162 lines
5.0 KiB
Python
162 lines
5.0 KiB
Python
import logging
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import os
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import torch
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import pickle
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import numpy as np
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from agent.dataset.d3il_dataset.base_dataset import TrajectoryDataset
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from agent.dataset.d3il_dataset import sim_framework_path
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from .geo_transform import quat2euler
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class Pushing_Dataset(TrajectoryDataset):
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def __init__(
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self,
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data_directory: os.PathLike,
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device="cpu",
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obs_dim: int = 20,
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action_dim: int = 2,
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max_len_data: int = 256,
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window_size: int = 1,
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):
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super().__init__(
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data_directory=data_directory,
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device=device,
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obs_dim=obs_dim,
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action_dim=action_dim,
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max_len_data=max_len_data,
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window_size=window_size,
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)
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logging.info("Loading Block Push Dataset")
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inputs = []
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actions = []
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masks = []
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# for root, dirs, files in os.walk(self.data_directory):
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#
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# for mode_dir in dirs:
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# state_files = glob.glob(os.path.join(root, mode_dir) + "/env*")
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# data_dir = os.path.join(sim_framework_path(data_directory), "local")
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# data_dir = sim_framework_path(data_directory)
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# state_files = glob.glob(data_dir + "/env*")
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bp_data_dir = sim_framework_path("data/pushing/all_data")
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state_files = np.load(sim_framework_path(data_directory), allow_pickle=True)
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for file in state_files:
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with open(os.path.join(bp_data_dir, file), "rb") as f:
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env_state = pickle.load(f)
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# lengths.append(len(env_state['robot']['des_c_pos']))
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zero_obs = np.zeros((1, self.max_len_data, self.obs_dim), dtype=np.float32)
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zero_action = np.zeros(
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(1, self.max_len_data, self.action_dim), dtype=np.float32
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)
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zero_mask = np.zeros((1, self.max_len_data), dtype=np.float32)
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# robot and box positions
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robot_des_pos = env_state["robot"]["des_c_pos"][:, :2]
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robot_c_pos = env_state["robot"]["c_pos"][:, :2]
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red_box_pos = env_state["red-box"]["pos"][:, :2]
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red_box_quat = np.tan(quat2euler(env_state["red-box"]["quat"])[:, -1:])
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green_box_pos = env_state["green-box"]["pos"][:, :2]
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green_box_quat = np.tan(quat2euler(env_state["green-box"]["quat"])[:, -1:])
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red_target_pos = env_state["red-target"]["pos"][:, :2]
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green_target_pos = env_state["green-target"]["pos"][:, :2]
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input_state = np.concatenate(
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(
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robot_des_pos,
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robot_c_pos,
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red_box_pos,
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red_box_quat,
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green_box_pos,
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green_box_quat,
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),
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axis=-1,
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)
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vel_state = robot_des_pos[1:] - robot_des_pos[:-1]
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valid_len = len(input_state) - 1
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zero_obs[0, :valid_len, :] = input_state[:-1]
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zero_action[0, :valid_len, :] = vel_state
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zero_mask[0, :valid_len] = 1
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inputs.append(zero_obs)
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actions.append(zero_action)
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masks.append(zero_mask)
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# shape: B, T, n
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self.observations = torch.from_numpy(np.concatenate(inputs)).to(device).float()
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self.actions = torch.from_numpy(np.concatenate(actions)).to(device).float()
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self.masks = torch.from_numpy(np.concatenate(masks)).to(device).float()
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self.num_data = len(self.observations)
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self.slices = self.get_slices()
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def get_slices(self):
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slices = []
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min_seq_length = np.inf
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for i in range(self.num_data):
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T = self.get_seq_length(i)
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min_seq_length = min(T, min_seq_length)
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if T - self.window_size < 0:
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print(
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f"Ignored short sequence #{i}: len={T}, window={self.window_size}"
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)
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else:
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slices += [
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(i, start, start + self.window_size)
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for start in range(T - self.window_size + 1)
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] # slice indices follow convention [start, end)
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return slices
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def get_seq_length(self, idx):
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return int(self.masks[idx].sum().item())
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def get_all_actions(self):
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result = []
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# mask out invalid actions
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for i in range(len(self.masks)):
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T = int(self.masks[i].sum().item())
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result.append(self.actions[i, :T, :])
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return torch.cat(result, dim=0)
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def get_all_observations(self):
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result = []
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# mask out invalid observations
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for i in range(len(self.masks)):
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T = int(self.masks[i].sum().item())
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result.append(self.observations[i, :T, :])
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return torch.cat(result, dim=0)
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def __len__(self):
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return len(self.slices)
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def __getitem__(self, idx):
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i, start, end = self.slices[idx]
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obs = self.observations[i, start:end]
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act = self.actions[i, start:end]
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mask = self.masks[i, start:end]
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return obs, act, mask
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