mirror of https://github.com/kendryte/nncase.git
79 lines
2.1 KiB
Python
79 lines
2.1 KiB
Python
# Copyright 2019-2021 Canaan Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# pylint: disable=invalid-name, unused-argument, import-outside-toplevel
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import pytest
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import torch
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from onnx_test_runner import OnnxTestRunner
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def _make_module(in_shape, out_channel, kernel_size, dim):
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class SqueezeModule(torch.nn.Module):
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def __init__(self):
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super(SqueezeModule, self).__init__()
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self.conv2d = torch.nn.Conv2d(in_shape[1], out_channel, kernel_size)
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def forward(self, x):
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x = self.conv2d(x)
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x = torch.squeeze(x)
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# There is something wrong when converting pytorch into onnx. Use tf.squeeze(x, dim) instead.
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# x = torch.squeeze(x, dim)
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return x
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return SqueezeModule()
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in_shapes = [
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[1, 4, 60, 72],
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[1, 3, 224, 224]
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]
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out_channels = [
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1,
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3,
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]
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kernel_sizes = [
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1,
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3,
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]
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axes = [
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0,
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1,
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2,
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3
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]
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@pytest.mark.parametrize('in_shape', in_shapes)
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@pytest.mark.parametrize('out_channel', out_channels)
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@pytest.mark.parametrize('kernel_size', kernel_sizes)
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@pytest.mark.parametrize('axis', axes)
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def test_squeeze(in_shape, out_channel, kernel_size, axis, request):
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out_shape = [in_shape[0], out_channel, in_shape[2] -
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kernel_size + 1, in_shape[3] - kernel_size + 1]
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dim = axis if out_shape[axis] == 1 else None
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module = _make_module(in_shape, out_channel, kernel_size, dim)
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runner = OnnxTestRunner(request.node.name)
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model_file = runner.from_torch(module, in_shape)
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runner.run(model_file)
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if __name__ == "__main__":
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pytest.main(['-vv', 'test_squeeze.py'])
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