update 20240902

contrast/config.py

@@ -0,0 +1,84 @@
+import torch
+import torchvision.transforms as T
+
+
+class Config:
+    # network settings
+    backbone = 'vit'  # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5, PPLCNET_x2_5]
+    metric = 'softmax'  # [cosface, arcface, softmax]
+    cbam = True
+    embedding_size = 256  # 256
+    drop_ratio = 0.5
+    img_size = 224
+
+    teacher = 'vit'  # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5, PPLCNET_x2_5]
+    student = 'resnet'
+    # 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*4//5),
+        # 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/0625_train/train'
+    test_root = "./data/2250_train/val/"
+    # test_root = "./data/0625_train/val"
+
+    test_list = "./data/2250_train/val_pair.txt"
+    test_group_json = "./data/2250_train/cross_same.json"
+    # test_group_json = "./data/0625_train/cross_same.json"
+    # test_list = "./data/test_data_100/val_pair.txt"
+
+    # training settings
+    checkpoints = "checkpoints/vit_b_16_0815/"  # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3]
+    restore = True
+    # restore_model = "checkpoints/renet18_2250_0315/best_resnet18_2250_0315.pth"    # best_resnet18_1491_0306.pth
+    restore_model = "checkpoints/vit_b_16_0730/best.pth"  # best_resnet18_1491_0306.pth
+
+    # test_model = "./checkpoints/renet18_1887_0311/best_resnet18_1887_0311.pth"
+    testbackbone = 'resnet18'  # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5]
+    # test_val = "./data/2250_train"
+    test_val = "./data/0625_train"
+    test_model = "checkpoints/resnet18_0721/best.pth"
+
+    train_batch_size = 128  # 256
+    test_batch_size = 256  # 256
+
+
+    epoch = 300
+    optimizer = 'adamw'  # ['sgd', 'adam'， 'adamw']
+    lr = 1e-3  # 1e-2
+    lr_step = 10  # 10
+    lr_decay = 0.95  # 0.98
+    weight_decay = 5e-4
+    loss = 'focal_loss'  # ['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
+    # group_test = False
+
+config = Config()