Pikaliov
4ff36ce188
Add dataloaders (v1/v2), analysis scripts, and documentation for working with UAV-GeoLoc (World-UAV). Co-authored-by: Cursor <cursoragent@cursor.com>
309 lines
13 KiB
Python
309 lines
13 KiB
Python
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 import AerialDatasetEvalGroup, AerialDatasetEvalVanilia
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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 tqdm import tqdm
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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='group', help='Model architecture')
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parser.add_argument('--model_path', type=str, default='./world', help='Path to save model checkpoints')
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# Group Config
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parser.add_argument('--group_arch', type=str, default='groupdinonet', help='Group architecture')
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parser.add_argument('--group_config', type=str, default='none', help='Group configuration')
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# Backbone Config
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parser.add_argument('--backbone_arch', type=str, default='dinov2_vits14', help='Backbone architecture')
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parser.add_argument('--pretrain_flag', type=bool, default=True, help='Flag to use pre-trained weights')
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# Agg Config
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parser.add_argument('--agg_arch', type=str, default='multiconvap', help='Aggregation architecture')
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parser.add_argument('--agg_in_channels', type=int, default=384, help='Input channels for aggregation')
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parser.add_argument('--agg_out_channels', type=int, default=384, help='Output channels for aggregation')
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parser.add_argument('--agg_s1', type=int, default=1, help='Aggregation s1 parameter')
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parser.add_argument('--agg_s2', type=int, default=1, help='Aggregation s2 parameter')
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parser.add_argument('--agg_LPN', type=bool, default=False, help='Use LPN for aggregation')
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# Dataset Paths
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parser.add_argument('--dataset_root_dir', type=str, default='/media/guan/新加卷/EdgeBing/TestData/test_40_midref_rot0/', 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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parser.add_argument('--save_dir_path', type=str, default='/media/Shen/Data/RingoData/DenseUAV/test/db.txt', help='Root directory of the dataset')
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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' 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=1, 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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# Build the config dictionaries dynamically based on parsed args
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group_config = {
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"group_arch": args.group_arch,
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"group_config": {args.group_config}
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}
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backbone_config = {
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"backbone_arch": args.backbone_arch,
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"pretrain_flag": args.pretrain_flag
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}
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agg_config = {
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"agg_arch": args.agg_arch,
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"agg_config": {
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"in_channels": args.agg_in_channels,
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"out_channels": args.agg_out_channels,
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"s1": args.agg_s1,
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"s2": args.agg_s2,
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"LPN": args.agg_LPN
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}
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}
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config = {
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"mode": args.mode,
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"model_path": args.model_path,
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"group": group_config,
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"backbone": backbone_config,
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"agg": agg_config,
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"dataset_root_dir": args.dataset_root_dir,
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"checkpoint_path": args.checkpoint_path,
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"save_dir_path":args.save_dir_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 config
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#-------------------------------------------------------------------------------------------#
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# Test Config
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#-------------------------------------------------------------------------------------------#
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config = parse_config()
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if not os.path.exists(config['save_dir_path']):
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os.mkdir(config['save_dir_path'])
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# test angle
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angle_list = list(range(0, 1))
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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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if config["mode"] == "vanilia":
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model = model.BackboneGlobal(config['backbone']['backbone_arch'],
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config['backbone']['pretrain_flag'],
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config['agg']['agg_arch'],
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config['agg']['agg_config'])
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else:
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model = model.GrounpDinoGlobal(config['group']['group_arch'],
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config['agg']['agg_arch'],
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config['agg']['agg_config'])
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for angle in tqdm(angle_list):
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if config["mode"] == "vanilia":
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eva_dataset_query = AerialDatasetEvalVanilia(data_dir=config['dataset_root_dir'],
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mode='query',
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angle=angle,
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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 = AerialDatasetEvalVanilia(data_dir=config['dataset_root_dir'],
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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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else:
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eva_dataset_query = AerialDatasetEvalGroup(data_dir=config["dataset_root_dir"],
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mode='query',
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angle=angle,
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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 = AerialDatasetEvalGroup(data_dir=config["dataset_root_dir"],
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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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# model = model.GrounpGlobal(config.group['group_arch'],
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# config.agg['agg_arch'],
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# config.agg['agg_config'])
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model_state_dict = torch.load(config['checkpoint_path'], map_location='cuda:0')
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model.load_state_dict(model_state_dict, strict=False)
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model = model.to(config['device'])
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# pos_gt = eval_dataloader_db.dataset.get_gt_npy()
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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["mode"],LPN=config['agg']['agg_config']['LPN'])
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print(config['checkpoint_path'])
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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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save_result_txt = config['save_dir_path'] + '/' + str(angle) + '.txt'
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with open(save_result_txt, 'w') as f_w:
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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))
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f_w.write(info + '\n')
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f_w.close()
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# vis and save retrieval results
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# save_vis_dir = config.dataset_root_dir + '/' + 'vis' + '/'
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# if not os.path.exists(save_vis_dir):
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# os.makedirs(save_vis_dir)
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# temp_path = os.path.join(config.dataset_root_dir, 'reference_images')
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# DB_path = sorted(glob.glob(f'{temp_path}/{"*.png"}'))
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# # save top 1 flase or wrong
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# with open(config.save_pred_txt, 'w') as f:
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# for i in range(predictions.shape[0]):
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# query_path = eval_dataloader_query.dataset.getitem(i)
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# if np.any(np.in1d(predictions[i,0], really_pos_gt[i][1])):
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# num = 1
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# else:
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# num = 0
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# pred_path = DB_path[predictions[i,0]]
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# info = query_path + ' ' + pred_path + ' ' + str(num) + '\n'
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# f.write(info)
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# for i in range(predictions.shape[0]):
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# query_path = eval_dataloader_query.dataset.getitem(i)
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# fig, axs = plt.subplots(2, 6, figsize=(15, 5))
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# query_img = plt.imread(query_path)
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# for j in range(2):
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# for k in range(6):
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# if j == 0 and k == 0:
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# axs[j, k].imshow(query_img)
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# axs[j, k].axis('off') # 不显示坐标轴
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# elif j==0 and k != 0:
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# if np.any(np.in1d(predictions[i,k], really_pos_gt[i][1] )):
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# db_img_path = DB_path[predictions[i,k]]
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# db_img = plt.imread(db_img_path)
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# axs[j, k].imshow(db_img)
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# # 创建一个矩形框
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# rect = patches.Rectangle((10, 10), 2, 2, linewidth=10, edgecolor='blue', facecolor='none')
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# # 将矩形框添加到图像上，根据图像尺寸调整框的大小
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# rect.set_transform(axs[j, k].transData) # 将框的坐标系设置为数据坐标系
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# axs[j, k].add_patch(rect)
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# axs[j,k].axis('off') # 不显示坐标轴
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# else:
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# db_img_path = DB_path[predictions[i,k]]
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# db_img = plt.imread(db_img_path)
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# axs[j, k].imshow(db_img)
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# # 创建一个矩形框
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# rect = patches.Rectangle((10, 10), 2, 2, linewidth=10, edgecolor='red', facecolor='none')
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# # 将矩形框添加到图像上，根据图像尺寸调整框的大小
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# rect.set_transform(axs[j, k].transData) # 将框的坐标系设置为数据坐标系
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# axs[j, k].add_patch(rect)
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# axs[j, k].axis('off') # 不显示坐标轴
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# if j ==1:
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# try:
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# db_img_path = DB_path[really_pos_gt[i][1][k]]
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# db_img = plt.imread(db_img_path)
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# axs[j, k].imshow(db_img)
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# axs[j, k].axis('off') # 不显示坐标轴
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# except:
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# break
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# save_one_path = save_vis_dir + str(i) + '.png'
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# plt.savefig(save_one_path, dpi=300)
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