參考
face_mask_detection參考
Nvidia TLT(Transfer Learning Toolkit) 轉移學習工具 安裝所需工具
參考資料集
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$ docker run --name face-mask --runtime=nvidia --gpus all -it -v /home/user/Data/DeepStreamer/tlt-experiments/:/workspace/tlt-experiments -p 8888:8888 nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3 /bin/bash
root@0aa10155a3bd:/workspace/tlt-experiments# exit
exit
$ docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
0aa10155a3bd nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3 "install_ngc_cli.sh …" 8 minutes ago Up 6 minutes 0.0.0.0:8888->8888/tcp face-mask
$ docker exec -it face-mask /bin/bash
# cd tlt-experiments/face-mask-detection
# python -m pip install -r requirements.txt
修改 face-mask-detection/tlt_specs/detectnet_v2_tfrecords_kitti_trainval.txt
root_directory_path: "/workspace/tlt-experiments/face-mask-detection-data/kitti/train"
修改 face-mask-detection/tlt_specs/detectnet_v2_train_resnet18_kitti.txt
tfrecords_path: "/workspace/tlt-experiments/face-mask-detection-data/kitti/tfrecords/kitti_trainval/*"
image_directory_path: "/workspace/tlt-experiments/face-mask-detection-data/kitti/train"
pretrained_model_file: "/workspace/tlt-experiments/detectnet_v2/pretrained_resnet18/tlt_pretrained_detectnet_v2_vresnet18/resnet18.hdf5"
修改 face-mask-detection/tlt_specs/detectnet_v2_retrain_resnet18_kitti.txt
tfrecords_path: "/workspace/tlt-experiments/face-mask-detection-data/kitti/tfrecords/kitti_trainval/*"
mage_directory_path: "/workspace/tlt-experiments/face-mask-detection-data/kitti/train"
pretrained_model_file: "/workspace/tlt-experiments/face-mask-detection-data/detectnet_v2/experiment_dir_pruned/resnet18_nopool_bn_detectnet_v2_pruned.tlt"
修改 face-mask-detection/tlt_specs/detectnet_v2_inference_kitti_tlt.txt
model: "/workspace/tlt-experiments/detectnet_v2/experiment_dir_retrain/weights/resnet18_detector_pruned.tlt"
修改 face-mask-detection/tlt_specs/detectnet_v2_inference_kitti_etlt.txt
trt_engine: "/workspace/tlt-experiments/detectnet_v2/experiment_dir_final_unpruned/resnet18_detector.trt"
產生 kitti 格式的標註
# vi data2kitti.sh
/usr/bin/python data2kitti.py --kaggle-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/Kaggle Medical Mask Dataset/medical-masks-dataset' --mafa-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/MAFA Dataset' --fddb-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/FDDB Dataset' --widerface-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/Wider Face Dataset' --kitti-base-path '/workspace/tlt-experiments/face-mask-detection-data/kitti' --train
/usr/bin/python data2kitti.py --kaggle-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/Kaggle Medical Mask Dataset/medical-masks-dataset' --mafa-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/MAFA Dataset' --fddb-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/FDDB Dataset' --widerface-dataset-path '/workspace/tlt-experiments/face-mask-detection-data/Wider Face Dataset' --kitti-base-path '/workspace/tlt-experiments/face-mask-detection-data/kitti' --val
# jupyter notebook --ip 0.0.0.0 --allow-root
To access the notebook, open this file in a browser:
file:///root/.local/share/jupyter/runtime/nbserver-241-open.html
Or copy and paste one of these URLs:
http://0aa10155a3bd:8888/?token=29cbb63890972635e00320fe6c66bda30b2c81daa7400f3d
or http://127.0.0.1:8888/?token=29cbb63890972635e00320fe6c66bda30b2c81daa7400f3d
用瀏覽器開啟 http://127.0.0.1:8888/?token=29cbb63890972635e00320fe6c66bda30b2c81daa7400f3d
進入 http://192.168.0.101:8888/notebooks/face-mask-detection.ipynb
修改下列環境變數到正確目錄
# Setting up env variables for cleaner command line commands.
%env KEY=tlt_encode
# User directory
%env USER_EXPERIMENT_DIR=/workspace/tlt-experiments/detectnet_v2
# Download directory
%env DATA_DOWNLOAD_DIR=/workspace/tlt-experiments/face-mask-detection-data/kitti
%env SPECS_DIR=/workspace/tlt-experiments/face-mask-detection/tlt_specs
產生 tfrecords
!tlt-dataset-convert -d $SPECS_DIR/detectnet_v2_tfrecords_kitti_trainval.txt \
-o $DATA_DOWNLOAD_DIR/tfrecords/kitti_trainval/kitti_trainval
查詢 tlt_pretrained_detectnet_v2
!ngc registry model list nvidia/tlt_pretrained_detectnet_v2:*
!mkdir -p $USER_EXPERIMENT_DIR/pretrained_resnet18/
下載 tlt_pretrained_detectnet_v2
!ngc registry model download-version nvidia/tlt_pretrained_detectnet_v2:resnet18 \
--dest $USER_EXPERIMENT_DIR/pretrained_resnet18
訓練開始
!tlt-train detectnet_v2 -e $SPECS_DIR/detectnet_v2_train_resnet18_kitti.txt \
-r $USER_EXPERIMENT_DIR/experiment_dir_unpruned \
-k $KEY \
-n resnet18_detector
測試訓練結果
!tlt-evaluate detectnet_v2 -e $SPECS_DIR/detectnet_v2_train_resnet18_kitti.txt\
-m $USER_EXPERIMENT_DIR/experiment_dir_unpruned/weights/resnet18_detector.tlt \
-k $KEY
修剪模型， -ph (threshold), 越大產生的模型越小，推論速度越快，但會不準
!mkdir -p $USER_EXPERIMENT_DIR/experiment_dir_pruned
!tlt-prune -m $USER_EXPERIMENT_DIR/experiment_dir_unpruned/weights/resnet18_detector.tlt \
-o $USER_EXPERIMENT_DIR/experiment_dir_pruned/resnet18_nopool_bn_detectnet_v2_pruned.tlt \
-eq union \
-pth 0.7 \
-k $KEY
再次訓練修剪過的模型
!tlt-train detectnet_v2 -e $SPECS_DIR/detectnet_v2_retrain_resnet18_kitti.txt \
-r $USER_EXPERIMENT_DIR/experiment_dir_retrain \
-k $KEY \
-n resnet18_detector_pruned
測試再次訓練的模型
!tlt-evaluate detectnet_v2 -e $SPECS_DIR/detectnet_v2_retrain_resnet18_kitti.txt \
-m $USER_EXPERIMENT_DIR/experiment_dir_retrain/weights/resnet18_detector_pruned.tlt \
-k $KEY
利用模型推論測試的圖形
!tlt-infer detectnet_v2 -e $SPECS_DIR/detectnet_v2_inference_kitti_tlt.txt \
-o $USER_EXPERIMENT_DIR/tlt_infer_testing \
-i $DATA_DOWNLOAD_DIR/test/images \
-k $KEY
輸出可被 DeepStream 使用的模型
!mkdir -p $USER_EXPERIMENT_DIR/experiment_dir_final
# Removing a pre-existing copy of the etlt if there has been any.
import os
output_file=os.path.join(os.environ['USER_EXPERIMENT_DIR'],
"experiment_dir_final/resnet18_detector_thermal.etlt")
if os.path.exists(output_file):
os.system("rm {}".format(output_file))
!tlt-export detectnet_v2 \
-m $USER_EXPERIMENT_DIR/experiment_dir_retrain/weights/resnet18_detector_pruned.tlt \
-o $USER_EXPERIMENT_DIR/experiment_dir_final/resnet18_detector_thermal.etlt \
-k $KEY
修改 ds_configs/config_infer_primary_masknet_gpu.txt
tlt-encoded-model=/home/nano/Data/DeepStream/face-mask-detection/experiment_dir_final/resnet18_detector_thermal.etlt
# 不要用 model-engine-file
修改 ds_configs/deepstream_app_source1_camera_masknet_gpu.txt
完成
$ deepstream-app -c deepstream_app_source1_camera_masknet_gpu.txt
