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1
NVIDIA Clara SDK Study
2019/04/30
Macy Kung
2
Introduction to NVIDIA Clara SDK
- The NVIDIA Clara SDK make it easy deploy and integrated with consumer
to enable medical imaging organ annotation predictions
- It reduce the entry of medical imaging developers in medical imaging training
models, let developers focus on developing application platforms
- Transfer Learning Toolkit
- AI Assisted Annotation Toolkit
3
Transfer Learning Toolkit
- Transfer Learning Toolkit support Train or Fine-Tune medical images model that using
to images inference and export to TensorRT model
- NVIDIA provided per-tained models such as Liver, Hippocapus, Brain, Lung,
Prostate, Left Atrium, Pancreas, Spleen…etc.
- train model
- export models
tlt-pull -k $API_KEY -m segmentation_hippocampus -v 1 -d /var/tmp/test_models/
hippocampus -o nvtltea -t med
tlt-train segmentation 
-e $TRAIN_CONFIG 
-d $DATA_LIST 
-r $TRAIN_OUTPUT_DIR
# checkpoint dir is the same as
TRAIN_OUTPUT_DIR where we saved the
checkpoint
env CHECKPOINT=/mnt/aiaa_spleen/output
# export the optimized model
tlt-export 
--model_name 'model' 
--model_file_path ${CHECKPOINT} 
--input_node_names 'cond/Merge' 
--output_node_names 'NV_OUTPUT_LABEL'
4
Transfer Learning Toolkit
- Fine-tune Model
# Choose the model to download
MODEL_NAME=annotation_spleen
VERSION=1
API_KEY=YOUR_NGC_API_KEY
TRAIN_FINETUNE_CONFIG=/opt/nvidia/medical/annotation/examples/MSD_Task09_Spleen/
config_train_finetune_aiaa_Task09_Spleen.json
PRETRAINED_CHECKPOINT_DIR=/mnt/aiaa_spleen/pretrained_model
tlt-train segmentation 
--config $TRAIN_CONFIG 
--file_root $FILE_ROOT 
--data_list $DATA_LIST 
--checkpoint $PRETRAINED_CHECKPOINT_DIR/model.ckpt 
--model_dir $TRAIN_OUTPUT_DIR
5
Transfer Learning Toolkit
- Evaluation
# Evaluation variables
EVAL_CONFIG=/opt/nvidia/medical/annotation/examples/MSD_Task09_Spleen/
config_eval_aiaa_Task09_Spleen.json
# checkpoint dir is the same as TRAIN_OUTPUT_DIR where we saved the checkpoint
CHECKPOINT=/mnt/aiaa_spleen/output
EVAL_OUTPUT_PATH=/mnt/aiaa_spleen/output/eval
EVAL_REPORT=/mnt/aiaa_spleen/output/eval/mean_dice_raw_results.txt
SUMMARY_REPORT=/mnt/aiaa_spleen/output/eval/mean_dice_summary_results.txt
# show evaluation configuration
cat $EVAL_CONFIG
echo "Run evaluation on "$DATA_LIST
mkdir $EVAL_OUTPUT_PATH
tlt-evaluate 
--model_save_path $CHECKPOINT/model.trt.pb 
--config $EVAL_CONFIG 
--model_format "trtmodel" 
--file_root $FILE_ROOT 
--output_path $EVAL_OUTPUT_PATH 
--data_file $DATA_LIST 
--setup standard
6
Transfer Learning Toolkit
- Inference
# Inference variables
env INFERENCE_CONFIG=/mnt/aiaa_spleen/config_infer_aiaa_Task09_Spleen.json
# checkpoint dir is the same as TRAIN_OUTPUT_DIR where we saved the checkpoint
env CHECKPOINT=/mnt/aiaa_spleen/output
env INFERENCE_OUTPUT_PATH=/mnt/aiaa_spleen/output/inference
# show inference configuration
cat $INFERENCE_CONFIG
echo "Run inference on "$DATA_LIST
mkdir $INFERENCE_OUTPUT_PATH
tlt-infer 
--model_save_path $CHECKPOINT/model.trt.pb 
--config $INFERENCE_CONFIG 
--model_format "trtmodel" 
--file_root $DATA_ROOT 
--output_path $INFERENCE_OUTPUT_PATH 
--data_file $DATA_LIST 
--setup standard
7
Transfer Learning Toolkit
- Inference can be used to open the inference result with the MITK tool.
# Inference variables
env INFERENCE_CONFIG=/mnt/aiaa_spleen/config_infer_aiaa_Task09_Spleen.json
# checkpoint dir is the same as TRAIN_OUTPUT_DIR where we saved the checkpoint
env CHECKPOINT=/mnt/aiaa_spleen/output
env INFERENCE_OUTPUT_PATH=/mnt/aiaa_spleen/output/inference
# show inference configuration
cat $INFERENCE_CONFIG
echo "Run inference on "$DATA_LIST
mkdir $INFERENCE_OUTPUT_PATH
tlt-infer 
--model_save_path $CHECKPOINT/model.trt.pb 
--config $INFERENCE_CONFIG 
--model_format "trtmodel" 
--file_root $DATA_ROOT 
--output_path $INFERENCE_OUTPUT_PATH 
--data_file $DATA_LIST 
--setup standard
8
AI Assisted Annotation Toolkit
- AI Assisted Annotation Tookit provides user application integrated with medical
image data models to implement model inference
- AI Assisted Annotation Server (AIAAS) support to carry training model
Model Configuration file
{
"model_format": "trtmodel",
"model_path": "/var/tmp/models/annotation_spleen/model.trt.pb",
"thresh": 0.5,
"result_dir": "/var/tmp/experiments/aiaa",
"input_nodes":
{
"image": "cond/Merge"
},
"output_nodes":
{
"model": "NV_OUTPUT_LABEL"
},
"batch_size": 1,
"pre_transforms":
[
{
"name": "transforms.VolumeTo4dArray",
"fields": ["image", "label"]
},
{
"name": "transforms.NormalizeNonzeroIntensities",
"fields": "image"
},
{
"name": "transforms.AddExtremePointsChannel",
"image_field": "image",
"label_field": "label",
"sigma": 3,
"pert": 0
} ],
9
AI Assisted Annotation Toolkit
- Assisted Annotation Server loaded the configuration file
engine_pool.json is AIAA Server Configuration file
[{
"name": "MyAnnotationSpleenModel",
"class": "Dextr3dCroppedEngine",
"module": "medical.aas.dextr3dcropped",
"config_file": "/var/tmp/models/annotation_spleen.json",
"labels": ["spleen"]
}, {
"name": "MyAnnotationLiverModel",
"class": "Dextr3dCroppedEngine",
"module": "medical.aas.dextr3dcropped",
"config_file": "/var/tmp/models/annotation_liver.json",
"labels": ["liver"]
}]
10
AI Assisted Annotation Toolkit
- Start the AI Assisted Annotation Server
- AI Assisted Annotation Server is working
/opt/nvidia/medical/aas/start_aas.sh --config /var/tmp/models/engine_pool.json
curl http://$MACHINE_IP:5000/v1/models
[{"description": "", "name": "MyAnnotationLiverModel",
"labels": ["liver"],
"internal name": "Dextr3dCroppedEngine"},
{"description": "",
"name": "MyAnnotationSpleenModel",
"labels": ["spleen"],
“internal name": "Dextr3dCroppedEngine"}]
11
AI Assisted Annotation Toolkit
- AI Assisted Annotation Client
- Support C++ API & Python API
- Documention home : https://docs.nvidia.com/clara/aiaa/sdk-api/docs/index.html
- Integration MITK
- Integration Web Application
DEMO: https://github.com/windwaterbo/nvidia_ai_assisted_integration
12
Integrating into our Application
AI-Assisted
Annotation
Client
AI-Assisted
Annotation
Server
AI Platform
13
Nvidia Clara SDK License
SOFTWARE DEVELOPER KITS, SAMPLES AND TOOLS LICENSE AGREEMENT

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NVIDIA Clara SDK study

  • 1. 1 NVIDIA Clara SDK Study 2019/04/30 Macy Kung
  • 2. 2 Introduction to NVIDIA Clara SDK - The NVIDIA Clara SDK make it easy deploy and integrated with consumer to enable medical imaging organ annotation predictions - It reduce the entry of medical imaging developers in medical imaging training models, let developers focus on developing application platforms - Transfer Learning Toolkit - AI Assisted Annotation Toolkit
  • 3. 3 Transfer Learning Toolkit - Transfer Learning Toolkit support Train or Fine-Tune medical images model that using to images inference and export to TensorRT model - NVIDIA provided per-tained models such as Liver, Hippocapus, Brain, Lung, Prostate, Left Atrium, Pancreas, Spleen…etc. - train model - export models tlt-pull -k $API_KEY -m segmentation_hippocampus -v 1 -d /var/tmp/test_models/ hippocampus -o nvtltea -t med tlt-train segmentation -e $TRAIN_CONFIG -d $DATA_LIST -r $TRAIN_OUTPUT_DIR # checkpoint dir is the same as TRAIN_OUTPUT_DIR where we saved the checkpoint env CHECKPOINT=/mnt/aiaa_spleen/output # export the optimized model tlt-export --model_name 'model' --model_file_path ${CHECKPOINT} --input_node_names 'cond/Merge' --output_node_names 'NV_OUTPUT_LABEL'
  • 4. 4 Transfer Learning Toolkit - Fine-tune Model # Choose the model to download MODEL_NAME=annotation_spleen VERSION=1 API_KEY=YOUR_NGC_API_KEY TRAIN_FINETUNE_CONFIG=/opt/nvidia/medical/annotation/examples/MSD_Task09_Spleen/ config_train_finetune_aiaa_Task09_Spleen.json PRETRAINED_CHECKPOINT_DIR=/mnt/aiaa_spleen/pretrained_model tlt-train segmentation --config $TRAIN_CONFIG --file_root $FILE_ROOT --data_list $DATA_LIST --checkpoint $PRETRAINED_CHECKPOINT_DIR/model.ckpt --model_dir $TRAIN_OUTPUT_DIR
  • 5. 5 Transfer Learning Toolkit - Evaluation # Evaluation variables EVAL_CONFIG=/opt/nvidia/medical/annotation/examples/MSD_Task09_Spleen/ config_eval_aiaa_Task09_Spleen.json # checkpoint dir is the same as TRAIN_OUTPUT_DIR where we saved the checkpoint CHECKPOINT=/mnt/aiaa_spleen/output EVAL_OUTPUT_PATH=/mnt/aiaa_spleen/output/eval EVAL_REPORT=/mnt/aiaa_spleen/output/eval/mean_dice_raw_results.txt SUMMARY_REPORT=/mnt/aiaa_spleen/output/eval/mean_dice_summary_results.txt # show evaluation configuration cat $EVAL_CONFIG echo "Run evaluation on "$DATA_LIST mkdir $EVAL_OUTPUT_PATH tlt-evaluate --model_save_path $CHECKPOINT/model.trt.pb --config $EVAL_CONFIG --model_format "trtmodel" --file_root $FILE_ROOT --output_path $EVAL_OUTPUT_PATH --data_file $DATA_LIST --setup standard
  • 6. 6 Transfer Learning Toolkit - Inference # Inference variables env INFERENCE_CONFIG=/mnt/aiaa_spleen/config_infer_aiaa_Task09_Spleen.json # checkpoint dir is the same as TRAIN_OUTPUT_DIR where we saved the checkpoint env CHECKPOINT=/mnt/aiaa_spleen/output env INFERENCE_OUTPUT_PATH=/mnt/aiaa_spleen/output/inference # show inference configuration cat $INFERENCE_CONFIG echo "Run inference on "$DATA_LIST mkdir $INFERENCE_OUTPUT_PATH tlt-infer --model_save_path $CHECKPOINT/model.trt.pb --config $INFERENCE_CONFIG --model_format "trtmodel" --file_root $DATA_ROOT --output_path $INFERENCE_OUTPUT_PATH --data_file $DATA_LIST --setup standard
  • 7. 7 Transfer Learning Toolkit - Inference can be used to open the inference result with the MITK tool. # Inference variables env INFERENCE_CONFIG=/mnt/aiaa_spleen/config_infer_aiaa_Task09_Spleen.json # checkpoint dir is the same as TRAIN_OUTPUT_DIR where we saved the checkpoint env CHECKPOINT=/mnt/aiaa_spleen/output env INFERENCE_OUTPUT_PATH=/mnt/aiaa_spleen/output/inference # show inference configuration cat $INFERENCE_CONFIG echo "Run inference on "$DATA_LIST mkdir $INFERENCE_OUTPUT_PATH tlt-infer --model_save_path $CHECKPOINT/model.trt.pb --config $INFERENCE_CONFIG --model_format "trtmodel" --file_root $DATA_ROOT --output_path $INFERENCE_OUTPUT_PATH --data_file $DATA_LIST --setup standard
  • 8. 8 AI Assisted Annotation Toolkit - AI Assisted Annotation Tookit provides user application integrated with medical image data models to implement model inference - AI Assisted Annotation Server (AIAAS) support to carry training model Model Configuration file { "model_format": "trtmodel", "model_path": "/var/tmp/models/annotation_spleen/model.trt.pb", "thresh": 0.5, "result_dir": "/var/tmp/experiments/aiaa", "input_nodes": { "image": "cond/Merge" }, "output_nodes": { "model": "NV_OUTPUT_LABEL" }, "batch_size": 1, "pre_transforms": [ { "name": "transforms.VolumeTo4dArray", "fields": ["image", "label"] }, { "name": "transforms.NormalizeNonzeroIntensities", "fields": "image" }, { "name": "transforms.AddExtremePointsChannel", "image_field": "image", "label_field": "label", "sigma": 3, "pert": 0 } ],
  • 9. 9 AI Assisted Annotation Toolkit - Assisted Annotation Server loaded the configuration file engine_pool.json is AIAA Server Configuration file [{ "name": "MyAnnotationSpleenModel", "class": "Dextr3dCroppedEngine", "module": "medical.aas.dextr3dcropped", "config_file": "/var/tmp/models/annotation_spleen.json", "labels": ["spleen"] }, { "name": "MyAnnotationLiverModel", "class": "Dextr3dCroppedEngine", "module": "medical.aas.dextr3dcropped", "config_file": "/var/tmp/models/annotation_liver.json", "labels": ["liver"] }]
  • 10. 10 AI Assisted Annotation Toolkit - Start the AI Assisted Annotation Server - AI Assisted Annotation Server is working /opt/nvidia/medical/aas/start_aas.sh --config /var/tmp/models/engine_pool.json curl http://$MACHINE_IP:5000/v1/models [{"description": "", "name": "MyAnnotationLiverModel", "labels": ["liver"], "internal name": "Dextr3dCroppedEngine"}, {"description": "", "name": "MyAnnotationSpleenModel", "labels": ["spleen"], “internal name": "Dextr3dCroppedEngine"}]
  • 11. 11 AI Assisted Annotation Toolkit - AI Assisted Annotation Client - Support C++ API & Python API - Documention home : https://docs.nvidia.com/clara/aiaa/sdk-api/docs/index.html - Integration MITK - Integration Web Application DEMO: https://github.com/windwaterbo/nvidia_ai_assisted_integration
  • 12. 12 Integrating into our Application AI-Assisted Annotation Client AI-Assisted Annotation Server AI Platform
  • 13. 13 Nvidia Clara SDK License SOFTWARE DEVELOPER KITS, SAMPLES AND TOOLS LICENSE AGREEMENT

Notas del editor

  1. Transfer Learning Toolkit 提供用戶Train or Fine-Tune 醫學圖像 Model,可以用於醫學圖像推理(Inference),還可以導出TensorRT模型 AI Assisted Annotation Tookit 提供用戶的應用平台與醫學圖像數據模型結合,達到模型推理與醫學圖像應用平台的ai annotation結合
  2. ‣ Brain Tumor segmentation ‣ Liver and Tumor segmentation ‣ Hippocampus segmentation ‣ Lung Tumor segmentation ‣ Prostate segmentation ‣ Left Atrium segmentation ‣ Pancreas and Tumor segmentation ‣ Colon Tumor segmentation ‣ Hepatic Vessel segmentation ‣ Spleen segmentation ‣ Chest X-ray classification
  3. ‣ Brain Tumor segmentation ‣ Liver and Tumor segmentation ‣ Hippocampus segmentation ‣ Lung Tumor segmentation ‣ Prostate segmentation ‣ Left Atrium segmentation ‣ Pancreas and Tumor segmentation ‣ Colon Tumor segmentation ‣ Hepatic Vessel segmentation ‣ Spleen segmentation ‣ Chest X-ray classification
  4. ‣ Brain Tumor segmentation ‣ Liver and Tumor segmentation ‣ Hippocampus segmentation ‣ Lung Tumor segmentation ‣ Prostate segmentation ‣ Left Atrium segmentation ‣ Pancreas and Tumor segmentation ‣ Colon Tumor segmentation ‣ Hepatic Vessel segmentation ‣ Spleen segmentation ‣ Chest X-ray classification
  5. ‣ Brain Tumor segmentation ‣ Liver and Tumor segmentation ‣ Hippocampus segmentation ‣ Lung Tumor segmentation ‣ Prostate segmentation ‣ Left Atrium segmentation ‣ Pancreas and Tumor segmentation ‣ Colon Tumor segmentation ‣ Hepatic Vessel segmentation ‣ Spleen segmentation ‣ Chest X-ray classification
  6. ‣ Brain Tumor segmentation ‣ Liver and Tumor segmentation ‣ Hippocampus segmentation ‣ Lung Tumor segmentation ‣ Prostate segmentation ‣ Left Atrium segmentation ‣ Pancreas and Tumor segmentation ‣ Colon Tumor segmentation ‣ Hepatic Vessel segmentation ‣ Spleen segmentation ‣ Chest X-ray classification
  7. curl http://10.91.4.191:5000/v1/models
  8. curl http://10.91.4.191:5000/v1/models https://docs.nvidia.com/clara/aiaa/sdk-api/docs/index.html