import os import h5py import torch import os import numpy as np from PIL import Image,ImageDraw import torch.nn.functional as F from open_clip.transform import ResizeMaxSize,_convert_to_rgb,det_image_transform,ResizeLongest from torchvision.transforms import ToTensor,Normalize import matplotlib.pyplot as plt from torchvision import transforms from pycocotools.coco import COCO from src.segment_anything import sam_model_registry from math import sqrt from vis_sd_featsv2 import plot_pca os.environ["CUDA_VISIBLE_DEVICES"] = "0" def load_data(coco): image_ids=[] img_ids = coco.getImgIds() cat_ids = coco.getCatIds() for img_id in img_ids: img = coco.loadImgs(img_id)[0] ann_ids = coco.getAnnIds(imgIds=img['id'], catIds=cat_ids, iscrowd=None) anns = coco.loadAnns(ann_ids) anns = [ann for ann in anns if ann['iscrowd'] == 0] if len(anns) == 0: continue image_ids.append(img_id) torch.manual_seed(42) image_ids = [image_ids[i] for i in torch.randperm(len(image_ids))] return image_ids def build_SAM(): try: vfm = sam_model_registry['vit_l'](checkpoint='/mnt/SSD8T/home/wjj/code/ProxyCLIP/sam_ckpts/sam_vit_l_0b3195.pth').half() except Exception as e: raise RuntimeError(f"Failed to load SAM model: {e}") return vfm mean=[0.485, 0.456, 0.406] std=[0.229, 0.224, 0.225] normalize = Normalize(mean=mean, std=std) SAM_transform=transforms.Compose([ ResizeLongest(560, fill=0), _convert_to_rgb, ToTensor(), normalize ]) _transform=transforms.Compose([ ResizeLongest(560, fill=0), _convert_to_rgb,]) with torch.no_grad(): device="cuda" coco_path='/mnt/SSD8T/home/wjj/dataset/standard_coco/annotations/instances_train2017.json' img_path='/mnt/SSD8T/home/wjj/dataset/standard_coco/train2017' cache_path = "/mnt/SSD8T/home/wjj/code/distilldift/train/cache/Dift_COCO_dift_merged_fp16.h5" cache=h5py.File(cache_path, 'r')[str(0)] weights = torch.load("/mnt/SSD8T/home/wjj/code/DeCLIP/EVAB_COCO_117K_topk10.pth", map_location="cpu") coco=COCO(coco_path) image_ids=load_data(coco) sam=build_SAM().to(device) image_select=5 img_name = coco.loadImgs(image_ids[image_select])[0]['file_name'] weight = weights[image_select] match_id = weight[np.random.choice(10)] matching_sample = image_ids[match_id] matching_sample_info = coco.imgs[matching_sample] knn_image_name=matching_sample_info['file_name'] knn_image_path = os.path.join(img_path, knn_image_name) knn_image= Image.open(knn_image_path) knn_image_tensor = SAM_transform(knn_image).unsqueeze(0).to(torch.float16).to(device) # ------- 1. 特征提取，注意区分raw和norm ------- # 对KNN图片 knn_sam_feats_raw = sam.image_encoder(knn_image_tensor).flatten(start_dim=-2).transpose(-2, -1).to(torch.float32).to(device) knn_sd_feats_raw = torch.from_numpy(cache[knn_image_name][()]).unsqueeze(0).flatten(start_dim=-2).transpose(-2, -1).to(torch.float32).to(device) knn_sam_feats = F.normalize(knn_sam_feats_raw, dim=2) knn_sd_feats = F.normalize(knn_sd_feats_raw, dim=2) # 对当前图片 image_path = os.path.join(img_path, img_name) image = Image.open(image_path) image_tensor = SAM_transform(image).unsqueeze(0).to(torch.float16).to(device) sd_feats_raw = torch.from_numpy(cache[img_name][()]).unsqueeze(0).flatten(start_dim=-2).transpose(-2, -1).to(torch.float32).to(device) sam_feats_raw = sam.image_encoder(image_tensor).flatten(start_dim=-2).transpose(-2, -1).to(torch.float32).to(device) sd_feats = F.normalize(sd_feats_raw, dim=2) sam_feats = F.normalize(sam_feats_raw, dim=2) w_sam = 0.9 w_sd = 0.1 sam_weighted = (w_sam ** 0.5) * sam_feats sd_weighted = (w_sd ** 0.5) * sd_feats sd_sam_feats_raw = torch.cat([sam_weighted, sd_weighted], dim=2) sd_sam_feats=F.normalize(sd_sam_feats_raw, dim=2) # ------- 2. 相似度热力图 ------- sim_sd = torch.einsum('bic,bjc->bij', sd_feats, sd_feats) # [bs, n_sd, n_sd] sim_sam = torch.einsum('bic,bjc->bij', sam_feats, sam_feats) # [bs, n_sd, n_sd] sim_sam = (sim_sam - torch.mean(sim_sam) * 1.2) * 3.0 sim_sd_sam = torch.einsum('bic,bjc->bij', sd_sam_feats, sd_sam_feats) target_size = (560, 560) low_res_size = (35, 35) low_res_token_choosen = (8, 10) token_chosen = int( low_res_token_choosen[0] * low_res_size[1] + low_res_token_choosen[1] ) token_x_low_res = token_chosen % low_res_size[0] token_y_low_res = token_chosen // low_res_size[0] token_x_img = int(((token_x_low_res+ 0.5) / low_res_size[0]) * target_size[0]) token_y_img = int(((token_y_low_res+ 0.5) / low_res_size[1]) * target_size[1]) output_dir = "sam_vis" if not os.path.exists(output_dir): os.mkdir(output_dir) sim_sd = sim_sd[:, token_chosen, :] # 1, h*w sim_sam = sim_sam[:, token_chosen, :] # 1, h*w sim_sd_sam = sim_sd_sam[:, token_chosen, :] # 1, h*w vis_img1=_transform(image) # 2. sim_sd, sim_dino, sim_sd_dino热力图 sim_maps = [sim_sd, sim_sam, sim_sd_sam] sim_np_maps = [] for sim in sim_maps: sim_map = sim.view(1, 1, low_res_size[0], low_res_size[1]) sim_map_up = F.interpolate(sim_map, size=target_size, mode="bilinear", align_corners=False) sim_map_np = sim_map_up.squeeze().cpu().numpy() sim_np_maps.append(sim_map_np) # 3. 可视化 fig, axes = plt.subplots(1, 4, figsize=(20, 6)) # 第一列：原图 axes[0].imshow(vis_img1) axes[0].scatter([token_x_img], [token_y_img], c='red', s=100, marker="o", edgecolors='black', linewidths=2) axes[0].set_title('Image') axes[0].axis('off') # 后三列：三个相似度热力图 titles = [ 'Token Similarity (SD)', 'Token Similarity (SAM)', 'Token Similarity (SD+SAM)' ] for i in range(3): axes[i+1].imshow(sim_np_maps[i], cmap='jet') axes[i+1].set_title(titles[i]) axes[i+1].axis('off') plt.tight_layout() plt.savefig(os.path.join(output_dir, "token_similarity_vis.png")) plt.close(fig) # ===== PCA 部分 ===== # 用未归一化特征 # 注意：sam_feats, sd_feats, sd_sam_feats 需要未归一化版本 # 你第二段代码里对 sd_feats 和 sam_feats 直接 normalize 了 # 所以需要提前保存一份未归一化的特征 sd_sam_feats_raw = torch.cat([ (w_sam ** 0.5) * sam_feats_raw, (w_sd ** 0.5) * sd_feats_raw ], dim=2) feats_to_pca = [sam_feats_raw, sd_feats_raw, sd_sam_feats_raw] pca_titles = ["SAM PCA", "SD PCA", "SAM+SD PCA"] pca_paths = [] for i, feats in enumerate(feats_to_pca): feats_np = feats[0].cpu().numpy() pca_path = os.path.join(output_dir, f"pca_vis_{i}.png") plot_pca(feats_np, pca_path, target_size) pca_paths.append(pca_path) pca_imgs = [ transforms.Resize(target_size, interpolation=transforms.InterpolationMode.NEAREST)(Image.open(p)) for p in pca_paths ] fig, axes = plt.subplots(1, 4, figsize=(20, 6)) axes[0].imshow(vis_img1) axes[0].set_title('Image') axes[0].axis('off') for i in range(3): axes[i+1].imshow(pca_imgs[i]) axes[i+1].set_title(pca_titles[i]) axes[i+1].axis('off') plt.tight_layout() plt.savefig(os.path.join(output_dir, "pca_vis.png")) plt.close(fig)