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/best_20250228.pth"   
    # test_model = "checkpoints/zhanting_res_801.pth"
    # test_model = "checkpoints/zhanting_res_abroad_8021.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()