# import torch
# import torchvision.transforms as T
#
#
# class Config:
#     # network settings
#     backbone = 'resnet18'  # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5, PPLCNET_x2_5]
#     metric = 'arcface'  # [cosface, arcface]
#     cbam = True
#     embedding_size = 256
#     drop_ratio = 0.5
#     img_size = 224
#
#     batch_size = 8
#
#     # data preprocess
#     # input_shape = [1, 128, 128]
#     """transforms.RandomCrop(size),
#         transforms.RandomVerticalFlip(p=0.5),
#         transforms.RandomHorizontalFlip(),
#         RandomRotate(15, 0.3),
#         # RandomGaussianBlur()"""
#
#     train_transform = T.Compose([
#         T.ToTensor(),
#         T.Resize((img_size, img_size)),
#         # T.RandomCrop(img_size),
#         # T.RandomHorizontalFlip(p=0.5),
#         T.RandomRotation(180),
#         T.ColorJitter(brightness=0.5),
#         T.ConvertImageDtype(torch.float32),
#         T.Normalize(mean=[0.5], std=[0.5]),
#     ])
#     test_transform = T.Compose([
#         T.ToTensor(),
#         T.Resize((img_size, img_size)),
#         T.ConvertImageDtype(torch.float32),
#         T.Normalize(mean=[0.5], std=[0.5]),
#     ])
#
#     # dataset
#     train_root = './data/2250_train/train' # 初始筛选过一次的数据集
#     # train_root = './data/0612_train/train'
#     test_root = "./data/2250_train/val/"
#     # test_root = "./data/0612_train/val"
#     test_list = "./data/2250_train/val_pair.txt"
#
#     test_group_json = "./2250_train/cross_same_0508.json"
#
#
#     # test_list = "./data/test_data_100/val_pair.txt"
#
#     # training settings
#     checkpoints = "checkpoints/resnet18_0613/"  # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3]
#     restore = False
#     # restore_model = "checkpoints/renet18_2250_0315/best_resnet18_2250_0315.pth"    # best_resnet18_1491_0306.pth
#     restore_model = "checkpoints/resnet18_0515/best.pth"  # best_resnet18_1491_0306.pth
#
#     # test_model = "checkpoints/renet18_2250_0314/best_resnet18_2250_0314.pth"
#     testbackbone = 'resnet18'  # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5]
#     test_val = "D:/比对/cl"
#     # test_val = "./data/test_data_100"
#
#     # test_model = "checkpoints/zhanting_res_801.pth"
#     test_model = "checkpoints/resnet18_0515/v11.pth"
#
#
#
#     train_batch_size = 512  # 256
#     test_batch_size = 256  # 256
#
#     epoch = 300
#     optimizer = 'sgd'  # ['sgd', 'adam']
#     lr = 1.5e-2  # 1e-2
#     lr_step = 5  # 10
#     lr_decay = 0.95  # 0.98
#     weight_decay = 5e-4
#     loss = 'cross_entropy'  # ['focal_loss', 'cross_entropy']
#     # device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
#     device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
#
#     pin_memory = True  # if memory is large, set it True to speed up a bit
#     num_workers = 4  # dataloader
#
#     group_test = True
#
# config = Config()