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未验证 提交 787f91b6 编写于 作者: W Walter 提交者： GitHub
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Merge pull request #1970 from HydrogenSulfate/cppinfer_tipc 

add Cppinfer tipc
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test_tipc/README.md
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......@@ -35,15 +35,16 @@
│ ├── MobileNetV3 # MobileNetV3系列模型测试配置文件目录
│ │ ├── MobileNetV3_large_x1_0_train_infer_python.txt #基础训练预测配置文件
│ │ ├── MobileNetV3_large_x1_0_train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt #多机多卡训练预测配置文件
│ │ ├── MobileNetV3_large_x1_0_train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt #多机多卡训练预测配置文件
│ │ └── MobileNetV3_large_x1_0_paddle2onnx_infer_python.txt #paddle2onnx推理测试配置文件
│ └── ResNet # ResNet系列模型测试配置文件目录
│ ├── ResNet50_vd_train_infer_python.txt #基础训练预测配置文件
│ ├── ResNet50_vd_train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt #多机多卡训练预测配置文件
│ ├── ResNet50_vd_train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt #多机多卡训练预测配置文件
│ ├── ResNet50_vd_train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt #混合精度训练预测配置文件
│ └── ResNet50_vd_paddle2onnx_infer_python.txt #paddle2onnx推理测试配置文件
| ......
│ │ ├── MobileNetV3_large_x1_0_train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt #混合精度训练预测配置文件
│ │ ├── MobileNetV3_large_x1_0_paddle2onnx_infer_python.txt #paddle2onnx推理测试配置文件
│ │ └── ......
│ ├──ResNet # ResNet系列模型测试配置文件目录
│ │ ├── ResNet50_vd_train_infer_python.txt #基础训练预测配置文件
│ │ ├── ResNet50_vd_train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt #多机多卡训练预测配置文件
│ │ ├── ResNet50_vd_train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt #混合精度训练预测配置文件
│ │ ├── ResNet50_vd_paddle2onnx_infer_python.txt #paddle2onnx推理测试配置文件
│ │ └── ......
│ └── ......
├── docs
│ ├── guide.png
│ └── test.png
......
test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:MobileNetV3_large_x1_0
cpp_infer_type:cls
cls_inference_model_dir:./MobileNetV3_large_x1_0_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/PP-ShiTu/PPShiTu_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
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===========================cpp_infer_params===========================
model_name:PPShiTu
cpp_infer_type:shitu
feature_inference_model_dir:./feature_inference/
det_inference_model_dir:./det_inference
feature_inference_model_dir:./general_PPLCNet_x2_5_lite_v1.0_infer/
det_inference_model_dir:./picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
det_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
infer_quant:False
inference_cmd:./deploy/cpp_shitu/build/pp_shitu -c inference_drink.yaml
use_gpu:True|False
enable_mkldnn:True|False
cpu_threads:1|6
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False|True
precision:fp32|fp16
use_tensorrt:False
precision:fp32
data_dir:./dataset/drink_dataset_v1.0
benchmark:True
generate_yaml_cmd:python3 test_tipc/generate_cpp_yaml.py
transform_index_cmd:python3 deploy/cpp_shitu/tools/transform_id_map.py -c inference_drink.yaml
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
transform_index_cmd:python3.7 deploy/cpp_shitu/tools/transform_id_map.py -c inference_drink.yaml
test_tipc/config/PPHGNet/PPHGNet_small_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:PPHGNet_small
cpp_infer_type:cls
cls_inference_model_dir:./PPHGNet_small_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/PPHGNet/PPHGNet_tiny_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:PPHGNet_tiny
cpp_infer_type:cls
cls_inference_model_dir:./PPHGNet_tiny_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/PPLCNet/PPLCNet_x0_25_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:PPLCNet_x0_25
cpp_infer_type:cls
cls_inference_model_dir:./PPLCNet_x0_25_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_25_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/PPLCNet/PPLCNet_x0_35_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:PPLCNet_x0_35
cpp_infer_type:cls
cls_inference_model_dir:./PPLCNet_x0_35_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_35_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/PPLCNet/PPLCNet_x0_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:PPLCNet_x0_5
cpp_infer_type:cls
cls_inference_model_dir:./PPLCNet_x0_5_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/PPLCNet/PPLCNet_x0_75_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:PPLCNet_x0_75
cpp_infer_type:cls
cls_inference_model_dir:./PPLCNet_x0_75_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_75_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:PPLCNet_x1_0
cpp_infer_type:cls
cls_inference_model_dir:./PPLCNet_x1_0_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/PPLCNet/PPLCNet_x1_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:PPLCNet_x1_5
cpp_infer_type:cls
cls_inference_model_dir:./PPLCNet_x1_5_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_5_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/PPLCNet/PPLCNet_x2_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:PPLCNet_x2_0
cpp_infer_type:cls
cls_inference_model_dir:./PPLCNet_x2_0_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_0_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/PPLCNet/PPLCNet_x2_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:PPLCNet_x2_5
cpp_infer_type:cls
cls_inference_model_dir:./PPLCNet_x2_5_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/PPLCNetV2/PPLCNetV2_base_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:PPLCNetV2_base
cpp_infer_type:cls
cls_inference_model_dir:./PPLCNetV2_base_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:ResNet50
cpp_infer_type:cls
cls_inference_model_dir:./ResNet50_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
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===========================cpp_infer_params===========================
model_name:ResNet50_vd
cpp_infer_type:cls
cls_inference_model_dir:./cls_inference/
cls_inference_model_dir:./ResNet50_vd_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/ResNet50_vd_inference.tar
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:True|False
cpu_threads:1|6
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False|True
precision:fp32|fp16
image_dir:./dataset/ILSVRC2012/val
benchmark:True
generate_yaml_cmd:python3 test_tipc/generate_cpp_yaml.py
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/config/SwinTransformer/SwinTransformer_tiny_patch4_window7_224_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 0 → 100644
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===========================cpp_infer_params===========================
model_name:SwinTransformer_tiny_patch4_window7_224
cpp_infer_type:cls
cls_inference_model_dir:./SwinTransformer_tiny_patch4_window7_224_infer/
det_inference_model_dir:
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar
det_inference_url:
infer_quant:False
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
use_gpu:True|False
enable_mkldnn:False
cpu_threads:1
batch_size:1
use_tensorrt:False
precision:fp32
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
benchmark:False
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
test_tipc/docs/test_inference_cpp.md
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# C++预测功能测试
# Linux GPU/CPU C++ 推理功能测试
C++预测功能测试的主程序为`test_inference_cpp.sh`，可以测试基于C++预测库的模型推理功能。
Linux GPU/CPU C++ 推理功能测试的主程序为`test_inference_cpp.sh`，可以测试基于C++预测引擎的推理功能。
## 1. 测试结论汇总
基于训练是否使用量化，进行本测试的模型可以分为`正常模型`和`量化模型`，这两类模型对应的C++预测功能汇总如下：
- 推理相关：
| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
| ---- | ---- | ---- | :----: | :----: | :----: |
| 正常模型 | GPU | 1/6 | fp32/fp16 | - | - |
| 正常模型 | CPU | 1/6 | - | fp32 | 支持 |
| 量化模型 | GPU | 1/6 | int8 | - | - |
| 量化模型 | CPU | 1/6 | - | int8 | 支持 |
| 算法名称 | 模型名称 | device_CPU | device_GPU |
| :----: | :----: | :----: | :----: |
| MobileNetV3 | MobileNetV3_large_x1_0 | 支持 | 支持 |
| PP-ShiTu | PPShiTu_general_rec、PPShiTu_mainbody_det | 支持 | 支持 |
| PP-ShiTu | PPShiTu_mainbody_det | 支持 | 支持 |
| PPHGNet | PPHGNet_small | 支持 | 支持 |
| PPHGNet | PPHGNet_tiny | 支持 | 支持 |
| PPLCNet | PPLCNet_x0_25 | 支持 | 支持 |
| PPLCNet | PPLCNet_x0_35 | 支持 | 支持 |
| PPLCNet | PPLCNet_x0_5 | 支持 | 支持 |
| PPLCNet | PPLCNet_x0_75 | 支持 | 支持 |
| PPLCNet | PPLCNet_x1_0 | 支持 | 支持 |
| PPLCNet | PPLCNet_x1_5 | 支持 | 支持 |
| PPLCNet | PPLCNet_x2_0 | 支持 | 支持 |
| PPLCNet | PPLCNet_x2_5 | 支持 | 支持 |
| PPLCNetV2 | PPLCNetV2_base | 支持 | 支持 |
| ResNet | ResNet50 | 支持 | 支持 |
| ResNet | ResNet50_vd | 支持 | 支持 |
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | 支持 | 支持 |
## 2. 测试流程
运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。
## 2. 测试流程(以**ResNet50**为例)
### 2.1 功能测试
先运行`prepare.sh`准备数据和模型，然后运行`test_inference_cpp.sh`进行测试，最终在```test_tipc/output```目录下生成`cpp_infer_*.log`后缀的日志文件。
<details>
<summary><b>准备数据、准备推理模型、编译opencv、编译（下载）Paddle Inference、编译C++预测Demo（已写入prepare.sh自动执行，点击以展开详细内容或者折叠）
</b></summary>
### 2.1 准备数据和推理模型
#### 2.1.1 准备数据
默认使用`./deploy/images/ILSVRC2012_val_00000010.jpeg`作为测试输入图片。
#### 2.1.2 准备推理模型
* 如果已经训练好了模型，可以参考[模型导出](../../docs/zh_CN/inference_deployment/export_model.md)，导出`inference model`，并将导出路径设置为`./deploy/models/ResNet50_infer`，
导出完毕后文件结构如下
```shell
bash test_tipc/prepare.sh test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt cpp_infer
./deploy/models/ResNet50_infer/
├── inference.pdmodel
├── inference.pdiparams
└── inference.pdiparams.info
```
### 2.2 准备环境
#### 2.2.1 运行准备
配置合适的编译和执行环境，其中包括编译器，cuda等一些基础库，建议安装docker环境，[参考链接](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/docker/linux-docker.html)。
# 用法1:
bash test_tipc/test_inference_cpp.sh test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
bash test_tipc/test_inference_cpp.sh test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 1
#### 2.2.2 编译opencv库
* 首先需要从opencv官网上下载Linux环境下的源码，以3.4.7版本为例，下载及解压缩命令如下：
```
cd deploy/cpp
wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
tar -xvf 3.4.7.tar.gz
```
运行预测指令后，在`test_tipc/output`文件夹下自动会保存运行日志，包括以下文件：
* 编译opencv，首先设置opencv源码路径(`root_path`)以及安装路径(`install_path`)，`root_path`为下载的opencv源码路径，`install_path`为opencv的安装路径。在本例中，源码路径即为当前目录下的`opencv-3.4.7/`。
```shell
test_tipc/output/
|- results_cpp.log # 运行指令状态的日志
|- cls_cpp_infer_cpu_usemkldnn_False_threads_1_precision_fp32_batchsize_1.log # CPU上不开启Mkldnn，线程数设置为1，测试batch_size=1条件下的预测运行日志
|- cls_cpp_infer_cpu_usemkldnn_False_threads_6_precision_fp32_batchsize_1.log # CPU上不开启Mkldnn，线程数设置为6，测试batch_size=1条件下的预测运行日志
|- cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log # GPU上不开启TensorRT，测试batch_size=1的fp32精度预测日志
|- cls_cpp_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log # GPU上开启TensorRT，测试batch_size=1的fp16精度预测日志
......
cd ./opencv-3.4.7
export root_path=$PWD
export install_path=${root_path}/opencv3
```
其中results_cpp.log中包含了每条指令的运行状态，如果运行成功会输出：
* 然后在opencv源码路径下，按照下面的命令进行编译。
```shell
rm -rf build
mkdir build
cd build
cmake .. \
-DCMAKE_INSTALL_PREFIX=${install_path} \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_SHARED_LIBS=OFF \
-DWITH_IPP=OFF \
-DBUILD_IPP_IW=OFF \
-DWITH_LAPACK=OFF \
-DWITH_EIGEN=OFF \
-DCMAKE_INSTALL_LIBDIR=lib64 \
-DWITH_ZLIB=ON \
-DBUILD_ZLIB=ON \
-DWITH_JPEG=ON \
-DBUILD_JPEG=ON \
-DWITH_PNG=ON \
-DBUILD_PNG=ON \
-DWITH_TIFF=ON \
-DBUILD_TIFF=ON
make -j
make install
```
Run successfully with command - ./deploy/cpp/build/clas_system -c inference_cls.yaml 2>&1|tee test_tipc/output/cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log
......
* `make install`完成之后，会在该文件夹下生成opencv头文件和库文件，用于后面的代码编译。
以opencv3.4.7版本为例，最终在安装路径下的文件结构如下所示。**注意**：不同的opencv版本，下述的文件结构可能不同。
```shell
opencv3/
├── bin :可执行文件
├── include :头文件
├── lib64 :库文件
└── share :部分第三方库
```
#### 2.2.3 下载或者编译Paddle预测库
* 有2种方式获取Paddle预测库，下面进行详细介绍。
##### 预测库源码编译
* 如果希望获取最新预测库特性，可以从Paddle github上克隆最新代码，源码编译预测库。
* 可以参考[Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)的说明，从github上获取Paddle代码，然后进行编译，生成最新的预测库。使用git获取代码方法如下。
```shell
git clone https://github.com/PaddlePaddle/Paddle.git
```
* 进入Paddle目录后，使用如下命令编译。
```shell
rm -rf build
mkdir build
cd build
cmake .. \
-DWITH_CONTRIB=OFF \
-DWITH_MKL=ON \
-DWITH_MKLDNN=ON \
-DWITH_TESTING=OFF \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_INFERENCE_API_TEST=OFF \
-DON_INFER=ON \
-DWITH_PYTHON=ON
make -j
make inference_lib_dist
```
如果运行失败，会输出：
更多编译参数选项可以参考Paddle C++预测库官网：[https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)。
* 编译完成之后，可以在`build/paddle_inference_install_dir/`文件下看到生成了以下文件及文件夹。
```
Run failed with command - ./deploy/cpp/build/clas_system -c inference_cls.yaml 2>&1|tee test_tipc/output/cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log
......
build/paddle_inference_install_dir/
├── CMakeCache.txt
├── paddle
├── third_party
└── version.txt
```
可以很方便的根据results_cpp.log中的内容判定哪一个指令运行错误。
其中`paddle`就是之后进行C++预测时所需的Paddle库，`version.txt`中包含当前预测库的版本信息。
##### 直接下载安装
### 2.2 精度测试
* [Paddle预测库官网](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html)上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本。
使用compare_results.py脚本比较模型预测的结果是否符合预期，主要步骤包括：
- 提取日志中的预测坐标；
- 从本地文件中提取保存好的坐标结果；
- 比较上述两个结果是否符合精度预期，误差大于设置阈值时会报错。
以`manylinux_cuda11.1_cudnn8.1_avx_mkl_trt7_gcc8.2`版本为例，使用下述命令下载并解压：
```shell
wget https://paddle-inference-lib.bj.bcebos.com/2.2.2/cxx_c/Linux/GPU/x86-64_gcc8.2_avx_mkl_cuda11.1_cudnn8.1.1_trt7.2.3.4/paddle_inference.tgz
tar -xvf paddle_inference.tgz
```
最终会在当前的文件夹中生成`paddle_inference/`的子文件夹,文件内容和上述的paddle_inference_install_dir一样。
#### 2.2.4 编译C++预测Demo
* 编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。
```shell
# 在deploy/cpp下执行以下命令
bash tools/build.sh
```
具体地，`tools/build.sh`中内容如下。
```shell
OPENCV_DIR=your_opencv_dir
LIB_DIR=your_paddle_inference_dir
CUDA_LIB_DIR=your_cuda_lib_dir
CUDNN_LIB_DIR=your_cudnn_lib_dir
TENSORRT_DIR=your_tensorrt_lib_dir
BUILD_DIR=build
rm -rf ${BUILD_DIR}
mkdir ${BUILD_DIR}
cd ${BUILD_DIR}
cmake .. \
-DPADDLE_LIB=${LIB_DIR} \
-DWITH_MKL=ON \
-DDEMO_NAME=clas_system \
-DWITH_GPU=OFF \
-DWITH_STATIC_LIB=OFF \
-DWITH_TENSORRT=OFF \
-DTENSORRT_DIR=${TENSORRT_DIR} \
-DOPENCV_DIR=${OPENCV_DIR} \
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
-DCUDA_LIB=${CUDA_LIB_DIR} \
make -j
```
上述命令中，
* `OPENCV_DIR`为opencv编译安装的地址（本例中需修改为`opencv-3.4.7/opencv3`文件夹的路径）；
* `LIB_DIR`为下载的Paddle预测库（`paddle_inference`文件夹），或编译生成的Paddle预测库（`build/paddle_inference_install_dir`文件夹）的路径；
* `CUDA_LIB_DIR`为cuda库文件地址，在docker中一般为`/usr/local/cuda/lib64`；
* `CUDNN_LIB_DIR`为cudnn库文件地址，在docker中一般为`/usr/lib64`。
* `TENSORRT_DIR`是tensorrt库文件地址，在dokcer中一般为`/usr/local/TensorRT-7.2.3.4/`，TensorRT需要结合GPU使用。
在执行上述命令，编译完成之后，会在当前路径下生成`build`文件夹，其中生成一个名为`clas_system`的可执行文件。
</details>
* 可执行以下命令，自动完成上述准备环境中的所需内容
```shell
bash test_tipc/prepare.sh test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt cpp_infer
```
### 2.3 功能测试
测试方法如下所示，希望测试不同的模型文件，只需更换为自己的参数配置文件，即可完成对应模型的测试。
#### 使用方式
运行命令：
```shell
python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/cls_cpp_*.txt --log_file=./test_tipc/output/cls_cpp_*.log --atol=1e-3 --rtol=1e-3
bash test_tipc/test_inference_cpp.sh ${your_params_file}
```
参数介绍：
- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下
- log_file: 指向运行test_tipc/test_inference_cpp.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持cpp_infer_*.log格式传入
- atol: 设置的绝对误差
- rtol: 设置的相对误差
以`ResNet50`的`Linux GPU/CPU C++推理测试`为例，命令如下所示。
#### 运行结果
```shell
bash test_tipc/test_inference_cpp.sh test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
```
正常运行效果如下图：
<img src="compare_cpp_right.png" width="1000">
输出结果如下，表示命令运行成功。
出现不一致结果时的运行输出：
<img src="compare_cpp_wrong.png" width="1000">
```shell
Run successfully with command - ./deploy/cpp/build/clas_system -c inference_cls.yaml > ./test_tipc/output/ResNet50/cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log 2>&1!
Run successfully with command - ./deploy/cpp/build/clas_system -c inference_cls.yaml > ./test_tipc/output/ResNet50/cls_cpp_infer_cpu_usemkldnn_False_threads_1_precision_fp32_batchsize_1.log 2>&1!
```
最终log中会打印出结果，如下所示
```log
You are using Paddle compiled with TensorRT, but TensorRT dynamic library is not found. Ignore this if TensorRT is not needed.
=======Paddle Class inference config======
Global:
infer_imgs: ./deploy/images/ILSVRC2012_val_00000010.jpeg
inference_model_dir: ./deploy/models/ResNet50_infer
batch_size: 1
use_gpu: True
enable_mkldnn: True
cpu_num_threads: 10
enable_benchmark: True
use_fp16: False
ir_optim: True
use_tensorrt: False
gpu_mem: 8000
enable_profile: False
PreProcess:
transform_ops:
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 0.00392157
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ""
channel_num: 3
- ToCHWImage: ~
PostProcess:
main_indicator: Topk
Topk:
topk: 5
class_id_map_file: ./ppcls/utils/imagenet1k_label_list.txt
SavePreLabel:
save_dir: ./pre_label/
=======End of Paddle Class inference config======
img_file_list length: 1
Current image path: ./deploy/images/ILSVRC2012_val_00000010.jpeg
Current total inferen time cost: 5449.39 ms.
Top1: class_id: 153, score: 0.4144, label: Maltese dog, Maltese terrier, Maltese
Top2: class_id: 332, score: 0.3909, label: Angora, Angora rabbit
Top3: class_id: 229, score: 0.0514, label: Old English sheepdog, bobtail
Top4: class_id: 204, score: 0.0430, label: Lhasa, Lhasa apso
Top5: class_id: 265, score: 0.0420, label: toy poodle
## 3. 更多教程
```
详细log位于`./test_tipc/output/ResNet50/cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log`和`./test_tipc/output/ResNet50/cls_cpp_infer_cpu_usemkldnn_False_threads_1_precision_fp32_batchsize_1.log`中。
本文档为功能测试用，更详细的c++预测使用教程请参考：[服务器端C++预测](../../docs/zh_CN/inference_deployment/)
如果运行失败，也会在终端中输出运行失败的日志信息以及对应的运行命令。可以基于该命令，分析运行失败的原因。
test_tipc/generate_cpp_yaml.py
浏览文件 @ 787f91b6
......@@ -66,6 +66,10 @@ def main():
"test_images")
config["IndexProcess"]["index_dir"] = os.path.join(args.data_dir,
"index")
config["IndexProcess"]["image_root"] = os.path.join(args.data_dir,
"gallery")
config["IndexProcess"]["data_file"] = os.path.join(args.data_dir,
"drink_label.txt")
assert args.cls_model_dir
assert args.det_model_dir
config["Global"]["det_inference_model_dir"] = args.det_model_dir
......
test_tipc/prepare.sh
浏览文件 @ 787f91b6
......@@ -12,7 +12,7 @@ dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
function func_parser_key(){
function func_parser_key() {
strs=$1
IFS=":"
array=(${strs})
......@@ -20,7 +20,7 @@ function func_parser_key(){
echo ${tmp}
}
function func_parser_value(){
function func_parser_value() {
strs=$1
IFS=":"
array=(${strs})
......@@ -33,46 +33,84 @@ function func_parser_value(){
fi
}
function func_get_url_file_name(){
function func_get_url_file_name() {
strs=$1
IFS="/"
array=(${strs})
tmp=${array[${#array[@]}-1]}
tmp=${array[${#array[@]} - 1]}
echo ${tmp}
}
model_name=$(func_parser_value "${lines[1]}")
if [ ${MODE} = "cpp_infer" ];then
if [[ $FILENAME == *infer_cpp_linux_gpu_cpu.txt ]];then
if [ ${MODE} = "cpp_infer" ]; then
if [ -d "./deploy/cpp/opencv-3.4.7/opencv3/" ] && [ $(md5sum ./deploy/cpp/opencv-3.4.7.tar.gz | awk -F ' ' '{print $1}') = "faa2b5950f8bee3f03118e600c74746a" ]; then
echo "################### build opencv skipped ###################"
else
echo "################### build opencv ###################"
rm -rf ./deploy/cpp/opencv-3.4.7.tar.gz ./deploy/cpp/opencv-3.4.7/
pushd ./deploy/cpp/
wget -nc https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/opencv-3.4.7.tar.gz
tar -xf opencv-3.4.7.tar.gz
cd opencv-3.4.7/
install_path=$(pwd)/opencv3
rm -rf build
mkdir build
cd build
cmake .. \
-DCMAKE_INSTALL_PREFIX=${install_path} \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_SHARED_LIBS=OFF \
-DWITH_IPP=OFF \
-DBUILD_IPP_IW=OFF \
-DWITH_LAPACK=OFF \
-DWITH_EIGEN=OFF \
-DCMAKE_INSTALL_LIBDIR=lib64 \
-DWITH_ZLIB=ON \
-DBUILD_ZLIB=ON \
-DWITH_JPEG=ON \
-DBUILD_JPEG=ON \
-DWITH_PNG=ON \
-DBUILD_PNG=ON \
-DWITH_TIFF=ON \
-DBUILD_TIFF=ON
make -j
make install
cd ../../
popd
echo "################### build opencv finished ###################"
fi
if [[ $FILENAME == *infer_cpp_linux_gpu_cpu.txt ]]; then
cpp_type=$(func_parser_value "${lines[2]}")
cls_inference_model_dir=$(func_parser_value "${lines[3]}")
det_inference_model_dir=$(func_parser_value "${lines[4]}")
cls_inference_url=$(func_parser_value "${lines[5]}")
det_inference_url=$(func_parser_value "${lines[6]}")
if [[ $cpp_type == "cls" ]];then
if [[ $cpp_type == "cls" ]]; then
eval "wget -nc $cls_inference_url"
tar xf "${model_name}_inference.tar"
eval "mv inference $cls_inference_model_dir"
tar xf "${model_name}_infer.tar"
cd dataset
rm -rf ILSVRC2012
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_infer.tar
tar xf whole_chain_infer.tar
ln -s whole_chain_infer ILSVRC2012
cd ..
elif [[ $cpp_type == "shitu" ]];then
elif [[ $cpp_type == "shitu" ]]; then
eval "wget -nc $cls_inference_url"
tar_name=$(func_get_url_file_name "$cls_inference_url")
model_dir=${tar_name%.*}
eval "tar xf ${tar_name}"
eval "mv ${model_dir} ${cls_inference_model_dir}"
eval "wget -nc $det_inference_url"
tar_name=$(func_get_url_file_name "$det_inference_url")
model_dir=${tar_name%.*}
eval "tar xf ${tar_name}"
eval "mv ${model_dir} ${det_inference_model_dir}"
cd dataset
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
tar -xf drink_dataset_v1.0.tar
......@@ -90,7 +128,7 @@ model_name=$(func_parser_value "${lines[1]}")
model_url_value=$(func_parser_value "${lines[35]}")
model_url_key=$(func_parser_key "${lines[35]}")
if [[ $FILENAME == *GeneralRecognition* ]];then
if [[ $FILENAME == *GeneralRecognition* ]]; then
cd dataset
rm -rf Aliproduct
rm -rf train_reg_all_data.txt
......@@ -108,12 +146,12 @@ if [[ $FILENAME == *GeneralRecognition* ]];then
exit 0
fi
if [[ $FILENAME == *use_dali* ]];then
if [[ $FILENAME == *use_dali* ]]; then
python_name=$(func_parser_value "${lines[2]}")
${python_name} -m pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/nightly --upgrade nvidia-dali-nightly-cuda102
fi
if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "lite_train_whole_infer" ];then
if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "lite_train_whole_infer" ]; then
# pretrain lite train data
cd dataset
rm -rf ILSVRC2012
......@@ -125,7 +163,7 @@ if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "lite_train_whole_infer"
mv val.txt val_list.txt
cp -r train/* val/
cd ../../
elif [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ];then
elif [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ]; then
# download data
cd dataset
rm -rf ILSVRC2012
......@@ -140,14 +178,14 @@ elif [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ];then
eval "wget -nc $model_url_value"
if [[ $model_url_key == *inference* ]]; then
rm -rf inference
tar xf "${model_name}_inference.tar"
tar xf "${model_name}_infer.tar"
fi
if [[ $model_name == "SwinTransformer_large_patch4_window7_224" || $model_name == "SwinTransformer_large_patch4_window12_384" ]];then
if [[ $model_name == "SwinTransformer_large_patch4_window7_224" || $model_name == "SwinTransformer_large_patch4_window12_384" ]]; then
cmd="mv ${model_name}_22kto1k_pretrained.pdparams ${model_name}_pretrained.pdparams"
eval $cmd
fi
elif [ ${MODE} = "whole_train_whole_infer" ];then
elif [ ${MODE} = "whole_train_whole_infer" ]; then
cd dataset
rm -rf ILSVRC2012
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_CIFAR100.tar
......@@ -159,7 +197,7 @@ elif [ ${MODE} = "whole_train_whole_infer" ];then
cd ../../
fi
if [ ${MODE} = "serving_infer" ];then
if [ ${MODE} = "serving_infer" ]; then
# prepare serving env
python_name=$(func_parser_value "${lines[2]}")
${python_name} -m pip install install paddle-serving-server-gpu==0.7.0.post102
......@@ -187,7 +225,7 @@ if [ ${MODE} = "serving_infer" ];then
unset https_proxy
fi
if [ ${MODE} = "paddle2onnx_infer" ];then
if [ ${MODE} = "paddle2onnx_infer" ]; then
# prepare paddle2onnx env
python_name=$(func_parser_value "${lines[2]}")
inference_model_url=$(func_parser_value "${lines[10]}")
......@@ -201,11 +239,9 @@ if [ ${MODE} = "paddle2onnx_infer" ];then
wget -nc ${inference_model_url}
tar xf ${tar_name}
cd ../../
fi
if [ ${MODE} = "benchmark_train" ];then
if [ ${MODE} = "benchmark_train" ]; then
pip install -r requirements.txt
cd dataset
rm -rf ILSVRC2012
......
test_tipc/test_inference_cpp.sh
浏览文件 @ 787f91b6
......@@ -6,12 +6,11 @@ GPUID=$2
if [[ ! $GPUID ]];then
GPUID=0
fi
dataline=$(awk 'NR==1, NR==16{print}' $FILENAME)
dataline=$(awk 'NR==1, NR==19{print}' $FILENAME)
# parser params
IFS=$'\n'
lines=(${dataline})
# parser cpp inference model
model_name=$(func_parser_value "${lines[1]}")
cpp_infer_type=$(func_parser_value "${lines[2]}")
......@@ -31,7 +30,7 @@ cpp_benchmark_value=$(func_parser_value "${lines[16]}")
generate_yaml_cmd=$(func_parser_value "${lines[17]}")
transform_index_cmd=$(func_parser_value "${lines[18]}")
LOG_PATH="./test_tipc/output"
LOG_PATH="./test_tipc/output/${model_name}"
mkdir -p ${LOG_PATH}
status_log="${LOG_PATH}/results_cpp.log"
# generate_yaml_cmd="python3 test_tipc/generate_cpp_yaml.py"
......@@ -58,11 +57,10 @@ function func_shitu_cpp_inference(){
precison="int8"
fi
_save_log_path="${_log_path}/shitu_cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
eval $transform_index_cmd
command="${generate_yaml_cmd} --type shitu --batch_size ${batch_size} --mkldnn ${use_mkldnn} --gpu ${use_gpu} --cpu_thread ${threads} --tensorrt False --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --det_model_dir ${cpp_det_infer_model_dir} --gpu_id ${GPUID}"
eval $command
eval $transform_index_cmd
command="${_script} 2>&1|tee ${_save_log_path}"
command="${_script} > ${_save_log_path} 2>&1"
eval $command
last_status=${PIPESTATUS[0]}
status_check $last_status "${command}" "${status_log}"
......@@ -83,13 +81,13 @@ function func_shitu_cpp_inference(){
fi
for batch_size in ${cpp_batch_size_list[*]}; do
_save_log_path="${_log_path}/shitu_cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
eval $transform_index_cmd
command="${generate_yaml_cmd} --type shitu --batch_size ${batch_size} --mkldnn False --gpu ${use_gpu} --cpu_thread 1 --tensorrt ${use_trt} --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --det_model_dir ${cpp_det_infer_model_dir} --gpu_id ${GPUID}"
eval $command
eval $transform_index_cmd
command="${_script} 2>&1|tee ${_save_log_path}"
command="${_script} > ${_save_log_path} 2>&1"
eval $command
last_status=${PIPESTATUS[0]}
status_check $last_status "${_script}" "${status_log}"
status_check $last_status "${command}" "${status_log}"
done
done
done
......@@ -124,7 +122,7 @@ function func_cls_cpp_inference(){
command="${generate_yaml_cmd} --type cls --batch_size ${batch_size} --mkldnn ${use_mkldnn} --gpu ${use_gpu} --cpu_thread ${threads} --tensorrt False --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --gpu_id ${GPUID}"
eval $command
command1="${_script} 2>&1|tee ${_save_log_path}"
command1="${_script} > ${_save_log_path} 2>&1"
eval ${command1}
last_status=${PIPESTATUS[0]}
status_check $last_status "${command1}" "${status_log}"
......@@ -147,7 +145,7 @@ function func_cls_cpp_inference(){
_save_log_path="${_log_path}/cls_cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
command="${generate_yaml_cmd} --type cls --batch_size ${batch_size} --mkldnn False --gpu ${use_gpu} --cpu_thread 1 --tensorrt ${use_trt} --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --gpu_id ${GPUID}"
eval $command
command="${_script} 2>&1|tee ${_save_log_path}"
command="${_script} > ${_save_log_path} 2>&1"
eval $command
last_status=${PIPESTATUS[0]}
status_check $last_status "${command}" "${status_log}"
......@@ -195,49 +193,11 @@ if [[ $cpp_infer_type == "shitu" ]]; then
cd ..
fi
if [ -d "opencv-3.4.7/opencv3/" ] && [ $(md5sum opencv-3.4.7.tar.gz | awk -F ' ' '{print $1}') = "faa2b5950f8bee3f03118e600c74746a" ];then
echo "################### build opencv skipped ###################"
else
echo "################### build opencv ###################"
rm -rf opencv-3.4.7.tar.gz opencv-3.4.7/
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/opencv-3.4.7.tar.gz
tar -xf opencv-3.4.7.tar.gz
cd opencv-3.4.7/
install_path=$(pwd)/opencv3
rm -rf build
mkdir build
cd build
cmake .. \
-DCMAKE_INSTALL_PREFIX=${install_path} \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_SHARED_LIBS=OFF \
-DWITH_IPP=OFF \
-DBUILD_IPP_IW=OFF \
-DWITH_LAPACK=OFF \
-DWITH_EIGEN=OFF \
-DCMAKE_INSTALL_LIBDIR=lib64 \
-DWITH_ZLIB=ON \
-DBUILD_ZLIB=ON \
-DWITH_JPEG=ON \
-DBUILD_JPEG=ON \
-DWITH_PNG=ON \
-DBUILD_PNG=ON \
-DWITH_TIFF=ON \
-DBUILD_TIFF=ON
make -j
make install
cd ../../
echo "################### build opencv finished ###################"
fi
echo "################### build PaddleClas demo ####################"
OPENCV_DIR=$(pwd)/opencv-3.4.7/opencv3/
# LIB_DIR=/work/project/project/test/paddle_inference/
LIB_DIR=$(pwd)/Paddle/build/paddle_inference_install_dir/
# pwd = /workspace/hesensen/PaddleClas/deploy/cpp_shitu
OPENCV_DIR=$(dirname $PWD)/cpp/opencv-3.4.7/opencv3/
LIB_DIR=$(dirname $PWD)/cpp/paddle_inference/
CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`)
CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`)
......
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