# Diffusion-Policy

## Installation

```bash
cd roboverse_learn/algorithms/diffusion_policy
pip install -e .
cd ../../../

pip install pandas wandb
```

## Usage of Training


1. First run 'data2zarr_dp.py' to get zarr data

```shell
# --------- instruction format ---------#

python roboverse_learn/algorithms/diffusion_policy/data2zarr_dp.py  <task_name> <expert_data_num> <metadata_dir> <split offset>

# The format of 'task_name' should be {task}_{robot}
# e.g.StackCube_franka / CloseBox_franka

# 'expert_data_num' means number of training data
# you can define it by yourself, but it should be less than the number of data in 'metadata_dir'

# 'metadata_dir' means the location of metadata

# --------- instruction example ---------#

python roboverse_learn/algorithms/diffusion_policy/data2zarr_dp.py CloseBox_franka 10 ~/Project/RoboVerse/RoboVerse/data_isaaclab/demo/CloseBox/robot-franka 1
```

The processed zarr data will be saved in **'data_policy'**. If you wanna move the zarr data to headless server, please make sure the new location is the same as **'data_policy'** in the headless server.

**ATTENTION**:

In this script, we define 'joint_qpos' as the *state* and 'joint_qpos_target' as the *action*. If you wanna change, just modify the code in line 84 and line 85 in **'roboverse_learn/algorithms/diffusion_policy/data2zarr_dp.py'**.

By the way, if you change the 'joint_qpos' and 'joint_qpos_target' to other parameters, please check *agent_pos:shape* and *action:shape* in **roboverse_learn/algorithms/diffusion_policy/diffusion_policy/config/task/default_task.yaml** to make sure they are consistent with the shape of new parameters.

2. Then run 'train.sh' to train DP

```shell
# --------- instruction format ---------#
# example task: using 100 CloseBox Franka demo to train a diffusion policy
python roboverse_learn/algorithms/diffusion_policy/train.py --config-name=robot_dp.yaml \
                task.name=CloseBox_franka_100 \
                task.dataset.zarr_path="data_policy/CloseBox_franka_5.zarr" \
                training.debug=False \
                training.seed=0 \
                training.device="cuda:0" \
                exp_name=CloseBox_franka_100_dp \
                horizon=4 \
                n_obs_steps=2 \
                n_action_steps=2
```

you can modify some parameters (including *training epoch*, *batch_size*) in **'roboverse_learn/algorithms/diffusion_policy/diffusion_policy/config/robot_dp.yaml'**

Here list some important parameters which can consider to change:
- horizon (line 12)
- n_obs_steps (line 13)
- n_action_steps (line 14)
- dataloader: batch_size (line 76)
- val_dataloader: batch_size (line 83)
- num_epochs (line 104)
- checkpoint_every (line 113)
- val_every (line 114)

## Usage of Validation

Use following instruction to run validation (customize some input parameters):

```bash
python roboverse_learn/eval.py --task CloseBox --sim isaaclab --checkpoint_path XXX(absolute_path)
```