print(">>> [Epoch was updated]")
for b in range(len(dataset.files) // config.batch_size):
scheduler.optimizer.zero_grad()
try:
batch_x, batch_y = dataset.slide_seq2seq_batch(
config.batch_size, config.max_seq)
batch_x = torch.from_numpy(batch_x).contiguous().to(
config.device, non_blocking=True, dtype=torch.int)
batch_y = torch.from_numpy(batch_y).contiguous().to(
config.device, non_blocking=True, dtype=torch.int)
except IndexError:
continue
start_time = time.time()
mt.train()
sample = mt.forward(batch_x)
metrics = metric_set(sample, batch_y)
loss = metrics['loss']
loss.backward()
scheduler.step()
end_time = time.time()
if config.debug:
print("[Loss]: {}".format(loss))
train_summary_writer.add_scalar('loss',
metrics['loss'],
global_step=idx)
train_summary_writer.add_scalar('accuracy',
metrics['accuracy'],
global_step=idx)
print(">>> [Epoch was updated]")
for b in range(len(dataset.files) // config.batch_size):
scheduler.optimizer.zero_grad()
try:
batch_x, batch_y = dataset.slide_seq2seq_batch(
config.batch_size, config.max_seq)
batch_x = torch.from_numpy(batch_x).contiguous().to(
config.device, non_blocking=True, dtype=torch.int)
batch_y = torch.from_numpy(batch_y).contiguous().to(
config.device, non_blocking=True, dtype=torch.int)
except IndexError:
continue
start_time = time.time()
mt.train()
sample, _ = mt.forward(batch_x)
metrics = metric_set(sample, batch_y)
loss = metrics['loss']
with amp.scale_loss(loss, scheduler.optimizer) as scaled_loss:
scaled_loss.backward()
scheduler.step()
end_time = time.time()
if config.debug:
print("[Loss]: {}".format(loss))
train_summary_writer.add_scalar('loss',
metrics['loss'],
global_step=idx)
train_summary_writer.add_scalar('accuracy',
metrics['accuracy'],