def train_one_epoch(self, epoch, accum_iter):
self.model.train()
self.lr_scheduler.step()
average_meter_set = AverageMeterSet()
tqdm_dataloader = tqdm(self.train_loader)
for batch_idx, batch in enumerate(tqdm_dataloader):
batch_size = batch[0].size(0)
batch = [x.to(self.device) for x in batch]
self.optimizer.zero_grad()
loss = self.calculate_loss(batch)
loss.backward()
self.optimizer.step()
average_meter_set.update('loss', loss.item())
tqdm_dataloader.set_description(
'Epoch {}, loss {:.3f} '.format(epoch+1, average_meter_set['loss'].avg))
accum_iter += batch_size
if self._needs_to_log(accum_iter):
tqdm_dataloader.set_description('Logging to Tensorboard')
log_data = {
'state_dict': (self._create_state_dict()),
'epoch': epoch,
'accum_iter': accum_iter,
}
log_data.update(average_meter_set.averages())
self.log_extra_train_info(log_data)
self.logger_service.log_train(log_data)
return accum_iter
def validate(self, epoch, accum_iter):
self.model.eval()
average_meter_set = AverageMeterSet()
with torch.no_grad():
tqdm_dataloader = tqdm(self.val_loader)
for batch_idx, batch in enumerate(tqdm_dataloader):
batch = [x.to(self.device) for x in batch]
metrics = self.calculate_metrics(batch)
for k, v in metrics.items():
average_meter_set.update(k, v)
description_metrics = ['NDCG@%d' % k for k in self.metric_ks[:3]] +\
['Recall@%d' % k for k in self.metric_ks[:3]]
description = 'Val: ' + ', '.join(s + ' {:.3f}' for s in description_metrics)
description = description.replace('NDCG', 'N').replace('Recall', 'R')
description = description.format(*(average_meter_set[k].avg for k in description_metrics))
tqdm_dataloader.set_description(description)
log_data = {
'state_dict': (self._create_state_dict()),
'epoch': epoch,
'accum_iter': accum_iter,
}
log_data.update(average_meter_set.averages())
self.logger_service.log_val(log_data)
def test(self):
print('Test best model with test set!')
best_model = torch.load(
os.path.join(self.export_root, 'models',
'best_acc_model.pth')).get('model_state_dict')
self.model.load_state_dict(best_model)
self.model.eval()
average_meter_set = AverageMeterSet()
with torch.no_grad():
tqdm_dataloader = tqdm(self.test_loader)
for batch_idx, batch in enumerate(tqdm_dataloader):
batch = [x.to(self.device) for x in batch]
metrics, preds = self.calculate_metrics(batch)
for k, v in metrics.items():
average_meter_set.update(k, v)
description_metrics = ['NDCG@%d' % k for k in self.metric_ks[:3]] +\
['Recall@%d' % k for k in self.metric_ks[:3]]
description = 'Val: ' + ', '.join(s + ' {:.3f}'
for s in description_metrics)
description = description.replace('NDCG',
'N').replace('Recall', 'R')
description = description.format(
*(average_meter_set[k].avg for k in description_metrics))
tqdm_dataloader.set_description(description)
average_metrics = average_meter_set.averages()
with open(
os.path.join(self.export_root, 'logs',
'test_metrics.json'), 'w') as f:
json.dump(average_metrics, f, indent=4)
print(average_metrics)
def validate(self, epoch, accum_iter, mode, doLog=True, **kwargs):
if mode == 'val':
loader = self.val_loader
elif mode == 'test':
loader = self.test_loader
else:
raise ValueError
self.model.eval()
average_meter_set = AverageMeterSet()
num_instance = 0
with torch.no_grad():
tqdm_dataloader = tqdm(loader) if not self.pilot else loader
for batch_idx, batch in enumerate(tqdm_dataloader):
if self.pilot and batch_idx >= self.pilot_batch_cnt:
# print('Break validation due to pilot mode')
break
batch = {k: v.to(self.device) for k, v in batch.items()}
batch_size = next(iter(batch.values())).size(0)
num_instance += batch_size
metrics = self.calculate_metrics(batch)
for k, v in metrics.items():
average_meter_set.update(k, v)
if not self.pilot:
description_metrics = ['NDCG@%d' % k for k in self.metric_ks] +\
['Recall@%d' % k for k in self.metric_ks]
description = '{}: '.format(mode.capitalize()) + ', '.join(
s + ' {:.4f}' for s in description_metrics)
description = description.replace('NDCG', 'N').replace(
'Recall', 'R')
description = description.format(
*(average_meter_set[k].avg
for k in description_metrics))
tqdm_dataloader.set_description(description)
log_data = {
'state_dict': (self._create_state_dict(epoch, accum_iter)),
'epoch': epoch,
'accum_iter': accum_iter,
'num_eval_instance': num_instance,
}
log_data.update(average_meter_set.averages())
log_data.update(kwargs)
if doLog:
if mode == 'val':
self.logger_service.log_val(log_data)
elif mode == 'test':
self.logger_service.log_test(log_data)
else:
raise ValueError
return log_data
def validate(self, epoch, accum_iter):
self.model.eval()
self.all_preds = []
average_meter_set = AverageMeterSet()
with torch.no_grad():
tqdm_dataloader = tqdm(self.val_loader)
for batch_idx, batch in enumerate(tqdm_dataloader):
batch = [x.to(self.device) for x in batch]
metrics, preds = self.calculate_metrics(batch)
for p in preds:
self.all_preds.append(p.tolist())
for k, v in metrics.items():
average_meter_set.update(k, v)
description_metrics = ['NDCG@%d' % k for k in self.metric_ks[:3]] + \
['Recall@%d' % k for k in self.metric_ks[:3]]
description = 'Val: ' + ', '.join(s + ' {:.3f}'
for s in description_metrics)
description = description.replace('NDCG',
'N').replace('Recall', 'R')
description = description.format(
*(average_meter_set[k].avg for k in description_metrics))
tqdm_dataloader.set_description(description)
log_data = {
'state_dict': (self._create_state_dict()),
'epoch': epoch + 1,
'accum_iter': accum_iter,
}
log_data.update(average_meter_set.averages())
self.log_extra_val_info(log_data)
self.logger_service.log_val(log_data)
df = pd.DataFrame(self.all_preds,
columns=[
'prediction_' + str(i)
for i in range(len(self.all_preds[0]))
])
if not os.path.isdir(self.args.output_predictions_folder):
os.makedirs(self.args.output_predictions_folder)
with open(
os.path.join(self.args.output_predictions_folder,
'config.json'), 'w') as f:
self.args.recommender = "BERT4rec"
self.args.seed = str(self.args.model_init_seed)
args_dict = {}
args_dict['args'] = vars(self.args)
f.write(json.dumps(args_dict, indent=4, sort_keys=True))
df.to_csv(self.args.output_predictions_folder + "/predictions.csv",
index=False)
def train_one_epoch(self, epoch, accum_iter):
self.model.train()
average_meter_set = AverageMeterSet()
tqdm_dataloader = tqdm(self.train_loader)
for batch_idx, batch in enumerate(tqdm_dataloader):
batch = self.batch_to_device(batch)
batch_size = self.args.train_batch_size
# forward pass
self.optimizer.zero_grad()
loss = self.calculate_loss(batch)
# backward pass
loss.backward()
self.optimizer.step()
# update metrics
average_meter_set.update('loss', loss.item())
average_meter_set.update('lr', self.optimizer.defaults['lr'])
tqdm_dataloader.set_description('Epoch {}, loss {:.3f} '.format(epoch + 1, average_meter_set['loss'].avg))
accum_iter += batch_size
if self._needs_to_log(accum_iter):
tqdm_dataloader.set_description('Logging to Tensorboard')
log_data = {
'state_dict': (self._create_state_dict()),
'epoch': epoch+1,
'accum_iter': accum_iter,
}
log_data.update(average_meter_set.averages())
self.log_extra_train_info(log_data)
self.logger_service.log_train(log_data)
if self.args.local and batch_idx == 20:
break
# adapt learning rate
if self.args.enable_lr_schedule:
self.lr_scheduler.step()
if epoch % self.lr_scheduler.step_size == 0:
print(self.optimizer.defaults['lr'])
return accum_iter
def validate(self, epoch, accum_iter):
self.model.eval()
average_meter_set = AverageMeterSet()
with torch.no_grad():
tqdm_dataloader = tqdm(self.val_loader)
for batch_idx, batch in enumerate(tqdm_dataloader):
batch = [x.to(self.device) for x in batch]
metrics = self.calculate_metrics(batch)
for k, v in metrics.items():
average_meter_set.update(k, v)
description_metrics = ['NDCG@%d' % k for k in self.metric_ks[:3]] + \
['Recall@%d' % k for k in self.metric_ks[:3]]
if 'accuracy' in self.args.metrics_to_log:
description_metrics = ['accuracy']
description = 'Val: ' + ', '.join(s + ' {:.3f}' for s in description_metrics)
description = description.replace('NDCG', 'N').replace('Recall', 'R')
description = description.format(*(average_meter_set[k].avg for k in description_metrics))
tqdm_dataloader.set_description(description)
log_data = {
'state_dict': (self._create_state_dict()),
'epoch': epoch + 1,
'accum_iter': accum_iter,
'user_embedding': self.model.embedding.user.weight.cpu().detach().numpy()
if self.args.dump_useritem_embeddings == 'True'
and self.model.embedding.user is not None
else None,
'item_embedding': self.model.embedding.token.weight.cpu().detach().numpy()
if self.args.dump_useritem_embeddings == 'True'
else None,
}
log_data.update(average_meter_set.averages())
self.log_extra_val_info(log_data)
self.logger_service.log_val(log_data)
def train_one_epoch(self, epoch, accum_iter, train_loader, **kwargs):
self.model.train()
average_meter_set = AverageMeterSet()
num_instance = 0
tqdm_dataloader = tqdm(
train_loader) if not self.pilot else train_loader
for batch_idx, batch in enumerate(tqdm_dataloader):
if self.pilot and batch_idx >= self.pilot_batch_cnt:
# print('Break training due to pilot mode')
break
batch_size = next(iter(batch.values())).size(0)
batch = {k: v.to(self.device) for k, v in batch.items()}
num_instance += batch_size
if self.total_anneal_steps > 0:
anneal = min(self.anneal_cap,
1. * self.update_count / self.total_anneal_steps)
else:
anneal = self.anneal_cap
self.optimizer.zero_grad()
loss = self.calculate_loss(batch, anneal)
if isinstance(loss, tuple):
loss, extra_info = loss
for k, v in extra_info.items():
average_meter_set.update(k, v)
loss.backward()
if self.clip_grad_norm is not None:
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
self.clip_grad_norm)
self.optimizer.step()
self.update_count += 1
average_meter_set.update('loss', loss.item())
if not self.pilot:
tqdm_dataloader.set_description(
'Epoch {}, loss {:.3f} '.format(
epoch, average_meter_set['loss'].avg))
accum_iter += batch_size
if self._needs_to_log(accum_iter):
if not self.pilot:
tqdm_dataloader.set_description('Logging')
log_data = {
# 'state_dict': (self._create_state_dict()),
'epoch': epoch,
'accum_iter': accum_iter,
}
log_data.update(average_meter_set.averages())
log_data.update(kwargs)
self.log_extra_train_info(log_data)
self.logger_service.log_train(log_data)
log_data = {
# 'state_dict': (self._create_state_dict()),
'epoch': epoch,
'accum_iter': accum_iter,
'num_train_instance': num_instance,
}
log_data.update(average_meter_set.averages())
log_data.update(kwargs)
self.log_extra_train_info(log_data)
self.logger_service.log_train(log_data)
return accum_iter
