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import sys
import os
import pathlib
ROOT_DIR = str(pathlib.Path(__file__).parent.parent.parent)
sys.path.append(ROOT_DIR)
os.chdir(ROOT_DIR)
import os
import json
import hydra
import torch
from omegaconf import OmegaConf
import pathlib
import copy
import numpy as np
import random
import dill
import h5py
from tqdm import tqdm
from termcolor import colored
from hydra.core.hydra_config import HydraConfig
from diffusion_policy.workspace.base_workspace import BaseWorkspace
from diffusion_policy.policy.diffusion_unet_lowdim_policy import DiffusionUnetLowdimPolicy
from diffusion_policy.policy.diffusion_transformer_lowdim_policy import DiffusionTransformerLowdimPolicy
from diffusion_policy.env_runner.base_lowdim_runner import BaseLowdimRunner
import robomimic.utils.file_utils as FileUtils
import robomimic.utils.env_utils as EnvUtils
from diffusion_policy.gym_util.video_recording_wrapper import VideoRecorder
OmegaConf.register_new_resolver("eval", eval, replace=True)
# %%
class DatacollectDiffusionLowdimWorkspace(BaseWorkspace):
include_keys = ['global_step', 'epoch']
def __init__(self, cfg: OmegaConf, output_dir=None):
super().__init__(cfg, output_dir=output_dir)
# Load payload from checkpoint
if cfg.checkpoint_dir is None:
checkpoint_dir_dict = {
'pusht_lowdim': {
'datacollect_diffusion_unet_lowdim': '',
'datacollect_diffusion_transformer_lowdim': '',
},
'lift_lowdim': {
'datacollect_diffusion_unet_lowdim': 'logs/pretrain/lift_lowdim/train_diffusion_cnn/checkpoints/epoch=0010-test_mean_score=0.680.ckpt',
'datacollect_diffusion_transformer_lowdim': 'logs/pretrain/lift_lowdim/train_diffusion_transformer/checkpoints/epoch=0015-test_mean_score=0.400.ckpt',
},
'can_lowdim': {
'datacollect_diffusion_unet_lowdim': 'logs/pretrain/can_lowdim/train_diffusion_cnn/checkpoints/epoch=0015-test_mean_score=0.600.ckpt',
'datacollect_diffusion_transformer_lowdim': 'logs/pretrain/can_lowdim/train_diffusion_transformer/checkpoints/epoch=0060-test_mean_score=0.380.ckpt',
},
'square_lowdim': {
'datacollect_diffusion_unet_lowdim': 'logs/pretrain/square_lowdim/train_diffusion_cnn/checkpoints/epoch=0040-test_mean_score=0.520.ckpt',
'datacollect_diffusion_transformer_lowdim': 'logs/pretrain/square_lowdim/train_diffusion_transformer/checkpoints/epoch=0450-test_mean_score=0.520.ckpt',
},
'transport_lowdim': {
'datacollect_diffusion_unet_lowdim': 'logs/pretrain/transport_lowdim/train_diffusion_cnn/checkpoints/epoch=0150-test_mean_score=0.480.ckpt',
'datacollect_diffusion_transformer_lowdim': 'logs/pretrain/transport_lowdim/train_diffusion_transformer/checkpoints/epoch=0450-test_mean_score=0.240.ckpt',
},
'tool_hang_lowdim': {
'datacollect_diffusion_unet_lowdim': '',
'datacollect_diffusion_transformer_lowdim': 'logs/pretrain/tool_hang_lowdim/train_diffusion_transformer/checkpoints/epoch=0500-test_mean_score=0.440.ckpt',
},
'kitchen_lowdim': {
'datacollect_diffusion_unet_lowdim': '',
'datacollect_diffusion_transformer_lowdim': '',
},
}
checkpoint_dir = checkpoint_dir_dict[cfg.task_name][cfg.name]
else:
checkpoint_dir = cfg.checkpoint_dir
ckpt_file = pathlib.Path(checkpoint_dir)
assert ckpt_file.is_file()
print(colored(f"Collecting from: {ckpt_file}", "green", attrs=["bold"]))
payload = torch.load(ckpt_file.open('rb'), pickle_module=dill)
self.pretrained_cfg = payload['cfg']
# set seed
seed = cfg.collecting.seed
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
# configure model
if self.pretrained_cfg.policy._target_ == 'diffusion_policy.policy.diffusion_unet_lowdim_policy.DiffusionUnetLowdimPolicy':
self.model: DiffusionUnetLowdimPolicy
self.model = hydra.utils.instantiate(self.pretrained_cfg.policy)
self.ema_model: DiffusionUnetLowdimPolicy = None
if self.pretrained_cfg.training.use_ema:
self.ema_model = copy.deepcopy(self.model)
elif self.pretrained_cfg.policy._target_ == 'diffusion_policy.policy.diffusion_transformer_lowdim_policy.DiffusionTransformerLowdimPolicy':
self.model: DiffusionTransformerLowdimPolicy
self.model = hydra.utils.instantiate(self.pretrained_cfg.policy)
self.ema_model: DiffusionTransformerLowdimPolicy = None
if self.pretrained_cfg.training.use_ema:
self.ema_model = copy.deepcopy(self.model)
else:
raise ValueError(f"Unknown policy type: {self.pretrained_cfg.policy._target_}")
# Load weights from pretrained models
exclude_keys = ['optimizer']
self.load_payload(payload, exclude_keys=exclude_keys, include_keys=None)
def run(self):
cfg = copy.deepcopy(self.cfg)
run_dir = HydraConfig.get().run.dir
cfg.task.env_runner['n_train_vis'] = 0
cfg.task.env_runner['n_test_vis'] = 0
cfg.task.env_runner['n_train'] = 0
cfg.task.env_runner['n_test'] = cfg.collecting.num_episodes
cfg.task.env_runner['n_envs'] = min(100, cfg.collecting.num_episodes)
# configure env runner
env_runner: BaseLowdimRunner
env_runner = hydra.utils.instantiate(
cfg.task.env_runner,
output_dir=self.output_dir,
return_intermediate_state=True,
collect_data=True,
use_oracle_ac=False,
)
assert isinstance(env_runner, BaseLowdimRunner)
assert env_runner.return_intermediate_state and env_runner.collect_data and (not env_runner.use_oracle_ac), "Wrong configs in collect mode"
# device transfer
device = torch.device(cfg.collecting.device)
policy = self.model
if self.ema_model is not None:
policy = self.ema_model
policy.to(device)
# Collect data
policy.eval()
runner_log, all_episodes = env_runner.run(policy)
# Writing data to h5 file
rollout_num_episodes = len(all_episodes['observations'])
data_collect_file = os.path.join(run_dir, f"collect_{cfg.task_name}.hdf5")
data_writer = h5py.File(data_collect_file, "w")
data_grp = data_writer.create_group("data")
total_samples = 0
all_successes = []
for i in range(rollout_num_episodes):
states = []
successes = []
for t in range(len(all_episodes['infos'][i])):
states.append(all_episodes['infos'][i][t]['states'])
successes.append(all_episodes['infos'][i][t]['success'])
if np.sum(successes) > 0:
first_succ_idx = np.argmax(successes) # No need to +1 here since we have success flag at reset
else:
first_succ_idx = len(all_episodes['actions'][i])
states = np.array(states)
successes = np.array(successes)
all_successes.append(np.max(successes))
ep_data_grp = data_grp.create_group(f"episode_{i}")
ep_data_grp.create_dataset("obs", data=np.array(all_episodes['observations'][i][:first_succ_idx]))
ep_data_grp.create_dataset("next_obs", data=np.array(all_episodes['observations'][i][1:first_succ_idx + 1]))
ep_data_grp.create_dataset("actions", data=np.array(all_episodes['actions'][i][:first_succ_idx]))
ep_data_grp.create_dataset("rewards", data=np.array(all_episodes['rewards'][i][:first_succ_idx]))
ep_data_grp.create_dataset("dones", data=np.array(all_episodes['terminals'][i][:first_succ_idx])) # this may not contain any done
ep_data_grp.create_dataset("states", data=states[:first_succ_idx + 1])
ep_data_grp.create_dataset("successes", data=successes[1:first_succ_idx + 1])
ep_data_grp.attrs["model_file"] = all_episodes['infos'][i][0]['model'] # model xml for this episode
ep_data_grp.attrs["num_samples"] = len(all_episodes['actions'][i]) # number of transitions in this episode
total_samples += len(all_episodes['actions'][i])
data_grp.attrs["total"] = total_samples
data_grp.attrs["env_args"] = json.dumps(env_runner.env_meta, indent=4)
data_writer.close()
json_log = dict()
for key, value in runner_log.items():
if 'video' not in key:
json_log[key] = float(value)
json.dump(json_log, open(os.path.join(run_dir, f"collect_{cfg.task_name}.json"), 'w'), indent=2, sort_keys=True)
print(colored(f"Avg. Performance: {np.mean(all_successes):.4f}", "green", attrs=['bold']))
print(colored(f"Dumped to: {run_dir}\n", 'green'))
if cfg.collecting.render_image:
del env_runner
print(f"Rendering video from collected data...")
replay_collected_data(data_collect_file, run_dir, cfg.task.env_runner.render_hw[0], cfg.task.env_runner.render_hw[1])
def replay_collected_data(dataset_path, run_dir, cam_width, cam_height):
env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path=dataset_path)
env = EnvUtils.create_env_for_data_processing(
env_meta=env_meta,
camera_names=['agentview'],
camera_height=cam_height,
camera_width=cam_width,
reward_shaping=True,
)
# Read data from offline dataset
f = h5py.File(dataset_path, "r")
demos = list(f["data"].keys())
inds = np.argsort([int(elem.split("_")[-1]) for elem in demos])
demos = [demos[i] for i in inds]
video_recoder = VideoRecorder.create_h264(
fps=10,
codec='h264',
input_pix_fmt='rgb24',
crf=22,
thread_type='FRAME',
thread_count=1
)
video_path = os.path.join(run_dir, "videos")
os.makedirs(video_path, exist_ok=True)
for ind in tqdm(range(len(demos))):
ep = demos[ind]
# prepare initial state to reload from
states = f["data/{}/states".format(ep)][()]
initial_state = dict(states=states[0])
initial_state["model"] = f["data/{}".format(ep)].attrs["model_file"]
env.reset()
obs = env.reset_to(initial_state)
# Reset video writer
video_recoder.stop()
video_recoder.start(f"{video_path}/episode_{ind}.mp4")
video_recoder.write_frame(obs['agentview_image']) # Write initial state
traj_len = states.shape[0]
assert video_recoder.is_ready()
for t in tqdm(range(1, traj_len), leave=False):
# reset to simulator state to get observation
next_obs = env.reset_to({"states": states[t]})
video_recoder.write_frame(next_obs['agentview_image'])
@hydra.main(
version_base=None,
config_path=str(pathlib.Path(__file__).parent.parent.joinpath("config")),
config_name=pathlib.Path(__file__).stem)
def main(cfg):
workspace = DatacollectDiffusionLowdimWorkspace(cfg)
workspace.run()
if __name__ == "__main__":
main()
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