Jaywxy
commented
6 months ago 我将semanticstf的val数据改为22序列,用semantickitti的API @查看点云和他的标签,也发现了点云数量和标签输入不一致,你问你最后复现了吗?
python visualize.py --sequence 22 --dataset /mnt/860evo_500/dataset/semantic-kitti/
INTERFACE: Dataset /mnt/860evo_500/dataset/semantic-kitti/ Config config/semantic-kitti.yaml Sequence 22 Predictions None ignore_semantics False do_instances False ignore_safety False offset 0
Opening config file config/semantic-kitti.yaml
Sequence folder exists! Using sequence from /mnt/860evo_500/dataset/semantic-kitti/sequences/22/velodyne
Labels folder exists! Using labels from /mnt/860evo_500/dataset/semantic-kitti/sequences/22/labels
Using semantics in visualizer
Points shape: (139240, 3)
Label shape: (111392,)
Traceback (most recent call last):
File "visualize.py", line 154, in
Hi, I'm raising an issue because I'm having a problem using SemanticSTF.
When I load xyz point data and label data from dataloader to use the validation set of SemanticSTF, I get an error that they have different sample lengths as follows.
File "~/clip3d/dataloader/dataset.py", line 1072, in __getitem__ labels = labels[inds] IndexError: index 124898 is out of bounds for axis 0 with size 111392When I actually print it out, it looks like this: on the left is the xyz point data, and on the right is the label data.
(138785, 4) (111028, 1) (137495, 4) (109996, 1) (138860, 4) (111088, 1) (139240, 4) (111392, 1)I'm currently using a different codebase(2DPASS), so there may be differences, but I don't see anything in
SemanticSTF/PointDR/core/datasets/semantic_stf.py, I don't see any code that specifically addresses this or makes a difference, so I'm wondering if there's something wrong with the dataset you uploaded.code below is the dataset class that load xyz point data and label data from SemanticSTF.
import os import yaml import numpy as np from PIL import Image from torch.utils import data def absoluteFilePaths(directory, num_vote): for dirpath, _, filenames in os.walk(directory): filenames.sort() for f in filenames: for _ in range(num_vote): yield os.path.abspath(os.path.join(dirpath, f)) class SemanticKITTISTF(data.Dataset): def __init__(self, config, data_path, num_vote=1): with open(config['dataset_params']['label_mapping'], 'r') as stream: semkittiyaml = yaml.safe_load(stream) self.config = config # self.corruption = corruption self.imageset = 'val' self.num_vote = num_vote self.learning_map = semkittiyaml['learning_map'] original_data_path = data_path self.im_idx = [] self.im_idx += absoluteFilePaths('/'.join([data_path.replace('sequences', 'SemanticSTF'), 'val', 'velodyne']), num_vote) self.im_idx = sorted(self.im_idx) # added calib_path = os.path.join(original_data_path, '08', "calib.txt") calib = self.read_calib(calib_path) self.proj_matrix = np.matmul(calib["P2"], calib["Tr"]) print('dataloader corruption_dataset init length im_idx', len(self.im_idx)) self.no_image = False # if config['model_params']['model_architecture'] == "baseline" or config['baseline_only'] is True: if config['model_params']['model_architecture'] != "arch_2dpass" or config['baseline_only'] is True: self.no_image = True # print(self.im_idx[0]) # print(self.im_idx[0].replace('velodyne', 'labels')[:-3] + 'label') def __len__(self): 'Denotes the total number of samples' return len(self.im_idx) @staticmethod def read_calib(calib_path): """ :param calib_path: Path to a calibration text file. :return: dict with calibration matrices. """ calib_all = {} with open(calib_path, 'r') as f: for line in f.readlines(): if line == '\n': break key, value = line.split(':', 1) calib_all[key] = np.array([float(x) for x in value.split()]) # reshape matrices calib_out = {} calib_out['P2'] = calib_all['P2'].reshape(3, 4) # 3x4 projection matrix for left camera calib_out['Tr'] = np.identity(4) # 4x4 matrix calib_out['Tr'][:3, :4] = calib_all['Tr'].reshape(3, 4) return calib_out def __getitem__(self, index): # print('SemanticKITTIC getitem') raw_data = np.fromfile(self.im_idx[index], dtype=np.float32).reshape((-1, 4)) origin_len = len(raw_data) raw_data = raw_data[:, :4] points = raw_data[:, :3] annotated_data = np.fromfile(self.im_idx[index].replace('velodyne', 'labels')[:-3] + 'label', dtype=np.uint32).reshape((-1, 1)) instance_label = annotated_data >> 16 annotated_data = annotated_data & 0xFFFF # delete high 16 digits binary ## change invalid (snow, fog, rain ...) to ignore label idx_20 = np.where(annotated_data == 20) annotated_data[idx_20] = 0 print(raw_data.shape, annotated_data.shape) print(np.unique(annotated_data)) if self.config['dataset_params']['ignore_label'] != 0: annotated_data -= 1 annotated_data[annotated_data == -1] = self.config['dataset_params']['ignore_label'] if not self.no_image: # added image_file = self.im_idx[index].replace('velodyne', 'image_2').replace('.bin', '.png') image = Image.open(image_file) data_dict = {} data_dict['xyz'] = points data_dict['labels'] = annotated_data.astype(np.uint8) data_dict['instance_label'] = instance_label data_dict['signal'] = raw_data[:, 3:4] data_dict['origin_len'] = origin_len if not self.no_image: data_dict['img'] = image # added data_dict['proj_matrix'] = self.proj_matrix # added return data_dict, self.im_idx[index]Thank you in advance for your kind response. Sincerly,
这是为命令加了--ignore_semantic de 结果......这是啥图啊
xiaoaoran
Hi, I'm raising an issue because I'm having a problem using SemanticSTF.
When I load xyz point data and label data from dataloader to use the validation set of SemanticSTF, I get an error that they have different sample lengths as follows.
When I actually print it out, it looks like this: on the left is the xyz point data, and on the right is the label data.
I'm currently using a different codebase(2DPASS), so there may be differences, but I don't see anything in
SemanticSTF/PointDR/core/datasets/semantic_stf.py, I don't see any code that specifically addresses this or makes a difference, so I'm wondering if there's something wrong with the dataset you uploaded.code below is the dataset class that load xyz point data and label data from SemanticSTF.
Thank you in advance for your kind response. Sincerly,