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utils.py
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utils.py
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import numpy as np
import torch
import os
import h5py
from torch.utils.data import TensorDataset, DataLoader
import IPython
e = IPython.embed
class EpisodicDataset(torch.utils.data.Dataset):
def __init__(self, episode_ids, dataset_dir, camera_names, norm_stats):
super(EpisodicDataset).__init__()
self.episode_ids = episode_ids
self.dataset_dir = dataset_dir
self.camera_names = camera_names
self.norm_stats = norm_stats
self.is_sim = None
self.__getitem__(0) # initialize self.is_sim
def __len__(self):
return len(self.episode_ids)
def __getitem__(self, index):
sample_full_episode = False # hardcode
episode_id = self.episode_ids[index]
dataset_path = os.path.join(self.dataset_dir, f'episode_{episode_id}.hdf5')
with h5py.File(dataset_path, 'r') as root:
is_sim = root.attrs['sim']
original_action_shape = root['/action'].shape
episode_len = original_action_shape[0]
if sample_full_episode:
start_ts = 0
else:
start_ts = np.random.choice(episode_len)
# get observation at start_ts only
qpos = root['/observations/qpos'][start_ts]
qvel = root['/observations/qvel'][start_ts]
image_dict = dict()
for cam_name in self.camera_names:
image_dict[cam_name] = root[f'/observations/images/{cam_name}'][start_ts]
# get all actions after and including start_ts
if is_sim:
action = root['/action'][start_ts:]
action_len = episode_len - start_ts
else:
action = root['/action'][max(0, start_ts - 1):] # hack, to make timesteps more aligned
action_len = episode_len - max(0, start_ts - 1) # hack, to make timesteps more aligned
self.is_sim = is_sim
padded_action = np.zeros(original_action_shape, dtype=np.float32)
padded_action[:action_len] = action
is_pad = np.zeros(episode_len)
is_pad[action_len:] = 1
# new axis for different cameras
all_cam_images = []
for cam_name in self.camera_names:
all_cam_images.append(image_dict[cam_name])
all_cam_images = np.stack(all_cam_images, axis=0)
# construct observations
image_data = torch.from_numpy(all_cam_images)
qpos_data = torch.from_numpy(qpos).float()
action_data = torch.from_numpy(padded_action).float()
is_pad = torch.from_numpy(is_pad).bool()
# channel last
image_data = torch.einsum('k h w c -> k c h w', image_data)
# normalize image and change dtype to float
image_data = image_data / 255.0
action_data = (action_data - self.norm_stats["action_mean"]) / self.norm_stats["action_std"]
qpos_data = (qpos_data - self.norm_stats["qpos_mean"]) / self.norm_stats["qpos_std"]
return image_data, qpos_data, action_data, is_pad
def get_norm_stats(dataset_dir, num_episodes):
all_qpos_data = []
all_action_data = []
for episode_idx in range(num_episodes):
dataset_path = os.path.join(dataset_dir, f'episode_{episode_idx}.hdf5')
with h5py.File(dataset_path, 'r') as root:
qpos = root['/observations/qpos'][()]
qvel = root['/observations/qvel'][()]
action = root['/action'][()]
all_qpos_data.append(torch.from_numpy(qpos))
all_action_data.append(torch.from_numpy(action))
all_qpos_data = torch.stack(all_qpos_data)
all_action_data = torch.stack(all_action_data)
all_action_data = all_action_data
# normalize action data
action_mean = all_action_data.mean(dim=[0, 1], keepdim=True)
action_std = all_action_data.std(dim=[0, 1], keepdim=True)
action_std = torch.clip(action_std, 1e-2, np.inf) # clipping
# normalize qpos data
qpos_mean = all_qpos_data.mean(dim=[0, 1], keepdim=True)
qpos_std = all_qpos_data.std(dim=[0, 1], keepdim=True)
qpos_std = torch.clip(qpos_std, 1e-2, np.inf) # clipping
stats = {"action_mean": action_mean.numpy().squeeze(), "action_std": action_std.numpy().squeeze(),
"qpos_mean": qpos_mean.numpy().squeeze(), "qpos_std": qpos_std.numpy().squeeze(),
"example_qpos": qpos}
return stats
def load_data(dataset_dir, num_episodes, camera_names, batch_size_train, batch_size_val):
print(f'\nData from: {dataset_dir}\n')
# obtain train test split
train_ratio = 0.8
shuffled_indices = np.random.permutation(num_episodes)
train_indices = shuffled_indices[:int(train_ratio * num_episodes)]
val_indices = shuffled_indices[int(train_ratio * num_episodes):]
# obtain normalization stats for qpos and action
norm_stats = get_norm_stats(dataset_dir, num_episodes)
# construct dataset and dataloader
train_dataset = EpisodicDataset(train_indices, dataset_dir, camera_names, norm_stats)
val_dataset = EpisodicDataset(val_indices, dataset_dir, camera_names, norm_stats)
train_dataloader = DataLoader(train_dataset, batch_size=batch_size_train, shuffle=True, pin_memory=True, num_workers=1, prefetch_factor=1)
val_dataloader = DataLoader(val_dataset, batch_size=batch_size_val, shuffle=True, pin_memory=True, num_workers=1, prefetch_factor=1)
return train_dataloader, val_dataloader, norm_stats, train_dataset.is_sim
### env utils
def sample_box_pose():
x_range = [0.0, 0.2]
y_range = [0.4, 0.6]
z_range = [0.05, 0.05]
ranges = np.vstack([x_range, y_range, z_range])
cube_position = np.random.uniform(ranges[:, 0], ranges[:, 1])
cube_quat = np.array([1, 0, 0, 0])
return np.concatenate([cube_position, cube_quat])
def sample_insertion_pose():
# Peg
x_range = [0.1, 0.2]
y_range = [0.4, 0.6]
z_range = [0.05, 0.05]
ranges = np.vstack([x_range, y_range, z_range])
peg_position = np.random.uniform(ranges[:, 0], ranges[:, 1])
peg_quat = np.array([1, 0, 0, 0])
peg_pose = np.concatenate([peg_position, peg_quat])
# Socket
x_range = [-0.2, -0.1]
y_range = [0.4, 0.6]
z_range = [0.05, 0.05]
ranges = np.vstack([x_range, y_range, z_range])
socket_position = np.random.uniform(ranges[:, 0], ranges[:, 1])
socket_quat = np.array([1, 0, 0, 0])
socket_pose = np.concatenate([socket_position, socket_quat])
return peg_pose, socket_pose
### helper functions
def compute_dict_mean(epoch_dicts):
result = {k: None for k in epoch_dicts[0]}
num_items = len(epoch_dicts)
for k in result:
value_sum = 0
for epoch_dict in epoch_dicts:
value_sum += epoch_dict[k]
result[k] = value_sum / num_items
return result
def detach_dict(d):
new_d = dict()
for k, v in d.items():
new_d[k] = v.detach()
return new_d
def set_seed(seed):
torch.manual_seed(seed)
np.random.seed(seed)