# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # -------------------------------------------------------- # References: # NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer # -------------------------------------------------------- # Inherited from dataset v2, seq2seq general version import cv2 import numpy as np import torch import os from PIL import Image from typing import Tuple import yaml import pickle import tqdm from torch.utils.data import Dataset from misc import angle_difference, get_data_path, get_delta_np, normalize_data, to_local_coords from project_functions import reproject_depth_to_other_pose_seq2seq, project_to_2d_image_seq2seq, resize_image_half class BaseDataset(Dataset): def __init__( self, data_folder: str, data_split_folder: str, dataset_name: str, image_size: Tuple[int, int], min_dist_cat: int, max_dist_cat: int, len_traj_pred: int, traj_stride: int, context_size: int, transform: object, traj_names: str, normalize: bool = True, predefined_index: list = None, goals_per_obs: int = 1, ): self.data_folder = data_folder self.data_split_folder = data_split_folder self.dataset_name = dataset_name self.goals_per_obs = goals_per_obs traj_names_file = os.path.join(data_split_folder, traj_names) with open(traj_names_file, "r") as f: file_lines = f.read() self.traj_names = file_lines.split("\n") if "" in self.traj_names: self.traj_names.remove("") self.image_size = image_size self.distance_categories = list(range(min_dist_cat, max_dist_cat + 1)) self.min_dist_cat = self.distance_categories[0] self.max_dist_cat = self.distance_categories[-1] self.len_traj_pred = len_traj_pred self.traj_stride = traj_stride self.context_size = context_size self.normalize = normalize # load data/data_config.yaml with open("config/data_config.yaml", "r") as f: all_data_config = yaml.safe_load(f) dataset_names = list(all_data_config.keys()) dataset_names.sort() # use this index to retrieve the dataset name from the data_config.yaml self.data_config = all_data_config[self.dataset_name] self.transform = transform self._load_index(predefined_index) self.ACTION_STATS = {} for key in all_data_config['action_stats']: self.ACTION_STATS[key] = np.expand_dims(all_data_config['action_stats'][key], axis=0) def _load_index(self, predefined_index) -> None: """ Generates a list of tuples of (obs_traj_name, goal_traj_name, obs_time, goal_time) for each observation in the dataset """ if predefined_index: print(f"****** Using a predefined evaluation index... {predefined_index}******") with open(predefined_index, "rb") as f: self.index_to_data = pickle.load(f) return else: print("****** Evaluating from NON PREDEFINED index... ******") index_to_data_path = os.path.join( self.data_split_folder, f"dataset_dist_{self.min_dist_cat}_to_{self.max_dist_cat}_n{self.context_size}_len_traj_pred_{self.len_traj_pred}.pkl", ) self.index_to_data, self.goals_index = self._build_index() with open(index_to_data_path, "wb") as f: pickle.dump((self.index_to_data, self.goals_index), f) def _build_index(self, use_tqdm: bool = False): """ Build an index consisting of tuples (trajectory name, time, max goal distance) """ samples_index = [] goals_index = [] for traj_name in tqdm.tqdm(self.traj_names, disable=not use_tqdm, dynamic_ncols=True): traj_data = self._get_trajectory(traj_name) traj_len = len(traj_data["position"]) for goal_time in range(0, traj_len): goals_index.append((traj_name, goal_time)) begin_time = self.context_size - 1 end_time = traj_len - self.len_traj_pred for curr_time in range(begin_time, end_time, self.traj_stride): max_goal_distance = min(self.max_dist_cat, traj_len - curr_time - 1) min_goal_distance = max(self.min_dist_cat, -curr_time) samples_index.append((traj_name, curr_time, min_goal_distance, max_goal_distance)) return samples_index, goals_index def _get_trajectory(self, trajectory_name): with open(os.path.join(self.data_folder, trajectory_name, "traj_data.pkl"), "rb") as f: traj_data = pickle.load(f) for k,v in traj_data.items(): traj_data[k] = v.astype('float') return traj_data def __len__(self) -> int: return len(self.index_to_data) # ============ seq2seq ============ def _compute_projected_images(self, traj_data, context_times, rgb_seq, goal_times_np): """ 使用多帧历史 (context_times) 的 depth/rgb/pose;重投影到多个目标位姿 (goal_times_np)。 返回: np.ndarray, 形状 (B, H, W, 3) ;B = len(goal_times_np) """ K = traj_data["K"] # (3,3) depth_seq = traj_data["depth"][context_times] # (T, H, W) poses_src_seq = traj_data["pose"][context_times]# (T, 4, 4) H, W = depth_seq.shape[-2:] poses_dst_seq = traj_data["pose"][goal_times_np] # (B, 4, 4) # 先用 seq2seq 得到每个目标位姿的点云/颜色 points_3d_all, colors_all = reproject_depth_to_other_pose_seq2seq( K=K, depth_maps=depth_seq, # (T,H,W) rgb_imgs=rgb_seq, # (T,H,W,3) poses_src=poses_src_seq, # (T,4,4) poses_dst=poses_dst_seq # (B,4,4) ) # 再做 z-buffer 投成图像 images = project_to_2d_image_seq2seq( K=K, points_3d=points_3d_all, # List[(Ni,3)], 长度 B colors=colors_all, # List[(Ni,3)], 长度 B image_size=(H, W) ) # (B, H, W, 3) return images # ============================================================ def _compute_actions(self, traj_data, curr_time, goal_time): start_index = curr_time end_index = curr_time + self.len_traj_pred + 1 yaw = traj_data["yaw"][start_index:end_index] positions = traj_data["point"][start_index:end_index] goal_pos = traj_data["point"][goal_time] goal_yaw = traj_data["yaw"][goal_time] if len(yaw.shape) == 2: yaw = yaw.squeeze(1) if yaw.shape != (self.len_traj_pred + 1,): raise ValueError("is used?") waypoints_pos = to_local_coords(positions, positions[0], yaw[0]) waypoints_yaw = angle_difference(yaw[0], yaw) actions = np.concatenate([waypoints_pos, waypoints_yaw.reshape(-1, 1)], axis=-1) actions = actions[1:] goal_pos = to_local_coords(goal_pos, positions[0], yaw[0]) goal_yaw = angle_difference(yaw[0], goal_yaw) if self.normalize: actions[:, :3] /= self.data_config["metric_waypoint_spacing"] goal_pos[:, :3] /= self.data_config["metric_waypoint_spacing"] goal_pos = np.concatenate([goal_pos, goal_yaw.reshape(-1, 1)], axis=-1) return actions, goal_pos class TrainingDataset(BaseDataset): def __init__( self, data_folder: str, data_split_folder: str, dataset_name: str, image_size: Tuple[int, int], min_dist_cat: int, max_dist_cat: int, len_traj_pred: int, traj_stride: int, context_size: int, transform: object, traj_names: str = 'traj_names.txt', normalize: bool = True, predefined_index: list = None, goals_per_obs: int = 1, ): super().__init__(data_folder, data_split_folder, dataset_name, image_size, min_dist_cat, max_dist_cat, len_traj_pred, traj_stride, context_size, transform, traj_names, normalize, predefined_index, goals_per_obs) def __getitem__(self, i: int) -> Tuple[torch.Tensor]: try: f_curr, curr_time, min_goal_dist, max_goal_dist = self.index_to_data[i] goal_offset = np.random.randint(min_goal_dist, max_goal_dist + 1, size=(self.goals_per_obs)) goal_time = (curr_time + goal_offset).astype('int') # (B,) rel_time = (goal_offset).astype('float')/(128.) # TODO: tune this const # 历史帧时间序列 context_times = list(range(curr_time - self.context_size + 1, curr_time + 1)) true_context = [(f_curr, t) for t in context_times] goal_context = [(f_curr, t) for t in goal_time] context = true_context + goal_context obs_image = torch.stack([self.transform(Image.open(get_data_path(self.data_folder, f, t))) for f, t in context]) # aug rgb_imgs = [cv2.imread(get_data_path(self.data_folder, f_img, t_img)) for f_img, t_img in true_context] rgb_imgs = [cv2.cvtColor(rgb_img, cv2.COLOR_BGR2RGB) for rgb_img in rgb_imgs] rgb_imgs = np.stack(rgb_imgs, axis=0) # Load other trajectory data curr_traj_data = self._get_trajectory(f_curr) # 计算动作/目标 _, goal_pos = self._compute_actions(curr_traj_data, curr_time, goal_time) goal_pos[:, :3] = normalize_data(goal_pos[:, :3], self.ACTION_STATS) # 使用多历史帧 → 多目标帧重投影（不再传单帧 rgb） projected_images = self._compute_projected_images(curr_traj_data, context_times, rgb_imgs, goal_time) # (B,H,W,3) # 转成张量 projected_tensor_list = [self.transform(Image.fromarray(img)) for img in projected_images] projected_tensor = torch.stack(projected_tensor_list, dim=0) # # ===================== 保存图像 ===================== # vis_root = './visualizations-seq2seq' # sample_dir = os.path.join(vis_root, f'{self.dataset_name}', f'sample_{i}') # os.makedirs(sample_dir, exist_ok=True) # # 1) 历史帧（rgb_imgs: (T,H,W,3)） # T = rgb_imgs.shape[0] # for t_idx in range(T): # img_t = rgb_imgs[t_idx] # if img_t.dtype != np.uint8: # img_t = np.clip(img_t, 0, 255).astype(np.uint8) # Image.fromarray(img_t).save(os.path.join(sample_dir, f'hist_{t_idx:03d}.png')) # # 2) 目标 GT 帧（与 goal_context 对齐） # for j, (_, t_goal) in enumerate(goal_context): # p = get_data_path(self.data_folder, f_curr, int(t_goal)) # gt = Image.open(p).convert("RGB") # gt.save(os.path.join(sample_dir, f'gt_{j:03d}_t{int(t_goal)}.png')) # # 3) 投影结果（projected_images: (B,H,W,3)） # B = projected_images.shape[0] # for j in range(B): # proj = projected_images[j] # if proj.dtype != np.uint8: # proj = np.clip(proj, 0, 255).astype(np.uint8) # Image.fromarray(proj).save(os.path.join(sample_dir, f'proj_{j:03d}.png')) # # ==================================================== return ( torch.as_tensor(obs_image, dtype=torch.float32), torch.as_tensor(goal_pos, dtype=torch.float32), torch.as_tensor(rel_time, dtype=torch.float32), torch.as_tensor(projected_tensor, dtype=torch.float32), ) except Exception as e: print(f"Exception in {self.dataset_name}", e) raise Exception(e) class EvalDataset(BaseDataset): def __init__( self, data_folder: str, data_split_folder: str, dataset_name: str, image_size: Tuple[int, int], min_dist_cat: int, max_dist_cat: int, len_traj_pred: int, traj_stride: int, context_size: int, transform: object, traj_names: str, normalize: bool = True, predefined_index: list = None, goals_per_obs: int = 1, ): super().__init__(data_folder, data_split_folder, dataset_name, image_size, min_dist_cat, max_dist_cat, len_traj_pred, traj_stride, context_size, transform, traj_names, normalize, predefined_index, goals_per_obs) def __getitem__(self, i: int) -> Tuple[torch.Tensor]: try: f_curr, curr_time, _, _ = self.index_to_data[i] context_times = list(range(curr_time - self.context_size + 1, curr_time + 1)) pred_times = list(range(curr_time + 1, curr_time + self.len_traj_pred + 1)) # 未来 B 帧 context = [(f_curr, t) for t in context_times] pred = [(f_curr, t) for t in pred_times] obs_image = torch.stack([self.transform(Image.open(get_data_path(self.data_folder, f, t))) for f, t in context]) pred_image = torch.stack([self.transform(Image.open(get_data_path(self.data_folder, f, t))) for f, t in pred]) curr_traj_data = self._get_trajectory(f_curr) # 动作/delta actions, _ = self._compute_actions(curr_traj_data, curr_time, np.array(pred_times)) actions[:, :3] = normalize_data(actions[:, :3], self.ACTION_STATS) delta = get_delta_np(actions) rgb_imgs = [cv2.imread(get_data_path(self.data_folder, f_img, t_img)) for f_img, t_img in context] rgb_imgs = [cv2.cvtColor(rgb_img, cv2.COLOR_BGR2RGB) for rgb_img in rgb_imgs] rgb_imgs = np.stack(rgb_imgs, axis=0) # 多历史帧 → 多目标帧重投影（B 张投影图） projected_images = self._compute_projected_images(curr_traj_data, context_times, rgb_imgs, np.array(pred_times)) projected_tensor_list = [self.transform(Image.fromarray(img)) for img in projected_images] projected_tensor = torch.stack(projected_tensor_list, dim=0) print(f"Index {i}, projected_images shape: {projected_images.shape}, projected_tensor shape: {projected_tensor.size()}") return ( torch.tensor([i], dtype=torch.float32), # for logging purposes torch.as_tensor(obs_image, dtype=torch.float32), torch.as_tensor(pred_image, dtype=torch.float32), torch.as_tensor(delta, dtype=torch.float32), torch.as_tensor(projected_tensor, dtype=torch.float32), ) except Exception as e: print(f"Exception in {self.dataset_name}", e) raise Exception(e) class TrajectoryEvalDataset(BaseDataset): def __init__( self, data_folder: str, data_split_folder: str, dataset_name: str, image_size: Tuple[int, int], min_dist_cat: int, max_dist_cat: int, len_traj_pred: int, traj_stride: int, context_size: int, transform: object, traj_names: str, normalize: bool = True, predefined_index: list = None, goals_per_obs: int = 1, ): super().__init__(data_folder, data_split_folder, dataset_name, image_size, min_dist_cat, max_dist_cat, len_traj_pred, traj_stride, context_size, transform, traj_names, normalize, predefined_index, goals_per_obs) def _sample_goal(self, trajectory_name, curr_time, min_goal_dist, max_goal_dist): """ Sample a goal from the future in the same trajectory. Returns: (trajectory_name, goal_time, goal_is_negative) """ goal_offset = np.random.randint(min_goal_dist, max_goal_dist + 1) goal_time = curr_time + int(goal_offset) return trajectory_name, goal_time, False def __getitem__(self, i: int) -> Tuple[torch.Tensor]: try: f_curr, curr_time, min_goal_dist, max_goal_dist = self.index_to_data[i] f_goal, goal_time, _ = self._sample_goal(f_curr, curr_time, min_goal_dist, max_goal_dist) context_times = list(range(curr_time - self.context_size + 1, curr_time + 1)) context = [(f_curr, t) for t in context_times] obs_image = torch.stack([self.transform(Image.open(get_data_path(self.data_folder, f, t))) for f, t in context]) goal_image = self.transform(Image.open(get_data_path(self.data_folder, f_goal, goal_time))).unsqueeze(0) curr_traj_data = self._get_trajectory(f_curr) # Compute actions, goal_pos, projected images actions, goal_pos = self._compute_actions(curr_traj_data, curr_time, np.array([goal_time])) rgb_imgs = [cv2.imread(get_data_path(self.data_folder, f_img, t_img)) for f_img, t_img in context] rgb_imgs = [cv2.cvtColor(rgb_img, cv2.COLOR_BGR2RGB) for rgb_img in rgb_imgs] rgb_imgs = np.stack(rgb_imgs, axis=0) projected_images = self._compute_projected_images(curr_traj_data, context_times, rgb_imgs, np.array([goal_time])) projected_tensor_list = [self.transform(Image.fromarray(img)) for img in projected_images] projected_tensor = torch.stack(projected_tensor_list, dim=0) return ( torch.tensor([i], dtype=torch.float32), # for logging purposes torch.as_tensor(obs_image, dtype=torch.float32), torch.as_tensor(goal_image, dtype=torch.float32), torch.as_tensor(actions, dtype=torch.float32), torch.as_tensor(goal_pos, dtype=torch.float32), torch.as_tensor(projected_tensor, dtype=torch.float32), ) except Exception as e: print(f"Exception in {self.dataset_name}", e) raise Exception(e)