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| // Tencent is pleased to support the open source community by making ncnn available. | |
| // | |
| // Copyright (C) 2021 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_convolution1d(int w, int h, int outh, int kernel, int dilation, int stride, int pad, int bias) | |
| { | |
| 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 * h * kernel); | |
| int activation_type = RAND() % 6; // 0 1 2 3 4 5 | |
| 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<ncnn::Mat> weights(bias ? 2 : 1); | |
| weights[0] = RandomMat(outh * h * kernel); | |
| if (bias) | |
| weights[1] = RandomMat(outh); | |
| int ret = test_layer<ncnn::Convolution1D>("Convolution1D", pd, weights, a); | |
| if (ret != 0) | |
| { | |
| fprintf(stderr, "test_convolution1d failed w=%d h=%d outh=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, outh, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]); | |
| } | |
| return ret; | |
| } | |
| static int test_convolution1d_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]; | |
| const int b0 = i % 2; | |
| const int b1 = 1 - b1; | |
| int ret = 0 | |
| || test_convolution1d(9, 1, 1, k, d, s, p, b0) | |
| || test_convolution1d(9, 1, 3, k, d, s, p, b1) | |
| || test_convolution1d(9, 1, 7, k, d, s, p, b0) | |
| || test_convolution1d(9, 1, 15, k, d, s, p, b1) | |
| || test_convolution1d(9, 1, 31, k, d, s, p, b0) | |
| || test_convolution1d(9, 3, 1, k, d, s, p, b1) | |
| || test_convolution1d(9, 3, 3, k, d, s, p, b0) | |
| || test_convolution1d(9, 3, 7, k, d, s, p, b1) | |
| || test_convolution1d(9, 3, 15, k, d, s, p, b0) | |
| || test_convolution1d(9, 3, 31, k, d, s, p, b1) | |
| || test_convolution1d(9, 7, 1, k, d, s, p, b0) | |
| || test_convolution1d(9, 7, 3, k, d, s, p, b1) | |
| || test_convolution1d(9, 7, 7, k, d, s, p, b0) | |
| || test_convolution1d(9, 7, 15, k, d, s, p, b1) | |
| || test_convolution1d(9, 7, 31, k, d, s, p, b0) | |
| || test_convolution1d(9, 15, 1, k, d, s, p, b1) | |
| || test_convolution1d(9, 15, 3, k, d, s, p, b0) | |
| || test_convolution1d(9, 15, 7, k, d, s, p, b1) | |
| || test_convolution1d(9, 15, 15, k, d, s, p, b0) | |
| || test_convolution1d(9, 15, 31, k, d, s, p, b1) | |
| || test_convolution1d(9, 31, 1, k, d, s, p, b0) | |
| || test_convolution1d(9, 31, 3, k, d, s, p, b1) | |
| || test_convolution1d(9, 31, 7, k, d, s, p, b0) | |
| || test_convolution1d(9, 31, 15, k, d, s, p, b1) | |
| || test_convolution1d(25, 28, 31, k, d, s, p, b0) | |
| || test_convolution1d(25, 31, 28, k, d, s, p, b1) | |
| || test_convolution1d(25, 28, 28, k, d, s, p, b0) | |
| || test_convolution1d(25, 24, 28, k, d, s, p, b1) | |
| || test_convolution1d(25, 24, 31, k, d, s, p, b0) | |
| || test_convolution1d(25, 28, 24, k, d, s, p, b1) | |
| || test_convolution1d(25, 31, 24, k, d, s, p, b0) | |
| || test_convolution1d(25, 24, 24, k, d, s, p, b1) | |
| || test_convolution1d(25, 28, 48, k, d, s, p, b0) | |
| || test_convolution1d(25, 31, 48, k, d, s, p, b1) | |
| || test_convolution1d(25, 24, 48, k, d, s, p, b0) | |
| || test_convolution1d(25, 48, 28, k, d, s, p, b1) | |
| || test_convolution1d(25, 48, 31, k, d, s, p, b0) | |
| || test_convolution1d(25, 48, 24, k, d, s, p, b1) | |
| || test_convolution1d(25, 31, 31, k, d, s, p, b0) | |
| || test_convolution1d(25, 48, 48, k, d, s, p, b1); | |
| if (ret != 0) | |
| return -1; | |
| } | |
| return 0 | |
| || test_convolution1d(7, 1, 4, 3, 1, 1, 1, 1) | |
| || test_convolution1d(14, 1, 4, 3, 1, 2, 1, 1) | |
| || test_convolution1d(15, 4, 4, 3, 1, 1, 1, 1) | |
| || test_convolution1d(15, 8, 8, 3, 1, 1, 1, 1) | |
| || test_convolution1d(11, 8, 16, 3, 1, 1, 1, 1) | |
| || test_convolution1d(13, 16, 24, 3, 1, 1, 1, 1) | |
| || test_convolution1d(8, 16, 24, 3, 1, 1, 1, 0) | |
| || test_convolution1d(4, 16, 24, 3, 1, 1, 1, 1) | |
| || test_convolution1d(4, 16, 24, 3, 1, 1, 1, 0) | |
| || test_convolution1d(6, 64, 64, 3, 1, 2, 0, 1); | |
| } | |
| static int test_convolution1d_dynamic(int w, int h, int outh, int kernel, int dilation, int stride, int pad, int bias) | |
| { | |
| 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(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<ncnn::Mat> as(bias ? 3 : 2); | |
| as[0] = a; | |
| as[1] = RandomMat(kernel, h, outh); | |
| if (bias) | |
| as[2] = RandomMat(outh); | |
| std::vector<ncnn::Mat> weights(0); | |
| int ret = test_layer<ncnn::Convolution1D>("Convolution1D", pd, weights, as); | |
| if (ret != 0) | |
| { | |
| fprintf(stderr, "test_convolution1d_dynamic failed w=%d h=%d outh=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, outh, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]); | |
| } | |
| return ret; | |
| } | |
| static int test_convolution1d_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_convolution1d_dynamic(11, 1, 1, k, d, s, p, 1) | |
| || test_convolution1d_dynamic(11, 4, 13, k, d, s, p, 0) | |
| || test_convolution1d_dynamic(11, 13, 4, k, d, s, p, 1) | |
| || test_convolution1d_dynamic(11, 12, 12, k, d, s, p, 0) | |
| || test_convolution1d_dynamic(11, 8, 12, k, d, s, p, 1) | |
| || test_convolution1d_dynamic(11, 8, 13, k, d, s, p, 0) | |
| || test_convolution1d_dynamic(11, 13, 8, k, d, s, p, 1) | |
| || test_convolution1d_dynamic(11, 12, 16, k, d, s, p, 0) | |
| || test_convolution1d_dynamic(11, 15, 15, k, d, s, p, 0) | |
| || test_convolution1d_dynamic(11, 16, 16, k, d, s, p, 0); | |
| if (ret != 0) | |
| return -1; | |
| } | |
| return 0; | |
| } | |
| int main() | |
| { | |
| SRAND(7767517); | |
| return test_convolution1d_0() || test_convolution1d_1(); | |
| } | |