def evaluate_llhx(frame, input_loader, n_marg_llh, use_q_llh, mode, device):
avgr = Averager()
for x, y in input_loader:
x = x.to(device)
avgr.update(frame.llh(x, None, n_marg_llh, use_q_llh, mode),
nrep=len(x))
return avgr.avg
def train(model, optimizer, source_loader, args):
model.train()
acc_avg = Averager()
loss_avg = Averager()
iter_source = iter(source_loader)
num_iter = len(iter_source)
for i in range(num_iter):
sx, sy = iter_source.next()
if args.cuda:
sx, sy = sx.cuda(), sy.cuda()
_, logits = model(sx)
criterion = nn.CrossEntropyLoss()
loss = criterion(logits, sy)
optimizer.zero_grad()
loss.backward()
optimizer.step()
acc = count_acc(logits, sy)
acc_avg.add(acc)
loss_avg.add(loss.item())
acc = acc_avg.item()
loss = loss_avg.item()
return loss, acc
def validation(model, val_loader):
'''
evaluation function for validation data
'''
print("Start validation.\n")
val_loss_hist = Averager()
with torch.no_grad():
for images, targets, _ in val_loader:
images = torch.stack(images).to(CFG.device).float()
bboxes = [
target['boxes'].to(CFG.device).float() for target in targets
]
labels = [
target['labels'].to(CFG.device).float() for target in targets
]
batch_size = images.shape[0]
target_res = dict()
target_res['bbox'] = bboxes
target_res['cls'] = labels
# target_res["img_scale"] = torch.tensor([1.0] * batch_size, dtype=torch.float).to(CFG.device)
# target_res["img_size"] = torch.tensor([images[0].shape[-2:]] * batch_size, dtype=torch.float).to(CFG.device)
# forward pass & calculate loss
output = model(images, target_res)
loss_value = output['loss'].detach().item()
val_loss_hist.update(loss_value, batch_size)
del images, targets, bboxes
return val_loss_hist.value
def train(self, epoch, dataloader):
use_cuda = torch.cuda.is_available()
self.net.train()
for m in self.net.modules(): ### freeze是处理batchnorm的
if isinstance(m, _BatchNorm):
if self.args["train"]["freeze"]:
m.eval()
loss_avg = Averager()
lls_avg = Averager()
for batch_idx, sample in enumerate(dataloader):
sequence_idx = sample[0]
data, target, name = sample[0], sample[1], sample[2]
if use_cuda:
data = data.cuda()
target = target.cuda()
if len(sample) > 3:
args = sample[3:]
else:
args = None
total_loss, loss_list = self.warp(
data, target, False, args) #### warp一般是并行，返回一组batch的损失
loss_avg.update(total_loss.mean().detach().cpu().numpy())
loss_list = tuple([l.cpu().numpy() for l in loss_list])
info = "Finish training %d out of %d, " % (batch_idx + 1,
len(dataloader))
for lid, l in enumerate(loss_list):
info += "loss %d: %.4f, " % (lid, float(np.mean(l)))
print(info)
lls_avg.update(loss_list)
self.optimizer.zero_grad()
loss_scalar = torch.mean(total_loss)
if self.half: # Automated Mix Precision
with amp.scale_loss(loss_scalar,
self.optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss_scalar.backward()
torch.nn.utils.clip_grad_value_(self.net.parameters(), 2)
self.optimizer.step()
self.scheduler.step(epoch=epoch)
self.__writeLossLog(
"Train",
epoch,
meanloss=loss_avg.val(),
loss_list=lls_avg.val(),
lr=self.scheduler.get_lr()[0],
)
def __init__(self,
algo,
dataset,
kernel_fn,
base_model_fn,
num_particles=10,
resume=False,
resume_epoch=None,
resume_lr=1e-4):
self.algo = algo
self.dataset = dataset
self.kernel_fn = kernel_fn
self.num_particles = num_particles
print("running {} on {}".format(algo, dataset))
if self.dataset == 'regression':
self.data = toy.generate_regression_data(80, 200)
(self.train_data,
self.train_targets), (self.test_data,
self.test_targets) = self.data
elif self.dataset == 'classification':
self.train_data, self.train_targets = toy.generate_classification_data(
100)
self.test_data, self.test_targets = toy.generate_classification_data(
200)
else:
raise NotImplementedError
if kernel_fn == 'rbf':
self.kernel = rbf_fn
else:
raise NotImplementedError
models = [base_model_fn().cuda() for _ in range(num_particles)]
self.models = models
param_set, state_dict = extract_parameters(self.models)
self.state_dict = state_dict
self.param_set = torch.nn.Parameter(param_set.clone(),
requires_grad=True)
self.optimizer = torch.optim.Adam([{
'params': self.param_set,
'lr': 1e-3
}])
if self.dataset == 'regression':
self.loss_fn = torch.nn.MSELoss()
elif self.dataset == 'classification':
self.loss_fn = torch.nn.CrossEntropyLoss()
self.kernel_width_averager = Averager(shape=())
def perform_lambda_test(n_episodes, n_avg):
averager = Averager(
GymEpisodeTaskFactory(env, n_episodes, SarsaLambdaFactory(env)))
alphas = np.arange(
1, 15
) / N_TILINGS # Those are again divided by N_TILINGS in sarsa to give final alpha value
results = defaultdict(lambda: np.zeros(len(alphas)))
for lam in [0, .68, .84, .92, .96, .98, .99]:
for i, alpha in np.ndenumerate(alphas):
results[lam][i] = averager.average((lam, alpha),
n_avg,
merge=average_steps_per_episode)
plot_scatters_from_dict(results, 'lambda={}', alphas)
def validation(model, criterion, evaluation_loader, converter, opt):
""" validation or evaluation """
n_correct = 0
norm_ED = 0
length_of_data = 0
infer_time = 0
valid_loss_avg = Averager()
for i, (image_tensors, labels) in enumerate(evaluation_loader):
batch_size = image_tensors.size(0)
length_of_data = length_of_data + batch_size
image = image_tensors.to(device)
# For max length prediction
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)
text_for_loss, length_for_loss = converter.encode(
labels, batch_max_length=opt.batch_max_length)
start_time = time.time()
preds, global_feature, local_feature, attention_weights, transformed_imgs, control_points = model(
image, text_for_pred, is_train=False)
forward_time = time.time() - start_time
preds = preds[:, :text_for_loss.shape[1] - 1, :]
target = text_for_loss[:, 1:] # without [GO] Symbol
cost = criterion(preds.contiguous().view(-1, preds.shape[-1]),
target.contiguous().view(-1))
# select max probabilty (greedy decoding) then decode index to character
preds_score, preds_index = preds.max(2)
preds_str = converter.decode(preds_index, length_for_pred)
labels = converter.decode(text_for_loss[:, 1:], length_for_loss)
infer_time += forward_time
valid_loss_avg.add(cost)
# calculate accuracy.
batch_n_correct, batch_char_acc = compute_loss(preds_str, labels, opt)
n_correct += batch_n_correct
norm_ED += batch_char_acc
accuracy = n_correct / float(length_of_data) * 100
norm_ED = norm_ED / float(length_of_data) * 100
return valid_loss_avg.val(
), accuracy, norm_ED, preds_str, labels, infer_time, length_of_data
def train_base(args, base_loader, model, criterion, optimizer, epoch):
losses, accs = [Averager() for i in range(2)]
model.train()
for i, (imgs, lbls) in enumerate(base_loader):
imgs, lbls = imgs.to(args.device), lbls.to(args.device)
features = model(imgs)
predicts = model.classify(features)
loss = criterion(predicts, lbls)
acc = accuracy_(predicts, lbls)
optimizer.zero_grad()
loss.backward()
optimizer.step()
model.weight_norm()
losses.add(loss.item()), accs.add(acc)
print(" [%d/%d] base loss:%.3f, acc:%.2f" %
(i + 1, len(base_loader), losses.item(), accs.item()),
end=' \r')
wandb.log({
'base_train_loss': losses.item(),
'base_train_acc': accs.item()
})
print("Epoch %d, base loss:%.4f, acc:%.3f" %
(epoch, losses.item(), accs.item()))
def train(opt):
""" dataset preparation """
train_dataset, valid_loader = dataset_preparation(opt)
""" model configuration """
converter, model = model_configuration(opt)
# weight initialization
weight_initialization(model)
# set data parallel for multi-GPU
model = set_data_parallel(model, opt)
""" setup loss """
criterion = setup_loss(opt)
# loss averager
loss_avg = Averager()
# filter that only require gradient decent
filtered_parameters = filter(model)
# setup optimizer
optimizer = setup_optimizer(filtered_parameters, opt)
""" final options """
# print(opt)
final_options(opt)
""" start training """
best_accuracy, best_norm_ED, i, start_time = start_training(opt)
# Train all epochs
train_all_epochs(best_accuracy, best_norm_ED, converter, criterion, i,
loss_avg, model, opt, optimizer, start_time,
train_dataset, valid_loader)
def train_stage(self, data_train_stage1, data_train_stage2, model, epoch):
print('Training Epoch {}:'.format(epoch + 1))
model.train()
aid_set = list(set(data_train_stage1.aid))
avg_loss = Averager()
data_train = data_train_stage1
n_samples = data_train.shape[0]
n_batch = int(np.ceil(n_samples / self.batchsize))
if self.sample_method == 'normal':
list_prob = []
for aid in aid_set:
list_prob.append(data_train_stage1[data_train_stage1.aid == aid].shape[0])
list_prob_sum = sum(list_prob)
for i in range(len(list_prob)):
list_prob[i] = list_prob[i] / list_prob_sum
elif self.sample_method == 'sqrt':
list_prob = []
for aid in aid_set:
list_prob.append(math.sqrt(data_train_stage1[data_train_stage1.aid == aid].shape[0]))
list_prob_sum = sum(list_prob)
for i in range(len(list_prob)):
list_prob[i] = list_prob[i] / list_prob_sum
for i_batch in tqdm.tqdm(range(n_batch)):
if (self.sample_method == 'normal') or (self.sample_method == 'sqrt'):
batch_aid_set = np.random.choice(aid_set, size=self.task_count, replace=False, p=list_prob)#random.sample(aid_set, 5)
elif self.sample_method == 'unit':
batch_aid_set = random.sample(aid_set, self.task_count)
list_sup_x, list_sup_y, list_qry_x, list_qry_y = list(), list(), list(), list()
for aid in batch_aid_set:
batch_sup = data_train[data_train.aid == aid].sample(self.batchsize)
batch_qry = data_train[data_train.aid == aid].sample(self.batchsize)
batch_sup_x = batch_sup[self.train_col]
batch_sup_y = batch_sup[self.label_col].values
batch_qry_x = batch_qry[self.train_col]
batch_qry_y = batch_qry[self.label_col].values
list_sup_x.append(batch_sup_x)
list_sup_y.append(batch_sup_y)
list_qry_x.append(batch_qry_x)
list_qry_y.append(batch_qry_y)
loss = model.global_update(list_sup_x, list_sup_y, list_qry_x, list_qry_y)
avg_loss.add(loss.item())
print('Training Epoch {}; Loss {}; '.format(epoch + 1, avg_loss.item()))
def val(model, args, val_loader, label):
model.eval()
vl = Averager()
va = Averager()
with torch.no_grad():
for i, batch in tqdm(enumerate(val_loader, 1), total=len(val_loader)):
data, index_label = batch[0].cuda(), batch[1]
logits = model(data, mode='val')
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
vl.add(loss.item())
va.add(acc)
vl = vl.item()
va = va.item()
return vl, va
def test(model, target_loader, args):
model.eval()
acc_avg = Averager()
with torch.no_grad():
iter_target = iter(target_loader)
num_iter = len(iter_target)
for i in range(num_iter):
tx, ty = iter_target.next()
if torch.cuda.is_available():
tx, ty = tx.cuda(), ty.cuda()
_, logits = model(tx)
acc = count_acc(logits, ty)
acc_avg.add(acc)
acc = acc_avg.item()
return acc
def test(model, label, args, few_shot_params):
if args.debug:
n_test = 10
print_freq = 2
else:
n_test = 1000
print_freq = 100
test_file = args.dataset_dir + 'test.json'
test_datamgr = SetDataManager(test_file, args.dataset_dir, args.image_size,
mode = 'val',n_episode = n_test ,**few_shot_params)
loader = test_datamgr.get_data_loader(aug=False)
test_acc_record = np.zeros((n_test,))
warmup_state = torch.load(osp.join(args.checkpoint_dir, 'max_acc' + '.pth'))['params']
model.load_state_dict(warmup_state, strict=False)
model.eval()
ave_acc = Averager()
with torch.no_grad():
for i, batch in enumerate(loader, 1):
data, index_label = batch[0].cuda(), batch[1].cuda()
logits = model(data, 'test')
acc = count_acc(logits, label)
ave_acc.add(acc)
test_acc_record[i - 1] = acc
if i % print_freq == 0:
print('batch {}: {:.2f}({:.2f})'.format(i, ave_acc.item() * 100, acc * 100))
m, pm = compute_confidence_interval(test_acc_record)
# print('Val Best Epoch {}, Acc {:.4f}, Test Acc {:.4f}'.format(trlog['max_acc_epoch'], trlog['max_acc'],
# ave_acc.item()))
print('Test Acc {:.4f} + {:.4f}'.format(m, pm))
acc_str = '%4.2f' % (m * 100)
with open(args.save_dir + '/result.txt', 'a') as f:
f.write('%s %s\n' % (acc_str, args.name))
def main(self):
print('Starting Training...')
loss_hist = Averager()
loss = []
validation_losses = []
precisions = []
itr = 1
for epoch in range(self.num_epochs):
self.model.train()
loss_hist.reset()
for images, targets in self.train_data_loader:
images = list(image.to(self.device) for image in images)
# targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
targets = [{k: v.long().to(self.device)
for k, v in t.items()} for t in targets]
loss_dict = self.model(images, targets)
losses = sum(loss for loss in loss_dict.values())
self.validate()
loss_value = losses.item()
loss_hist.send(loss_value)
loss.append(loss_value)
self.optimizer.zero_grad()
losses.backward()
self.optimizer.step()
if math.isnan(loss_value):
plot_grad_flow(self.model.named_parameters())
raise ValueError('Loss is nan')
if itr % 50 == 0:
print(f"Iteration #{itr} loss: {loss_value}")
itr += 1
# update the learning rate
if self.lr_scheduler is not None:
self.lr_scheduler.step()
if self.val_dataset:
precision, validation_loss = self.validate()
precisions.append(precision)
validation_losses.append(validation_loss)
print(f'Mean Precision for Validation Data: {precision}')
print(f'Validation Loss: {validation_loss}')
print(f"Epoch #{epoch} loss: {loss_hist.value}")
print('Finished!')
return loss, precisions, validation_losses
def validate(val_loader, model, device):
model.eval()
itr = 1
loss_hist = Averager()
loss_hist.reset()
for images, targets, image_ids in val_loader:
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
loss_dict = model(images, targets)
losses = sum(loss for loss in loss_dict)
loss_value = losses.item()
loss_hist.send(loss_value)
if itr % 20 == 0:
print(f"Iteration: {itr} loss: {loss_hist.value}")
itr += 1
return loss_hist.value
def perform_sarsa_lambda_comparison(n_episodes, n_avg):
alphas = np.arange(
0.2, 2.2, 0.2
) # Those are divided by N_TILINGS in sarsa to give final alpha value
lam = 0.84
results = defaultdict(lambda: np.zeros(len(alphas)))
averager = Averager(
GymEpisodeTaskFactory(env, n_episodes, SarsaLambdaFactory(env)))
for i, alpha in np.ndenumerate(alphas):
results['Sarsa(Lam) with replacing'][i] = -averager.average(
(lam, alpha), n_avg, merge=average_steps_per_episode)
averager = Averager(
GymEpisodeTaskFactory(env, n_episodes,
TrueOnlineSarsaLambdaFactory(env)))
for i, alpha in np.ndenumerate(alphas):
results['True Online Sarsa(Lam)'][i] = -averager.average(
(lam, alpha), n_avg, merge=average_steps_per_episode)
plot_scatters_from_dict(results, '{}', alphas)
def train(opt):
""" dataset preparation """
if not opt.data_filtering_off:
print(
'Filtering the images containing characters which are not in opt.character'
)
print(
'Filtering the images whose label is longer than opt.batch_max_length'
)
opt.select_data = opt.select_data.split('-')
opt.batch_ratio = opt.batch_ratio.split('-')
train_dataset = Batch_Balanced_Dataset(opt)
log = open(f'./saved_models/{opt.exp_name}/log_dataset.txt', 'a')
AlignCollate_valid = AlignCollate(imgH=opt.imgH,
imgW=opt.imgW,
keep_ratio_with_pad=opt.PAD)
valid_dataset, valid_dataset_log = hierarchical_dataset(
root=opt.valid_data, opt=opt)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=opt.batch_size,
shuffle=
True, # 'True' to check training progress with validation function.
num_workers=int(opt.workers),
collate_fn=AlignCollate_valid,
pin_memory=True)
log.write(valid_dataset_log)
print('-' * 80)
log.write('-' * 80 + '\n')
log.close()
""" model configuration """
if 'CTC' in opt.Prediction:
if opt.baiduCTC:
converter = CTCLabelConverterForBaiduWarpctc(opt.character)
else:
converter = CTCLabelConverter(opt.character)
else:
converter = AttnLabelConverter(opt.character)
opt.num_class = len(converter.character)
if opt.rgb:
opt.input_channel = 3
model = Model(opt)
print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial,
opt.input_channel, opt.output_channel, opt.hidden_size,
opt.num_class, opt.batch_max_length, opt.Transformation,
opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction)
# weight initialization
for name, param in model.named_parameters():
if 'localization_fc2' in name:
print(f'Skip {name} as it is already initialized')
continue
try:
if 'bias' in name:
init.constant_(param, 0.0)
elif 'weight' in name:
init.kaiming_normal_(param)
except Exception as e: # for batchnorm.
if 'weight' in name:
param.data.fill_(1)
continue
# data parallel for multi-GPU
model = torch.nn.DataParallel(model).to(device)
model.train()
if opt.saved_model != '':
print(f'loading pretrained model from {opt.saved_model}')
if opt.FT:
model.load_state_dict(torch.load(opt.saved_model), strict=False)
else:
model.load_state_dict(torch.load(opt.saved_model))
print("Model:")
print(model)
""" setup loss """
if 'CTC' in opt.Prediction:
if opt.baiduCTC:
# need to install warpctc. see our guideline.
from warpctc_pytorch import CTCLoss
criterion = CTCLoss()
else:
criterion = torch.nn.CTCLoss(zero_infinity=True).to(device)
else:
criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(
device) # ignore [GO] token = ignore index 0
# loss averager
loss_avg = Averager()
# filter that only require gradient decent
filtered_parameters = []
params_num = []
for p in filter(lambda p: p.requires_grad, model.parameters()):
filtered_parameters.append(p)
params_num.append(np.prod(p.size()))
print('Trainable params num : ', sum(params_num))
# [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())]
# setup optimizer
if opt.adam:
optimizer = optim.Adam(filtered_parameters,
lr=opt.lr,
betas=(opt.beta1, 0.999))
else:
optimizer = optim.Adadelta(filtered_parameters,
lr=opt.lr,
rho=opt.rho,
eps=opt.eps)
print("Optimizer:")
print(optimizer)
""" final options """
# print(opt)
with open(f'./saved_models/{opt.exp_name}/opt.txt', 'a') as opt_file:
opt_log = '------------ Options -------------\n'
args = vars(opt)
for k, v in args.items():
opt_log += f'{str(k)}: {str(v)}\n'
opt_log += '---------------------------------------\n'
print(opt_log)
opt_file.write(opt_log)
""" start training """
start_iter = 0
if opt.saved_model != '':
try:
start_iter = int(opt.saved_model.split('_')[-1].split('.')[0])
print(f'continue to train, start_iter: {start_iter}')
except:
pass
start_time = time.time()
best_accuracy = -1
best_norm_ED = -1
iteration = start_iter
while (True):
# train part
image_tensors, labels = train_dataset.get_batch()
image = image_tensors.to(device)
text, length = converter.encode(labels,
batch_max_length=opt.batch_max_length)
batch_size = image.size(0)
if 'CTC' in opt.Prediction:
preds = model(image, text)
preds_size = torch.IntTensor([preds.size(1)] * batch_size)
if opt.baiduCTC:
preds = preds.permute(1, 0, 2) # to use CTCLoss format
cost = criterion(preds, text, preds_size, length) / batch_size
else:
preds = preds.log_softmax(2).permute(1, 0, 2)
cost = criterion(preds, text, preds_size, length)
else:
preds = model(image, text[:, :-1]) # align with Attention.forward
target = text[:, 1:] # without [GO] Symbol
cost = criterion(preds.view(-1, preds.shape[-1]),
target.contiguous().view(-1))
model.zero_grad()
cost.backward()
torch.nn.utils.clip_grad_norm_(
model.parameters(),
opt.grad_clip) # gradient clipping with 5 (Default)
optimizer.step()
loss_avg.add(cost)
# validation part
if (
iteration + 1
) % opt.valInterval == 0 or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0'
elapsed_time = time.time() - start_time
# for log
with open(f'./saved_models/{opt.exp_name}/log_train.txt',
'a') as log:
model.eval()
with torch.no_grad():
valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation(
model, criterion, valid_loader, converter, opt)
model.train()
# training loss and validation loss
loss_log = f'[{iteration+1}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}'
loss_avg.reset()
current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}'
# keep best accuracy model (on valid dataset)
if current_accuracy > best_accuracy:
best_accuracy = current_accuracy
torch.save(
model.state_dict(),
f'./saved_models/{opt.exp_name}/best_accuracy.pth')
if current_norm_ED > best_norm_ED:
best_norm_ED = current_norm_ED
torch.save(
model.state_dict(),
f'./saved_models/{opt.exp_name}/best_norm_ED.pth')
best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}'
loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}'
print(loss_model_log)
log.write(loss_model_log + '\n')
# show some predicted results
dashed_line = '-' * 80
head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F'
predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n'
for gt, pred, confidence in zip(labels[:5], preds[:5],
confidence_score[:5]):
if 'Attn' in opt.Prediction:
gt = gt[:gt.find('[s]')]
pred = pred[:pred.find('[s]')]
predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n'
predicted_result_log += f'{dashed_line}'
print(predicted_result_log)
log.write(predicted_result_log + '\n')
# save model per 1e+5 iter.
if (iteration + 1) % 1e+5 == 0:
torch.save(
model.state_dict(),
f'./saved_models/{opt.exp_name}/iter_{iteration+1}.pth')
if (iteration + 1) == opt.num_iter:
print('end the training')
sys.exit()
iteration += 1
def validation(model, criterion, evaluation_loader, converter, opt):
""" validation or evaluation """
n_correct = 0
norm_ED = 0
length_of_data = 0
infer_time = 0
valid_loss_avg = Averager()
for i, (image_tensors, labels) in enumerate(evaluation_loader):
batch_size = image_tensors.size(0)
length_of_data = length_of_data + batch_size
image = image_tensors.to(device)
# For max length prediction
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)
text_for_loss, length_for_loss = converter.encode(
labels, batch_max_length=opt.batch_max_length)
start_time = time.time()
if 'CTC' in opt.Prediction:
preds = model(image, text_for_pred).log_softmax(2)
forward_time = time.time() - start_time
# Calculate evaluation loss for CTC deocder.
preds_size = torch.IntTensor([preds.size(1)] * batch_size)
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_for_loss, preds_size, length_for_loss)
torch.backends.cudnn.enabled = True
# Select max probabilty (greedy decoding) then decode index to character
_, preds_index = preds.max(2)
preds_index = preds_index.transpose(1, 0).contiguous().view(-1)
preds_str = converter.decode(preds_index.data, preds_size.data)
else:
preds = model(image, text_for_pred, is_train=False)
forward_time = time.time() - start_time
preds = preds[:, :text_for_loss.shape[1] - 1, :]
target = text_for_loss[:, 1:] # without [GO] Symbol
cost = criterion(preds.contiguous().view(-1, preds.shape[-1]),
target.contiguous().view(-1))
# select max probabilty (greedy decoding) then decode index to character
_, preds_index = preds.max(2)
preds_str = converter.decode(preds_index, length_for_pred)
labels = converter.decode(text_for_loss[:, 1:], length_for_loss)
infer_time += forward_time
valid_loss_avg.add(cost)
# calculate accuracy.
for pred, gt in zip(preds_str, labels):
if 'Attn' in opt.Prediction:
pred = pred[:pred.find(
'[s]')] # prune after "end of sentence" token ([s])
gt = gt[:gt.find('[s]')]
if pred == gt:
n_correct += 1
if len(gt) == 0:
norm_ED += 1
else:
norm_ED += edit_distance(pred, gt) / len(gt)
accuracy = n_correct / float(length_of_data) * 100
return valid_loss_avg.val(
), accuracy, norm_ED, preds_str, labels, infer_time, length_of_data
def train(opt):
""" dataset preparation """
opt.select_data = opt.select_data.split('-')
opt.batch_ratio = opt.batch_ratio.split('-')
#import ipdb;ipdb.set_trace()
train_dataset = Batch_Balanced_Dataset(opt)
AlignCollate_valid = AlignCollate(imgH=opt.imgH,
imgW=opt.imgW,
keep_ratio_with_pad=opt.PAD)
valid_dataset = hierarchical_dataset(root=opt.valid_data, opt=opt)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=opt.batch_size,
shuffle=
True, # 'True' to check training progress with validation function.
num_workers=int(opt.workers),
collate_fn=AlignCollate_valid,
pin_memory=True)
print('-' * 80)
""" model configuration """
if 'CTC' in opt.Prediction:
converter = CTCLabelConverter(opt.character)
else:
converter = AttnLabelConverter(opt.character)
opt.num_class = len(converter.character)
if opt.rgb:
opt.input_channel = 3
model = Model(opt)
print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial,
opt.input_channel, opt.output_channel, opt.hidden_size,
opt.num_class, opt.batch_max_length, opt.Transformation,
opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction)
# weight initialization
for name, param in model.named_parameters():
if 'localization_fc2' in name:
print(f'Skip {name} as it is already initialized')
continue
try:
if 'bias' in name:
init.constant_(param, 0.0)
elif 'weight' in name:
init.kaiming_normal_(param)
except Exception as e: # for batchnorm.
if 'weight' in name:
param.data.fill_(1)
continue
# data parallel for multi-GPU
model = torch.nn.DataParallel(model).to(device)
model.train()
if opt.continue_model != '':
print(f'loading pretrained model from {opt.continue_model}')
model.load_state_dict(torch.load(opt.continue_model))
print("Model:")
#print(model)
""" setup loss """
if 'CTC' in opt.Prediction:
criterion = torch.nn.CTCLoss(zero_infinity=True).to(device)
else:
criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(
device) # ignore [GO] token = ignore index 0
# loss averager
loss_avg = Averager()
# filter that only require gradient decent
filtered_parameters = []
params_num = []
for p in filter(lambda p: p.requires_grad, model.parameters()):
filtered_parameters.append(p)
params_num.append(np.prod(p.size()))
print('Trainable params num : ', sum(params_num))
# [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())]
# setup optimizer
if opt.adam:
optimizer = optim.Adam(filtered_parameters,
lr=opt.lr,
betas=(opt.beta1, 0.999))
else:
optimizer = optim.Adadelta(filtered_parameters,
lr=opt.lr,
rho=opt.rho,
eps=opt.eps)
print("Optimizer:")
print(optimizer)
""" final options """
# print(opt)
with open(f'./saved_models/{opt.experiment_name}/opt.txt',
'a',
encoding="utf-8") as opt_file:
opt_log = '------------ Options -------------\n'
args = vars(opt)
for k, v in args.items():
opt_log += f'{str(k)}: {str(v)}\n'
opt_log += '---------------------------------------\n'
print(opt_log)
opt_file.write(opt_log)
""" start training """
start_iter = 0
if opt.continue_model != '':
start_iter = int(opt.continue_model.split('_')[-1].split('.')[0])
print(f'continue to train, start_iter: {start_iter}')
start_time = time.time()
best_accuracy = -1
best_norm_ED = 1e+6
i = start_iter
while (True):
# train part
image_tensors, labels = train_dataset.get_batch()
image = image_tensors.to(device)
text, length = converter.encode(labels,
batch_max_length=opt.batch_max_length)
batch_size = image.size(0)
#import ipdb;ipdb.set_trace()
if 'CTC' in opt.Prediction:
preds = model(image, text).log_softmax(2)
preds_size = torch.IntTensor([preds.size(1)] *
batch_size).to(device)
preds = preds.permute(1, 0, 2) # to use CTCLoss format
# To avoid ctc_loss issue, disabled cudnn for the computation of the ctc_loss
# https://github.com/jpuigcerver/PyLaia/issues/16
torch.backends.cudnn.enabled = False
cost = criterion(preds, text, preds_size, length)
torch.backends.cudnn.enabled = True
else:
preds = model(image, text[:, :-1]) # align with Attention.forward
target = text[:, 1:] # without [GO] Symbol
cost = criterion(preds.view(-1, preds.shape[-1]),
target.contiguous().view(-1))
model.zero_grad()
cost.backward()
torch.nn.utils.clip_grad_norm_(
model.parameters(),
opt.grad_clip) # gradient clipping with 5 (Default)
optimizer.step()
loss_avg.add(cost)
# validation part
if i % opt.valInterval == 0:
elapsed_time = time.time() - start_time
print(
f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}'
)
# for log
with open(f'./saved_models/{opt.experiment_name}/log_train.txt',
'a',
encoding="utf-8") as log:
log.write(
f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}\n'
)
loss_avg.reset()
model.eval()
with torch.no_grad():
valid_loss, current_accuracy, current_norm_ED, preds, labels, infer_time, length_of_data = validation(
model, criterion, valid_loader, converter, opt)
model.train()
for pred, gt in zip(preds[:5], labels[:5]):
if 'Attn' in opt.Prediction:
pred = pred[:pred.find('[s]')]
gt = gt[:gt.find('[s]')]
print(f'{pred:20s}, gt: {gt:20s}, {str(pred == gt)}')
#pred = pred.encode('utf-8')
#gt = gt.encode('utf-8')
log.write(
f'{pred:20s}, gt: {gt:20s}, {str(pred == gt)}\n')
valid_log = f'[{i}/{opt.num_iter}] valid loss: {valid_loss:0.5f}'
valid_log += f' accuracy: {current_accuracy:0.3f}, norm_ED: {current_norm_ED:0.2f}'
print(valid_log)
log.write(valid_log + '\n')
# keep best accuracy model
if current_accuracy > best_accuracy:
best_accuracy = current_accuracy
torch.save(
model.state_dict(),
f'./saved_models/{opt.experiment_name}/best_accuracy.pth'
)
if current_norm_ED < best_norm_ED:
best_norm_ED = current_norm_ED
torch.save(
model.state_dict(),
f'./saved_models/{opt.experiment_name}/best_norm_ED.pth'
)
best_model_log = f'best_accuracy: {best_accuracy:0.3f}, best_norm_ED: {best_norm_ED:0.2f}'
print(best_model_log)
log.write(best_model_log + '\n')
# save model per 1e+5 iter.
if (i + 1) % 1e+5 == 0:
torch.save(model.state_dict(),
f'./saved_models/{opt.experiment_name}/iter_{i+1}.pth')
if i == opt.num_iter:
print('end the training')
sys.exit()
i += 1
def train(opt):
plotDir = os.path.join(opt.exp_dir,opt.exp_name,'plots')
if not os.path.exists(plotDir):
os.makedirs(plotDir)
lib.print_model_settings(locals().copy())
""" dataset preparation """
if not opt.data_filtering_off:
print('Filtering the images containing characters which are not in opt.character')
print('Filtering the images whose label is longer than opt.batch_max_length')
# see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130
opt.select_data = opt.select_data.split('-')
opt.batch_ratio = opt.batch_ratio.split('-')
#considering the real images for discriminator
opt.batch_size = opt.batch_size*2
train_dataset = Batch_Balanced_Dataset(opt)
log = open(os.path.join(opt.exp_dir,opt.exp_name,'log_dataset.txt'), 'a')
AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD)
valid_dataset, valid_dataset_log = hierarchical_dataset(root=opt.valid_data, opt=opt)
valid_loader = torch.utils.data.DataLoader(
valid_dataset, batch_size=opt.batch_size,
shuffle=False, # 'True' to check training progress with validation function.
num_workers=int(opt.workers),
collate_fn=AlignCollate_valid, pin_memory=True)
log.write(valid_dataset_log)
print('-' * 80)
log.write('-' * 80 + '\n')
log.close()
""" model configuration """
if 'CTC' in opt.Prediction:
converter = CTCLabelConverter(opt.character)
else:
converter = AttnLabelConverter(opt.character)
opt.num_class = len(converter.character)
if opt.rgb:
opt.input_channel = 3
model = AdaINGen(opt)
ocrModel = Model(opt)
disModel = MsImageDisV1(opt)
print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel,
opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction,
opt.SequenceModeling, opt.Prediction)
# weight initialization
for currModel in [model, ocrModel, disModel]:
for name, param in currModel.named_parameters():
if 'localization_fc2' in name:
print(f'Skip {name} as it is already initialized')
continue
try:
if 'bias' in name:
init.constant_(param, 0.0)
elif 'weight' in name:
init.kaiming_normal_(param)
except Exception as e: # for batchnorm.
if 'weight' in name:
param.data.fill_(1)
continue
# data parallel for multi-GPU
ocrModel = torch.nn.DataParallel(ocrModel).to(device)
if not opt.ocrFixed:
ocrModel.train()
else:
ocrModel.module.Transformation.eval()
ocrModel.module.FeatureExtraction.eval()
ocrModel.module.AdaptiveAvgPool.eval()
# ocrModel.module.SequenceModeling.eval()
ocrModel.module.Prediction.eval()
model = torch.nn.DataParallel(model).to(device)
model.train()
disModel = torch.nn.DataParallel(disModel).to(device)
disModel.train()
if opt.modelFolderFlag:
if len(glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_synth.pth")))>0:
opt.saved_synth_model = glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_synth.pth"))[-1]
if len(glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_dis.pth")))>0:
opt.saved_dis_model = glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_dis.pth"))[-1]
#loading pre-trained model
if opt.saved_ocr_model != '' and opt.saved_ocr_model != 'None':
print(f'loading pretrained ocr model from {opt.saved_ocr_model}')
if opt.FT:
ocrModel.load_state_dict(torch.load(opt.saved_ocr_model), strict=False)
else:
ocrModel.load_state_dict(torch.load(opt.saved_ocr_model))
print("OCRModel:")
print(ocrModel)
if opt.saved_synth_model != '' and opt.saved_synth_model != 'None':
print(f'loading pretrained synth model from {opt.saved_synth_model}')
if opt.FT:
model.load_state_dict(torch.load(opt.saved_synth_model), strict=False)
else:
model.load_state_dict(torch.load(opt.saved_synth_model))
print("SynthModel:")
print(model)
if opt.saved_dis_model != '' and opt.saved_dis_model != 'None':
print(f'loading pretrained discriminator model from {opt.saved_dis_model}')
if opt.FT:
disModel.load_state_dict(torch.load(opt.saved_dis_model), strict=False)
else:
disModel.load_state_dict(torch.load(opt.saved_dis_model))
print("DisModel:")
print(disModel)
""" setup loss """
if 'CTC' in opt.Prediction:
ocrCriterion = torch.nn.CTCLoss(zero_infinity=True).to(device)
else:
ocrCriterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0
recCriterion = torch.nn.L1Loss()
styleRecCriterion = torch.nn.L1Loss()
# loss averager
loss_avg_ocr = Averager()
loss_avg = Averager()
loss_avg_dis = Averager()
loss_avg_ocrRecon_1 = Averager()
loss_avg_ocrRecon_2 = Averager()
loss_avg_gen = Averager()
loss_avg_imgRecon = Averager()
loss_avg_styRecon = Averager()
##---------------------------------------##
# filter that only require gradient decent
filtered_parameters = []
params_num = []
for p in filter(lambda p: p.requires_grad, model.parameters()):
filtered_parameters.append(p)
params_num.append(np.prod(p.size()))
print('Trainable params num : ', sum(params_num))
# [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())]
# setup optimizer
if opt.optim=='adam':
optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, opt.beta2), weight_decay=opt.weight_decay)
else:
optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps, weight_decay=opt.weight_decay)
print("SynthOptimizer:")
print(optimizer)
#filter parameters for OCR training
ocr_filtered_parameters = []
ocr_params_num = []
for p in filter(lambda p: p.requires_grad, ocrModel.parameters()):
ocr_filtered_parameters.append(p)
ocr_params_num.append(np.prod(p.size()))
print('OCR Trainable params num : ', sum(ocr_params_num))
# setup optimizer
if opt.optim=='adam':
ocr_optimizer = optim.Adam(ocr_filtered_parameters, lr=opt.lr, betas=(opt.beta1, opt.beta2), weight_decay=opt.weight_decay)
else:
ocr_optimizer = optim.Adadelta(ocr_filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps, weight_decay=opt.weight_decay)
print("OCROptimizer:")
print(ocr_optimizer)
#filter parameters for OCR training
dis_filtered_parameters = []
dis_params_num = []
for p in filter(lambda p: p.requires_grad, disModel.parameters()):
dis_filtered_parameters.append(p)
dis_params_num.append(np.prod(p.size()))
print('Dis Trainable params num : ', sum(dis_params_num))
# setup optimizer
if opt.optim=='adam':
dis_optimizer = optim.Adam(dis_filtered_parameters, lr=opt.lr, betas=(opt.beta1, opt.beta2), weight_decay=opt.weight_decay)
else:
dis_optimizer = optim.Adadelta(dis_filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps, weight_decay=opt.weight_decay)
print("DisOptimizer:")
print(dis_optimizer)
##---------------------------------------##
""" final options """
with open(os.path.join(opt.exp_dir,opt.exp_name,'opt.txt'), 'a') as opt_file:
opt_log = '------------ Options -------------\n'
args = vars(opt)
for k, v in args.items():
opt_log += f'{str(k)}: {str(v)}\n'
opt_log += '---------------------------------------\n'
print(opt_log)
opt_file.write(opt_log)
""" start training """
start_iter = 0
if opt.saved_synth_model != '' and opt.saved_synth_model != 'None':
try:
start_iter = int(opt.saved_synth_model.split('_')[-2].split('.')[0])
print(f'continue to train, start_iter: {start_iter}')
except:
pass
#get schedulers
scheduler = get_scheduler(optimizer,opt)
ocr_scheduler = get_scheduler(ocr_optimizer,opt)
dis_scheduler = get_scheduler(dis_optimizer,opt)
start_time = time.time()
best_accuracy = -1
best_norm_ED = -1
best_accuracy_ocr = -1
best_norm_ED_ocr = -1
iteration = start_iter
cntr=0
while(True):
# train part
if opt.lr_policy !="None":
scheduler.step()
ocr_scheduler.step()
dis_scheduler.step()
image_tensors_all, labels_1_all, labels_2_all = train_dataset.get_batch()
# ## comment
# pdb.set_trace()
# for imgCntr in range(image_tensors.shape[0]):
# save_image(tensor2im(image_tensors[imgCntr]),'temp/'+str(imgCntr)+'.png')
# pdb.set_trace()
# ###
# print(cntr)
cntr+=1
disCnt = int(image_tensors_all.size(0)/2)
image_tensors, image_tensors_real, labels_gt, labels_2 = image_tensors_all[:disCnt], image_tensors_all[disCnt:disCnt+disCnt], labels_1_all[:disCnt], labels_2_all[:disCnt]
image = image_tensors.to(device)
image_real = image_tensors_real.to(device)
batch_size = image.size(0)
##-----------------------------------##
#generate text(labels) from ocr.forward
if opt.ocrFixed:
# ocrModel.eval()
length_for_pred = torch.IntTensor([opt.batch_max_length] * batch_size).to(device)
text_for_pred = torch.LongTensor(batch_size, opt.batch_max_length + 1).fill_(0).to(device)
if 'CTC' in opt.Prediction:
preds = ocrModel(image, text_for_pred)
preds = preds[:, :text_for_loss.shape[1] - 1, :]
preds_size = torch.IntTensor([preds.size(1)] * batch_size)
_, preds_index = preds.max(2)
labels_1 = converter.decode(preds_index.data, preds_size.data)
else:
preds = ocrModel(image, text_for_pred, is_train=False)
_, preds_index = preds.max(2)
labels_1 = converter.decode(preds_index, length_for_pred)
for idx, pred in enumerate(labels_1):
pred_EOS = pred.find('[s]')
labels_1[idx] = pred[:pred_EOS] # prune after "end of sentence" token ([s])
# ocrModel.train()
else:
labels_1 = labels_gt
##-----------------------------------##
text_1, length_1 = converter.encode(labels_1, batch_max_length=opt.batch_max_length)
text_2, length_2 = converter.encode(labels_2, batch_max_length=opt.batch_max_length)
#forward pass from style and word generator
images_recon_1, images_recon_2, style = model(image, text_1, text_2)
if 'CTC' in opt.Prediction:
if not opt.ocrFixed:
#ocr training with orig image
preds_ocr = ocrModel(image, text_1)
preds_size_ocr = torch.IntTensor([preds_ocr.size(1)] * batch_size)
preds_ocr = preds_ocr.log_softmax(2).permute(1, 0, 2)
ocrCost_train = ocrCriterion(preds_ocr, text_1, preds_size_ocr, length_1)
#content loss for reconstructed images
preds_1 = ocrModel(images_recon_1, text_1)
preds_size_1 = torch.IntTensor([preds_1.size(1)] * batch_size)
preds_1 = preds_1.log_softmax(2).permute(1, 0, 2)
preds_2 = ocrModel(images_recon_2, text_2)
preds_size_2 = torch.IntTensor([preds_2.size(1)] * batch_size)
preds_2 = preds_2.log_softmax(2).permute(1, 0, 2)
ocrCost_1 = ocrCriterion(preds_1, text_1, preds_size_1, length_1)
ocrCost_2 = ocrCriterion(preds_2, text_2, preds_size_2, length_2)
# ocrCost = 0.5*( ocrCost_1 + ocrCost_2 )
else:
if not opt.ocrFixed:
#ocr training with orig image
preds_ocr = ocrModel(image, text_1[:, :-1]) # align with Attention.forward
target_ocr = text_1[:, 1:] # without [GO] Symbol
ocrCost_train = ocrCriterion(preds_ocr.view(-1, preds_ocr.shape[-1]), target_ocr.contiguous().view(-1))
#content loss for reconstructed images
preds_1 = ocrModel(images_recon_1, text_1[:, :-1], is_train=False) # align with Attention.forward
target_1 = text_1[:, 1:] # without [GO] Symbol
preds_2 = ocrModel(images_recon_2, text_2[:, :-1], is_train=False) # align with Attention.forward
target_2 = text_2[:, 1:] # without [GO] Symbol
ocrCost_1 = ocrCriterion(preds_1.view(-1, preds_1.shape[-1]), target_1.contiguous().view(-1))
ocrCost_2 = ocrCriterion(preds_2.view(-1, preds_2.shape[-1]), target_2.contiguous().view(-1))
# ocrCost = 0.5*(ocrCost_1+ocrCost_2)
if not opt.ocrFixed:
#training OCR
ocrModel.zero_grad()
ocrCost_train.backward()
# torch.nn.utils.clip_grad_norm_(ocrModel.parameters(), opt.grad_clip) # gradient clipping with 5 (Default)
ocr_optimizer.step()
#if ocr is fixed; ignore this loss
loss_avg_ocr.add(ocrCost_train)
else:
loss_avg_ocr.add(torch.tensor(0.0))
#Domain discriminator: Dis update
disModel.zero_grad()
disCost = opt.disWeight*0.5*(disModel.module.calc_dis_loss(images_recon_1.detach(), image_real) + disModel.module.calc_dis_loss(images_recon_2.detach(), image))
disCost.backward()
# torch.nn.utils.clip_grad_norm_(disModel.parameters(), opt.grad_clip) # gradient clipping with 5 (Default)
dis_optimizer.step()
loss_avg_dis.add(disCost)
# #[Style Encoder] + [Word Generator] update
#Adversarial loss
disGenCost = 0.5*(disModel.module.calc_gen_loss(images_recon_1)+disModel.module.calc_gen_loss(images_recon_2))
#Input reconstruction loss
recCost = recCriterion(images_recon_1,image)
#Pair style reconstruction loss
if opt.styleReconWeight == 0.0:
styleRecCost = torch.tensor(0.0)
else:
if opt.styleDetach:
styleRecCost = styleRecCriterion(model(images_recon_2, None, None, styleFlag=True), style.detach())
else:
styleRecCost = styleRecCriterion(model(images_recon_2, None, None, styleFlag=True), style)
#OCR Content cost
ocrCost = 0.5*(ocrCost_1+ocrCost_2)
cost = opt.ocrWeight*ocrCost + opt.reconWeight*recCost + opt.disWeight*disGenCost + opt.styleReconWeight*styleRecCost
model.zero_grad()
ocrModel.zero_grad()
disModel.zero_grad()
cost.backward()
# torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default)
optimizer.step()
loss_avg.add(cost)
#Individual losses
loss_avg_ocrRecon_1.add(opt.ocrWeight*0.5*ocrCost_1)
loss_avg_ocrRecon_2.add(opt.ocrWeight*0.5*ocrCost_2)
loss_avg_gen.add(opt.disWeight*disGenCost)
loss_avg_imgRecon.add(opt.reconWeight*recCost)
loss_avg_styRecon.add(opt.styleReconWeight*styleRecCost)
# validation part
if (iteration + 1) % opt.valInterval == 0 or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0'
#Save training images
os.makedirs(os.path.join(opt.exp_dir,opt.exp_name,'trainImages',str(iteration)), exist_ok=True)
for trImgCntr in range(batch_size):
try:
save_image(tensor2im(image[trImgCntr].detach()),os.path.join(opt.exp_dir,opt.exp_name,'trainImages',str(iteration),str(trImgCntr)+'_input_'+labels_gt[trImgCntr]+'.png'))
save_image(tensor2im(images_recon_1[trImgCntr].detach()),os.path.join(opt.exp_dir,opt.exp_name,'trainImages',str(iteration),str(trImgCntr)+'_recon_'+labels_1[trImgCntr]+'.png'))
save_image(tensor2im(images_recon_2[trImgCntr].detach()),os.path.join(opt.exp_dir,opt.exp_name,'trainImages',str(iteration),str(trImgCntr)+'_pair_'+labels_2[trImgCntr]+'.png'))
except:
print('Warning while saving training image')
elapsed_time = time.time() - start_time
# for log
with open(os.path.join(opt.exp_dir,opt.exp_name,'log_train.txt'), 'a') as log:
model.eval()
ocrModel.module.Transformation.eval()
ocrModel.module.FeatureExtraction.eval()
ocrModel.module.AdaptiveAvgPool.eval()
ocrModel.module.SequenceModeling.eval()
ocrModel.module.Prediction.eval()
disModel.eval()
with torch.no_grad():
valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation_synth_lrw_res(
iteration, model, ocrModel, disModel, recCriterion, styleRecCriterion, ocrCriterion, valid_loader, converter, opt)
model.train()
if not opt.ocrFixed:
ocrModel.train()
else:
# ocrModel.module.Transformation.eval()
# ocrModel.module.FeatureExtraction.eval()
# ocrModel.module.AdaptiveAvgPool.eval()
ocrModel.module.SequenceModeling.train()
# ocrModel.module.Prediction.eval()
disModel.train()
# training loss and validation loss
loss_log = f'[{iteration+1}/{opt.num_iter}] Train OCR loss: {loss_avg_ocr.val():0.5f}, Train Synth loss: {loss_avg.val():0.5f}, Train Dis loss: {loss_avg_dis.val():0.5f}, Valid OCR loss: {valid_loss[0]:0.5f}, Valid Synth loss: {valid_loss[1]:0.5f}, Valid Dis loss: {valid_loss[2]:0.5f}, Elapsed_time: {elapsed_time:0.5f}'
current_model_log_ocr = f'{"Current_accuracy_OCR":17s}: {current_accuracy[0]:0.3f}, {"Current_norm_ED_OCR":17s}: {current_norm_ED[0]:0.2f}'
current_model_log_1 = f'{"Current_accuracy_recon":17s}: {current_accuracy[1]:0.3f}, {"Current_norm_ED_recon":17s}: {current_norm_ED[1]:0.2f}'
current_model_log_2 = f'{"Current_accuracy_pair":17s}: {current_accuracy[2]:0.3f}, {"Current_norm_ED_pair":17s}: {current_norm_ED[2]:0.2f}'
#plotting
lib.plot.plot(os.path.join(plotDir,'Train-OCR-Loss'), loss_avg_ocr.val().item())
lib.plot.plot(os.path.join(plotDir,'Train-Synth-Loss'), loss_avg.val().item())
lib.plot.plot(os.path.join(plotDir,'Train-Dis-Loss'), loss_avg_dis.val().item())
lib.plot.plot(os.path.join(plotDir,'Train-OCR-Recon1-Loss'), loss_avg_ocrRecon_1.val().item())
lib.plot.plot(os.path.join(plotDir,'Train-OCR-Recon2-Loss'), loss_avg_ocrRecon_2.val().item())
lib.plot.plot(os.path.join(plotDir,'Train-Gen-Loss'), loss_avg_gen.val().item())
lib.plot.plot(os.path.join(plotDir,'Train-ImgRecon1-Loss'), loss_avg_imgRecon.val().item())
lib.plot.plot(os.path.join(plotDir,'Train-StyRecon2-Loss'), loss_avg_styRecon.val().item())
lib.plot.plot(os.path.join(plotDir,'Valid-OCR-Loss'), valid_loss[0].item())
lib.plot.plot(os.path.join(plotDir,'Valid-Synth-Loss'), valid_loss[1].item())
lib.plot.plot(os.path.join(plotDir,'Valid-Dis-Loss'), valid_loss[2].item())
lib.plot.plot(os.path.join(plotDir,'Valid-OCR-Recon1-Loss'), valid_loss[3].item())
lib.plot.plot(os.path.join(plotDir,'Valid-OCR-Recon2-Loss'), valid_loss[4].item())
lib.plot.plot(os.path.join(plotDir,'Valid-Gen-Loss'), valid_loss[5].item())
lib.plot.plot(os.path.join(plotDir,'Valid-ImgRecon1-Loss'), valid_loss[6].item())
lib.plot.plot(os.path.join(plotDir,'Valid-StyRecon2-Loss'), valid_loss[7].item())
lib.plot.plot(os.path.join(plotDir,'Orig-OCR-WordAccuracy'), current_accuracy[0])
lib.plot.plot(os.path.join(plotDir,'Recon-OCR-WordAccuracy'), current_accuracy[1])
lib.plot.plot(os.path.join(plotDir,'Pair-OCR-WordAccuracy'), current_accuracy[2])
lib.plot.plot(os.path.join(plotDir,'Orig-OCR-CharAccuracy'), current_norm_ED[0])
lib.plot.plot(os.path.join(plotDir,'Recon-OCR-CharAccuracy'), current_norm_ED[1])
lib.plot.plot(os.path.join(plotDir,'Pair-OCR-CharAccuracy'), current_norm_ED[2])
# keep best accuracy model (on valid dataset)
if current_accuracy[1] > best_accuracy:
best_accuracy = current_accuracy[1]
torch.save(model.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_accuracy.pth'))
torch.save(disModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_accuracy_dis.pth'))
if current_norm_ED[1] > best_norm_ED:
best_norm_ED = current_norm_ED[1]
torch.save(model.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_norm_ED.pth'))
torch.save(disModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_norm_ED_dis.pth'))
best_model_log = f'{"Best_accuracy_Recon":17s}: {best_accuracy:0.3f}, {"Best_norm_ED_Recon":17s}: {best_norm_ED:0.2f}'
# keep best accuracy model (on valid dataset)
if current_accuracy[0] > best_accuracy_ocr:
best_accuracy_ocr = current_accuracy[0]
if not opt.ocrFixed:
torch.save(ocrModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_accuracy_ocr.pth'))
if current_norm_ED[0] > best_norm_ED_ocr:
best_norm_ED_ocr = current_norm_ED[0]
if not opt.ocrFixed:
torch.save(ocrModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'best_norm_ED_ocr.pth'))
best_model_log_ocr = f'{"Best_accuracy_ocr":17s}: {best_accuracy_ocr:0.3f}, {"Best_norm_ED_ocr":17s}: {best_norm_ED_ocr:0.2f}'
loss_model_log = f'{loss_log}\n{current_model_log_ocr}\n{current_model_log_1}\n{current_model_log_2}\n{best_model_log_ocr}\n{best_model_log}'
print(loss_model_log)
log.write(loss_model_log + '\n')
# show some predicted results
dashed_line = '-' * 80
head = f'{"Ground Truth":32s} | {"Prediction":25s} | Confidence Score & T/F'
predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n'
for gt_ocr, pred_ocr, confidence_ocr, gt_1, pred_1, confidence_1, gt_2, pred_2, confidence_2 in zip(labels[0][:5], preds[0][:5], confidence_score[0][:5], labels[1][:5], preds[1][:5], confidence_score[1][:5], labels[2][:5], preds[2][:5], confidence_score[2][:5]):
if 'Attn' in opt.Prediction:
# gt_ocr = gt_ocr[:gt_ocr.find('[s]')]
pred_ocr = pred_ocr[:pred_ocr.find('[s]')]
# gt_1 = gt_1[:gt_1.find('[s]')]
pred_1 = pred_1[:pred_1.find('[s]')]
# gt_2 = gt_2[:gt_2.find('[s]')]
pred_2 = pred_2[:pred_2.find('[s]')]
predicted_result_log += f'{"ocr"}: {gt_ocr:27s} | {pred_ocr:25s} | {confidence_ocr:0.4f}\t{str(pred_ocr == gt_ocr)}\n'
predicted_result_log += f'{"recon"}: {gt_1:25s} | {pred_1:25s} | {confidence_1:0.4f}\t{str(pred_1 == gt_1)}\n'
predicted_result_log += f'{"pair"}: {gt_2:26s} | {pred_2:25s} | {confidence_2:0.4f}\t{str(pred_2 == gt_2)}\n'
predicted_result_log += f'{dashed_line}'
print(predicted_result_log)
log.write(predicted_result_log + '\n')
loss_avg_ocr.reset()
loss_avg.reset()
loss_avg_dis.reset()
loss_avg_ocrRecon_1.reset()
loss_avg_ocrRecon_2.reset()
loss_avg_gen.reset()
loss_avg_imgRecon.reset()
loss_avg_styRecon.reset()
lib.plot.flush()
lib.plot.tick()
# save model per 1e+5 iter.
if (iteration) % 1e+5 == 0:
torch.save(
model.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'iter_'+str(iteration+1)+'_synth.pth'))
if not opt.ocrFixed:
torch.save(
ocrModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'iter_'+str(iteration+1)+'_ocr.pth'))
torch.save(
disModel.state_dict(), os.path.join(opt.exp_dir,opt.exp_name,'iter_'+str(iteration+1)+'_dis.pth'))
if (iteration + 1) == opt.num_iter:
print('end the training')
sys.exit()
iteration += 1
def train(opt):
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()
set_gpu(args.gpu)
dataset = MiniImageNet('test')
sampler = CategoriesSampler(dataset.label, args.batch, args.way,
args.shot + args.query)
loader = DataLoader(dataset,
batch_sampler=sampler,
num_workers=8,
pin_memory=True)
model = Convnet().cuda()
model.load_state_dict(torch.load(args.load))
model.eval()
ave_acc = Averager()
for i, batch in enumerate(loader, 1):
# data, _ = [_.cuda() for _ in batch]
data, mask, bg, _ = [_.cuda() for _ in batch]
p = args.shot * args.test_way
data_spt, data_query = data[:p], data[p:]
mask_spt = mask[:p]
mask_spt[mask_spt != 0] = 1
N, c, w, h = data.size()
data_spt_repeat = data_spt.unsqueeze(0).repeat(N, 1, 1, 1, 1)
mask_spt_repeat = mask_spt.unsqueeze(0).repeat(N, 1, 1, 1, 1)
bg_repeat = bg.unsqueeze(0).repeat(p, 1, 1, 1,
1).permute(1, 0, 2, 3, 4)
merge_spt = data_spt_repeat * mask_spt_repeat.float() + bg_repeat * (
1 - mask_spt_repeat.float())
trlog = {}
trlog['args'] = vars(args)
trlog['train_loss'] = []
trlog['val_loss'] = []
trlog['train_acc'] = []
trlog['val_acc'] = []
trlog['max_acc'] = 0.0
timer = Timer()
for epoch in range(1, max_epoch + 1):
lr_scheduler.step()
model.train()
tl = Averager()
ta = Averager()
for i, batch in enumerate(train_loader, 1):
data, _ = [_ for _ in batch]
p = shot * train_way
data_shot, data_query = data[:p], data[p:]
proto = model(data_shot)
proto = proto.reshape(shot, train_way, -1).mean(dim=0)
label = torch.arange(train_way).repeat(2)
label = label.type(torch.LongTensor)
logits = euclidean_metric(model(data_query), proto)
loss = F.cross_entropy(logits, label)
distance = distance_metric(args.distance, model)
# optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
#lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
steplr_after = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=len(train_loader) * args.epochs, eta_min=1e-5)
lr_scheduler = WarmupScheduler(optimizer,
multiplier=1,
total_epoch=args.warmup_epochs *
len(train_loader),
after_scheduler=steplr_after)
best_acc = 0
train_loss, train_acc, valid_loss, valid_acc = [
Averager() for i in range(4)
]
for epoch in range(1, args.epochs + 1):
############## Train ##############
model.train()
for i, batch in enumerate(train_loader):
data, _ = [d.to(args.device) for d in batch]
p = args.shot * args.train_way
data_support, data_query = data[:p], data[
p:] #(shot*way) #(query*way)
proto = model(data_support)
trlog['train_loss'] = []
trlog['val_loss'] = []
trlog['train_acc'] = []
trlog['val_acc'] = []
trlog['max_acc'] = 0.0
trlog['max_acc_epoch'] = 0
timer = Timer()
global_count = 0
writer = SummaryWriter(comment=args.save_path)
for epoch in range(1, args.max_epoch + 1):
if args.lr_decay:
lr_scheduler.step()
model.train()
tl = Averager()
ta = Averager()
label = torch.arange(args.way).repeat(args.query)
if torch.cuda.is_available():
label = label.type(torch.cuda.LongTensor)
else:
label = label.type(torch.LongTensor)
for i, batch in enumerate(train_loader, 1):
global_count = global_count + 1
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
p = args.shot * args.way
def train():
""" dataset preparation """
train_dataset_lmdb = LmdbDataset(cfg.lmdb_trainset_dir_name)
val_dataset_lmdb = LmdbDataset(cfg.lmdb_valset_dir_name)
train_loader = torch.utils.data.DataLoader(
train_dataset_lmdb, batch_size=cfg.batch_size,
collate_fn=data_collate,
shuffle=True,
num_workers=int(cfg.workers),
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(
val_dataset_lmdb, batch_size=cfg.batch_size,
collate_fn=data_collate,
shuffle=True, # 'True' to check training progress with validation function.
num_workers=int(cfg.workers),
pin_memory=True)
# --------------------训练过程---------------------------------
model = advancedEAST()
if int(cfg.train_task_id[-3:]) != 256:
id_num = cfg.train_task_id[-3:]
idx_dic = {'384': 256, '512': 384, '640': 512, '736': 640}
model.load_state_dict(torch.load('./saved_model/3T{}_best_loss.pth'.format(idx_dic[id_num])))
elif os.path.exists('./saved_model/3T{}_best_loss.pth'.format(cfg.train_task_id)):
model.load_state_dict(torch.load('./saved_model/3T{}_best_loss.pth'.format(cfg.train_task_id)))
model = model.to(device)
optimizer = optim.Adam(model.parameters(), lr=cfg.lr, weight_decay=cfg.decay)
loss_func = quad_loss
train_Loss_list = []
val_Loss_list = []
'''start training'''
start_iter = 0
if cfg.saved_model != '':
try:
start_iter = int(cfg.saved_model.split('_')[-1].split('.')[0])
print('continue to train, start_iter: {}'.format(start_iter))
except Exception as e:
print(e)
pass
start_time = time.time()
best_mF1_score = 0
i = start_iter
step_num = 0
start_time = time.time()
loss_avg = Averager()
val_loss_avg = Averager()
eval_p_r_f = eval_pre_rec_f1()
while(True):
model.train()
# train part
# training-----------------------------
for image_tensors, labels, gt_xy_list in train_loader:
step_num += 1
batch_x = image_tensors.to(device).float()
batch_y = labels.to(device).float() # float64转float32
out = model(batch_x)
loss = loss_func(batch_y, out)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_avg.add(loss)
train_Loss_list.append(loss_avg.val())
if i == 5 or (i + 1) % 10 == 0:
eval_p_r_f.add(out, gt_xy_list) # 非常耗时！！！
# save model per 100 epochs.
if (i + 1) % 1e+2 == 0:
torch.save(model.state_dict(), './saved_models/{}/{}_iter_{}.pth'.format(cfg.train_task_id, cfg.train_task_id, step_num+1))
print('Epoch:[{}/{}] Training Loss: {:.3f}'.format(i + 1, cfg.epoch_num, train_Loss_list[-1].item()))
loss_avg.reset()
if i == 5 or (i + 1) % 10 == 0:
mPre, mRec, mF1_score = eval_p_r_f.val()
print('Training meanPrecision:{:.2f}% meanRecall:{:.2f}% meanF1-score:{:.2f}%'.format(mPre, mRec, mF1_score))
eval_p_r_f.reset()
# evaluation--------------------------------
if (i + 1) % cfg.valInterval == 0:
elapsed_time = time.time() - start_time
print('Elapsed time:{}s'.format(round(elapsed_time)))
model.eval()
for image_tensors, labels, gt_xy_list in valid_loader:
batch_x = image_tensors.to(device)
batch_y = labels.to(device).float() # float64转float32
out = model(batch_x)
loss = loss_func(batch_y, out)
val_loss_avg.add(loss)
val_Loss_list.append(val_loss_avg.val())
eval_p_r_f.add(out, gt_xy_list)
mPre, mRec, mF1_score = eval_p_r_f.val()
print('validation meanPrecision:{:.2f}% meanRecall:{:.2f}% meanF1-score:{:.2f}%'.format(mPre, mRec, mF1_score))
eval_p_r_f.reset()
if mF1_score > best_mF1_score: # 记录最佳模型
best_mF1_score = mF1_score
torch.save(model.state_dict(), './saved_models/{}/{}_best_mF1_score_{:.3f}.pth'.format(cfg.train_task_id, cfg.train_task_id, mF1_score))
torch.save(model.state_dict(), './saved_model/{}_best_mF1_score.pth'.format(cfg.train_task_id))
print('Validation loss:{:.3f}'.format(val_loss_avg.val().item()))
val_loss_avg.reset()
if i == cfg.epoch_num:
torch.save(model.state_dict(), './saved_models/{}/{}_iter_{}.pth'.format(cfg.train_task_id, cfg.train_task_id, i+1))
print('End the training')
break
i += 1
sys.exit()
def train(opt, tb):
""" dataset preparation """
if not opt.data_filtering_off:
print('Filtering the images containing characters which are not in opt.character')
print('Filtering the images whose label is longer than opt.batch_max_length')
# see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130
opt.select_data = opt.select_data.split('-')
opt.batch_ratio = opt.batch_ratio.split('-')
train_dataset = Batch_Balanced_Dataset(opt)
log = open(f'./saved_models/{opt.experiment_name}/log_dataset.txt', 'a')
AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD)
valid_dataset, valid_dataset_log = hierarchical_dataset(root=opt.valid_data, opt=opt)
valid_loader = torch.utils.data.DataLoader(
valid_dataset, batch_size=opt.batch_size,
shuffle=True, # 'True' to check training progress with validation function.
num_workers=int(opt.workers),
collate_fn=AlignCollate_valid, pin_memory=True)
log.write(valid_dataset_log)
print('-' * 80)
log.write('-' * 80 + '\n')
log.close()
""" model configuration """
if 'CTC' in opt.Prediction:
converter = CTCLabelConverter(opt.character)
else:
converter = AttnLabelConverter(opt.character)
opt.num_class = len(converter.character)
if opt.rgb:
opt.input_channel = 3
model = Model(opt)
print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel,
opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction,
opt.SequenceModeling, opt.Prediction)
# weight initialization
for name, param in model.named_parameters():
if 'localization_fc2' in name:
print(f'Skip {name} as it is already initialized')
continue
try:
if 'bias' in name:
init.constant_(param, 0.0)
elif 'weight' in name:
init.kaiming_normal_(param)
except Exception as e: # for batchnorm.
if 'weight' in name:
param.data.fill_(1)
continue
# data parallel for multi-GPU
model = torch.nn.DataParallel(model).to(device)
model.train()
if opt.saved_model != '':
print(f'loading pretrained model from {opt.saved_model}')
if opt.FT:
model.load_state_dict(torch.load(opt.saved_model), strict=False)
else:
model.load_state_dict(torch.load(opt.saved_model))
print("Model:")
print(model)
""" setup loss """
if 'CTC' in opt.Prediction:
criterion = torch.nn.CTCLoss(zero_infinity=True).to(device)
else:
criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0
# loss averager
loss_avg = Averager()
# filter that only require gradient decent
filtered_parameters = []
params_num = []
print("~~~~~~~~~~~~Gradient Descent~~~~~~~~~~~~~")
#print(model.parameters())
#print(model.)
for p in filter(lambda p: p.requires_grad, model.parameters()):
filtered_parameters.append(p)
params_num.append(np.prod(p.size()))
print('Filtered parameters for gradient descent: \n', len(filtered_parameters))
print('Trainable params num : ', sum(params_num))
# [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())]
# setup optimizer
if opt.adam:
optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999))
else:
optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps)
print("Optimizer:")
print(optimizer)
""" final options """
# print(opt)
with open(f'./saved_models/{opt.experiment_name}/opt.txt', 'a') as opt_file:
opt_log = '------------ Options -------------\n'
args = vars(opt)
for k, v in args.items():
opt_log += f'{str(k)}: {str(v)}\n'
opt_log += '---------------------------------------\n'
print(opt_log)
opt_file.write(opt_log)
""" start training """
start_iter = 0
if opt.saved_model != '':
try:
start_iter = int(opt.saved_model.split('_')[-1].split('.')[0])
print(f'continue to train, start_iter: {start_iter}')
except:
pass
start_time = time.time()
best_accuracy = -1
best_norm_ED = -1
i = start_iter
while(True):
# train part
image_tensors, labels = train_dataset.get_batch()
image = image_tensors.to(device)
text, length = converter.encode(labels, batch_max_length=opt.batch_max_length)
batch_size = image.size(0)
if 'CTC' in opt.Prediction:
preds = model(image, text).log_softmax(2)
preds_size = torch.IntTensor([preds.size(1)] * batch_size)
preds = preds.permute(1, 0, 2)
# (ctc_a) For PyTorch 1.2.0 and 1.3.0. To avoid ctc_loss issue, disabled cudnn for the computation of the ctc_loss
# https://github.com/jpuigcerver/PyLaia/issues/16
torch.backends.cudnn.enabled = False
cost = criterion(preds, text.to(device), preds_size.to(device), length.to(device))
torch.backends.cudnn.enabled = True
# # (ctc_b) To reproduce our pretrained model / paper, use our previous code (below code) instead of (ctc_a).
# # With PyTorch 1.2.0, the below code occurs NAN, so you may use PyTorch 1.1.0.
# # Thus, the result of CTCLoss is different in PyTorch 1.1.0 and PyTorch 1.2.0.
# # See https://github.com/clovaai/deep-text-recognition-benchmark/issues/56#issuecomment-526490707
# cost = criterion(preds, text, preds_size, length)
else:
preds = model(image, text[:, :-1]) # align with Attention.forward
print(preds[0][0])
target = text[:, 1:] # without [GO] Symbol
print(target[0])
cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1))
model.zero_grad()
cost.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default)
optimizer.step()
loss_avg.add(cost)
# validation part
if i % opt.valInterval == 0:
elapsed_time = time.time() - start_time
# for log
with open(f'./saved_models/{opt.experiment_name}/log_train.txt', 'a') as log:
model.eval()
with torch.no_grad():
valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels, infer_time, length_of_data = validation(
model, criterion, valid_loader, converter, opt)
model.train()
# training loss and validation loss
loss_log = f'[{i}/{opt.num_iter}] Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss:0.5f}, Elapsed_time: {elapsed_time:0.5f}'
tb.add_scalar('Training Loss vs Iteration', loss_avg.val(), i) # Record to Tensorboard
tb.add_scalar('Validation Loss vs Iteration', valid_loss, i) # Record to Tensorboard
loss_avg.reset()
current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"Current_norm_ED":17s}: {current_norm_ED:0.2f}'
tb.add_scalar('Current Accuracy vs Iteration', current_accuracy, i) # Record to Tensorboard
tb.add_scalar('Current Norm ED vs Iteration', current_norm_ED, i) # Record to Tensorboard
# keep best accuracy model (on valid dataset)
if current_accuracy > best_accuracy:
best_accuracy = current_accuracy
torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/best_accuracy.pth')
if current_norm_ED > best_norm_ED:
best_norm_ED = current_norm_ED
torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/best_norm_ED.pth')
best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}'
loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}'
print(loss_model_log)
log.write(loss_model_log + '\n')
# show some predicted results
dashed_line = '-' * 80
head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F'
predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n'
for gt, pred, confidence in zip(labels[:5], preds[:5], confidence_score[:5]):
if 'Attn' in opt.Prediction:
gt = gt[:gt.find('[s]')]
pred = pred[:pred.find('[s]')]
predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\t{str(pred == gt)}\n'
predicted_result_log += f'{dashed_line}'
print(predicted_result_log)
log.write(predicted_result_log + '\n')
# save model per 1e+5 iter.
if (i + 1) % 1e+5 == 0:
torch.save(
model.state_dict(), f'./saved_models/{opt.experiment_name}/iter_{i+1}.pth')
if i == opt.num_iter:
print('end the training')
sys.exit()
i += 1
def train(opt):
lib.print_model_settings(locals().copy())
if 'Attn' in opt.Prediction:
converter = AttnLabelConverter(opt.character)
text_len = opt.batch_max_length+2
else:
converter = CTCLabelConverter(opt.character)
text_len = opt.batch_max_length
opt.classes = converter.character
""" dataset preparation """
if not opt.data_filtering_off:
print('Filtering the images containing characters which are not in opt.character')
print('Filtering the images whose label is longer than opt.batch_max_length')
# see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130
log = open(os.path.join(opt.exp_dir,opt.exp_name,'log_dataset.txt'), 'a')
AlignCollate_valid = AlignPairCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD)
train_dataset = LmdbStyleDataset(root=opt.train_data, opt=opt)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=opt.batch_size*2, #*2 to sample different images from training encoder and discriminator real images
shuffle=True, # 'True' to check training progress with validation function.
num_workers=int(opt.workers),
collate_fn=AlignCollate_valid, pin_memory=True, drop_last=True)
print('-' * 80)
valid_dataset = LmdbStyleDataset(root=opt.valid_data, opt=opt)
valid_loader = torch.utils.data.DataLoader(
valid_dataset, batch_size=opt.batch_size*2, #*2 to sample different images from training encoder and discriminator real images
shuffle=False, # 'True' to check training progress with validation function.
num_workers=int(opt.workers),
collate_fn=AlignCollate_valid, pin_memory=True, drop_last=True)
print('-' * 80)
log.write('-' * 80 + '\n')
log.close()
text_dataset = text_gen(opt)
text_loader = torch.utils.data.DataLoader(
text_dataset, batch_size=opt.batch_size,
shuffle=True,
num_workers=int(opt.workers),
pin_memory=True, drop_last=True)
opt.num_class = len(converter.character)
c_code_size = opt.latent
cEncoder = GlobalContentEncoder(opt.num_class, text_len, opt.char_embed_size, c_code_size)
ocrModel = ModelV1(opt)
genModel = styleGANGen(opt.size, opt.latent, opt.latent, opt.n_mlp, channel_multiplier=opt.channel_multiplier)
g_ema = styleGANGen(opt.size, opt.latent, opt.latent, opt.n_mlp, channel_multiplier=opt.channel_multiplier)
disEncModel = styleGANDis(opt.size, channel_multiplier=opt.channel_multiplier, input_dim=opt.input_channel, code_s_dim=c_code_size)
accumulate(g_ema, genModel, 0)
# uCriterion = torch.nn.MSELoss()
# sCriterion = torch.nn.MSELoss()
# if opt.contentLoss == 'vis' or opt.contentLoss == 'seq':
# ocrCriterion = torch.nn.L1Loss()
# else:
if 'CTC' in opt.Prediction:
ocrCriterion = torch.nn.CTCLoss(zero_infinity=True).to(device)
else:
print('Not implemented error')
sys.exit()
# ocrCriterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0
cEncoder= torch.nn.DataParallel(cEncoder).to(device)
cEncoder.train()
genModel = torch.nn.DataParallel(genModel).to(device)
g_ema = torch.nn.DataParallel(g_ema).to(device)
genModel.train()
g_ema.eval()
disEncModel = torch.nn.DataParallel(disEncModel).to(device)
disEncModel.train()
ocrModel = torch.nn.DataParallel(ocrModel).to(device)
if opt.ocrFixed:
if opt.Transformation == 'TPS':
ocrModel.module.Transformation.eval()
ocrModel.module.FeatureExtraction.eval()
ocrModel.module.AdaptiveAvgPool.eval()
# ocrModel.module.SequenceModeling.eval()
ocrModel.module.Prediction.eval()
else:
ocrModel.train()
g_reg_ratio = opt.g_reg_every / (opt.g_reg_every + 1)
d_reg_ratio = opt.d_reg_every / (opt.d_reg_every + 1)
optimizer = optim.Adam(
list(genModel.parameters())+list(cEncoder.parameters()),
lr=opt.lr * g_reg_ratio,
betas=(0 ** g_reg_ratio, 0.99 ** g_reg_ratio),
)
dis_optimizer = optim.Adam(
disEncModel.parameters(),
lr=opt.lr * d_reg_ratio,
betas=(0 ** d_reg_ratio, 0.99 ** d_reg_ratio),
)
ocr_optimizer = optim.Adam(
ocrModel.parameters(),
lr=opt.lr,
betas=(0.9, 0.99),
)
## Loading pre-trained files
if opt.modelFolderFlag:
if len(glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_synth.pth")))>0:
opt.saved_synth_model = glob.glob(os.path.join(opt.exp_dir,opt.exp_name,"iter_*_synth.pth"))[-1]
if opt.saved_ocr_model !='' and opt.saved_ocr_model !='None':
print(f'loading pretrained ocr model from {opt.saved_ocr_model}')
checkpoint = torch.load(opt.saved_ocr_model)
ocrModel.load_state_dict(checkpoint)
# if opt.saved_gen_model !='' and opt.saved_gen_model !='None':
# print(f'loading pretrained gen model from {opt.saved_gen_model}')
# checkpoint = torch.load(opt.saved_gen_model, map_location=lambda storage, loc: storage)
# genModel.module.load_state_dict(checkpoint['g'])
# g_ema.module.load_state_dict(checkpoint['g_ema'])
if opt.saved_synth_model != '' and opt.saved_synth_model != 'None':
print(f'loading pretrained synth model from {opt.saved_synth_model}')
checkpoint = torch.load(opt.saved_synth_model)
# styleModel.load_state_dict(checkpoint['styleModel'])
# mixModel.load_state_dict(checkpoint['mixModel'])
genModel.load_state_dict(checkpoint['genModel'])
g_ema.load_state_dict(checkpoint['g_ema'])
disEncModel.load_state_dict(checkpoint['disEncModel'])
ocrModel.load_state_dict(checkpoint['ocrModel'])
optimizer.load_state_dict(checkpoint["optimizer"])
dis_optimizer.load_state_dict(checkpoint["dis_optimizer"])
ocr_optimizer.load_state_dict(checkpoint["ocr_optimizer"])
# if opt.imgReconLoss == 'l1':
# recCriterion = torch.nn.L1Loss()
# elif opt.imgReconLoss == 'ssim':
# recCriterion = ssim
# elif opt.imgReconLoss == 'ms-ssim':
# recCriterion = msssim
# loss averager
loss_avg_dis = Averager()
loss_avg_gen = Averager()
loss_avg_unsup = Averager()
loss_avg_sup = Averager()
log_r1_val = Averager()
log_avg_path_loss_val = Averager()
log_avg_mean_path_length_avg = Averager()
log_ada_aug_p = Averager()
loss_avg_ocr_sup = Averager()
loss_avg_ocr_unsup = Averager()
""" final options """
with open(os.path.join(opt.exp_dir,opt.exp_name,'opt.txt'), 'a') as opt_file:
opt_log = '------------ Options -------------\n'
args = vars(opt)
for k, v in args.items():
opt_log += f'{str(k)}: {str(v)}\n'
opt_log += '---------------------------------------\n'
print(opt_log)
opt_file.write(opt_log)
""" start training """
start_iter = 0
if opt.saved_synth_model != '' and opt.saved_synth_model != 'None':
try:
start_iter = int(opt.saved_synth_model.split('_')[-2].split('.')[0])
print(f'continue to train, start_iter: {start_iter}')
except:
pass
#get schedulers
scheduler = get_scheduler(optimizer,opt)
dis_scheduler = get_scheduler(dis_optimizer,opt)
ocr_scheduler = get_scheduler(ocr_optimizer,opt)
start_time = time.time()
iteration = start_iter
cntr=0
mean_path_length = 0
d_loss_val = 0
r1_loss = torch.tensor(0.0, device=device)
g_loss_val = 0
path_loss = torch.tensor(0.0, device=device)
path_lengths = torch.tensor(0.0, device=device)
mean_path_length_avg = 0
# loss_dict = {}
accum = 0.5 ** (32 / (10 * 1000))
ada_augment = torch.tensor([0.0, 0.0], device=device)
ada_aug_p = opt.augment_p if opt.augment_p > 0 else 0.0
ada_aug_step = opt.ada_target / opt.ada_length
r_t_stat = 0
epsilon = 10e-50
# sample_z = torch.randn(opt.n_sample, opt.latent, device=device)
while(True):
# print(cntr)
# train part
if opt.lr_policy !="None":
scheduler.step()
dis_scheduler.step()
ocr_scheduler.step()
image_input_tensors, _, labels, _ = iter(train_loader).next()
labels_z_c = iter(text_loader).next()
image_input_tensors = image_input_tensors.to(device)
gt_image_tensors = image_input_tensors[:opt.batch_size].detach()
real_image_tensors = image_input_tensors[opt.batch_size:].detach()
labels_gt = labels[:opt.batch_size]
requires_grad(cEncoder, False)
requires_grad(genModel, False)
requires_grad(disEncModel, True)
requires_grad(ocrModel, False)
text_z_c, length_z_c = converter.encode(labels_z_c, batch_max_length=opt.batch_max_length)
text_gt, length_gt = converter.encode(labels_gt, batch_max_length=opt.batch_max_length)
z_c_code = cEncoder(text_z_c)
noise_style = mixing_noise_style(opt.batch_size, opt.latent, opt.mixing, device)
style=[]
style.append(noise_style[0]*z_c_code)
if len(noise_style)>1:
style.append(noise_style[1]*z_c_code)
if opt.zAlone:
#to validate orig style gan results
newstyle = []
newstyle.append(style[0][:,:opt.latent])
if len(style)>1:
newstyle.append(style[1][:,:opt.latent])
style = newstyle
fake_img,_ = genModel(style, input_is_latent=opt.input_latent)
# #unsupervised code prediction on generated image
# u_pred_code = disEncModel(fake_img, mode='enc')
# uCost = uCriterion(u_pred_code, z_code)
# #supervised code prediction on gt image
# s_pred_code = disEncModel(gt_image_tensors, mode='enc')
# sCost = uCriterion(s_pred_code, gt_phoc_tensors)
#Domain discriminator
fake_pred = disEncModel(fake_img)
real_pred = disEncModel(real_image_tensors)
disCost = d_logistic_loss(real_pred, fake_pred)
# dis_cost = disCost + opt.gamma_e*uCost + opt.beta*sCost
loss_avg_dis.add(disCost)
# loss_avg_sup.add(opt.beta*sCost)
# loss_avg_unsup.add(opt.gamma_e * uCost)
disEncModel.zero_grad()
disCost.backward()
dis_optimizer.step()
d_regularize = cntr % opt.d_reg_every == 0
if d_regularize:
real_image_tensors.requires_grad = True
real_pred = disEncModel(real_image_tensors)
r1_loss = d_r1_loss(real_pred, real_image_tensors)
disEncModel.zero_grad()
(opt.r1 / 2 * r1_loss * opt.d_reg_every + 0 * real_pred[0]).backward()
dis_optimizer.step()
log_r1_val.add(r1_loss)
# Recognizer update
if not opt.ocrFixed and not opt.zAlone:
requires_grad(disEncModel, False)
requires_grad(ocrModel, True)
if 'CTC' in opt.Prediction:
preds_recon = ocrModel(gt_image_tensors, text_gt, is_train=True)
preds_size = torch.IntTensor([preds_recon.size(1)] * opt.batch_size)
preds_recon_softmax = preds_recon.log_softmax(2).permute(1, 0, 2)
ocrCost = ocrCriterion(preds_recon_softmax, text_gt, preds_size, length_gt)
else:
print("Not implemented error")
sys.exit()
ocrModel.zero_grad()
ocrCost.backward()
# torch.nn.utils.clip_grad_norm_(ocrModel.parameters(), opt.grad_clip) # gradient clipping with 5 (Default)
ocr_optimizer.step()
loss_avg_ocr_sup.add(ocrCost)
else:
loss_avg_ocr_sup.add(torch.tensor(0.0))
# [Word Generator] update
# image_input_tensors, _, labels, _ = iter(train_loader).next()
labels_z_c = iter(text_loader).next()
# image_input_tensors = image_input_tensors.to(device)
# gt_image_tensors = image_input_tensors[:opt.batch_size]
# real_image_tensors = image_input_tensors[opt.batch_size:]
# labels_gt = labels[:opt.batch_size]
requires_grad(cEncoder, True)
requires_grad(genModel, True)
requires_grad(disEncModel, False)
requires_grad(ocrModel, False)
text_z_c, length_z_c = converter.encode(labels_z_c, batch_max_length=opt.batch_max_length)
z_c_code = cEncoder(text_z_c)
noise_style = mixing_noise_style(opt.batch_size, opt.latent, opt.mixing, device)
style=[]
style.append(noise_style[0]*z_c_code)
if len(noise_style)>1:
style.append(noise_style[1]*z_c_code)
if opt.zAlone:
#to validate orig style gan results
newstyle = []
newstyle.append(style[0][:,:opt.latent])
if len(style)>1:
newstyle.append(style[1][:,:opt.latent])
style = newstyle
fake_img,_ = genModel(style, input_is_latent=opt.input_latent)
fake_pred = disEncModel(fake_img)
disGenCost = g_nonsaturating_loss(fake_pred)
if opt.zAlone:
ocrCost = torch.tensor(0.0)
else:
#Compute OCR prediction (Reconstruction of content)
# text_for_pred = torch.LongTensor(opt.batch_size, opt.batch_max_length + 1).fill_(0).to(device)
# length_for_pred = torch.IntTensor([opt.batch_max_length] * opt.batch_size).to(device)
if 'CTC' in opt.Prediction:
preds_recon = ocrModel(fake_img, text_z_c, is_train=False)
preds_size = torch.IntTensor([preds_recon.size(1)] * opt.batch_size)
preds_recon_softmax = preds_recon.log_softmax(2).permute(1, 0, 2)
ocrCost = ocrCriterion(preds_recon_softmax, text_z_c, preds_size, length_z_c)
else:
print("Not implemented error")
sys.exit()
genModel.zero_grad()
cEncoder.zero_grad()
gen_enc_cost = disGenCost + opt.ocrWeight * ocrCost
grad_fake_OCR = torch.autograd.grad(ocrCost, fake_img, retain_graph=True)[0]
loss_grad_fake_OCR = 10**6*torch.mean(grad_fake_OCR**2)
grad_fake_adv = torch.autograd.grad(disGenCost, fake_img, retain_graph=True)[0]
loss_grad_fake_adv = 10**6*torch.mean(grad_fake_adv**2)
if opt.grad_balance:
gen_enc_cost.backward(retain_graph=True)
grad_fake_OCR = torch.autograd.grad(ocrCost, fake_img, create_graph=True, retain_graph=True)[0]
grad_fake_adv = torch.autograd.grad(disGenCost, fake_img, create_graph=True, retain_graph=True)[0]
a = opt.ocrWeight * torch.div(torch.std(grad_fake_adv), epsilon+torch.std(grad_fake_OCR))
if a is None:
print(ocrCost, disGenCost, torch.std(grad_fake_adv), torch.std(grad_fake_OCR))
if a>1000 or a<0.0001:
print(a)
ocrCost = a.detach() * ocrCost
gen_enc_cost = disGenCost + ocrCost
gen_enc_cost.backward(retain_graph=True)
grad_fake_OCR = torch.autograd.grad(ocrCost, fake_img, create_graph=False, retain_graph=True)[0]
grad_fake_adv = torch.autograd.grad(disGenCost, fake_img, create_graph=False, retain_graph=True)[0]
loss_grad_fake_OCR = 10 ** 6 * torch.mean(grad_fake_OCR ** 2)
loss_grad_fake_adv = 10 ** 6 * torch.mean(grad_fake_adv ** 2)
with torch.no_grad():
gen_enc_cost.backward()
else:
gen_enc_cost.backward()
loss_avg_gen.add(disGenCost)
loss_avg_ocr_unsup.add(opt.ocrWeight * ocrCost)
optimizer.step()
g_regularize = cntr % opt.g_reg_every == 0
if g_regularize:
path_batch_size = max(1, opt.batch_size // opt.path_batch_shrink)
# image_input_tensors, _, labels, _ = iter(train_loader).next()
labels_z_c = iter(text_loader).next()
# image_input_tensors = image_input_tensors.to(device)
# gt_image_tensors = image_input_tensors[:path_batch_size]
# labels_gt = labels[:path_batch_size]
text_z_c, length_z_c = converter.encode(labels_z_c[:path_batch_size], batch_max_length=opt.batch_max_length)
# text_gt, length_gt = converter.encode(labels_gt, batch_max_length=opt.batch_max_length)
z_c_code = cEncoder(text_z_c)
noise_style = mixing_noise_style(path_batch_size, opt.latent, opt.mixing, device)
style=[]
style.append(noise_style[0]*z_c_code)
if len(noise_style)>1:
style.append(noise_style[1]*z_c_code)
if opt.zAlone:
#to validate orig style gan results
newstyle = []
newstyle.append(style[0][:,:opt.latent])
if len(style)>1:
newstyle.append(style[1][:,:opt.latent])
style = newstyle
fake_img, grad = genModel(style, return_latents=True, g_path_regularize=True, mean_path_length=mean_path_length)
decay = 0.01
path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1))
mean_path_length_orig = mean_path_length + decay * (path_lengths.mean() - mean_path_length)
path_loss = (path_lengths - mean_path_length_orig).pow(2).mean()
mean_path_length = mean_path_length_orig.detach().item()
genModel.zero_grad()
cEncoder.zero_grad()
weighted_path_loss = opt.path_regularize * opt.g_reg_every * path_loss
if opt.path_batch_shrink:
weighted_path_loss += 0 * fake_img[0, 0, 0, 0]
weighted_path_loss.backward()
optimizer.step()
# mean_path_length_avg = (
# reduce_sum(mean_path_length).item() / get_world_size()
# )
#commented above for multi-gpu , non-distributed setting
mean_path_length_avg = mean_path_length
accumulate(g_ema, genModel, accum)
log_avg_path_loss_val.add(path_loss)
log_avg_mean_path_length_avg.add(torch.tensor(mean_path_length_avg))
log_ada_aug_p.add(torch.tensor(ada_aug_p))
if get_rank() == 0:
if wandb and opt.wandb:
wandb.log(
{
"Generator": g_loss_val,
"Discriminator": d_loss_val,
"Augment": ada_aug_p,
"Rt": r_t_stat,
"R1": r1_val,
"Path Length Regularization": path_loss_val,
"Mean Path Length": mean_path_length,
"Real Score": real_score_val,
"Fake Score": fake_score_val,
"Path Length": path_length_val,
}
)
# validation part
if (iteration + 1) % opt.valInterval == 0 or iteration == 0: # To see training progress, we also conduct validation when 'iteration == 0'
#generate paired content with similar style
labels_z_c_1 = iter(text_loader).next()
labels_z_c_2 = iter(text_loader).next()
text_z_c_1, length_z_c_1 = converter.encode(labels_z_c_1, batch_max_length=opt.batch_max_length)
text_z_c_2, length_z_c_2 = converter.encode(labels_z_c_2, batch_max_length=opt.batch_max_length)
z_c_code_1 = cEncoder(text_z_c_1)
z_c_code_2 = cEncoder(text_z_c_2)
style_c1_s1 = []
style_c2_s1 = []
style_s1 = mixing_noise_style(opt.batch_size, opt.latent, opt.mixing, device)
style_c1_s1.append(style_s1[0]*z_c_code_1)
style_c2_s1.append(style_s1[0]*z_c_code_2)
if len(style_s1)>1:
style_c1_s1.append(style_s1[1]*z_c_code_1)
style_c2_s1.append(style_s1[1]*z_c_code_2)
noise_style = mixing_noise_style(opt.batch_size, opt.latent, opt.mixing, device)
style_c1_s2 = []
style_c1_s2.append(noise_style[0]*z_c_code_1)
if len(noise_style)>1:
style_c1_s2.append(noise_style[1]*z_c_code_1)
if opt.zAlone:
#to validate orig style gan results
newstyle = []
newstyle.append(style_c1_s1[0][:,:opt.latent])
if len(style_c1_s1)>1:
newstyle.append(style_c1_s1[1][:,:opt.latent])
style_c1_s1 = newstyle
style_c2_s1 = newstyle
style_c1_s2 = newstyle
fake_img_c1_s1, _ = g_ema(style_c1_s1, input_is_latent=opt.input_latent)
fake_img_c2_s1, _ = g_ema(style_c2_s1, input_is_latent=opt.input_latent)
fake_img_c1_s2, _ = g_ema(style_c1_s2, input_is_latent=opt.input_latent)
if not opt.zAlone:
#Run OCR prediction
if 'CTC' in opt.Prediction:
preds = ocrModel(fake_img_c1_s1, text_z_c_1, is_train=False)
preds_size = torch.IntTensor([preds.size(1)] * opt.batch_size)
_, preds_index = preds.max(2)
preds_str_fake_img_c1_s1 = converter.decode(preds_index.data, preds_size.data)
preds = ocrModel(fake_img_c2_s1, text_z_c_2, is_train=False)
preds_size = torch.IntTensor([preds.size(1)] * opt.batch_size)
_, preds_index = preds.max(2)
preds_str_fake_img_c2_s1 = converter.decode(preds_index.data, preds_size.data)
preds = ocrModel(fake_img_c1_s2, text_z_c_1, is_train=False)
preds_size = torch.IntTensor([preds.size(1)] * opt.batch_size)
_, preds_index = preds.max(2)
preds_str_fake_img_c1_s2 = converter.decode(preds_index.data, preds_size.data)
preds = ocrModel(gt_image_tensors, text_gt, is_train=False)
preds_size = torch.IntTensor([preds.size(1)] * gt_image_tensors.shape[0])
_, preds_index = preds.max(2)
preds_str_gt = converter.decode(preds_index.data, preds_size.data)
else:
print("Not implemented error")
sys.exit()
else:
preds_str_fake_img_c1_s1 = [':None:'] * fake_img_c1_s1.shape[0]
preds_str_gt = [':None:'] * fake_img_c1_s1.shape[0]
os.makedirs(os.path.join(opt.trainDir,str(iteration)), exist_ok=True)
for trImgCntr in range(opt.batch_size):
try:
save_image(tensor2im(fake_img_c1_s1[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_c1_s1_'+labels_z_c_1[trImgCntr]+'_ocr:'+preds_str_fake_img_c1_s1[trImgCntr]+'.png'))
if not opt.zAlone:
save_image(tensor2im(fake_img_c2_s1[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_c2_s1_'+labels_z_c_2[trImgCntr]+'_ocr:'+preds_str_fake_img_c2_s1[trImgCntr]+'.png'))
save_image(tensor2im(fake_img_c1_s2[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_c1_s2_'+labels_z_c_1[trImgCntr]+'_ocr:'+preds_str_fake_img_c1_s2[trImgCntr]+'.png'))
if trImgCntr<gt_image_tensors.shape[0]:
save_image(tensor2im(gt_image_tensors[trImgCntr].detach()),os.path.join(opt.trainDir,str(iteration),str(trImgCntr)+'_gt_act:'+labels_gt[trImgCntr]+'_ocr:'+preds_str_gt[trImgCntr]+'.png'))
except:
print('Warning while saving training image')
elapsed_time = time.time() - start_time
# for log
with open(os.path.join(opt.exp_dir,opt.exp_name,'log_train.txt'), 'a') as log:
# training loss and validation loss
loss_log = f'[{iteration+1}/{opt.num_iter}] \
Train Dis loss: {loss_avg_dis.val():0.5f}, Train Gen loss: {loss_avg_gen.val():0.5f},\
Train UnSup OCR loss: {loss_avg_ocr_unsup.val():0.5f}, Train Sup OCR loss: {loss_avg_ocr_sup.val():0.5f}, \
Train R1-val loss: {log_r1_val.val():0.5f}, Train avg-path-loss: {log_avg_path_loss_val.val():0.5f}, \
Train mean-path-length loss: {log_avg_mean_path_length_avg.val():0.5f}, Train ada-aug-p: {log_ada_aug_p.val():0.5f}, \
Elapsed_time: {elapsed_time:0.5f}'
#plotting
lib.plot.plot(os.path.join(opt.plotDir,'Train-Dis-Loss'), loss_avg_dis.val().item())
lib.plot.plot(os.path.join(opt.plotDir,'Train-Gen-Loss'), loss_avg_gen.val().item())
lib.plot.plot(os.path.join(opt.plotDir,'Train-UnSup-OCR-Loss'), loss_avg_ocr_unsup.val().item())
lib.plot.plot(os.path.join(opt.plotDir,'Train-Sup-OCR-Loss'), loss_avg_ocr_sup.val().item())
lib.plot.plot(os.path.join(opt.plotDir,'Train-r1_val'), log_r1_val.val().item())
lib.plot.plot(os.path.join(opt.plotDir,'Train-path_loss_val'), log_avg_path_loss_val.val().item())
lib.plot.plot(os.path.join(opt.plotDir,'Train-mean_path_length_avg'), log_avg_mean_path_length_avg.val().item())
lib.plot.plot(os.path.join(opt.plotDir,'Train-ada_aug_p'), log_ada_aug_p.val().item())
print(loss_log)
loss_avg_dis.reset()
loss_avg_gen.reset()
loss_avg_ocr_unsup.reset()
loss_avg_ocr_sup.reset()
log_r1_val.reset()
log_avg_path_loss_val.reset()
log_avg_mean_path_length_avg.reset()
log_ada_aug_p.reset()
lib.plot.flush()
lib.plot.tick()
# save model per 1e+5 iter.
if (iteration) % 1e+4 == 0:
torch.save({
'cEncoder':cEncoder.state_dict(),
'genModel':genModel.state_dict(),
'g_ema':g_ema.state_dict(),
'ocrModel':ocrModel.state_dict(),
'disEncModel':disEncModel.state_dict(),
'optimizer':optimizer.state_dict(),
'ocr_optimizer':ocr_optimizer.state_dict(),
'dis_optimizer':dis_optimizer.state_dict()},
os.path.join(opt.exp_dir,opt.exp_name,'iter_'+str(iteration+1)+'_synth.pth'))
if (iteration + 1) == opt.num_iter:
print('end the training')
sys.exit()
iteration += 1
cntr+=1
args = parser.parse_args()
pprint(vars(args))
set_gpu(args.gpu)
dataset = MiniImageNet('test')
sampler = CategoriesSampler(dataset.label,
args.batch, args.way, args.folds * args.shot + args.query)
loader = DataLoader(dataset, batch_sampler=sampler,
num_workers=8, pin_memory=True)
model = Convnet().cuda()
model.load_state_dict(torch.load(args.load))
model.eval()
ave_acc = Averager()
s_label = torch.arange(args.train_way).repeat(args.shot).view(args.shot * args.train_way)
s_onehot = torch.zeros(s_label.size(0), 20)
s_onehot = s_onehot.scatter_(1, s_label.unsqueeze(dim=1), 1).cuda()
for i, batch in enumerate(loader, 1):
data, _ = [_.cuda() for _ in batch]
k = args.way * args.shot
data_shot, meta_support, data_query = data[:k], data[k:2*k], data[2*k:]
#p = inter_fold(model, args, data_shot)
x = model(data_shot)
x = x.reshape(args.shot, args.way, -1).mean(dim=0)
p = x
def train(opt):
lib.print_model_settings(locals().copy())
# train_transform = transforms.Compose([
# # transforms.RandomResizedCrop(input_size),
# transforms.Resize((opt.imgH, opt.imgW)),
# # transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
# ])
# val_transform = transforms.Compose([
# transforms.Resize((opt.imgH, opt.imgW)),
# # transforms.CenterCrop(input_size),
# transforms.ToTensor(),
# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
# ])
AlignFontCollateObj = AlignFontCollate(imgH=opt.imgH,
imgW=opt.imgW,
keep_ratio_with_pad=opt.PAD)
train_dataset = fontDataset(imgDir=opt.train_img_dir,
annFile=opt.train_ann_file,
transform=None,
numClasses=opt.numClasses)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=opt.batch_size,
shuffle=
False, # 'True' to check training progress with validation function.
sampler=data_sampler(train_dataset,
shuffle=True,
distributed=opt.distributed),
num_workers=int(opt.workers),
collate_fn=AlignFontCollateObj,
pin_memory=True,
drop_last=False)
# numClasses = len(train_dataset.Idx2F)
numClasses = np.unique(train_dataset.fontIdx).size
train_loader = sample_data(train_loader)
print('-' * 80)
numTrainSamples = len(train_dataset)
# valid_dataset = LmdbStyleDataset(root=opt.valid_data, opt=opt)
valid_dataset = fontDataset(imgDir=opt.train_img_dir,
annFile=opt.val_ann_file,
transform=None,
F2Idx=train_dataset.F2Idx,
Idx2F=train_dataset.Idx2F,
numClasses=opt.numClasses)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=opt.batch_size,
shuffle=
False, # 'True' to check training progress with validation function.
sampler=data_sampler(valid_dataset,
shuffle=False,
distributed=opt.distributed),
num_workers=int(opt.workers),
collate_fn=AlignFontCollateObj,
pin_memory=True,
drop_last=False)
numTestSamples = len(valid_dataset)
print('numClasses', numClasses)
print('numTrainSamples', numTrainSamples)
print('numTestSamples', numTestSamples)
vggFontModel = VGGFontModel(models.vgg19(pretrained=opt.preTrained),
numClasses).to(device)
for name, param in vggFontModel.classifier.named_parameters():
try:
if 'bias' in name:
init.constant_(param, 0.0)
elif 'weight' in name:
init.kaiming_normal_(param)
except Exception as e: # for batchnorm.
print('Exception in weight init' + name)
if 'weight' in name:
param.data.fill_(1)
continue
if opt.optim == "sgd":
print('SGD optimizer')
optimizer = optim.SGD(vggFontModel.parameters(),
lr=opt.lr,
momentum=0.9)
elif opt.optim == "adam":
print('Adam optimizer')
optimizer = optim.Adam(vggFontModel.parameters(), lr=opt.lr)
#get schedulers
scheduler = get_scheduler(optimizer, opt)
criterion = torch.nn.CrossEntropyLoss()
if opt.modelFolderFlag:
if len(
glob.glob(
os.path.join(opt.exp_dir, opt.exp_name,
"iter_*_vggFont.pth"))) > 0:
opt.saved_font_model = glob.glob(
os.path.join(opt.exp_dir, opt.exp_name,
"iter_*_vggFont.pth"))[-1]
## Loading pre-trained files
if opt.saved_font_model != '' and opt.saved_font_model != 'None':
print(f'loading pretrained synth model from {opt.saved_font_model}')
checkpoint = torch.load(opt.saved_font_model,
map_location=lambda storage, loc: storage)
vggFontModel.load_state_dict(checkpoint['vggFontModel'])
optimizer.load_state_dict(checkpoint["optimizer"])
scheduler.load_state_dict(checkpoint["scheduler"])
# print('Model Initialization')
#
# print('Loaded checkpoint')
if opt.distributed:
vggFontModel = torch.nn.parallel.DistributedDataParallel(
vggFontModel,
device_ids=[opt.local_rank],
output_device=opt.local_rank,
broadcast_buffers=False,
find_unused_parameters=True)
vggFontModel.train()
# print('Loaded distributed')
if opt.distributed:
vggFontModel_module = vggFontModel.module
else:
vggFontModel_module = vggFontModel
# print('Loading module')
# loss averager
loss_train = Averager()
loss_val = Averager()
train_acc = Averager()
val_acc = Averager()
train_acc_5 = Averager()
val_acc_5 = Averager()
""" final options """
with open(os.path.join(opt.exp_dir, opt.exp_name, 'opt.txt'),
'a') as opt_file:
opt_log = '------------ Options -------------\n'
args = vars(opt)
for k, v in args.items():
opt_log += f'{str(k)}: {str(v)}\n'
opt_log += '---------------------------------------\n'
print(opt_log)
opt_file.write(opt_log)
""" start training """
start_iter = 0
if opt.saved_font_model != '' and opt.saved_font_model != 'None':
try:
start_iter = int(opt.saved_font_model.split('_')[-2].split('.')[0])
print(f'continue to train, start_iter: {start_iter}')
except:
pass
iteration = start_iter
cntr = 0
# trainCorrect=0
# tCntr=0
while (True):
# print(cntr)
# train part
start_time = time.time()
if not opt.testFlag:
image_input_tensors, labels_gt = next(train_loader)
image_input_tensors = image_input_tensors.to(device)
labels_gt = labels_gt.view(-1).to(device)
preds = vggFontModel(image_input_tensors)
loss = criterion(preds, labels_gt)
vggFontModel.zero_grad()
loss.backward()
optimizer.step()
# _, preds_max = preds.max(dim=1)
# trainCorrect += (preds_max == labels_gt).sum()
# tCntr+=preds_max.shape[0]
acc1, acc5 = getNumCorrect(preds,
labels_gt,
topk=(1, min(numClasses, 5)))
train_acc.addScalar(acc1, preds.shape[0])
train_acc_5.addScalar(acc5, preds.shape[0])
loss_train.add(loss)
if opt.lr_policy != "None":
scheduler.step()
# print
if get_rank() == 0:
if (
iteration + 1
) % opt.valInterval == 0 or iteration == 0 or opt.testFlag: # To see training progress, we also conduct validation when 'iteration == 0'
#validation
# iCntr=torch.tensor(0.0).to(device)
# valCorrect=torch.tensor(0.0).to(device)
vggFontModel.eval()
print('Inside val', iteration)
for vCntr, (image_input_tensors,
labels_gt) in enumerate(valid_loader):
# print('vCntr--',vCntr)
if opt.debugFlag and vCntr > 2:
break
with torch.no_grad():
image_input_tensors = image_input_tensors.to(device)
labels_gt = labels_gt.view(-1).to(device)
preds = vggFontModel(image_input_tensors)
loss = criterion(preds, labels_gt)
loss_val.add(loss)
# _, preds_max = preds.max(dim=1)
# valCorrect += (preds_max == labels_gt).sum()
# iCntr+=preds_max.shape[0]
acc1, acc5 = getNumCorrect(preds,
labels_gt,
topk=(1, min(numClasses,
5)))
val_acc.addScalar(acc1, preds.shape[0])
val_acc_5.addScalar(acc5, preds.shape[0])
vggFontModel.train()
elapsed_time = time.time() - start_time
#DO HERE
with open(
os.path.join(opt.exp_dir, opt.exp_name,
'log_train.txt'), 'a') as log:
# print('COUNT-------',val_acc_5.n_count)
# training loss and validation loss
loss_log = f'[{iteration+1}/{opt.num_iter}] \
Train loss: {loss_train.val():0.5f}, Val loss: {loss_val.val():0.5f}, \
Train Top-1 Acc: {train_acc.val()*100:0.5f}, Train Top-5 Acc: {train_acc_5.val()*100:0.5f}, \
Val Top-1 Acc: {val_acc.val()*100:0.5f}, Val Top-5 Acc: {val_acc_5.val()*100:0.5f}, \
Elapsed_time: {elapsed_time:0.5f}'
#plotting
lib.plot.plot(os.path.join(opt.plotDir, 'Train-Loss'),
loss_train.val().item())
lib.plot.plot(os.path.join(opt.plotDir, 'Val-Loss'),
loss_val.val().item())
lib.plot.plot(os.path.join(opt.plotDir, 'Train-Top-1-Acc'),
train_acc.val() * 100)
lib.plot.plot(os.path.join(opt.plotDir, 'Train-Top-5-Acc'),
train_acc_5.val() * 100)
lib.plot.plot(os.path.join(opt.plotDir, 'Val-Top-1-Acc'),
val_acc.val() * 100)
lib.plot.plot(os.path.join(opt.plotDir, 'Val-Top-5-Acc'),
val_acc_5.val() * 100)
print(loss_log)
log.write(loss_log + "\n")
loss_train.reset()
loss_val.reset()
train_acc.reset()
val_acc.reset()
train_acc_5.reset()
val_acc_5.reset()
# trainCorrect=0
# tCntr=0
lib.plot.flush()
# save model per 30000 iter.
if (iteration) % 15000 == 0:
torch.save(
{
'vggFontModel': vggFontModel_module.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict()
},
os.path.join(opt.exp_dir, opt.exp_name, 'iter_' +
str(iteration + 1) + '_vggFont.pth'))
lib.plot.tick()
if (iteration + 1) == opt.num_iter:
print('end the training')
sys.exit()
iteration += 1
cntr += 1
