97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
# Copyright (C) 2018-2023 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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from pytorch_layer_test_class import PytorchLayerTest
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class TestPixelShuffle(PytorchLayerTest):
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def _prepare_input(self):
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return (np.random.randn(*self.shape).astype(np.float32),)
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def create_model(self, upscale_factor):
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import torch
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import torch.nn.functional as F
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class aten_pixel_shuffle(torch.nn.Module):
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def __init__(self, upscale_factor):
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super(aten_pixel_shuffle, self).__init__()
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self.upscale_factor = upscale_factor
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def forward(self, x):
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return F.pixel_shuffle(x, self.upscale_factor)
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return aten_pixel_shuffle(upscale_factor), None, "aten::pixel_shuffle"
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@pytest.mark.parametrize(("upscale_factor,shape"), [(3, [1, 9, 4, 4]),
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(2, [1, 2, 3, 8, 4, 4]),])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_pixel_shuffle(self, upscale_factor, shape, ie_device, precision, ir_version):
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self.shape = shape
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self._test(*self.create_model(upscale_factor),
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ie_device, precision, ir_version)
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class TestPixelUnshuffle(PytorchLayerTest):
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def _prepare_input(self):
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return (np.random.randn(*self.shape).astype(np.float32),)
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def create_model(self, upscale_factor):
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import torch
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import torch.nn.functional as F
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class aten_pixel_unshuffle(torch.nn.Module):
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def __init__(self, upscale_factor):
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super(aten_pixel_unshuffle, self).__init__()
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self.upscale_factor = upscale_factor
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def forward(self, x):
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return F.pixel_unshuffle(x, self.upscale_factor)
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return aten_pixel_unshuffle(upscale_factor), None, "aten::pixel_unshuffle"
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@pytest.mark.parametrize(("upscale_factor,shape"), [(3, [1, 1, 12, 12]),
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(2, [1, 2, 3, 2, 8, 8]),])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_pixel_unshuffle(self, upscale_factor, shape, ie_device, precision, ir_version):
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self.shape = shape
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self._test(*self.create_model(upscale_factor),
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ie_device, precision, ir_version)
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class TestChannelShuffle(PytorchLayerTest):
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def _prepare_input(self):
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return (np.random.randn(*self.shape).astype(np.float32),)
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def create_model(self, groups):
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import torch
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import torch.nn.functional as F
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class aten_channel_shuffle(torch.nn.Module):
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def __init__(self, upscale_factor):
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super(aten_channel_shuffle, self).__init__()
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self.upscale_factor = upscale_factor
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def forward(self, x):
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return F.channel_shuffle(x, self.upscale_factor)
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return aten_channel_shuffle(groups), None, "aten::channel_shuffle"
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@pytest.mark.parametrize(("groups,shape"), [
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(3, [1, 9, 4, 4]),
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(2, [1, 8, 8, 4, 4]),
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(4, [4, 4, 2]),
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(5, [4, 10, 2, 10, 1, 1]),
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(1, [2, 3, 4])
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_channel_shuffle(self, groups, shape, ie_device, precision, ir_version):
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self.shape = shape
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self._test(*self.create_model(groups),
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ie_device, precision, ir_version)
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