6a9608b075
* Fix GroupConvolution weigts * Update src/frontends/pytorch/src/utils.hpp * Update convnd.cpp
162 lines
7.9 KiB
Python
162 lines
7.9 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 TestConv2D(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(2, 3, 25, 25).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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import torch.nn.functional as F
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class aten_conv2d(torch.nn.Module):
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def __init__(self):
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super(aten_conv2d, 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 F.conv2d(x, self.weight, self.bias, self.strides, self.pads, self.dilations, self.groups)
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ref_net = None
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return aten_conv2d(), ref_net, "aten::conv2d"
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@pytest.mark.parametrize("params",
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[{'weights_shape': [1, 3, 3, 3], 'strides': 1, 'pads': 0, 'dilations': 1, 'groups': 1},
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{'weights_shape': [1, 3, 3, 3], 'strides': 2, 'pads': 0, 'dilations': 1, 'groups': 1},
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{'weights_shape': [1, 3, 3, 3], 'strides': 1, 'pads': 1, 'dilations': 1, 'groups': 1},
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{'weights_shape': [1, 3, 3, 3], 'strides': 1, 'pads': 0, 'dilations': 2, 'groups': 1},
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{'weights_shape': [1, 3, 3, 3], 'strides': 1, 'pads': [0, 1], 'dilations': 1,
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'groups': 1},
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{'weights_shape': [1, 3, 3, 3], 'strides': 1, 'pads': [1, 0], 'dilations': 1,
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'groups': 1},
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{'weights_shape': [1, 3, 3, 3], 'strides': 1, 'pads': 'same', 'dilations': 1,
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'groups': 1},
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{'weights_shape': [1, 3, 3, 3], 'strides': 1, 'pads': 'valid', 'dilations': 1,
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'groups': 1},
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{'weights_shape': [3, 1, 3, 3], 'strides': 1, 'pads': 0, 'dilations': 1, '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_conv2d(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)
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class TestConv1D(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(2, 3, 25).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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import torch.nn.functional as F
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class aten_conv1d(torch.nn.Module):
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def __init__(self):
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super(aten_conv1d, 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 F.conv1d(x, self.weight, self.bias, self.strides, self.pads, self.dilations, self.groups)
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ref_net = None
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return aten_conv1d(), ref_net, "aten::conv1d"
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@pytest.mark.parametrize("params",
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[{'weights_shape': [3, 3, 3], 'strides': 1, 'pads': 0, 'dilations': 1, 'groups': 1},
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{'weights_shape': [3, 3, 3], 'strides': 2, 'pads': 0, 'dilations': 1, 'groups': 1},
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{'weights_shape': [3, 3, 3], 'strides': 1, 'pads': 1, 'dilations': 1, 'groups': 1},
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{'weights_shape': [3, 3, 3], 'strides': 1, 'pads': 0, 'dilations': 2, 'groups': 1},
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{'weights_shape': [3, 3, 3], 'strides': 1, 'pads': 'same', 'dilations': 1, 'groups': 1},
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{'weights_shape': [3, 3, 3], 'strides': 1, 'pads': 'valid', 'dilations': 1, 'groups': 1},
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{'weights_shape': [3, 1, 3], 'strides': 1, 'pads': 0, 'dilations': 1, '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_conv1d(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)
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class TestConv3D(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(2, 3, 25, 25, 25).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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import torch.nn.functional as F
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class aten_conv3d(torch.nn.Module):
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def __init__(self):
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super(aten_conv3d, 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 F.conv3d(x, self.weight, self.bias, self.strides, self.pads, self.dilations, self.groups)
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ref_net = None
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return aten_conv3d(), ref_net, "aten::conv3d"
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@pytest.mark.parametrize("params",
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[{'weights_shape': [1, 3, 3, 3, 3], 'strides': 1, 'pads': 0, 'dilations': 1, 'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': 2, 'pads': 0, 'dilations': 1, 'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': 1, 'pads': 1, 'dilations': 1, 'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': 1, 'pads': 0, 'dilations': 2, 'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': 1, 'pads': [0, 1, 0], 'dilations': 1,
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'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': 1, 'pads': [1, 0, 0], 'dilations': 1,
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'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': 1, 'pads': [0, 0, 1], 'dilations': 1,
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'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': 1, 'pads': [1, 1, 0], 'dilations': 1,
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'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': 1, 'pads': [0, 1, 1], 'dilations': 1,
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'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': 1, 'pads': [1, 0, 1], 'dilations': 1,
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'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': 1, 'pads': 'same', 'dilations': 1,
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'groups': 1},
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{'weights_shape': [1, 3, 3, 3, 3], 'strides': 1, 'pads': 'valid', 'dilations': 1,
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'groups': 1},
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{'weights_shape': [3, 1, 3, 3, 3], 'strides': 1, 'pads': 0, 'dilations': 1, '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_conv3d(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)
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