| 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 BaseLowdimDataset |
|
|
| class PushTLowdimDataset(BaseLowdimDataset): |
| def __init__(self, |
| zarr_path, |
| horizon=1, |
| pad_before=0, |
| pad_after=0, |
| obs_key='keypoint', |
| state_key='state', |
| action_key='action', |
| seed=42, |
| val_ratio=0.0, |
| max_train_episodes=None |
| ): |
| super().__init__() |
| self.replay_buffer = ReplayBuffer.copy_from_path( |
| zarr_path, keys=[obs_key, state_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 |
| 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.obs_key = obs_key |
| self.state_key = state_key |
| self.action_key = action_key |
| 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() |
| 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[self.action_key]) |
|
|
| def __len__(self) -> int: |
| return len(self.sampler) |
|
|
| def _sample_to_data(self, sample): |
| keypoint = sample[self.obs_key] |
| state = sample[self.state_key] |
| agent_pos = state[:,:2] |
| obs = np.concatenate([ |
| keypoint.reshape(keypoint.shape[0], -1), |
| agent_pos], axis=-1) |
|
|
| data = { |
| 'obs': obs, |
| 'action': sample[self.action_key], |
| } |
| 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 |
|
|
