mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-18 13:42:18 +08:00
nihuini
110 lines
4.2 KiB
C++
110 lines
4.2 KiB
C++
// Tencent is pleased to support the open source community by making ncnn available.
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//
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// Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
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//
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// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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// in compliance with the License. You may obtain a copy of the License at
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//
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// https://opensource.org/licenses/BSD-3-Clause
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//
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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#include "testutil.h"
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#include "layer/deconvolutiondepthwise.h"
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static int test_deconvolutiondepthwise(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int group)
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{
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ncnn::Mat a = RandomMat(w, h, c);
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ncnn::ParamDict pd;
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pd.set(0, outch);// num_output
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pd.set(1, kernel);// kernel_w
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pd.set(2, dilation);// dilation_w
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pd.set(3, stride);// stride_w
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pd.set(4, pad);// pad_w
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pd.set(5, bias);// bias_term
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pd.set(6, outch/group*c/group*kernel*kernel*group);
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pd.set(7, group);
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int activation_type = RAND() % 5;// 0 1 2 3 4
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ncnn::Mat activation_params(2);
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activation_params[0] = RandomFloat(-1, 0);// alpha
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activation_params[1] = RandomFloat(0, 1);// beta
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pd.set(9, activation_type);
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pd.set(10, activation_params);
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std::vector<ncnn::Mat> weights(2);
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weights[0] = RandomMat(outch/group*c/group*kernel*kernel*group);
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weights[1] = RandomMat(outch);
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_vulkan_compute = true;
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opt.use_int8_inference = false;
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int ret = test_layer<ncnn::DeconvolutionDepthWise>("DeconvolutionDepthWise", pd, weights, opt, a);
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if (ret != 0)
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{
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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]);
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}
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return ret;
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}
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static int test_deconvolutiondepthwise_0()
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{
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static const int kdsp[16][4] = {
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{1, 1, 1, 0},
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{1, 1, 2, 0},
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{2, 1, 1, 1},
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{2, 1, 2, 1},
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{3, 1, 1, 1},
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{3, 1, 2, 1},
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{3, 2, 1, 1},
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{4, 1, 1, 2},
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{4, 1, 2, 2},
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{4, 2, 1, 2},
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{5, 1, 1, 2},
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{5, 1, 2, 2},
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{5, 2, 2, 2},
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{7, 1, 1, 3},
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{7, 1, 2, 3},
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{7, 2, 1, 3},
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};
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for (int i=0; i<16; i++)
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{
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int ret = 0
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|| test_deconvolutiondepthwise(9, 7, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 1)
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|| test_deconvolutiondepthwise(9, 7, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 1)
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|| test_deconvolutiondepthwise(9, 7, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 2)
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|| test_deconvolutiondepthwise(9, 7, 3, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 3)
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|| test_deconvolutiondepthwise(9, 7, 4, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 2)
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|| test_deconvolutiondepthwise(9, 7, 4, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 4)
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|| test_deconvolutiondepthwise(9, 7, 7, 7, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 7)
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|| test_deconvolutiondepthwise(9, 7, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 2)
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|| test_deconvolutiondepthwise(9, 7, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 8)
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|| test_deconvolutiondepthwise(9, 7, 12, 12, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 4)
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|| test_deconvolutiondepthwise(9, 7, 15, 15, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 15)
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|| test_deconvolutiondepthwise(9, 7, 16, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 2)
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|| test_deconvolutiondepthwise(9, 7, 16, 16, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 16)
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;
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if (ret != 0)
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return -1;
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}
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return 0;
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}
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int main()
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{
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SRAND(7767517);
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return test_deconvolutiondepthwise_0();
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}
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