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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
-
- import numpy as np
- import pytest
-
- import mindspore.context as context
- from mindspore.common.tensor import Tensor
- from mindspore.nn import BatchNorm2d
- from mindspore.nn import Cell
- from mindspore.ops import composite as C
-
-
- class Batchnorm_Net(Cell):
- def __init__(self, c, weight, bias, moving_mean, moving_var_init):
- super(Batchnorm_Net, self).__init__()
- self.bn = BatchNorm2d(c, eps=0.00001, momentum=0.1, beta_init=bias, gamma_init=weight,
- moving_mean_init=moving_mean, moving_var_init=moving_var_init)
-
- def construct(self, input_data):
- x = self.bn(input_data)
- return x
-
-
- class Grad(Cell):
- def __init__(self, network):
- super(Grad, self).__init__()
- self.grad = C.GradOperation(get_all=True, sens_param=True)
- self.network = network
-
- def construct(self, input_data, sens):
- gout = self.grad(self.network)(input_data, sens)
- return gout
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_cpu
- @pytest.mark.env_onecard
- def test_train_forward():
- x = np.array([[
- [[1, 3, 3, 5], [2, 4, 6, 8], [3, 6, 7, 7], [4, 3, 8, 2]],
- [[5, 7, 6, 3], [3, 5, 6, 7], [9, 4, 2, 5], [7, 5, 8, 1]]]]).astype(np.float32)
- expect_output = np.array([[[[-0.6059, 0.3118, 0.3118, 1.2294],
- [-0.1471, 0.7706, 1.6882, 2.6059],
- [0.3118, 1.6882, 2.1471, 2.1471],
- [0.7706, 0.3118, 2.6059, -0.1471]],
-
- [[0.9119, 1.8518, 1.3819, -0.0281],
- [-0.0281, 0.9119, 1.3819, 1.8518],
- [2.7918, 0.4419, -0.4981, 0.9119],
- [1.8518, 0.9119, 2.3218, -0.9680]]]]).astype(np.float32)
-
- weight = np.ones(2).astype(np.float32)
- bias = np.ones(2).astype(np.float32)
- moving_mean = np.ones(2).astype(np.float32)
- moving_var_init = np.ones(2).astype(np.float32)
- error = np.ones(shape=[1, 2, 4, 4]) * 1.0e-4
-
- context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
- bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias), Tensor(moving_mean), Tensor(moving_var_init))
- bn_net.set_train()
- output = bn_net(Tensor(x))
- diff = output.asnumpy() - expect_output
- assert np.all(diff < error)
- assert np.all(-diff < error)
-
- context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
- bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias), Tensor(moving_mean), Tensor(moving_var_init))
- bn_net.set_train(False)
- output = bn_net(Tensor(x))
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