718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
428 lines
16 KiB
C++
428 lines
16 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 "layer/convolution.h"
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#include "testutil.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);
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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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// larget epsilon for winograd optimization
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if (kernel == 3 && dilation == 1 && stride == 1 && c >= 16 && outch >= 16)
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{
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Randomize(a, -1, 1);
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Randomize(weights[0], -1, 1);
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epsilon = 0.002;
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}
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int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, a, epsilon);
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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, -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, 2},
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{4, 1, 2, -233},
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{4, 2, 1, -234},
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{5, 1, 1, -234},
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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_convolution(9, 7, 1, 1, k, d, s, p, 1)
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|| test_convolution(9, 7, 4, 13, k, d, s, p, 0)
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|| test_convolution(9, 7, 13, 4, k, d, s, p, 1)
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|| test_convolution(9, 7, 12, 12, k, d, s, p, 0)
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|| test_convolution(9, 7, 8, 12, k, d, s, p, 1)
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|| test_convolution(9, 7, 8, 13, k, d, s, p, 0)
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|| test_convolution(9, 7, 13, 8, k, d, s, p, 1)
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|| test_convolution(9, 7, 12, 16, k, d, s, p, 0)
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|| test_convolution(9, 7, 15, 15, k, d, s, p, 0)
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|| test_convolution(9, 7, 16, 16, k, d, s, p, 0)
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|| test_convolution(18, 17, 1, 1, k, d, s, p, 1)
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|| test_convolution(18, 17, 4, 13, k, d, s, p, 0)
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|| test_convolution(18, 17, 13, 4, k, d, s, p, 1)
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|| test_convolution(18, 17, 12, 12, k, d, s, p, 0)
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|| test_convolution(18, 17, 8, 12, k, d, s, p, 1)
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|| test_convolution(18, 17, 8, 13, k, d, s, p, 0)
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|| test_convolution(18, 17, 13, 8, k, d, s, p, 1)
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|| test_convolution(18, 17, 12, 16, k, d, s, p, 0)
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|| test_convolution(18, 17, 15, 15, k, d, s, p, 0)
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|| test_convolution(18, 17, 16, 16, k, d, s, p, 0)
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|| test_convolution(25, 33, 1, 1, k, d, s, p, 1)
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|| test_convolution(25, 33, 4, 13, k, d, s, p, 0)
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|| test_convolution(25, 33, 13, 4, k, d, s, p, 1)
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|| test_convolution(25, 33, 12, 12, k, d, s, p, 0)
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|| test_convolution(25, 33, 8, 12, k, d, s, p, 1)
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|| test_convolution(25, 33, 8, 13, k, d, s, p, 0)
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|| test_convolution(25, 33, 13, 8, k, d, s, p, 1)
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|| test_convolution(25, 33, 12, 16, k, d, s, p, 0)
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|| test_convolution(25, 33, 15, 15, k, d, s, p, 0)
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|| test_convolution(25, 33, 16, 16, k, d, s, p, 0);
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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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|| test_convolution(7, 5, 1, 4, 3, 1, 1, 1, 1)
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|| test_convolution(14, 5, 1, 4, 3, 1, 2, 1, 1)
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|| test_convolution(11, 5, 2, 12, 2, 2, 2, 1, 1)
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|| test_convolution(15, 11, 4, 4, 3, 1, 1, 1, 1)
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|| test_convolution(15, 11, 8, 8, 3, 1, 1, 1, 1)
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|| test_convolution(11, 11, 8, 16, 3, 1, 1, 1, 1)
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|| test_convolution(13, 16, 16, 24, 3, 1, 1, 1, 1)
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|| test_convolution(20, 19, 24, 24, 3, 1, 1, 1, 1)
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|| test_convolution(8, 8, 16, 24, 3, 1, 1, 1, 0)
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|| test_convolution(4, 8, 16, 24, 3, 1, 1, 1, 1)
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|| test_convolution(4, 20, 16, 24, 3, 1, 1, 1, 0)
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|| test_convolution(6, 7, 64, 64, 3, 1, 2, 0, 1)
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|| test_convolution(15, 17, 24, 32, 1, 1, 1, 0, 0)
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|| test_convolution(15, 17, 24, 32, 1, 1, 2, 0, 1)
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|| test_convolution(15, 17, 24, 32, 3, 1, 2, 0, 1)
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|| test_convolution(15, 17, 32, 24, 1, 1, 1, 0, 0)
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|| test_convolution(15, 17, 32, 24, 1, 1, 2, 0, 1)
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|| test_convolution(15, 17, 32, 24, 3, 1, 2, 0, 1)
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|| test_convolution(15, 17, 32, 28, 1, 1, 1, 0, 0)
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|| test_convolution(15, 17, 32, 28, 1, 1, 2, 0, 1)
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|| test_convolution(15, 17, 32, 28, 3, 1, 2, 0, 1)
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|| test_convolution(15, 17, 26, 32, 1, 1, 1, 0, 0)
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|| test_convolution(15, 17, 26, 32, 1, 1, 2, 0, 1)
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|| test_convolution(15, 17, 26, 32, 3, 1, 2, 0, 1)
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|| test_convolution(15, 17, 32, 26, 1, 1, 1, 0, 0)
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|| test_convolution(15, 17, 32, 26, 1, 1, 2, 0, 1)
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|| test_convolution(15, 17, 32, 26, 3, 1, 2, 0, 1)
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|| test_convolution(30, 30, 32, 26, 3, 1, 1, 1, 0);
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}
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static int test_convolution_vec(int w, 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);
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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 * w * 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 * w * kernel * kernel);
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if (bias)
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weights[1] = RandomMat(outch);
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int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, a);
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if (ret != 0)
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{
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fprintf(stderr, "test_convolution_vec failed w=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d act=%d actparams=[%f,%f]\n", w, 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_2()
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{
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return 0
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|| test_convolution_vec(1, 1, 1, 1, 1, 0, 1)
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|| test_convolution_vec(11, 12, 1, 1, 1, 0, 0)
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|| test_convolution_vec(20, 15, 1, 1, 1, 0, 1)
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|| test_convolution_vec(12, 20, 1, 1, 1, 0, 0)
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|| test_convolution_vec(3, 24, 1, 1, 1, 0, 1)
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|| test_convolution_vec(24, 5, 1, 1, 1, 0, 0)
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|| test_convolution_vec(32, 24, 1, 1, 1, 0, 1)
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|| test_convolution_vec(12, 32, 1, 1, 1, 0, 0)
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|| test_convolution_vec(64, 20, 1, 1, 1, 0, 1)
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|| test_convolution_vec(64, 128, 1, 1, 1, 0, 0);
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}
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static int test_convolution_dynamic(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, 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(19, 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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std::vector<ncnn::Mat> as(bias ? 3 : 2);
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as[0] = a;
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as[1] = RandomMat(kernel, kernel, c, outch);
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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<ncnn::Convolution>("Convolution", pd, weights, as);
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if (ret != 0)
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{
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fprintf(stderr, "test_convolution_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]\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_3()
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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_convolution_dynamic(11, 10, 1, 1, k, d, s, p, 1)
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|| test_convolution_dynamic(11, 10, 4, 13, k, d, s, p, 0)
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|| test_convolution_dynamic(11, 10, 13, 4, k, d, s, p, 1)
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|| test_convolution_dynamic(11, 10, 12, 12, k, d, s, p, 0)
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|| test_convolution_dynamic(11, 10, 8, 12, k, d, s, p, 1)
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|| test_convolution_dynamic(11, 10, 8, 13, k, d, s, p, 0)
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|| test_convolution_dynamic(11, 10, 13, 8, k, d, s, p, 1)
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|| test_convolution_dynamic(11, 10, 12, 16, k, d, s, p, 0)
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|| test_convolution_dynamic(11, 10, 15, 15, k, d, s, p, 0)
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|| test_convolution_dynamic(11, 10, 16, 16, k, d, s, p, 0);
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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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#if NCNN_INT8
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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);
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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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pd.set(8, requant ? 101 : 1); // int8_scale_term
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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 ? 5 : 4);
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weights[0] = RandomMat(outch * c * kernel * kernel);
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ncnn::Mat weight_scales = scales_mat(weights[0], outch, c * kernel * kernel, c * kernel * kernel);
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ncnn::Mat input_scales = scales_mat(a, 1, w * h * c, a.cstep);
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ncnn::Mat top_scales = requant ? scales_mat(a, 1, w * h * c, a.cstep) : ncnn::Mat();
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if (bias)
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{
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weights[1] = RandomMat(outch);
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weights[2] = weight_scales;
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weights[3] = input_scales;
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weights[4] = top_scales;
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}
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else
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{
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weights[1] = weight_scales;
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weights[2] = input_scales;
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weights[3] = top_scales;
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}
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int flag = TEST_LAYER_DISABLE_GPU_TESTING;
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int ret = test_layer<ncnn::Convolution>("Convolution", pd, weights, a, requant ? 1.0f : 0.001f, 0, flag);
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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 act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, requant, 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_1()
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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, 2},
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{4, 1, 2, -233},
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{4, 2, 1, -234},
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{5, 1, 1, -234},
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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_convolution_int8(9, 7, 1, 1, k, d, s, p, 1)
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|| test_convolution_int8(9, 7, 2, 2, k, d, s, p, 1)
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|| test_convolution_int8(9, 7, 3, 3, k, d, s, p, 1)
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|| test_convolution_int8(9, 7, 4, 4, k, d, s, p, 1)
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|| test_convolution_int8(9, 7, 7, 7, k, d, s, p, 1)
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|| test_convolution_int8(9, 7, 8, 8, k, d, s, p, 1)
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|| test_convolution_int8(9, 7, 15, 15, k, d, s, p, 1)
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|| test_convolution_int8(9, 7, 16, 15, k, d, s, p, 1)
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|| test_convolution_int8(9, 7, 15, 16, k, d, s, p, 1)
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|| test_convolution_int8(9, 7, 16, 16, k, d, s, p, 1);
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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 < 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_convolution_int8(9, 7, 1, 1, k, d, s, p, 1, true)
|
|
|| test_convolution_int8(9, 7, 1, 1, k, d, s, p, 1, true)
|
|
|| test_convolution_int8(9, 7, 2, 2, k, d, s, p, 1, true)
|
|
|| test_convolution_int8(9, 7, 3, 3, k, d, s, p, 1, true)
|
|
|| test_convolution_int8(9, 7, 4, 4, k, d, s, p, 1, true)
|
|
|| test_convolution_int8(9, 7, 7, 7, k, d, s, p, 1, true)
|
|
|| test_convolution_int8(9, 7, 8, 8, k, d, s, p, 1, true)
|
|
|| test_convolution_int8(9, 7, 15, 15, k, d, s, p, 1, true)
|
|
|| test_convolution_int8(9, 7, 16, 15, k, d, s, p, 1, true)
|
|
|| test_convolution_int8(9, 7, 15, 16, k, d, s, p, 1, true)
|
|
|| test_convolution_int8(9, 7, 16, 16, k, d, s, p, 1, true);
|
|
|
|
if (ret != 0)
|
|
return -1;
|
|
}
|
|
|
|
return 0
|
|
|| test_convolution_int8(11, 11, 8, 16, 3, 1, 1, 1, 1)
|
|
|| test_convolution_int8(13, 16, 16, 24, 3, 1, 1, 1, 1)
|
|
|| test_convolution_int8(8, 8, 16, 24, 3, 1, 1, 1, 0)
|
|
|| test_convolution_int8(4, 8, 16, 24, 3, 1, 1, 1, 1)
|
|
|| test_convolution_int8(4, 20, 16, 24, 3, 1, 1, 1, 0)
|
|
|| test_convolution_int8(6, 7, 64, 64, 3, 1, 2, 0, 1)
|
|
|| test_convolution_int8(25, 33, 16, 15, 3, 1, 1, 1, 0);
|
|
}
|
|
#endif // NCNN_INT8
|
|
|
|
int main()
|
|
{
|
|
SRAND(7767517);
|
|
|
|
#if NCNN_INT8
|
|
return 0
|
|
|| test_convolution_0()
|
|
|| test_convolution_1()
|
|
|| test_convolution_2()
|
|
|| test_convolution_3();
|
|
#else
|
|
return 0
|
|
|| test_convolution_0()
|
|
|| test_convolution_2()
|
|
|| test_convolution_3();
|
|
#endif
|
|
}
|