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63 lines
2.1 KiB
63 lines
2.1 KiB
4 years ago
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# 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.operations import _grad_ops as G
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.relu_grad = G.ReluGrad()
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def construct(self, y_backprop, x):
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return self.relu_grad(y_backprop, x)
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def get_output(y_backprop, x, enable_graph_kernel=False):
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if enable_graph_kernel:
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context.set_context(enable_graph_kernel=True)
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net = Net()
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output = net(y_backprop, x)
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return output
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def test_relu_grad(shape1, shape2, dtype):
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x = Tensor(np.random.normal(0, 10, shape1).astype(dtype))
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y_backprop = Tensor(np.random.normal(0, 10, shape2).astype(dtype))
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expect = get_output(y_backprop, x, False)
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output = get_output(y_backprop, x, True)
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expect_np = expect.asnumpy().copy()
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output_np = output.asnumpy().copy()
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assert np.allclose(expect_np, output_np, 0.0001, 0.0001)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_relu_grad_gpu():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_relu_grad((4, 3), (4, 3), np.int32)
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test_relu_grad((12, 1), (12, 1), np.float16)
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def test_relu_grad_ascend():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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test_relu_grad((4, 3), (4, 3), np.int32)
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test_relu_grad((12, 1), (12, 1), np.float16)
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