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
214 lines
8.9 KiB
C++
214 lines
8.9 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/convolutiondepthwise1d.h"
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#include "testutil.h"
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static int test_convolutiondepthwise1d(int w, int h, int outh, int kernel, int dilation, int stride, int pad, int bias, int group)
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{
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ncnn::Mat a = RandomMat(w, h);
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ncnn::ParamDict pd;
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pd.set(0, outh); // 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, outh / group * h / group * kernel * kernel * group);
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pd.set(7, group);
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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(2);
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weights[0] = RandomMat(outh / group * h / group * kernel * kernel * group);
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weights[1] = RandomMat(outh);
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int ret = test_layer<ncnn::ConvolutionDepthWise1D>("ConvolutionDepthWise1D", pd, weights, a);
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if (ret != 0)
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{
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fprintf(stderr, "test_convolutiondepthwise1d failed w=%d h=%d outh=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d act=%d actparams=[%f,%f]\n", w, h, outh, kernel, dilation, stride, pad, bias, group, activation_type, activation_params[0], activation_params[1]);
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}
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return ret;
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}
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static int test_convolutiondepthwise1d_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_convolutiondepthwise1d(15, 1, 1, k, d, s, p, 1, 1)
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|| test_convolutiondepthwise1d(15, 2, 2, k, d, s, p, 0, 1)
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|| test_convolutiondepthwise1d(15, 2, 2, k, d, s, p, 1, 2)
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|| test_convolutiondepthwise1d(15, 3, 3, k, d, s, p, 0, 3)
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|| test_convolutiondepthwise1d(15, 4, 2, k, d, s, p, 1, 2)
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|| test_convolutiondepthwise1d(15, 4, 4, k, d, s, p, 0, 4)
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|| test_convolutiondepthwise1d(15, 7, 7, k, d, s, p, 1, 7)
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|| test_convolutiondepthwise1d(15, 8, 8, k, d, s, p, 0, 2)
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|| test_convolutiondepthwise1d(15, 8, 8, k, d, s, p, 1, 8)
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|| test_convolutiondepthwise1d(15, 12, 12, k, d, s, p, 0, 4)
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|| test_convolutiondepthwise1d(15, 15, 15, k, d, s, p, 1, 15)
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|| test_convolutiondepthwise1d(15, 16, 8, k, d, s, p, 0, 2)
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|| test_convolutiondepthwise1d(15, 16, 16, k, d, s, p, 1, 16)
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|| test_convolutiondepthwise1d(18, 1, 1, k, d, s, p, 1, 1)
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|| test_convolutiondepthwise1d(18, 2, 2, k, d, s, p, 0, 1)
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|| test_convolutiondepthwise1d(18, 2, 2, k, d, s, p, 1, 2)
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|| test_convolutiondepthwise1d(18, 3, 3, k, d, s, p, 0, 3)
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|| test_convolutiondepthwise1d(18, 4, 2, k, d, s, p, 1, 2)
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|| test_convolutiondepthwise1d(18, 4, 4, k, d, s, p, 0, 4)
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|| test_convolutiondepthwise1d(18, 7, 7, k, d, s, p, 1, 7)
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|| test_convolutiondepthwise1d(18, 8, 8, k, d, s, p, 0, 2)
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|| test_convolutiondepthwise1d(18, 8, 8, k, d, s, p, 1, 8)
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|| test_convolutiondepthwise1d(18, 12, 12, k, d, s, p, 0, 4)
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|| test_convolutiondepthwise1d(18, 15, 15, k, d, s, p, 1, 15)
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|| test_convolutiondepthwise1d(18, 16, 8, k, d, s, p, 0, 2)
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|| test_convolutiondepthwise1d(18, 16, 16, k, d, s, p, 1, 16)
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|| test_convolutiondepthwise1d(25, 1, 1, k, d, s, p, 1, 1)
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|| test_convolutiondepthwise1d(25, 2, 2, k, d, s, p, 0, 1)
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|| test_convolutiondepthwise1d(25, 2, 2, k, d, s, p, 1, 2)
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|| test_convolutiondepthwise1d(25, 3, 3, k, d, s, p, 0, 3)
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|| test_convolutiondepthwise1d(25, 4, 2, k, d, s, p, 1, 2)
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|| test_convolutiondepthwise1d(25, 4, 4, k, d, s, p, 0, 4)
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|| test_convolutiondepthwise1d(25, 7, 7, k, d, s, p, 1, 7)
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|| test_convolutiondepthwise1d(25, 8, 8, k, d, s, p, 0, 2)
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|| test_convolutiondepthwise1d(25, 8, 8, k, d, s, p, 1, 8)
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|| test_convolutiondepthwise1d(25, 12, 12, k, d, s, p, 0, 4)
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|| test_convolutiondepthwise1d(25, 15, 15, k, d, s, p, 1, 15)
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|| test_convolutiondepthwise1d(25, 16, 8, k, d, s, p, 0, 2)
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|| test_convolutiondepthwise1d(25, 16, 16, k, d, s, p, 1, 16);
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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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static int test_convolutiondepthwise1d_dynamic(int w, int h, int outh, int kernel, int dilation, int stride, int pad, int bias, int group)
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{
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ncnn::Mat a = RandomMat(w, h);
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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(7, group);
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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, h / group, outh);
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if (bias)
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as[2] = RandomMat(outh);
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std::vector<ncnn::Mat> weights(0);
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int ret = test_layer<ncnn::ConvolutionDepthWise1D>("ConvolutionDepthWise1D", pd, weights, as);
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if (ret != 0)
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{
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fprintf(stderr, "test_convolutiondepthwise1d_dynamic failed w=%d h=%d outh=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d act=%d actparams=[%f,%f]\n", w, h, outh, kernel, dilation, stride, pad, bias, group, activation_type, activation_params[0], activation_params[1]);
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}
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return ret;
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}
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static int test_convolutiondepthwise1d_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_convolutiondepthwise1d_dynamic(11, 1, 1, k, d, s, p, 1, 1)
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|| test_convolutiondepthwise1d_dynamic(11, 2, 2, k, d, s, p, 0, 1)
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|| test_convolutiondepthwise1d_dynamic(11, 2, 2, k, d, s, p, 1, 2)
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|| test_convolutiondepthwise1d_dynamic(11, 3, 3, k, d, s, p, 0, 3)
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|| test_convolutiondepthwise1d_dynamic(11, 4, 2, k, d, s, p, 1, 2)
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|| test_convolutiondepthwise1d_dynamic(11, 4, 4, k, d, s, p, 0, 4)
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|| test_convolutiondepthwise1d_dynamic(11, 7, 7, k, d, s, p, 1, 7)
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|| test_convolutiondepthwise1d_dynamic(11, 8, 8, k, d, s, p, 0, 2)
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|| test_convolutiondepthwise1d_dynamic(11, 8, 8, k, d, s, p, 1, 8)
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|| test_convolutiondepthwise1d_dynamic(11, 12, 12, k, d, s, p, 0, 4)
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|| test_convolutiondepthwise1d_dynamic(11, 15, 15, k, d, s, p, 1, 15)
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|| test_convolutiondepthwise1d_dynamic(11, 16, 8, k, d, s, p, 0, 2)
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|| test_convolutiondepthwise1d_dynamic(11, 16, 16, k, d, s, p, 1, 16);
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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_convolutiondepthwise1d_0() || test_convolutiondepthwise1d_1();
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}
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