// 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/deconvolution.h" #include "testutil.h" static int test_deconvolution(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int output_pad_right, int output_pad_bottom, int output_w, int output_h) { ncnn::Mat a = RandomMat(w, h, c); if (output_w > 0 && output_h > 0 && pad != -233 && pad != -234) { pad = -233; } 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 * c * kernel * kernel); 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); pd.set(18, output_pad_right); pd.set(19, output_pad_bottom); pd.set(20, output_w); pd.set(21, output_h); std::vector weights(2); weights[0] = RandomMat(outch * c * kernel * kernel); weights[1] = RandomMat(outch); int ret = test_layer("Deconvolution", pd, weights, a); if (ret != 0) { fprintf(stderr, "test_deconvolution failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f] output_pad_right=%d output_pad_bottom=%d output_w=%d output_h=%d\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1], output_pad_right, output_pad_bottom, output_w, output_h); } return ret; } static int test_deconvolution_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, -233}, {4, 1, 2, -234}, {4, 2, 1, -234}, {5, 1, 1, 2}, {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_deconvolution(9, 7, 1, 1, k, d, s, p, 1, 0, 0, 0, 0) || test_deconvolution(9, 7, 4, 13, k, d, s, p, 0, 1, 1, 7, 5) || test_deconvolution(9, 7, 13, 4, k, d, s, p, 1, 1, 0, 0, 0) || test_deconvolution(9, 7, 4, 8, k, d, s, p, 0, 0, 1, 0, 0) || test_deconvolution(9, 7, 8, 4, k, d, s, p, 1, 0, 0, 7, 5) || test_deconvolution(9, 7, 8, 13, k, d, s, p, 0, 2, 2, 0, 0) || test_deconvolution(9, 7, 13, 8, k, d, s, p, 1, 2, 0, 0, 0) || test_deconvolution(9, 7, 16, 16, k, d, s, p, 0, 0, 2, 7, 5); if (ret != 0) return -1; } return 0 || test_deconvolution(7, 5, 24, 32, 4, 2, 2, 2, 1, 0, 0, 0, 0) || test_deconvolution(7, 5, 32, 24, 4, 2, 2, 2, 1, 0, 0, 0, 0) || test_deconvolution(7, 5, 28, 32, 4, 2, 2, 2, 1, 0, 0, 0, 0) || test_deconvolution(7, 5, 32, 28, 4, 2, 2, 2, 1, 0, 0, 0, 0) || test_deconvolution(7, 5, 26, 32, 4, 2, 2, 2, 1, 0, 0, 0, 0) || test_deconvolution(7, 5, 32, 26, 4, 2, 2, 2, 1, 0, 0, 0, 0); } int main() { SRAND(7767517); return test_deconvolution_0(); }