corentin/torch_utils
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Trainer verbose argument implementation

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Corentin 2021-01-23 04:18:05 +09:00
commit b43b8b14d6
1 changed files with 14 additions and 10 deletions
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trainer.py
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@ -23,12 +23,13 @@ class Trainer:
epoch_skip: int, summary_per_epoch: int, image_per_epoch: int,
data_dtype=None, label_dtype=None,
train_pre_process: Optional[nn.Module] = None, data_is_label: bool = False,
logger=DummyLogger()):
logger=DummyLogger(), verbose=True):
super().__init__()
self.device = device
self.output_dir = output_dir
self.data_is_label = data_is_label
self.logger = logger
self.verbose = verbose
self.should_stop = False
self.batch_generator_train = batch_generator_train
@ -129,16 +130,17 @@ class Trainer:
try:
while not self.should_stop and self.batch_generator_train.epoch < epochs:
epoch = self.batch_generator_train.epoch
print()
print(' ' * os.get_terminal_size()[0], end='\r')
print(f'Epoch {self.batch_generator_train.epoch}')
if self.verbose:
print()
print(' ' * os.get_terminal_size()[0], end='\r')
print(f'Epoch {self.batch_generator_train.epoch}')
while not self.should_stop and epoch == self.batch_generator_train.epoch:
self.batch_inputs = torch.as_tensor(
self.batch_generator_train.batch_data, dtype=self.data_dtype, device=self.device)
self.batch_labels = torch.as_tensor(
self.batch_generator_train.batch_label, dtype=self.label_dtype, device=self.device)
if self.benchmark_step > 1:
if self.verbose and self.benchmark_step > 1:
speed = self.benchmark_step / (time.time() - self.benchmark_time)
print(
f'Step {self.batch_generator_train.global_step}, {speed:0.02f} steps/s'
@ -167,7 +169,8 @@ class Trainer:
self.save_summaries()
self.batch_generator_train.next_batch()
except KeyboardInterrupt:
print()
if self.verbose:
print()
train_stop_time = time.time()
self.writer_train.close()
@ -228,10 +231,11 @@ class Trainer:
self.batch_inputs, self.processed_inputs, self.batch_labels, self.network_outputs,
val_inputs, val_pre_process, val_labels, val_outputs)
speed = self.benchmark_step / (time.time() - self.benchmark_time)
print(f'Step {global_step}, '
f'loss {self.running_loss / self.running_count:.03e} {val_loss / val_count:.03e}, '
f'{speed:0.02f} steps/s, {speed * self.batch_generator_train.batch_size:0.02f} input/sec')
if self.verbose:
speed = self.benchmark_step / (time.time() - self.benchmark_time)
print(f'Step {global_step}, '
f'loss {self.running_loss / self.running_count:.03e} {val_loss / val_count:.03e}, '
f'{speed:0.02f} steps/s, {speed * self.batch_generator_train.batch_size:0.02f} input/sec')
torch.save(self.network, os.path.join(self.output_dir, f'model_{global_step}.pt'))
self.benchmark_time = time.time()
self.benchmark_step = 0