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fix bug Expected object of backend CUDA but got backend CPU for argu… #191

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fix bug Expected object of backend CUDA but got backend CPU for argu… #191

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9496f5e
fix bug Expected object of backend CUDA but got backend CPU for argu…
anonymone Aug 20, 2019
ad2819b
fix bugs that loggong can not displayed in console
anonymone Dec 26, 2019
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2 changes: 1 addition & 1 deletion seq2seq/evaluator/evaluator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@ def evaluate(self, model, data):
match = 0
total = 0

device = None if torch.cuda.is_available() else -1
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
batch_iterator = torchtext.data.BucketIterator(
dataset=data, batch_size=self.batch_size,
sort=True, sort_key=lambda x: len(x.src),
Expand Down
13 changes: 5 additions & 8 deletions seq2seq/trainer/supervised_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,8 +46,6 @@ def __init__(self, expt_dir='experiment', loss=NLLLoss(), batch_size=64,
os.makedirs(self.expt_dir)
self.batch_size = batch_size

self.logger = logging.getLogger(__name__)

def _train_batch(self, input_variable, input_lengths, target_variable, model, teacher_forcing_ratio):
loss = self.loss
# Forward propagation
Expand All @@ -67,12 +65,11 @@ def _train_batch(self, input_variable, input_lengths, target_variable, model, te

def _train_epoches(self, data, model, n_epochs, start_epoch, start_step,
dev_data=None, teacher_forcing_ratio=0):
log = self.logger

print_loss_total = 0 # Reset every print_every
epoch_loss_total = 0 # Reset every epoch

device = None if torch.cuda.is_available() else -1
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
batch_iterator = torchtext.data.BucketIterator(
dataset=data, batch_size=self.batch_size,
sort=False, sort_within_batch=True,
Expand All @@ -85,7 +82,7 @@ def _train_epoches(self, data, model, n_epochs, start_epoch, start_step,
step = start_step
step_elapsed = 0
for epoch in range(start_epoch, n_epochs + 1):
log.debug("Epoch: %d, Step: %d" % (epoch, step))
logging.debug("Epoch: %d, Step: %d" % (epoch, step))

batch_generator = batch_iterator.__iter__()
# consuming seen batches from previous training
Expand Down Expand Up @@ -113,7 +110,7 @@ def _train_epoches(self, data, model, n_epochs, start_epoch, start_step,
step / total_steps * 100,
self.loss.name,
print_loss_avg)
log.info(log_msg)
logging.info(log_msg)

# Checkpoint
if step % self.checkpoint_every == 0 or step == total_steps:
Expand All @@ -136,7 +133,7 @@ def _train_epoches(self, data, model, n_epochs, start_epoch, start_step,
else:
self.optimizer.update(epoch_loss_avg, epoch)

log.info(log_msg)
logging.info(log_msg)

def train(self, model, data, num_epochs=5,
resume=False, dev_data=None,
Expand Down Expand Up @@ -179,7 +176,7 @@ def train(self, model, data, num_epochs=5,
optimizer = Optimizer(optim.Adam(model.parameters()), max_grad_norm=5)
self.optimizer = optimizer

self.logger.info("Optimizer: %s, Scheduler: %s" % (self.optimizer.optimizer, self.optimizer.scheduler))
logging.info("Optimizer: %s, Scheduler: %s" % (self.optimizer.optimizer, self.optimizer.scheduler))

self._train_epoches(data, model, num_epochs,
start_epoch, step, dev_data=dev_data,
Expand Down