Initial import: World-UAV prepro
Add dataloaders (v1/v2), analysis scripts, and documentation for working with UAV-GeoLoc (World-UAV). Co-authored-by: Cursor <cursoragent@cursor.com>
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GeoLoc-UAV-main/eval_simidataset_parser_anyloc_rot.py
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GeoLoc-UAV-main/eval_simidataset_parser_anyloc_rot.py
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from torch.utils.data import DataLoader
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from dataclasses import dataclass,field
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from eval import eval
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import os
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import torch
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from torchvision import transforms as T
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from dataset.World_rot import WorldDatasetEvalVanilia, WorldDatasetEvalGroup
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from models import model
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import glob
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import numpy as np
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import argparse
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from models.anyloc import AnyModel
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def get_parser():
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parser = argparse.ArgumentParser(description="Configuration for training the model")
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# Model Configurations
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parser.add_argument('--mode', type=str, default='dinov2_vitl14', help='Model architecture')
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parser.add_argument('--model', type=str, default='vanilia', help='Path to save model checkpoints')
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# Dataset Paths
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parser.add_argument('--dataset_root', type=str, default='/media/guan/新加卷/EdgeBing/WorldLoc/ya1/', help='Root directory of the dataset')
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parser.add_argument('--test_txt', type=str, default='/media/Shen/Data/RingoData/DenseUAV/test/db.txt', help='Root directory of the dataset')
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parser.add_argument('--save_txt', type=str, default='/media/Shen/Data/RingoData/DenseUAV/test/db.txt', help='Root directory of the dataset')
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#'/media/Shen/Data/RingoData/WorldLoc/TestData/vpair test_40_midref_rot0'
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# Checkpoint Config
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parser.add_argument('--checkpoint_path', type=str, default=None, help='Path to start from a checkpoint')
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# Training Parameters
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parser.add_argument('--num_workers', type=int, default=0 if os.name == 'nt' else 4, help='Number of workers for data loading')
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parser.add_argument('--device', type=str, default='cuda:0' if torch.cuda.is_available() else 'cpu', help='Device for training')
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parser.add_argument('--cudnn_benchmark', type=bool, default=True, help='Use cudnn benchmark for performance')
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parser.add_argument('--cudnn_deterministic', type=bool, default=False, help='Make cudnn deterministic')
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# Training Settings
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parser.add_argument('--mixed_precision', type=bool, default=True, help='Use mixed precision training')
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parser.add_argument('--custom_sampling', type=bool, default=True, help='Use custom sampling')
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parser.add_argument('--seed', type=int, default=1, help='Random seed')
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parser.add_argument('--epochs', type=int, default=30, help='Number of epochs to train')
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parser.add_argument('--batch_size', type=int, default=32, help='Batch size')
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parser.add_argument('--verbose', type=bool, default=True, help='Verbose output during training')
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parser.add_argument('--gpu_ids', type=tuple, default=(1,), help='GPU IDs for training')
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# Optimizer Config
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parser.add_argument('--clip_grad', type=float, default=100.0, help='Clip gradients (None or float)')
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parser.add_argument('--decay_exclude_bias', type=bool, default=False, help='Exclude bias from decay')
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parser.add_argument('--grad_checkpointing', type=bool, default=False, help='Use gradient checkpointing')
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# Loss Config
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parser.add_argument('--label_smoothing', type=float, default=0.1, help='Label smoothing factor')
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# Learning Rate
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parser.add_argument('--lr', type=float, default=0.001, help='Learning rate')
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parser.add_argument('--scheduler', type=str, default='cosine', help='Learning rate scheduler')
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parser.add_argument('--warmup_epochs', type=float, default=0.1, help='Warmup epochs for learning rate')
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parser.add_argument('--lr_end', type=float, default=0.0001, help='End learning rate for polynomial scheduler')
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return parser
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def parse_config():
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parser = get_parser()
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args = parser.parse_args()
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config = {
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"mode": args.mode,
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"model": args.model,
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"dataset_root_dir": args.dataset_root,
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"test_index_txt": args.test_txt,
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"save_txt":args.save_txt,
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"checkpoint_path": args.checkpoint_path,
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"num_workers": args.num_workers,
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"device": args.device,
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"cudnn_benchmark": args.cudnn_benchmark,
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"cudnn_deterministic": args.cudnn_deterministic,
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"mixed_precision": args.mixed_precision,
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"custom_sampling": args.custom_sampling,
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"seed": args.seed,
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"epochs": args.epochs,
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"batch_size": args.batch_size,
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"verbose": args.verbose,
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"gpu_ids": args.gpu_ids,
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"clip_grad": args.clip_grad,
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"decay_exclude_bias": args.decay_exclude_bias,
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"grad_checkpointing": args.grad_checkpointing,
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"label_smoothing": args.label_smoothing,
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"lr": args.lr,
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"scheduler": args.scheduler,
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"warmup_epochs": args.warmup_epochs,
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"lr_end": args.lr_end
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}
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return args, config
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#-------------------------------------------------------------------------------------------#
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# Train Config
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#-------------------------------------------------------------------------------------------#
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args, config = parse_config()
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IMAGENET_MEAN_STD = {'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225]}
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eval_transform = T.Compose([
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T.Resize((224, 224), interpolation=T.InterpolationMode.BILINEAR),
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T.ToTensor(),
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T.Normalize(mean=IMAGENET_MEAN_STD["mean"], std=IMAGENET_MEAN_STD["std"]),
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])
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model = AnyModel(model_name=config['mode'],
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pretrained=True)
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model = model.to(config["device"])
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#------------------------------------------------------------Eval---------------------------------------------------------------------#
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rotation_angles = list(range(0, 360, 5))
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height_rot_list = [f"height100_rot{angle}" for angle in rotation_angles]
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with open(config['save_txt'], 'w') as f_w:
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with open(config["test_index_txt"],"r") as val_test:
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for line in val_test:
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for height_mode in height_rot_list:
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if config["model"] == 'vanilia':
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eva_dataset_query = WorldDatasetEvalVanilia(data_dir=config["dataset_root_dir"],
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name=line.strip('\n'),
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mode='query',
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height_mode=height_mode,
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transforms=eval_transform)
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eval_dataloader_query = DataLoader(eva_dataset_query,
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batch_size=config["batch_size"],
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num_workers=config["num_workers"],
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shuffle=not config["custom_sampling"],
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pin_memory=True)
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eva_dataset_db = WorldDatasetEvalVanilia(data_dir=config["dataset_root_dir"],
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name=line.strip('\n'),
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mode='DB',
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transforms=eval_transform)
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eval_dataloader_db = DataLoader(eva_dataset_db,
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batch_size=config["batch_size"],
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num_workers=config["num_workers"],
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shuffle=not config["custom_sampling"],
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pin_memory=True)
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pos_gt = eval_dataloader_db.dataset.get_gt()
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result, predictions, really_pos_gt = eval.evaluate(config, model, eval_dataloader_query, eval_dataloader_db, pos_gt, mode=config["model"],LPN=False)
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print('top 1: ', round(result[0]*100,2), 'top 5: ', round(result[1]*100,2), 'top 10: ', round(result[2]*100,2)) #vanilia
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f_w.write(str(height_mode) + ' ' + str(round(result[0]*100,2)) + ' ' + str(round(result[1]*100,2)) + '\n')
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