// Tencent is pleased to support the open source community by making ncnn available. // // Copyright (C) 2019 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. #include "layer/convolutiondepthwise1d.h" #include "testutil.h" static int test_convolutiondepthwise1d(int w, int h, int outh, int kernel, int dilation, int stride, int pad, int bias, int group) { ncnn::Mat a = RandomMat(w, h); ncnn::ParamDict pd; pd.set(0, outh); // num_output pd.set(1, kernel); // kernel_w pd.set(2, dilation); // dilation_w pd.set(3, stride); // stride_w pd.set(4, pad); // pad_w pd.set(5, bias); // bias_term pd.set(6, outh / group * h / group * kernel * kernel * group); pd.set(7, group); int activation_type = RAND() % 7; // 0 1 2 3 4 5 6 ncnn::Mat activation_params(2); activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha activation_params[1] = RandomFloat(0, 1); // beta pd.set(9, activation_type); pd.set(10, activation_params); std::vector weights(2); weights[0] = RandomMat(outh / group * h / group * kernel * kernel * group); weights[1] = RandomMat(outh); int ret = test_layer("ConvolutionDepthWise1D", pd, weights, a); if (ret != 0) { fprintf(stderr, "test_convolutiondepthwise1d failed w=%d h=%d outh=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d act=%d actparams=[%f,%f]\n", w, h, outh, kernel, dilation, stride, pad, bias, group, activation_type, activation_params[0], activation_params[1]); } return ret; } static int test_convolutiondepthwise1d_0() { static const int kdsp[16][4] = { {1, 1, 1, 0}, {1, 1, 2, 0}, {2, 1, 1, 1}, {2, 1, 2, -233}, {3, 1, 1, 1}, {3, 1, 2, 1}, {3, 2, 1, 1}, {4, 1, 1, 2}, {4, 1, 2, -233}, {4, 2, 1, -234}, {5, 1, 1, -234}, {5, 1, 2, 2}, {5, 2, 2, 2}, {7, 1, 1, 3}, {7, 1, 2, 3}, {7, 2, 1, -233}, }; for (int i = 0; i < 16; i++) { const int k = kdsp[i][0]; const int d = kdsp[i][1]; const int s = kdsp[i][2]; const int p = kdsp[i][3]; int ret = 0 || test_convolutiondepthwise1d(15, 1, 1, k, d, s, p, 1, 1) || test_convolutiondepthwise1d(15, 2, 2, k, d, s, p, 0, 1) || test_convolutiondepthwise1d(15, 2, 2, k, d, s, p, 1, 2) || test_convolutiondepthwise1d(15, 3, 3, k, d, s, p, 0, 3) || test_convolutiondepthwise1d(15, 4, 2, k, d, s, p, 1, 2) || test_convolutiondepthwise1d(15, 4, 4, k, d, s, p, 0, 4) || test_convolutiondepthwise1d(15, 7, 7, k, d, s, p, 1, 7) || test_convolutiondepthwise1d(15, 8, 8, k, d, s, p, 0, 2) || test_convolutiondepthwise1d(15, 8, 8, k, d, s, p, 1, 8) || test_convolutiondepthwise1d(15, 12, 12, k, d, s, p, 0, 4) || test_convolutiondepthwise1d(15, 15, 15, k, d, s, p, 1, 15) || test_convolutiondepthwise1d(15, 16, 8, k, d, s, p, 0, 2) || test_convolutiondepthwise1d(15, 16, 16, k, d, s, p, 1, 16) || test_convolutiondepthwise1d(18, 1, 1, k, d, s, p, 1, 1) || test_convolutiondepthwise1d(18, 2, 2, k, d, s, p, 0, 1) || test_convolutiondepthwise1d(18, 2, 2, k, d, s, p, 1, 2) || test_convolutiondepthwise1d(18, 3, 3, k, d, s, p, 0, 3) || test_convolutiondepthwise1d(18, 4, 2, k, d, s, p, 1, 2) || test_convolutiondepthwise1d(18, 4, 4, k, d, s, p, 0, 4) || test_convolutiondepthwise1d(18, 7, 7, k, d, s, p, 1, 7) || test_convolutiondepthwise1d(18, 8, 8, k, d, s, p, 0, 2) || test_convolutiondepthwise1d(18, 8, 8, k, d, s, p, 1, 8) || test_convolutiondepthwise1d(18, 12, 12, k, d, s, p, 0, 4) || test_convolutiondepthwise1d(18, 15, 15, k, d, s, p, 1, 15) || test_convolutiondepthwise1d(18, 16, 8, k, d, s, p, 0, 2) || test_convolutiondepthwise1d(18, 16, 16, k, d, s, p, 1, 16) || test_convolutiondepthwise1d(25, 1, 1, k, d, s, p, 1, 1) || test_convolutiondepthwise1d(25, 2, 2, k, d, s, p, 0, 1) || test_convolutiondepthwise1d(25, 2, 2, k, d, s, p, 1, 2) || test_convolutiondepthwise1d(25, 3, 3, k, d, s, p, 0, 3) || test_convolutiondepthwise1d(25, 4, 2, k, d, s, p, 1, 2) || test_convolutiondepthwise1d(25, 4, 4, k, d, s, p, 0, 4) || test_convolutiondepthwise1d(25, 7, 7, k, d, s, p, 1, 7) || test_convolutiondepthwise1d(25, 8, 8, k, d, s, p, 0, 2) || test_convolutiondepthwise1d(25, 8, 8, k, d, s, p, 1, 8) || test_convolutiondepthwise1d(25, 12, 12, k, d, s, p, 0, 4) || test_convolutiondepthwise1d(25, 15, 15, k, d, s, p, 1, 15) || test_convolutiondepthwise1d(25, 16, 8, k, d, s, p, 0, 2) || test_convolutiondepthwise1d(25, 16, 16, k, d, s, p, 1, 16); if (ret != 0) return -1; } return 0; } static int test_convolutiondepthwise1d_dynamic(int w, int h, int outh, int kernel, int dilation, int stride, int pad, int bias, int group) { ncnn::Mat a = RandomMat(w, h); ncnn::ParamDict pd; pd.set(0, 0); pd.set(1, 0); pd.set(2, dilation); pd.set(3, stride); pd.set(4, pad); pd.set(5, bias); pd.set(6, 0); pd.set(7, group); pd.set(19, 1); // dynamic weight int activation_type = RAND() % 7; // 0 1 2 3 4 5 6 ncnn::Mat activation_params(2); activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : RandomFloat(-1, 0); // alpha activation_params[1] = RandomFloat(0, 1); // beta pd.set(9, activation_type); pd.set(10, activation_params); std::vector as(bias ? 3 : 2); as[0] = a; as[1] = RandomMat(kernel, h / group, outh); if (bias) as[2] = RandomMat(outh); std::vector weights(0); int ret = test_layer("ConvolutionDepthWise1D", pd, weights, as); if (ret != 0) { fprintf(stderr, "test_convolutiondepthwise1d_dynamic failed w=%d h=%d outh=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d act=%d actparams=[%f,%f]\n", w, h, outh, kernel, dilation, stride, pad, bias, group, activation_type, activation_params[0], activation_params[1]); } return ret; } static int test_convolutiondepthwise1d_1() { static const int kdsp[7][4] = { {1, 1, 1, 0}, {1, 1, 2, 0}, {2, 1, 1, 1}, {2, 1, 2, -233}, {3, 1, 1, 1}, {3, 1, 2, 1}, {3, 2, 1, -234}, }; for (int i = 0; i < 7; i++) { const int k = kdsp[i][0]; const int d = kdsp[i][1]; const int s = kdsp[i][2]; const int p = kdsp[i][3]; int ret = 0 || test_convolutiondepthwise1d_dynamic(11, 1, 1, k, d, s, p, 1, 1) || test_convolutiondepthwise1d_dynamic(11, 2, 2, k, d, s, p, 0, 1) || test_convolutiondepthwise1d_dynamic(11, 2, 2, k, d, s, p, 1, 2) || test_convolutiondepthwise1d_dynamic(11, 3, 3, k, d, s, p, 0, 3) || test_convolutiondepthwise1d_dynamic(11, 4, 2, k, d, s, p, 1, 2) || test_convolutiondepthwise1d_dynamic(11, 4, 4, k, d, s, p, 0, 4) || test_convolutiondepthwise1d_dynamic(11, 7, 7, k, d, s, p, 1, 7) || test_convolutiondepthwise1d_dynamic(11, 8, 8, k, d, s, p, 0, 2) || test_convolutiondepthwise1d_dynamic(11, 8, 8, k, d, s, p, 1, 8) || test_convolutiondepthwise1d_dynamic(11, 12, 12, k, d, s, p, 0, 4) || test_convolutiondepthwise1d_dynamic(11, 15, 15, k, d, s, p, 1, 15) || test_convolutiondepthwise1d_dynamic(11, 16, 8, k, d, s, p, 0, 2) || test_convolutiondepthwise1d_dynamic(11, 16, 16, k, d, s, p, 1, 16); if (ret != 0) return -1; } return 0; } int main() { SRAND(7767517); return test_convolutiondepthwise1d_0() || test_convolutiondepthwise1d_1(); }