diff --git a/test_tipc/README.md b/test_tipc/README.md index 4869f6e11ddc78b7c05c7805bfb25ba7e41b683d..42fcb8faf62083241386a2d3767e880b695726b0 100644 --- a/test_tipc/README.md +++ b/test_tipc/README.md @@ -35,10 +35,12 @@ │ ├── 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_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt #C++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 │ └── 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_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt #C++推理测试配置文件 │ └── 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 | ...... ├── docs @@ -99,7 +101,7 @@ bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/MobileNetV3/Mo ## 4 开始测试 -各功能测试中涉及混合精度、裁剪、量化等训练相关，及mkldnn、Tensorrt等多种预测相关参数配置，请点击下方相应链接了解更多细节和使用教程： +各功能测试中涉及混合精度、裁剪、量化等训练相关，及mkldnn、Tensorrt等多种预测相关参数配置，请点击下方相应链接了解更多细节和使用教程： - [test_train_inference_python 使用](docs/test_train_inference_python.md)：测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。 - [test_inference_cpp 使用](docs/test_inference_cpp.md) ：测试基于C++的模型推理。 diff --git a/test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..7a1f69fb21c693a5e532148e9829d230478ac218 --- /dev/null +++ b/test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:MobileNetV3_large_x1_0 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/MobileNetV3_large_x1_0_infer/inference.pdmodel +cls_params_path:./deploy/models/MobileNetV3_large_x1_0_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PP-ShiTu/PPShiTu_general_rec_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PP-ShiTu/PPShiTu_general_rec_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..7a025e80af56d4dcd53fadf9221bee1c92f68790 --- /dev/null +++ b/test_tipc/config/PP-ShiTu/PPShiTu_general_rec_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:general_PPLCNet_x2_5_lite_v1.0 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/general_PPLCNet_x2_5_lite_v1.0_infer/inference.pdmodel +cls_params_path:./deploy/models/general_PPLCNet_x2_5_lite_v1.0_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PP-ShiTu/PPShiTu_mainbody_det_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PP-ShiTu/PPShiTu_mainbody_det_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..b1067c2561c71fb357f0455f32e6a9ceb3129861 --- /dev/null +++ b/test_tipc/config/PP-ShiTu/PPShiTu_mainbody_det_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:picodet_PPLCNet_x2_5_mainbody_lite_v1.0 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/inference.pdmodel +cls_params_path:./deploy/models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PPHGNet/PPHGNet_small_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PPHGNet/PPHGNet_small_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..566f34530bf99da8ab0f87bd64d0d5e377a008be --- /dev/null +++ b/test_tipc/config/PPHGNet/PPHGNet_small_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:PPHGNet_small +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/PPHGNet_small_infer/inference.pdmodel +cls_params_path:./deploy/models/PPHGNet_small_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PPHGNet/PPHGNet_tiny_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PPHGNet/PPHGNet_tiny_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..0420a43eb650fc27ff3840d9ea153787243f379c --- /dev/null +++ b/test_tipc/config/PPHGNet/PPHGNet_tiny_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:PPHGNet_tiny +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/PPHGNet_tiny_infer/inference.pdmodel +cls_params_path:./deploy/models/PPHGNet_tiny_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PPLCNet/PPLCNet_x0_25_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PPLCNet/PPLCNet_x0_25_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..93d6f352e4c4993e31b10952581fb8f1fe496cbe --- /dev/null +++ b/test_tipc/config/PPLCNet/PPLCNet_x0_25_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:PPLCNet_x0_25 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/PPLCNet_x0_25_infer/inference.pdmodel +cls_params_path:./deploy/models/PPLCNet_x0_25_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PPLCNet/PPLCNet_x0_35_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PPLCNet/PPLCNet_x0_35_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f41fe3db1088c73016d87894e7e263d0d825d52 --- /dev/null +++ b/test_tipc/config/PPLCNet/PPLCNet_x0_35_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:PPLCNet_x0_35 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/PPLCNet_x0_35_infer/inference.pdmodel +cls_params_path:./deploy/models/PPLCNet_x0_35_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PPLCNet/PPLCNet_x0_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PPLCNet/PPLCNet_x0_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..e2ae4d14d1281674df7ba7e016ffcafcb303f2e7 --- /dev/null +++ b/test_tipc/config/PPLCNet/PPLCNet_x0_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:PPLCNet_x0_5 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/PPLCNet_x0_5_infer/inference.pdmodel +cls_params_path:./deploy/models/PPLCNet_x0_5_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PPLCNet/PPLCNet_x0_75_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PPLCNet/PPLCNet_x0_75_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..bf377e7a470becc98665136cc9cde0a5470b5aff --- /dev/null +++ b/test_tipc/config/PPLCNet/PPLCNet_x0_75_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:PPLCNet_x0_75 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/PPLCNet_x0_75_infer/inference.pdmodel +cls_params_path:./deploy/models/PPLCNet_x0_75_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..f16deb1b0c1eeab3888799b335ef6f992d3986d3 --- /dev/null +++ b/test_tipc/config/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:PPLCNet_x1_0 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/PPLCNet_x1_0_infer/inference.pdmodel +cls_params_path:./deploy/models/PPLCNet_x1_0_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PPLCNet/PPLCNet_x1_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PPLCNet/PPLCNet_x1_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..fc3a2e75b37327975cdd1b110b5d0dafb3bbe5bd --- /dev/null +++ b/test_tipc/config/PPLCNet/PPLCNet_x1_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:PPLCNet_x1_5 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/PPLCNet_x1_5_infer/inference.pdmodel +cls_params_path:./deploy/models/PPLCNet_x1_5_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PPLCNet/PPLCNet_x2_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PPLCNet/PPLCNet_x2_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..52cf9b0d0e6cb135757c4f4e7ac4f60f4e10d374 --- /dev/null +++ b/test_tipc/config/PPLCNet/PPLCNet_x2_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:PPLCNet_x2_0 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/PPLCNet_x2_0_infer/inference.pdmodel +cls_params_path:./deploy/models/PPLCNet_x2_0_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PPLCNet/PPLCNet_x2_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PPLCNet/PPLCNet_x2_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..b26c82cec352558b6e99e4d715b0de7b70780398 --- /dev/null +++ b/test_tipc/config/PPLCNet/PPLCNet_x2_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:PPLCNet_x2_5 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/PPLCNet_x2_5_infer/inference.pdmodel +cls_params_path:./deploy/models/PPLCNet_x2_5_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/PPLCNetV2/PPLCNetV2_base_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/PPLCNetV2/PPLCNetV2_base_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..e2ae4d14d1281674df7ba7e016ffcafcb303f2e7 --- /dev/null +++ b/test_tipc/config/PPLCNetV2/PPLCNetV2_base_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:PPLCNet_x0_5 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/PPLCNet_x0_5_infer/inference.pdmodel +cls_params_path:./deploy/models/PPLCNet_x0_5_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..60d9a40436b4e85ddb918c8e3a2939d71ef04f66 --- /dev/null +++ b/test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:ResNet50 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/ResNet50_infer/inference.pdmodel +cls_params_path:./deploy/models/ResNet50_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt index 51c73f13d46c3a8793f9b5db92a74e0aa7b4e599..dfdd5b7f55f1397a7568c72b260fa3f4c9a916ad 100644 --- a/test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +++ b/test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -1,18 +1,12 @@ -===========================cpp_infer_params=========================== +# model load config model_name:ResNet50_vd -cpp_infer_type:cls -cls_inference_model_dir:./cls_inference/ -det_inference_model_dir: -cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/ResNet50_vd_inference.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 -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 +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/ResNet50_vd_infer/inference.pdmodel +cls_params_path:./deploy/models/ResNet50_vd_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/config/SwinTransformer/SwinTransformer_tiny_patch4_window7_224_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/config/SwinTransformer/SwinTransformer_tiny_patch4_window7_224_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..8a76d870e8a8ffc39877d658e6a23e58611fa5e9 --- /dev/null +++ b/test_tipc/config/SwinTransformer/SwinTransformer_tiny_patch4_window7_224_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -0,0 +1,12 @@ +# model load config +model_name:SwinTransformer_tiny_patch4_window7_224 +use_gpu:True|False +gpu_id:0 +gpu_mem:4000 +cpu_math_library_num_threads:10 + +# cls config +cls_model_path:./deploy/models/SwinTransformer_tiny_patch4_window7_224_infer/inference.pdmodel +cls_params_path:./deploy/models/SwinTransformer_tiny_patch4_window7_224_infer/inference.pdiparams +resize_short_size:256 +crop_size:224 \ No newline at end of file diff --git a/test_tipc/docs/test_inference_cpp.md b/test_tipc/docs/test_inference_cpp.md index eabf8774d37454461fe28a5fc1d5ed0b3135fcad..37a73a7067f5b6e2f8e7aaca698b224a42be03ab 100644 --- a/test_tipc/docs/test_inference_cpp.md +++ b/test_tipc/docs/test_inference_cpp.md @@ -1,86 +1,311 @@ -# 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 | 支持 | 支持 | +| PP-ShiTu | PPShiTu_mainbody_det | 暂不支持 | 暂不支持 | +| 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。 + +
+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） + + +### 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。 +
+ +* 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/configs/ResNet50/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/configs/ResNet50/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +``` -正常运行效果如下图： - +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： - +```shell +Run successfully with command - ./deploy/cpp/build/clas_system -c ./deploy/configs/inference_cls.yaml > ./test_tipc/output/ResNet50/infer_cpp/infer_cpp_use_gpu.log 2>&1 ! +Run successfully with command - ./deploy/cpp/build/clas_system -c ./deploy/configs/inference_cls.yaml > ./test_tipc/output/ResNet50/infer_cpp/infer_cpp_use_cpu.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/infer_cpp/infer_cpp_use_gpu.log`和`./test_tipc/output/ResNet50/infer_cpp/infer_cpp_use_cpu.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。 diff --git a/test_tipc/test_inference_cpp.sh b/test_tipc/test_inference_cpp.sh index 129e439562980a233924995141ea864d052f6dfb..36ff584cd969d8ec1349f80d5020a28f7214bc74 100644 --- a/test_tipc/test_inference_cpp.sh +++ b/test_tipc/test_inference_cpp.sh @@ -1,303 +1,65 @@ #!/bin/bash source test_tipc/common_func.sh +function func_parser_key_cpp(){ + strs=$1 + IFS=" " + array=(${strs}) + tmp=${array[0]} + echo ${tmp} +} + +function func_parser_value_cpp(){ + strs=$1 + IFS=":" + array=(${strs}) + tmp=${array[1]} + echo ${tmp} +} + FILENAME=$1 -GPUID=$2 -if [[ ! $GPUID ]];then - GPUID=0 -fi -dataline=$(awk 'NR==1, NR==16{print}' $FILENAME) + +dataline=$(cat ${FILENAME}) +lines=(${dataline}) # parser params +dataline=$(awk 'NR==1, NR==14{print}' $FILENAME) IFS=$'\n' lines=(${dataline}) -# parser cpp inference model -model_name=$(func_parser_value "${lines[1]}") -cpp_infer_type=$(func_parser_value "${lines[2]}") -cpp_infer_model_dir=$(func_parser_value "${lines[3]}") -cpp_det_infer_model_dir=$(func_parser_value "${lines[4]}") -cpp_infer_is_quant=$(func_parser_value "${lines[7]}") -# parser cpp inference -inference_cmd=$(func_parser_value "${lines[8]}") -cpp_use_gpu_list=$(func_parser_value "${lines[9]}") -cpp_use_mkldnn_list=$(func_parser_value "${lines[10]}") -cpp_cpu_threads_list=$(func_parser_value "${lines[11]}") -cpp_batch_size_list=$(func_parser_value "${lines[12]}") -cpp_use_trt_list=$(func_parser_value "${lines[13]}") -cpp_precision_list=$(func_parser_value "${lines[14]}") -cpp_image_dir_value=$(func_parser_value "${lines[15]}") -cpp_benchmark_value=$(func_parser_value "${lines[16]}") -generate_yaml_cmd=$(func_parser_value "${lines[17]}") -transform_index_cmd=$(func_parser_value "${lines[18]}") - -LOG_PATH="./test_tipc/output" +# parser load config +model_name=$(func_parser_value_cpp "${lines[1]}") +use_gpu_key=$(func_parser_key_cpp "${lines[2]}") +use_gpu_value=$(func_parser_value_cpp "${lines[2]}") +LOG_PATH="./test_tipc/output/${model_name}/infer_cpp" mkdir -p ${LOG_PATH} -status_log="${LOG_PATH}/results_cpp.log" -# generate_yaml_cmd="python3 test_tipc/generate_cpp_yaml.py" - -function func_shitu_cpp_inference(){ - IFS='|' - _script=$1 - _model_dir=$2 - _log_path=$3 - _img_dir=$4 - _flag_quant=$5 - # inference - - for use_gpu in ${cpp_use_gpu_list[*]}; do - if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then - for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do - if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then - continue - fi - for threads in ${cpp_cpu_threads_list[*]}; do - for batch_size in ${cpp_batch_size_list[*]}; do - precision="fp32" - if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then - precison="int8" - fi - _save_log_path="${_log_path}/shitu_cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log" - - 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}" - eval $command - last_status=${PIPESTATUS[0]} - status_check $last_status "${command}" "${status_log}" - done - done - done - elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then - for use_trt in ${cpp_use_trt_list[*]}; do - for precision in ${cpp_precision_list[*]}; do - if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then - continue - fi - if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then - continue - fi - if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then - continue - 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_${batch_size}.log" - command="${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}" - eval $command - last_status=${PIPESTATUS[0]} - status_check $last_status "${_script}" "${status_log}" - done - done - done - else - echo "Does not support hardware other than CPU and GPU Currently!" - fi - done -} +status_log="${LOG_PATH}/results_infer_cpp.log" -function func_cls_cpp_inference(){ +line_inference_model_dir=3 +line_use_gpu=5 +function func_infer_cpp(){ + # inference cpp IFS='|' - _script=$1 - _model_dir=$2 - _log_path=$3 - _img_dir=$4 - _flag_quant=$5 - # inference - - for use_gpu in ${cpp_use_gpu_list[*]}; do - if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then - for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do - if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then - continue - fi - for threads in ${cpp_cpu_threads_list[*]}; do - for batch_size in ${cpp_batch_size_list[*]}; do - precision="fp32" - if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then - precison="int8" - fi - _save_log_path="${_log_path}/cls_cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log" - - 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}" - eval ${command1} - last_status=${PIPESTATUS[0]} - status_check $last_status "${command1}" "${status_log}" - done - done - done - elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then - for use_trt in ${cpp_use_trt_list[*]}; do - for precision in ${cpp_precision_list[*]}; do - if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then - continue - fi - if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then - continue - fi - if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then - continue - fi - for batch_size in ${cpp_batch_size_list[*]}; do - _save_log_path="${_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}" - eval $command - last_status=${PIPESTATUS[0]} - status_check $last_status "${command}" "${status_log}" - done - done - done + for use_gpu in ${use_gpu_value[*]}; do + if [[ ${use_gpu} = "True" ]]; then + _save_log_path="${LOG_PATH}/infer_cpp_use_gpu.log" else - echo "Does not support hardware other than CPU and GPU Currently!" + _save_log_path="${LOG_PATH}/infer_cpp_use_cpu.log" fi + # run infer cpp + inference_cpp_cmd="./deploy/cpp/build/clas_system" + inference_cpp_cfg="./deploy/configs/inference_cls.yaml" + set_model_name_cmd="sed -i '${line_inference_model_dir}s#: .*#: ./deploy/models/${model_name}_infer#' '${inference_cpp_cfg}'" + set_use_gpu_cmd="sed -i '${line_use_gpu}s#: .*#: ${use_gpu}#' '${inference_cpp_cfg}'" + eval $set_model_name_cmd + eval $set_use_gpu_cmd + infer_cpp_full_cmd="${inference_cpp_cmd} -c ${inference_cpp_cfg} > ${_save_log_path} 2>&1 " + eval $infer_cpp_full_cmd + last_status=${PIPESTATUS[0]} + status_check $last_status "${infer_cpp_full_cmd}" "${status_log}" "${model_name}" done } +echo "################### run test cpp inference ###################" -if [[ $cpp_infer_type == "cls" ]]; then - cd deploy/cpp -elif [[ $cpp_infer_type == "shitu" ]]; then - cd deploy/cpp_shitu -else - echo "Only support cls and shitu" - exit 0 -fi - -if [[ $cpp_infer_type == "shitu" ]]; then - echo "################### update cmake ###################" - wget -nc https://github.com/Kitware/CMake/releases/download/v3.22.0/cmake-3.22.0.tar.gz - tar xf cmake-3.22.0.tar.gz - cd ./cmake-3.22.0 - export root_path=$PWD - export install_path=${root_path}/cmake - eval "./bootstrap --prefix=${install_path}" - make -j - make install - export PATH=${install_path}/bin:$PATH - cd .. - echo "################### update cmake done ###################" - - echo "################### build faiss ###################" - apt-get install -y libopenblas-dev - git clone https://github.com/facebookresearch/faiss.git - cd faiss - export faiss_install_path=$PWD/faiss_install - eval "cmake -B build . -DFAISS_ENABLE_PYTHON=OFF -DCMAKE_INSTALL_PREFIX=${faiss_install_path}" - make -C build -j faiss - make -C build install - 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=$(pwd)/Paddle/build/paddle_inference_install_dir/ -CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`) -CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`) - -BUILD_DIR=build -rm -rf ${BUILD_DIR} -mkdir ${BUILD_DIR} -cd ${BUILD_DIR} -if [[ $cpp_infer_type == cls ]]; then - cmake .. \ - -DPADDLE_LIB=${LIB_DIR} \ - -DWITH_MKL=ON \ - -DWITH_GPU=ON \ - -DWITH_STATIC_LIB=OFF \ - -DWITH_TENSORRT=OFF \ - -DOPENCV_DIR=${OPENCV_DIR} \ - -DCUDNN_LIB=${CUDNN_LIB_DIR} \ - -DCUDA_LIB=${CUDA_LIB_DIR} \ - -DTENSORRT_DIR=${TENSORRT_DIR} -else - cmake ..\ - -DPADDLE_LIB=${LIB_DIR} \ - -DWITH_MKL=ON \ - -DWITH_GPU=ON \ - -DWITH_STATIC_LIB=OFF \ - -DWITH_TENSORRT=OFF \ - -DOPENCV_DIR=${OPENCV_DIR} \ - -DCUDNN_LIB=${CUDNN_LIB_DIR} \ - -DCUDA_LIB=${CUDA_LIB_DIR} \ - -DTENSORRT_DIR=${TENSORRT_DIR} \ - -DFAISS_DIR=${faiss_install_path} \ - -DFAISS_WITH_MKL=OFF -fi -make -j -cd ../../../ -# cd ../../ -echo "################### build PaddleClas demo finished ###################" - - -# set cuda device -# GPUID=$2 -# if [ ${#GPUID} -le 0 ];then -# env="export CUDA_VISIBLE_DEVICES=0" -# else -# env="export CUDA_VISIBLE_DEVICES=${GPUID}" -# fi -# set CUDA_VISIBLE_DEVICES -# eval $env - - -echo "################### run test ###################" -export Count=0 -IFS="|" -infer_quant_flag=(${cpp_infer_is_quant}) -for infer_model in ${cpp_infer_model_dir[*]}; do - #run inference - is_quant=${infer_quant_flag[Count]} - if [[ $cpp_infer_type == "cls" ]]; then - func_cls_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_image_dir_value}" ${is_quant} - else - func_shitu_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_image_dir_value}" ${is_quant} - fi - Count=$(($Count + 1)) -done +func_infer_cpp \ No newline at end of file