50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
# Copyright (C) 2018-2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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import torch
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from pytorch_layer_test_class import PytorchLayerTest
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@pytest.mark.parametrize('dimension', (0, 1, 2))
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@pytest.mark.parametrize('size', (1, 2))
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@pytest.mark.parametrize('step', (1, 2, 3, 4))
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@pytest.mark.parametrize('input_shape',
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[
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[2, 2, 5], [3, 3, 3, 3], [2, 3, 4, 5]
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])
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class TestUnfold(PytorchLayerTest):
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def _prepare_input(self):
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return (self.input_tensor, )
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def create_model(self, dimension, size, step):
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class aten_unfold(torch.nn.Module):
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def __init__(self, dimension, size, step) -> None:
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super().__init__()
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self.dimension = dimension
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self.size = size
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self.step = step
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def forward(self, input_tensor):
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return input_tensor.unfold(dimension=self.dimension, size=self.size, step=self.step)
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ref_net = None
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return aten_unfold(dimension, size, step), ref_net, "aten::unfold"
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.precommit_torch_export
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@pytest.mark.precommit_fx_backend
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def test_unfold(self, ie_device, precision, ir_version, dimension, size, step, input_shape):
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self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
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dyn_shape = True
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if ie_device == "GPU" and size == 1 and step == 1:
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dyn_shape = False
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self._test(*self.create_model(dimension, size, step),
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ie_device, precision, ir_version, dynamic_shapes=dyn_shape)
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