feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake
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
This commit is contained in:
wangzhengyang
49
3rdparty/opencv-4.5.4/modules/dnn/src/tengine4dnn/include/tengine_graph_convolution.hpp
vendored
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49
3rdparty/opencv-4.5.4/modules/dnn/src/tengine4dnn/include/tengine_graph_convolution.hpp
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@ -0,0 +1,49 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one
|
||||
* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
|
||||
* regarding copyright ownership. The ASF licenses this file
|
||||
* to you under the Apache License, Version 2.0 (the
|
||||
* License); you may not use this file except in compliance
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||||
* with the License. You may obtain a copy of the License at
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||||
*
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* http://www.apache.org/licenses/LICENSE-2.0
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||||
*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* AS IS BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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||||
* KIND, either express or implied. See the License for the
|
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* specific language governing permissions and limitations
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* under the License.
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*/
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/*
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* Copyright (c) 2020, OPEN AI LAB
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* Author: qtang@openailab.com
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*/
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#ifndef TENGINE_GRAPH_CONVOLUTION_HPP
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#define TENGINE_GRAPH_CONVOLUTION_HPP
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#define FLOAT_TO_REALSIZE (4)
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#ifdef HAVE_TENGINE
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#include "tengine_c_api.h"
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namespace cv
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{
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namespace dnn
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{
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teng_graph_t tengine_init(const char* name , float* input_, int inch, int group, int in_h, int in_w,
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float *output_, int out_b, int outch, int out_h, int out_w,
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float *kernel_,int kernel_s , int kernel_h, int kernel_w,
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float *teg_bias, int stride_h,int stride_w,
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int pad_h, int pad_w, int dilation_h, int dilation_w,
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size_t wstep, const std::string padMode , teng_graph_t& graph, int nstripes) ;
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bool tengine_forward(teng_graph_t& graph) ;
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bool tengine_release(teng_graph_t& graph) ;
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}
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}
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#endif
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#endif /* TENGINE_GRAPH_CONVOLUTION_HPP */
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373
3rdparty/opencv-4.5.4/modules/dnn/src/tengine4dnn/src/tengine_graph_convolution.cpp
vendored
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373
3rdparty/opencv-4.5.4/modules/dnn/src/tengine4dnn/src/tengine_graph_convolution.cpp
vendored
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@ -0,0 +1,373 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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||||
* distributed with this work for additional information
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||||
* regarding copyright ownership. The ASF licenses this file
|
||||
* to you under the Apache License, Version 2.0 (the
|
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* License); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* AS IS BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
|
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* specific language governing permissions and limitations
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* under the License.
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*/
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/*
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* Copyright (c) 2020, OPEN AI LAB
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* Author: qtang@openailab.com
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*/
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#include "../../precomp.hpp"
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#include <iostream>
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#include <vector>
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#include <opencv2/core/utils/configuration.private.hpp>
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#include <opencv2/core/utils/logger.hpp>
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#include "../include/tengine_graph_convolution.hpp"
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#ifdef HAVE_TENGINE
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#include "tengine_c_api.h"
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namespace cv
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{
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namespace dnn
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{
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static int create_input_node(teng_graph_t graph, const char* node_name, int inch, int in_h, int in_w)
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{
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node_t node = teng_create_graph_node(graph, node_name, "InputOp");
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tensor_t tensor = teng_create_graph_tensor(graph, node_name, TENGINE_DT_FP32);
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teng_set_node_output_tensor(node, 0, tensor, TENSOR_TYPE_INPUT);
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int dims[4] = {1, inch, in_h, in_w};
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teng_set_tensor_shape(tensor, dims, 4);
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teng_release_graph_tensor(tensor);
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teng_release_graph_node(node);
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return 0;
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}
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static int create_conv_node(teng_graph_t graph, const char* node_name, const char* input_name, int in_h, int in_w, int out_h, int out_w,
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int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, int pad_w, int inch, int outch, int group,
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int dilation_h, int dilation_w, int activation, std::string padMode)
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{
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node_t conv_node = teng_create_graph_node(graph, node_name, "Convolution");
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tensor_t input_tensor = teng_get_graph_tensor(graph, input_name);
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if (input_tensor == NULL)
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{
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CV_LOG_WARNING(NULL,"Tengine: input_tensor is NULL." );
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return -1;
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}
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teng_set_node_input_tensor(conv_node, 0, input_tensor);
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teng_release_graph_tensor(input_tensor);
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/* output */
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tensor_t output_tensor = teng_create_graph_tensor(graph, node_name, TENGINE_DT_FP32);
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teng_set_node_output_tensor(conv_node, 0, output_tensor, TENSOR_TYPE_VAR);
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teng_release_graph_tensor(output_tensor);
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/* weight */
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std::string weight_name(node_name);
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weight_name += "/weight";
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node_t w_node = teng_create_graph_node(graph, weight_name.c_str(), "Const");
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tensor_t w_tensor = teng_create_graph_tensor(graph, weight_name.c_str(), TENGINE_DT_FP32);
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teng_set_node_output_tensor(w_node, 0, w_tensor, TENSOR_TYPE_CONST);
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teng_set_node_input_tensor(conv_node, 1, w_tensor);
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int w_dims[] = {outch, inch / group, kernel_h, kernel_w};
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teng_set_tensor_shape(w_tensor, w_dims, 4);
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teng_release_graph_node(w_node);
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teng_release_graph_tensor(w_tensor);
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/* bias */
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std::string bias_name(node_name);
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bias_name += "/bias";
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node_t b_node = teng_create_graph_node(graph, bias_name.c_str(), "Const");
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tensor_t b_tensor = teng_create_graph_tensor(graph, bias_name.c_str(), TENGINE_DT_FP32);
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teng_set_node_output_tensor(b_node, 0, b_tensor, TENSOR_TYPE_CONST);
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int b_dims[] = {outch};
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teng_set_tensor_shape(b_tensor, b_dims, 1);
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teng_set_node_input_tensor(conv_node, 2, b_tensor);
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teng_release_graph_node(b_node);
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teng_release_graph_tensor(b_tensor);
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int pad_h1 = pad_h;
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int pad_w1 = pad_w;
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if (!padMode.empty())
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{
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if (padMode == "SAME")
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{
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int out_h_temp = (in_h-kernel_h + 2*pad_h)/stride_h + 1;
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int out_w_temp = (in_w-kernel_w + 2*pad_w)/stride_w + 1;
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if (out_h_temp < out_h)
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pad_h1 += 1;
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if (out_w_temp < out_w)
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pad_w1 += 1;
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}
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}
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/* attr */
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teng_set_node_attr_int(conv_node, "kernel_h", &kernel_h);
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teng_set_node_attr_int(conv_node, "kernel_w", &kernel_w);
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teng_set_node_attr_int(conv_node, "stride_h", &stride_h);
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teng_set_node_attr_int(conv_node, "stride_w", &stride_w);
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teng_set_node_attr_int(conv_node, "pad_h0", &pad_h);
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teng_set_node_attr_int(conv_node, "pad_w0", &pad_w);
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teng_set_node_attr_int(conv_node, "pad_h1", &pad_h1);
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teng_set_node_attr_int(conv_node, "pad_w1", &pad_w1);
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teng_set_node_attr_int(conv_node, "output_channel", &outch);
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teng_set_node_attr_int(conv_node, "input_channel", &inch);
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teng_set_node_attr_int(conv_node, "group", &group);
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teng_set_node_attr_int(conv_node, "dilation_h", &dilation_h);
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teng_set_node_attr_int(conv_node, "dilation_w", &dilation_w);
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// set_node_attr_int(conv_node, "activation", &activation);
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teng_release_graph_node(conv_node);
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return 0;
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}
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static teng_graph_t create_conv_graph(const char* layer_name, float* input_data, int inch, int group, int in_h, int in_w,
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float* output_data, int outch, int out_h, int out_w,
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int kernel_h, int kernel_w,
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int stride_h,int stride_w,
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int pad_h, int pad_w, int dilation_h, int dilation_w, int activation,
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float* teg_weight, float* teg_bias, std::string padMode, int nstripes)
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{
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node_t conv_node = NULL;
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tensor_t input_tensor = NULL;
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tensor_t output_tensor = NULL;
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tensor_t weight_tensor = NULL;
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tensor_t bias_tensor = NULL;
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/* create graph for convolution */
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int in_size = in_h * in_w * inch;
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int out_size = out_h * out_w * outch;
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int weight_size = outch * (inch / group) * kernel_w * kernel_h;
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int bias_size = outch;
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int buf_size = 0;
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int input_num = 0;
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/* create graph */
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teng_graph_t graph = teng_create_graph(NULL, NULL, NULL);
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bool ok = true;
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if(graph == NULL)
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{
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CV_LOG_WARNING(NULL,"Tengine: create_graph failed." );
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ok = false;
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}
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const char* input_name = "data";
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const char* conv_name = layer_name;
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if (ok && create_input_node(graph, input_name, inch, in_h, in_w) < 0)
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{
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CV_LOG_WARNING(NULL,"Tengine: create_input_node failed." );
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ok = false;
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}
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if (ok && create_conv_node(graph, conv_name, input_name, in_h, in_w, out_h, out_w, kernel_h, kernel_w,
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stride_h, stride_w, pad_h, pad_w, inch, outch, group, dilation_h, dilation_w, activation, padMode) < 0)
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{
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CV_LOG_WARNING(NULL,"Tengine: create conv node failed." );
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ok = false;
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}
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/* set input/output node */
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const char* inputs_name[] = {input_name};
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const char* outputs_name[] = {conv_name};
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if (ok && teng_set_graph_input_node(graph, inputs_name, sizeof(inputs_name) / sizeof(char*)) < 0)
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{
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CV_LOG_WARNING(NULL,"Tengine: set inputs failed." );
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ok = false;
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}
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if (ok && teng_set_graph_output_node(graph, outputs_name, sizeof(outputs_name) / sizeof(char*)) < 0)
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{
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CV_LOG_WARNING(NULL,"Tengine: set outputs failed." );
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ok = false;
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}
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/* set input data */
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if (ok)
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{
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input_tensor = teng_get_graph_input_tensor(graph, 0, 0);
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buf_size = teng_get_tensor_buffer_size(input_tensor);
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if (buf_size != in_size * FLOAT_TO_REALSIZE)
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{
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CV_LOG_WARNING(NULL,"Tengine: Input data size check failed.");
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ok = false;
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}
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}
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if (ok)
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{
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teng_set_tensor_buffer(input_tensor, (float *)input_data, buf_size);
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teng_release_graph_tensor(input_tensor);
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/* create convolution node */
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/* set weight node */
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conv_node = teng_get_graph_node(graph, conv_name);
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weight_tensor = teng_get_node_input_tensor(conv_node, 1);
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buf_size = teng_get_tensor_buffer_size(weight_tensor);
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if (buf_size != weight_size * FLOAT_TO_REALSIZE)
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{
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CV_LOG_WARNING(NULL,"Tengine: Input weight size check failed.");
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ok = false;
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}
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}
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if (ok)
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{
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teng_set_tensor_buffer(weight_tensor, teg_weight, buf_size);
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/* set bias node */
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input_num = teng_get_node_input_number(conv_node);
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if (input_num > 2)
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{
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bias_tensor = teng_get_node_input_tensor(conv_node, 2);
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buf_size = teng_get_tensor_buffer_size(bias_tensor);
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if (buf_size != bias_size * FLOAT_TO_REALSIZE)
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{
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CV_LOG_WARNING(NULL,"Tengine: Input bias size check failed.");
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ok = false;
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}
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else teng_set_tensor_buffer(bias_tensor, teg_bias, buf_size);
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}
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}
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/* prerun */
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if (ok && teng_prerun_graph_multithread(graph, TENGINE_CLUSTER_BIG, nstripes) < 0)
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{
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CV_LOG_WARNING(NULL, "Tengine: prerun_graph failed.");
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ok = false;
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}
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if (ok)
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{
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/* set output data */
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output_tensor = teng_get_node_output_tensor(conv_node, 0);
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int ret = teng_set_tensor_buffer(output_tensor, output_data, out_size * FLOAT_TO_REALSIZE);
|
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if(ret)
|
||||
{
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CV_LOG_WARNING(NULL,"Tengine: Set output tensor buffer failed." );
|
||||
ok = false;
|
||||
}
|
||||
}
|
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|
||||
if (false == ok)
|
||||
{
|
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teng_destroy_graph(graph) ;
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return NULL ;
|
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}
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return graph;
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}
|
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static bool tengine_init_flag = false;
|
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teng_graph_t tengine_init(const char* layer_name, float* input_, int inch, int group, int in_h, int in_w,
|
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float *output_, int out_b, int outch, int out_h, int out_w,
|
||||
float *kernel_, int kernel_s ,int kernel_h, int kernel_w,
|
||||
float *teg_bias, int stride_h,int stride_w,
|
||||
int pad_h, int pad_w, int dilation_h, int dilation_w,
|
||||
size_t wstep, const std::string padMode, teng_graph_t &graph, int nstripes)
|
||||
{
|
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std::vector<float> teg_weight_vec;
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float *teg_weight = NULL;
|
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int kernel_inwh = (inch / group) * kernel_w * kernel_h;
|
||||
// Do not using the activation fuse mode, just convolution only.
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int activation = -1;
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|
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if (!(kernel_s == 2 && kernel_h == kernel_w && pad_h == pad_w
|
||||
&& dilation_h == dilation_w && stride_h == stride_w
|
||||
&& out_b == 1 && pad_h < 10)) // just for Conv2D
|
||||
{
|
||||
// printf("return : just for Conv2D\n");
|
||||
return NULL;
|
||||
}
|
||||
|
||||
{
|
||||
/* printf("Tengine(%s): input (1 x %d x %d x %d),output (%d x %d x %d x %d), kernel (%d x %d), stride (%d x %d), dilation (%d x %d), pad (%d x %d).\n",
|
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layer_name, inch, in_h, in_w,
|
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out_b, outch, out_h, out_w,
|
||||
kernel_w, kernel_h,
|
||||
stride_w, stride_h,
|
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dilation_w, dilation_h,
|
||||
pad_w, pad_h);
|
||||
*/
|
||||
// weight
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||||
if (kernel_inwh != wstep)
|
||||
{
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||||
teg_weight_vec.resize(kernel_inwh * outch);
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||||
teg_weight = &teg_weight_vec[0];
|
||||
for (int i=0; i<outch; i++)
|
||||
{
|
||||
memcpy(teg_weight+i*kernel_inwh, kernel_+i*wstep, kernel_inwh*FLOAT_TO_REALSIZE);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
teg_weight = kernel_;
|
||||
}
|
||||
|
||||
/* initial the resoruce of tengine */
|
||||
if(false == tengine_init_flag)
|
||||
{
|
||||
init_tengine();
|
||||
tengine_init_flag = true;
|
||||
}
|
||||
|
||||
/* create the convolution graph */
|
||||
graph = create_conv_graph(layer_name, input_, inch, group, in_h, in_w,
|
||||
output_, outch, out_h, out_w,
|
||||
kernel_h, kernel_w, stride_h,stride_w,
|
||||
pad_h, pad_w, dilation_h, dilation_w, activation,
|
||||
teg_weight, teg_bias, padMode, nstripes);
|
||||
if(NULL == graph )
|
||||
{
|
||||
return NULL;
|
||||
}
|
||||
}
|
||||
return graph ;
|
||||
}
|
||||
|
||||
bool tengine_forward(teng_graph_t &graph)
|
||||
{
|
||||
/* run */
|
||||
if(teng_run_graph(graph, 1) < 0)
|
||||
{
|
||||
CV_LOG_WARNING(NULL,"Tengine: run_graph failed.");
|
||||
return false ;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
bool tengine_release(teng_graph_t &graph)
|
||||
{
|
||||
teng_postrun_graph(graph);
|
||||
teng_destroy_graph(graph);
|
||||
return true;
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif
|
||||
Reference in New Issue
Block a user