mirror of
https://github.com/amazon-science/chronos-forecasting
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30 lines
880 B
Python
30 lines
880 B
Python
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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import torch
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from chronos.utils import left_pad_and_stack_1D
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@pytest.mark.parametrize(
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"tensors",
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[
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[
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torch.tensor([2.0, 3.0], dtype=dtype),
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torch.tensor([4.0, 5.0, 6.0], dtype=dtype),
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torch.tensor([7.0, 8.0, 9.0, 10.0], dtype=dtype),
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]
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for dtype in [torch.int, torch.float16, torch.float32]
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],
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)
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def test_pad_and_stack(tensors: list):
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stacked_and_padded = left_pad_and_stack_1D(tensors)
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assert stacked_and_padded.dtype == torch.float32
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assert stacked_and_padded.shape == (len(tensors), max(len(t) for t in tensors))
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ref = torch.concat(tensors).to(dtype=stacked_and_padded.dtype)
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assert torch.sum(torch.nan_to_num(stacked_and_padded, nan=0)) == torch.sum(ref)
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