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 WorldDatasetEval 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" : "groupnet", #group "group_config": { "none" } } def default_backbone_config(): return { "backbone_arch" : "dinov2_vitb14", } def default_agg_config(): return { "agg_arch": "convap", #convap "agg_config": { "in_channels": 768, #256 #512 "out_channels": 768, #256 "s1": 1, "s2": 1 } } @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/guan/新加卷/EdgeBing/WorldLoc" train_query_txt: str = "/media/guan/新加卷/EdgeBing/WorldLoc/Index/train_query.txt" # val_index val_index_txt = "/media/guan/新加卷/EdgeBing/WorldLoc/Index/val.txt" # test_index test_index_txt = "/media/guan/新加卷/EdgeBing/WorldLoc/Index/test.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 = 28 # keep in mind real_batch_size = 2 * batch_size 128 verbose: bool = True gpu_ids: tuple = (0,1,2,3) # 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.agg['agg_arch'], config.agg['agg_config']) # model = model.GrounpGlobal(config.group['group_arch'], # config.agg['agg_arch'], # config.agg['agg_config']) model_state_dict = torch.load("world/dinov2-base/094102/weights_e18_0.1987.pth") model.load_state_dict(model_state_dict, strict=False) model = model.to(config.device) with open(config.val_index_txt,"r") as val_test: for line in val_test: eva_dataset_query = WorldDatasetEval(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 = WorldDatasetEval(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, ranks=[1, 5, 10], name=line.strip('\n')) print(line.strip('\n'), 'top 1: ', round(result[0]*100,2), 'top 5: ', round(result[1]*100,2), 'top 10: ', round(result[2]*100,2)) # vis and save retrieval results # save_vis_dir = config.dataset_root_dir + '/' + line.strip('\n') + '/' + 'resnet18' + '/' # if not os.path.exists(save_vis_dir): # os.makedirs(save_vis_dir) # temp_path = os.path.join(config.dataset_root_dir, line.strip('\n'), 'DB', 'img') # DB_path = sorted(glob.glob(f'{temp_path}/{"*.png"}')) # for i in range(predictions.shape[0]): # query_path = eval_dataloader_query.dataset.getitem(i) # fig, axs = plt.subplots(2, 6, figsize=(15, 5)) # query_img = plt.imread(query_path) # for j in range(2): # for k in range(6): # if j == 0 and k == 0: # axs[j, k].imshow(query_img) # axs[j, k].axis('off') # 不显示坐标轴 # elif j==0 and k != 0: # if np.any(np.in1d(predictions[i,k], really_pos_gt[i][1] )): # db_img_path = DB_path[predictions[i,k]] # db_img = plt.imread(db_img_path) # axs[j, k].imshow(db_img) # # 创建一个矩形框 # rect = patches.Rectangle((0, 0), 1, 1, linewidth=10, edgecolor='green', facecolor='none') # # 将矩形框添加到图像上，根据图像尺寸调整框的大小 # rect.set_transform(axs[j, k].transData) # 将框的坐标系设置为数据坐标系 # axs[j, k].add_patch(rect) # axs[j,k].axis('off') # 不显示坐标轴 # else: # db_img_path = DB_path[predictions[i,k]] # db_img = plt.imread(db_img_path) # axs[j, k].imshow(db_img) # # 创建一个矩形框 # rect = patches.Rectangle((0, 0), 1, 1, linewidth=10, edgecolor='red', facecolor='none') # # 将矩形框添加到图像上，根据图像尺寸调整框的大小 # rect.set_transform(axs[j, k].transData) # 将框的坐标系设置为数据坐标系 # axs[j, k].add_patch(rect) # axs[j, k].axis('off') # 不显示坐标轴 # if j ==1: # try: # db_img_path = DB_path[really_pos_gt[i][1][k]] # db_img = plt.imread(db_img_path) # axs[j, k].imshow(db_img) # axs[j, k].axis('off') # 不显示坐标轴 # except: # break # save_one_path = save_vis_dir + str(i) + '.png' # plt.savefig(save_one_path, dpi=300)