190 lines
6.8 KiB
Python
190 lines
6.8 KiB
Python
import numpy as np
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import torch
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import os
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import h5py
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from torch.utils.data import TensorDataset, DataLoader
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import IPython
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e = IPython.embed
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class EpisodicDataset(torch.utils.data.Dataset):
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def __init__(self, episode_ids, dataset_dir, camera_names, norm_stats):
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super(EpisodicDataset).__init__()
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self.episode_ids = episode_ids
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self.dataset_dir = dataset_dir
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self.camera_names = camera_names
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self.norm_stats = norm_stats
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self.is_sim = None
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self.__getitem__(0) # initialize self.is_sim
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def __len__(self):
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return len(self.episode_ids)
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def __getitem__(self, index):
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sample_full_episode = False # hardcode
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episode_id = self.episode_ids[index]
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dataset_path = os.path.join(self.dataset_dir, f'episode_{episode_id}.hdf5')
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with h5py.File(dataset_path, 'r') as root:
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is_sim = root.attrs['sim']
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original_action_shape = root['/action'].shape
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episode_len = original_action_shape[0]
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if sample_full_episode:
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start_ts = 0
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else:
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start_ts = np.random.choice(episode_len)
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# get observation at start_ts only
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qpos = root['/observations/qpos'][start_ts]
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qvel = root['/observations/qvel'][start_ts]
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image_dict = dict()
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for cam_name in self.camera_names:
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image_dict[cam_name] = root[f'/observations/images/{cam_name}'][start_ts]
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# get all actions after and including start_ts
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if is_sim:
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action = root['/action'][start_ts:]
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action_len = episode_len - start_ts
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else:
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action = root['/action'][max(0, start_ts - 1):] # hack, to make timesteps more aligned
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action_len = episode_len - max(0, start_ts - 1) # hack, to make timesteps more aligned
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self.is_sim = is_sim
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padded_action = np.zeros(original_action_shape, dtype=np.float32)
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padded_action[:action_len] = action
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is_pad = np.zeros(episode_len)
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is_pad[action_len:] = 1
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# new axis for different cameras
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all_cam_images = []
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for cam_name in self.camera_names:
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all_cam_images.append(image_dict[cam_name])
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all_cam_images = np.stack(all_cam_images, axis=0)
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# construct observations
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image_data = torch.from_numpy(all_cam_images)
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qpos_data = torch.from_numpy(qpos).float()
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action_data = torch.from_numpy(padded_action).float()
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is_pad = torch.from_numpy(is_pad).bool()
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# channel last
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image_data = torch.einsum('k h w c -> k c h w', image_data)
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# normalize image and change dtype to float
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image_data = image_data / 255.0
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action_data = (action_data - self.norm_stats["action_mean"]) / self.norm_stats["action_std"]
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qpos_data = (qpos_data - self.norm_stats["qpos_mean"]) / self.norm_stats["qpos_std"]
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return image_data, qpos_data, action_data, is_pad
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def get_norm_stats(dataset_dir, num_episodes):
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all_qpos_data = []
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all_action_data = []
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for episode_idx in range(num_episodes):
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dataset_path = os.path.join(dataset_dir, f'episode_{episode_idx}.hdf5')
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with h5py.File(dataset_path, 'r') as root:
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qpos = root['/observations/qpos'][()]
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qvel = root['/observations/qvel'][()]
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action = root['/action'][()]
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all_qpos_data.append(torch.from_numpy(qpos))
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all_action_data.append(torch.from_numpy(action))
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all_qpos_data = torch.stack(all_qpos_data)
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all_action_data = torch.stack(all_action_data)
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all_action_data = all_action_data
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# normalize action data
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action_mean = all_action_data.mean(dim=[0, 1], keepdim=True)
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action_std = all_action_data.std(dim=[0, 1], keepdim=True)
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action_std = torch.clip(action_std, 1e-2, 10) # clipping
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# normalize qpos data
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qpos_mean = all_qpos_data.mean(dim=[0, 1], keepdim=True)
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qpos_std = all_qpos_data.std(dim=[0, 1], keepdim=True)
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qpos_std = torch.clip(qpos_std, 1e-2, 10) # clipping
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stats = {"action_mean": action_mean.numpy().squeeze(), "action_std": action_std.numpy().squeeze(),
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"qpos_mean": qpos_mean.numpy().squeeze(), "qpos_std": qpos_std.numpy().squeeze(),
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"example_qpos": qpos}
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return stats
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def load_data(dataset_dir, num_episodes, camera_names, batch_size_train, batch_size_val):
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print(f'\nData from: {dataset_dir}\n')
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# obtain train test split
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train_ratio = 0.8
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shuffled_indices = np.random.permutation(num_episodes)
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train_indices = shuffled_indices[:int(train_ratio * num_episodes)]
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val_indices = shuffled_indices[int(train_ratio * num_episodes):]
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# obtain normalization stats for qpos and action
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norm_stats = get_norm_stats(dataset_dir, num_episodes)
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# construct dataset and dataloader
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train_dataset = EpisodicDataset(train_indices, dataset_dir, camera_names, norm_stats)
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val_dataset = EpisodicDataset(val_indices, dataset_dir, camera_names, norm_stats)
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train_dataloader = DataLoader(train_dataset, batch_size=batch_size_train, shuffle=True, pin_memory=True, num_workers=1, prefetch_factor=1)
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val_dataloader = DataLoader(val_dataset, batch_size=batch_size_val, shuffle=True, pin_memory=True, num_workers=1, prefetch_factor=1)
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return train_dataloader, val_dataloader, norm_stats, train_dataset.is_sim
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### env utils
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def sample_box_pose():
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x_range = [0.0, 0.2]
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y_range = [0.4, 0.6]
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z_range = [0.05, 0.05]
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ranges = np.vstack([x_range, y_range, z_range])
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cube_position = np.random.uniform(ranges[:, 0], ranges[:, 1])
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cube_quat = np.array([1, 0, 0, 0])
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return np.concatenate([cube_position, cube_quat])
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def sample_insertion_pose():
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# Peg
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x_range = [0.1, 0.2]
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y_range = [0.4, 0.6]
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z_range = [0.05, 0.05]
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ranges = np.vstack([x_range, y_range, z_range])
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peg_position = np.random.uniform(ranges[:, 0], ranges[:, 1])
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peg_quat = np.array([1, 0, 0, 0])
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peg_pose = np.concatenate([peg_position, peg_quat])
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# Socket
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x_range = [-0.2, -0.1]
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y_range = [0.4, 0.6]
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z_range = [0.05, 0.05]
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ranges = np.vstack([x_range, y_range, z_range])
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socket_position = np.random.uniform(ranges[:, 0], ranges[:, 1])
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socket_quat = np.array([1, 0, 0, 0])
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socket_pose = np.concatenate([socket_position, socket_quat])
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return peg_pose, socket_pose
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### helper functions
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def compute_dict_mean(epoch_dicts):
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result = {k: None for k in epoch_dicts[0]}
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num_items = len(epoch_dicts)
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for k in result:
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value_sum = 0
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for epoch_dict in epoch_dicts:
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value_sum += epoch_dict[k]
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result[k] = value_sum / num_items
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return result
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def detach_dict(d):
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new_d = dict()
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for k, v in d.items():
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new_d[k] = v.detach()
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return new_d
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def set_seed(seed):
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torch.manual_seed(seed)
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np.random.seed(seed)
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