mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-19 23:03:17 +08:00
nihui
305 lines
14 KiB
C++
305 lines
14 KiB
C++
// Copyright 2019 Tencent
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// SPDX-License-Identifier: BSD-3-Clause
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#include "testutil.h"
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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)
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{
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ncnn::Mat a = RandomMat(w, h, c);
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if (output_w > 0 && output_h > 0 && pad != -233 && pad != -234)
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{
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pad = -233;
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}
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ncnn::ParamDict pd;
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pd.set(0, outch);
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pd.set(1, kernel);
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pd.set(2, dilation);
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pd.set(3, stride);
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pd.set(4, pad);
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pd.set(5, bias);
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pd.set(6, outch * c * kernel * kernel);
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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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pd.set(18, output_pad_right);
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pd.set(19, output_pad_bottom);
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pd.set(20, output_w);
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pd.set(21, output_h);
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std::vector<ncnn::Mat> weights(2);
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weights[0] = RandomMat(outch * c * kernel * kernel);
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weights[1] = RandomMat(outch);
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int ret = test_layer("Deconvolution", pd, weights, a);
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if (ret != 0)
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{
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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);
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return ret;
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}
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = true;
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opt.use_fp16_packed = false;
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opt.use_fp16_storage = false;
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opt.use_fp16_arithmetic = false;
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opt.use_bf16_packed = false;
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opt.use_bf16_storage = false;
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opt.use_sgemm_convolution = false;
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opt.use_winograd_convolution = false;
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ret = test_layer_opt("Deconvolution", pd, weights, opt, a);
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if (ret != 0)
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{
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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);
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return ret;
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}
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}
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{
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_packing_layout = true;
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opt.use_fp16_packed = true;
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opt.use_fp16_storage = true;
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opt.use_fp16_arithmetic = true;
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opt.use_bf16_packed = false;
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opt.use_bf16_storage = false;
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opt.use_sgemm_convolution = false;
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opt.use_winograd_convolution = false;
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ret = test_layer_opt("Deconvolution", pd, weights, opt, a);
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if (ret != 0)
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{
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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);
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return ret;
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}
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}
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return ret;
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}
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static int test_deconvolution_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, -233},
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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, -233},
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{4, 1, 2, -234},
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{4, 2, 1, -234},
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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, -233},
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};
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for (int i = 0; i < 16; i++)
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{
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const int k = kdsp[i][0];
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const int d = kdsp[i][1];
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const int s = kdsp[i][2];
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const int p = kdsp[i][3];
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int ret = 0
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|| test_deconvolution(9, 7, 1, 1, k, d, s, p, 1, 0, 0, 0, 0)
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|| test_deconvolution(9, 7, 4, 13, k, d, s, p, 0, 1, 1, 7, 5)
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|| test_deconvolution(9, 7, 13, 4, k, d, s, p, 1, 1, 0, 0, 0)
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|| test_deconvolution(9, 7, 4, 8, k, d, s, p, 0, 0, 1, 0, 0)
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|| test_deconvolution(9, 7, 8, 4, k, d, s, p, 1, 0, 0, 7, 5)
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|| test_deconvolution(7, 7, 12, 12, k, d, s, p, 1, 0, 1, 0, 0)
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|| test_deconvolution(4, 5, 12, 11, k, d, s, p, 0, 0, 1, 1, 0)
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|| test_deconvolution(9, 7, 8, 13, k, d, s, p, 0, 2, 2, 0, 0)
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|| test_deconvolution(9, 7, 13, 8, k, d, s, p, 1, 2, 0, 0, 0)
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|| test_deconvolution(9, 7, 16, 16, k, d, s, p, 0, 0, 2, 7, 5);
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if (ret != 0)
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return -1;
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}
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// tier coverage for small outch and various elempack
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return 0
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|| test_deconvolution(5, 4, 7, 3, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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|| test_deconvolution(6, 7, 2, 3, 2, 1, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution(5, 3, 1, 3, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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|| test_deconvolution(8, 6, 8, 2, 3, 2, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution(8, 4, 8, 15, 1, 1, 2, 0, 0, 0, 1, 0, 0)
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// kernel transform num_input < 4 paths
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|| test_deconvolution(4, 5, 1, 8, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 2, 7, 1, 1, 1, 0, 1, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 2, 8, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution(4, 5, 2, 1, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 1, 5, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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// elempack==1 large inch for inner tier cascading
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|| test_deconvolution(3, 3, 14, 2, 3, 1, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution(3, 3, 14, 1, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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// avx512 tier coverage
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|| test_deconvolution(5, 4, 1, 16, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 2, 16, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 8, 16, 3, 1, 2, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution(4, 3, 5, 16, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 24, 8, 3, 1, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 20, 8, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution(4, 5, 17, 8, 1, 1, 1, 0, 1, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 7, 5, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution(4, 3, 24, 5, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 20, 5, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution(4, 3, 17, 5, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution(4, 3, 24, 2, 3, 1, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 17, 2, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 20, 2, 3, 1, 2, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution(4, 3, 7, 1, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 16, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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|| test_deconvolution(4, 3, 24, 1, 3, 1, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution(5, 4, 20, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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|| test_deconvolution(4, 3, 17, 1, 3, 1, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution(7, 5, 24, 32, 4, 2, 2, 2, 1, 0, 0, 0, 0)
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|| test_deconvolution(7, 5, 32, 24, 4, 2, 2, 2, 1, 0, 0, 0, 0)
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|| test_deconvolution(7, 5, 28, 32, 4, 2, 2, 2, 1, 0, 0, 0, 0)
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|| test_deconvolution(7, 5, 32, 28, 4, 2, 2, 2, 1, 0, 0, 0, 0)
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|| test_deconvolution(7, 5, 26, 32, 4, 2, 2, 2, 1, 0, 0, 0, 0)
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|| test_deconvolution(7, 5, 32, 26, 4, 2, 2, 2, 1, 0, 0, 0, 0);
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}
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static int test_deconvolution_dynamic(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)
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{
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ncnn::Mat a = RandomMat(w, h, c);
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if (output_w > 0 && output_h > 0 && pad != -233 && pad != -234)
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{
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pad = -233;
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}
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ncnn::ParamDict pd;
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pd.set(0, 0);
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pd.set(1, 0);
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pd.set(2, dilation);
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pd.set(3, stride);
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pd.set(4, pad);
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pd.set(5, bias);
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pd.set(6, 0);
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pd.set(28, 1); // dynamic weight
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int activation_type = RAND() % 7; // 0 1 2 3 4 5 6
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ncnn::Mat activation_params(2);
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activation_params[0] = (activation_type == 6) ? RandomFloat(0, 1) : 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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pd.set(18, output_pad_right);
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pd.set(19, output_pad_bottom);
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pd.set(20, output_w);
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pd.set(21, output_h);
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std::vector<ncnn::Mat> as(bias ? 3 : 2);
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as[0] = a;
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as[1] = RandomMat(kernel, kernel, outch, c);
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if (bias)
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as[2] = RandomMat(outch);
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std::vector<ncnn::Mat> weights(0);
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int ret = test_layer("Deconvolution", pd, weights, as);
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if (ret != 0)
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{
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fprintf(stderr, "test_deconvolution_dynamic 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);
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}
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return ret;
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}
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static int test_deconvolution_1()
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{
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static const int kdsp[7][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, -233},
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{3, 1, 1, 1},
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{3, 1, 2, 1},
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{3, 2, 1, -234},
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};
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for (int i = 0; i < 7; i++)
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{
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const int k = kdsp[i][0];
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const int d = kdsp[i][1];
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const int s = kdsp[i][2];
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const int p = kdsp[i][3];
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int ret = 0
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|| test_deconvolution_dynamic(9, 7, 1, 1, k, d, s, p, 1, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(9, 7, 4, 13, k, d, s, p, 0, 1, 1, 7, 5)
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|| test_deconvolution_dynamic(9, 7, 13, 4, k, d, s, p, 1, 1, 0, 0, 0)
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|| test_deconvolution_dynamic(9, 7, 4, 8, k, d, s, p, 0, 0, 1, 0, 0)
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|| test_deconvolution_dynamic(9, 7, 8, 4, k, d, s, p, 1, 0, 0, 7, 5)
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|| test_deconvolution_dynamic(7, 7, 12, 12, k, d, s, p, 1, 0, 1, 0, 0)
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|| test_deconvolution_dynamic(4, 5, 12, 11, k, d, s, p, 0, 0, 1, 1, 0)
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|| test_deconvolution_dynamic(9, 7, 8, 13, k, d, s, p, 0, 2, 2, 0, 0)
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|| test_deconvolution_dynamic(9, 7, 13, 8, k, d, s, p, 1, 2, 0, 0, 0)
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|| test_deconvolution_dynamic(9, 7, 16, 16, k, d, s, p, 0, 0, 2, 7, 5);
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if (ret != 0)
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return -1;
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}
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// tier coverage for small outch and various elempack
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return 0
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|| test_deconvolution_dynamic(5, 4, 7, 3, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(6, 7, 2, 3, 2, 1, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 3, 1, 3, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(8, 6, 8, 2, 3, 2, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(8, 4, 8, 15, 1, 1, 2, 0, 0, 0, 1, 0, 0)
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// kernel transform num_input < 4 paths
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|| test_deconvolution_dynamic(4, 5, 1, 8, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 2, 7, 1, 1, 1, 0, 1, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 2, 8, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(4, 5, 2, 1, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 1, 5, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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// elempack==1 large inch for inner tier cascading
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|| test_deconvolution_dynamic(3, 3, 14, 2, 3, 1, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(3, 3, 14, 1, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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// avx512 tier coverage
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|| test_deconvolution_dynamic(5, 4, 1, 16, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 2, 16, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 8, 16, 3, 1, 2, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(4, 3, 5, 16, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 24, 8, 3, 1, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 20, 8, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(4, 5, 17, 8, 1, 1, 1, 0, 1, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 7, 5, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(4, 3, 24, 5, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 20, 5, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(4, 3, 17, 5, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(4, 3, 24, 2, 3, 1, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 17, 2, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 20, 2, 3, 1, 2, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(4, 3, 7, 1, 3, 1, 1, 1, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 16, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(4, 3, 24, 1, 3, 1, 1, 1, 1, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(5, 4, 20, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
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|| test_deconvolution_dynamic(4, 3, 17, 1, 3, 1, 1, 1, 1, 0, 0, 0, 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_deconvolution_0() || test_deconvolution_1();
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}
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