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85 lines
3.8 KiB
85 lines
3.8 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.common.tensor import Tensor
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from mindspore.common.parameter import Parameter
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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def __init__(self, input_scale, input_bias, input_mean, input_variance, is_training):
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super(Net, self).__init__()
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self.fused_bn_ex = P.BatchNorm(is_training=is_training, epsilon=1e-5, momentum=0.9)
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self.scale = Parameter(input_scale, name='scale')
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self.bias = Parameter(input_bias, name='b')
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self.mean = Parameter(input_mean, name='mean')
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self.variance = Parameter(input_variance, name='variance')
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def construct(self, input_x):
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return self.fused_bn_ex(input_x, self.scale, self.bias, self.mean, self.variance)
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def get_output(x, weight, bias, moving_mean, moving_var, is_training, 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(Tensor(weight), Tensor(bias), Tensor(moving_mean), Tensor(moving_var), is_training)
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output = net(Tensor(x))
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return output, net.mean, net.variance
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def test_bn_train():
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x = np.random.normal(0, 1, [1, 2, 4, 4]).astype(np.float32)
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weight = np.random.normal(0, 1, [2,]).astype(np.float32)
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bias = np.random.normal(0, 1, [2,]).astype(np.float32)
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moving_mean = np.random.normal(0, 1, [2,]).astype(np.float32)
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moving_var = np.random.normal(0, 1, [2,]).astype(np.float32)
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train_expect = get_output(x, weight, bias, moving_mean, moving_var, True, False)
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train_output = get_output(x, weight, bias, moving_mean, moving_var, True, True)
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assert np.allclose(train_expect[0][0].asnumpy(), train_output[0][0].asnumpy(), 0.0001, 0.0001)
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assert np.allclose(train_expect[0][3].asnumpy(), train_output[0][3].asnumpy(), 0.0001, 0.0001)
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assert np.allclose(train_expect[0][4].asnumpy(), train_output[0][4].asnumpy(), 0.0001, 0.0001)
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assert np.allclose(train_expect[1].data.asnumpy(), train_output[1].data.asnumpy(), 0.0001, 0.0001)
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assert np.allclose(train_expect[2].data.asnumpy(), train_output[2].data.asnumpy(), 0.0001, 0.0001)
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def test_bn_infer():
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x = np.random.normal(5, 1, [1, 2, 4, 4]).astype(np.float32)
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weight = np.random.normal(5, 1, [2,]).astype(np.float32)
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bias = np.random.normal(5, 1, [2,]).astype(np.float32)
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moving_mean = np.random.normal(5, 1, [2,]).astype(np.float32)
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moving_var = np.random.normal(5, 1, [2,]).astype(np.float32)
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infer_expect = get_output(x, weight, bias, moving_mean, moving_var, False, False)
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infer_output = get_output(x, weight, bias, moving_mean, moving_var, False, True)
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assert np.allclose(infer_expect[0][0].asnumpy(), infer_output[0][0].asnumpy(), 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_bn_train_gpu():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_bn_train()
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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_bn_infer_gpu():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_bn_infer()
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