text stringlengths 1 93.6k |
|---|
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())]
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# setup optimizer
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if opt.adam:
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# optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) # , weight_decay=3.0e-05
|
optimizer = optim.AdamW(filtered_parameters, lr=opt.lr,
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betas=(opt.beta1, 0.999),
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eps=8.e-8,
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weight_decay=0.05,
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amsgrad=False)
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else:
|
optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps)
|
print("Optimizer:")
|
print(optimizer)
|
if opt.lr_scheduler:
|
lr_scheduler = CosineAnnealingLR(
|
epochs=opt.num_epochs,
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step_each_epoch=opt.one_epoch_steps,
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warmup_epoch=2,
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last_epoch=-1)(optimizer=optimizer)
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else:
|
lr_scheduler = None
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""" final options """
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# print(opt)
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with open(f'./saved_models/{opt.exp_name}/opt.txt', 'a') as opt_file:
|
opt_log = '------------ Options -------------\n'
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args = vars(opt)
|
for k, v in args.items():
|
if k != "character":
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opt_log += f'{str(k)}: {str(v)}\n'
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opt_log += '---------------------------------------\n'
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print(opt_log)
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opt_file.write(opt_log)
|
""" start training """
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start_iter = 0
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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
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iteration = start_iter
|
while(True):
|
# train part
|
image_tensors, labels = train_dataset.get_batch()
|
image = image_tensors.to(device)
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text, length = converter.encode(labels, batch_max_length=opt.batch_max_length)
|
batch_size = image.size(0)
|
if 'CTC' == opt.Prediction:
|
preds = model(image, text)
|
preds_size = torch.IntTensor([preds.size(1)] * batch_size)
|
if opt.baiduCTC:
|
preds = preds.permute(1, 0, 2) # to use CTCLoss format
|
cost = criterion(preds, text, preds_size, length) / batch_size
|
else:
|
preds = preds.log_softmax(2).permute(1, 0, 2)
|
cost = criterion(preds, text, preds_size, length)
|
elif 'DCTC' in opt.Prediction:
|
preds = model(image, text)
|
preds = preds.permute(1, 0, 2)
|
targets_dict = {
|
'targets': text,
|
'target_lengths': length
|
}
|
cost = criterion(logits=preds, targets_dict=targets_dict)
|
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 opt.lr_scheduler:
|
lr_scheduler.step()
|
# 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)
|
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