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| // Tencent is pleased to support the open source community by making ncnn available. | |
| // | |
| // Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved. | |
| // | |
| // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except | |
| // in compliance with the License. You may obtain a copy of the License at | |
| // | |
| // https://opensource.org/licenses/BSD-3-Clause | |
| // | |
| // 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. | |
| static int test_batchnorm(const ncnn::Mat& a, float eps) | |
| { | |
| int channels; | |
| if (a.dims == 1) channels = a.w; | |
| if (a.dims == 2) channels = a.h; | |
| if (a.dims == 3 || a.dims == 4) channels = a.c; | |
| ncnn::ParamDict pd; | |
| pd.set(0, channels); // channels | |
| pd.set(1, eps); // eps | |
| std::vector<ncnn::Mat> weights(4); | |
| weights[0] = RandomMat(channels); | |
| weights[1] = RandomMat(channels); | |
| weights[2] = RandomMat(channels); | |
| weights[3] = RandomMat(channels); | |
| // var must be positive | |
| Randomize(weights[2], 0.001f, 2.f); | |
| int ret = test_layer<ncnn::BatchNorm>("BatchNorm", pd, weights, a); | |
| if (ret != 0) | |
| { | |
| fprintf(stderr, "test_batchnorm failed a.dims=%d a=(%d %d %d %d) eps=%f\n", a.dims, a.w, a.h, a.d, a.c, eps); | |
| } | |
| return ret; | |
| } | |
| static int test_batchnorm_0() | |
| { | |
| return 0 | |
| || test_batchnorm(RandomMat(5, 6, 7, 24), 0.f) | |
| || test_batchnorm(RandomMat(5, 6, 7, 24), 0.01f) | |
| || test_batchnorm(RandomMat(7, 8, 9, 12), 0.f) | |
| || test_batchnorm(RandomMat(7, 8, 9, 12), 0.001f) | |
| || test_batchnorm(RandomMat(3, 4, 5, 13), 0.f) | |
| || test_batchnorm(RandomMat(3, 4, 5, 13), 0.f) | |
| || test_batchnorm(RandomMat(3, 4, 6, 32), 0.f) | |
| || test_batchnorm(RandomMat(3, 4, 5, 32), 0.001f); | |
| } | |
| static int test_batchnorm_1() | |
| { | |
| return 0 | |
| || test_batchnorm(RandomMat(5, 7, 24), 0.f) | |
| || test_batchnorm(RandomMat(5, 7, 24), 0.01f) | |
| || test_batchnorm(RandomMat(7, 9, 12), 0.f) | |
| || test_batchnorm(RandomMat(7, 9, 12), 0.001f) | |
| || test_batchnorm(RandomMat(3, 5, 13), 0.f) | |
| || test_batchnorm(RandomMat(3, 5, 13), 0.001f) | |
| || test_batchnorm(RandomMat(3, 5, 16), 0.001f) | |
| || test_batchnorm(RandomMat(3, 5, 32), 0.001f); | |
| } | |
| static int test_batchnorm_2() | |
| { | |
| return 0 | |
| || test_batchnorm(RandomMat(15, 24), 0.f) | |
| || test_batchnorm(RandomMat(15, 24), 0.01f) | |
| || test_batchnorm(RandomMat(17, 12), 0.f) | |
| || test_batchnorm(RandomMat(17, 12), 0.001f) | |
| || test_batchnorm(RandomMat(19, 15), 0.f) | |
| || test_batchnorm(RandomMat(19, 15), 0.001f) | |
| || test_batchnorm(RandomMat(128, 16), 0.f) | |
| || test_batchnorm(RandomMat(16, 128), 0.001f); | |
| } | |
| static int test_batchnorm_3() | |
| { | |
| return 0 | |
| || test_batchnorm(RandomMat(128), 0.f) | |
| || test_batchnorm(RandomMat(128), 0.001f) | |
| || test_batchnorm(RandomMat(124), 0.f) | |
| || test_batchnorm(RandomMat(124), 0.1f) | |
| || test_batchnorm(RandomMat(127), 0.f) | |
| || test_batchnorm(RandomMat(127), 0.1f); | |
| } | |
| int main() | |
| { | |
| SRAND(7767517); | |
| return 0 | |
| || test_batchnorm_0() | |
| || test_batchnorm_1() | |
| || test_batchnorm_2() | |
| || test_batchnorm_3(); | |
| } | |