forked from mindspore-Ecosystem/mindspore
57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.nn as nn
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import mindspore.common.dtype as mstype
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from mindspore.common.initializer import Normal
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from mindspore import Tensor
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from mindspore import context
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_conv2d_depthwiseconv2d_str():
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net = nn.Conv2d(128, 128, (2, 3), stride=4, pad_mode='valid', padding=0, group=128, weight_init='normal')
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input_data = Tensor(np.ones([3, 128, 127, 114]), dtype=mstype.float32)
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output = net(input_data)
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assert output.shape == (3, 128, 32, 28)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_conv2d_depthwiseconv2d_initializer():
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net = nn.Conv2d(128, 128, (2, 3), stride=4, pad_mode='valid', padding=0, group=128, weight_init=Normal())
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input_data = Tensor(np.ones([3, 128, 127, 114]), dtype=mstype.float32)
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output = net(input_data)
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assert output.shape == (3, 128, 32, 28)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_conv2d_depthwiseconv2d_tensor():
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weight_init = Tensor(np.random.randn(128, 1, 2, 3).astype(np.float32))
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net = nn.Conv2d(128, 128, (2, 3), stride=4, pad_mode='valid', padding=0, group=128, weight_init=weight_init)
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input_data = Tensor(np.ones([3, 128, 127, 114]), dtype=mstype.float32)
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output = net(input_data)
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assert output.shape == (3, 128, 32, 28)
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