def make_coco_transforms(image_set): normalize = T.Compose([ T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800] if image_set == 'train': return T.Compose([ T.RandomHorizontalFlip(), T.RandomSelect( T.RandomResize(scales, max_size=1333), T.Compose([ T.RandomResize([400, 500, 600]), T.RandomSizeCrop(384, 600), T.RandomResize(scales, max_size=1333), ])), normalize, ]) if image_set == 'val': return T.Compose([ T.RandomResize([800], max_size=1333), normalize, ]) raise ValueError(f'unknown {image_set}')
# add args.device args.device = torch.device('cpu') args.pin_memory = False if not args.disable_cuda and torch.cuda.is_available(): args.device = torch.device('cuda') args.pin_memory = True return args args = cli() print("Loading dataset...") # load train data preprocess = transforms.Compose([ transforms.Normalize(), transforms.RandomApply(transforms.HFlip(), 0.5), transforms.RescaleRelative(), transforms.Crop(args.square_edge), transforms.CenterPad(args.square_edge), ]) class rtpose_lightning(pl.LightningModule): def __init__(self, args, preprocess, target_transforms, model): super(rtpose_lightning, self).__init__() self.args = args self.preprocess = preprocess self.model = model self.target_transforms = target_transforms