1# Copyright 2021 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15 16import numpy as np 17import pytest 18 19import mindspore.context as context 20import mindspore.nn as nn 21from mindspore import Tensor 22from mindspore.ops import operations as P 23from mindspore.ops import composite as C 24from mindspore.common import dtype as mstype 25 26 27class PReLUOpNet(nn.Cell): 28 def __init__(self): 29 super(PReLUOpNet, self).__init__() 30 self.prelu = P.PReLU() 31 32 def construct(self, x, weight): 33 return self.prelu(x, weight) 34 35 36class PReLUOpGradNet(nn.Cell): 37 def __init__(self, net): 38 super(PReLUOpGradNet, self).__init__() 39 self.forward = net 40 self.grad = C.GradOperation(get_all=True, sens_param=False) 41 42 def construct(self, x, weight): 43 return self.grad(self.forward)(x, weight) 44 45 46def judge_result_correct(result, expect): 47 result = result.asnumpy() 48 expect = expect.asnumpy() 49 assert result.dtype == expect.dtype 50 assert result.shape == expect.shape 51 assert np.allclose(result, expect, rtol=1.e-2) 52 53 54def test_prelu(x, weight, expect_forward, expect_dx, expect_dw): 55 prelu_forward = PReLUOpNet() 56 prelu_backward = PReLUOpGradNet(prelu_forward) 57 forward_output = prelu_forward(x, weight) 58 judge_result_correct(forward_output, expect_forward) 59 60 backward_output = prelu_backward(x, weight) 61 assert len(backward_output) == 2 62 judge_result_correct(backward_output[0], expect_dx) 63 judge_result_correct(backward_output[1], expect_dw) 64 65 66context.set_context(device_target="GPU", mode=context.GRAPH_MODE) 67dtypes = [mstype.float16, mstype.float32] 68 69 70@pytest.mark.level0 71@pytest.mark.platform_x86_gpu_training 72@pytest.mark.env_onecard 73def test_prelu_single_weight(): 74 x = np.arange(-10, 26).reshape((2, 3, 2, 3)) * 0.7 75 weight = np.array([0.6]) 76 expect_forward = np.where(x >= 0, x, weight * x) 77 expect_dx = np.where(x > 0, 1, weight) 78 expect_dw = np.sum(np.where(x >= 0, 0, x)).reshape((1,)) 79 80 for dtype in dtypes: 81 x = Tensor(x, dtype) 82 weight = Tensor(weight, dtype) 83 expect_forward = Tensor(expect_forward, dtype) 84 expect_dx = Tensor(expect_dx, dtype) 85 expect_dw = Tensor(expect_dw, dtype) 86 test_prelu(x, weight, expect_forward, expect_dx, expect_dw) 87 88 89@pytest.mark.level0 90@pytest.mark.platform_x86_gpu_training 91@pytest.mark.env_onecard 92def test_prelu_multiple_weight(): 93 x = np.arange(-10, 26).reshape((2, 3, 2, 3)) * 0.6 94 weight = np.array([0.2, 0.3, 0.4]) 95 expect_forward = np.array([[[[-1.20, -1.08, -0.96], 96 [-0.84, -0.72, -0.60]], 97 [[-0.72, -0.54, -0.36], 98 [-0.18, 0.00, 0.60]], 99 [[1.20, 1.80, 2.40], 100 [3.00, 3.60, 4.20]]], 101 [[[4.80, 5.40, 6.00], 102 [6.60, 7.20, 7.80]], 103 [[8.40, 9.00, 9.60], 104 [10.20, 10.80, 11.40]], 105 [[12.00, 12.60, 13.20], 106 [13.80, 14.40, 15.00]]]]) 107 expect_dx = np.array([[[[0.2, 0.2, 0.2], 108 [0.2, 0.2, 0.2]], 109 [[0.3, 0.3, 0.3], 110 [0.3, 0.3, 1.0]], 111 [[1.0, 1.0, 1.0], 112 [1.0, 1.0, 1.0]]], 113 [[[1.0, 1.0, 1.0], 114 [1.0, 1.0, 1.0]], 115 [[1.0, 1.0, 1.0], 116 [1.0, 1.0, 1.0]], 117 [[1.0, 1.0, 1.0], 118 [1.0, 1.0, 1.0]]]]) 119 expect_dw = np.array([-27.0, -6.0, 0.0]) 120 121 for dtype in dtypes: 122 x = Tensor(x, dtype) 123 weight = Tensor(weight, dtype) 124 expect_forward = Tensor(expect_forward, dtype) 125 expect_dx = Tensor(expect_dx, dtype) 126 expect_dw = Tensor(expect_dw, dtype) 127 test_prelu(x, weight, expect_forward, expect_dx, expect_dw) 128 129 130@pytest.mark.level0 131@pytest.mark.platform_x86_gpu_training 132@pytest.mark.env_onecard 133def test_prelu_single_weight_0_D(): 134 x = np.array(-0.8) 135 weight = np.array([0.6]) 136 expect_forward = np.array(-0.48) 137 expect_dx = np.array(0.6) 138 expect_dw = np.array([-0.8]) 139 140 for dtype in dtypes: 141 x = Tensor(x, dtype) 142 weight = Tensor(weight, dtype) 143 expect_forward = Tensor(expect_forward, dtype) 144 expect_dx = Tensor(expect_dx, dtype) 145 expect_dw = Tensor(expect_dw, dtype) 146 test_prelu(x, weight, expect_forward, expect_dx, expect_dw) 147 148 149@pytest.mark.level0 150@pytest.mark.platform_x86_gpu_training 151@pytest.mark.env_onecard 152def test_prelu_single_weight_1_D(): 153 x = np.arange(-10, 26).reshape((36,)) * 0.7 154 weight = np.array([0.6]) 155 expect_forward = np.where(x >= 0, x, weight * x) 156 expect_dx = np.where(x > 0, 1, weight) 157 expect_dw = np.sum(np.where(x >= 0, 0, x)).reshape((1,)) 158 159 for dtype in dtypes: 160 x = Tensor(x, dtype) 161 weight = Tensor(weight, dtype) 162 expect_forward = Tensor(expect_forward, dtype) 163 expect_dx = Tensor(expect_dx, dtype) 164 expect_dw = Tensor(expect_dw, dtype) 165 test_prelu(x, weight, expect_forward, expect_dx, expect_dw) 166 167 168@pytest.mark.level0 169@pytest.mark.platform_x86_gpu_training 170@pytest.mark.env_onecard 171def test_prelu_single_weight_2_D(): 172 x = np.arange(-10, 26).reshape((4, 9)) * 0.7 173 weight = np.array([0.6]) 174 expect_forward = np.where(x >= 0, x, weight * x) 175 expect_dx = np.where(x > 0, 1, weight) 176 expect_dw = np.sum(np.where(x >= 0, 0, x)).reshape((1,)) 177 178 for dtype in dtypes: 179 x = Tensor(x, dtype) 180 weight = Tensor(weight, dtype) 181 expect_forward = Tensor(expect_forward, dtype) 182 expect_dx = Tensor(expect_dx, dtype) 183 expect_dw = Tensor(expect_dw, dtype) 184 test_prelu(x, weight, expect_forward, expect_dx, expect_dw) 185 186 187@pytest.mark.level0 188@pytest.mark.platform_x86_gpu_training 189@pytest.mark.env_onecard 190def test_prelu_multiple_weight_2_D(): 191 x = np.arange(-6, 6).reshape((3, 4)) * 0.6 192 weight = np.array([0.2, 0.4, 0.7, 0.9]) 193 expect_forward = np.array([[-0.72, -1.20, -1.68, -1.62], 194 [-0.24, -0.24, 0.00, 0.60], 195 [1.20, 1.80, 2.40, 3.00]]) 196 expect_dx = np.array([[0.2, 0.4, 0.7, 0.9], 197 [0.2, 0.4, 0.7, 1.0], 198 [1.0, 1.0, 1.0, 1.0]]) 199 expect_dw = np.array([-4.8, -3.6, -2.4, -1.8]) 200 201 for dtype in dtypes: 202 x = Tensor(x, dtype) 203 weight = Tensor(weight, dtype) 204 expect_forward = Tensor(expect_forward, dtype) 205 expect_dx = Tensor(expect_dx, dtype) 206 expect_dw = Tensor(expect_dw, dtype) 207 test_prelu(x, weight, expect_forward, expect_dx, expect_dw) 208