def get_sample(self):
data_dict = AttrDict()
data_dict.images = np.random.rand(self.spec['max_seq_len'], 3, self.img_sz, self.img_sz).astype(np.float32)
data_dict.states = np.random.rand(self.spec['max_seq_len'], self.spec['state_dim']).astype(np.float32)
data_dict.actions = np.random.rand(self.spec['max_seq_len'] - 1, self.spec['n_actions']).astype(np.float32)
return data_dict
def __getitem__(self, index):
# sample start index in data range
seq = self._sample_seq()
start_idx = np.random.randint(
0, seq.states.shape[0] - self.subseq_len - 1)
output = AttrDict(
states=seq.states[start_idx:start_idx + self.subseq_len],
actions=seq.actions[start_idx:start_idx + self.subseq_len - 1],
pad_mask=np.ones((self.subseq_len, )),
)
if self.remove_goal:
output.states = output.states[..., :int(output.states.shape[-1] /
2)]
return output
