File size: 3,584 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 99 100 101 102 103 | 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, downsample_mask)
from diffusion_policy.model.common.normalizer import LinearNormalizer
from diffusion_policy.dataset.base_dataset import BaseImageDataset
from diffusion_policy.common.normalize_util import get_image_range_normalizer
class PushTImageDataset(BaseImageDataset):
def __init__(self,
zarr_path,
horizon=1,
pad_before=0,
pad_after=0,
seed=42,
val_ratio=0.0,
max_train_episodes=None
):
super().__init__()
self.replay_buffer = ReplayBuffer.copy_from_path(
zarr_path, keys=['img', 'state', 'action'])
val_mask = get_val_mask(
n_episodes=self.replay_buffer.n_episodes,
val_ratio=val_ratio,
seed=seed)
train_mask = ~val_mask
train_mask = downsample_mask(
mask=train_mask,
max_n=max_train_episodes,
seed=seed)
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
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 = {
'action': self.replay_buffer['action'],
'agent_pos': self.replay_buffer['state'][...,:2]
}
normalizer = LinearNormalizer()
normalizer.fit(data=data, last_n_dims=1, mode=mode, **kwargs)
normalizer['image'] = get_image_range_normalizer()
return normalizer
def __len__(self) -> int:
return len(self.sampler)
def _sample_to_data(self, sample):
agent_pos = sample['state'][:,:2].astype(np.float32) # (agent_posx2, block_posex3)
image = np.moveaxis(sample['img'],-1,1)/255
data = {
'obs': {
'image': image, # T, 3, 96, 96
'agent_pos': agent_pos, # T, 2
},
'action': sample['action'].astype(np.float32) # T, 2
}
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
def test():
import os
zarr_path = os.path.expanduser('~/dev/diffusion_policy/data/pusht/pusht_cchi_v7_replay.zarr')
dataset = PushTImageDataset(zarr_path, horizon=16)
# from matplotlib import pyplot as plt
# normalizer = dataset.get_normalizer()
# nactions = normalizer['action'].normalize(dataset.replay_buffer['action'])
# diff = np.diff(nactions, axis=0)
# dists = np.linalg.norm(np.diff(nactions, axis=0), axis=-1)
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