| from typing import Dict |
| import torch |
| import numpy as np |
| import copy |
| import h5py |
| from tqdm import tqdm |
|
|
| from diffusion_policy.common.pytorch_util import dict_apply |
| from diffusion_policy.common.replay_buffer import ReplayBuffer |
| from diffusion_policy.common.sampler import SequenceSampler, get_val_mask |
| from diffusion_policy.model.common.normalizer import LinearNormalizer, SingleFieldLinearNormalizer |
| from diffusion_policy.dataset.base_dataset import BaseLowdimDataset |
|
|
|
|
| class Hdf5LowdimDataset(BaseLowdimDataset): |
| def __init__( |
| self, |
| dataset_dir=None, |
| horizon=1, |
| pad_before=0, |
| pad_after=0, |
| abs_action=True, |
| seed=42, |
| val_ratio=0.0, |
| dense_reward=False |
| ): |
| super().__init__() |
| if not abs_action: |
| raise NotImplementedError("Not implemented for relative actions") |
|
|
| self.replay_buffer = ReplayBuffer.create_empty_numpy() |
| with h5py.File(dataset_dir, 'r') as f: |
| demos = list(f["data"].keys()) |
| inds = np.argsort([int(elem.split("_")[-1]) for elem in demos]) |
| demos = [demos[i] for i in inds] |
|
|
| for idx in tqdm(range(len(demos)), desc="Loading hdf5 to ReplayBuffer"): |
| ep = demos[idx] |
| demo = f['data'][ep] |
| episode = { |
| 'obs': demo['obs'][:].astype(np.float32), |
| 'action': demo['actions'][:].astype(np.float32), |
| 'reward': demo['rewards'][:].astype(np.float32) if dense_reward else demo['successes'][:].astype(np.float32), |
| } |
| self.replay_buffer.add_episode(episode) |
|
|
| val_mask = get_val_mask( |
| n_episodes=self.replay_buffer.n_episodes, |
| val_ratio=val_ratio, |
| seed=seed) |
| train_mask = ~val_mask |
| self.sampler = SequenceSampler( |
| replay_buffer=self.replay_buffer, |
| sequence_length=horizon, |
| pad_before=pad_before, |
| pad_after=pad_after, |
| episode_mask=train_mask) |
|
|
| self.train_mask = train_mask |
| self.horizon = horizon |
| self.pad_before = pad_before |
| self.pad_after = pad_after |
| self.dataset_path = dataset_dir |
|
|
| 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): |
| data = { |
| 'obs': self.replay_buffer['obs'], |
| 'action': self.replay_buffer['action'], |
| 'reward': self.replay_buffer['reward'], |
| } |
| if 'range_eps' not in kwargs: |
| |
| kwargs['range_eps'] = 5e-2 |
| normalizer = LinearNormalizer() |
| normalizer.fit(data=data, last_n_dims=1, mode=mode, **kwargs) |
| 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]: |
| data = self.sampler.sample_sequence(idx) |
| torch_data = dict_apply(data, torch.from_numpy) |
| return torch_data |
|
|
