// 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/deconvolutiondepthwise.h" #include "testutil.h" static int test_deconvolutiondepthwise(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int group) { ncnn::Mat a = RandomMat(w, h, c); ncnn::ParamDict pd; pd.set(0, outch); // 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, outch / group * c / group * kernel * kernel * group); pd.set(7, group); int activation_type = RAND() % 5; // 0 1 2 3 4 ncnn::Mat activation_params(2); activation_params[0] = 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(outch / group * c / group * kernel * kernel * group); weights[1] = RandomMat(outch); ncnn::Option opt; opt.num_threads = 1; opt.use_vulkan_compute = true; opt.use_int8_inference = false; int ret = test_layer("DeconvolutionDepthWise", pd, weights, opt, a); if (ret != 0) { fprintf(stderr, "test_deconvolutiondepthwise failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, group, activation_type, activation_params[0], activation_params[1]); } return ret; } static int test_deconvolutiondepthwise_0() { static const int kdsp[16][4] = { {1, 1, 1, 0}, {1, 1, 2, 0}, {2, 1, 1, 1}, {2, 1, 2, 1}, {3, 1, 1, 1}, {3, 1, 2, 1}, {3, 2, 1, 1}, {4, 1, 1, 2}, {4, 1, 2, 2}, {4, 2, 1, 2}, {5, 1, 1, 2}, {5, 1, 2, 2}, {5, 2, 2, 2}, {7, 1, 1, 3}, {7, 1, 2, 3}, {7, 2, 1, 3}, }; for (int i = 0; i < 16; i++) { int ret = 0 || test_deconvolutiondepthwise(9, 7, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 1) || test_deconvolutiondepthwise(9, 7, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 1) || test_deconvolutiondepthwise(9, 7, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 2) || test_deconvolutiondepthwise(9, 7, 3, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 3) || test_deconvolutiondepthwise(9, 7, 4, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 2) || test_deconvolutiondepthwise(9, 7, 4, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 4) || test_deconvolutiondepthwise(9, 7, 7, 7, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 7) || test_deconvolutiondepthwise(9, 7, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 2) || test_deconvolutiondepthwise(9, 7, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 8) || test_deconvolutiondepthwise(9, 7, 12, 12, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 4) || test_deconvolutiondepthwise(9, 7, 15, 15, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 15) || test_deconvolutiondepthwise(9, 7, 16, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 2) || test_deconvolutiondepthwise(9, 7, 16, 16, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 16); if (ret != 0) return -1; } return 0; } int main() { SRAND(7767517); return test_deconvolutiondepthwise_0(); }