forked from mindspore-Ecosystem/mindspore
374 lines
16 KiB
Python
374 lines
16 KiB
Python
# Copyright 2019-2022 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.common.parameter import ParameterTuple
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from mindspore.nn import BatchNorm2d, BatchNorm1d, SGD
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from mindspore.nn import Cell
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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class BatchNormNet(Cell):
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def __init__(self, c, weight, bias, moving_mean, moving_var_init, use_batch_statistics=None):
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super(BatchNormNet, 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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use_batch_statistics=use_batch_statistics)
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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.level1
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@pytest.mark.platform_x86_gpu_training
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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.PYNATIVE_MODE, device_target="GPU")
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bn_net = BatchNormNet(2, Tensor(weight), Tensor(bias),
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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="GPU")
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bn_net = BatchNormNet(2, Tensor(weight), Tensor(bias),
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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="GPU")
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bn_net = BatchNormNet(2, Tensor(weight), Tensor(bias),
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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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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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bn_net = BatchNormNet(2, Tensor(weight), Tensor(bias),
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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.level1
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@pytest.mark.platform_x86_gpu_training
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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="GPU")
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bn_net = BatchNormNet(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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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_train_stats_false_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([[[[3.707105, 5.121315, 5.121315, 6.535525],
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[4.41421, 5.8284197, 7.24263, 8.656839],
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[5.121315, 7.24263, 7.9497347, 7.9497347],
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[5.8284197, 5.121315, 8.656839, 4.41421]],
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[[6.535525, 7.9497347, 7.24263, 5.121315],
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[5.121315, 6.535525, 7.24263, 7.9497347],
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[9.363945, 5.8284197, 4.41421, 6.535525],
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[7.9497347, 6.535525, 8.656839, 3.707105]]]]).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) * 3
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moving_mean = np.zeros(2).astype(np.float32)
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moving_var_init = np.ones(2).astype(np.float32) * 2
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error = np.ones(shape=[1, 2, 4, 4]) * 1.0e-4
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use_batch_statistics = False
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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bn_net = BatchNormNet(2, Tensor(weight), Tensor(bias), Tensor(moving_mean),
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Tensor(moving_var_init), use_batch_statistics)
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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="GPU")
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bn_net = BatchNormNet(2, Tensor(weight), Tensor(bias), Tensor(moving_mean),
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Tensor(moving_var_init), use_batch_statistics)
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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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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_infer_backward():
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expect_output = np.array([[[[-0.3224156, -0.3840524], [1.1337637, -1.0998858]],
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[[-0.1724273, -0.877854], [0.0422135, 0.5828123]],
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[[-1.1006137, 1.1447179], [0.9015862, 0.5024918]]]]).astype(np.float32)
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np.random.seed(1)
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x_np = np.random.randn(1, 3, 2, 2).astype(np.float32)
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input_grad_np = np.random.randn(1, 3, 2, 2).astype(np.float32)
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ms_input = Tensor(x_np)
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weight = Tensor(np.ones(3).astype(np.float32))
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bias = Tensor(np.zeros(3).astype(np.float32))
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moving_mean = Tensor(np.zeros(3).astype(np.float32))
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moving_var_init = Tensor(np.ones(3).astype(np.float32))
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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ms_net = BatchNormNet(3, weight, bias, moving_mean, moving_var_init)
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ms_net.set_train(False)
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ms_grad = Grad(ms_net)
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ms_out_grad_np = ms_grad(ms_input, Tensor(input_grad_np))
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assert np.allclose(ms_out_grad_np[0].asnumpy(), expect_output)
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class BatchNorm1DNet(Cell):
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def __init__(self, affine=True, gamma_init='ones', beta_init='zeros', moving_mean_init='zeros',
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moving_var_init='ones', use_batch_statistics=None):
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super(BatchNorm1DNet, self).__init__()
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self.bn1 = BatchNorm1d(2, eps=0.00001, momentum=0.1, affine=affine, gamma_init=gamma_init, beta_init=beta_init,
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moving_mean_init=moving_mean_init, moving_var_init=moving_var_init,
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use_batch_statistics=use_batch_statistics)
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def construct(self, x):
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x = self.bn1(x)
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return x
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class GradByListNet(Cell):
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def __init__(self, network):
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super(GradByListNet, self).__init__()
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self.grad = C.GradOperation(get_all=True, sens_param=True, get_by_list=True)
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self.network = network
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self.params = ParameterTuple(network.trainable_params())
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def construct(self, x, dy):
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grad_op = self.grad(self.network, self.params)
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output = grad_op(x, dy)
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return output
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_1d_train():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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bn_net = BatchNorm1DNet(use_batch_statistics=None)
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grad_net = GradByListNet(bn_net)
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optimizer = SGD(bn_net.trainable_params(), learning_rate=0.01, momentum=0.9)
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bn_net.set_train(True)
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x1 = np.array([[1.6243454, -0.6117564],
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[-0.5281718, -1.0729686],
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[0.86540765, -2.3015387],
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[1.7448118, -0.7612069],
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[0.3190391, -0.24937038]]).astype(np.float32)
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dy1 = np.array([[1.4621079, -2.0601406],
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[-0.3224172, -0.38405436],
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[1.1337694, -1.0998913],
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[-0.1724282, -0.8778584],
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[0.04221375, 0.58281523]]).astype(np.float32)
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x2 = np.array([[-0.19183555, -0.887629],
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[-0.7471583, 1.6924546],
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[0.05080776, -0.6369957],
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[0.19091548, 2.1002553],
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[0.12015896, 0.6172031]]).astype(np.float32)
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dy2 = np.array([[0.30017033, -0.35224986],
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[-1.1425182, -0.34934273],
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[-0.20889424, 0.5866232],
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[0.8389834, 0.9311021],
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[0.2855873, 0.8851412]]).astype(np.float32)
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x_train = [x1, x2]
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dy_train = [dy1, dy2]
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dx1 = np.array([[0.8120, -2.0371],
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[-0.2202, 0.5837],
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[0.8040, 0.1950],
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[-1.1823, -0.2786],
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[-0.2135, 1.5371]]).astype(np.float32)
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gamma1 = np.array([0.9821, 0.9873]).astype(np.float32)
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beta1 = np.array([-0.0214, 0.0384]).astype(np.float32)
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mean1 = np.array([0.7246, -0.8994]).astype(np.float32)
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variance1 = np.array([0.9036, 0.6559]).astype(np.float32)
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dx2 = np.array([[1.1955, -0.4247],
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[-0.2425, -0.6789],
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[-1.4563, 0.3237],
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[0.8752, 0.3351],
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[-0.3719, 0.4448]]).astype(np.float32)
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gamma2 = np.array([0.9370, 0.9687]).astype(np.float32)
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beta2 = np.array([-0.0415, 0.0559]).astype(np.float32)
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mean2 = np.array([-0.0314, 0.4294]).astype(np.float32)
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variance2 = np.array([0.2213, 1.6822]).astype(np.float32)
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exp_dx = [dx1, dx2]
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exp_gamma = [gamma1, gamma2]
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exp_beta = [beta1, beta2]
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exp_mean = [mean1, mean2]
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exp_variance = [variance1, variance2]
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for data in zip(x_train, dy_train, exp_dx, exp_gamma, exp_beta, exp_mean, exp_variance):
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output = grad_net(Tensor(data[0]), Tensor(data[1]))
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assert np.allclose(output[0][0].asnumpy(), data[2], atol=1.0e-4)
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optimizer(output[1])
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assert np.allclose(bn_net.bn1.gamma.asnumpy(), data[3], atol=1.0e-4)
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assert np.allclose(bn_net.bn1.beta.asnumpy(), data[4], atol=1.0e-4)
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assert np.allclose(bn_net.bn1.moving_mean.asnumpy(), data[5], atol=1.0e-4)
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assert np.allclose(bn_net.bn1.moving_variance.asnumpy(), data[6], atol=1.0e-4)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_1d_eval():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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gamma_init = Tensor(np.array([0.93700373, 0.96870345]).astype(np.float32))
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beta_init = Tensor(np.array([-0.04145495, 0.05593072]).astype(np.float32))
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mean_init = Tensor(np.array([-0.03142229, 0.4294087]).astype(np.float32))
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variance_init = Tensor(np.array([0.2212921, 1.6822311]).astype(np.float32))
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bn_net = BatchNorm1DNet(affine=False, gamma_init=gamma_init, beta_init=beta_init, moving_mean_init=mean_init,
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moving_var_init=variance_init, use_batch_statistics=None)
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bn_net.set_train(False)
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x1 = np.array([[-1.1006192, 1.1447237],
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[0.9015907, 0.50249434],
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[0.90085596, -0.68372786],
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[-0.12289023, -0.93576944],
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[-0.26788807, 0.53035545]]).astype(np.float32)
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x2 = np.array([[-0.7543979, 1.2528682],
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[0.5129298, -0.29809284],
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[0.48851815, -0.07557172],
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[1.1316293, 1.5198169],
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[2.1855755, -1.3964963]]).astype(np.float32)
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x_test = [x1, x2]
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y1 = np.array([[-2.1711, 0.5902],
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[1.8169, 0.1105],
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[1.8155, -0.7754],
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[-0.2236, -0.9637],
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[-0.5125, 0.1313]]).astype(np.float32)
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y2 = np.array([[-1.4815, 0.6710],
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[1.0428, -0.4874],
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[0.9942, -0.3212],
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[2.2751, 0.8703],
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[4.3744, -1.3078]]).astype(np.float32)
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y_test = [y1, y2]
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for x, y in zip(x_test, y_test):
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y_pred = bn_net(Tensor(x))
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assert np.allclose(y_pred.asnumpy(), y, atol=1.0e-4)
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def test_batch_norm_forward_functional(nptype):
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"""
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Feature: test batch_norm forward for given input dtype.
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Description: test inputs for given input dtype.
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Expectation: the result match with expected result.
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"""
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input_x = Tensor(np.ones([2, 2]).astype(nptype))
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running_mean = Tensor(np.ones([2]).astype(nptype))
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running_var = Tensor(np.ones([2]).astype(nptype))
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weight = Tensor(np.ones([2]).astype(nptype))
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bias = Tensor(np.ones([2]).astype(nptype))
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output = F.batch_norm(input_x, running_mean, running_var, weight, bias)
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expected = np.array([[1., 1.], [1., 1.]]).astype(nptype)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_batch_norm_forward_float32_functional():
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"""
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Feature: test batch_norm forward.
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Description: test float32 inputs.
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Expectation: the result match with expected result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_batch_norm_forward_functional(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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test_batch_norm_forward_functional(np.float32)
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