code/model_vis_tools/vis_sd_featsv3.py

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)