forked from mindspore-Ecosystem/mindspore
66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
# Copyright 2021 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.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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class NetConv3dTranspose(nn.Cell):
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def __init__(self):
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super(NetConv3dTranspose, self).__init__()
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in_channel = 2
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out_channel = 2
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kernel_size = 2
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self.conv_trans = P.Conv3DTranspose(in_channel, out_channel,
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kernel_size,
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pad_mode="pad",
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pad=1,
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stride=1,
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dilation=1,
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group=1)
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def construct(self, x, w):
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return self.conv_trans(x, w)
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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_conv3d_transpose():
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x = Tensor(np.arange(1 * 2 * 3 * 3 * 3).reshape(1, 2, 3, 3, 3).astype(np.float32))
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w = Tensor(np.ones((2, 2, 2, 2, 2)).astype(np.float32))
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expect = np.array([[[[[320., 336.],
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[368., 384.]],
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[[464., 480.],
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[512., 528.]]],
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[[[320., 336.],
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[368., 384.]],
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[[464., 480.],
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[512., 528.]]]]]).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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conv3dtranspose = NetConv3dTranspose()
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output = conv3dtranspose(x, w)
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assert (output.asnumpy() == expect).all()
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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conv3dtranspose = NetConv3dTranspose()
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output = conv3dtranspose(x, w)
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assert (output.asnumpy() == expect).all()
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