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 _train_image_transform(self): transform = transforms.Compose([ transforms.Resize(256), transforms.RandomCrop(224), transforms.RandomHorizontalFlip(), transforms.RandomVerticalFlip(), transforms.RandomRotation(0.2), transforms.ColorJitter(0.1, 0, 0, 0), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) return transform
data_dir = args.data_dir save_dir = args.save_dir num_classes = args.num_classes channel = args.channel clip_min_value = args.clip_min_value clip_max_value = args.clip_max_value mean = args.mean std = args.std num_epochs = args.num_epochs train_batch_size = args.train_batch_size lr = args.lr # 定义训练和验证时的transforms train_transforms = T.Compose([ T.RandomVerticalFlip(0.5), T.RandomHorizontalFlip(0.5), T.ResizeStepScaling(0.5, 2.0, 0.25), T.RandomPaddingCrop(1000), T.Clip(min_val=clip_min_value, max_val=clip_max_value), T.Normalize( min_val=clip_min_value, max_val=clip_max_value, mean=mean, std=std), ]) eval_transforms = T.Compose([ T.Clip(min_val=clip_min_value, max_val=clip_max_value), T.Normalize( min_val=clip_min_value, max_val=clip_max_value, mean=mean, std=std), ]) train_list = osp.join(data_dir, 'train.txt') val_list = osp.join(data_dir, 'val.txt')
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
print("------------Preparing Model...----------------") net = ACCNN(n_classes=n_classes, pre_trained=True, dataset=dataset, fold=fold, virtualize=True, using_fl=fl).to(DEVICE) print("------------%s Model Already be Prepared------------" % net_name) input_img_size = net.input_size IMG_MEAN = [0.449] IMG_STD = [0.226] transform_train = transforms.Compose([ transforms.Resize( input_img_size ), # 缩放将图片的最小边缩放为 input_img_size,因此如果输入是非正方形的,那么输出也不是正方形的 transforms.RandomHorizontalFlip(), transforms.RandomRotation(30), transforms.ToTensor(), transforms.Normalize(IMG_MEAN, IMG_STD), ]) transform_test = transforms.Compose([ transforms.Resize( input_img_size ), # 缩放将图片的最小边缩放为 input_img_size,因此如果输入是非正方形的,那么输出也不是正方形的 transforms.ToTensor(), transforms.Normalize(IMG_MEAN, IMG_STD), ]) print("------------Preparing Data...----------------") if dataset == "JAFFE": test_data = JAFFE(is_train=False, transform=transform_test,
help='learning rate', default=0.01, type=float) return parser.parse_args() args = parse_args() data_dir = args.data_dir save_dir = args.save_dir channel = args.channel num_epochs = args.num_epochs train_batch_size = args.train_batch_size lr = args.lr # 定义训练和验证时的transforms train_transforms = T.Compose([T.RandomHorizontalFlip(0.5), T.Normalize()]) eval_transforms = T.Compose([T.Normalize()]) train_list = osp.join(data_dir, 'train.txt') val_list = osp.join(data_dir, 'val.txt') label_list = osp.join(data_dir, 'labels.txt') # 定义数据读取器 train_reader = Reader(data_dir=data_dir, file_list=train_list, label_list=label_list, transforms=train_transforms, shuffle=True) eval_reader = Reader(data_dir=data_dir,
IMG_MEAN = [0.449] IMG_STD = [0.226] # for RGB images # IMG_MEAN = [0.485, 0.456, 0.406] # IMG_STD = [0.229, 0.224, 0.225] crop_img_size = int(net.input_size * 1.2) input_img_size = net.input_size transform_using_crop = opt.tr_using_crop if transform_using_crop: transform_train = transforms.Compose([ transforms.Resize(crop_img_size), transforms.TenCrop(input_img_size), transforms.Lambda(lambda crops: torch.stack([ transforms.Normalize(IMG_MEAN, IMG_STD) (transforms.ToTensor()(transforms.RandomHorizontalFlip()( transforms.RandomRotation(30)(crop)))) for crop in crops ])), ]) transform_test = transforms.Compose([ transforms.Resize(crop_img_size), transforms.TenCrop(input_img_size), transforms.Lambda(lambda crops: torch.stack([ transforms.Normalize(IMG_MEAN, IMG_STD) (transforms.ToTensor()(crop)) for crop in crops ])), ]) else: transform_train = transforms.Compose([ transforms.Resize( input_img_size ), # 缩放将图片的最小边缩放为 input_img_size,因此如果输入是非正方形的,那么输出也不是正方形的