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from typing import Dict, List
import torch
import numpy as np
import h5py
from tqdm import tqdm
import zarr
import os
import shutil
import copy
import json
import hashlib
from filelock import FileLock
from threadpoolctl import threadpool_limits
import concurrent.futures
import multiprocessing
from omegaconf import OmegaConf
from equi_diffpo.common.pytorch_util import dict_apply
from equi_diffpo.dataset.base_dataset import BaseImageDataset, LinearNormalizer
from equi_diffpo.model.common.normalizer import LinearNormalizer, SingleFieldLinearNormalizer
from equi_diffpo.model.common.rotation_transformer import RotationTransformer
from equi_diffpo.codecs.imagecodecs_numcodecs import register_codecs, Jpeg2k
from equi_diffpo.common.replay_buffer import ReplayBuffer
from equi_diffpo.common.sampler import SequenceSampler, get_val_mask
from equi_diffpo.common.normalize_util import (
robomimic_abs_action_only_normalizer_from_stat,
get_range_normalizer_from_stat,
get_voxel_identity_normalizer,
get_image_range_normalizer,
get_identity_normalizer_from_stat,
array_to_stats
)
register_codecs()
class RobomimicReplayPointCloudDataset(BaseImageDataset):
def __init__(self,
shape_meta: dict,
dataset_path: str,
horizon=1,
pad_before=0,
pad_after=0,
n_obs_steps=None,
abs_action=False,
rotation_rep='rotation_6d', # ignored when abs_action=False
use_legacy_normalizer=False,
use_cache=False,
seed=42,
val_ratio=0.0,
n_demo=100,
):
self.n_demo = n_demo
rotation_transformer = RotationTransformer(
from_rep='axis_angle', to_rep=rotation_rep)
replay_buffer = None
if use_cache:
cache_zarr_path = dataset_path + f'.{n_demo}.' + '.zarr.zip'
cache_lock_path = cache_zarr_path + '.lock'
print('Acquiring lock on cache.')
with FileLock(cache_lock_path):
if not os.path.exists(cache_zarr_path):
# cache does not exists
try:
print('Cache does not exist. Creating!')
# store = zarr.DirectoryStore(cache_zarr_path)
replay_buffer = _convert_point_cloud_to_replay(
store=zarr.MemoryStore(),
shape_meta=shape_meta,
dataset_path=dataset_path,
abs_action=abs_action,
rotation_transformer=rotation_transformer,
n_demo=n_demo)
print('Saving cache to disk.')
with zarr.ZipStore(cache_zarr_path) as zip_store:
replay_buffer.save_to_store(
store=zip_store
)
except Exception as e:
shutil.rmtree(cache_zarr_path)
raise e
else:
print('Loading cached ReplayBuffer from Disk.')
with zarr.ZipStore(cache_zarr_path, mode='r') as zip_store:
replay_buffer = ReplayBuffer.copy_from_store(
src_store=zip_store, store=zarr.MemoryStore())
print('Loaded!')
else:
replay_buffer = _convert_point_cloud_to_replay(
store=zarr.MemoryStore(),
shape_meta=shape_meta,
dataset_path=dataset_path,
abs_action=abs_action,
rotation_transformer=rotation_transformer,
n_demo=n_demo)
rgb_keys = list()
pc_keys = list()
lowdim_keys = list()
obs_shape_meta = shape_meta['obs']
for key, attr in obs_shape_meta.items():
type = attr.get('type', 'low_dim')
if type == 'rgb':
rgb_keys.append(key)
if type == 'point_cloud':
pc_keys.append(key)
elif type == 'low_dim':
lowdim_keys.append(key)
# for key in rgb_keys:
# replay_buffer[key].compressor.numthreads=1
key_first_k = dict()
if n_obs_steps is not None:
# only take first k obs from images
for key in rgb_keys + pc_keys + lowdim_keys:
key_first_k[key] = n_obs_steps
val_mask = get_val_mask(
n_episodes=replay_buffer.n_episodes,
val_ratio=val_ratio,
seed=seed)
train_mask = ~val_mask
sampler = SequenceSampler(
replay_buffer=replay_buffer,
sequence_length=horizon,
pad_before=pad_before,
pad_after=pad_after,
episode_mask=train_mask,
key_first_k=key_first_k)
self.replay_buffer = replay_buffer
self.sampler = sampler
self.shape_meta = shape_meta
self.rgb_keys = rgb_keys
self.pc_keys = pc_keys
self.lowdim_keys = lowdim_keys
self.abs_action = abs_action
self.n_obs_steps = n_obs_steps
self.train_mask = train_mask
self.horizon = horizon
self.pad_before = pad_before
self.pad_after = pad_after
self.use_legacy_normalizer = use_legacy_normalizer
def get_validation_dataset(self):
val_set = copy.copy(self)
val_set.sampler = SequenceSampler(
replay_buffer=self.replay_buffer,
sequence_length=self.horizon,
pad_before=self.pad_before,
pad_after=self.pad_after,
episode_mask=~self.train_mask
)
val_set.train_mask = ~self.train_mask
return val_set
def get_normalizer(self, mode='limits', **kwargs) -> LinearNormalizer:
data = {
'action': self.replay_buffer['action'],
'robot0_eef_pos': self.replay_buffer['robot0_eef_pos'][...,:],
'robot0_eef_quat': self.replay_buffer['robot0_eef_quat'][...,:],
'robot0_gripper_qpos': self.replay_buffer['robot0_gripper_qpos'][...,:],
'point_cloud': self.replay_buffer['point_cloud'],
}
normalizer = LinearNormalizer()
normalizer.fit(data=data, last_n_dims=1, mode=mode, **kwargs)
# normalizer['point_cloud'] = SingleFieldLinearNormalizer.create_identity()
return normalizer
def get_all_actions(self) -> torch.Tensor:
return torch.from_numpy(self.replay_buffer['action'])
def __len__(self):
return len(self.sampler)
def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
threadpool_limits(1)
data = self.sampler.sample_sequence(idx)
# to save RAM, only return first n_obs_steps of OBS
# since the rest will be discarded anyway.
# when self.n_obs_steps is None
# this slice does nothing (takes all)
T_slice = slice(self.n_obs_steps)
obs_dict = dict()
for key in self.rgb_keys:
# move channel last to channel first
# T,H,W,C
# convert uint8 image to float32
obs_dict[key] = np.moveaxis(data[key][T_slice],-1,1
).astype(np.float32) / 255.
# T,C,H,W
del data[key]
for key in self.pc_keys:
obs_dict[key] = data[key][T_slice].astype(np.float32)
del data[key]
for key in self.lowdim_keys:
obs_dict[key] = data[key][T_slice].astype(np.float32)
del data[key]
torch_data = {
'obs': dict_apply(obs_dict, torch.from_numpy),
'action': torch.from_numpy(data['action'].astype(np.float32))
}
return torch_data
def _convert_actions(raw_actions, abs_action, rotation_transformer):
actions = raw_actions
if abs_action:
is_dual_arm = False
if raw_actions.shape[-1] == 14:
# dual arm
raw_actions = raw_actions.reshape(-1,2,7)
is_dual_arm = True
pos = raw_actions[...,:3]
rot = raw_actions[...,3:6]
gripper = raw_actions[...,6:]
rot = rotation_transformer.forward(rot)
raw_actions = np.concatenate([
pos, rot, gripper
], axis=-1).astype(np.float32)
if is_dual_arm:
raw_actions = raw_actions.reshape(-1,20)
actions = raw_actions
return actions
def _convert_point_cloud_to_replay(store, shape_meta, dataset_path, abs_action, rotation_transformer,
n_workers=None, max_inflight_tasks=None, n_demo=100):
if n_workers is None:
n_workers = multiprocessing.cpu_count()
if max_inflight_tasks is None:
max_inflight_tasks = n_workers * 5
# parse shape_meta
pc_keys = list()
rgb_keys = list()
lowdim_keys = list()
# construct compressors and chunks
obs_shape_meta = shape_meta['obs']
for key, attr in obs_shape_meta.items():
shape = attr['shape']
type = attr.get('type', 'low_dim')
if type == 'rgb':
rgb_keys.append(key)
elif type == 'point_cloud':
pc_keys.append(key)
elif type == 'low_dim':
lowdim_keys.append(key)
root = zarr.group(store)
data_group = root.require_group('data', overwrite=True)
meta_group = root.require_group('meta', overwrite=True)
with h5py.File(dataset_path) as file:
# count total steps
demos = file['data']
episode_ends = list()
prev_end = 0
n_demo = min(n_demo, len(demos))
for i in range(n_demo):
demo = demos[f'demo_{i}']
episode_length = demo['actions'].shape[0]
episode_end = prev_end + episode_length
prev_end = episode_end
episode_ends.append(episode_end)
n_steps = episode_ends[-1]
episode_starts = [0] + episode_ends[:-1]
_ = meta_group.array('episode_ends', episode_ends,
dtype=np.int64, compressor=None, overwrite=True)
# save lowdim data
for key in tqdm(lowdim_keys + ['action'], desc="Loading lowdim data"):
data_key = 'obs/' + key
if key == 'action':
data_key = 'actions'
this_data = list()
for i in range(n_demo):
demo = demos[f'demo_{i}']
this_data.append(demo[data_key][:].astype(np.float32))
this_data = np.concatenate(this_data, axis=0)
if key == 'action':
this_data = _convert_actions(
raw_actions=this_data,
abs_action=abs_action,
rotation_transformer=rotation_transformer
)
assert this_data.shape == (n_steps,) + tuple(shape_meta['action']['shape'])
else:
assert this_data.shape == (n_steps,) + tuple(shape_meta['obs'][key]['shape'])
_ = data_group.array(
name=key,
data=this_data,
shape=this_data.shape,
chunks=this_data.shape,
compressor=None,
dtype=this_data.dtype
)
def pc_copy(zarr_arr, zarr_idx, hdf5_arr, hdf5_idx):
try:
zarr_arr[zarr_idx] = hdf5_arr[hdf5_idx]
_ = zarr_arr[zarr_idx]
return True
except Exception as e:
return False
def img_copy(zarr_arr, zarr_idx, hdf5_arr, hdf5_idx):
try:
zarr_arr[zarr_idx] = hdf5_arr[hdf5_idx]
# make sure we can successfully decode
_ = zarr_arr[zarr_idx]
return True
except Exception as e:
return False
with tqdm(total=n_steps*len(rgb_keys), desc="Loading image data", mininterval=1.0) as pbar:
# one chunk per thread, therefore no synchronization needed
with concurrent.futures.ThreadPoolExecutor(max_workers=n_workers) as executor:
futures = set()
for key in rgb_keys:
data_key = 'obs/' + key
shape = tuple(shape_meta['obs'][key]['shape'])
c,h,w = shape
this_compressor = Jpeg2k(level=50)
img_arr = data_group.require_dataset(
name=key,
shape=(n_steps,h,w,c),
chunks=(1,h,w,c),
compressor=this_compressor,
dtype=np.uint8
)
for episode_idx in range(n_demo):
demo = demos[f'demo_{episode_idx}']
hdf5_arr = demo['obs'][key]
for hdf5_idx in range(hdf5_arr.shape[0]):
if len(futures) >= max_inflight_tasks:
# limit number of inflight tasks
completed, futures = concurrent.futures.wait(futures,
return_when=concurrent.futures.FIRST_COMPLETED)
for f in completed:
if not f.result():
raise RuntimeError('Failed to encode image!')
pbar.update(len(completed))
zarr_idx = episode_starts[episode_idx] + hdf5_idx
futures.add(
executor.submit(img_copy,
img_arr, zarr_idx, hdf5_arr, hdf5_idx))
completed, futures = concurrent.futures.wait(futures)
for f in completed:
if not f.result():
raise RuntimeError('Failed to encode image!')
pbar.update(len(completed))
with tqdm(total=n_steps*len(pc_keys), desc="Loading point cloud data", mininterval=1.0) as pbar:
# one chunk per thread, therefore no synchronization needed
with concurrent.futures.ThreadPoolExecutor(max_workers=n_workers) as executor:
futures = set()
for key in pc_keys:
data_key = key
shape = tuple(shape_meta['obs'][key]['shape'])
n, c = shape
img_arr = data_group.require_dataset(
name=key,
shape=(n_steps, n, c),
chunks=(1, n, c),
dtype=np.float32
)
for episode_idx in range(n_demo):
demo = demos[f'demo_{episode_idx}']
hdf5_arr = demo['obs'][key]
for hdf5_idx in range(hdf5_arr.shape[0]):
if len(futures) >= max_inflight_tasks:
# limit number of inflight tasks
completed, futures = concurrent.futures.wait(futures,
return_when=concurrent.futures.FIRST_COMPLETED)
for f in completed:
if not f.result():
raise RuntimeError('Failed to encode image!')
pbar.update(len(completed))
zarr_idx = episode_starts[episode_idx] + hdf5_idx
futures.add(
executor.submit(pc_copy,
img_arr, zarr_idx, hdf5_arr, hdf5_idx))
completed, futures = concurrent.futures.wait(futures)
for f in completed:
if not f.result():
raise RuntimeError('Failed to encode image!')
pbar.update(len(completed))
replay_buffer = ReplayBuffer(root)
return replay_buffer
def normalizer_from_stat(stat):
max_abs = np.maximum(stat['max'].max(), np.abs(stat['min']).max())
scale = np.full_like(stat['max'], fill_value=1/max_abs)
offset = np.zeros_like(stat['max'])
return SingleFieldLinearNormalizer.create_manual(
scale=scale,
offset=offset,
input_stats_dict=stat
)
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