zzy34407230
/
mindspore2022

# Copyright 2019 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.common.parameter import ParameterTuple
from mindspore.nn import BatchNorm2d, BatchNorm1d, SGD
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, use_batch_statistics=None):
        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,
                              use_batch_statistics=use_batch_statistics)

    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_gpu_training
@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.PYNATIVE_MODE, device_target="GPU")
    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="GPU")
    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="GPU")
    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))

    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
    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))


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_train_backward():
    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)
    grad = np.array([[
        [[1, 2, 7, 1], [4, 2, 1, 3], [1, 6, 5, 2], [2, 4, 3, 2]],
        [[9, 4, 3, 5], [1, 3, 7, 6], [5, 7, 9, 9], [1, 4, 6, 8]]]]).astype(np.float32)
    expect_output = np.array([[[[-0.69126546, -0.32903028, 1.9651246, -0.88445705],
                                [0.6369296, -0.37732816, -0.93275493, -0.11168876],
                                [-0.7878612, 1.3614, 0.8542711, -0.52222186],
                                [-0.37732816, 0.5886317, -0.11168876, -0.28073236]],

                               [[1.6447213, -0.38968924, -1.0174079, -0.55067265],
                                [-2.4305856, -1.1751484, 0.86250514, 0.5502673],
                                [0.39576983, 0.5470243, 1.1715001, 1.6447213],
                                [-1.7996241, -0.7051701, 0.7080077, 0.5437813]]]]).astype(np.float32)

    weight = Tensor(np.ones(2).astype(np.float32))
    bias = Tensor(np.ones(2).astype(np.float32))
    moving_mean = Tensor(np.ones(2).astype(np.float32))
    moving_var_init = Tensor(np.ones(2).astype(np.float32))
    error = np.ones(shape=[1, 2, 4, 4]) * 1.0e-6

    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    bn_net = Batchnorm_Net(2, weight, bias, moving_mean, moving_var_init)
    bn_net.set_train()
    bn_grad = Grad(bn_net)
    output = bn_grad(Tensor(x), Tensor(grad))
    diff = output[0].asnumpy() - expect_output
    assert np.all(diff < error)
    assert np.all(-diff < error)


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_train_stats_false_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([[[[3.707105, 5.121315, 5.121315, 6.535525],
                                [4.41421, 5.8284197, 7.24263, 8.656839],
                                [5.121315, 7.24263, 7.9497347, 7.9497347],
                                [5.8284197, 5.121315, 8.656839, 4.41421]],

                               [[6.535525, 7.9497347, 7.24263, 5.121315],
                                [5.121315, 6.535525, 7.24263, 7.9497347],
                                [9.363945, 5.8284197, 4.41421, 6.535525],
                                [7.9497347, 6.535525, 8.656839, 3.707105]]]]).astype(np.float32)

    weight = np.ones(2).astype(np.float32)
    bias = np.ones(2).astype(np.float32) * 3
    moving_mean = np.zeros(2).astype(np.float32)
    moving_var_init = np.ones(2).astype(np.float32) * 2
    error = np.ones(shape=[1, 2, 4, 4]) * 1.0e-4
    use_batch_statistics = False

    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
    bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias), Tensor(moving_mean),
                           Tensor(moving_var_init), use_batch_statistics)
    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="GPU")
    bn_net = Batchnorm_Net(2, Tensor(weight), Tensor(bias), Tensor(moving_mean),
                           Tensor(moving_var_init), use_batch_statistics)
    bn_net.set_train()
    output = bn_net(Tensor(x))
    diff = output.asnumpy() - expect_output
    assert np.all(diff < error)
    assert np.all(-diff < error)


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_infer_backward():
    expect_output = np.array([[[[-0.3224156, -0.3840524], [1.1337637, -1.0998858]],
                               [[-0.1724273, -0.877854], [0.0422135, 0.5828123]],
                               [[-1.1006137, 1.1447179], [0.9015862, 0.5024918]]]]).astype(np.float32)
    np.random.seed(1)
    x_np = np.random.randn(1, 3, 2, 2).astype(np.float32)
    input_grad_np = np.random.randn(1, 3, 2, 2).astype(np.float32)
    ms_input = Tensor(x_np)
    weight = Tensor(np.ones(3).astype(np.float32))
    bias = Tensor(np.zeros(3).astype(np.float32))
    moving_mean = Tensor(np.zeros(3).astype(np.float32))
    moving_var_init = Tensor(np.ones(3).astype(np.float32))
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    ms_net = Batchnorm_Net(3, weight, bias, moving_mean, moving_var_init)
    ms_net.set_train(False)
    ms_grad = Grad(ms_net)
    ms_out_grad_np = ms_grad(ms_input, Tensor(input_grad_np))
    assert np.allclose(ms_out_grad_np[0].asnumpy(), expect_output)


class BatchNorm1d_Net(Cell):
    def __init__(self, affine=True, gamma_init='ones', beta_init='zeros', moving_mean_init='zeros',
                 moving_var_init='ones', use_batch_statistics=None):
        super(BatchNorm1d_Net, self).__init__()
        self.bn1 = BatchNorm1d(2, eps=0.00001, momentum=0.1, affine=affine, gamma_init=gamma_init, beta_init=beta_init,
                               moving_mean_init=moving_mean_init, moving_var_init=moving_var_init,
                               use_batch_statistics=use_batch_statistics)

    def construct(self, x):
        x = self.bn1(x)
        return x

class GradByListNet(Cell):
    def __init__(self, network):
        super(GradByListNet, self).__init__()
        self.grad = C.GradOperation(get_all=True, sens_param=True, get_by_list=True)
        self.network = network
        self.params = ParameterTuple(network.trainable_params())

    def construct(self, x, dy):
        grad_op = self.grad(self.network, self.params)
        output = grad_op(x, dy)
        return output


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_1d_train():
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    bn_net = BatchNorm1d_Net(use_batch_statistics=None)
    grad_net = GradByListNet(bn_net)
    optimizer = SGD(bn_net.trainable_params(), learning_rate=0.01, momentum=0.9)
    bn_net.set_train(True)

    x1 = np.array([[1.6243454, -0.6117564],
                   [-0.5281718, -1.0729686],
                   [0.86540765, -2.3015387],
                   [1.7448118, -0.7612069],
                   [0.3190391, -0.24937038]]).astype(np.float32)
    dy1 = np.array([[1.4621079, -2.0601406],
                    [-0.3224172, -0.38405436],
                    [1.1337694, -1.0998913],
                    [-0.1724282, -0.8778584],
                    [0.04221375, 0.58281523]]).astype(np.float32)
    x2 = np.array([[-0.19183555, -0.887629],
                   [-0.7471583, 1.6924546],
                   [0.05080776, -0.6369957],
                   [0.19091548, 2.1002553],
                   [0.12015896, 0.6172031]]).astype(np.float32)
    dy2 = np.array([[0.30017033, -0.35224986],
                    [-1.1425182, -0.34934273],
                    [-0.20889424, 0.5866232],
                    [0.8389834, 0.9311021],
                    [0.2855873, 0.8851412]]).astype(np.float32)
    x_train = [x1, x2]
    dy_train = [dy1, dy2]

    dx1 = np.array([[0.8120, -2.0371],
                    [-0.2202, 0.5837],
                    [0.8040, 0.1950],
                    [-1.1823, -0.2786],
                    [-0.2135, 1.5371]]).astype(np.float32)
    gamma1 = np.array([0.9821, 0.9873]).astype(np.float32)
    beta1 = np.array([-0.0214, 0.0384]).astype(np.float32)
    mean1 = np.array([0.7246, -0.8994]).astype(np.float32)
    variance1 = np.array([0.9036, 0.6559]).astype(np.float32)

    dx2 = np.array([[1.1955, -0.4247],
                    [-0.2425, -0.6789],
                    [-1.4563, 0.3237],
                    [0.8752, 0.3351],
                    [-0.3719, 0.4448]]).astype(np.float32)
    gamma2 = np.array([0.9370, 0.9687]).astype(np.float32)
    beta2 = np.array([-0.0415, 0.0559]).astype(np.float32)
    mean2 = np.array([-0.0314, 0.4294]).astype(np.float32)
    variance2 = np.array([0.2213, 1.6822]).astype(np.float32)

    exp_dx = [dx1, dx2]
    exp_gamma = [gamma1, gamma2]
    exp_beta = [beta1, beta2]
    exp_mean = [mean1, mean2]
    exp_variance = [variance1, variance2]

    for data in zip(x_train, dy_train, exp_dx, exp_gamma, exp_beta, exp_mean, exp_variance):
        output = grad_net(Tensor(data[0]), Tensor(data[1]))
        assert np.allclose(output[0][0].asnumpy(), data[2], atol=1.0e-4)
        optimizer(output[1])
        assert np.allclose(bn_net.bn1.gamma.asnumpy(), data[3], atol=1.0e-4)
        assert np.allclose(bn_net.bn1.beta.asnumpy(), data[4], atol=1.0e-4)
        assert np.allclose(bn_net.bn1.moving_mean.asnumpy(), data[5], atol=1.0e-4)
        assert np.allclose(bn_net.bn1.moving_variance.asnumpy(), data[6], atol=1.0e-4)


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_1d_eval():
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    gamma_init = Tensor(np.array([0.93700373, 0.96870345]).astype(np.float32))
    beta_init = Tensor(np.array([-0.04145495, 0.05593072]).astype(np.float32))
    mean_init = Tensor(np.array([-0.03142229, 0.4294087]).astype(np.float32))
    variance_init = Tensor(np.array([0.2212921, 1.6822311]).astype(np.float32))
    bn_net = BatchNorm1d_Net(affine=False, gamma_init=gamma_init, beta_init=beta_init, moving_mean_init=mean_init,
                             moving_var_init=variance_init, use_batch_statistics=None)
    bn_net.set_train(False)

    x1 = np.array([[-1.1006192, 1.1447237],
                   [0.9015907, 0.50249434],
                   [0.90085596, -0.68372786],
                   [-0.12289023, -0.93576944],
                   [-0.26788807, 0.53035545]]).astype(np.float32)
    x2 = np.array([[-0.7543979, 1.2528682],
                   [0.5129298, -0.29809284],
                   [0.48851815, -0.07557172],
                   [1.1316293, 1.5198169],
                   [2.1855755, -1.3964963]]).astype(np.float32)
    x_test = [x1, x2]

    y1 = np.array([[-2.1711, 0.5902],
                   [1.8169, 0.1105],
                   [1.8155, -0.7754],
                   [-0.2236, -0.9637],
                   [-0.5125, 0.1313]]).astype(np.float32)
    y2 = np.array([[-1.4815, 0.6710],
                   [1.0428, -0.4874],
                   [0.9942, -0.3212],
                   [2.2751, 0.8703],
                   [4.3744, -1.3078]]).astype(np.float32)
    y_test = [y1, y2]

    for x, y in zip(x_test, y_test):
        y_pred = bn_net(Tensor(x))
        assert np.allclose(y_pred.asnumpy(), y, atol=1.0e-4)