# 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)