53 lines
2.2 KiB
Python
53 lines
2.2 KiB
Python
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import pytest
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import torch
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import torchtext
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from torchtext.data.example import Example
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from pytorch_lightning.utilities.apply_func import move_data_to_device
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def _get_torchtext_data_iterator(include_lengths=False):
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text_field = torchtext.data.Field(sequential=True, pad_first=False, # nosec
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init_token="<s>", eos_token="</s>", # nosec
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include_lengths=include_lengths) # nosec
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example1 = Example.fromdict({"text": "a b c a c"}, {"text": ("text", text_field)})
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example2 = Example.fromdict({"text": "b c a a"}, {"text": ("text", text_field)})
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example3 = Example.fromdict({"text": "c b a"}, {"text": ("text", text_field)})
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dataset = torchtext.data.Dataset(
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[example1, example2, example3],
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{"text": text_field},
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)
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text_field.build_vocab(dataset)
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iterator = torchtext.data.Iterator(dataset, batch_size=3,
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sort_key=None, device=None,
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batch_size_fn=None,
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train=True, repeat=False, shuffle=None,
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sort=None, sort_within_batch=None)
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return iterator, text_field
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@pytest.mark.parametrize('include_lengths', [False, True])
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@pytest.mark.parametrize(['device'], [pytest.param(torch.device('cuda', 0))])
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test assumes GPU machine")
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def test_batch_move_data_to_device_torchtext_include_lengths(include_lengths, device):
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data_iterator, _ = _get_torchtext_data_iterator(include_lengths=include_lengths)
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data_iter = iter(data_iterator)
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batch = next(data_iter)
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batch_on_device = move_data_to_device(batch, device)
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if include_lengths:
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# tensor with data
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assert (batch_on_device.text[0].device == device)
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# tensor with length of data
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assert (batch_on_device.text[1].device == device)
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else:
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assert (batch_on_device.text.device == device)
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@pytest.mark.parametrize('include_lengths', [False, True])
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def test_batch_move_data_to_device_torchtext_include_lengths_cpu(include_lengths):
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test_batch_move_data_to_device_torchtext_include_lengths(include_lengths, torch.device('cpu'))
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