138 lines
8.4 KiB
Python
138 lines
8.4 KiB
Python
       
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# 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 TestConv2D(PytorchLayerTest):
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def _prepare_input(self, ndim=4):
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import numpy as np
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input_shape = (1, 3, 10, 10, 10)
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return (np.random.randn(*input_shape[:ndim]).astype(np.float32),)
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def create_model(self, weights_shape, strides, pads, dilations, groups, bias):
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import torch
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class aten_convolution_mode(torch.nn.Module):
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def __init__(self):
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super(aten_convolution_mode, self).__init__()
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self.weight = torch.randn(weights_shape)
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self.bias = None
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if bias:
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self.bias = torch.randn(weights_shape[0])
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self.strides = strides
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self.pads = pads
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self.dilations = dilations
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self.groups = groups
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def forward(self, x):
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return torch._convolution_mode(x, self.weight, self.bias, self.strides, self.pads, self.dilations,
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self.groups)
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ref_net = None
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return aten_convolution_mode(), ref_net, "aten::_convolution_mode"
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@pytest.mark.parametrize("params",
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[
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{'weights_shape': [1, 3, 3], 'strides': [1], 'pads': 'same', 'dilations': [1],
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'groups': 1},
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{'weights_shape': [1, 3, 3], 'strides': [1], 'pads': 'valid', 'dilations': [1],
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'groups': 1},
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{'weights_shape': [1, 3, 3], 'strides': [1], 'pads': 'same', 'dilations': [2],
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'groups': 1},
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{'weights_shape': [1, 3, 3], 'strides': [1], 'pads': 'valid', 'dilations': [2],
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'groups': 1},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'pads': 'same', 'dilations': [1],
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'groups': 3},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'pads': 'valid', 'dilations': [1],
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'groups': 3},
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{'weights_shape': [1, 3, 3], 'strides': [2], 'pads': 'valid', 'dilations': [1],
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'groups': 1},
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{'weights_shape': [1, 3, 3], 'strides': [2], 'pads': 'valid', 'dilations': [2],
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'groups': 1},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'pads': 'same', 'dilations': [2],
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'groups': 3},
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{'weights_shape': [3, 1, 1], 'strides': [1], 'pads': 'valid', 'dilations': [2],
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'groups': 3},
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])
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@pytest.mark.parametrize("bias", [True, False])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_convolution_mode_1d(self, params, bias, ie_device, precision, ir_version):
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self._test(*self.create_model(**params, bias=bias),
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ie_device, precision, ir_version, dynamic_shapes=params['groups'] == 1,
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kwargs_to_prepare_input={'ndim': 3})
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@pytest.mark.parametrize("params",
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[
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{'weights_shape': [1, 3, 3, 3], 'strides': [1, 1], 'pads': 'same', 'dilations': [1, 1],
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'groups': 1},
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{'weights_shape': [1, 3, 3, 3], 'strides': [1, 1], 'pads': 'valid',
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'dilations': [1, 1], 'groups': 1},
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{'weights_shape': [1, 3, 3, 3], 'strides': [1, 1], 'pads': 'same', 'dilations': [2, 2],
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'groups': 1},
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{'weights_shape': [1, 3, 3, 3], 'strides': [1, 1], 'pads': 'valid',
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'dilations': [2, 2], 'groups': 1},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': 'same', 'dilations': [1, 1],
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'groups': 3},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': 'valid',
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'dilations': [1, 1], 'groups': 3},
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{'weights_shape': [1, 3, 3, 3], 'strides': [2, 2], 'pads': 'valid',
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'dilations': [1, 1], 'groups': 1},
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{'weights_shape': [1, 3, 3, 3], 'strides': [2, 2], 'pads': 'valid',
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'dilations': [2, 2], 'groups': 1},
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{'weights_shape': [1, 3, 3, 3], 'strides': [2, 1], 'pads': 'valid',
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'dilations': [1, 1], 'groups': 1},
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{'weights_shape': [3, 1, 1, 1], 'strides': [2, 2], 'pads': 'valid',
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'dilations': [1, 1], 'groups': 3},
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{'weights_shape': [3, 1, 1, 1], 'strides': [2, 2], 'pads': 'valid',
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'dilations': [2, 2], 'groups': 3},
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{'weights_shape': [3, 1, 1, 1], 'strides': [2, 1], 'pads': 'valid',
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'dilations': [1, 1], 'groups': 3},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': 'same', 'dilations': [2, 1],
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'groups': 3},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': 'valid',
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'dilations': [2, 1], 'groups': 3},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': 'same', 'dilations': [2, 2],
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'groups': 3},
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{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': 'valid',
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'dilations': [2, 2], 'groups': 3},
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])
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@pytest.mark.parametrize("bias", [True, False])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_convolution_mode_2d(self, params, bias, ie_device, precision, ir_version):
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self._test(*self.create_model(**params, bias=bias),
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ie_device, precision, ir_version, dynamic_shapes=params['groups'] == 1)
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@pytest.mark.parametrize("params",
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[
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': [1, 1, 1], 'pads': 'same',
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'dilations': [1, 1, 1], 'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': [1, 1, 1], 'pads': 'valid',
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'dilations': [1, 1, 1], 'groups': 1},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': 'same',
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'dilations': [1, 1, 1], 'groups': 3},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': 'valid',
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'dilations': [1, 1, 1], 'groups': 3},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': [2, 2, 1], 'pads': 'valid',
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'dilations': [1, 1, 1], 'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': [2, 2, 2], 'pads': 'valid',
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'dilations': [1, 1, 1], 'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': [2, 2, 2], 'pads': 'valid',
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'dilations': [2, 2, 2], 'groups': 1},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': 'same',
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'dilations': [2, 1, 2], 'groups': 3},
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{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': 'valid',
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'dilations': [2, 1, 2], 'groups': 3},
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])
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@pytest.mark.parametrize("bias", [True, False])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_convolution_mode_3d(self, params, bias, ie_device, precision, ir_version):
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self._test(*self.create_model(**params, bias=bias),
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ie_device, precision, ir_version, dynamic_shapes=params['groups'] == 1,
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kwargs_to_prepare_input={'ndim': 5})
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