import os import sys import torch import argparse import torch from eval import eval from torchvision import transforms as T import numpy as np import glob from torch.utils.data import DataLoader from dataset.World import DenseUAVDatasetEvalVanilia from dataset.World import AerialDatasetEvalVanilia from models.anyloc import AnyModel def get_parser(): parser = argparse.ArgumentParser(description="Configuration for training the model") # Model Configurations parser.add_argument('--mode', type=str, default='dinov2_vitg14', help='Model architecture') parser.add_argument('--model', type=str, default='vanilia', help='Path to save model checkpoints') # Dataset Paths parser.add_argument('--dataset_query', type=str, default='/media/guan/新加卷/DenseUAV/DenseUAV/test/query.txt', help='Root directory of the dataset') parser.add_argument('--dataset_db', type=str, default='/media/guan/新加卷/DenseUAV/DenseUAV/test/db.txt', help='Root directory of the dataset') parser.add_argument('--dataset_gt', type=str, default='/media/guan/新加卷/DenseUAV/DenseUAV/test/gt.txt', help='Root directory of the dataset') parser.add_argument('--dataset_root_dir', type=str, default='/media/guan/新加卷/EdgeBing/TestData/test_40_midref_rot90/', help='Root directory of the dataset') #'/media/Shen/Data/RingoData/WorldLoc/TestData/vpair test_40_midref_rot0' # Checkpoint Config parser.add_argument('--checkpoint_path', type=str, default="/media/guan/新加卷/Code(1)/Code/vit_base_eva_gta_same_area.pth", help='Path to start from a checkpoint') parser.add_argument('--save_dir_path', type=str, default='/media/Shen/Data/RingoData/DenseUAV/test/db.txt', help='Root directory of the dataset') # Training Parameters parser.add_argument('--num_workers', type=int, default=0 if os.name == 'nt' else 4, help='Number of workers for data loading') parser.add_argument('--device', type=str, default='cuda:0' if torch.cuda.is_available() else 'cpu', help='Device for training') parser.add_argument('--cudnn_benchmark', type=bool, default=True, help='Use cudnn benchmark for performance') parser.add_argument('--cudnn_deterministic', type=bool, default=False, help='Make cudnn deterministic') # Training Settings parser.add_argument('--mixed_precision', type=bool, default=True, help='Use mixed precision training') parser.add_argument('--custom_sampling', type=bool, default=True, help='Use custom sampling') parser.add_argument('--seed', type=int, default=1, help='Random seed') parser.add_argument('--epochs', type=int, default=30, help='Number of epochs to train') parser.add_argument('--batch_size', type=int, default=1, help='Batch size') parser.add_argument('--verbose', type=bool, default=True, help='Verbose output during training') parser.add_argument('--gpu_ids', type=tuple, default=(0,), help='GPU IDs for training') # Optimizer Config parser.add_argument('--clip_grad', type=float, default=100.0, help='Clip gradients (None or float)') parser.add_argument('--decay_exclude_bias', type=bool, default=False, help='Exclude bias from decay') parser.add_argument('--grad_checkpointing', type=bool, default=False, help='Use gradient checkpointing') # Loss Config parser.add_argument('--label_smoothing', type=float, default=0.1, help='Label smoothing factor') # Learning Rate parser.add_argument('--lr', type=float, default=0.001, help='Learning rate') parser.add_argument('--scheduler', type=str, default='cosine', help='Learning rate scheduler') parser.add_argument('--warmup_epochs', type=float, default=0.1, help='Warmup epochs for learning rate') parser.add_argument('--lr_end', type=float, default=0.0001, help='End learning rate for polynomial scheduler') return parser def parse_config(): parser = get_parser() args = parser.parse_args() config = { "mode": args.mode, "model": args.model, # "dataset_query": args.dataset_query, # "dataset_db": args.dataset_db, # "dataset_gt": args.dataset_gt, "dataset_root_dir":args.dataset_root_dir, "checkpoint_path": args.checkpoint_path, "save_dir_path":args.save_dir_path, "num_workers": args.num_workers, "device": args.device, "cudnn_benchmark": args.cudnn_benchmark, "cudnn_deterministic": args.cudnn_deterministic, "mixed_precision": args.mixed_precision, "custom_sampling": args.custom_sampling, "seed": args.seed, "epochs": args.epochs, "batch_size": args.batch_size, "verbose": args.verbose, "gpu_ids": args.gpu_ids, "clip_grad": args.clip_grad, "decay_exclude_bias": args.decay_exclude_bias, "grad_checkpointing": args.grad_checkpointing, "label_smoothing": args.label_smoothing, "lr": args.lr, "scheduler": args.scheduler, "warmup_epochs": args.warmup_epochs, "lr_end": args.lr_end, "LPN":False } return args, config #-------------------------------------------------------------------------------------------# # Train Config #-------------------------------------------------------------------------------------------# args, config = parse_config() IMAGENET_MEAN_STD = {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]} eval_transform = T.Compose([ T.Resize((224, 224), interpolation=T.InterpolationMode.BILINEAR), T.ToTensor(), T.Normalize(mean=IMAGENET_MEAN_STD["mean"], std=IMAGENET_MEAN_STD["std"]), ]) model = AnyModel(model_name=config['mode'], pretrained=True) model = model.to(config["device"]) # eva_dataset_query = DenseUAVDatasetEvalVanilia(txt=config['dataset_query'], # mode='query', # gt_txt=config["dataset_gt"], # transforms=eval_transform) # eval_dataloader_query = DataLoader(eva_dataset_query, # batch_size=config['batch_size'], # num_workers=config['num_workers'], # shuffle=not config['custom_sampling'], # pin_memory=True) # eva_dataset_db = DenseUAVDatasetEvalVanilia(txt=config['dataset_db'], # mode='DB', # gt_txt=config["dataset_gt"], # transforms=eval_transform) # eval_dataloader_db = DataLoader(eva_dataset_db, # batch_size=config['batch_size'], # num_workers=config['num_workers'], # shuffle=not config['custom_sampling'], # pin_memory=True) # pos_gt = eval_dataloader_db.dataset.get_gt() if not os.path.exists(config['save_dir_path']): os.mkdir(config['save_dir_path']) # test angle angle_list = list(range(0, 1)) for angle in angle_list: eva_dataset_query = AerialDatasetEvalVanilia(data_dir=config['dataset_root_dir'], mode='query', angle=angle, transforms=eval_transform) eval_dataloader_query = DataLoader(eva_dataset_query, batch_size=config['batch_size'], num_workers=config['num_workers'], shuffle=not config['custom_sampling'], pin_memory=True) eva_dataset_db = AerialDatasetEvalVanilia(data_dir=config['dataset_root_dir'], mode='DB', transforms=eval_transform) eval_dataloader_db = DataLoader(eva_dataset_db, batch_size=config['batch_size'], num_workers=config['num_workers'], shuffle=not config['custom_sampling'], pin_memory=True) pos_gt = eval_dataloader_db.dataset.get_gt() result, predictions, really_pos_gt = eval.evaluate(config, model, eval_dataloader_query, eval_dataloader_db, pos_gt, mode=config["model"], LPN=config['LPN']) print(config['checkpoint_path']) print('top 1: ', round(result[0]*100,2), 'top 5: ', round(result[1]*100,2), 'top 10: ', round(result[2]*100,2)) #vanilia save_result_txt = config['save_dir_path'] + '/' + str(angle) + '.txt' with open(save_result_txt, 'w') as f_w: info = 'top 1: '+ str(round(result[0]*100,2)) + ' top 5: ' +str(round(result[1]*100,2)) + ' top 10: ' + str(round(result[2]*100,2)) f_w.write(info + '\n') f_w.close() # with open("/media/guan/新加卷/Code/result/anyloc/denseuav_g.txt", "w") as f_w: # for i in range(predictions.shape[0]): # query_path = eval_dataloader_query.dataset.getitem(i) # if np.any(np.in1d(predictions[i,0], really_pos_gt[i][1])): # num = 1 # else: # num = 0 # pred_path = eval_dataloader_db.dataset.samples[predictions[i,0]] # info = query_path + ' ' + pred_path + ' ' + str(num) + '\n' # f_w.write(info)