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add cpp inference for TIPC
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│ ├── MobileNetV3 # MobileNetV3系列模型测试配置文件目录
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│ ├── MobileNetV3 # MobileNetV3系列模型测试配置文件目录
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│ │ ├── MobileNetV3_large_x1_0_train_infer_python.txt #基础训练预测配置文件
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│ │ ├── MobileNetV3_large_x1_0_train_infer_python.txt #基础训练预测配置文件
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│ │ ├── 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
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│ │ ├── 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
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│ │ ├── MobileNetV3_large_x1_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt #C++推理测试配置文件
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│ │ └── 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
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│ │ └── 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
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│ └── ResNet # ResNet系列模型测试配置文件目录
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│ └── ResNet # ResNet系列模型测试配置文件目录
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│ ├── ResNet50_vd_train_infer_python.txt #基础训练预测配置文件
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│ ├── ResNet50_vd_train_infer_python.txt #基础训练预测配置文件
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│ ├── 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
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│ ├── 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
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│ ├── ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt #C++推理测试配置文件
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│ └── 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
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│ └── 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
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| ......
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| ......
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├── docs
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├── docs
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# model load config
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model_name:MobileNetV3_large_x1_0
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/MobileNetV3_large_x1_0_infer/inference.pdmodel
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cls_params_path:./deploy/models/MobileNetV3_large_x1_0_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:general_PPLCNet_x2_5_lite_v1.0
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/general_PPLCNet_x2_5_lite_v1.0_infer/inference.pdmodel
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cls_params_path:./deploy/models/general_PPLCNet_x2_5_lite_v1.0_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:picodet_PPLCNet_x2_5_mainbody_lite_v1.0
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/inference.pdmodel
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cls_params_path:./deploy/models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:PPHGNet_small
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/PPHGNet_small_infer/inference.pdmodel
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cls_params_path:./deploy/models/PPHGNet_small_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:PPHGNet_tiny
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/PPHGNet_tiny_infer/inference.pdmodel
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cls_params_path:./deploy/models/PPHGNet_tiny_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:PPLCNet_x0_25
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/PPLCNet_x0_25_infer/inference.pdmodel
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cls_params_path:./deploy/models/PPLCNet_x0_25_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:PPLCNet_x0_35
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/PPLCNet_x0_35_infer/inference.pdmodel
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cls_params_path:./deploy/models/PPLCNet_x0_35_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:PPLCNet_x0_5
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/PPLCNet_x0_5_infer/inference.pdmodel
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cls_params_path:./deploy/models/PPLCNet_x0_5_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:PPLCNet_x0_75
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/PPLCNet_x0_75_infer/inference.pdmodel
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cls_params_path:./deploy/models/PPLCNet_x0_75_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:PPLCNet_x1_0
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/PPLCNet_x1_0_infer/inference.pdmodel
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cls_params_path:./deploy/models/PPLCNet_x1_0_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:PPLCNet_x1_5
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/PPLCNet_x1_5_infer/inference.pdmodel
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cls_params_path:./deploy/models/PPLCNet_x1_5_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:PPLCNet_x2_0
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/PPLCNet_x2_0_infer/inference.pdmodel
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cls_params_path:./deploy/models/PPLCNet_x2_0_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:PPLCNet_x2_5
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/PPLCNet_x2_5_infer/inference.pdmodel
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cls_params_path:./deploy/models/PPLCNet_x2_5_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:PPLCNet_x0_5
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/PPLCNet_x0_5_infer/inference.pdmodel
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cls_params_path:./deploy/models/PPLCNet_x0_5_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# model load config
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model_name:ResNet50
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/ResNet50_infer/inference.pdmodel
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cls_params_path:./deploy/models/ResNet50_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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===========================cpp_infer_params===========================
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# model load config
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model_name:ResNet50_vd
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model_name:ResNet50_vd
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cpp_infer_type:cls
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cls_inference_model_dir:./cls_inference/
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det_inference_model_dir:
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cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/ResNet50_vd_inference.tar
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det_inference_url:
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infer_quant:False
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inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
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use_gpu:True|False
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use_gpu:True|False
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enable_mkldnn:True|False
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gpu_id:0
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cpu_threads:1|6
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gpu_mem:4000
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batch_size:1
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cpu_math_library_num_threads:10
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use_tensorrt:False|True
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precision:fp32|fp16
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# cls config
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image_dir:./dataset/ILSVRC2012/val
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cls_model_path:./deploy/models/ResNet50_vd_infer/inference.pdmodel
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benchmark:True
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cls_params_path:./deploy/models/ResNet50_vd_infer/inference.pdiparams
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generate_yaml_cmd:python3 test_tipc/generate_cpp_yaml.py
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crop_size:224
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# model load config
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model_name:SwinTransformer_tiny_patch4_window7_224
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use_gpu:True|False
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gpu_id:0
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gpu_mem:4000
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cpu_math_library_num_threads:10
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# cls config
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cls_model_path:./deploy/models/SwinTransformer_tiny_patch4_window7_224_infer/inference.pdmodel
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cls_params_path:./deploy/models/SwinTransformer_tiny_patch4_window7_224_infer/inference.pdiparams
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resize_short_size:256
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crop_size:224
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# C++预测功能测试
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# Linux GPU/CPU C++ 推理功能测试
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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。
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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。
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## 1. 测试结论汇总
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## 1. 测试结论汇总
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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：
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- 推理相关：
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| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
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| 算法名称 | 模型名称 | device_CPU | device_GPU |
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| ---- | ---- | ---- | :----: | :----: | :----: |
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| :----: | :----: | :----: | :----: |
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| 正常模型 | GPU | 1/6 | fp32/fp16 | - | - |
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| MobileNetV3 | MobileNetV3_large_x1_0 | 支持 | 支持 |
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| 正常模型 | CPU | 1/6 | - | fp32 | 支持 |
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| PP-ShiTu | PPShiTu_general_rec | 支持 | 支持 |
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| 量化模型 | GPU | 1/6 | int8 | - | - |
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| PP-ShiTu | PPShiTu_mainbody_det | 暂不支持 | 暂不支持 |
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| 量化模型 | CPU | 1/6 | - | int8 | 支持 |
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| PPHGNet | PPHGNet_small | 支持 | 支持 |
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| PPHGNet | PPHGNet_tiny | 支持 | 支持 |
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| PPLCNet | PPLCNet_x0_25 | 支持 | 支持 |
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| PPLCNet | PPLCNet_x0_35 | 支持 | 支持 |
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| PPLCNet | PPLCNet_x0_5 | 支持 | 支持 |
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| PPLCNet | PPLCNet_x0_75 | 支持 | 支持 |
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| PPLCNet | PPLCNet_x1_0 | 支持 | 支持 |
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| PPLCNet | PPLCNet_x1_5 | 支持 | 支持 |
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| PPLCNet | PPLCNet_x2_0 | 支持 | 支持 |
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| PPLCNet | PPLCNet_x2_5 | 支持 | 支持 |
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| PPLCNetV2 | PPLCNetV2_base | 支持 | 支持 |
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| ResNet | ResNet50 | 支持 | 支持 |
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| ResNet | ResNet50_vd | 支持 | 支持 |
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| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | 支持 | 支持 |
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## 2. 测试流程
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## 2. 测试流程(以**ResNet50**为例)
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运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。
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### 2.1 功能测试
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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。
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<details>
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<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）
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</b></summary>
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### 2.1 准备数据和推理模型
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#### 2.1.1 准备数据
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默认使用`./deploy/images/ILSVRC2012_val_00000010.jpeg`作为测试输入图片。
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#### 2.1.2 准备推理模型
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||||||
|
* 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
|
```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
|
||||||
# 用法1:
|
├── inference.pdiparams
|
||||||
bash test_tipc/test_inference_cpp.sh test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
|
└── inference.pdiparams.info
|
||||||
# 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
|
|
||||||
```
|
```
|
||||||
|
|
||||||
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：
|
### 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)。
|
||||||
|
|
||||||
|
#### 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
|
||||||
|
```
|
||||||
|
|
||||||
|
* 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
|
```shell
|
||||||
test_tipc/output/
|
cd ./opencv-3.4.7
|
||||||
|- results_cpp.log # 运行指令状态的日志
|
export root_path=$PWD
|
||||||
|- cls_cpp_infer_cpu_usemkldnn_False_threads_1_precision_fp32_batchsize_1.log # CPU上不开启Mkldnn，线程数设置为1，测试batch_size=1条件下的预测运行日志
|
export install_path=${root_path}/opencv3
|
||||||
|- 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
|
|
||||||
......
|
|
||||||
```
|
```
|
||||||
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。
|
||||||
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
|
|
||||||
......
|
|
||||||
```
|
|
||||||
如果运行失败，会输出：
|
|
||||||
```
|
|
||||||
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
|
|
||||||
......
|
|
||||||
```
|
|
||||||
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。
|
|
||||||
|
|
||||||
|
|
||||||
### 2.2 精度测试
|
|
||||||
|
|
||||||
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。
|
|
||||||
|
|
||||||
#### 使用方式
|
|
||||||
运行命令：
|
|
||||||
```shell
|
```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
|
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
|
||||||
```
|
```
|
||||||
|
|
||||||
参数介绍：
|
* `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。
|
||||||
- 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
|
|
||||||
|
|
||||||
#### 运行结果
|
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。
|
||||||
|
|
||||||
正常运行效果如下图：
|
```shell
|
||||||
<img src="compare_cpp_right.png" width="1000">
|
opencv3/
|
||||||
|
├── bin :可执行文件
|
||||||
|
├── include :头文件
|
||||||
|
├── lib64 :库文件
|
||||||
|
└── share :部分第三方库
|
||||||
|
```
|
||||||
|
|
||||||
出现不一致结果时的运行输出：
|
#### 2.2.3 下载或者编译Paddle预测库
|
||||||
<img src="compare_cpp_wrong.png" width="1000">
|
|
||||||
|
* 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
|
||||||
|
```
|
||||||
|
|
||||||
|
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)。
|
||||||
|
|
||||||
|
|
||||||
## 3. 更多教程
|
* 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。
|
||||||
|
|
||||||
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/)
|
```
|
||||||
|
build/paddle_inference_install_dir/
|
||||||
|
├── CMakeCache.txt
|
||||||
|
├── paddle
|
||||||
|
├── third_party
|
||||||
|
└── version.txt
|
||||||
|
```
|
||||||
|
|
||||||
|
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
|
||||||
|
|
||||||
|
* [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。
|
||||||
|
|
||||||
|
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/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。
|
||||||
|
|
||||||
|
```shell
|
||||||
|
bash test_tipc/test_inference_cpp.sh ${your_params_file}
|
||||||
|
```
|
||||||
|
|
||||||
|
以`ResNet50`的`Linux GPU/CPU C++推理测试`为例，命令如下所示。
|
||||||
|
|
||||||
|
```shell
|
||||||
|
bash test_tipc/test_inference_cpp.sh test_tipc/configs/ResNet50/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
|
||||||
|
```
|
||||||
|
|
||||||
|
输出结果如下，表示命令运行成功。
|
||||||
|
|
||||||
|
```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
|
||||||
|
|
||||||
|
```
|
||||||
|
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。
|
||||||
|
|
||||||
|
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。
|
||||||
|
|||||||
@ -1,303 +1,65 @@
|
|||||||
#!/bin/bash
|
#!/bin/bash
|
||||||
source test_tipc/common_func.sh
|
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
|
FILENAME=$1
|
||||||
GPUID=$2
|
|
||||||
if [[ ! $GPUID ]];then
|
dataline=$(cat ${FILENAME})
|
||||||
GPUID=0
|
lines=(${dataline})
|
||||||
fi
|
|
||||||
dataline=$(awk 'NR==1, NR==16{print}' $FILENAME)
|
|
||||||
|
|
||||||
# parser params
|
# parser params
|
||||||
|
dataline=$(awk 'NR==1, NR==14{print}' $FILENAME)
|
||||||
IFS=$'\n'
|
IFS=$'\n'
|
||||||
lines=(${dataline})
|
lines=(${dataline})
|
||||||
|
|
||||||
# parser cpp inference model
|
# parser load config
|
||||||
model_name=$(func_parser_value "${lines[1]}")
|
model_name=$(func_parser_value_cpp "${lines[1]}")
|
||||||
cpp_infer_type=$(func_parser_value "${lines[2]}")
|
use_gpu_key=$(func_parser_key_cpp "${lines[2]}")
|
||||||
cpp_infer_model_dir=$(func_parser_value "${lines[3]}")
|
use_gpu_value=$(func_parser_value_cpp "${lines[2]}")
|
||||||
cpp_det_infer_model_dir=$(func_parser_value "${lines[4]}")
|
LOG_PATH="./test_tipc/output/${model_name}/infer_cpp"
|
||||||
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"
|
|
||||||
mkdir -p ${LOG_PATH}
|
mkdir -p ${LOG_PATH}
|
||||||
status_log="${LOG_PATH}/results_cpp.log"
|
status_log="${LOG_PATH}/results_infer_cpp.log"
|
||||||
# generate_yaml_cmd="python3 test_tipc/generate_cpp_yaml.py"
|
|
||||||
|
|
||||||
function func_shitu_cpp_inference(){
|
line_inference_model_dir=3
|
||||||
|
line_use_gpu=5
|
||||||
|
function func_infer_cpp(){
|
||||||
|
# inference cpp
|
||||||
IFS='|'
|
IFS='|'
|
||||||
_script=$1
|
for use_gpu in ${use_gpu_value[*]}; do
|
||||||
_model_dir=$2
|
if [[ ${use_gpu} = "True" ]]; then
|
||||||
_log_path=$3
|
_save_log_path="${LOG_PATH}/infer_cpp_use_gpu.log"
|
||||||
_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}" "${status_log}"
|
|
||||||
done
|
|
||||||
done
|
|
||||||
done
|
|
||||||
else
|
else
|
||||||
echo "Does not support hardware other than CPU and GPU Currently!"
|
_save_log_path="${LOG_PATH}/infer_cpp_use_cpu.log"
|
||||||
fi
|
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
|
done
|
||||||
}
|
}
|
||||||
|
|
||||||
function func_cls_cpp_inference(){
|
echo "################### run test 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
|
func_infer_cpp
|
||||||
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
|
|
||||||
else
|
|
||||||
echo "Does not support hardware other than CPU and GPU Currently!"
|
|
||||||
fi
|
|
||||||
done
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
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
|
|
||||||
Loading…
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Reference in New Issue
Block a user