178 lines
7.1 KiB
Python
178 lines
7.1 KiB
Python
import numpy as np
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from PytorchBoot.dataset import BaseDataset
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import PytorchBoot.namespace as namespace
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import PytorchBoot.stereotype as stereotype
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from PytorchBoot.config import ConfigManager
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from PytorchBoot.utils.log_util import Log
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import torch
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import os
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import sys
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sys.path.append(r"/data/hofee/project/nbv_rec/nbv_reconstruction")
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from utils.data_load import DataLoadUtil
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from utils.pose import PoseUtil
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from utils.pts import PtsUtil
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@stereotype.dataset("seq_reconstruction_dataset")
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class SeqReconstructionDataset(BaseDataset):
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def __init__(self, config):
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super(SeqReconstructionDataset, self).__init__(config)
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self.config = config
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self.root_dir = config["root_dir"]
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self.split_file_path = config["split_file"]
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self.scene_name_list = self.load_scene_name_list()
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self.datalist = self.get_datalist()
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self.pts_num = config["pts_num"]
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self.type = config["type"]
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self.cache = config.get("cache")
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self.load_from_preprocess = config.get("load_from_preprocess", False)
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if self.type == namespace.Mode.TEST:
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#self.model_dir = config["model_dir"]
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self.filter_degree = config["filter_degree"]
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if self.type == namespace.Mode.TRAIN:
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scale_ratio = 1
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self.datalist = self.datalist*scale_ratio
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if self.cache:
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expr_root = ConfigManager.get("runner", "experiment", "root_dir")
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expr_name = ConfigManager.get("runner", "experiment", "name")
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self.cache_dir = os.path.join(expr_root, expr_name, "cache")
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# self.preprocess_cache()
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def load_scene_name_list(self):
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scene_name_list = []
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with open(self.split_file_path, "r") as f:
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for line in f:
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scene_name = line.strip()
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scene_name_list.append(scene_name)
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return scene_name_list
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def get_datalist(self):
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datalist = []
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for scene_name in self.scene_name_list:
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seq_num = DataLoadUtil.get_label_num(self.root_dir, scene_name)
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scene_max_coverage_rate = 0
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max_coverage_rate_list = []
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scene_max_cr_idx = 0
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for seq_idx in range(seq_num):
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label_path = DataLoadUtil.get_label_path(
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self.root_dir, scene_name, seq_idx
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)
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label_data = DataLoadUtil.load_label(label_path)
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max_coverage_rate = label_data["max_coverage_rate"]
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if max_coverage_rate > scene_max_coverage_rate:
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scene_max_coverage_rate = max_coverage_rate
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scene_max_cr_idx = seq_idx
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max_coverage_rate_list.append(max_coverage_rate)
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best_label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name, scene_max_cr_idx)
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best_label_data = DataLoadUtil.load_label(best_label_path)
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first_frame = best_label_data["best_sequence"][0]
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best_seq_len = len(best_label_data["best_sequence"])
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datalist.append({
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"scene_name": scene_name,
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"first_frame": first_frame,
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"best_seq_len": best_seq_len,
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"max_coverage_rate": scene_max_coverage_rate,
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"label_idx": scene_max_cr_idx,
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})
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return datalist
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def preprocess_cache(self):
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Log.info("preprocessing cache...")
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for item_idx in range(len(self.datalist)):
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self.__getitem__(item_idx)
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Log.success("finish preprocessing cache.")
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def load_from_cache(self, scene_name, curr_frame_idx):
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cache_name = f"{scene_name}_{curr_frame_idx}.txt"
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cache_path = os.path.join(self.cache_dir, cache_name)
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if os.path.exists(cache_path):
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data = np.loadtxt(cache_path)
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return data
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else:
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return None
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def save_to_cache(self, scene_name, curr_frame_idx, data):
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cache_name = f"{scene_name}_{curr_frame_idx}.txt"
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cache_path = os.path.join(self.cache_dir, cache_name)
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try:
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np.savetxt(cache_path, data)
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except Exception as e:
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Log.error(f"Save cache failed: {e}")
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def __getitem__(self, index):
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data_item_info = self.datalist[index]
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max_coverage_rate = data_item_info["max_coverage_rate"]
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scene_name = data_item_info["scene_name"]
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(
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scanned_views_pts,
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scanned_coverages_rate,
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scanned_n_to_world_pose,
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) = ([], [], [])
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view = data_item_info["first_frame"]
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frame_idx = view[0]
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coverage_rate = view[1]
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view_path = DataLoadUtil.get_path(self.root_dir, scene_name, frame_idx)
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cam_info = DataLoadUtil.load_cam_info(view_path, binocular=True)
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n_to_world_pose = cam_info["cam_to_world"]
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target_point_cloud = (
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DataLoadUtil.load_from_preprocessed_pts(view_path)
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)
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downsampled_target_point_cloud = PtsUtil.random_downsample_point_cloud(
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target_point_cloud, self.pts_num
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)
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scanned_views_pts.append(downsampled_target_point_cloud)
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scanned_coverages_rate.append(coverage_rate)
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n_to_world_6d = PoseUtil.matrix_to_rotation_6d_numpy(
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np.asarray(n_to_world_pose[:3, :3])
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)
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n_to_world_trans = n_to_world_pose[:3, 3]
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n_to_world_9d = np.concatenate([n_to_world_6d, n_to_world_trans], axis=0)
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scanned_n_to_world_pose.append(n_to_world_9d)
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# combined_scanned_views_pts = np.concatenate(scanned_views_pts, axis=0)
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# voxel_downsampled_combined_scanned_pts_np = PtsUtil.voxel_downsample_point_cloud(combined_scanned_views_pts, 0.002)
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# random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, self.pts_num)
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data_item = {
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"first_scanned_pts": np.asarray(scanned_views_pts, dtype=np.float32), # Ndarray(S x Nv x 3)
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"first_scanned_coverage_rate": scanned_coverages_rate, # List(S): Float, range(0, 1)
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"first_scanned_n_to_world_pose_9d": np.asarray(scanned_n_to_world_pose, dtype=np.float32), # Ndarray(S x 9)
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"seq_max_coverage_rate": max_coverage_rate, # Float, range(0, 1)
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"scene_name": scene_name, # String
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}
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return data_item
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def __len__(self):
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return len(self.datalist)
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# -------------- Debug ---------------- #
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if __name__ == "__main__":
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import torch
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seed = 0
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torch.manual_seed(seed)
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np.random.seed(seed)
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config = {
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"root_dir": "/data/hofee/data/new_full_data",
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"source": "seq_reconstruction_dataset",
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"split_file": "/data/hofee/data/sample.txt",
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"load_from_preprocess": True,
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"ratio": 0.5,
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"batch_size": 2,
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"filter_degree": 75,
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"num_workers": 0,
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"pts_num": 4096,
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"type": namespace.Mode.TRAIN,
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}
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ds = SeqReconstructionDataset(config)
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print(len(ds))
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print(ds.__getitem__(10))
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