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119 lines
4.9 KiB
119 lines
4.9 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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from mindspore.common.tensor import Tensor
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from mindspore.nn import BatchNorm2d
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from mindspore.nn import Cell
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from mindspore.ops import composite as C
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class Batchnorm_Net(Cell):
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def __init__(self, c, weight, bias, moving_mean, moving_var_init):
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super(Batchnorm_Net, self).__init__()
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self.bn = BatchNorm2d(c, eps=0.00001, momentum=0.1, beta_init=bias, gamma_init=weight,
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moving_mean_init=moving_mean, moving_var_init=moving_var_init)
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def construct(self, input_data):
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x = self.bn(input_data)
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return x
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class Grad(Cell):
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def __init__(self, network):
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super(Grad, self).__init__()
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self.grad = C.GradOperation(get_all=True, sens_param=True)
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self.network = network
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def construct(self, input_data, sens):
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gout = self.grad(self.network)(input_data, sens)
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return gout
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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_train_forward():
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x = np.array([[
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[[1, 3, 3, 5], [2, 4, 6, 8], [3, 6, 7, 7], [4, 3, 8, 2]],
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[[5, 7, 6, 3], [3, 5, 6, 7], [9, 4, 2, 5], [7, 5, 8, 1]]]]).astype(np.float32)
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expect_output = np.array([[[[-0.6059, 0.3118, 0.3118, 1.2294],
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[-0.1471, 0.7706, 1.6882, 2.6059],
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[0.3118, 1.6882, 2.1471, 2.1471],
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[0.7706, 0.3118, 2.6059, -0.1471]],
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[[0.9119, 1.8518, 1.3819, -0.0281],
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[-0.0281, 0.9119, 1.3819, 1.8518],
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[2.7918, 0.4419, -0.4981, 0.9119],
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[1.8518, 0.9119, 2.3218, -0.9680]]]]).astype(np.float32)
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weight = np.ones(2).astype(np.float32)
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bias = np.ones(2).astype(np.float32)
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moving_mean = np.ones(2).astype(np.float32)
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moving_var_init = np.ones(2).astype(np.float32)
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error = np.ones(shape=[1, 2, 4, 4]) * 1.0e-4
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias), Tensor(moving_mean), Tensor(moving_var_init))
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bn_net.set_train()
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output = bn_net(Tensor(x))
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diff = output.asnumpy() - expect_output
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assert np.all(diff < error)
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assert np.all(-diff < error)
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias), Tensor(moving_mean), Tensor(moving_var_init))
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bn_net.set_train(False)
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output = bn_net(Tensor(x))
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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_train_backward():
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x = np.array([[
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[[1, 3, 3, 5], [2, 4, 6, 8], [3, 6, 7, 7], [4, 3, 8, 2]],
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[[5, 7, 6, 3], [3, 5, 6, 7], [9, 4, 2, 5], [7, 5, 8, 1]]]]).astype(np.float32)
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grad = np.array([[
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[[1, 2, 7, 1], [4, 2, 1, 3], [1, 6, 5, 2], [2, 4, 3, 2]],
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[[9, 4, 3, 5], [1, 3, 7, 6], [5, 7, 9, 9], [1, 4, 6, 8]]]]).astype(np.float32)
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expect_output = np.array([[[[-0.69126546, -0.32903028, 1.9651246, -0.88445705],
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[0.6369296, -0.37732816, -0.93275493, -0.11168876],
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[-0.7878612, 1.3614, 0.8542711, -0.52222186],
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[-0.37732816, 0.5886317, -0.11168876, -0.28073236]],
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[[1.6447213, -0.38968924, -1.0174079, -0.55067265],
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[-2.4305856, -1.1751484, 0.86250514, 0.5502673],
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[0.39576983, 0.5470243, 1.1715001, 1.6447213],
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[-1.7996241, -0.7051701, 0.7080077, 0.5437813]]]]).astype(np.float32)
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weight = Tensor(np.ones(2).astype(np.float32))
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bias = Tensor(np.ones(2).astype(np.float32))
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moving_mean = Tensor(np.ones(2).astype(np.float32))
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moving_var_init = Tensor(np.ones(2).astype(np.float32))
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error = np.ones(shape=[1, 2, 4, 4]) * 1.0e-6
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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bn_net = Batchnorm_Net(2, weight, bias, moving_mean, moving_var_init)
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bn_net.set_train()
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bn_grad = Grad(bn_net)
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output = bn_grad(Tensor(x), Tensor(grad))
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diff = output[0].asnumpy() - expect_output
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assert np.all(diff < error)
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assert np.all(-diff < error)
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