def create_dataloader(logger): DATA_PATH = os.path.join('../', 'data') # create dataloader train_set = KittiRCNNDataset(root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TRAIN.SPLIT, mode='TRAIN', logger=logger, classes=cfg.CLASSES, rcnn_training_roi_dir=args.rcnn_training_roi_dir, rcnn_training_feature_dir=args.rcnn_training_feature_dir, gt_database_dir=args.gt_database) train_loader = DataLoader(train_set, batch_size=args.batch_size, pin_memory=True, num_workers=args.workers, shuffle=True, collate_fn=train_set.collate_batch, drop_last=True) if args.train_with_eval: test_set = KittiRCNNDataset(root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TRAIN.VAL_SPLIT, mode='EVAL', logger=logger, classes=cfg.CLASSES, rcnn_eval_roi_dir=args.rcnn_eval_roi_dir, rcnn_eval_feature_dir=args.rcnn_eval_feature_dir) test_loader = DataLoader(test_set, batch_size=1, shuffle=True, pin_memory=True, num_workers=args.workers, collate_fn=test_set.collate_batch) else: test_loader = None return train_loader, test_loader
def create_dataloader(logger): DATA_PATH = os.path.join('../../', 'data') # create dataloader mean_std = ([103.939, 116.779, 123.68], [1.0, 1.0, 1.0]) val_input_transform = standard_transforms.Compose([ extended_transforms.FlipChannels(), standard_transforms.ToTensor(), standard_transforms.Lambda(lambda x: x.mul_(255)), standard_transforms.Normalize(*mean_std) ]) train_set = KittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, image_size=cfg.PSP.IMAGE_SIZE, transform=val_input_transform, split=cfg.TRAIN.SPLIT, mode='TRAIN', logger=logger, classes=cfg.CLASSES, rcnn_training_roi_dir=args.rcnn_training_roi_dir, rcnn_training_feature_dir=args.rcnn_training_feature_dir, gt_database_dir=args.gt_database, bgr_file=args.rpn_bgr, mean_covariance_file=args.rpn_mean_covariance) train_loader = DataLoader(train_set, batch_size=args.batch_size, pin_memory=True, num_workers=args.workers, shuffle=True, collate_fn=train_set.collate_batch, drop_last=True) if args.train_with_eval: test_set = KittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, image_size=cfg.PSP.IMAGE_SIZE, transform=val_input_transform, split=cfg.TRAIN.VAL_SPLIT, mode='EVAL', logger=logger, classes=cfg.CLASSES, rcnn_eval_roi_dir=args.rcnn_eval_roi_dir, rcnn_eval_feature_dir=args.rcnn_eval_feature_dir) test_loader = DataLoader(test_set, batch_size=1, shuffle=True, pin_memory=True, num_workers=args.workers, collate_fn=test_set.collate_batch) else: test_loader = None return train_loader, test_loader
def create_dataloader(logger): # go to semester project folder to access data # data folder contains the data we downloaded from KITTI DATA_PATH = os.path.join('../../', 'data') # create dataloader train_set = KittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TRAIN.SPLIT, mode='TRAIN', logger=logger, classes=cfg.CLASSES, rcnn_training_roi_dir=args.rcnn_training_roi_dir, rcnn_training_feature_dir=args.rcnn_training_feature_dir, gt_database_dir=args.gt_database ) # this is the pickled database we made using generate_gt_database.py # Now that we have a torch_data.Dataset (train_set) we make a dataloader # see here for info : https://pytorch.org/tutorials/beginner/data_loading_tutorial.html train_loader = DataLoader(train_set, batch_size=args.batch_size, pin_memory=True, num_workers=args.workers, shuffle=True, collate_fn=train_set.collate_batch, drop_last=True) if args.train_with_eval: test_set = KittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TRAIN.VAL_SPLIT, mode='EVAL', logger=logger, classes=cfg.CLASSES, rcnn_eval_roi_dir=args.rcnn_eval_roi_dir, rcnn_eval_feature_dir=args.rcnn_eval_feature_dir) test_loader = DataLoader(test_set, batch_size=1, shuffle=True, pin_memory=True, num_workers=args.workers, collate_fn=test_set.collate_batch) else: test_loader = None return train_loader, test_loader
def create_dataloader(logger): mode = 'TEST' if args.test else 'EVAL' #DATA_PATH = os.path.join('..', 'data') # calls from app/frame_handler DATA_PATH = '../../test_dataset/0_drive_0064_sync' # create dataloader test_set = KittiRCNNDataset(root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TEST.SPLIT, mode=mode, random_select=True, rcnn_eval_roi_dir=None, rcnn_eval_feature_dir=None, classes=cfg.CLASSES, logger=logger) test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=8, collate_fn=test_set.collate_batch) return test_loader
def create_dataloader(logger): mode = 'TEST' if args.test else 'EVAL' #DATA_PATH = os.path.join('..', 'data') DATA_PATH = cfg.DATA_PATH # create dataloader test_set = KittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TEST.SPLIT, mode=mode, random_select=args.random_select, rcnn_eval_roi_dir=args.rcnn_eval_roi_dir, rcnn_eval_feature_dir=args.rcnn_eval_feature_dir, classes=cfg.CLASSES, logger=logger) test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=args.workers, collate_fn=test_set.collate_batch) return test_loader
def create_dataloader(logger): mode = 'TEST' if args.test else 'EVAL' if (mode == 'TEST'): DATA_PATH = '/media/jionie/my_disk/Kaggle/Lyft/input/3d-object-detection-for-autonomous-vehicles/test_root' else: DATA_PATH = '/media/jionie/my_disk/Kaggle/Lyft/input/3d-object-detection-for-autonomous-vehicles/train_root' # create dataloader test_set = KittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TEST.SPLIT, mode=mode, random_select=args.random_select, rcnn_eval_roi_dir=args.rcnn_eval_roi_dir, rcnn_eval_feature_dir=args.rcnn_eval_feature_dir, classes=cfg.CLASSES, logger=logger) test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=args.workers, collate_fn=test_set.collate_batch) return test_loader
def create_dataloader(logger): # create dataloader train_set = KittiRCNNDataset(root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TRAIN.SPLIT, mode='TRAIN', logger=logger, classes=cfg.CLASSES, noise=args.noise_kind, weakly_num=args.weakly_num) train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=0, collate_fn=train_set.collate_batch, drop_last=True) test_set = KittiRCNNDataset(root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TRAIN.VAL_SPLIT, mode='EVAL', logger=logger, classes=cfg.CLASSES) test_loader = DataLoader(test_set, batch_size=1, shuffle=False, pin_memory=True, num_workers=0, collate_fn=test_set.collate_batch) return train_loader, test_loader
def create_dataloader_da(logger): mode = 'TEST' if args.test else 'EVAL' DATA_PATH = os.path.join('../', 'data') source_test_set = KittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.DA.SOURCE.VAL_SPLIT, mode=mode, logger=logger, classes=cfg.CLASSES, rcnn_eval_roi_dir=args.rcnn_eval_roi_dir, rcnn_eval_feature_dir=args.rcnn_eval_feature_dir, is_source=True) source_test_loader = DataLoader(source_test_set, batch_size=1, shuffle=True, pin_memory=True, num_workers=args.workers, collate_fn=source_test_set.collate_batch) target_test_set = nuscenes2kittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.DA.TARGET.VAL_SPLIT, mode='EVAL', logger=logger, classes=cfg.CLASSES, rcnn_eval_roi_dir=args.rcnn_eval_roi_dir, rcnn_eval_feature_dir=args.rcnn_eval_feature_dir, is_source=False) target_test_loader = DataLoader(target_test_set, batch_size=1, shuffle=True, pin_memory=True, num_workers=args.workers, collate_fn=target_test_set.collate_batch) return source_test_loader, target_test_loader
def create_dataloader(logger): mode = 'TEST' if args.test else 'EVAL' DATA_PATH = os.path.join('../../', 'data') # create dataloader mean_std = ([103.939, 116.779, 123.68], [1.0, 1.0, 1.0]) val_input_transform = standard_transforms.Compose([ extended_transforms.FlipChannels(), standard_transforms.ToTensor(), standard_transforms.Lambda(lambda x: x.mul_(255)), standard_transforms.Normalize(*mean_std) ]) test_set = KittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, image_size=cfg.PSP.IMAGE_SIZE, transform=val_input_transform, split=cfg.TEST.SPLIT, mode=mode, random_select=args.random_select, rcnn_eval_roi_dir=args.rcnn_eval_roi_dir, rcnn_eval_feature_dir=args.rcnn_eval_feature_dir, classes=cfg.CLASSES, logger=logger, bgr_file=args.rpn_bgr, mean_covariance_file=args.rpn_mean_covariance) test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=args.workers, collate_fn=test_set.collate_batch) return test_loader
def create_dataloader(config, logger): mode = 'TEST' if config['test'] else 'EVAL' DATA_PATH = os.path.join('..', 'data') # create dataloader test_set = KittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TEST.SPLIT, mode=mode, random_select=config['random_select'], rcnn_eval_roi_dir=config['rcnn_eval_roi_dir'], rcnn_eval_feature_dir=config['rcnn_eval_feature_dir'], classes=cfg.CLASSES, logger=logger) test_loader = DataLoader(test_set, batch_size=config['batch_size'], shuffle=False, pin_memory=True, num_workers=config['workers'], collate_fn=test_set.collate_batch) return test_loader
def create_dataloader(logger): mode = 'TEST' if args.test else 'EVAL' DATA_PATH = os.path.join('/raid/meng/Dataset/Kitti/object') if args.eval_all: print('Args eval_all enabled, small_val set will be used') cfg.TEST.SPLIT = 'small_val' # create dataloader test_set = KittiRCNNDataset(root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TEST.SPLIT, mode=mode, random_select=args.random_select, classes=cfg.CLASSES, logger=logger) #,noise='label_noise') test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=args.workers, collate_fn=test_set.collate_batch) return test_loader
logger = create_logger(log_file) """ dataset = KittiRCNNDataset(root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TEST.SPLIT, mode=mode, random_select=False, rcnn_eval_roi_dir=None, rcnn_eval_feature_dir=None, classes=cfg.CLASSES, logger=logger) dataset[0] """ from lib.datasets.kitti_rcnn_dataset import KittiRCNNDataset train_set = KittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.TRAIN.SPLIT, mode='TRAIN', logger=logger, classes=cfg.CLASSES, rcnn_training_roi_dir=None, rcnn_training_feature_dir=None, gt_database_dir='tools/gt_database/train_gt_database_3level_Car.pkl') batch = [train_set[0]] batch_size = 1 ans_dict = {} for key in batch[0].keys(): if cfg.RPN.ENABLED and key == 'gt_boxes3d' or \ (cfg.RCNN.ENABLED and cfg.RCNN.ROI_SAMPLE_JIT and key in ['gt_boxes3d', 'roi_boxes3d']): max_gt = 0 for k in range(batch_size): max_gt = max(max_gt, batch[k][key].__len__())
def create_dataloader(logger): DATA_PATH = os.path.join('../', 'data') # create dataloader source_train_set = KittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.DA.SOURCE.SPLIT, mode='TRAIN', logger=logger, classes=cfg.CLASSES, rcnn_training_roi_dir=args.rcnn_training_roi_dir, rcnn_training_feature_dir=args.rcnn_training_feature_dir, gt_database_dir=args.gt_database, is_source=True) source_train_loader = DataLoader(source_train_set, batch_size=args.batch_size, pin_memory=True, num_workers=args.workers, shuffle=True, collate_fn=source_train_set.collate_batch, drop_last=True) if cfg.DA.ENABLED: target_train_set = nuscenes2kittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.DA.TARGET.SPLIT, mode='TRAIN', logger=logger, classes=cfg.CLASSES, rcnn_training_roi_dir=args.rcnn_training_roi_dir, rcnn_training_feature_dir=args.rcnn_training_feature_dir, gt_database_dir=args.gt_database, is_source=False) target_train_loader = DataLoader( target_train_set, batch_size=args.batch_size, pin_memory=True, num_workers=args.workers, shuffle=True, collate_fn=target_train_set.collate_batch, drop_last=True) if args.train_with_eval: source_test_set = KittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.DA.SOURCE.VAL_SPLIT, mode='EVAL', logger=logger, classes=cfg.CLASSES, rcnn_eval_roi_dir=args.rcnn_eval_roi_dir, rcnn_eval_feature_dir=args.rcnn_eval_feature_dir, is_source=True) source_test_loader = DataLoader( source_test_set, batch_size=1, shuffle=True, pin_memory=True, num_workers=args.workers, collate_fn=source_test_set.collate_batch) if cfg.DA.ENABLED: target_test_set = nuscenes2kittiRCNNDataset( root_dir=DATA_PATH, npoints=cfg.RPN.NUM_POINTS, split=cfg.DA.TARGET.VAL_SPLIT, mode='EVAL', logger=logger, classes=cfg.CLASSES, rcnn_eval_roi_dir=args.rcnn_eval_roi_dir, rcnn_eval_feature_dir=args.rcnn_eval_feature_dir, is_source=False) target_test_loader = DataLoader( target_test_set, batch_size=1, shuffle=True, pin_memory=True, num_workers=args.workers, collate_fn=target_test_set.collate_batch) else: source_test_loader = None target_test_loader = None return source_train_loader, target_train_loader, source_test_loader, target_test_loader