if epoch_counter % config['eval_every_n_epochs'] == 0:
# validation steps
with torch.no_grad():
model.eval()
valid_loss = 0.0
for counter, ((xis, xjs), _) in enumerate(valid_loader):
if train_gpu:
xis = xis.cuda()
xjs = xjs.cuda()
loss = (step(xis, xjs))
valid_loss += loss.item()
valid_loss /= counter
if valid_loss < best_valid_loss:
# save the model weights
best_valid_loss = valid_loss
torch.save(model.state_dict(),
os.path.join(model_checkpoints_folder, 'model.pth'))
train_writer.add_scalar('validation_loss',
valid_loss,
global_step=valid_n_iter)
valid_n_iter += 1
model.train()
def train(self):
#Data
train_loader, valid_loader = self.dataset.get_data_loaders()
#Model
model = ResNetSimCLR(**self.config["model"])
if self.device == 'cuda':
model = nn.DataParallel(model, device_ids=[i for i in range(self.config['gpu']['gpunum'])])
#model = model.to(self.device)
model = model.cuda()
print(model)
model = self._load_pre_trained_weights(model)
each_epoch_steps = len(train_loader)
total_steps = each_epoch_steps * self.config['train']['epochs']
warmup_steps = each_epoch_steps * self.config['train']['warmup_epochs']
scaled_lr = eval(self.config['train']['lr']) * self.batch_size / 256.
optimizer = torch.optim.Adam(
model.parameters(),
scaled_lr,
weight_decay=eval(self.config['train']['weight_decay']))
'''
optimizer = LARS(params=model.parameters(),
lr=eval(self.config['train']['lr']),
momentum=self.config['train']['momentum'],
weight_decay=eval(self.config['train']['weight_decay'],
eta=0.001,
max_epoch=self.config['train']['epochs'])
'''
# scheduler during warmup stage
lambda1 = lambda epoch:epoch*1.0 / int(warmup_steps)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)
if apex_support and self.config['train']['fp16_precision']:
model, optimizer = amp.initialize(model, optimizer,
opt_level='O2',
keep_batchnorm_fp32=True)
model_checkpoints_folder = os.path.join(self.writer.log_dir, 'checkpoints')
# save config file
_save_config_file(model_checkpoints_folder)
n_iter = 0
valid_n_iter = 0
best_valid_loss = np.inf
lr = eval(self.config['train']['lr'])
end = time.time()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
for epoch_counter in range(self.config['train']['epochs']):
model.train()
for i, ((xis, xjs), _) in enumerate(train_loader):
data_time.update(time.time() - end)
optimizer.zero_grad()
xis = xis.cuda()
xjs = xjs.cuda()
loss = self._step(model, xis, xjs, n_iter)
#print("Loss: ",loss.data.cpu())
losses.update(loss.item(), 2 * xis.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
print('Epoch: [{epoch}][{step}/{each_epoch_steps}] Loss {loss.val:.4f} Avg Loss {loss.avg:.4f} DataTime {datatime.val:.4f} BatchTime {batchtime.val:.4f} LR {lr})'.format(epoch=epoch_counter, step=i, each_epoch_steps=each_epoch_steps, loss=losses, datatime=data_time, batchtime=batch_time, lr=lr))
if n_iter % self.config['train']['log_every_n_steps'] == 0:
self.writer.add_scalar('train_loss', loss, global_step=n_iter)
if apex_support and self.config['train']['fp16_precision']:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
n_iter += 1
#adjust lr
if n_iter == warmup_steps:
# scheduler after warmup stage
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps-warmup_steps, eta_min=0, last_epoch=-1)
scheduler.step()
lr = scheduler.get_lr()[0]
self.writer.add_scalar('cosine_lr_decay', scheduler.get_lr()[0], global_step=n_iter)
sys.stdout.flush()
# validate the model if requested
if epoch_counter % self.config['train']['eval_every_n_epochs'] == 0:
valid_loss = self._validate(model, valid_loader)
if valid_loss < best_valid_loss:
# save the model weights
best_valid_loss = valid_loss
torch.save(model.state_dict(), os.path.join(model_checkpoints_folder, 'model.pth'))
self.writer.add_scalar('validation_loss', valid_loss, global_step=valid_n_iter)
valid_n_iter += 1
