def collate_fn(batch, train=True):
""" list of tensors to a batch tensors """
premise_batch, _ = pad_batch([row['premise'] for row in batch])
hypothesis_batch, _ = pad_batch([row['hypothesis'] for row in batch])
label_batch = [row['label'] for row in batch]
# PyTorch RNN requires batches to be transposed for speed and integration with CUDA
transpose = (lambda b: torch.stack(b).t_().squeeze(0).contiguous())
return (transpose(premise_batch), transpose(hypothesis_batch),
transpose(label_batch))
def collate_fn(batch):
text_batch, _ = pad_batch([row['text'] for row in batch])
label_batch = [row['label'] for row in batch]
to_tensor = (lambda b: torch.stack(b).squeeze(-1))
return text_batch, to_tensor(label_batch)
def test_pad_batch():
batch = [
torch.LongTensor([1, 2, 3]),
torch.LongTensor([1, 2]),
torch.LongTensor([1])
]
padded, lengths = pad_batch(batch, PADDING_INDEX)
padded = [r.tolist() for r in padded]
assert padded == [[1, 2, 3], [1, 2, PADDING_INDEX],
[1, PADDING_INDEX, PADDING_INDEX]]
assert lengths == [3, 2, 1]
def collate_fn(batch):
""" list of tensors to a batch variable """
# PyTorch RNN requires sorting decreasing size
batch = sorted(batch,
key=lambda row: len(row[input_key]),
reverse=True)
input_batch, input_lengths = pad_batch(
[row[input_key] for row in batch])
output_batch, output_lengths = pad_batch(
[row[output_key] for row in batch])
def batch_to_variable(batch):
# PyTorch RNN requires batches to be transposed for speed and integration with CUDA
return Variable(torch.stack(batch).t_().contiguous(),
volatile=not train)
# Async minibatch allocation for speed
# Reference: http://timdettmers.com/2015/03/09/deep-learning-hardware-guide/
cuda = lambda t: t.cuda(async=True) if torch.cuda.is_available() else t
return (cuda(batch_to_variable(input_batch)),
cuda(torch.LongTensor(input_lengths)),
cuda(batch_to_variable(output_batch)),
cuda(torch.LongTensor(output_lengths)))
def collate_fn(batch):
""" list of tensors to a batch tensors """
text_batch, _ = pad_batch([row['text'] for row in batch])
label_batch = [row['label'] for row in batch]
return [text_batch, torch.cat(label_batch)]