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 test(opt):
""" 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)
model = torch.nn.DataParallel(model).to(device)
# load model
print('loading pretrained model from %s' % opt.saved_model)
model.load_state_dict(torch.load(opt.saved_model, map_location=device))
opt.exp_name = '_'.join(opt.saved_model.split('/')[1:])
# print(model)
# return model
AlignCollate_evaluation = AlignCollate(imgH=opt.imgH,
imgW=opt.imgW,
keep_ratio_with_pad=opt.PAD)
eval_data, eval_data_log = hierarchical_dataset(root=opt.eval_data,
opt=opt)
evaluation_loader = torch.utils.data.DataLoader(
eval_data,
batch_size=opt.batch_size,
shuffle=False,
num_workers=int(opt.workers),
collate_fn=AlignCollate_evaluation,
pin_memory=True)
# _, accuracy_by_best_model, _, _, _, _, _, _ = validation(
# model, criterion, evaluation_loader, converter, opt)
for i, (image_tensors, labels) in enumerate(evaluation_loader):
# batch_size = image_tensors.size(0)
# text_for_pred = torch.LongTensor(batch_size, opt.batch_max_length + 1).fill_(0).to(device)
target_layer = model.module.FeatureExtraction.ConvNet.layer4[-1]
# model.eval()
# handle =model.module.Transformation.register_forward_hook(hook)
# model(image_tensors,text_for_pred)
input_tensor = image_tensors
# print(labels)
# print(input_tensor.shape,'input_tensor.shape')
# handle.remove()
# print(input_tensor)
# Create an input tensor image for your model..
# input_tensor=image_tensors
# Note: input_tensor can be a batch tensor with several images!
# print(labels)
# Construct the CAM object once, and then re-use it on many images:
cam = EigenCAM(model=model,
target_layer=target_layer,
use_cuda=opt.use_cuda)
# If target_category is None, the highest scoring category
# will be used for every image in the batch.
# target_category can also be an integer, or a list of different integers
# for every image in the batch.
text_for_loss, length_for_loss = converter.encode(
labels, batch_max_length=opt.batch_max_length)
target_category = text_for_loss
# You can also pass aug_smooth=True and eigen_smooth=True, to apply smoothing.
grayscale_cam = cam(input_tensor=input_tensor,
target_category=target_category)
# In this example grayscale_cam has only one image in the batch:
grayscale_cam = grayscale_cam[0, :]
loader = transforms.ToPILImage()
sourc_image = loader(image_tensors[0])
sourc_image = cv2.cvtColor(np.asarray(sourc_image), cv2.COLOR_RGB2BGR)
# rgb_img=loader(input_tensor[0].cpu())
# rgb_img.save('rgb_visual.bmp')
# rgb_img2=cv2.imread('rgb_visual.bmp')
rgb_img2 = np.float32(sourc_image) / 255
visualization = show_cam_on_image(rgb_img2, grayscale_cam)
sourc_image = cv2.resize(sourc_image, (0, 0),
fx=5,
fy=5,
interpolation=cv2.INTER_CUBIC)
visualization = cv2.resize(visualization, (0, 0),
fx=5,
fy=5,
interpolation=cv2.INTER_CUBIC)
cat_image = np.vstack((sourc_image, visualization))
cv2.imwrite('visual_image2/' + str(i) + '_2.bmp', cat_image)
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,
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.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 = []
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.saved_model != '':
start_iter = int(opt.saved_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)
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))
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}'
print(loss_log)
log.write(loss_log + '\n')
loss_avg.reset()
current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}'
print(current_model_log)
log.write(current_model_log + '\n')
# 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}'
print(best_model_log)
log.write(best_model_log + '\n')
# show some predicted results
print('-' * 80)
print(f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F')
log.write(f'{"Ground Truth":25s} | {"Prediction":25s} | {"Confidence Score"}\n')
print('-' * 80)
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]')]
print(f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}')
log.write(f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n')
print('-' * 80)
# 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, 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):
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('-')
#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 = AdaINGenV4(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
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_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_hole_tensors, image_mask_tensors = genRandomMasks(image_tensors)
image = image_tensors.to(device)
image_hole_tensors = image_hole_tensors.to(device)
image_mask_tensors = image_mask_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)
images_recon_1, images_recon_2, style = model(image_hole_tensors,
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 image reconstruction loss
if opt.imgReconLoss == 'ssim':
recCost = -1 * recCriterion(images_recon_1, image, val_range=2)
elif opt.imgReconLoss == 'ms-ssim':
recCost = -1 * recCriterion(
images_recon_1, image, val_range=2, normalize='relu')
else:
recCost = 0.5 * (recCriterion(image_mask_tensors * images_recon_1,
image_mask_tensors * image) +
recCriterion(
(1 - image_mask_tensors) * images_recon_1,
(1 - image_mask_tensors) * image))
#Pair style reconstruction loss
if opt.styleReconWeight == 0.0:
styleRecCost = torch.tensor(0.0)
else:
if opt.styleLoss == 'l1':
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)
elif opt.styleLoss == 'triplet':
if opt.styleDetach:
styleRecCost = styleRecCriterion(
model(images_recon_2, None, None, styleFlag=True),
style.detach(),
model(image_real, None, None, styleFlag=True))
else:
styleRecCost = styleRecCriterion(
model(images_recon_2, None, None, styleFlag=True),
style, model(image_real, None, None, styleFlag=True))
#OCR Content cost
ocrCost = 0.5 * (opt.ocrWeight_1 * ocrCost_1 +
opt.ocrWeight_2 * 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,
styleTestRecCriterion, 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')
# 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', encoding='utf-16')
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)
elif 'Attn' in opt.Prediction:
converter = AttnLabelConverter(opt.character)
elif 'Transformer' in opt.Prediction or 'Test' in opt.Prediction or 'Transformer' in opt.SequenceModeling:
converter = TransformerLabelConverter(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 = []
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-16') 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
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'./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()
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')
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":10s} | {"Prediction":10s} | 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 or 'Transformer' in opt.Prediction or 'Test' in opt.Prediction or 'Transformer' in opt.SequenceModeling:
gt = gt[:gt.find('[s]')]
pred = pred[:pred.find('[s]')]
predicted_result_log += f'{gt:10s} | {pred:10s} | {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+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 """
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(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
# 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
ocrModel = torch.nn.DataParallel(ocrModel).to(device)
ocrModel.train()
model = torch.nn.DataParallel(model).to(device)
model.train()
disModel = torch.nn.DataParallel(disModel).to(device)
disModel.train()
#loading pre-trained 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))
print("OCRModel:")
print(ocrModel)
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("SynthModel:")
print(model)
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))
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()
##---------------------------------------##
# 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
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
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 """
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
# cntr=0
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()
# ###
# print(cntr)
# cntr+=1
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, style = 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)
styleRecCost = styleRecCriterion(
model(images_recon_2, None, None, styleFlag=True), style.detach())
cost = opt.ocrWeight * ocrCost + opt.reconWeight * recCost + opt.disWeight * disGenCost + opt.styleReconWeight * styleRecCost
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_lrw(
iteration, model, ocrModel, disModel, recCriterion,
styleRecCriterion, 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(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(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 train(opt,
AMP,
WdB,
ralph_path,
train_data_path,
train_data_list,
test_data_path,
test_data_list,
experiment_name,
train_batch_size,
val_batch_size,
workers,
lr,
valInterval,
num_iter,
wdbprj,
continue_model='',
finetune=''):
HVD3P = pO.HVD or pO.DDP
os.makedirs(f'./saved_models/{experiment_name}', exist_ok=True)
# if OnceExecWorker and WdB:
# wandb.init(project=wdbprj, name=experiment_name)
# wandb.config.update(opt)
# load supplied ralph
with open(ralph_path, 'r') as f:
ralph_train = json.load(f)
print('[4] IN TRAIN; BEFORE MAKING DATASET')
train_dataset = ds_load.myLoadDS(train_data_list,
train_data_path,
ralph=ralph_train)
valid_dataset = ds_load.myLoadDS(test_data_list,
test_data_path,
ralph=ralph_train)
# SAVE RALPH FOR LATER USE
# with open(f'./saved_models/{experiment_name}/ralph.json', 'w+') as f:
# json.dump(train_dataset.ralph, f)
print('[5] DATASET DONE LOADING')
if OnceExecWorker:
print(pO)
print('Alphabet :', len(train_dataset.alph), train_dataset.alph)
for d in [train_dataset, valid_dataset]:
print('Dataset Size :', len(d.fns))
# print('Max LbW : ',max(list(map(len,d.tlbls))) )
# print('#Chars : ',sum([len(x) for x in d.tlbls]))
# print('Sample label :',d.tlbls[-1])
# print("Dataset :", sorted(list(map(len,d.tlbls))) )
print('-' * 80)
if opt.num_gpu > 1:
workers = workers * (1 if HVD3P else opt.num_gpu)
if HVD3P:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, num_replicas=opt.world_size, rank=opt.rank)
valid_sampler = torch.utils.data.distributed.DistributedSampler(
valid_dataset, num_replicas=opt.world_size, rank=opt.rank)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=train_batch_size,
shuffle=True if not HVD3P else False,
pin_memory=True,
num_workers=int(workers),
sampler=train_sampler if HVD3P else None,
worker_init_fn=WrkSeeder,
collate_fn=ds_load.SameTrCollate)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=val_batch_size,
pin_memory=True,
num_workers=int(workers),
sampler=valid_sampler if HVD3P else None)
model = OrigamiNet()
model.apply(init_bn)
# load finetune ckpt
if finetune != '':
model = load_finetune(model, finetune)
model.train()
if OnceExecWorker:
import pprint
[print(k, model.lreszs[k]) for k in sorted(model.lreszs.keys())]
biparams = list(
dict(filter(lambda kv: 'bias' in kv[0],
model.named_parameters())).values())
nonbiparams = list(
dict(filter(lambda kv: 'bias' not in kv[0],
model.named_parameters())).values())
if not pO.DDP:
model = model.to(device)
else:
model.cuda(opt.rank)
optimizer = optim.Adam(model.parameters(), lr=lr)
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,
gamma=10**(-1 /
90000))
# if OnceExecWorker and WdB:
# wandb.watch(model, log="all")
if pO.HVD:
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
optimizer = hvd.DistributedOptimizer(
optimizer, named_parameters=model.named_parameters())
# optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters(), compression=hvd.Compression.fp16)
if pO.DDP and opt.rank != 0:
random.seed()
np.random.seed()
# if AMP:
# model, optimizer = amp.initialize(model, optimizer, opt_level = "O1")
if pO.DP:
model = torch.nn.DataParallel(model)
elif pO.DDP:
model = pDDP(model,
device_ids=[opt.rank],
output_device=opt.rank,
find_unused_parameters=False)
model_ema = ModelEma(model)
if continue_model != '':
if OnceExecWorker:
print(f'loading pretrained model from {continue_model}')
checkpoint = torch.load(
continue_model, map_location=f'cuda:{opt.rank}' if HVD3P else None)
model.load_state_dict(checkpoint['model'], strict=True)
optimizer.load_state_dict(checkpoint['optimizer'])
model_ema._load_checkpoint(continue_model,
f'cuda:{opt.rank}' if HVD3P else None)
criterion = torch.nn.CTCLoss(reduction='none',
zero_infinity=True).to(device)
converter = CTCLabelConverter(train_dataset.ralph.values())
if OnceExecWorker:
with open(f'./saved_models/{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 += gin.operative_config_str()
opt_file.write(opt_log)
# if WdB:
# wandb.config.gin_str = gin.operative_config_str().splitlines()
print(optimizer)
print(opt_log)
start_time = time.time()
best_accuracy = -1
best_norm_ED = 1e+6
best_CER = 1e+6
i = 0
gAcc = 1
epoch = 1
btReplay = False and AMP
max_batch_replays = 3
if HVD3P: train_sampler.set_epoch(epoch)
titer = iter(train_loader)
while (True):
start_time = time.time()
model.zero_grad()
train_loss = Metric(pO, 'train_loss')
for j in trange(valInterval, leave=False, desc='Training'):
# Load a batch
try:
image_tensors, labels, fnames = next(titer)
except StopIteration:
epoch += 1
if HVD3P: train_sampler.set_epoch(epoch)
titer = iter(train_loader)
image_tensors, labels, fnames = next(titer)
# log filenames
# fnames = [f'{i}___{fname}' for fname in fnames]
# with open(f'./saved_models/{experiment_name}/filelog.txt', 'a+') as f:
# f.write('\n'.join(fnames) + '\n')
# Move to device
image = image_tensors.to(device)
text, length = converter.encode(labels)
batch_size = image.size(0)
replay_batch = True
maxR = 3
while replay_batch and maxR > 0:
maxR -= 1
# Forward pass
preds = model(image, text).float()
preds_size = torch.IntTensor([preds.size(1)] *
batch_size).to(device)
preds = preds.permute(1, 0, 2).log_softmax(2)
if i == 0 and OnceExecWorker:
print('Model inp : ', image.dtype, image.size())
print('CTC inp : ', preds.dtype, preds.size(),
preds_size[0])
# To avoid ctc_loss issue, disabled cudnn for the computation of the ctc_loss
torch.backends.cudnn.enabled = False
cost = criterion(preds, text.to(device), preds_size,
length.to(device)).mean() / gAcc
torch.backends.cudnn.enabled = True
train_loss.update(cost)
# cost tracking?
# with open(f'./saved_models/{experiment_name}/steplog.txt', 'a+') as f:
# f.write(f'Step {i} cost: {cost}\n')
optimizer.zero_grad()
default_optimizer_step = optimizer.step # added for batch replay
# Backward and step
if not AMP:
cost.backward()
replay_batch = False
else:
# with amp.scale_loss(cost, optimizer) as scaled_loss:
# scaled_loss.backward()
# if pO.HVD: optimizer.synchronize()
# if optimizer.step is default_optimizer_step or not btReplay:
# replay_batch = False
# elif maxR>0:
# optimizer.step()
pass
if btReplay:
pass #amp._amp_state.loss_scalers[0]._loss_scale = mx_sc
if (i + 1) % gAcc == 0:
if pO.HVD and AMP:
with optimizer.skip_synchronize():
optimizer.step()
else:
optimizer.step()
model.zero_grad()
model_ema.update(model, num_updates=i / 2)
if (i + 1) % (gAcc * 2) == 0:
lr_scheduler.step()
i += 1
# validation part
if True:
elapsed_time = time.time() - start_time
start_time = time.time()
model.eval()
with torch.no_grad():
valid_loss, current_accuracy, current_norm_ED, ted, bleu, preds, labels, infer_time = validation(
model_ema.ema, criterion, valid_loader, converter, opt, pO)
model.train()
v_time = time.time() - start_time
if OnceExecWorker:
if current_norm_ED < best_norm_ED:
best_norm_ED = current_norm_ED
checkpoint = {
'model': model.state_dict(),
'state_dict_ema': model_ema.ema.state_dict(),
'optimizer': optimizer.state_dict(),
}
torch.save(
checkpoint,
f'./saved_models/{experiment_name}/best_norm_ED.pth')
if ted < best_CER:
best_CER = ted
if current_accuracy > best_accuracy:
best_accuracy = current_accuracy
out = f'[{i}] Loss: {train_loss.avg:0.5f} time: ({elapsed_time:0.1f},{v_time:0.1f})'
out += f' vloss: {valid_loss:0.3f}'
out += f' CER: {ted:0.4f} NER: {current_norm_ED:0.4f} lr: {lr_scheduler.get_lr()[0]:0.5f}'
out += f' bAcc: {best_accuracy:0.1f}, bNER: {best_norm_ED:0.4f}, bCER: {best_CER:0.4f}, B: {bleu*100:0.2f}'
print(out)
with open(f'./saved_models/{experiment_name}/log_train.txt',
'a') as log:
log.write(out + '\n')
# if WdB:
# wandb.log({'lr': lr_scheduler.get_lr()[0], 'It':i, 'nED': current_norm_ED, 'B':bleu*100,
# 'tloss':train_loss.avg, 'AnED': best_norm_ED, 'CER':ted, 'bestCER':best_CER, 'vloss':valid_loss})
if DEBUG:
print(
f'[!!!] Iteration check. Value of i: {i} | Value of num_iter: {num_iter}'
)
# Change i == num_iter to i >= num_iter
# Add num_iter > 0 condition
if num_iter > 0 and i >= num_iter:
print('end the training')
#sys.exit()
break
def train(opt):
""" dataset preparation """
if opt.select_data == 'baidu':
train_set = BAIDUset(opt, opt.train_csv)
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=opt.batch_size,
shuffle=True,
num_workers=int(opt.workers),
collate_fn=BaiduCollate(opt.imgH, opt.imgW, keep_ratio=False))
val_set = BAIDUset(opt, opt.val_csv)
valid_loader = torch.utils.data.DataLoader(
val_set,
batch_size=opt.batch_size,
shuffle=True,
num_workers=int(opt.workers),
collate_fn=BaiduCollate(opt.imgH, opt.imgW, keep_ratio=False),
pin_memory=True)
else:
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,
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)
elif 'Bert' in opt.Prediction:
converter = TransformerConverter(opt.character, opt.max_seq)
elif 'SRN' in opt.Prediction:
converter = SRNConverter(opt.character, opt.SRN_PAD)
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).cuda()
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).cuda()
elif 'Bert' in opt.Prediction:
criterion = torch.nn.CrossEntropyLoss(ignore_index=0).cuda()
elif 'SRN' in opt.Prediction:
criterion = cal_performance
else:
criterion = torch.nn.CrossEntropyLoss(
ignore_index=0).cuda() # 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))
elif opt.ranger:
optimizer = Ranger(filtered_parameters, lr=opt.lr)
else:
optimizer = optim.Adadelta(filtered_parameters,
lr=opt.lr,
rho=opt.rho,
eps=opt.eps)
print("Optimizer:")
print(optimizer)
lrScheduler = lr_scheduler.MultiStepLR(optimizer, [5, 20, 30],
gamma=0.5) # 减小学习速率
""" 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
if opt.select_data == 'baidu':
train_iter = iter(train_loader)
step_per_epoch = len(train_set) / opt.batch_size
print('一代有多少step:', step_per_epoch)
else:
step_per_epoch = train_dataset.nums_samples / opt.batch_size
print('一代有多少step:', step_per_epoch)
while (True):
# try:
# train part
for p in model.parameters():
p.requires_grad = True
if opt.select_data == 'baidu':
try:
image_tensors, labels = train_iter.next()
except:
train_iter = iter(train_loader)
image_tensors, labels = train_iter.next()
else:
image_tensors, labels = train_dataset.get_batch()
image = image_tensors.cuda()
text, length = converter.encode(labels)
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) # to use CTCLoss format
cost = criterion(preds, text, preds_size, length)
elif 'Bert' in opt.Prediction:
pad_mask = None
# print(image.shape)
preds = model(image, pad_mask)
cost = criterion(preds[0].view(-1, preds[0].shape[-1]), text.contiguous().view(-1)) + \
criterion(preds[1].view(-1, preds[1].shape[-1]), text.contiguous().view(-1))
elif 'SRN' in opt.Prediction:
preds = model(image, None)
cost, n_correct = criterion(preds, text)
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)
if i % opt.disInterval == 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}'
)
start_time = time.time()
# validation part
if i % opt.valInterval == 0 and i > start_iter:
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()
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: {pred:20s}, gt: {gt:20s}, {str(pred == gt)}')
log.write(
f'pred: {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) % opt.saveInterval == 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 i > 0 and i % step_per_epoch == 0: # 调整学习速率
lrScheduler.step()
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 = 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):
""" training pipeline for our character recognition model """
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)
# Logging the experiment, so that we can refer to the performance of previous runs
log = open(f"./saved_models/{opt.exp_name}/log_dataset.txt", "a")
# Using params from user input to collation function for dataloader
AlignCollate_valid = AlignCollate(imgH=opt.imgH,
imgW=opt.imgW,
keep_ratio_with_pad=opt.PAD)
# Defining our validation dataloader
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()
# Using either CTC or Attention for char predictions
if "CTC" in opt.Prediction:
converter = CTCLabelConverter(opt.character)
else:
converter = AttnLabelConverter(opt.character)
opt.num_class = len(converter.character)
# Runnning our OCR model in grayscale or RGB
if opt.rgb:
opt.input_channel = 3
# Defining our model using user inputs
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
# Putting model in training mode
model.train()
# Using finetuning saved model from previous runs
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)
# Sending model to cpu or gpu, depending upon the avialbility
model.to(device)
# Setting up loss functions in the case of either CTC or Attention
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 of optimizer to be used
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)
# 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)
# Training iteration starts here
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
# Setting up initial metrics results and initializing the timer
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)
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) % 1e5 == 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