def test(model, dataloader, params):
val_data = tqdm(dataloader.data_iterator(data_type='test',
batch_size=params.batch_size),
total=(dataloader.size()[0] // params.batch_size))
metrics = Metrics()
loss_avg = RunningAverage()
with torch.no_grad():
for data, labels in val_data:
model.eval()
data = torch.tensor(data, dtype=torch.long).to(params.device)
labels = torch.tensor(labels, dtype=torch.long).to(params.device)
batch_masks = data != 0
loss, logits = model(data,
attention_mask=batch_masks,
labels=labels)
predicted = logits.max(2)[1]
metrics.update(batch_pred=predicted.cpu().numpy(),
batch_true=labels.cpu().numpy(),
batch_mask=batch_masks.cpu().numpy())
loss_avg.update(torch.mean(loss).item())
val_data.set_postfix(type='VAL',
loss='{:05.3f}'.format(loss_avg()))
metrics.loss = loss_avg()
return metrics
def validate(model, val_set, params):
val_data = tqdm(DataLoader(val_set,
batch_size=params.batch_size,
collate_fn=KeyphraseData.collate_fn),
total=(len(val_set) // params.batch_size))
metrics = Metrics()
loss_avg = RunningAverage()
with torch.no_grad():
model.eval()
for data, labels, mask in val_data:
data = data.to(params.device)
labels = labels.to(params.device)
mask = mask.to(params.device)
loss, logits = model(data, attention_mask=mask, labels=labels)
predicted = logits.max(2)[1]
metrics.update(batch_pred=predicted.cpu().numpy(),
batch_true=labels.cpu().numpy(),
batch_mask=mask.cpu().numpy())
loss_avg.update(torch.mean(loss).item())
val_data.set_postfix(type='VAL',
loss='{:05.3f}'.format(loss_avg()))
metrics.loss = loss_avg()
return metrics