def create_dataloader(opt, path, imgsz, stride, hyp=None, pad=0.0, prefix='',
void_classes=[], valid_classes=[], cache=False, rect=False, rank=-1):
cfg = get_cfg_defaults()
cfg.merge_from_file('../dataloaders/configs/sunrgbd.yaml')
cfg.merge_from_list(['DATASET.ANNOTATION_TYPE', 'bbox',
'DATASET.NO_TRANSFORMS', True,
'TRAIN.BATCH_SIZE', 1])
train_loader, val_loader, test_loader, num_class = make_data_loader(cfg)
dataloader = train_loader
return dataloader, dataloader.dataset
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import argparse
parser = argparse.ArgumentParser(description="Test sbd Loader")
parser.add_argument('config_file', help='config file path')
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
cfg = get_cfg_defaults()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
print(cfg)
sbd_train = SBDSegmentation(cfg, split='train')
dataloader = DataLoader(sbd_train, batch_size=2, shuffle=True, num_workers=2)
for ii, sample in enumerate(dataloader):
for jj in range(sample["image"].size()[0]):
img = sample['image'].numpy()
gt = sample['label'].numpy()
tmp = np.array(gt[jj]).astype(np.uint8)
segmap = decode_segmap(tmp, dataset='pascal')
img_tmp = np.transpose(img[jj], axes=[1, 2, 0])