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https://github.com/Tencent/ncnn.git
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nihui
41f031e045
* adjust tests * opt shader coverage op * update gpu_coverage.cpp atomicor coalesce
245 lines
9.9 KiB
C++
245 lines
9.9 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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#include "layer_type.h"
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#include <limits.h>
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static int test_deformableconv2d(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias)
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{
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const int kernel_extent_w = dilation * (kernel - 1) + 1;
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const int kernel_extent_h = dilation * (kernel - 1) + 1;
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const int out_w = (w + pad + pad - kernel_extent_w) / stride + 1;
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const int out_h = (h + pad + pad - kernel_extent_h) / stride + 1;
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std::vector<ncnn::Mat> a(3);
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a[0] = RandomMat(w, h, c);
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a[1] = RandomMat(out_w, out_h, kernel * kernel * 2);
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a[2] = RandomMat(out_w, out_h, kernel * kernel);
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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() % 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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std::vector<ncnn::Mat> weights(bias ? 2 : 1);
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weights[0] = RandomMat(outch * c * kernel * kernel);
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if (bias)
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weights[1] = RandomMat(outch);
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float epsilon = 0.001;
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int ret = test_layer("DeformableConv2D", pd, weights, a, 1, epsilon);
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if (ret != 0)
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{
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fprintf(stderr, "test_deformableconv2d failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
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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_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("DeformableConv2D", pd, weights, opt, a, 1, epsilon);
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if (ret != 0)
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{
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fprintf(stderr, "test_deformableconv2d failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
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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_storage = true;
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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("DeformableConv2D", pd, weights, opt, a, 1, epsilon);
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if (ret != 0)
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{
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fprintf(stderr, "test_deformableconv2d failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, activation_type, activation_params[0], activation_params[1]);
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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_deformableconv2d_0()
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{
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static const int kdsp[10][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, 0},
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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, 2, 1},
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{5, 1, 2, 2},
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{5, 2, 2, 2},
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};
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for (int i = 0; i < 4; 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_deformableconv2d(9, 7, 1, 1, k, d, s, p, 1)
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|| test_deformableconv2d(9, 7, 4, 13, k, d, s, p, 0)
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|| test_deformableconv2d(9, 7, 13, 4, k, d, s, p, 1)
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|| test_deformableconv2d(9, 7, 4, 8, k, d, s, p, 0)
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|| test_deformableconv2d(9, 7, 8, 4, k, d, s, p, 1)
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|| test_deformableconv2d(9, 7, 8, 13, k, d, s, p, 0)
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|| test_deformableconv2d(9, 7, 13, 8, k, d, s, p, 1)
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|| test_deformableconv2d(9, 7, 16, 16, k, d, s, p, 0);
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if (ret != 0)
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return -1;
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// separate channel packing from the small spatial boundary cases
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static const int channels[][2] = {
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{3, 3}, {12, 12}, {24, 24}, {48, 48}, {3, 12}, {12, 24}, {24, 48}, {48, 3}
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};
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for (int j = 0; j < 8; j++)
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{
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if (test_deformableconv2d(16, 16, channels[j][0], channels[j][1], k, d, s, p, 1) != 0)
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return -1;
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}
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}
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// complete the packing transitions on the 1x1 and 3x3 baselines
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static const int packing[][2] = {
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{3, 24}, {3, 48}, {12, 3}, {12, 48}, {24, 3}, {24, 12}, {48, 12}, {48, 24}
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};
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for (int j = 0; j < 8; j++)
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{
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if (test_deformableconv2d(16, 16, packing[j][0], packing[j][1], 1, 1, 1, 0, 1)
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|| test_deformableconv2d(16, 16, packing[j][0], packing[j][1], 3, 1, 1, 1, 1))
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return -1;
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}
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return 0
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|| test_deformableconv2d(7, 5, 24, 32, 4, 2, 2, 2, 1)
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|| test_deformableconv2d(7, 5, 32, 24, 4, 2, 2, 2, 1)
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|| test_deformableconv2d(7, 5, 28, 32, 4, 2, 2, 2, 1)
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|| test_deformableconv2d(7, 5, 32, 28, 4, 2, 2, 2, 1)
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|| test_deformableconv2d(7, 5, 26, 32, 4, 2, 2, 2, 1)
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|| test_deformableconv2d(7, 5, 32, 26, 4, 2, 2, 2, 1);
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}
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#if NCNN_VALIDATION
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static int test_deformableconv2d_load_param_activation(const ncnn::ParamDict& base, int activation_type, const ncnn::Mat& activation_params, int expected_ret)
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{
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ncnn::ParamDict pd = base;
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pd.set(9, activation_type);
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pd.set(10, activation_params);
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return test_layer_param(ncnn::LayerType::DeformableConv2D, pd, expected_ret);
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}
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static int test_deformableconv2d_load_param()
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{
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ncnn::ParamDict base;
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base.set(0, 8);
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base.set(1, 3);
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base.set(6, 216);
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if (test_layer_param(ncnn::LayerType::DeformableConv2D, base, 0) != 0)
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return -1;
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ncnn::Mat params(2);
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params[0] = 0.1f;
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params[1] = 0.5f;
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int ret = 0
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|| test_deformableconv2d_load_param_activation(base, 0, ncnn::Mat(), 0)
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|| test_deformableconv2d_load_param_activation(base, 0, ncnn::Mat(0), 0)
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|| test_deformableconv2d_load_param_activation(base, 0, params, 0)
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|| test_deformableconv2d_load_param_activation(base, 0, params.range(0, 1), 0)
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|| test_deformableconv2d_load_param_activation(base, 1, ncnn::Mat(), 0)
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|| test_deformableconv2d_load_param_activation(base, 1, ncnn::Mat(0), 0)
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|| test_deformableconv2d_load_param_activation(base, 1, params, 0)
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|| test_deformableconv2d_load_param_activation(base, 1, params.range(0, 1), 0)
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|| test_deformableconv2d_load_param_activation(base, 2, ncnn::Mat(), -1)
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|| test_deformableconv2d_load_param_activation(base, 2, ncnn::Mat(0), -1)
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|| test_deformableconv2d_load_param_activation(base, 2, params, 0)
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|| test_deformableconv2d_load_param_activation(base, 2, params.range(0, 1), 0)
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|| test_deformableconv2d_load_param_activation(base, 3, ncnn::Mat(), -1)
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|| test_deformableconv2d_load_param_activation(base, 3, ncnn::Mat(0), -1)
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|| test_deformableconv2d_load_param_activation(base, 3, params, 0)
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|| test_deformableconv2d_load_param_activation(base, 3, params.range(0, 1), -1)
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|| test_deformableconv2d_load_param_activation(base, 4, ncnn::Mat(), 0)
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|| test_deformableconv2d_load_param_activation(base, 4, ncnn::Mat(0), 0)
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|| test_deformableconv2d_load_param_activation(base, 4, params, 0)
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|| test_deformableconv2d_load_param_activation(base, 4, params.range(0, 1), 0)
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|| test_deformableconv2d_load_param_activation(base, 5, ncnn::Mat(), 0)
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|| test_deformableconv2d_load_param_activation(base, 5, ncnn::Mat(0), 0)
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|| test_deformableconv2d_load_param_activation(base, 5, params, 0)
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|| test_deformableconv2d_load_param_activation(base, 5, params.range(0, 1), 0)
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|| test_deformableconv2d_load_param_activation(base, 6, ncnn::Mat(), -1)
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|| test_deformableconv2d_load_param_activation(base, 6, ncnn::Mat(0), -1)
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|| test_deformableconv2d_load_param_activation(base, 6, params, 0)
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|| test_deformableconv2d_load_param_activation(base, 6, params.range(0, 1), -1)
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|| test_deformableconv2d_load_param_activation(base, 7, ncnn::Mat(), -1);
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if (ret != 0)
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return ret;
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ncnn::Mat missing_data(0);
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missing_data.w = 1;
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const ncnn::Mat bad[] = {ncnn::Mat(2, (size_t)1u), ncnn::Mat(2, (size_t)2u), ncnn::Mat(2, 2), ncnn::Mat(2, (size_t)16u, 4), missing_data};
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for (int i = 0; i < 5; i++)
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{
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if (test_layer_param(ncnn::LayerType::DeformableConv2D, base, 10, bad[i], -1) != 0)
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return -1;
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}
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ret = 0
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|| test_layer_param(ncnn::LayerType::DeformableConv2D, base, 0, 0, -1)
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|| test_layer_param(ncnn::LayerType::DeformableConv2D, base, 1, 0, -1)
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|| test_layer_param(ncnn::LayerType::DeformableConv2D, base, 2, 0, -1)
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|| test_layer_param(ncnn::LayerType::DeformableConv2D, base, 3, 0, -1)
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|| test_layer_param(ncnn::LayerType::DeformableConv2D, base, 1, INT_MAX, -1)
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|| test_layer_param(ncnn::LayerType::DeformableConv2D, base, 6, 217, -1);
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if (ret != 0)
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return ret;
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return 0;
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}
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#endif // NCNN_VALIDATION
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int main()
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{
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SRAND(7767517);
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return 0
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|| test_deformableconv2d_0()
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#if NCNN_VALIDATION
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|| test_deformableconv2d_load_param()
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#endif // NCNN_VALIDATION
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;
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}
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