elif FLAGS.dataset == 'scannet':
sys.path.append(os.path.join(ROOT_DIR, 'scannet'))
from scannet_detection_dataset import ScannetDetectionDataset, MAX_NUM_OBJ
from model_util_scannet import ScannetDatasetConfig
DATASET_CONFIG = ScannetDatasetConfig()
TEST_DATASET = ScannetDetectionDataset('val',
num_points=NUM_POINT,
augment=False,
use_color=FLAGS.use_color,
use_height=(not FLAGS.no_height))
elif FLAGS.dataset == 'mp3d':
sys.path.append(os.path.join(ROOT_DIR, 'mp3d'))
from mp3d_detection_dataset_debug import MP3DDetectionDataset, MAX_NUM_OBJ
from model_util_mp3d import MP3DDatasetConfig
DATASET_CONFIG = MP3DDatasetConfig()
TEST_DATASET = MP3DDetectionDataset(FLAGS.split,
num_points=NUM_POINT,
augment=False,
use_color=FLAGS.use_color,
use_height=(not FLAGS.no_height),
overfit=FLAGS.overfit,
data_type=FLAGS.data_type)
else:
print('Unknown dataset %s. Exiting...' % (FLAGS.dataset))
exit(-1)
print(len(TEST_DATASET))
TEST_DATALOADER = DataLoader(TEST_DATASET,
batch_size=BATCH_SIZE,
shuffle=FLAGS.shuffle_dataset,
"""
import open3d as o3d
import os
import sys
import h5py
import numpy as np
from torch.utils.data import Dataset
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(ROOT_DIR)
sys.path.append(os.path.join(ROOT_DIR, 'utils'))
import pc_util
from model_util_mp3d import rotate_aligned_boxes
from model_util_mp3d import MP3DDatasetConfig
DC = MP3DDatasetConfig()
#TODO
MAX_NUM_OBJ = 64
MAX_NUM_OBJ_FT = 256
MEAN_COLOR_RGB = np.array([109.8, 97.2, 83.8])
# for 800 training samples
#MAX_NUM_OBJ: 28
#MAX_NUM_POINTS: 968435
#MEAN_NUM_POINTS: 193136.24625
#MEAN_RGB: [0.51826599 0.49850078 0.46754714]
class MP3DDetectionDataset(Dataset):
def __init__(self,