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import utils
import torch
import numpy as np
from pathlib import Path
from datasets.core import TrajectoryDataset
class SimKitchenTrajectoryDataset(TrajectoryDataset):
def __init__(self, data_directory, prefetch=True, onehot_goals=False):
self.data_directory = Path(data_directory)
states = torch.from_numpy(np.load(self.data_directory / "observations_seq.npy"))
actions = torch.from_numpy(np.load(self.data_directory / "actions_seq.npy"))
goals = torch.load(self.data_directory / "onehot_goals.pth")
# The current values are in shape T x N x Dim, move to N x T x Dim
self.states, self.actions, self.goals = utils.transpose_batch_timestep(
states, actions, goals
)
self.Ts = np.load(self.data_directory / "existence_mask.npy").sum(axis=0).astype(int).tolist()
self.prefetch = prefetch
if self.prefetch:
self.obses = []
for i in range(len(self.Ts)):
self.obses.append(torch.load(self.data_directory / "obses" / f"{i:03d}.pth"))
self.onehot_goals = onehot_goals
def get_seq_length(self, idx):
return self.Ts[idx]
def get_all_actions(self):
result = []
# mask out invalid actions
for i in range(len(self.Ts)):
T = self.Ts[i]
result.append(self.actions[i, :T, :])
return torch.cat(result, dim=0)
def get_frames(self, idx, frames):
# obs, act, mask / obs, act, mask, goal
if self.prefetch:
obs = self.obses[idx][frames]
else:
obs = torch.load(self.data_directory / "obses" / f"{idx:03d}.pth")[frames]
obs = obs / 255.0
act = self.actions[idx, frames]
mask = torch.ones((len(frames)))
if self.onehot_goals:
goal = self.goals[idx, frames]
return obs, act, mask, goal
else:
return obs, act, mask
def __getitem__(self, idx):
T = self.Ts[idx]
return self.get_frames(idx, range(T))
def __len__(self):
return len(self.Ts) |