plot.tick() # save model per 1e+5 iter. if (iteration) % 1e+4 == 0: torch.save( { 'genModel': genModel.state_dict(), 'g_ema': g_ema.state_dict(), 'disEncModel': disEncModel.state_dict(), 'optimizer': optimizer.state_dict(), 'dis_optimizer': dis_optimizer.state_dict() }, os.path.join(opt.exp_dir, opt.exp_name, 'iter_' + str(iteration + 1) + '_synth.pth')) if (iteration + 1) == opt.num_iter: print('end the training') sys.exit() iteration += 1 cntr += 1
def train(opt): os.makedirs(opt.log, exist_ok=True) writer = SummaryWriter(opt.log) """ dataset preparation """ if not opt.data_filtering_off: print( 'Filtering the images containing characters which are not in opt.character' ) print( 'Filtering the images whose label is longer than opt.batch_max_length' ) opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') train_dataset = Batch_Balanced_Dataset(opt) log = open(f'./saved_models/{opt.exp_name}/log_dataset.txt', 'a') AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) valid_dataset, valid_dataset_log = hierarchical_dataset( root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle= True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() """ model configuration """ ctc_converter = CTCLabelConverter(opt.character) attn_converter = AttnLabelConverter(opt.character) opt.num_class = len(attn_converter.character) if opt.rgb: opt.input_channel = 3 model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length) # weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # data parallel for multi-GPU model = torch.nn.DataParallel(model).to(device) model.train() if opt.saved_model != '': print(f'loading pretrained model from {opt.saved_model}') if opt.FT: model.load_state_dict(torch.load(opt.saved_model), strict=False) else: model.load_state_dict(torch.load(opt.saved_model)) """ setup loss """ loss_avg = Averager() ctc_loss = torch.nn.CTCLoss(zero_infinity=True).to(device) attn_loss = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable params num : ', sum(params_num)) # setup optimizer if opt.adam: optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("Optimizer:") """ final options """ # print(opt) with open(f'./saved_models/{opt.exp_name}/opt.txt', 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_model != '': try: start_iter = int(opt.saved_model.split('_')[-1].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass start_time = time.time() best_accuracy = -1 best_norm_ED = -1 iteration = start_iter pbar = tqdm(range(opt.num_iter)) for iteration in pbar: # train part image_tensors, labels = train_dataset.get_batch() image = image_tensors.to(device) ctc_text, ctc_length = ctc_converter.encode( labels, batch_max_length=opt.batch_max_length) attn_text, attn_length = attn_converter.encode( labels, batch_max_length=opt.batch_max_length) batch_size = image.size(0) preds, refiner = model(image, attn_text[:, :-1]) refiner_size = torch.IntTensor([refiner.size(1)] * batch_size) refiner = refiner.log_softmax(2).permute(1, 0, 2) refiner_loss = ctc_loss(refiner, ctc_text, refiner_size, ctc_length) total_loss = opt.lambda_ctc * refiner_loss target = attn_text[:, 1:] # without [GO] Symbol for pred in preds: total_loss += opt.lambda_attn * attn_loss( pred.view(-1, pred.shape[-1]), target.contiguous().view(-1)) model.zero_grad() total_loss.backward() torch.nn.utils.clip_grad_norm_( model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) optimizer.step() loss_avg.add(total_loss) if loss_avg.val() <= 0.6: opt.grad_clip = 2 if loss_avg.val() <= 0.3: opt.grad_clip = 1 preds = (p.cpu() for p in preds) refiner = refiner.cpu() image = image.cpu() torch.cuda.empty_cache() writer.add_scalar('train_loss', loss_avg.val(), iteration) pbar.set_description('Iteration {0}/{1}, AvgLoss {2}'.format( iteration, opt.num_iter, loss_avg.val())) # validation part if (iteration + 1) % opt.valInterval == 0 or iteration == 0: elapsed_time = time.time() - start_time # for log with open(f'./saved_models/{opt.exp_name}/log_train.txt', 'a') as log: model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation( model, attn_loss, valid_loader, attn_converter, opt) model.train() # training loss and validation loss loss_log = f'[{iteration+1}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}' writer.add_scalar('Val_loss', valid_loss) pbar.set_description(loss_log) loss_avg.reset() current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}' # keep best accuracy model (on valid dataset) if current_accuracy > best_accuracy: best_accuracy = current_accuracy torch.save( model.state_dict(), f'./saved_models/{opt.exp_name}/best_accuracy_{str(best_accuracy)}.pth' ) if current_norm_ED > best_norm_ED: best_norm_ED = current_norm_ED torch.save( model.state_dict(), f'./saved_models/{opt.exp_name}/best_norm_ED.pth') best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}' loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}' # print(loss_model_log) log.write(loss_model_log + '\n') # show some predicted results dashed_line = '-' * 80 head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n' for gt, pred, confidence in zip(labels[:5], preds[:5], confidence_score[:5]): if 'Attn' or 'Transformer' in opt.Prediction: gt = gt[:gt.find('[s]')] pred = pred[:pred.find('[s]')] predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n' predicted_result_log += f'{dashed_line}' log.write(predicted_result_log + '\n') # save model per 1e+3 iter. if (iteration + 1) % 1e+3 == 0: torch.save(model.state_dict(), f'./saved_models/{opt.exp_name}/SCATTER_STR.pth') if (iteration + 1) == opt.num_iter: print('end the training') sys.exit()
def train(model, optimizer, scheduler, train_loader, val_loader): ''' train model ''' print("Start training.\n") early_stopping = EarlyStopping(patience=CFG.patience, verbose=True, trace_func=print) train_loss_hist = Averager() for epoch in tqdm(range(1, CFG.nepochs + 1)): now = time.localtime() print("%04d/%02d/%02d %02d:%02d:%02d" % (now.tm_year, now.tm_mon, now.tm_mday, now.tm_hour, now.tm_min, now.tm_sec)) model.train() train_loss_hist.reset() for step, (images, targets, _) in enumerate(train_loader): images = torch.stack(images).to(CFG.device).float() bboxes = [ target['boxes'].to(CFG.device).float() for target in targets ] labels = [ target['labels'].to(CFG.device).float() for target in targets ] batch_size = images.shape[0] target_res = dict() target_res['bbox'] = bboxes target_res['cls'] = labels # forward pass & calculate loss loss = model(images, target_res)['loss'] loss_value = loss.detach().item() train_loss_hist.update(loss_value, batch_size) # backward optimizer.zero_grad() # reset previous gradient loss.backward() # backward propagation optimizer.step() # parameters update # log train loss by step if (step + 1) % 25 == 0: print('Epoch [{}/{}], Step [{}/{}], Train Loss: {:.6f}'.format( epoch, CFG.nepochs, step + 1, len(train_loader), loss_value)) del images, targets, bboxes # scheduler step scheduler.step() # Print score after each epoch if ((epoch % CFG.print_freq) == 0) or (epoch == (CFG.nepochs)): val_loss = validation(model, val_loader) print("epoch:[%d] train_loss:[%.6f] val_loss:[%.6f]" % (epoch, train_loss_hist.value, val_loss)) # wandb.log({ # "Train Loss": train_loss, # "Val Loss": val_loss, # "Val mIoU": mIoU, # "Val pix_acc": acc, # "Seg": fig_mask, # }) if early_stopping(model=model, val_loss=val_loss): best_metric = {'epoch': epoch, 'val_loss': val_loss} torch.save( model.state_dict(), os.path.join( CFG.models_path, CFG.model_save_name, f"{CFG.model_save_name}_{str(epoch).zfill(2)}.pt")) if early_stopping.early_stop or epoch == CFG.nepochs: print("best model information") print(f"epoch : {best_metric['epoch']}") print(f"val_loss : {best_metric['val_loss']}") break print("Done")
def train(opt): """ dataset preparation """ opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') #import ipdb;ipdb.set_trace() train_dataset = Batch_Balanced_Dataset(opt) AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) valid_dataset = hierarchical_dataset(root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle= True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) print('-' * 80) """ model configuration """ if 'CTC' in opt.Prediction: converter = CTCLabelConverter(opt.character) else: converter = AttnLabelConverter(opt.character) opt.num_class = len(converter.character) if opt.rgb: opt.input_channel = 3 model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) # weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # data parallel for multi-GPU model = torch.nn.DataParallel(model).to(device) model.train() if opt.continue_model != '': print(f'loading pretrained model from {opt.continue_model}') model.load_state_dict(torch.load(opt.continue_model)) print("Model:") #print(model) """ setup loss """ if 'CTC' in opt.Prediction: criterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to( device) # ignore [GO] token = ignore index 0 # loss averager loss_avg = Averager() # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable params num : ', sum(params_num)) # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.adam: optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("Optimizer:") print(optimizer) """ final options """ # print(opt) with open(f'./saved_models/{opt.experiment_name}/opt.txt', 'a', encoding="utf-8") as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.continue_model != '': start_iter = int(opt.continue_model.split('_')[-1].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') start_time = time.time() best_accuracy = -1 best_norm_ED = 1e+6 i = start_iter while (True): # train part image_tensors, labels = train_dataset.get_batch() image = image_tensors.to(device) text, length = converter.encode(labels, batch_max_length=opt.batch_max_length) batch_size = image.size(0) #import ipdb;ipdb.set_trace() if 'CTC' in opt.Prediction: preds = model(image, text).log_softmax(2) preds_size = torch.IntTensor([preds.size(1)] * batch_size).to(device) preds = preds.permute(1, 0, 2) # to use CTCLoss format # To avoid ctc_loss issue, disabled cudnn for the computation of the ctc_loss # https://github.com/jpuigcerver/PyLaia/issues/16 torch.backends.cudnn.enabled = False cost = criterion(preds, text, preds_size, length) torch.backends.cudnn.enabled = True else: preds = model(image, text[:, :-1]) # align with Attention.forward target = text[:, 1:] # without [GO] Symbol cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) model.zero_grad() cost.backward() torch.nn.utils.clip_grad_norm_( model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) optimizer.step() loss_avg.add(cost) # validation part if i % opt.valInterval == 0: elapsed_time = time.time() - start_time print( f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}' ) # for log with open(f'./saved_models/{opt.experiment_name}/log_train.txt', 'a', encoding="utf-8") as log: log.write( f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}\n' ) loss_avg.reset() model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, labels, infer_time, length_of_data = validation( model, criterion, valid_loader, converter, opt) model.train() for pred, gt in zip(preds[:5], labels[:5]): if 'Attn' in opt.Prediction: pred = pred[:pred.find('[s]')] gt = gt[:gt.find('[s]')] print(f'{pred:20s}, gt: {gt:20s}, {str(pred == gt)}') #pred = pred.encode('utf-8') #gt = gt.encode('utf-8') log.write( f'{pred:20s}, gt: {gt:20s}, {str(pred == gt)}\n') valid_log = f'[{i}/{opt.num_iter}] valid loss: {valid_loss:0.5f}' valid_log += f' accuracy: {current_accuracy:0.3f}, norm_ED: {current_norm_ED:0.2f}' print(valid_log) log.write(valid_log + '\n') # keep best accuracy model if current_accuracy > best_accuracy: best_accuracy = current_accuracy torch.save( model.state_dict(), f'./saved_models/{opt.experiment_name}/best_accuracy.pth' ) if current_norm_ED < best_norm_ED: best_norm_ED = current_norm_ED torch.save( model.state_dict(), f'./saved_models/{opt.experiment_name}/best_norm_ED.pth' ) best_model_log = f'best_accuracy: {best_accuracy:0.3f}, best_norm_ED: {best_norm_ED:0.2f}' print(best_model_log) log.write(best_model_log + '\n') # save model per 1e+5 iter. if (i + 1) % 1e+5 == 0: torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/iter_{i+1}.pth') if i == opt.num_iter: print('end the training') sys.exit() i += 1
def train_fn(start_epochs, epochs, train_loader, val_loader, model, device, optimizer, best_loss, checkpoint_path, best_model_path, lr_scheduler=None): print("Starting Training") model.train() loss_hist = Averager() itr = 1 train_loss = [] validation_loss = [] for epoch in range(start_epochs, epochs + 1): loss_hist.reset() for images, targets, image_ids in train_loader: images = list(image.to(device) for image in images) # images = images.to(device) targets = [{k: v.to(device) for k, v in t.items()} for t in targets] loss_dict = model(images, targets) losses = sum(loss for loss in loss_dict.values()) loss_value = losses.item() loss_hist.send(loss_value) optimizer.zero_grad() losses.backward() optimizer.step() if itr % 10 == 0: print(f"Iteration #{itr} loss: {loss_value}") itr += 1 # update the learning rate if lr_scheduler is not None: lr_scheduler.step() # val_loss = validate(val_loader, model, device) print( f"Epoch #{epoch} Train loss: {loss_hist.value}, Validation Loss : Commented" ) train_loss.append(loss_hist.value) # validation_loss.append(val_loss) checkpoint = { 'epoch': epoch + 1, # 'best_loss': val_loss, 'state_dict': model.state_dict(), 'optimizer': optimizer.state_dict(), } save_ckp(checkpoint, False, checkpoint_path, best_model_path) # if best_loss <= val_loss: # print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(best_loss, val_loss)) # save_ckp(checkpoint, True, checkpoint_path, best_model_path) # best_loss = val_loss return model, train_loss #, validation_loss
def train(opt, tb): """ dataset preparation """ if not opt.data_filtering_off: print('Filtering the images containing characters which are not in opt.character') print('Filtering the images whose label is longer than opt.batch_max_length') # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') train_dataset = Batch_Balanced_Dataset(opt) log = open(f'./saved_models/{opt.experiment_name}/log_dataset.txt', 'a') AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) valid_dataset, valid_dataset_log = hierarchical_dataset(root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle=True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() """ model configuration """ if 'CTC' in opt.Prediction: converter = CTCLabelConverter(opt.character) else: converter = AttnLabelConverter(opt.character) opt.num_class = len(converter.character) if opt.rgb: opt.input_channel = 3 model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) # weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # data parallel for multi-GPU model = torch.nn.DataParallel(model).to(device) model.train() if opt.saved_model != '': print(f'loading pretrained model from {opt.saved_model}') if opt.FT: model.load_state_dict(torch.load(opt.saved_model), strict=False) else: model.load_state_dict(torch.load(opt.saved_model)) print("Model:") print(model) """ setup loss """ if 'CTC' in opt.Prediction: criterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0 # loss averager loss_avg = Averager() # filter that only require gradient decent filtered_parameters = [] params_num = [] print("~~~~~~~~~~~~Gradient Descent~~~~~~~~~~~~~") #print(model.parameters()) #print(model.) for p in filter(lambda p: p.requires_grad, model.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Filtered parameters for gradient descent: \n', len(filtered_parameters)) print('Trainable params num : ', sum(params_num)) # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer if opt.adam: optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) else: optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("Optimizer:") print(optimizer) """ final options """ # print(opt) with open(f'./saved_models/{opt.experiment_name}/opt.txt', 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_model != '': try: start_iter = int(opt.saved_model.split('_')[-1].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass start_time = time.time() best_accuracy = -1 best_norm_ED = -1 i = start_iter while(True): # train part image_tensors, labels = train_dataset.get_batch() image = image_tensors.to(device) text, length = converter.encode(labels, batch_max_length=opt.batch_max_length) batch_size = image.size(0) if 'CTC' in opt.Prediction: preds = model(image, text).log_softmax(2) preds_size = torch.IntTensor([preds.size(1)] * batch_size) preds = preds.permute(1, 0, 2) # (ctc_a) For PyTorch 1.2.0 and 1.3.0. To avoid ctc_loss issue, disabled cudnn for the computation of the ctc_loss # https://github.com/jpuigcerver/PyLaia/issues/16 torch.backends.cudnn.enabled = False cost = criterion(preds, text.to(device), preds_size.to(device), length.to(device)) torch.backends.cudnn.enabled = True # # (ctc_b) To reproduce our pretrained model / paper, use our previous code (below code) instead of (ctc_a). # # With PyTorch 1.2.0, the below code occurs NAN, so you may use PyTorch 1.1.0. # # Thus, the result of CTCLoss is different in PyTorch 1.1.0 and PyTorch 1.2.0. # # See https://github.com/clovaai/deep-text-recognition-benchmark/issues/56#issuecomment-526490707 # cost = criterion(preds, text, preds_size, length) else: preds = model(image, text[:, :-1]) # align with Attention.forward print(preds[0][0]) target = text[:, 1:] # without [GO] Symbol print(target[0]) cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) model.zero_grad() cost.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) optimizer.step() loss_avg.add(cost) # validation part if i % opt.valInterval == 0: elapsed_time = time.time() - start_time # for log with open(f'./saved_models/{opt.experiment_name}/log_train.txt', 'a') as log: model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation( model, criterion, valid_loader, converter, opt) model.train() # training loss and validation loss loss_log = f'[{i}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}' tb.add_scalar('Training Loss vs Iteration', loss_avg.val(), i) # Record to Tensorboard tb.add_scalar('Validation Loss vs Iteration', valid_loss, i) # Record to Tensorboard loss_avg.reset() current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}' tb.add_scalar('Current Accuracy vs Iteration', current_accuracy, i) # Record to Tensorboard tb.add_scalar('Current Norm ED vs Iteration', current_norm_ED, i) # Record to Tensorboard # keep best accuracy model (on valid dataset) if current_accuracy > best_accuracy: best_accuracy = current_accuracy torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/best_accuracy.pth') if current_norm_ED > best_norm_ED: best_norm_ED = current_norm_ED torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/best_norm_ED.pth') best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}' loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}' print(loss_model_log) log.write(loss_model_log + '\n') # show some predicted results dashed_line = '-' * 80 head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n' for gt, pred, confidence in zip(labels[:5], preds[:5], confidence_score[:5]): if 'Attn' in opt.Prediction: gt = gt[:gt.find('[s]')] pred = pred[:pred.find('[s]')] predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n' predicted_result_log += f'{dashed_line}' print(predicted_result_log) log.write(predicted_result_log + '\n') # save model per 1e+5 iter. if (i + 1) % 1e+5 == 0: torch.save( model.state_dict(), f'./saved_models/{opt.experiment_name}/iter_{i+1}.pth') if i == opt.num_iter: print('end the training') sys.exit() i += 1
def train(opt): """ dataset preparation """ if not opt.data_filtering_off: print( 'Filtering the images containing characters which are not in opt.character' ) print( 'Filtering the images whose label is longer than opt.batch_max_length' ) # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') # train_dataset (image, label) train_dataset = Batch_Balanced_Dataset(opt) log = open(f'./saved_models/{opt.exp_name}/log_dataset.txt', 'a') AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD) valid_dataset, valid_dataset_log = hierarchical_dataset( root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle= True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() """ model configuration """ if 'CTC' in opt.Prediction: if opt.baiduCTC: converter = CTCLabelConverterForBaiduWarpctc(opt.character) else: converter = CTCLabelConverter(opt.character) else: converter = AttnLabelConverter(opt.character) opt.num_class = len(converter.character) if opt.rgb: opt.input_channel = 3 model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction) print("Model:") print(model) total_num, true_grad_num, false_grad_num = calculate_model_params(model) print("Total parameters: ", total_num) print("Number of parameters requires grad: ", true_grad_num) print("Number of parameters do not require grad: ", false_grad_num) # weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # data parallel for multi-GPU model = torch.nn.DataParallel(model).to(device) model.train() if isinstance(model, torch.nn.DataParallel): model = model.module # load pretrained model if opt.saved_model != '': print(f'loading pretrained model from {opt.saved_model}') if opt.FT: model.load_pretrained_networks() elif opt.continue_train: model.load_checkpoint(opt.model_name) else: raise Exception('Something went wrong!') """ setup loss """ if 'CTC' in opt.Prediction: if opt.baiduCTC: # need to install warpctc. see our guideline. from warpctc_pytorch import CTCLoss criterion = CTCLoss() else: criterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to( device) # ignore [GO] token = ignore index 0 # loss averager loss_avg = Averager() log_dir = f'./saved_models/{opt.exp_name}' writer = SummaryWriter(log_dir) # """ final options """ # print(opt) with open(f'./saved_models/{opt.exp_name}/opt.txt', 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 start_time = time.time() best_accuracy = -1 best_norm_ED = -1 iteration = start_iter while (True): # train part image_tensors, labels = train_dataset.get_batch() image = image_tensors.to(device) text, length = converter.encode(labels, batch_max_length=opt.batch_max_length) batch_size = image.size(0) if 'CTC' in opt.Prediction: preds = model(image, text) preds_size = torch.IntTensor([preds.size(1)] * batch_size) if opt.baiduCTC: preds = preds.permute(1, 0, 2) # to use CTCLoss format cost = criterion(preds, text, preds_size, length) / batch_size else: preds = preds.log_softmax(2).permute(1, 0, 2) cost = criterion(preds, text, preds_size, length) else: preds = model(image, text[:, :-1]) # align with Attention.forward target = text[:, 1:] # without [GO] Symbol cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) model.zero_grad() cost.backward() torch.nn.utils.clip_grad_norm_( model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default) model.optimize_parameters() writer.add_scalar('train_loss', cost, iteration + 1) loss_avg.add(cost) # validation part if ( iteration + 1 ) % opt.valInterval == 0 or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0' elapsed_time = time.time() - start_time # for log with open(f'./saved_models/{opt.exp_name}/log_train.txt', 'a') as log: model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation( model, criterion, valid_loader, converter, opt, iteration) model.train() # training loss and validation loss loss_log = f'[{iteration+1}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}' writer.add_scalar('val_loss', valid_loss, iteration + 1) writer.add_scalar('accuracy', current_accuracy, iteration + 1) loss_avg.reset() current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}' # keep best accuracy model (on valid dataset) if current_accuracy > best_accuracy: best_accuracy = current_accuracy model.save_checkpoints(iteration, 'best_accuracy.pth') if current_norm_ED > best_norm_ED: best_norm_ED = current_norm_ED model.save_checkpoints(iteration, 'best_norm_ED.pth') best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}' loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}' print(loss_model_log) log.write(loss_model_log + '\n') # show some predicted results dashed_line = '-' * 80 head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n' for gt, pred, confidence in zip(labels[:5], preds[:5], confidence_score[:5]): if 'Attn' in opt.Prediction: gt = gt[:gt.find('[s]')] pred = pred[:pred.find('[s]')] predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n' predicted_result_log += f'{dashed_line}' print(predicted_result_log) log.write(predicted_result_log + '\n') # save model per 1e+5 iter. if (iteration + 1) % 1e+5 == 0: model.save_checkpoints(iteration + 1, opt.model_name) if (iteration + 1) == opt.num_iter: print('end the training') sys.exit() iteration += 1