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Update torch indexing and use Tensor.cpu() #91

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Merged
EntilZha merged 6 commits into from
Apr 21, 2021
Merged

Update torch indexing and use Tensor.cpu() #91

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c9e0d29
Update dan.py
GeneralPoxter Apr 17, 2021
da79a70
Update rnn.py
GeneralPoxter Apr 17, 2021
53e8aee
Update elmo.py
GeneralPoxter Apr 17, 2021
693ab25
Update rnn.py
GeneralPoxter Apr 18, 2021
a76eb9e
Update dan.py
GeneralPoxter Apr 18, 2021
42dbde5
Update elmo.py
GeneralPoxter Apr 18, 2021
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4 changes: 2 additions & 2 deletions qanta/guesser/dan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -466,7 +466,7 @@ def run_epoch(self, iterator: Iterator):

out = self.model(input_dict, lengths_dict, qanta_ids)
_, preds = torch.max(out, 1)
accuracy = torch.mean(torch.eq(preds, page).float()).data[0]
accuracy = torch.mean(torch.eq(preds, page).float()).cpu().data
batch_loss = self.criterion(out, page)
if is_train:
batch_loss.backward()
Expand All @@ -476,7 +476,7 @@ def run_epoch(self, iterator: Iterator):
self.optimizer.step()

batch_accuracies.append(accuracy)
batch_losses.append(batch_loss.data[0])
batch_losses.append(batch_loss.cpu().data)

epoch_end = time.time()

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4 changes: 2 additions & 2 deletions qanta/guesser/elmo.py
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Expand Up @@ -193,14 +193,14 @@ def run_epoch(self, batches, train=True):
self.model.zero_grad()
out = self.model(x_batch.cuda(), length_batch.cuda())
_, preds = torch.max(out, 1)
accuracy = torch.mean(torch.eq(preds, y_batch).float()).data[0]
accuracy = torch.mean(torch.eq(preds, y_batch).float()).cpu().data
batch_loss = self.criterion(out, y_batch)
if train:
batch_loss.backward()
torch.nn.utils.clip_grad_norm(self.model.parameters(), 0.25)
self.optimizer.step()
batch_accuracies.append(accuracy)
batch_losses.append(batch_loss.data[0])
batch_losses.append(batch_loss.cpu().data)
epoch_end = time.time()

return np.mean(batch_accuracies), np.mean(batch_losses), epoch_end - epoch_start
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4 changes: 2 additions & 2 deletions qanta/guesser/rnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -346,7 +346,7 @@ def run_epoch(self, iterator: Iterator):

out, hidden = self.model(text, lengths, hidden_init, qanta_ids)
_, preds = torch.max(out, 1)
accuracy = torch.mean(torch.eq(preds, page).float()).data[0]
accuracy = torch.mean(torch.eq(preds, page).float()).cpu().data
batch_loss = self.criterion(out, page)
if is_train:
batch_loss.backward()
Expand All @@ -356,7 +356,7 @@ def run_epoch(self, iterator: Iterator):
self.optimizer.step()

batch_accuracies.append(accuracy)
batch_losses.append(batch_loss.data[0])
batch_losses.append(batch_loss.cpu().data)

epoch_end = time.time()

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