def load_data_transformers(resize_reso=512, crop_reso=448, swap_num=[7, 7]): center_resize = 600 Normalize = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) data_transforms = { 'swap': transforms.Compose([ transforms.Randomswap((swap_num[0], swap_num[1])), ]), 'common_aug': transforms.Compose([ transforms.Resize((resize_reso, resize_reso)), transforms.RandomRotation(degrees=15), transforms.RandomCrop((crop_reso,crop_reso)), transforms.RandomHorizontalFlip(), ]), 'train_totensor': transforms.Compose([ transforms.Resize((crop_reso, crop_reso)), # ImageNetPolicy(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]), 'val_totensor': transforms.Compose([ transforms.Resize((crop_reso, crop_reso)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]), 'test_totensor': transforms.Compose([ transforms.Resize((resize_reso, resize_reso)), transforms.CenterCrop((crop_reso, crop_reso)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]), 'None': None, } return data_transforms
def _val_image_transform(self): transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) return transform
def transform(rgb, output_size): transformer = transforms.Compose( [ transforms.Resize((int(IWIDTH * (250.0 / IHEIGHT)), 250)), transforms.CenterCrop((228, 304)), transforms.Resize(output_size), ] ) rgb_np = transformer(rgb) rgb_np = np.asfarray(rgb_np, dtype="float") / 255 return rgb_np
def load_data_transformers(resize_reso=512, crop_reso=448, swap_num=[7, 7]): center_resize = 600 Normalize = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) data_transforms = { 'swap': transforms.Compose([ transforms.Resize((resize_reso, resize_reso)), transforms.RandomRotation(degrees=15), transforms.RandomCrop((crop_reso, crop_reso)), transforms.RandomHorizontalFlip(), transforms.Randomswap((swap_num[0], swap_num[1])), ]), 'food_swap': transforms.Compose([ transforms.Resize((resize_reso, resize_reso)), transforms.RandomRotation(degrees=90), #transforms.RandomCrop((crop_reso, crop_reso)), transforms.RandomHorizontalFlip(), transforms.RandomVerticalFlip(), transforms.RandomResizedCrop(size=crop_reso, scale=(0.75, 1)), transforms.Randomswap((swap_num[0], swap_num[1])), ]), 'food_unswap': transforms.Compose([ transforms.Resize((resize_reso, resize_reso)), transforms.RandomRotation(degrees=90), #transforms.RandomCrop((crop_reso, crop_reso)), transforms.RandomHorizontalFlip(), transforms.RandomVerticalFlip(), transforms.RandomResizedCrop(size=crop_reso, scale=(0.75, 1)), ]), 'unswap': transforms.Compose([ transforms.Resize((resize_reso, resize_reso)), transforms.RandomRotation(degrees=15), transforms.RandomCrop((crop_reso, crop_reso)), transforms.RandomHorizontalFlip(), ]), 'train_totensor': transforms.Compose([ transforms.Resize((crop_reso, crop_reso)), #ImageNetPolicy(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]), 'val_totensor': transforms.Compose([ transforms.Resize((crop_reso, crop_reso)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]), 'test_totensor': transforms.Compose([ transforms.Resize((resize_reso, resize_reso)), transforms.CenterCrop((crop_reso, crop_reso)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]), 'None': None, 'Centered_swap': transforms.Compose([ transforms.CenterCrop((center_resize, center_resize)), transforms.Resize((resize_reso, resize_reso)), transforms.RandomRotation(degrees=15), transforms.RandomCrop((crop_reso, crop_reso)), transforms.RandomHorizontalFlip(), transforms.Randomswap((swap_num[0], swap_num[1])), ]), 'Centered_unswap': transforms.Compose([ transforms.CenterCrop((center_resize, center_resize)), transforms.Resize((resize_reso, resize_reso)), transforms.RandomRotation(degrees=15), transforms.RandomCrop((crop_reso, crop_reso)), transforms.RandomHorizontalFlip(), ]), 'Tencrop': transforms.Compose([ transforms.Resize((resize_reso, resize_reso)), transforms.TenCrop((crop_reso, crop_reso)), transforms.Lambda(lambda crops: torch.stack( [transforms.ToTensor()(crop) for crop in crops])), ]) } return data_transforms
transforms.Compose([ transforms.Resize((512, 512)), transforms.RandomRotation(degrees=15), transforms.RandomCrop((448, 448)), transforms.RandomHorizontalFlip(), ]), 'totensor': transforms.Compose([ transforms.Resize((448, 448)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]), 'None': transforms.Compose([ transforms.Resize((512, 512)), transforms.CenterCrop((448, 448)), ]), } data_set = {} data_set['train'] = dataset(cfg, imgroot=rawdata_root, anno_pd=train_pd, unswap=data_transforms["unswap"], swap=data_transforms["swap"], totensor=data_transforms["totensor"], train=True) data_set['val'] = dataset(cfg, imgroot=rawdata_root, anno_pd=test_pd, unswap=data_transforms["None"], swap=data_transforms["None"],