mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-19 06:21:39 +08:00
nihuini
219 lines
7.4 KiB
C++
219 lines
7.4 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/convolution.h"
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static int test_convolution(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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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*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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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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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::Convolution>("Convolution", pd, weights, opt, a);
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if (ret != 0)
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{
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fprintf(stderr, "test_convolution 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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return ret;
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}
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static int test_convolution_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_convolution(9, 7, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
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|| test_convolution(9, 7, 4, 13, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0)
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|| test_convolution(9, 7, 13, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
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|| test_convolution(9, 7, 4, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0)
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|| test_convolution(9, 7, 8, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
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|| test_convolution(9, 7, 8, 13, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0)
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|| test_convolution(9, 7, 13, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
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|| test_convolution(9, 7, 16, 16, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0)
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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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void set_param(ncnn::Convolution* layer)
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{
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layer->use_int8_requantize = true;
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layer->top_blob_int8_scale = 64.f;
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return;
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}
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static int test_convolution_int8(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, bool requant = false)
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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*c*kernel*kernel);
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pd.set(8, 1);// int8_scale_term
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std::vector<ncnn::Mat> weights(bias ? 4 : 3);
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weights[0] = RandomMat(outch*c*kernel*kernel);
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if (bias)
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{
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weights[1] = RandomMat(outch);
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weights[2] = RandomMat(outch);
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weights[3] = RandomMat(1);
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}
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else
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{
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weights[1] = RandomMat(outch);
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weights[2] = RandomMat(1);
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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_vulkan_compute = false;
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opt.use_int8_inference = true;
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int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, opt, a, 0.001f, requant ? set_param : 0);
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if (ret != 0)
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{
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fprintf(stderr, "test_convolution_int8 failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d requant=%d\n", w, h, c, outch, kernel, dilation, stride, pad, bias, requant);
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}
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return 0;
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}
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static int test_convolution_1()
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{
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static const int kdsp[24][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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{2, 2, 1, 1},
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{2, 2, 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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{3, 2, 2, 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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{4, 2, 2, 2},
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{5, 1, 1, 2},
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{5, 1, 2, 2},
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{5, 2, 1, 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, 1, 3, 3},
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{7, 2, 1, 3},
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{7, 2, 2, 3},
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{7, 2, 3, 3},
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};
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for (int i=0; i<24; i++)
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{
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int ret = 0
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|| test_convolution_int8(9, 7, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
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|| test_convolution_int8(9, 7, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
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|| test_convolution_int8(9, 7, 3, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
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|| test_convolution_int8(9, 7, 4, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
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|| test_convolution_int8(9, 7, 7, 7, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
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|| test_convolution_int8(9, 7, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
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|| test_convolution_int8(9, 7, 15, 15, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
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|| test_convolution_int8(9, 7, 16, 16, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1)
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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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for (int i=0; i<20; i++)
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{
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int ret = 0
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|| test_convolution_int8(9, 7, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
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|| test_convolution_int8(9, 7, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
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|| test_convolution_int8(9, 7, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
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|| test_convolution_int8(9, 7, 3, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
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|| test_convolution_int8(9, 7, 4, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
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|| test_convolution_int8(9, 7, 7, 7, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
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|| test_convolution_int8(9, 7, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
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|| test_convolution_int8(9, 7, 15, 15, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
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|| test_convolution_int8(9, 7, 16, 16, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, true)
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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_convolution_0() || test_convolution_1();
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}
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