from data.robotic_contour_dataloader import get_train_dataloader, get_val_dataloader from utils.options import Options from data.utils.prepare_data import get_split from train_engine_contour import TrainEngine if __name__ == '__main__': opt = Options().opt train_files, test_files = get_split(opt.fold) train_dataloader = get_train_dataloader(train_files, opt) val_dataloader = get_val_dataloader(test_files, opt) engine = TrainEngine(opt) engine.set_data(train_dataloader, val_dataloader) engine.train_model()
num_workers=opt.workers, pin_memory=True) return train_dataloader def get_val_dataloader(file_list, opt): data_transform = Compose([ PadIfNeeded( min_height=opt.val_crop_height, min_width=opt.val_crop_width, p=1), CenterCrop(height=opt.val_crop_height, width=opt.val_crop_width, p=1), Normalize(p=1) ], p=1) val_dataset = RoboticsDataset(file_names=file_list, transform=data_transform, problem_type=opt.problem_type) val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.workers, pin_memory=True) return val_dataloader if __name__ == '__main__': # Test code for dataloader options = Options() train_files, test_files = prepare_data.get_split(0) get_train_dataloader(train_files, options.opt)