File size: 2,998 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 | from typing import Dict
import torch
import numpy as np
import copy
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 ParticleLowdimDataset(BaseLowdimDataset):
def __init__(self,
zarr_path,
horizon=1,
pad_before=0,
pad_after=0,
obs_key='obs',
action_key='action',
use_manual_normalizer=False,
seed=42,
val_ratio=0.0
):
super().__init__()
self.replay_buffer = ReplayBuffer.copy_from_path(
zarr_path, keys=[obs_key, action_key])
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.obs_key = obs_key
self.action_key = action_key
self.use_manual_normalizer = use_manual_normalizer
self.train_mask = train_mask
self.horizon = horizon
self.pad_before = pad_before
self.pad_after = pad_after
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 = self._sample_to_data(self.replay_buffer)
normalizer = LinearNormalizer()
if not self.use_manual_normalizer:
normalizer.fit(data=data, last_n_dims=1, mode=mode, **kwargs)
else:
raise NotImplementedError("Manual normalizer not implemented")
return normalizer
def get_all_actions(self) -> torch.Tensor:
return torch.from_numpy(self.replay_buffer['action'])
def __len__(self) -> int:
return len(self.sampler)
def _sample_to_data(self, sample):
obs = sample[self.obs_key] # T, D_o
data = {
'obs': obs,
'action': sample[self.action_key], # T, D_a
}
return data
def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
sample = self.sampler.sample_sequence(idx)
data = self._sample_to_data(sample)
torch_data = dict_apply(data, torch.from_numpy)
return torch_data
|