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# 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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""" test_parser_construct """ |
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import pytest |
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import numpy as np |
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from mindspore import context |
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from mindspore.nn import Cell |
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from mindspore.common.tensor import Tensor |
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from mindspore.ops import operations as P |
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from mindspore.ops.composite import GradOperation |
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def setup_module(): |
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend") |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_parser_construct(): |
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class ParentNet(Cell): |
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def __init__(self): |
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super().__init__() |
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self.relu = P.ReLU() |
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def construct(self, x): |
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return self.relu(x) |
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class UncleNet(Cell): |
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def __init__(self): |
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super(UncleNet, self).__init__() |
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self.sigmoid = P.Sigmoid() |
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def construct(self, x): |
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return self.sigmoid(x) |
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class Net(UncleNet, ParentNet): |
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def __init__(self): |
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super().__init__() |
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super(UncleNet, self).__init__() |
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def construct(self, x): |
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return super(UncleNet, self).construct(x) |
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input_np_x = np.ones([2, 3, 4, 5]).astype(np.float32) |
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out_np = np.ones([2, 3, 4, 5]).astype(np.float32) |
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input_me = Tensor(input_np_x) |
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output_grad_me = Tensor(out_np) |
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net = Net() |
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out_me = net(input_me) |
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net1 = Net() |
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grad = GradOperation(sens_param=True) |
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grad_op = grad(net1) |
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grad_me = grad_op(input_me, output_grad_me) |
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assert np.allclose(input_np_x, out_me.asnumpy(), 0.001, 0.001) |
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assert np.allclose(input_np_x, grad_me.asnumpy(), 0.001, 0.001) |