预测示例 (C)¶
本章节包含2部分内容：(1) 运行 C 示例程序；(2) C 预测程序开发说明。
运行 C 示例程序¶
1. 源码编译 C 预测库¶
Paddle Inference 的 C 预测库需要以源码编译的方式进行获取，请参照以下两个文档进行源码编译
编译完成后，在编译目录下的 paddle_inference_c_install_dir 即为 C 预测库，目录结构如下：
paddle_inference_c_install_dir
├── paddle
│ ├── include C 预测库头文件目录
│ │ └── pd_common.h
│ │ └── pd_config.h
│ │ └── pd_inference_api.h C 预测库头文件
│ │ └── pd_predictor.h
│ │ └── pd_tensor.h
│ │ └── pd_types.h
│ │ └── pd_utils.h
│ └── lib
│ ├── libpaddle_inference_c.a C 静态预测库文件
│ └── libpaddle_inference_c.so C 动态预测库文件
├── third_party
│ └── install 第三方链接库和头文件
│ ├── cryptopp
│ ├── gflags
│ ├── glog
│ ├── mkldnn
│ ├── mklml
│ ├── protobuf
│ └── xxhash
└── version.txt 版本信息与编译选项信息
其中 version.txt 文件中记录了该预测库的版本信息，包括Git Commit ID、使用OpenBlas或MKL数学库、CUDA/CUDNN版本号，如：
GIT COMMIT ID: 1bf4836580951b6fd50495339a7a75b77bf539f6
WITH_MKL: ON
WITH_MKLDNN: ON
WITH_GPU: ON
CUDA version: 9.0
CUDNN version: v7.6
CXX compiler version: 4.8.5
WITH_TENSORRT: ON
TensorRT version: v6
2. 准备预测部署模型¶
下载 ResNet50 模型后解压，得到 Paddle 预测格式的模型，位于文件夹 ResNet50 下。如需查看模型结构，可将 inference.pdmodel 加载到模型可视化工具 Netron 中打开。
wget https://paddle-inference-dist.bj.bcebos.com/Paddle-Inference-Demo/resnet50.tgz
tar zxf resnet50.tgz
# 获得模型目录即文件如下
resnet50/
├── inference.pdmodel
├── inference.pdiparams.info
└── inference.pdiparams
3. 准备预测部署程序¶
将以下代码保存为 c_demo.c 文件：
#include "pd_inference_api.h"
#include <memory.h>
#include <malloc.h>
int main() {
// 创建 Config 对象
PD_Config* config = PD_ConfigCreate();
// 设置预测模型路径，即为本小节第2步中下载的模型
const char* model_path = "./resnet50/inference.pdmodel";
const char* params_path = "./resnet50/inference.pdiparams";
PD_ConfigSetModel(config, model_path, params_path);
// 根据 Config 创建 Predictor, 并销毁 Config 对象
PD_Predictor* predictor = PD_PredictorCreate(config);
// 准备输入数据
int32_t input_shape[4] = {1, 3, 244, 244};
float* input_data = (float*)calloc(1 * 3 * 224 * 224, sizeof(float));
// 获取输入 Tensor
PD_OneDimArrayCstr* input_names = PD_PredictorGetInputNames(predictor);
PD_Tensor* input_tensor = PD_PredictorGetInputHandle(predictor, input_names->data[0]);
// 设置输入 Tensor 的维度信息及数据
PD_TensorReshape(input_tensor, 4, input_shape);
PD_TensorCopyFromCpuFloat(input_tensor, input_data);
// 执行预测
PD_PredictorRun(predictor);
// 获取预测输出 Tensor
PD_OneDimArrayCstr* output_names = PD_PredictorGetOutputNames(predictor);
PD_Tensor* output_tensor = PD_PredictorGetOutputHandle(predictor, output_names->data[0]);
// 获取预测输出 Tensor 信息
PD_OneDimArrayInt32* output_shape = PD_TensorGetShape(output_tensor);
int32_t out_size = 1;
for (size_t i = 0; i < output_shape->size; ++i) {
out_size = out_size * output_shape->data[i];
}
// 打印输出 Tensor 信息
printf("Output Tensor Name: %s\n", output_names->data[0]);
printf("Output Tensor Size: %d\n", out_size);
// 获取预测输出 Tensor 数据
float* out_data = (float*)malloc(out_size * sizeof(float));
PD_TensorCopyToCpuFloat(output_tensor, out_data);
// 销毁相关对象， 回收相关内存
free(out_data);
PD_OneDimArrayInt32Destroy(output_shape);
PD_TensorDestroy(output_tensor);
PD_OneDimArrayCstrDestroy(output_names);
PD_TensorDestroy(input_tensor);
PD_OneDimArrayCstrDestroy(input_names);
free(input_data);
PD_PredictorDestroy(predictor);
return 0;
}
4. 编译预测部署程序¶
将 paddle_inference_c_install_dir/paddle/include 目录下的所有头文件和动态库文件 paddle_inference_c_install_dir/paddle/lib/libpaddle_inference_c.so 拷贝到与预测源码同一目录，然后使用 GCC 进行编译：
# GCC 编译命令
gcc c_demo.c libpaddle_inference_c.so -o c_demo_prog
# 编译完成之后生成 c_demo_prog 可执行文件，编译目录内容如下
c_demo_dir/
│
├── c_demo.c 预测 C 源码程序，内容如本小节第3步所示
├── c_demo_prog 编译后的预测可执行程序
│
├── pd_inference_api.h C 预测库头文件
├── pd_common.h
├── pd_config.h
├── pd_utils.h
├── pd_predictor.h
├── pd_tensor.h
├── pd_types.h
├── libpaddle_fluid_c.so C 动态预测库文件
│
├── resnet50_model.tar.gz 本小节第2步中下载的预测模型
└── resnet50 本小节第2步中下载的预测模型解压后的模型文件
├── inference.pdmodel
├── inference.pdiparams.info
└── inference.pdiparams
5. 执行预测程序¶
注意：需要先将动态库文件 libpaddle_inference_c.so 所在路径加入 LD_LIBRARY_PATH，否则会出现无法找到库文件的错误。
# 执行预测程序
export LD_LIBRARY_PATH=`pwd`:$LD_LIBRARY_PATH
./c_demo_prog
成功执行之后，得到的预测输出结果如下：
# 程序输出结果如下
--- Running analysis [ir_graph_build_pass]
--- Running analysis [ir_graph_clean_pass]
--- Running analysis [ir_analysis_pass]
--- Running IR pass [simplify_with_basic_ops_pass]
--- Running IR pass [layer_norm_fuse_pass]
--- Fused 0 subgraphs into layer_norm op.
--- Running IR pass [attention_lstm_fuse_pass]
--- Running IR pass [seqconv_eltadd_relu_fuse_pass]
--- Running IR pass [seqpool_cvm_concat_fuse_pass]
--- Running IR pass [mul_lstm_fuse_pass]
--- Running IR pass [fc_gru_fuse_pass]
--- fused 0 pairs of fc gru patterns
--- Running IR pass [mul_gru_fuse_pass]
--- Running IR pass [seq_concat_fc_fuse_pass]
--- Running IR pass [squeeze2_matmul_fuse_pass]
--- Running IR pass [reshape2_matmul_fuse_pass]
WARNING: Logging before InitGoogleLogging() is written to STDERR
W1202 07:16:22.473459 3803 op_compat_sensible_pass.cc:219] Check the Attr(transpose_Y) of Op(matmul) in pass(reshape2_matmul_fuse_pass) failed!
W1202 07:16:22.473500 3803 map_matmul_to_mul_pass.cc:668] Reshape2MatmulFusePass in op compat failed.
--- Running IR pass [flatten2_matmul_fuse_pass]
--- Running IR pass [map_matmul_v2_to_mul_pass]
--- Running IR pass [map_matmul_v2_to_matmul_pass]
--- Running IR pass [map_matmul_to_mul_pass]
I1202 07:16:22.476769 3803 fuse_pass_base.cc:57] --- detected 1 subgraphs
--- Running IR pass [fc_fuse_pass]
I1202 07:16:22.478200 3803 fuse_pass_base.cc:57] --- detected 1 subgraphs
--- Running IR pass [repeated_fc_relu_fuse_pass]
--- Running IR pass [squared_mat_sub_fuse_pass]
--- Running IR pass [conv_bn_fuse_pass]
I1202 07:16:22.526548 3803 fuse_pass_base.cc:57] --- detected 53 subgraphs
--- Running IR pass [conv_eltwiseadd_bn_fuse_pass]
--- Running IR pass [conv_transpose_bn_fuse_pass]
--- Running IR pass [conv_transpose_eltwiseadd_bn_fuse_pass]
--- Running IR pass [is_test_pass]
--- Running IR pass [runtime_context_cache_pass]
--- Running analysis [ir_params_sync_among_devices_pass]
--- Running analysis [adjust_cudnn_workspace_size_pass]
--- Running analysis [inference_op_replace_pass]
--- Running analysis [ir_graph_to_program_pass]
I1202 07:16:22.576740 3803 analysis_predictor.cc:717] ======= optimize end =======
I1202 07:16:22.579823 3803 naive_executor.cc:98] --- skip [feed], feed -> inputs
I1202 07:16:22.581485 3803 naive_executor.cc:98] --- skip [save_infer_model/scale_0.tmp_1], fetch -> fetch
Output Tensor Name: save_infer_model/scale_0.tmp_1
Output Tensor Size: 1000
C 预测程序开发说明¶
使用 Paddle Inference 开发 C 预测程序仅需以下七个步骤：
(1) 引用头文件
#include "pd_inference_api.h"
(2) 创建配置对象，并指定预测模型路径，详细可参考 C API 文档 - Config 方法
// 创建 Config 对象
PD_Config* config = PD_ConfigCreate();
// 设置预测模型路径，即为本小节第2步中下载的模型
const char* model_path = "./resnet50/inference.pdmodel";
const char* params_path = "./resnet50/inference.pdiparams";
PD_ConfigSetModel(config, model_path, params_path);
(3) 根据Config创建预测对象，详细可参考 C API 文档 - Predictor 方法
// 根据 Config 创建 Predictor, 并销毁 Config 对象
PD_Predictor* predictor = PD_PredictorCreate(config);
(4) 设置模型输入Tensor，详细可参考 C API 文档 - Tensor 方法
// 准备输入数据
int32_t input_shape[4] = {1, 3, 244, 244};
float* input_data = (float*)calloc(1 * 3 * 224 * 224, sizeof(float));
// 获取输入 Tensor
PD_OneDimArrayCstr* input_names = PD_PredictorGetInputNames(predictor);
PD_Tensor* input_tensor = PD_PredictorGetInputHandle(predictor, input_names->data[0]);
// 设置输入 Tensor 的维度信息及数据
PD_TensorReshape(input_tensor, 4, input_shape);
PD_TensorCopyFromCpuFloat(input_tensor, input_data);
(5) 执行预测引擎，详细可参考 C API 文档 - Predictor 方法
// 执行预测
PD_PredictorRun(predictor);
(6) 获得预测结果，详细可参考 C API 文档 - Tensor 方法
// 获取预测输出 Tensor
PD_OneDimArrayCstr* output_names = PD_PredictorGetOutputNames(predictor);
PD_Tensor* output_tensor = PD_PredictorGetOutputHandle(predictor, output_names->data[0]);
// 获取预测输出 Tensor 信息
PD_OneDimArrayInt32* output_shape = PD_TensorGetShape(output_tensor);
int32_t out_size = 1;
for (size_t i = 0; i < output_shape->size; ++i) {
out_size = out_size * output_shape->data[i];
}
// 打印输出 Tensor 信息
printf("Output Tensor Name: %s\n", output_names->data[0]);
printf("Output Tensor Size: %d\n", out_size);
// 获取预测输出 Tensor 数据
float* out_data = (float*)malloc(out_size * sizeof(float));
PD_TensorCopyToCpuFloat(output_tensor, out_data);
(7) 销毁相关对象，回收相关内存
// 销毁相关对象， 回收相关内存
free(out_data);
PD_OneDimArrayInt32Destroy(output_shape);
PD_TensorDestroy(output_tensor);
PD_OneDimArrayCstrDestroy(output_names);
PD_TensorDestroy(input_tensor);
PD_OneDimArrayCstrDestroy(input_names);
free(input_data);
PD_PredictorDestroy(predictor);