a9efe5bd8d
* [PT FE] Extend upsample suport * Update tests/layer_tests/pytorch_tests/test_upsample.py Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com> --------- Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com>
141 lines
4.6 KiB
Python
141 lines
4.6 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from pytorch_layer_test_class import PytorchLayerTest
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class TestUpsample1D(PytorchLayerTest):
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def _prepare_input(self):
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import numpy as np
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return (np.random.randn(1, 3, 224).astype(np.float32),)
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def create_model(self, size, scale, mode):
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import torch
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import torch.nn.functional as F
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class aten_upsample(torch.nn.Module):
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def __init__(self, size, scale, mode):
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super().__init__()
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self.size = size
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self.scale = scale
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self.mode = mode
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def forward(self, x):
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return F.interpolate(x, self.size, scale_factor=self.scale, mode=self.mode)
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ref_net = None
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return aten_upsample(size, scale, mode), ref_net, F"aten::upsample_{mode}1d"
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@pytest.mark.parametrize("mode,size,scale", [
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('nearest', 300, None),
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('nearest', 200, None),
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('nearest', None, 2.5),
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('nearest', None, 0.75),
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('linear', 300, None),
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('linear', 200, None),
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('linear', None, 2.5,),
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('linear', None, 0.75),
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_upsample1d(self, mode, size, scale, ie_device, precision, ir_version):
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self._test(*self.create_model(size, scale, mode), ie_device,
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precision, ir_version, trace_model=True)
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class TestUpsample2D(PytorchLayerTest):
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def _prepare_input(self):
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import numpy as np
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return (np.random.randn(1, 3, 200, 200).astype(np.float32),)
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def create_model(self, size, scale, mode):
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import torch
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import torch.nn.functional as F
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class aten_upsample(torch.nn.Module):
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def __init__(self, size, scale, mode):
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super().__init__()
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self.size = size
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self.scale = scale
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self.mode = mode
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def forward(self, x):
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return F.interpolate(x, self.size, scale_factor=self.scale, mode=self.mode)
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ref_net = None
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return aten_upsample(size, scale, mode), ref_net, F"aten::upsample_{mode}2d"
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@pytest.mark.parametrize("mode,size,scale", [
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('nearest', 300, None),
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('nearest', 150, None),
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('nearest', (300, 400), None),
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('nearest', None, 2.5),
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('nearest', None, 0.75),
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('nearest', None, (1.5, 2)),
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('bilinear', 300, None),
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('bilinear', 150, None),
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('bilinear', (400, 480), None),
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('bilinear', None, 2.5,),
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('bilinear', None, 0.75),
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('bilinear', None, (1.2, 1.3)),
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('bicubic', 300, None),
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('bicubic', 150, None),
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('bicubic', (400, 480), None),
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('bicubic', None, 2.5,),
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('bicubic', None, 0.75),
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('bicubic', None, (1.2, 1.3))
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_upsample2d(self, mode, size, scale, ie_device, precision, ir_version):
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self._test(*self.create_model(size, scale, mode), ie_device,
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precision, ir_version, trace_model=True, **{"custom_eps": 1e-3})
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class TestUpsample3D(PytorchLayerTest):
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def _prepare_input(self):
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import numpy as np
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return (np.random.randn(1, 3, 100, 100, 100).astype(np.float32),)
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def create_model(self, size, scale, mode):
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import torch
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import torch.nn.functional as F
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class aten_upsample(torch.nn.Module):
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def __init__(self, size, scale, mode):
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super().__init__()
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self.size = size
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self.scale = scale
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self.mode = mode
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def forward(self, x):
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return F.interpolate(x, self.size, scale_factor=self.scale, mode=self.mode)
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ref_net = None
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return aten_upsample(size, scale, mode), ref_net, F"aten::upsample_{mode}3d"
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@pytest.mark.parametrize("mode,size,scale", [
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('nearest', 200, None),
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('nearest', 150, None),
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('nearest', (150, 200, 250), None),
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('nearest', None, 2.5),
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('nearest', None, 0.75),
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('nearest', None, (1.5, 2, 2.5)),
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('trilinear', 200, None),
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('trilinear', 150, None),
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('trilinear', (200, 240, 210), None),
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('trilinear', None, 2.5,),
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('trilinear', None, 0.75),
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('trilinear', None, (1.2, 1.1, 1.5)),
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_upsample3d(self, mode, size, scale, ie_device, precision, ir_version):
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self._test(*self.create_model(size, scale, mode), ie_device,
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precision, ir_version, trace_model=True, **{"custom_eps": 1e-3})
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