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
