Mikhail RyzhovandMaxim Vafin
4078bd9c19
* test * fixed tests * typo * fixed tests * rest of the tests * fixed rsub test * tmp fix * Revert "tmp fix" This reverts commit b8bf1e9492e13497895da488612c9a137ef840bc. * fixed test params * reset thirdparty/pugixml * Revert "fixed rsub test" This reverts commit 9b6be34b8666936e8124b6622fcc5185b640de92. * fixed typo * fixed test data * reset test_rsub * removed unused param * reverrted runner * simplified call * fixed random * changed logical to auto mode * Revert "fixed random" This reverts commit 8a4f20b24641144f823a7e1f1ff92038634acf32. * fixed test_all * replaced random_sample with randn * fixed rebase issue * reverted logical splitting * Update tests/layer_tests/pytorch_tests/test_repeat_interleave.py Co-authored-by: Maxim Vafin <maxim.vafin@intel.com> * Update tests/layer_tests/pytorch_tests/test_all.py Co-authored-by: Maxim Vafin <maxim.vafin@intel.com> * Apply suggestions from code review Co-authored-by: Maxim Vafin <maxim.vafin@intel.com> * fixed merge conflict --------- Co-authored-by: Maxim Vafin <maxim.vafin@intel.com>
61 lines
2.3 KiB
Python
61 lines
2.3 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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import torch
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from pytorch_layer_test_class import PytorchLayerTest
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from torchvision.ops import roi_align
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class TestROIAlign(PytorchLayerTest):
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def _prepare_input(self):
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return (self.input_tensor, self.boxes)
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def create_model(self, output_size, spatial_scale, sampling_ratio, aligned):
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class torchvision_roi_align(torch.nn.Module):
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def __init__(self, output_size, spatial_scale, sampling_ratio, aligned):
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super().__init__()
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self.output_size = output_size
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self.spatial_scale = spatial_scale
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self.sampling_ratio = sampling_ratio
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self.aligned = aligned
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def forward(self, input_tensor, rois):
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return roi_align(
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input_tensor,
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rois.to(dtype=input_tensor.dtype),
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self.output_size,
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self.spatial_scale,
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self.sampling_ratio,
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self.aligned,
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)
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ref_net = None
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return (torchvision_roi_align(output_size, spatial_scale, sampling_ratio, aligned),
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ref_net, "torchvision::roi_align")
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@pytest.mark.parametrize('input_shape', [
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[4, 5, 6, 7],
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])
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@pytest.mark.parametrize('boxes', (np.array([[1, 2, 2, 3, 3]]).astype(np.float32),
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np.array([[0, 1, 2, 5, 4],
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[2, 1, 2, 5, 4],
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[3, 1, 2, 5, 4]]).astype(np.float32)))
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@pytest.mark.parametrize('output_size', ((4, 5), (3, 2), 3))
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@pytest.mark.parametrize('spatial_scale', (0.5, 1.0))
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@pytest.mark.parametrize('sampling_ratio', (0, 1))
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@pytest.mark.parametrize('aligned', (True, False))
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_roi_align(self, ie_device, precision, ir_version, input_shape, boxes, output_size,
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spatial_scale, sampling_ratio, aligned):
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self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
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self.boxes = boxes
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self._test(*self.create_model(output_size, spatial_scale, sampling_ratio, aligned),
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ie_device, precision, ir_version, trace_model=True)
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