更新 detacttracking

detecttracking/contrast/feat_extract/config.py

@@ -0,0 +1,87 @@
+# 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()