File size: 4,613 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | 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 BlockPushLowdimDataset(BaseLowdimDataset):
def __init__(self,
zarr_path,
horizon=1,
pad_before=0,
pad_after=0,
obs_key='obs',
action_key='action',
obs_eef_target=True,
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.obs_eef_target = obs_eef_target
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:
x = data['obs']
stat = {
'max': np.max(x, axis=0),
'min': np.min(x, axis=0),
'mean': np.mean(x, axis=0),
'std': np.std(x, axis=0)
}
is_x = np.zeros(stat['max'].shape, dtype=bool)
is_y = np.zeros_like(is_x)
is_x[[0,3,6,8,10,13]] = True
is_y[[1,4,7,9,11,14]] = True
is_rot = ~(is_x|is_y)
def normalizer_with_masks(stat, masks):
global_scale = np.ones_like(stat['max'])
global_offset = np.zeros_like(stat['max'])
for mask in masks:
output_max = 1
output_min = -1
input_max = stat['max'][mask].max()
input_min = stat['min'][mask].min()
input_range = input_max - input_min
scale = (output_max - output_min) / input_range
offset = output_min - scale * input_min
global_scale[mask] = scale
global_offset[mask] = offset
return SingleFieldLinearNormalizer.create_manual(
scale=global_scale,
offset=global_offset,
input_stats_dict=stat
)
normalizer['obs'] = normalizer_with_masks(stat, [is_x, is_y, is_rot])
normalizer['action'] = SingleFieldLinearNormalizer.create_fit(
data['action'], 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) -> int:
return len(self.sampler)
def _sample_to_data(self, sample):
obs = sample[self.obs_key] # T, D_o
if not self.obs_eef_target:
obs[:,8:10] = 0
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
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