Running Pose Sampler

Creating Training and Testing Data

The data is created by running PERCH 2.0 6Dof flow on the YCB Video Dataset. For now it is for one object at a time only :

1. Follow the steps in Running-With-Docker Wiki under Using Docker Image to set up PERCH 2.0 and MaskRCNN with Docker (make sure you are able to run Step 12 before going further).

2. Make a new folder outside Docker for storing sampler data :

mkdir -p pose_sampler_data/sugar/test
mkdir -p pose_sampler_data/sugar/train

2. Mount the above folder while running Docker to /data/pose_sampler_data.

• If you are creating training data, config_docker.yaml should point to the training COCO annotation file
• If you are creating test data, config_docker.yaml should point to the test COCO annotation file

3. Next, check the run_ycb_6d function in fat_pose_image.py :

• It should be set to run a single required object
• If creating training data, the scene range should be 0 to 93
• If creating test data, the scene range should be 48 to 60
• The image range can be set as per requirement

Training Network

Training a pose sampling network using the costs calculated by PERCH 2.0.

1. Follow the steps in Running-With-Docker Wiki under "Using Docker Image" to set up PERCH 2.0 and MaskRCNN with Docker.

2. Clone the sampling CNN repo:

git clone https://github.com/SBPL-Cruz/perch_pose_sampler

3. Start the Docker image and mount the cloned folder to Docker at /pose_sampler
4. After getting into the Docker shell, run the following to make sure all Python modules are in the PYTHONPATH :

export PYTHONPATH=$PYTHONPATH:/pose_sampler/
export PYTHONPATH=$PYTHONPATH:/ros_python3_ws/src/perception/sbpl_perception/src/scripts/tools/fat_dataset

5. Run the training code :

# For visualizing poses during training : 
Xvfb :5 -screen 0 800x600x24 & export DISPLAY=:5; #skip this if not using the CPU version 
cd /pose_sampler/utils
python train_classification.py \
  --dataset /data/YCB_Video_Dataset \
  --dataset_type ycb \
  --dataset_annotation /data/YCB_Video_Dataset/instances_train_bbox_pose_sampler.json \
  --test_dataset_annotation /data/YCB_Video_Dataset/instances_keyframe_bbox_pose_sampler.json \
  --batchsize 40 --nepoch 50 --render_poses

6. Run tensorboard outside Docker to visualize in the browser :

cd perch_pose_sampler/utils
tensorboard --logdir experiments