from torch.utils.data import DataLoader from dataclasses import dataclass,field from eval import eval import os import torch from torchvision import transforms as T from dataset.World import WorldDatasetEvalVanilia, WorldDatasetEvalGroup from models import model import glob import matplotlib.pyplot as plt import matplotlib.patches as patches import numpy as np def default_group_config(): return { "group_arch" : "groupdinonet", #group "group_config": { "none" } } def default_backbone_config(): return { "backbone_arch" : "resnet18", #dinov2_vitb14,resnet18 "pretrain_flag":True } def default_agg_config(): return { "agg_arch": "multiconvap", #convap "agg_config": { "in_channels": 512, #256 #512，768 "out_channels": 512, #256 "s1": 1, "s2": 1, 'LPN':False } } @dataclass class Configuration: model: str = "resnet18" # Savepath for model checkpoints model_path: str = "./world" # model config group:dict = field(default_factory=default_group_config) backbone:dict = field(default_factory=default_backbone_config) agg:dict = field(default_factory=default_agg_config) # dataset dataset_root_dir: str = "/media/Shen/Data/RingoData/WorldLoc" train_query_txt: str = "/media/Shen/Data/RingoData/WorldLoc/Index/train_query.txt" # val_index val_index_txt = "/media/Shen/Data/RingoData/WorldLoc/Index/val.txt" # test_index test_index_txt = "/media/Shen/Data/RingoData/WorldLoc/Index/test_country.txt" save_pred_txt = "/media/Shen/Data/RingoData/WorldLoc/txt/new_rot/dinos-finetune.txt" # Checkpoint to start from checkpoint_start = None # set num_workers to 0 if on Windows num_workers: int = 0 if os.name == 'nt' else 4 # train on GPU if available device: str = 'cuda' if torch.cuda.is_available() else 'cpu' # for better performance cudnn_benchmark: bool = True # make cudnn deterministic cudnn_deterministic: bool = False # trainning mixed_precision: bool = True custom_sampling: bool = True # use custom sampling instead of random seed = 1 epochs: int = 30 batch_size: int = 10 # keep in mind real_batch_size = 2 * batch_size 128 verbose: bool = True gpu_ids: tuple = (1,) # GPU ids for training # Optimizer clip_grad = 100. # None | float decay_exclue_bias: bool = False grad_checkpointing: bool = False # Gradient Checkpointing # Loss label_smoothing: float = 0.1 # Learning Rate lr: float = 0.001 # 1 * 10^-4 for ViT | 1 * 10^-1 for CNN scheduler: str = "cosine" # "polynomial" | "cosine" | "constant" | None warmup_epochs: int = 0.1 lr_end: float = 0.0001 # only for "polynomial" #-------------------------------------------------------------------------------------------# # Train Config #-------------------------------------------------------------------------------------------# config = Configuration() 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 = model.BackboneGlobal(config.backbone['backbone_arch'], config.backbone['pretrain_flag'], config.agg['agg_arch'], config.agg['agg_config']) # model = model.GrounpGlobal(config.group['group_arch'], # config.agg['agg_arch'], # config.agg['agg_config']) # model = model.GrounpDinoGlobal(config.group['group_arch'], # config.agg['agg_arch'], # config.agg['agg_config']) model_state_dict = torch.load("PTH/city/resnet_frozen/weights_e5_0.6413.pth", map_location='cuda:1') model.load_state_dict(model_state_dict, strict=False) model = model.to(config.device) #------------------------------------------------------------Eval---------------------------------------------------------------------# result_list_recall = [] result_list_precision = [] with open(config.test_index_txt,"r") as val_test: for line in val_test: eva_dataset_query = WorldDatasetEvalVanilia(data_dir=config.dataset_root_dir, name=line.strip('\n'), mode='query', 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 = WorldDatasetEvalVanilia(data_dir=config.dataset_root_dir, name=line.strip('\n'), 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) # eva_dataset_query = WorldDatasetEvalGroup(data_dir=config.dataset_root_dir, # name=line.strip('\n'), # mode='query', # 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 = WorldDatasetEvalGroup(data_dir=config.dataset_root_dir, # name=line.strip('\n'), # 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='vanilia',LPN=config.agg['agg_config']['LPN']) print('top 1: ', round(result[0]*100,2), 'top 5: ', round(result[1]*100,2), 'top 10: ', round(result[2]*100,2)) #vanilia # ap@5 ap_list = [] for i in range(predictions.shape[0]): ex = np.isin(predictions[i, 5:], really_pos_gt[i][1]) num_all = np.sum(ex) / 5 * 100 ap_list.append(num_all) average_ap = np.mean(np.array(ap_list)) result_list_recall.append(result) result_list_precision.append(average_ap) result_array = np.array(result_list_recall) average_result = np.mean(result_array, axis=0) print('Average', 'top 1: ', round(average_result[0]*100,2), 'top 5: ', round(average_result[1]*100,2), 'top 10: ', round(average_result[2]*100,2)) result_precision = np.array(result_list_precision) av_p = np.mean(result_precision) print('AP@5 is', round(av_p,2)) # save top 1 flase or wrong # with open(config.save_pred_txt, 'w') as f: # 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.write(info)