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52 lines
1.7 KiB
52 lines
1.7 KiB
# 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.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.common.api import ms_function
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from mindspore.ops.composite import GradOperation
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class Grad(nn.Cell):
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def __init__(self, network):
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super(Grad, self).__init__()
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self.grad = GradOperation(get_all=True, sens_param=True)
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self.network = network
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@ms_function
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def construct(self, input_, output_grad):
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return self.grad(self.network)(input_, output_grad)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_net():
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x = np.arange(1 * 1 * 6 * 6).reshape((1, 1, 6, 6)).astype(np.float32)
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net = nn.AvgPool2d(kernel_size=3, stride=2, pad_mode='valid')
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out = net(Tensor(x))
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out_shape = out.asnumpy().shape
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sens = np.arange(int(np.prod(out_shape))).reshape(out_shape).astype(np.float32)
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backword_net = Grad(net)
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output = backword_net(Tensor(x), Tensor(sens))
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print(len(output))
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print(output[0].asnumpy())
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