File size: 3,558 Bytes
987ed1b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | 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:
# to prevent blowing up dims that barely change
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
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