Add files using upload-large-folder tool

analysis/model_vis_tools/vis_sd_featsv4.py

@@ -0,0 +1,207 @@
+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 build_DINOv2, plot_pca
+
+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(1120, fill=0),
+                _convert_to_rgb,
+                ToTensor(),
+                normalize
+                ])
+DINO_transform=transforms.Compose([
+                ResizeLongest(490, 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)
+    dino=build_DINOv2().to(device)
+    image_select=100
+    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_dino_feats_raw = dino.get_intermediate_layers(knn_image_tensor, reshape=True)[0].flatten(start_dim=-2).transpose(-2,-1) # 未归一化
+    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_dino_feats = F.normalize(knn_dino_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)
+    DINO_tensor=DINO_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)
+    
+    dino_feats_raw = dino.get_intermediate_layers(DINO_tensor, reshape=True)[0]
+   
+    _size = dino_feats_raw.shape[-2:]
+    sam_feats_raw = sam.image_encoder(image_tensor)
+    sam_feats_raw=F.interpolate(sam_feats_raw, size=_size, mode='bilinear', align_corners=False).flatten(start_dim=-2).transpose(-2,-1) # 未归一化
+    dino_feats_raw = dino_feats_raw.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)
+    dino_feats = F.normalize(dino_feats_raw, dim=2)
+
+    # w_sam = 0.3
+    # w_dino = 0.7
+    # sam_weighted = (w_sam ** 0.5) * sam_feats
+    # dino_weighted = (w_dino ** 0.5) * dino_feats
+    dino_sam_feats_raw = torch.cat([dino_feats, sam_feats], dim=2)
+    dino_sam_feats=F.normalize(dino_sam_feats_raw, dim=2)
+
+    # ------- 2. 相似度热力图 -------
+    sim_dino = torch.einsum('bic,bjc->bij', dino_feats, dino_feats)        # [bs, n_sd, n_sd]
+    sim_sam = torch.einsum('bic,bjc->bij', sam_feats, sam_feats)        # [bs, n_sd, n_sd]
+    sim_dino_sam = torch.einsum('bic,bjc->bij', dino_sam_feats, dino_sam_feats)
+
+    target_size = (560, 560)
+    low_res_size = (35, 35)
+    low_res_token_choosen = (7, 17)
+    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_dino = sim_dino[:, token_chosen, :]  # 1, h*w
+    sim_sam = sim_sam[:, token_chosen, :]  # 1, h*w
+    sim_dino_sam = sim_dino_sam[:, token_chosen, :]  # 1, h*w
+
+
+    vis_img1=_transform(image)
+
+    # 2. sim_sd, sim_dino, sim_sd_dino热力图
+    sim_maps = [sim_dino, sim_sam, sim_dino_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 (DINOv2)',
+        'Token Similarity (SAM)',
+        'Token Similarity (DINOv2+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
+   
+  
+    feats_to_pca = [sam_feats_raw, dino_feats_raw, dino_sam_feats_raw]
+    pca_titles = ["SAM PCA", "DINOv2 PCA", "DINOv2+SAM 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)

analysis/model_vis_tools/vis_sd_featsv5.1.py

@@ -0,0 +1,150 @@
+
+from diffusion_model.stable_diffusion import diffusion
+import torch
+import numpy as np
+import os
+from PIL import Image
+from pycocotools.coco import COCO
+from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
+    CenterCrop
+from open_clip.transform import ResizeLongest, _convert_to_rgb
+from torchvision import transforms
+import matplotlib.pyplot as plt
+import cv2
+from functools import reduce
+import torch.nn.functional as F
+from open_clip.factory import create_model
+
+# ! 可视化DINO和openai CLIP的余弦相似度
+def build_DINOv2():
+    model_name='dinov2_vitb14_reg'
+    hub_path = '/mnt/SSD8T/home/wjj/.cache/torch/hub/facebookresearch_dinov2_main'
+    try:
+        vfm = torch.hub.load(hub_path, model_name, source='local').half()
+    except Exception as e:
+        raise RuntimeError(f"Failed to load DINOv2 model '{model_name}': {e}")
+    return vfm
+
+def visualize_sd_dino_clip_att(
+    ori_img, sim_dino, sim_clip, token_choosen, output_path, attn_map_hw=(35, 35), vis_hw=(560, 560)
+):
+    """
+    Visualize the original image and the cosine similarity maps for both DINO and CLIP.
+    Token chosen is highlighted with a smaller red dot in all visualizations.
+    
+    :param ori_img: Input image (PIL.Image, numpy.ndarray, or torch.Tensor)
+    :param sim_dino: Cosine similarity matrix from DINO (shape [H*W, H*W])
+    :param sim_clip: Cosine similarity matrix from CLIP (shape [H*W, H*W])
+    :param token_choosen: Token coordinate (row, col)
+    :param output_path: Path to save the visualization
+    :param attn_map_hw: Attention map resolution (H, W)
+    :param vis_hw: Visualization resolution (H, W)
+    """
+    # 1. Preprocess the original image
+    if isinstance(ori_img, torch.Tensor):
+        img = ori_img
+        if img.ndim == 4:  # [1,3,H,W] -> [3,H,W]
+            img = img[0]
+        img = img.cpu().numpy()
+        img = np.transpose(img, (1, 2, 0))  # [H, W, 3]
+        img = (img * 255).clip(0, 255).astype(np.uint8)
+    elif isinstance(ori_img, Image.Image):
+        img = np.array(ori_img)
+        if img.dtype != np.uint8:
+            img = (img * 255).clip(0, 255).astype(np.uint8)
+    elif isinstance(ori_img, np.ndarray):
+        img = ori_img
+        if img.dtype != np.uint8:
+            img = (img * 255).clip(0, 255).astype(np.uint8)
+    else:
+        raise ValueError("ori_img should be torch.Tensor, PIL.Image, or np.ndarray")
+    if img.shape[2] == 4:  # RGBA to RGB
+        img = img[:, :, :3]
+    img_resized = cv2.resize(img, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+    # 2. Token coordinates
+    h_attn, w_attn = attn_map_hw
+    row, col = token_choosen
+    y_vis = int((row + 0.5) * vis_hw[0] / h_attn)
+    x_vis = int((col + 0.5) * vis_hw[1] / w_attn)
+
+    img_with_dot = img_resized.copy()
+    cv2.circle(img_with_dot, (x_vis, y_vis), radius=8, color=(0, 0, 0), thickness=-1)  # Smaller black circle
+    cv2.circle(img_with_dot, (x_vis, y_vis), radius=6, color=(255, 0, 0), thickness=-1)  # Smaller red circle
+
+    # 3. Process DINO similarity map
+    dino_att_map = sim_dino[row * w_attn + col].to(torch.float32).detach().cpu().numpy().reshape(h_attn, w_attn)
+    dino_att_map = (dino_att_map - dino_att_map.min()) / (dino_att_map.max() - dino_att_map.min() + 1e-8)
+    dino_att_map_up = cv2.resize(dino_att_map, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+    # Enhance contrast for DINO similarity map
+    dino_att_map_up = (dino_att_map_up ** 0.5)  # Apply gamma correction for better contrast
+    dino_overlay = (img_resized * 0.3 + (plt.cm.jet(dino_att_map_up)[:, :, :3] * 255).astype(np.uint8) * 0.7).astype(np.uint8)
+    cv2.circle(dino_overlay, (x_vis, y_vis), radius=8, color=(0, 0, 0), thickness=-1)  # Smaller black circle
+    cv2.circle(dino_overlay, (x_vis, y_vis), radius=6, color=(255, 0, 0), thickness=-1)  # Smaller red circle
+
+    # 4. Process CLIP similarity map
+    clip_att_map = sim_clip[row * w_attn + col].to(torch.float32).detach().cpu().numpy().reshape(h_attn, w_attn)
+    clip_att_map = (clip_att_map - clip_att_map.min()) / (clip_att_map.max() - clip_att_map.min() + 1e-8)
+    clip_att_map_up = cv2.resize(clip_att_map, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+    # Enhance contrast for CLIP similarity map
+    clip_att_map_up = (clip_att_map_up ** 0.5)  # Apply gamma correction for better contrast
+    clip_overlay = (img_resized * 0.3 + (plt.cm.jet(clip_att_map_up)[:, :, :3] * 255).astype(np.uint8) * 0.7).astype(np.uint8)
+    cv2.circle(clip_overlay, (x_vis, y_vis), radius=8, color=(0, 0, 0), thickness=-1)  # Smaller black circle
+    cv2.circle(clip_overlay, (x_vis, y_vis), radius=6, color=(255, 0, 0), thickness=-1)  # Smaller red circle
+
+    # 5. Combine all images into a single row
+    combined_img = np.concatenate([img_with_dot, dino_overlay, clip_overlay], axis=1)
+
+    # 6. Save the visualization
+    plt.figure(figsize=(18, 6))
+    plt.imshow(combined_img)
+    plt.axis('off')
+    plt.title("Original | DINO Cosine Similarity | CLIP Cosine Similarity")
+    plt.tight_layout()
+    plt.savefig(output_path, dpi=200, bbox_inches='tight')
+    plt.close()
+
+
+with torch.no_grad():
+    device="cuda:0"
+    dino=build_DINOv2().to(device)
+    clip = create_model('ViT-B-16', 'openai', device=device, precision="amp").eval().to(device).half()
+    image = Image.open("demo_images/bird.jpg")
+    dino_mean=[0.485, 0.456, 0.406]
+    dino_std=[0.229, 0.224, 0.225]
+    clip_mean = [0.48145466, 0.4578275, 0.40821073]
+    clip_std = [0.26862954, 0.26130258, 0.27577711]
+    DINO_transform = Compose([Resize((196, 196)),_convert_to_rgb,ToTensor(),Normalize(mean=dino_mean, std=dino_std)])
+    clip_transform=Compose([Resize((224, 224)),_convert_to_rgb,ToTensor(),Normalize(mean=clip_mean, std=clip_std)])
+    img_transform=Compose([Resize((224, 224)),_convert_to_rgb,])
+    dino_img = DINO_transform(image).unsqueeze(0).to(torch.float16).to(device)
+    clip_img = clip_transform(image).unsqueeze(0).to(torch.float16).to(device)
+    vis_img=img_transform(image)
+    # dino_feats_raw=dino.get_intermediate_layers(dino_img, reshape=True)[0].flatten(start_dim=-2).transpose(-2,-1)
+    dino_feats_raw=dino.get_intermediate_layers(dino_img, reshape=True)[0].flatten(start_dim=-2).transpose(-2,-1)
+    dino_feats = F.normalize(dino_feats_raw, dim=2)
+    sim_dino = torch.einsum('bic,bjc->bij', dino_feats, dino_feats).squeeze(0)
+    del dino
+    del dino_feats_raw
+    del dino_feats
+    clip_feats=clip.encode_dense(clip_img,mode="vanilla",normalize=True)
+    del clip
+    sim_clip = torch.einsum('bic,bjc->bij', clip_feats, clip_feats).squeeze(0)
+    del clip_feats
+    output_dir = "clip_dino_vis"
+    if not os.path.exists(output_dir):
+        os.mkdir(output_dir)
+
+    token_choosen=(7, 7)
+
+    visualize_sd_dino_clip_att(
+        ori_img=vis_img,                # Original image
+        sim_dino=sim_dino,              # DINO cosine similarity
+        sim_clip=sim_clip,              # CLIP cosine similarity
+        token_choosen=token_choosen,         # Token of interest
+        output_path=os.path.join(output_dir, "vis_combined.png"),  # Output file
+        attn_map_hw=(14, 14),           # Attention map resolution
+        vis_hw=(224, 224)               # Visualization resolution
+    )

analysis/model_vis_tools/vis_sd_featsv5.py

@@ -0,0 +1,262 @@
+
+from diffusion_model.stable_diffusion import diffusion
+import torch
+import numpy as np
+import os
+from PIL import Image
+from pycocotools.coco import COCO
+from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
+    CenterCrop
+from open_clip.transform import ResizeLongest, _convert_to_rgb
+from torchvision import transforms
+import matplotlib.pyplot as plt
+import cv2
+from functools import reduce
+import torch.nn.functional as F
+
+
+class SDNormalize(object):
+    def __call__(self, img):
+        return 2.0 * img - 1.0
+
+
+def build_DINOv2():
+    model_name='dinov2_vitb14_reg'
+    hub_path = '/mnt/SSD8T/home/wjj/.cache/torch/hub/facebookresearch_dinov2_main'
+    try:
+        vfm = torch.hub.load(hub_path, model_name, source='local').half()
+    except Exception as e:
+        raise RuntimeError(f"Failed to load DINOv2 model '{model_name}': {e}")
+    return vfm
+
+def visualize_self_att_raw(
+    ori_img, self_att_raw, token_choosen, output_dir, attn_map_hw=(35, 35), vis_hw=(560, 560)
+):
+    """
+    可视化self_att_raw中所有注意力图的token choosen位置的结果。
+    每幅图单独保存。
+    :param ori_img: 输入图像
+    :param self_att_raw: 原始注意力图，形状[10, 1225, 1225]
+    :param token_choosen: 选择的token坐标 (row, col)
+    :param output_dir: 输出文件名前缀
+    :param attn_map_hw: 注意力图的分辨率 (H, W)
+    :param vis_hw: 可视化图像的分辨率 (H, W)
+    """
+    # 1. 原图
+    if isinstance(ori_img, torch.Tensor):
+        img = ori_img
+        if img.ndim == 4:  # [1,3,H,W] -> [3,H,W]
+            img = img[0]
+        img = img.cpu().numpy()
+        img = np.transpose(img, (1, 2, 0))  # [H, W, 3]
+        img = (img * 255).clip(0, 255).astype(np.uint8)
+    elif isinstance(ori_img, Image.Image):
+        img = np.array(ori_img)
+        if img.dtype != np.uint8:
+            img = (img * 255).clip(0, 255).astype(np.uint8)
+    elif isinstance(ori_img, np.ndarray):
+        img = ori_img
+        if img.dtype != np.uint8:
+            img = (img * 255).clip(0, 255).astype(np.uint8)
+    else:
+        raise ValueError("ori_img should be torch.Tensor, PIL.Image, or np.ndarray")
+    if img.shape[2] == 4:  # RGBA to RGB
+        img = img[:, :, :3]
+    img_resized = cv2.resize(img, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+    # 2. token坐标
+    h_attn, w_attn = attn_map_hw
+    row, col = token_choosen
+    y_vis = int((row + 0.5) * vis_hw[0] / h_attn)
+    x_vis = int((col + 0.5) * vis_hw[1] / w_attn)
+
+    img_with_dot = img_resized.copy()
+    cv2.circle(img_with_dot, (x_vis, y_vis), radius=13, color=(0, 0, 0), thickness=-1)  # 黑色圆
+    cv2.circle(img_with_dot, (x_vis, y_vis), radius=11, color=(255, 0, 0), thickness=-1)  # 红色圆
+
+    # 3. 遍历 self_att_raw
+    num_layers = self_att_raw.shape[0]
+    for i in range(num_layers):
+        layer_att_map = self_att_raw[i, row * w_attn + col].to(torch.float32).detach().cpu().numpy().reshape(h_attn, w_attn)
+        layer_att_map = (layer_att_map - layer_att_map.min()) / (layer_att_map.max() - layer_att_map.min() + 1e-8)
+        layer_att_map_up = cv2.resize(layer_att_map, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+        # 绘制图像
+        fig, axs = plt.subplots(1, 2, figsize=(12, 6))
+        axs[0].imshow(img_with_dot)
+        axs[0].set_title(f"Original (with token) - Layer {i}")
+        axs[0].axis('off')
+
+        axs[1].imshow(layer_att_map_up, cmap='jet')
+        axs[1].set_title(f"Self-Attention (Layer {i})")
+        axs[1].axis('off')
+
+        plt.tight_layout()
+
+        # 保存每一层的可视化结果
+        output_path = os.path.join(output_dir,f"layer_{i}.png")
+        plt.savefig(output_path, dpi=200, bbox_inches='tight')
+        plt.close()
+
+def visualize_sd_dino_att(
+    ori_img, self_att, sim_dino_soft, sim_dino_refined, 
+    token_choosen, filename, attn_map_hw=(64, 64), vis_hw=(512, 512)
+):
+    """
+    可视化SD传播对DINO自相关的细化效果, 4列分别为:
+    1. 原图带token
+    2. SD自注意力传播
+    3. DINO自相关
+    4. 细化后的DINO自相关
+    """
+    # 1. 原图
+    # 支持PIL.Image、np.ndarray、tensor三种输入
+    if isinstance(ori_img, torch.Tensor):
+        img = ori_img
+        if img.ndim == 4:  # [1,3,H,W] -> [3,H,W]
+            img = img[0]
+        img = img.cpu().numpy()
+        img = np.transpose(img, (1, 2, 0))  # [H, W, 3]
+        img = (img * 255).clip(0, 255).astype(np.uint8)
+    elif isinstance(ori_img, Image.Image):
+        img = np.array(ori_img)
+        if img.dtype != np.uint8:
+            img = (img * 255).clip(0, 255).astype(np.uint8)
+    elif isinstance(ori_img, np.ndarray):
+        img = ori_img
+        if img.dtype != np.uint8:
+            img = (img * 255).clip(0, 255).astype(np.uint8)
+    else:
+        raise ValueError("ori_img should be torch.Tensor, PIL.Image, or np.ndarray")
+    if img.shape[2] == 4:  # RGBA to RGB
+        img = img[:, :, :3]
+    img_resized = cv2.resize(img, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+    # 2. token坐标
+    h_attn, w_attn = attn_map_hw
+    row, col = token_choosen
+    y_vis = int((row + 0.5) * vis_hw[0] / h_attn)
+    x_vis = int((col + 0.5) * vis_hw[1] / w_attn)
+    img_with_dot = img_resized.copy()
+    # 绘制黑色边缘的圆
+    cv2.circle(img_with_dot, (x_vis, y_vis), radius=13, color=(0, 0, 0), thickness=-1)  # 黑色圆
+
+    # 绘制红点
+    cv2.circle(img_with_dot, (x_vis, y_vis), radius=11, color=(255, 0, 0), thickness=-1)  # 红色圆
+
+    # 3. SD自注意力传播
+    att_map_sd = self_att[row * w_attn + col].to(torch.float32).detach().cpu().numpy().reshape(h_attn, w_attn)
+    att_map_sd = (att_map_sd - att_map_sd.min()) / (att_map_sd.max() - att_map_sd.min() + 1e-8)
+    att_map_sd_up = cv2.resize(att_map_sd, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+    # 4. DINO自相关（传播前）
+    att_map_dino = sim_dino_soft[row * w_attn + col].detach().cpu().numpy().reshape(h_attn, w_attn)
+    att_map_dino = (att_map_dino - att_map_dino.min()) / (att_map_dino.max() - att_map_dino.min() + 1e-8)
+    att_map_dino = att_map_dino.astype(np.float32) 
+    att_map_dino_up = cv2.resize(att_map_dino, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+    # 5. DINO传播后
+    att_map_dino_ref = sim_dino_refined[row * w_attn + col].detach().cpu().numpy().reshape(h_attn, w_attn)
+    att_map_dino_ref = (att_map_dino_ref - att_map_dino_ref.min()) / (att_map_dino_ref.max() - att_map_dino_ref.min() + 1e-8)
+    att_map_dino_ref = att_map_dino_ref.astype(np.float32) 
+    att_map_dino_ref_up = cv2.resize(att_map_dino_ref, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+    # 6. 绘图
+    fig, axs = plt.subplots(1, 4, figsize=(18, 5))
+
+    axs[0].imshow(img_with_dot)
+    axs[0].set_title("Original (with token)")
+    axs[0].axis('off')
+
+    axs[1].imshow(att_map_sd_up, cmap='jet')
+    axs[1].set_title(f'SD propagation (token {token_choosen})')
+    axs[1].axis('off')
+
+    axs[2].imshow(att_map_dino_up, cmap='jet')
+    axs[2].set_title(f'DINO sim (pre-propagate)')
+    axs[2].axis('off')
+
+    axs[3].imshow(att_map_dino_ref_up, cmap='jet')
+    axs[3].set_title(f'DINO sim (post-propagate)')
+    axs[3].axis('off')
+
+    plt.tight_layout()
+    plt.savefig(filename, dpi=200, bbox_inches='tight')
+    plt.close()
+
+with torch.no_grad():
+    attention_layers_to_use= [-4, -6]
+    sd_version='v2.1'
+    time_step=45
+    device="cuda:6"
+    # coco_path='/mnt/SSD8T/home/wjj/dataset/standard_coco/annotations/instances_train2017.json'
+    # img_path='/mnt/SSD8T/home/wjj/dataset/standard_coco/train2017'
+
+    # coco=COCO(coco_path)
+    # image_ids=load_data(coco)
+    dino=build_DINOv2().to(device)
+    sd=diffusion(attention_layers_to_use=attention_layers_to_use,model=sd_version, time_step=time_step, device=device,dtype=torch.float16)
+    # image_select=5
+    # img_name = coco.loadImgs(image_ids[image_select])[0]['file_name']
+    # image_path = os.path.join(img_path, img_name)
+    image_root='/mnt/SSD8T/home/wjj/dataset/standard_coco/train2017'
+    image_file=os.listdir(image_root)[999]
+    image_name=os.path.join(image_root, image_file)
+    # image = Image.open('demo_images/horses.jpg')
+    image = Image.open("demo_images/bird.jpg")
+    mean=[0.485, 0.456, 0.406]
+    std=[0.229, 0.224, 0.225]
+
+    normalize = Normalize(mean=mean, std=std)
+    DINO_transform=transforms.Compose([
+                                    ResizeLongest(490, fill=0),
+                                    _convert_to_rgb,
+                                    ToTensor(),
+                                    normalize])
+    sd_transform=transforms.Compose([ResizeLongest(560, fill=0),  _convert_to_rgb,ToTensor(), SDNormalize()])
+    img_transform=transforms.Compose([ResizeLongest(560, fill=0)])
+    dino_img=DINO_transform(image).unsqueeze(0).to(torch.float16).to(device)
+    sd_img = sd_transform(image).unsqueeze(0).to(torch.float16).to(device)
+
+    # dino_feats_raw=dino.get_intermediate_layers(dino_img, reshape=True)[0].flatten(start_dim=-2).transpose(-2,-1)
+    dino_feats_raw=dino.get_intermediate_layers(dino_img, reshape=True)[0].flatten(start_dim=-2).transpose(-2,-1)
+    dino_feats = F.normalize(dino_feats_raw, dim=2)
+    sim_dino = torch.einsum('bic,bjc->bij', dino_feats, dino_feats).squeeze(0)
+    # sd preprocess
+    # 1. 
+    sd.forward_wo_preprocess(sd_img, "")
+    vis_img=img_transform(image)
+    self_att_raw = torch.cat([sd.attention_maps[idx] for idx in attention_layers_to_use]).float()
+
+    self_att = self_att_raw / torch.amax(self_att_raw, dim=-2, keepdim=True) + 1e-5
+    self_att = torch.where(self_att < 0.2, 0, self_att)
+    self_att /= self_att.sum(dim=-1, keepdim=True) + 1e-5
+    self_att = reduce(torch.matmul, self_att, torch.eye(self_att.shape[-1], device=self_att.device)).to(sim_dino.dtype)
+    refined_sim_dino = self_att @ sim_dino @ self_att.transpose(0, 1)
+    alpha = 0.8 
+    refined_sim_dino = (1 - alpha) * sim_dino + alpha * refined_sim_dino
+
+    output_dir = "sd_vis"
+    if not os.path.exists(output_dir):
+        os.mkdir(output_dir)
+
+    token_choosen=(12, 20)
+
+    visualize_sd_dino_att(
+        ori_img=vis_img,                # [1,3,H,W]，归一化 0-1 float
+        self_att=self_att,             # (4096,4096)
+        sim_dino_soft=sim_dino,   # (4096,4096)
+        sim_dino_refined=refined_sim_dino, # (4096,4096)
+        token_choosen=token_choosen,
+        filename=os.path.join(output_dir, f"vis.png"),
+        attn_map_hw=(35, 35),
+        vis_hw=(560, 560)
+    )
+    visualize_self_att_raw(
+        ori_img=vis_img,
+        self_att_raw=self_att_raw,
+        token_choosen=token_choosen,
+        output_dir=output_dir,
+        attn_map_hw=(35, 35),
+        vis_hw=(560, 560)
+    )

analysis/model_vis_tools/vis_sd_featsv6.py

@@ -0,0 +1,161 @@
+
+import h5py
+from diffusion_model.stable_diffusion import diffusion
+import torch
+import numpy as np
+import os
+from PIL import Image
+from pycocotools.coco import COCO
+from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
+    CenterCrop
+from open_clip.transform import ResizeLongest, _convert_to_rgb
+from torchvision import transforms
+import matplotlib.pyplot as plt
+import cv2
+from functools import reduce
+import torch.nn.functional as F
+
+def build_DINOv2():
+    model_name='dinov2_vitb14_reg'
+    hub_path = '/mnt/SSD8T/home/wjj/.cache/torch/hub/facebookresearch_dinov2_main'
+    try:
+        vfm = torch.hub.load(hub_path, model_name, source='local').half()
+    except Exception as e:
+        raise RuntimeError(f"Failed to load DINOv2 model '{model_name}': {e}")
+    return vfm
+
+def visualize_sd_dino_att(
+    ori_img, self_att, sim_dino_soft, sim_dino_refined, 
+    token_choosen, filename, attn_map_hw=(64, 64), vis_hw=(512, 512)
+):
+    """
+    可视化SD传播对DINO自相关的细化效果, 4列分别为:
+    1. 原图带token
+    2. SD自注意力传播
+    3. DINO自相关
+    4. 细化后的DINO自相关
+    """
+    # 1. 原图
+    # 支持PIL.Image、np.ndarray、tensor三种输入
+    if isinstance(ori_img, torch.Tensor):
+        img = ori_img
+        if img.ndim == 4:  # [1,3,H,W] -> [3,H,W]
+            img = img[0]
+        img = img.cpu().numpy()
+        img = np.transpose(img, (1, 2, 0))  # [H, W, 3]
+        img = (img * 255).clip(0, 255).astype(np.uint8)
+    elif isinstance(ori_img, Image.Image):
+        img = np.array(ori_img)
+        if img.dtype != np.uint8:
+            img = (img * 255).clip(0, 255).astype(np.uint8)
+    elif isinstance(ori_img, np.ndarray):
+        img = ori_img
+        if img.dtype != np.uint8:
+            img = (img * 255).clip(0, 255).astype(np.uint8)
+    else:
+        raise ValueError("ori_img should be torch.Tensor, PIL.Image, or np.ndarray")
+    if img.shape[2] == 4:  # RGBA to RGB
+        img = img[:, :, :3]
+    img_resized = cv2.resize(img, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+    # 2. token坐标
+    h_attn, w_attn = attn_map_hw
+    row, col = token_choosen
+    y_vis = int((row + 0.5) * vis_hw[0] / h_attn)
+    x_vis = int((col + 0.5) * vis_hw[1] / w_attn)
+    img_with_dot = img_resized.copy()
+    cv2.circle(img_with_dot, (x_vis, y_vis), radius=7, color=(255,0,0), thickness=-1)  # 红点
+
+    # 3. SD自注意力传播
+    att_map_sd = self_att[row * w_attn + col].to(torch.float32).detach().cpu().numpy().reshape(h_attn, w_attn)
+    att_map_sd = (att_map_sd - att_map_sd.min()) / (att_map_sd.max() - att_map_sd.min() + 1e-8)
+    att_map_sd_up = cv2.resize(att_map_sd, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+    # 4. DINO自相关（传播前）
+    att_map_dino = sim_dino_soft[row * w_attn + col].detach().cpu().numpy().reshape(h_attn, w_attn)
+    att_map_dino = (att_map_dino - att_map_dino.min()) / (att_map_dino.max() - att_map_dino.min() + 1e-8)
+    att_map_dino = att_map_dino.astype(np.float32) 
+    att_map_dino_up = cv2.resize(att_map_dino, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+    # 5. DINO传播后
+    att_map_dino_ref = sim_dino_refined[row * w_attn + col].detach().cpu().numpy().reshape(h_attn, w_attn)
+    att_map_dino_ref = (att_map_dino_ref - att_map_dino_ref.min()) / (att_map_dino_ref.max() - att_map_dino_ref.min() + 1e-8)
+    att_map_dino_ref = att_map_dino_ref.astype(np.float32) 
+    att_map_dino_ref_up = cv2.resize(att_map_dino_ref, vis_hw, interpolation=cv2.INTER_LINEAR)
+
+    # 6. 绘图
+    fig, axs = plt.subplots(1, 4, figsize=(18, 5))
+
+    axs[0].imshow(img_with_dot)
+    axs[0].set_title("Original (with token)")
+    axs[0].axis('off')
+
+    axs[1].imshow(att_map_sd_up, cmap='jet')
+    axs[1].set_title(f'SD propagation (token {token_choosen})')
+    axs[1].axis('off')
+
+    axs[2].imshow(att_map_dino_up, cmap='jet')
+    axs[2].set_title(f'DINO sim (pre-propagate)')
+    axs[2].axis('off')
+
+    axs[3].imshow(att_map_dino_ref_up, cmap='jet')
+    axs[3].set_title(f'DINO sim (post-propagate)')
+    axs[3].axis('off')
+
+    plt.tight_layout()
+    plt.savefig(filename, dpi=200, bbox_inches='tight')
+    plt.close()
+
+attention_layers_to_use= [-4, -6]
+sd_version='v2.1'
+time_step=45
+device="cuda:2"
+dino=build_DINOv2().to(device)
+image_root='/mnt/SSD8T/home/wjj/dataset/standard_coco/train2017'
+cache=h5py.File("/mnt/SSD8T/home/wjj/code/DeCLIP/sd_self_attn_cache/sd_self_attn_coco.h5", 'r', swmr=True)
+image_file=os.listdir(image_root)[5]
+cache_attn=torch.from_numpy(cache[image_file][()]).to(device)
+
+image_name=os.path.join(image_root, image_file)
+# image = Image.open('demo_images/horses.jpg')
+image = Image.open(image_name)
+mean=[0.485, 0.456, 0.406]
+std=[0.229, 0.224, 0.225]
+
+normalize = Normalize(mean=mean, std=std)
+DINO_transform=transforms.Compose([
+                                ResizeLongest(490, fill=0),
+                                _convert_to_rgb,
+                                ToTensor(),
+                                normalize])
+img_transform=transforms.Compose([ResizeLongest(560, fill=0)])
+dino_img=DINO_transform(image).unsqueeze(0).to(torch.float16).to(device)
+
+# dino_feats_raw=dino.get_intermediate_layers(dino_img, reshape=True)[0].flatten(start_dim=-2).transpose(-2,-1)
+dino_feats_raw=dino.get_intermediate_layers(dino_img, reshape=True)[0].flatten(start_dim=-2).transpose(-2,-1)
+dino_feats = F.normalize(dino_feats_raw, dim=2)
+sim_dino = torch.einsum('bic,bjc->bij', dino_feats, dino_feats).squeeze(0)
+self_att=cache_attn.to(sim_dino.dtype)
+refined_sim_dino = self_att @ sim_dino @ self_att.transpose(0, 1)
+# alpha = 0.1  # 你可以把它设置成任意[0,1]的数，逐渐试
+# sim_dino_mixed = (1 - alpha) * sim_dino_softmax + alpha * sim_dino_refined
+
+output_dir = "sd_vis"
+if not os.path.exists(output_dir):
+    os.mkdir(output_dir)
+
+token_choosen=(15, 15)
+vis_img = img_transform(image)
+visualize_sd_dino_att(
+    ori_img=vis_img,                # [1,3,H,W]，归一化 0-1 float
+    self_att=self_att,             # (4096,4096)
+    sim_dino_soft=sim_dino,   # (4096,4096)
+    sim_dino_refined=refined_sim_dino, # (4096,4096)
+    token_choosen=token_choosen,
+    filename=os.path.join(output_dir, f"vis.png"),
+    attn_map_hw=(35, 35),
+    vis_hw=(560, 560)
+)
+
+
+# visualize_sd_self_att(sd_img, self_att, (30,30), os.path.join(output_dir,"vis.png"),)

analysis/prompt_ensemble_ablation/configs/fvit_vitb16_ovcoco_clearclip_single_prompt.py

@@ -0,0 +1,23 @@
+# ClearCLIP EVA-B Single Prompt 消融实验配置
+# 使用原始 EVA-CLIP + QQ attention (ClearCLIP方式)
+# feature_mode='qq'
+#
+# 注意：必须设置 force_reload_embed=True
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+_base_ = '../../CLIPSelf/F-ViT/work_dirs/clearclip_ovcoco/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_clearclip.py'
+
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            # 使用 single prompt embedding (消融实验)
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_b_16_eva_single_prompt.pt',
+            # 强制从配置文件重新加载 embedding
+            force_reload_embed=True,
+        ),
+    ),
+)

analysis/prompt_ensemble_ablation/configs/fvit_vitb16_ovcoco_declip_ensemble.py

@@ -0,0 +1,208 @@
+# DeCLIP EVA-B Prompt Ensemble 配置
+# 使用 EVAB_dinov2B_epoch2.pth 检测器权重
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+find_unused_parameters = True
+num_classes = 65
+class_weight = [
+    1.0, 1.0, 1.0, 1.0, 0, 0, 1.0, 1.0, 1.0, 1.0, 1.0, 0, 0, 1.0, 1.0, 0, 0,
+    1.0, 1.0, 1.0, 1.0, 0, 1.0, 0, 1.0, 1.0, 1.0, 0, 1.0, 0, 1.0, 1.0, 0, 1.0,
+    0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0, 1.0, 0, 1.0, 1.0,
+    1.0, 1.0, 1.0, 1.0, 0, 1.0, 1.0, 1.0, 0, 1.0, 1.0, 1.0, 1.0, 0, 1.0, 0.6
+]
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+
+model = dict(
+    type='FViT',
+    backbone=dict(
+        type='EvaCLIPViT',
+        model_name='EVA02-CLIP-B-16',
+        pretrained='/opt/tiger/xiaomoguhzz/EVA02_CLIP_B_psz16_s8B.pt',
+        norm_cfg=norm_cfg,
+        out_indices=[3, 5, 7, 11],
+        feature_mode='csa'),  # DeCLIP: Q*Q^T + K*K^T, 无 scale, 无残差, 无 MLP
+    neck=dict(
+        type='FPN',
+        in_channels=[768, 768, 768, 768],
+        out_channels=256,
+        num_outs=5,
+        norm_cfg=norm_cfg),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[0.0, 0.0, 0.0, 0.0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0),
+        num_convs=2),
+    roi_head=dict(
+        type='FViTRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='FViTBBoxHead',
+            in_channels=256,
+            fc_out_channels=512,
+            roi_feat_size=7,
+            num_classes=num_classes,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0.0, 0.0, 0.0, 0.0],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=True,
+            loss_cls=dict(
+                type='CustomCrossEntropyLoss',
+                use_sigmoid=False,
+                loss_weight=1.0,
+                class_weight=class_weight),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0),
+            norm_cfg=norm_cfg,
+            fixed_temperature=0,
+            learned_temperature=50.0,
+            vlm_temperature=75.0,
+            alpha=0.1,
+            beta=0.8,
+            # Prompt Ensemble embedding
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_b_16_eva_ensemble.pt',
+            force_reload_embed=True,
+            seen_classes='datasets/mscoco_seen_classes.json',
+            all_classes='datasets/mscoco_65_classes.json',
+            num_shared_convs=4,
+            num_shared_fcs=2,
+            num_cls_fcs=1,
+            num_reg_fcs=1),
+        vlm_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(
+                type='RoIAlign',
+                output_size=1,
+                sampling_ratio=0,
+                use_torchvision=True),
+            out_channels=512,
+            featmap_strides=[16])),
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=False,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.01,
+            nms=dict(type='nms', iou_threshold=0.4),
+            max_per_img=100)))
+
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=True, base_batch_size=64)
+
+dataset_type = 'CocoDatasetOV'
+image_size = (640, 640)
+file_client_args = dict(backend='disk')
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=image_size,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', pad_to_square=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    val=dict(
+        type=dataset_type,
+        ann_file='data/coco/zero-shot/instances_val2017_all_2.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file='data/coco/zero-shot/instances_val2017_all_2.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=test_pipeline))
+
+evaluation = dict(interval=1, metric=['bbox'])
+optimizer = dict(type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.1)
+optimizer_config = dict(grad_clip=dict(max_norm=1.0, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=250,
+    warmup_ratio=0.001,
+    step=[100])
+runner = dict(type='EpochBasedRunner', max_epochs=3)
+fp16 = dict(loss_scale=512.0)

analysis/prompt_ensemble_ablation/configs/fvit_vitb16_ovcoco_declip_single_prompt.py

@@ -0,0 +1,209 @@
+# DeCLIP EVA-B Single Prompt 消融实验配置
+# 使用 EVAB_dinov2B_epoch2.pth 检测器权重
+# 注意：backbone.pretrained 指向 DeCLIP 预训练的 backbone，检测器权重通过命令行加载
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+find_unused_parameters = True
+num_classes = 65
+class_weight = [
+    1.0, 1.0, 1.0, 1.0, 0, 0, 1.0, 1.0, 1.0, 1.0, 1.0, 0, 0, 1.0, 1.0, 0, 0,
+    1.0, 1.0, 1.0, 1.0, 0, 1.0, 0, 1.0, 1.0, 1.0, 0, 1.0, 0, 1.0, 1.0, 0, 1.0,
+    0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0, 1.0, 0, 1.0, 1.0,
+    1.0, 1.0, 1.0, 1.0, 0, 1.0, 1.0, 1.0, 0, 1.0, 1.0, 1.0, 1.0, 0, 1.0, 0.6
+]
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+
+model = dict(
+    type='FViT',
+    backbone=dict(
+        type='EvaCLIPViT',
+        model_name='EVA02-CLIP-B-16',
+        pretrained='/opt/tiger/xiaomoguhzz/EVA02_CLIP_B_psz16_s8B.pt',
+        norm_cfg=norm_cfg,
+        out_indices=[3, 5, 7, 11],
+        feature_mode='csa'),  # DeCLIP: Q*Q^T + K*K^T, 无 scale, 无残差, 无 MLP
+    neck=dict(
+        type='FPN',
+        in_channels=[768, 768, 768, 768],
+        out_channels=256,
+        num_outs=5,
+        norm_cfg=norm_cfg),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[0.0, 0.0, 0.0, 0.0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0),
+        num_convs=2),
+    roi_head=dict(
+        type='FViTRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='FViTBBoxHead',
+            in_channels=256,
+            fc_out_channels=512,
+            roi_feat_size=7,
+            num_classes=num_classes,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0.0, 0.0, 0.0, 0.0],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=True,
+            loss_cls=dict(
+                type='CustomCrossEntropyLoss',
+                use_sigmoid=False,
+                loss_weight=1.0,
+                class_weight=class_weight),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0),
+            norm_cfg=norm_cfg,
+            fixed_temperature=0,
+            learned_temperature=50.0,
+            vlm_temperature=75.0,
+            alpha=0.1,
+            beta=0.8,
+            # Single Prompt embedding
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_b_16_eva_single_prompt.pt',
+            force_reload_embed=True,
+            seen_classes='datasets/mscoco_seen_classes.json',
+            all_classes='datasets/mscoco_65_classes.json',
+            num_shared_convs=4,
+            num_shared_fcs=2,
+            num_cls_fcs=1,
+            num_reg_fcs=1),
+        vlm_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(
+                type='RoIAlign',
+                output_size=1,
+                sampling_ratio=0,
+                use_torchvision=True),
+            out_channels=512,
+            featmap_strides=[16])),
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=False,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.01,
+            nms=dict(type='nms', iou_threshold=0.4),
+            max_per_img=100)))
+
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=True, base_batch_size=64)
+
+dataset_type = 'CocoDatasetOV'
+image_size = (640, 640)
+file_client_args = dict(backend='disk')
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=image_size,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', pad_to_square=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    val=dict(
+        type=dataset_type,
+        ann_file='data/coco/zero-shot/instances_val2017_all_2.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file='data/coco/zero-shot/instances_val2017_all_2.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=test_pipeline))
+
+evaluation = dict(interval=1, metric=['bbox'])
+optimizer = dict(type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.1)
+optimizer_config = dict(grad_clip=dict(max_norm=1.0, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=250,
+    warmup_ratio=0.001,
+    step=[100])
+runner = dict(type='EpochBasedRunner', max_epochs=3)
+fp16 = dict(loss_scale=512.0)

analysis/prompt_ensemble_ablation/configs/fvit_vitb16_ovcoco_ensemble.py

@@ -0,0 +1,27 @@
+# Prompt Ensemble 配置
+# 基于 CLIPSelf EVA-ViT-B/16，使用 prompt ensemble embedding
+#
+# 注意：必须设置 force_reload_embed=True，否则会使用 checkpoint 中保存的 embedding
+# 而不是配置文件中指定的 embedding 文件
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+_base_ = '../../CLIPSelf/F-ViT/configs/ov_coco/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_eva_original.py'
+
+model = dict(
+    backbone=dict(
+        pretrained='/opt/tiger/xiaomoguhzz/EVA02_CLIP_B_psz16_s8B.pt',
+        feature_mode='maskclip',
+    ),
+    roi_head=dict(
+        bbox_head=dict(
+            # 使用 prompt ensemble embedding
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_b_16_eva_ensemble.pt',
+            # 强制从配置文件重新加载 embedding，忽略 checkpoint 中保存的 embedding
+            force_reload_embed=True,
+        ),
+    ),
+)

analysis/prompt_ensemble_ablation/configs/fvit_vitb16_ovcoco_maskclip_ensemble.py

@@ -0,0 +1,22 @@
+# MaskCLIP EVA-B Ensemble 配置
+# 使用原始 EVA-CLIP (不经过 CLIPSelf 训练)
+#
+# 注意：必须设置 force_reload_embed=True
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+_base_ = '../../CLIPSelf/F-ViT/configs/declip/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_maskclip.py'
+
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            # 使用 prompt ensemble embedding
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_b_16_eva_ensemble.pt',
+            # 强制从配置文件重新加载 embedding
+            force_reload_embed=True,
+        ),
+    ),
+)

analysis/prompt_ensemble_ablation/configs/fvit_vitb16_ovcoco_maskclip_single_prompt.py

@@ -0,0 +1,22 @@
+# MaskCLIP EVA-B Single Prompt 消融实验配置
+# 使用原始 EVA-CLIP (不经过 CLIPSelf 训练)
+#
+# 注意：必须设置 force_reload_embed=True
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+_base_ = '../../CLIPSelf/F-ViT/configs/declip/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_maskclip.py'
+
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            # 使用 single prompt embedding (消融实验)
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_b_16_eva_single_prompt.pt',
+            # 强制从配置文件重新加载 embedding
+            force_reload_embed=True,
+        ),
+    ),
+)

analysis/prompt_ensemble_ablation/configs/fvit_vitb16_ovcoco_single_prompt.py

@@ -0,0 +1,27 @@
+# Single Prompt 消融实验配置
+# 基于 CLIPSelf EVA-ViT-B/16，使用 single prompt embedding 替代 prompt ensemble
+#
+# 注意：必须设置 force_reload_embed=True，否则会使用 checkpoint 中保存的 embedding
+# 而不是配置文件中指定的 embedding 文件
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+_base_ = '../../CLIPSelf/F-ViT/configs/ov_coco/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_eva_original.py'
+
+model = dict(
+    backbone=dict(
+        pretrained='/opt/tiger/xiaomoguhzz/EVA02_CLIP_B_psz16_s8B.pt',
+        feature_mode='maskclip',
+    ),
+    roi_head=dict(
+        bbox_head=dict(
+            # 使用 single prompt embedding (消融实验)
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_b_16_eva_single_prompt.pt',
+            # 强制从配置文件重新加载 embedding，忽略 checkpoint 中保存的 embedding
+            force_reload_embed=True,
+        ),
+    ),
+)

analysis/prompt_ensemble_ablation/configs/fvit_vitl14_ovcoco_clearclip_ensemble.py

@@ -0,0 +1,23 @@
+# ClearCLIP EVA-L Ensemble 配置
+# 使用原始 EVA-CLIP Large + QQ attention (ClearCLIP方式)
+# feature_mode='qq'
+#
+# 注意：必须设置 force_reload_embed=True
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+_base_ = '../../CLIPSelf/F-ViT/configs/declip/fvit_vitl14_upsample_fpn_bs64_3e_ovcoco_clearclip.py'
+
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            # 使用 prompt ensemble embedding
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_l_14_336_eva_ensemble.pt',
+            # 强制从配置文件重新加载 embedding
+            force_reload_embed=True,
+        ),
+    ),
+)

analysis/prompt_ensemble_ablation/configs/fvit_vitl14_ovcoco_clearclip_single_prompt.py

@@ -0,0 +1,23 @@
+# ClearCLIP EVA-L Single Prompt 消融实验配置
+# 使用原始 EVA-CLIP Large + QQ attention (ClearCLIP方式)
+# feature_mode='qq'
+#
+# 注意：必须设置 force_reload_embed=True
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+_base_ = '../../CLIPSelf/F-ViT/configs/declip/fvit_vitl14_upsample_fpn_bs64_3e_ovcoco_clearclip.py'
+
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            # 使用 single prompt embedding (消融实验)
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_l_14_336_eva_single_prompt.pt',
+            # 强制从配置文件重新加载 embedding
+            force_reload_embed=True,
+        ),
+    ),
+)

analysis/prompt_ensemble_ablation/configs/fvit_vitl14_ovcoco_declip_ensemble.py

@@ -0,0 +1,208 @@
+# DeCLIP EVA-L Prompt Ensemble 配置
+# 使用 EVAL_dinov2L_epoch3.pth 检测器权重
+# image_size=896, model_name='EVA02-CLIP-L-14-336'
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+find_unused_parameters = True
+num_classes = 65
+class_weight = [
+    1.0, 1.0, 1.0, 1.0, 0, 0, 1.0, 1.0, 1.0, 1.0, 1.0, 0, 0, 1.0, 1.0, 0, 0,
+    1.0, 1.0, 1.0, 1.0, 0, 1.0, 0, 1.0, 1.0, 1.0, 0, 1.0, 0, 1.0, 1.0, 0, 1.0,
+    0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0, 1.0, 0, 1.0, 1.0,
+    1.0, 1.0, 1.0, 1.0, 0, 1.0, 1.0, 1.0, 0, 1.0, 1.0, 1.0, 1.0, 0, 1.0, 0.6
+]
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+
+model = dict(
+    type='FViT',
+    backbone=dict(
+        type='EvaCLIPViT',
+        model_name='EVA02-CLIP-L-14-336',
+        pretrained='/opt/tiger/xiaomoguhzz/EVA02_CLIP_L_336_psz14_s6B.pt',
+        norm_cfg=norm_cfg,
+        out_indices=[6, 10, 14, 23],
+        feature_mode='csa'),  # DeCLIP: Q*Q^T + K*K^T, 无 scale, 无残差, 无 MLP
+    neck=dict(
+        type='FPN',
+        in_channels=[1024, 1024, 1024, 1024],
+        out_channels=256,
+        num_outs=5,
+        norm_cfg=norm_cfg),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[3.5, 7, 14, 28, 56]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[0.0, 0.0, 0.0, 0.0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0),
+        num_convs=2),
+    roi_head=dict(
+        type='FViTRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[3.5, 7, 14, 28]),
+        bbox_head=dict(
+            type='FViTBBoxHead',
+            in_channels=256,
+            fc_out_channels=768,
+            roi_feat_size=7,
+            num_classes=num_classes,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0.0, 0.0, 0.0, 0.0],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=True,
+            loss_cls=dict(
+                type='CustomCrossEntropyLoss',
+                use_sigmoid=False,
+                loss_weight=1.0,
+                class_weight=class_weight),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0),
+            norm_cfg=norm_cfg,
+            learned_temperature=50.0,
+            vlm_temperature=75.0,
+            alpha=0.1,
+            beta=0.8,
+            # Prompt Ensemble embedding (EVA-L)
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_l_14_336_eva_ensemble.pt',
+            force_reload_embed=True,
+            seen_classes='datasets/mscoco_seen_classes.json',
+            all_classes='datasets/mscoco_65_classes.json',
+            num_shared_convs=4,
+            num_shared_fcs=2,
+            num_cls_fcs=1,
+            num_reg_fcs=1),
+        vlm_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(
+                type='RoIAlign',
+                output_size=1,
+                sampling_ratio=0,
+                use_torchvision=True),
+            out_channels=768,
+            featmap_strides=[14])),
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=False,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.01,
+            nms=dict(type='nms', iou_threshold=0.4),
+            max_per_img=100)))
+
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=True, base_batch_size=64)
+
+dataset_type = 'CocoDatasetOV'
+image_size = (896, 896)
+file_client_args = dict(backend='disk')
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=image_size,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[122.771, 116.746, 104.094],
+                std=[68.501, 66.632, 70.323],
+                to_rgb=True),
+            dict(type='Pad', pad_to_square=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=4,
+    val=dict(
+        type=dataset_type,
+        ann_file='data/coco/zero-shot/instances_val2017_all_2.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file='data/coco/zero-shot/instances_val2017_all_2.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=test_pipeline))
+
+evaluation = dict(interval=1, metric=['bbox'])
+optimizer = dict(type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.1)
+optimizer_config = dict(grad_clip=dict(max_norm=1.0, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=250,
+    warmup_ratio=0.001,
+    step=[100])
+runner = dict(type='EpochBasedRunner', max_epochs=3)
+fp16 = dict(loss_scale=512.0)

analysis/prompt_ensemble_ablation/configs/fvit_vitl14_ovcoco_declip_single_prompt.py

@@ -0,0 +1,208 @@
+# DeCLIP EVA-L Single Prompt 消融实验配置
+# 使用 EVAL_dinov2L_epoch3.pth 检测器权重
+# image_size=896, model_name='EVA02-CLIP-L-14-336'
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+find_unused_parameters = True
+num_classes = 65
+class_weight = [
+    1.0, 1.0, 1.0, 1.0, 0, 0, 1.0, 1.0, 1.0, 1.0, 1.0, 0, 0, 1.0, 1.0, 0, 0,
+    1.0, 1.0, 1.0, 1.0, 0, 1.0, 0, 1.0, 1.0, 1.0, 0, 1.0, 0, 1.0, 1.0, 0, 1.0,
+    0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0, 1.0, 0, 1.0, 1.0,
+    1.0, 1.0, 1.0, 1.0, 0, 1.0, 1.0, 1.0, 0, 1.0, 1.0, 1.0, 1.0, 0, 1.0, 0.6
+]
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+
+model = dict(
+    type='FViT',
+    backbone=dict(
+        type='EvaCLIPViT',
+        model_name='EVA02-CLIP-L-14-336',
+        pretrained='/opt/tiger/xiaomoguhzz/EVA02_CLIP_L_336_psz14_s6B.pt',
+        norm_cfg=norm_cfg,
+        out_indices=[6, 10, 14, 23],
+        feature_mode='csa'),  # DeCLIP: Q*Q^T + K*K^T, 无 scale, 无残差, 无 MLP
+    neck=dict(
+        type='FPN',
+        in_channels=[1024, 1024, 1024, 1024],
+        out_channels=256,
+        num_outs=5,
+        norm_cfg=norm_cfg),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[3.5, 7, 14, 28, 56]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[0.0, 0.0, 0.0, 0.0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0),
+        num_convs=2),
+    roi_head=dict(
+        type='FViTRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[3.5, 7, 14, 28]),
+        bbox_head=dict(
+            type='FViTBBoxHead',
+            in_channels=256,
+            fc_out_channels=768,
+            roi_feat_size=7,
+            num_classes=num_classes,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0.0, 0.0, 0.0, 0.0],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=True,
+            loss_cls=dict(
+                type='CustomCrossEntropyLoss',
+                use_sigmoid=False,
+                loss_weight=1.0,
+                class_weight=class_weight),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0),
+            norm_cfg=norm_cfg,
+            learned_temperature=50.0,
+            vlm_temperature=75.0,
+            alpha=0.1,
+            beta=0.8,
+            # Single Prompt embedding (EVA-L)
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_l_14_336_eva_single_prompt.pt',
+            force_reload_embed=True,
+            seen_classes='datasets/mscoco_seen_classes.json',
+            all_classes='datasets/mscoco_65_classes.json',
+            num_shared_convs=4,
+            num_shared_fcs=2,
+            num_cls_fcs=1,
+            num_reg_fcs=1),
+        vlm_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(
+                type='RoIAlign',
+                output_size=1,
+                sampling_ratio=0,
+                use_torchvision=True),
+            out_channels=768,
+            featmap_strides=[14])),
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=False,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.01,
+            nms=dict(type='nms', iou_threshold=0.4),
+            max_per_img=100)))
+
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=True, base_batch_size=64)
+
+dataset_type = 'CocoDatasetOV'
+image_size = (896, 896)
+file_client_args = dict(backend='disk')
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=image_size,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[122.771, 116.746, 104.094],
+                std=[68.501, 66.632, 70.323],
+                to_rgb=True),
+            dict(type='Pad', pad_to_square=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=4,
+    val=dict(
+        type=dataset_type,
+        ann_file='data/coco/zero-shot/instances_val2017_all_2.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file='data/coco/zero-shot/instances_val2017_all_2.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=test_pipeline))
+
+evaluation = dict(interval=1, metric=['bbox'])
+optimizer = dict(type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.1)
+optimizer_config = dict(grad_clip=dict(max_norm=1.0, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=250,
+    warmup_ratio=0.001,
+    step=[100])
+runner = dict(type='EpochBasedRunner', max_epochs=3)
+fp16 = dict(loss_scale=512.0)

analysis/prompt_ensemble_ablation/configs/fvit_vitl14_ovcoco_ensemble.py

@@ -0,0 +1,26 @@
+# CLIPSelf EVA-L Ensemble 配置
+# 基于 CLIPSelf EVA-ViT-L/14-336
+#
+# 注意：必须设置 force_reload_embed=True，否则会使用 checkpoint 中保存的 embedding
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+_base_ = '../../CLIPSelf/F-ViT/configs/ov_coco/fvit_vitl14_upsample_fpn_bs64_3e_ovcoco_eva_original.py'
+
+model = dict(
+    backbone=dict(
+        pretrained='/opt/tiger/xiaomoguhzz/EVA02_CLIP_L_336_psz14_s6B.pt',
+        feature_mode='maskclip',
+    ),
+    roi_head=dict(
+        bbox_head=dict(
+            # 使用 prompt ensemble embedding
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_l_14_336_eva_ensemble.pt',
+            # 强制从配置文件重新加载 embedding，忽略 checkpoint 中保存的 embedding
+            force_reload_embed=True,
+        ),
+    ),
+)

analysis/prompt_ensemble_ablation/configs/fvit_vitl14_ovcoco_maskclip_ensemble.py

@@ -0,0 +1,26 @@
+# MaskCLIP EVA-L Ensemble 配置
+# 使用原始 EVA-CLIP Large (不经过 CLIPSelf 训练)
+#
+# 注意：必须设置 force_reload_embed=True
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+_base_ = '../../CLIPSelf/F-ViT/configs/ov_coco/fvit_vitl14_upsample_fpn_bs64_3e_ovcoco_eva_original.py'
+
+model = dict(
+    backbone=dict(
+        pretrained='/opt/tiger/xiaomoguhzz/EVA02_CLIP_L_336_psz14_s6B.pt',
+        feature_mode='maskclip',
+    ),
+    roi_head=dict(
+        bbox_head=dict(
+            # 使用 prompt ensemble embedding
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_l_14_336_eva_ensemble.pt',
+            # 强制从配置文件重新加载 embedding
+            force_reload_embed=True,
+        ),
+    ),
+)

analysis/prompt_ensemble_ablation/configs/fvit_vitl14_ovcoco_maskclip_single_prompt.py

@@ -0,0 +1,26 @@
+# MaskCLIP EVA-L Single Prompt 消融实验配置
+# 使用原始 EVA-CLIP Large (不经过 CLIPSelf 训练)
+#
+# 注意：必须设置 force_reload_embed=True
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+_base_ = '../../CLIPSelf/F-ViT/configs/ov_coco/fvit_vitl14_upsample_fpn_bs64_3e_ovcoco_eva_original.py'
+
+model = dict(
+    backbone=dict(
+        pretrained='/opt/tiger/xiaomoguhzz/EVA02_CLIP_L_336_psz14_s6B.pt',
+        feature_mode='maskclip',
+    ),
+    roi_head=dict(
+        bbox_head=dict(
+            # 使用 single prompt embedding (消融实验)
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_l_14_336_eva_single_prompt.pt',
+            # 强制从配置文件重新加载 embedding
+            force_reload_embed=True,
+        ),
+    ),
+)

analysis/prompt_ensemble_ablation/configs/fvit_vitl14_ovcoco_single_prompt.py

@@ -0,0 +1,26 @@
+# CLIPSelf EVA-L Single Prompt 消融实验配置
+# 基于 CLIPSelf EVA-ViT-L/14-336
+#
+# 注意：必须设置 force_reload_embed=True，否则会使用 checkpoint 中保存的 embedding
+
+custom_imports = dict(
+    imports=['datasets', 'models'],
+    allow_failed_imports=False
+)
+
+_base_ = '../../CLIPSelf/F-ViT/configs/ov_coco/fvit_vitl14_upsample_fpn_bs64_3e_ovcoco_eva_original.py'
+
+model = dict(
+    backbone=dict(
+        pretrained='/opt/tiger/xiaomoguhzz/EVA02_CLIP_L_336_psz14_s6B.pt',
+        feature_mode='maskclip',
+    ),
+    roi_head=dict(
+        bbox_head=dict(
+            # 使用 single prompt embedding (消融实验)
+            class_embed='/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/prompt_ensemble_ablation/embeddings/coco_eva02_clip_l_14_336_eva_single_prompt.pt',
+            # 强制从配置文件重新加载 embedding，忽略 checkpoint 中保存的 embedding
+            force_reload_embed=True,
+        ),
+    ),
+)

analysis/prompt_ensemble_ablation/results/clearclip_b_results.txt

@@ -0,0 +1,32 @@
+================================================
+ClearCLIP-B (QQ mode) Ablation Results
+Date: Thu Jan 22 04:26:36 AM UTC 2026
+================================================
+
+## clearclip_evab_single
+Config: fvit_vitb16_ovcoco_clearclip_single_prompt.py
+
+Results:
+OrderedDict([('base_ap50', 43.736), ('novel_ap50', 26.62), ('all_ap50', 39.259), ('bbox_mAP', 0.201), ('bbox_mAP_50', 0.393), ('bbox_mAP_75', 0.19), ('bbox_mAP_s', 0.086), ('bbox_mAP_m', 0.205), ('bbox_mAP_l', 0.318), ('bbox_mAP_copypaste', '0.201 0.393 0.190 0.086 0.205 0.318')])
+
+----------------------------------------
+
+## clearclip_evab_ensemble
+Config: fvit_vitb16_ovcoco_clearclip_ensemble.py
+
+Results:
+OrderedDict([('base_ap50', 43.999), ('novel_ap50', 26.743), ('all_ap50', 39.486), ('bbox_mAP', 0.203), ('bbox_mAP_50', 0.395), ('bbox_mAP_75', 0.193), ('bbox_mAP_s', 0.087), ('bbox_mAP_m', 0.207), ('bbox_mAP_l', 0.322), ('bbox_mAP_copypaste', '0.203 0.395 0.193 0.087 0.207 0.322')])
+
+----------------------------------------
+
+
+================================================
+Summary
+================================================
+
+| Model | Method | base_ap50 | novel_ap50 | all_ap50 | bbox_mAP |
+|-------|--------|-----------|------------|----------|----------|
+| ClearCLIP-B | Single | 43.736 | 26.62 | 39.259 | 0.201 |
+| ClearCLIP-B | Ensemble | 43.999 | 26.743 | 39.486 | 0.203 |
+
+Completed at: Thu Jan 22 04:30:51 AM UTC 2026

analysis/prompt_ensemble_ablation/results/clearclip_l_results.txt

@@ -0,0 +1,32 @@
+================================================
+ClearCLIP-L (QQ mode) Ablation Results
+Date: Thu Jan 22 06:40:03 AM UTC 2026
+================================================
+
+## clearclip_eval_single
+Config: fvit_vitl14_ovcoco_clearclip_single_prompt.py
+
+Results:
+OrderedDict([('base_ap50', 55.841), ('novel_ap50', 27.962), ('all_ap50', 48.549), ('bbox_mAP', 0.25), ('bbox_mAP_50', 0.485), ('bbox_mAP_75', 0.238), ('bbox_mAP_s', 0.155), ('bbox_mAP_m', 0.275), ('bbox_mAP_l', 0.35), ('bbox_mAP_copypaste', '0.250 0.485 0.238 0.155 0.275 0.350')])
+
+----------------------------------------
+
+## clearclip_eval_ensemble
+Config: fvit_vitl14_ovcoco_clearclip_ensemble.py
+
+Results:
+OrderedDict([('base_ap50', 56.091), ('novel_ap50', 30.003), ('all_ap50', 49.268), ('bbox_mAP', 0.255), ('bbox_mAP_50', 0.493), ('bbox_mAP_75', 0.244), ('bbox_mAP_s', 0.156), ('bbox_mAP_m', 0.282), ('bbox_mAP_l', 0.356), ('bbox_mAP_copypaste', '0.255 0.493 0.244 0.156 0.282 0.356')])
+
+----------------------------------------
+
+
+================================================
+Summary
+================================================
+
+| Model | Method | base_ap50 | novel_ap50 | all_ap50 | bbox_mAP |
+|-------|--------|-----------|------------|----------|----------|
+| ClearCLIP-L | Single | 55.841 | 27.962 | 48.549 | 0.25 |
+| ClearCLIP-L | Ensemble | 56.091 | 30.003 | 49.268 | 0.255 |
+
+Completed at: Thu Jan 22 06:44:06 AM UTC 2026

analysis/prompt_ensemble_ablation/results/declip_ablation_results.txt

@@ -0,0 +1,54 @@
+================================================
+DeCLIP Prompt Ensemble Ablation Results
+Date: Thu Jan 22 03:46:09 AM UTC 2026
+================================================
+
+## declip_evab_single_prompt
+Config: fvit_vitb16_ovcoco_declip_single_prompt.py
+Checkpoint: EVAB_dinov2B_epoch2.pth
+
+Results:
+OrderedDict([('base_ap50', 56.66), ('novel_ap50', 43.644), ('all_ap50', 53.256), ('bbox_mAP', 0.288), ('bbox_mAP_50', 0.533), ('bbox_mAP_75', 0.284), ('bbox_mAP_s', 0.152), ('bbox_mAP_m', 0.315), ('bbox_mAP_l', 0.408), ('bbox_mAP_copypaste', '0.288 0.533 0.284 0.152 0.315 0.408')])
+
+----------------------------------------
+
+## declip_evab_ensemble
+Config: fvit_vitb16_ovcoco_declip_ensemble.py
+Checkpoint: EVAB_dinov2B_epoch2.pth
+
+Results:
+OrderedDict([('base_ap50', 56.718), ('novel_ap50', 43.634), ('all_ap50', 53.296), ('bbox_mAP', 0.289), ('bbox_mAP_50', 0.533), ('bbox_mAP_75', 0.284), ('bbox_mAP_s', 0.154), ('bbox_mAP_m', 0.316), ('bbox_mAP_l', 0.41), ('bbox_mAP_copypaste', '0.289 0.533 0.284 0.154 0.316 0.410')])
+
+----------------------------------------
+
+## declip_eval_single_prompt
+Config: fvit_vitl14_ovcoco_declip_single_prompt.py
+Checkpoint: EVAL_dinov2L_epoch3.pth
+
+Results:
+OrderedDict([('base_ap50', 65.598), ('novel_ap50', 49.358), ('all_ap50', 61.35), ('bbox_mAP', 0.347), ('bbox_mAP_50', 0.614), ('bbox_mAP_75', 0.358), ('bbox_mAP_s', 0.252), ('bbox_mAP_m', 0.382), ('bbox_mAP_l', 0.442), ('bbox_mAP_copypaste', '0.347 0.614 0.358 0.252 0.382 0.442')])
+
+----------------------------------------
+
+## declip_eval_ensemble
+Config: fvit_vitl14_ovcoco_declip_ensemble.py
+Checkpoint: EVAL_dinov2L_epoch3.pth
+
+Results:
+OrderedDict([('base_ap50', 65.767), ('novel_ap50', 50.327), ('all_ap50', 61.729), ('bbox_mAP', 0.349), ('bbox_mAP_50', 0.617), ('bbox_mAP_75', 0.361), ('bbox_mAP_s', 0.255), ('bbox_mAP_m', 0.383), ('bbox_mAP_l', 0.446), ('bbox_mAP_copypaste', '0.349 0.617 0.361 0.255 0.383 0.446')])
+
+----------------------------------------
+
+
+================================================
+Summary
+================================================
+
+| Model | Method | base_ap50 | novel_ap50 | all_ap50 | bbox_mAP |
+|-------|--------|-----------|------------|----------|----------|
+| DeCLIP-B | Single | 56.66 | 43.644 | 53.256 | 0.288 |
+| DeCLIP-B | Ensemble | 56.718 | 43.634 | 53.296 | 0.289 |
+| DeCLIP-L | Single | 65.598 | 49.358 | 61.35 | 0.347 |
+| DeCLIP-L | Ensemble | 65.767 | 50.327 | 61.729 | 0.349 |
+
+Completed at: Thu Jan 22 03:53:32 AM UTC 2026

analysis/prompt_ensemble_ablation/results/maskclip_b_clipself_l_results.txt

@@ -0,0 +1,50 @@
+================================================
+MaskCLIP-B + CLIPSelf-L Ablation Results
+Date: Thu Jan 22 04:10:11 AM UTC 2026
+================================================
+
+## maskclip_evab_single
+Config: fvit_vitb16_ovcoco_maskclip_single_prompt.py
+
+Results:
+OrderedDict([('base_ap50', 43.4), ('novel_ap50', 17.65), ('all_ap50', 36.665), ('bbox_mAP', 0.187), ('bbox_mAP_50', 0.367), ('bbox_mAP_75', 0.173), ('bbox_mAP_s', 0.081), ('bbox_mAP_m', 0.184), ('bbox_mAP_l', 0.305), ('bbox_mAP_copypaste', '0.187 0.367 0.173 0.081 0.184 0.305')])
+
+----------------------------------------
+
+## maskclip_evab_ensemble
+Config: fvit_vitb16_ovcoco_maskclip_ensemble.py
+
+Results:
+OrderedDict([('base_ap50', 43.815), ('novel_ap50', 17.386), ('all_ap50', 36.903), ('bbox_mAP', 0.188), ('bbox_mAP_50', 0.369), ('bbox_mAP_75', 0.175), ('bbox_mAP_s', 0.081), ('bbox_mAP_m', 0.186), ('bbox_mAP_l', 0.306), ('bbox_mAP_copypaste', '0.188 0.369 0.175 0.081 0.186 0.306')])
+
+----------------------------------------
+
+## clipself_eval_single
+Config: fvit_vitl14_ovcoco_single_prompt.py
+
+Results:
+OrderedDict([('base_ap50', 63.843), ('novel_ap50', 44.475), ('all_ap50', 58.777), ('bbox_mAP', 0.329), ('bbox_mAP_50', 0.588), ('bbox_mAP_75', 0.335), ('bbox_mAP_s', 0.229), ('bbox_mAP_m', 0.37), ('bbox_mAP_l', 0.418), ('bbox_mAP_copypaste', '0.329 0.588 0.335 0.229 0.370 0.418')])
+
+----------------------------------------
+
+## clipself_eval_ensemble
+Config: fvit_vitl14_ovcoco_ensemble.py
+
+Results:
+OrderedDict([('base_ap50', 64.147), ('novel_ap50', 44.508), ('all_ap50', 59.011), ('bbox_mAP', 0.331), ('bbox_mAP_50', 0.59), ('bbox_mAP_75', 0.339), ('bbox_mAP_s', 0.228), ('bbox_mAP_m', 0.372), ('bbox_mAP_l', 0.422), ('bbox_mAP_copypaste', '0.331 0.590 0.339 0.228 0.372 0.422')])
+
+----------------------------------------
+
+
+================================================
+Summary
+================================================
+
+| Model | Method | base_ap50 | novel_ap50 | all_ap50 | bbox_mAP |
+|-------|--------|-----------|------------|----------|----------|
+| MaskCLIP-B | Single | 43.4 | 17.65 | 36.665 | 0.187 |
+| MaskCLIP-B | Ensemble | 43.815 | 17.386 | 36.903 | 0.188 |
+| CLIPSelf-L | Single | 63.843 | 44.475 | 58.777 | 0.329 |
+| CLIPSelf-L | Ensemble | 64.147 | 44.508 | 59.011 | 0.331 |
+
+Completed at: Thu Jan 22 04:19:18 AM UTC 2026

analysis/prompt_ensemble_ablation/scripts/generate_all_embeddings.sh

@@ -0,0 +1,95 @@
+#!/bin/bash
+# 生成所有模型的 single prompt 和 ensemble embeddings
+# 用于 prompt ensemble 消融实验
+
+set -e
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_DIR="$(dirname "$SCRIPT_DIR")"
+DECLIP_ROOT="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private"
+FVIT_DIR="$DECLIP_ROOT/CLIPSelf/F-ViT"
+
+# 设置 PYTHONPATH 以使用项目中的 open_clip
+export PYTHONPATH="$DECLIP_ROOT/src:$PYTHONPATH"
+
+# COCO 类别文件 (80 classes)
+CLASS_FILE="$FVIT_DIR/datasets/mscoco_all_classes.json"
+
+# 输出目录
+OUT_DIR="$PROJECT_DIR/embeddings"
+
+# EVA-CLIP 权重路径 (需要先运行 build_env/download_eva_clip.sh 下载)
+EVA_B_CKPT="/opt/tiger/xiaomoguhzz/EVA02_CLIP_B_psz16_s8B.pt"
+EVA_L_CKPT="/opt/tiger/xiaomoguhzz/EVA02_CLIP_L_336_psz14_s6B.pt"
+
+echo "=========================================="
+echo "Prompt Ensemble Ablation: Generate Embeddings"
+echo "=========================================="
+echo "Output directory: $OUT_DIR"
+echo ""
+
+# 检查 EVA-CLIP 权重是否存在
+if [ ! -f "$EVA_B_CKPT" ]; then
+    echo "WARNING: EVA-CLIP Base weights not found at $EVA_B_CKPT"
+    echo "Please run: bash build_env/download_eva_clip.sh"
+    echo "Skipping EVA-CLIP models..."
+    SKIP_EVA=true
+else
+    SKIP_EVA=false
+fi
+
+# 1. EVA-CLIP Base
+if [ "$SKIP_EVA" = false ]; then
+    echo "[1/4] EVA-CLIP Base (EVA02-CLIP-B-16)"
+    python "$SCRIPT_DIR/generate_single_prompt_embeddings.py" \
+        --class_file "$CLASS_FILE" \
+        --out_dir "$OUT_DIR" \
+        --model_name EVA02-CLIP-B-16 \
+        --pretrained eva \
+        --cache_dir "$EVA_B_CKPT" \
+        --mode both
+else
+    echo "[1/4] EVA-CLIP Base - SKIPPED (weights not found)"
+fi
+
+# 2. EVA-CLIP Large
+if [ "$SKIP_EVA" = false ] && [ -f "$EVA_L_CKPT" ]; then
+    echo ""
+    echo "[2/4] EVA-CLIP Large (EVA02-CLIP-L-14-336)"
+    python "$SCRIPT_DIR/generate_single_prompt_embeddings.py" \
+        --class_file "$CLASS_FILE" \
+        --out_dir "$OUT_DIR" \
+        --model_name EVA02-CLIP-L-14-336 \
+        --pretrained eva \
+        --cache_dir "$EVA_L_CKPT" \
+        --mode both
+else
+    echo ""
+    echo "[2/4] EVA-CLIP Large - SKIPPED (weights not found at $EVA_L_CKPT)"
+fi
+
+# 3. OpenAI CLIP Base (会自动下载)
+echo ""
+echo "[3/4] OpenAI CLIP Base (ViT-B-16)"
+python "$SCRIPT_DIR/generate_single_prompt_embeddings.py" \
+    --class_file "$CLASS_FILE" \
+    --out_dir "$OUT_DIR" \
+    --model_name ViT-B-16 \
+    --pretrained openai \
+    --mode both
+
+# 4. OpenAI CLIP Large (会自动下载)
+echo ""
+echo "[4/4] OpenAI CLIP Large (ViT-L-14)"
+python "$SCRIPT_DIR/generate_single_prompt_embeddings.py" \
+    --class_file "$CLASS_FILE" \
+    --out_dir "$OUT_DIR" \
+    --model_name ViT-L-14 \
+    --pretrained openai \
+    --mode both
+
+echo ""
+echo "=========================================="
+echo "All embeddings generated!"
+echo "=========================================="
+ls -lh "$OUT_DIR"

analysis/prompt_ensemble_ablation/scripts/generate_single_prompt_embeddings.py

@@ -0,0 +1,224 @@
+#!/usr/bin/env python3
+"""
+生成不使用 Prompt Ensemble 的 Text Embeddings
+用于消融实验：对比 prompt ensemble vs single prompt 的性能差异
+
+背景：审稿人问题 (3) - optimization for misalignment (prompt engineering)
+实验目的：验证 prompt ensemble 技术对 V-L 对齐的贡献
+"""
+
+import argparse
+import json
+import os
+import torch
+from tqdm import tqdm
+
+
+def article(name):
+    return "an" if name[0] in "aeiou" else "a"
+
+
+def processed_name(name, rm_dot=False):
+    res = name.replace("_", " ").replace("/", " or ").lower()
+    if rm_dot:
+        res = res.rstrip(".")
+    return res
+
+
+# 单模板 (用于消融实验)
+SINGLE_TEMPLATE = "a photo of a {}."
+
+# 多模板 (原始 prompt ensemble，用于对比)
+MULTIPLE_TEMPLATES = [
+    "There is {article} {} in the scene.",
+    "There is the {} in the scene.",
+    "a photo of {article} {} in the scene.",
+    "a photo of the {} in the scene.",
+    "a photo of one {} in the scene.",
+    "itap of {article} {}.",
+    "itap of my {}.",
+    "itap of the {}.",
+    "a photo of {article} {}.",
+    "a photo of my {}.",
+    "a photo of the {}.",
+    "a photo of one {}.",
+    "a photo of many {}.",
+    "a good photo of {article} {}.",
+    "a good photo of the {}.",
+    "a bad photo of {article} {}.",
+    "a bad photo of the {}.",
+    "a photo of a nice {}.",
+    "a photo of the nice {}.",
+    "a photo of a cool {}.",
+    "a photo of the cool {}.",
+    "a photo of a weird {}.",
+    "a photo of the weird {}.",
+    "a photo of a small {}.",
+    "a photo of the small {}.",
+    "a photo of a large {}.",
+    "a photo of the large {}.",
+    "a photo of a clean {}.",
+    "a photo of the clean {}.",
+    "a photo of a dirty {}.",
+    "a photo of the dirty {}.",
+    "a bright photo of {article} {}.",
+    "a bright photo of the {}.",
+    "a dark photo of {article} {}.",
+    "a dark photo of the {}.",
+    "a photo of a hard to see {}.",
+    "a photo of the hard to see {}.",
+    "a low resolution photo of {article} {}.",
+    "a low resolution photo of the {}.",
+    "a cropped photo of {article} {}.",
+    "a cropped photo of the {}.",
+    "a close-up photo of {article} {}.",
+    "a close-up photo of the {}.",
+    "a jpeg corrupted photo of {article} {}.",
+    "a jpeg corrupted photo of the {}.",
+    "a blurry photo of {article} {}.",
+    "a blurry photo of the {}.",
+    "a pixelated photo of {article} {}.",
+    "a pixelated photo of the {}.",
+    "a black and white photo of the {}.",
+    "a black and white photo of {article} {}.",
+    "a plastic {}.",
+    "the plastic {}.",
+    "a toy {}.",
+    "the toy {}.",
+    "a plushie {}.",
+    "the plushie {}.",
+    "a cartoon {}.",
+    "the cartoon {}.",
+    "an embroidered {}.",
+    "the embroidered {}.",
+    "a painting of the {}.",
+    "a painting of a {}.",
+]
+
+
+def build_text_embedding_single_prompt(categories, model, tokenizer, device='cuda'):
+    """使用单一模板生成 text embedding (无 prompt ensemble)"""
+    with torch.no_grad():
+        all_text_embeddings = []
+        for category in tqdm(categories, desc="Single prompt"):
+            text = SINGLE_TEMPLATE.format(processed_name(category, rm_dot=True))
+            tokens = tokenizer([text])
+            if device == 'cuda':
+                tokens = tokens.cuda()
+            text_embedding = model.encode_text(tokens)
+            text_embedding = text_embedding / text_embedding.norm(dim=-1, keepdim=True)
+            all_text_embeddings.append(text_embedding.squeeze(0))
+        
+        all_text_embeddings = torch.stack(all_text_embeddings, dim=0)
+    
+    return all_text_embeddings
+
+
+def build_text_embedding_ensemble(categories, model, tokenizer, device='cuda'):
+    """使用多模板生成 text embedding (prompt ensemble)"""
+    with torch.no_grad():
+        all_text_embeddings = []
+        for category in tqdm(categories, desc="Prompt ensemble"):
+            texts = [
+                template.format(
+                    processed_name(category, rm_dot=True), 
+                    article=article(category)
+                )
+                for template in MULTIPLE_TEMPLATES
+            ]
+            texts = [
+                "This is " + text if text.startswith("a") or text.startswith("the") else text
+                for text in texts
+            ]
+            tokens = tokenizer(texts)
+            if device == 'cuda':
+                tokens = tokens.cuda()
+            text_embeddings = model.encode_text(tokens)
+            text_embeddings = text_embeddings / text_embeddings.norm(dim=-1, keepdim=True)
+            text_embedding = text_embeddings.mean(dim=0)
+            text_embedding = text_embedding / text_embedding.norm()
+            all_text_embeddings.append(text_embedding)
+        
+        all_text_embeddings = torch.stack(all_text_embeddings, dim=0)
+    
+    return all_text_embeddings
+
+
+def load_categories(class_file):
+    """从类别 JSON 文件加载类别名称"""
+    print(f'Loading {class_file}')
+    with open(class_file, 'r') as f:
+        cat_names = json.load(f)
+    cat_names = cat_names + ['background']
+    print(f'Categories ({len(cat_names)}): {cat_names[:5]}...{cat_names[-3:]}')
+    return cat_names
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Generate text embeddings with/without prompt ensemble')
+    parser.add_argument('--class_file', type=str, required=True, help='Path to class names JSON (e.g., mscoco_all_classes.json)')
+    parser.add_argument('--out_dir', type=str, required=True, help='Output directory for embeddings')
+    parser.add_argument('--model_name', type=str, required=True, 
+                        choices=['EVA02-CLIP-B-16', 'EVA02-CLIP-L-14-336', 'ViT-B-16', 'ViT-L-14'],
+                        help='Model name')
+    parser.add_argument('--pretrained', type=str, required=True,
+                        help='Pretrained source (eva, openai)')
+    parser.add_argument('--cache_dir', type=str, default='',
+                        help='Path to checkpoint file (for EVA-CLIP)')
+    parser.add_argument('--mode', type=str, default='both', choices=['single', 'ensemble', 'both'],
+                        help='Generate single prompt, ensemble, or both')
+    args = parser.parse_args()
+
+    # 加载类别
+    categories = load_categories(args.class_file)
+    
+    # 加载模型
+    print(f'\nLoading model: {args.model_name} (pretrained={args.pretrained})')
+    from open_clip import create_model, get_tokenizer
+    
+    model = create_model(
+        model_name=args.model_name, 
+        pretrained=args.pretrained,
+        cache_dir=args.cache_dir if args.cache_dir else None
+    )
+    tokenizer = get_tokenizer(model_name=args.model_name)
+    
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    model = model.to(device)
+    model.eval()
+    
+    # 确定输出文件名前缀
+    model_suffix = args.model_name.lower().replace('-', '_').replace('/', '_')
+    pretrained_suffix = args.pretrained.lower()
+    
+    os.makedirs(args.out_dir, exist_ok=True)
+    
+    # 生成 embeddings
+    if args.mode in ['single', 'both']:
+        print('\n=== Generating Single Prompt Embeddings ===')
+        single_embeddings = build_text_embedding_single_prompt(categories, model, tokenizer, device)
+        single_embeddings = single_embeddings.cpu().float()
+        print(f'Shape: {single_embeddings.shape}')
+        
+        # 保存为 dict 格式 (与原始格式兼容)
+        single_dict = {k: v for k, v in zip(categories, single_embeddings)}
+        out_path = os.path.join(args.out_dir, f'coco_{model_suffix}_{pretrained_suffix}_single_prompt.pt')
+        torch.save(single_dict, out_path)
+        print(f'Saved: {out_path}')
+    
+    if args.mode in ['ensemble', 'both']:
+        print('\n=== Generating Prompt Ensemble Embeddings ===')
+        ensemble_embeddings = build_text_embedding_ensemble(categories, model, tokenizer, device)
+        ensemble_embeddings = ensemble_embeddings.cpu().float()
+        print(f'Shape: {ensemble_embeddings.shape}')
+        
+        ensemble_dict = {k: v for k, v in zip(categories, ensemble_embeddings)}
+        out_path = os.path.join(args.out_dir, f'coco_{model_suffix}_{pretrained_suffix}_ensemble.pt')
+        torch.save(ensemble_dict, out_path)
+        print(f'Saved: {out_path}')
+    
+    print('\nDone!')
+
+
+if __name__ == '__main__':
+    main()

analysis/prompt_ensemble_ablation/scripts/run_all_ablation.sh

@@ -0,0 +1,211 @@
+#!/bin/bash
+# Prompt Ensemble 消融实验完整测试脚本
+# 包含: CLIPSelf (Base/Large), MaskCLIP (Base/Large), DeCLIP (Base/Large)
+# 每个模型测试 Single Prompt vs Ensemble
+
+set -e
+
+# 路径配置
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ABLATION_DIR="$(dirname "${SCRIPT_DIR}")"
+FVIT_DIR="${ABLATION_DIR}/../CLIPSelf/F-ViT"
+RESULT_DIR="${ABLATION_DIR}/results"
+RESULT_FILE="${RESULT_DIR}/all_ablation_results.txt"
+
+# ==================== 权重路径 ====================
+# CLIPSelf 权重
+CLIPSELF_B_CKPT="/opt/tiger/xiaomoguhzz/fvit_eva_vitb16_ovcoco_clipself_proposals.pth"
+CLIPSELF_L_CKPT="/opt/tiger/xiaomoguhzz/fvit_eva_vitl14_ovcoco_clipself_proposals.pth"
+
+# MaskCLIP 权重 (原始 EVA-CLIP，经过检测器训练)
+MASKCLIP_B_CKPT="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/work_dirs/fvit_vitb16_ovcoco_maskclip/epoch_3.pth"
+# MaskCLIP Large 权重 - 需要提供路径
+MASKCLIP_L_CKPT=""  # TODO: 填写 MaskCLIP Large 权重路径
+
+# DeCLIP 权重
+DECLIP_B_CKPT="/opt/tiger/xiaomoguhzz/declip2_ovcoco_detector/EVAB_dinov2B_epoch2.pth"
+DECLIP_L_CKPT="/opt/tiger/xiaomoguhzz/declip2_ovcoco_detector/EVAL_dinov2L_epoch3.pth"
+
+# ==================== 配置文件路径 ====================
+CONFIG_DIR="${ABLATION_DIR}/configs"
+
+# CLIPSelf 配置
+CLIPSELF_B_SINGLE="${CONFIG_DIR}/fvit_vitb16_ovcoco_single_prompt.py"
+CLIPSELF_B_ENSEMBLE="${CONFIG_DIR}/fvit_vitb16_ovcoco_ensemble.py"
+CLIPSELF_L_SINGLE="${CONFIG_DIR}/fvit_vitl14_ovcoco_single_prompt.py"
+CLIPSELF_L_ENSEMBLE="${CONFIG_DIR}/fvit_vitl14_ovcoco_ensemble.py"
+
+# MaskCLIP 配置
+MASKCLIP_B_SINGLE="${CONFIG_DIR}/fvit_vitb16_ovcoco_maskclip_single_prompt.py"
+MASKCLIP_B_ENSEMBLE="${CONFIG_DIR}/fvit_vitb16_ovcoco_maskclip_ensemble.py"
+MASKCLIP_L_SINGLE="${CONFIG_DIR}/fvit_vitl14_ovcoco_maskclip_single_prompt.py"
+MASKCLIP_L_ENSEMBLE="${CONFIG_DIR}/fvit_vitl14_ovcoco_maskclip_ensemble.py"
+
+# DeCLIP 配置
+DECLIP_B_SINGLE="${CONFIG_DIR}/fvit_vitb16_ovcoco_declip_single_prompt.py"
+DECLIP_B_ENSEMBLE="${CONFIG_DIR}/fvit_vitb16_ovcoco_declip_ensemble.py"
+DECLIP_L_SINGLE="${CONFIG_DIR}/fvit_vitl14_ovcoco_declip_single_prompt.py"
+DECLIP_L_ENSEMBLE="${CONFIG_DIR}/fvit_vitl14_ovcoco_declip_ensemble.py"
+
+# 创建结果目录
+mkdir -p "${RESULT_DIR}"
+
+# 初始化结果文件
+echo "================================================" > "${RESULT_FILE}"
+echo "Prompt Ensemble Ablation - All Models Results" >> "${RESULT_FILE}"
+echo "Date: $(date)" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "" >> "${RESULT_FILE}"
+
+cd "${FVIT_DIR}"
+export PYTHONPATH="$(pwd)/..":$PYTHONPATH
+
+# 运行单个测试的函数
+run_test() {
+    local name=$1
+    local config=$2
+    local checkpoint=$3
+    local tmpdir=$4
+    local log_file="${RESULT_DIR}/${name}.log"
+    
+    # 检查权重是否存在
+    if [ ! -f "${checkpoint}" ]; then
+        echo "SKIP: ${name} - 权重不存在: ${checkpoint}"
+        echo "## ${name} - SKIPPED (权重不存在)" >> "${RESULT_FILE}"
+        echo "" >> "${RESULT_FILE}"
+        return
+    fi
+    
+    echo ""
+    echo "================================================"
+    echo "Running: ${name}"
+    echo "Config: ${config}"
+    echo "Checkpoint: ${checkpoint}"
+    echo "================================================"
+    
+    echo "## ${name}" >> "${RESULT_FILE}"
+    echo "Config: $(basename ${config})" >> "${RESULT_FILE}"
+    echo "Checkpoint: $(basename ${checkpoint})" >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+    
+    # 运行测试并保存日志
+    torchrun --nproc_per_node=8 --master_port=12346 \
+        test.py \
+        "${config}" \
+        "${checkpoint}" \
+        --launcher pytorch \
+        --eval bbox \
+        --tmpdir "${tmpdir}" \
+        2>&1 | tee "${log_file}"
+    
+    # 提取结果
+    echo "Results:" >> "${RESULT_FILE}"
+    grep -E "OrderedDict" "${log_file}" | tail -1 >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+    echo "----------------------------------------" >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+    
+    echo "Completed: ${name}"
+}
+
+echo ""
+echo "=========================================="
+echo "开始 Prompt Ensemble 消融实验"
+echo "=========================================="
+
+# ==================== 1. CLIPSelf 测试 ====================
+echo ""
+echo "========== CLIPSelf Tests =========="
+
+# CLIPSelf Base
+run_test "clipself_evab_single" "${CLIPSELF_B_SINGLE}" "${CLIPSELF_B_CKPT}" "/tmp/clipself_b_single"
+run_test "clipself_evab_ensemble" "${CLIPSELF_B_ENSEMBLE}" "${CLIPSELF_B_CKPT}" "/tmp/clipself_b_ensemble"
+
+# CLIPSelf Large
+run_test "clipself_eval_single" "${CLIPSELF_L_SINGLE}" "${CLIPSELF_L_CKPT}" "/tmp/clipself_l_single"
+run_test "clipself_eval_ensemble" "${CLIPSELF_L_ENSEMBLE}" "${CLIPSELF_L_CKPT}" "/tmp/clipself_l_ensemble"
+
+# ==================== 2. MaskCLIP 测试 ====================
+echo ""
+echo "========== MaskCLIP Tests =========="
+
+# MaskCLIP Base
+run_test "maskclip_evab_single" "${MASKCLIP_B_SINGLE}" "${MASKCLIP_B_CKPT}" "/tmp/maskclip_b_single"
+run_test "maskclip_evab_ensemble" "${MASKCLIP_B_ENSEMBLE}" "${MASKCLIP_B_CKPT}" "/tmp/maskclip_b_ensemble"
+
+# MaskCLIP Large (如果权重存在)
+if [ -n "${MASKCLIP_L_CKPT}" ]; then
+    run_test "maskclip_eval_single" "${MASKCLIP_L_SINGLE}" "${MASKCLIP_L_CKPT}" "/tmp/maskclip_l_single"
+    run_test "maskclip_eval_ensemble" "${MASKCLIP_L_ENSEMBLE}" "${MASKCLIP_L_CKPT}" "/tmp/maskclip_l_ensemble"
+else
+    echo "SKIP: MaskCLIP Large - 权重路径未设置"
+    echo "## MaskCLIP Large - SKIPPED (权重路径未设置)" >> "${RESULT_FILE}"
+fi
+
+# ==================== 3. DeCLIP 测试 ====================
+echo ""
+echo "========== DeCLIP Tests =========="
+
+# DeCLIP Base
+run_test "declip_evab_single" "${DECLIP_B_SINGLE}" "${DECLIP_B_CKPT}" "/tmp/declip_b_single"
+run_test "declip_evab_ensemble" "${DECLIP_B_ENSEMBLE}" "${DECLIP_B_CKPT}" "/tmp/declip_b_ensemble"
+
+# DeCLIP Large
+run_test "declip_eval_single" "${DECLIP_L_SINGLE}" "${DECLIP_L_CKPT}" "/tmp/declip_l_single"
+run_test "declip_eval_ensemble" "${DECLIP_L_ENSEMBLE}" "${DECLIP_L_CKPT}" "/tmp/declip_l_ensemble"
+
+# ==================== 汇总结果 ====================
+echo "" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "Summary Table" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "" >> "${RESULT_FILE}"
+echo "| Model | Size | Method | base_ap50 | novel_ap50 | all_ap50 | bbox_mAP |" >> "${RESULT_FILE}"
+echo "|-------|------|--------|-----------|------------|----------|----------|" >> "${RESULT_FILE}"
+
+# 从日志中提取并格式化结果
+extract_result() {
+    local log=$1
+    local model=$2
+    local size=$3
+    local method=$4
+    
+    if [ -f "${RESULT_DIR}/${log}.log" ]; then
+        result_line=$(grep "OrderedDict" "${RESULT_DIR}/${log}.log" | tail -1)
+        if [ -n "${result_line}" ]; then
+            base_ap50=$(echo "${result_line}" | grep -oP "base_ap50', \K[0-9.]+" || echo "N/A")
+            novel_ap50=$(echo "${result_line}" | grep -oP "novel_ap50', \K[0-9.]+" || echo "N/A")
+            all_ap50=$(echo "${result_line}" | grep -oP "all_ap50', \K[0-9.]+" || echo "N/A")
+            bbox_mAP=$(echo "${result_line}" | grep -oP "bbox_mAP', \K[0-9.]+" || echo "N/A")
+            echo "| ${model} | ${size} | ${method} | ${base_ap50} | ${novel_ap50} | ${all_ap50} | ${bbox_mAP} |" >> "${RESULT_FILE}"
+        fi
+    fi
+}
+
+# CLIPSelf 结果
+extract_result "clipself_evab_single" "CLIPSelf" "Base" "Single"
+extract_result "clipself_evab_ensemble" "CLIPSelf" "Base" "Ensemble"
+extract_result "clipself_eval_single" "CLIPSelf" "Large" "Single"
+extract_result "clipself_eval_ensemble" "CLIPSelf" "Large" "Ensemble"
+
+# MaskCLIP 结果
+extract_result "maskclip_evab_single" "MaskCLIP" "Base" "Single"
+extract_result "maskclip_evab_ensemble" "MaskCLIP" "Base" "Ensemble"
+extract_result "maskclip_eval_single" "MaskCLIP" "Large" "Single"
+extract_result "maskclip_eval_ensemble" "MaskCLIP" "Large" "Ensemble"
+
+# DeCLIP 结果
+extract_result "declip_evab_single" "DeCLIP" "Base" "Single"
+extract_result "declip_evab_ensemble" "DeCLIP" "Base" "Ensemble"
+extract_result "declip_eval_single" "DeCLIP" "Large" "Single"
+extract_result "declip_eval_ensemble" "DeCLIP" "Large" "Ensemble"
+
+echo "" >> "${RESULT_FILE}"
+echo "Completed at: $(date)" >> "${RESULT_FILE}"
+
+echo ""
+echo "================================================"
+echo "所有测试完成!"
+echo "结果保存在: ${RESULT_FILE}"
+echo "================================================"
+cat "${RESULT_FILE}"

analysis/prompt_ensemble_ablation/scripts/run_clearclip_b.sh

@@ -0,0 +1,125 @@
+#!/bin/bash
+# 测试 ClearCLIP Base 的 Prompt Ensemble 消融
+# feature_mode='qq', 共 2 个测试
+
+set -e
+
+# 路径配置
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ABLATION_DIR="$(dirname "${SCRIPT_DIR}")"
+FVIT_DIR="${ABLATION_DIR}/../CLIPSelf/F-ViT"
+RESULT_DIR="${ABLATION_DIR}/results"
+RESULT_FILE="${RESULT_DIR}/clearclip_b_results.txt"
+
+# 权重路径
+CLEARCLIP_B_CKPT="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/work_dirs/clearclip_ovcoco/epoch_3.pth"
+
+# 配置文件
+CONFIG_DIR="${ABLATION_DIR}/configs"
+CLEARCLIP_B_SINGLE="${CONFIG_DIR}/fvit_vitb16_ovcoco_clearclip_single_prompt.py"
+CLEARCLIP_B_ENSEMBLE="${CONFIG_DIR}/fvit_vitb16_ovcoco_clearclip_ensemble.py"
+
+# 创建结果目录
+mkdir -p "${RESULT_DIR}"
+
+# 初始化结果文件
+echo "================================================" > "${RESULT_FILE}"
+echo "ClearCLIP-B (QQ mode) Ablation Results" >> "${RESULT_FILE}"
+echo "Date: $(date)" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "" >> "${RESULT_FILE}"
+
+# 检查权重文件
+echo "检查权重文件..."
+if [ ! -f "${CLEARCLIP_B_CKPT}" ]; then
+    echo "错误: 权重不存在: ${CLEARCLIP_B_CKPT}"
+    exit 1
+fi
+echo "权重文件检查通过"
+
+cd "${FVIT_DIR}"
+export PYTHONPATH="$(pwd)/..":$PYTHONPATH
+
+# 运行测试函数
+run_test() {
+    local name=$1
+    local config=$2
+    local checkpoint=$3
+    local tmpdir=$4
+    local log_file="${RESULT_DIR}/${name}.log"
+    
+    echo ""
+    echo "================================================"
+    echo "Running: ${name}"
+    echo "Config: $(basename ${config})"
+    echo "Checkpoint: $(basename ${checkpoint})"
+    echo "================================================"
+    
+    echo "## ${name}" >> "${RESULT_FILE}"
+    echo "Config: $(basename ${config})" >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+    
+    torchrun --nproc_per_node=8 --master_port=12346 \
+        test.py \
+        "${config}" \
+        "${checkpoint}" \
+        --launcher pytorch \
+        --eval bbox \
+        --tmpdir "${tmpdir}" \
+        2>&1 | tee "${log_file}"
+    
+    echo "Results:" >> "${RESULT_FILE}"
+    grep -E "OrderedDict" "${log_file}" | tail -1 >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+    echo "----------------------------------------" >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+}
+
+echo ""
+echo "开始测试 ClearCLIP-B (共 2 个)..."
+
+# 1. ClearCLIP Base Single
+run_test "clearclip_evab_single" "${CLEARCLIP_B_SINGLE}" "${CLEARCLIP_B_CKPT}" "/tmp/clearclip_b_single"
+
+# 2. ClearCLIP Base Ensemble
+run_test "clearclip_evab_ensemble" "${CLEARCLIP_B_ENSEMBLE}" "${CLEARCLIP_B_CKPT}" "/tmp/clearclip_b_ensemble"
+
+# 汇总结果
+echo "" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "Summary" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "" >> "${RESULT_FILE}"
+echo "| Model | Method | base_ap50 | novel_ap50 | all_ap50 | bbox_mAP |" >> "${RESULT_FILE}"
+echo "|-------|--------|-----------|------------|----------|----------|" >> "${RESULT_FILE}"
+
+for log in clearclip_evab_single clearclip_evab_ensemble; do
+    if [ -f "${RESULT_DIR}/${log}.log" ]; then
+        model="ClearCLIP-B"
+        if [[ "${log}" == *"single"* ]]; then
+            method="Single"
+        else
+            method="Ensemble"
+        fi
+        
+        result_line=$(grep "OrderedDict" "${RESULT_DIR}/${log}.log" | tail -1)
+        if [ -n "${result_line}" ]; then
+            base_ap50=$(echo "${result_line}" | grep -oP "base_ap50', \K[0-9.]+" || echo "N/A")
+            novel_ap50=$(echo "${result_line}" | grep -oP "novel_ap50', \K[0-9.]+" || echo "N/A")
+            all_ap50=$(echo "${result_line}" | grep -oP "all_ap50', \K[0-9.]+" || echo "N/A")
+            bbox_mAP=$(echo "${result_line}" | grep -oP "bbox_mAP', \K[0-9.]+" || echo "N/A")
+            echo "| ${model} | ${method} | ${base_ap50} | ${novel_ap50} | ${all_ap50} | ${bbox_mAP} |" >> "${RESULT_FILE}"
+        fi
+    fi
+done
+
+echo "" >> "${RESULT_FILE}"
+echo "Completed at: $(date)" >> "${RESULT_FILE}"
+
+echo ""
+echo "================================================"
+echo "所有测试完成!"
+echo "结果保存在: ${RESULT_FILE}"
+echo "================================================"
+echo ""
+cat "${RESULT_FILE}"

analysis/prompt_ensemble_ablation/scripts/run_clearclip_l.sh

@@ -0,0 +1,125 @@
+#!/bin/bash
+# 测试 ClearCLIP Large 的 Prompt Ensemble 消融
+# feature_mode='qq', 共 2 个测试
+
+set -e
+
+# 路径配置
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ABLATION_DIR="$(dirname "${SCRIPT_DIR}")"
+FVIT_DIR="${ABLATION_DIR}/../CLIPSelf/F-ViT"
+RESULT_DIR="${ABLATION_DIR}/results"
+RESULT_FILE="${RESULT_DIR}/clearclip_l_results.txt"
+
+# 权重路径
+CLEARCLIP_L_CKPT="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/work_dirs/clearclip_ovcoco_large/epoch_3.pth"
+
+# 配置文件
+CONFIG_DIR="${ABLATION_DIR}/configs"
+CLEARCLIP_L_SINGLE="${CONFIG_DIR}/fvit_vitl14_ovcoco_clearclip_single_prompt.py"
+CLEARCLIP_L_ENSEMBLE="${CONFIG_DIR}/fvit_vitl14_ovcoco_clearclip_ensemble.py"
+
+# 创建结果目录
+mkdir -p "${RESULT_DIR}"
+
+# 初始化结果文件
+echo "================================================" > "${RESULT_FILE}"
+echo "ClearCLIP-L (QQ mode) Ablation Results" >> "${RESULT_FILE}"
+echo "Date: $(date)" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "" >> "${RESULT_FILE}"
+
+# 检查权重文件
+echo "检查权重文件..."
+if [ ! -f "${CLEARCLIP_L_CKPT}" ]; then
+    echo "错误: 权重不存在: ${CLEARCLIP_L_CKPT}"
+    exit 1
+fi
+echo "权重文件检查通过"
+
+cd "${FVIT_DIR}"
+export PYTHONPATH="$(pwd)/..":$PYTHONPATH
+
+# 运行测试函数
+run_test() {
+    local name=$1
+    local config=$2
+    local checkpoint=$3
+    local tmpdir=$4
+    local log_file="${RESULT_DIR}/${name}.log"
+    
+    echo ""
+    echo "================================================"
+    echo "Running: ${name}"
+    echo "Config: $(basename ${config})"
+    echo "Checkpoint: $(basename ${checkpoint})"
+    echo "================================================"
+    
+    echo "## ${name}" >> "${RESULT_FILE}"
+    echo "Config: $(basename ${config})" >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+    
+    torchrun --nproc_per_node=8 --master_port=12346 \
+        test.py \
+        "${config}" \
+        "${checkpoint}" \
+        --launcher pytorch \
+        --eval bbox \
+        --tmpdir "${tmpdir}" \
+        2>&1 | tee "${log_file}"
+    
+    echo "Results:" >> "${RESULT_FILE}"
+    grep -E "OrderedDict" "${log_file}" | tail -1 >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+    echo "----------------------------------------" >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+}
+
+echo ""
+echo "开始测试 ClearCLIP-L (共 2 个)..."
+
+# 1. ClearCLIP Large Single
+run_test "clearclip_eval_single" "${CLEARCLIP_L_SINGLE}" "${CLEARCLIP_L_CKPT}" "/tmp/clearclip_l_single"
+
+# 2. ClearCLIP Large Ensemble
+run_test "clearclip_eval_ensemble" "${CLEARCLIP_L_ENSEMBLE}" "${CLEARCLIP_L_CKPT}" "/tmp/clearclip_l_ensemble"
+
+# 汇总结果
+echo "" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "Summary" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "" >> "${RESULT_FILE}"
+echo "| Model | Method | base_ap50 | novel_ap50 | all_ap50 | bbox_mAP |" >> "${RESULT_FILE}"
+echo "|-------|--------|-----------|------------|----------|----------|" >> "${RESULT_FILE}"
+
+for log in clearclip_eval_single clearclip_eval_ensemble; do
+    if [ -f "${RESULT_DIR}/${log}.log" ]; then
+        model="ClearCLIP-L"
+        if [[ "${log}" == *"single"* ]]; then
+            method="Single"
+        else
+            method="Ensemble"
+        fi
+        
+        result_line=$(grep "OrderedDict" "${RESULT_DIR}/${log}.log" | tail -1)
+        if [ -n "${result_line}" ]; then
+            base_ap50=$(echo "${result_line}" | grep -oP "base_ap50', \K[0-9.]+" || echo "N/A")
+            novel_ap50=$(echo "${result_line}" | grep -oP "novel_ap50', \K[0-9.]+" || echo "N/A")
+            all_ap50=$(echo "${result_line}" | grep -oP "all_ap50', \K[0-9.]+" || echo "N/A")
+            bbox_mAP=$(echo "${result_line}" | grep -oP "bbox_mAP', \K[0-9.]+" || echo "N/A")
+            echo "| ${model} | ${method} | ${base_ap50} | ${novel_ap50} | ${all_ap50} | ${bbox_mAP} |" >> "${RESULT_FILE}"
+        fi
+    fi
+done
+
+echo "" >> "${RESULT_FILE}"
+echo "Completed at: $(date)" >> "${RESULT_FILE}"
+
+echo ""
+echo "================================================"
+echo "所有测试完成!"
+echo "结果保存在: ${RESULT_FILE}"
+echo "================================================"
+echo ""
+cat "${RESULT_FILE}"

analysis/prompt_ensemble_ablation/scripts/run_declip_ablation.sh

@@ -0,0 +1,163 @@
+#!/bin/bash
+# DeCLIP Prompt Ensemble 消融实验脚本
+# 运行 4 个测试: EVA-B/L x Single/Ensemble
+# 结果保存到 results/declip_ablation_results.txt
+
+set -e
+
+# 路径配置
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ABLATION_DIR="$(dirname "${SCRIPT_DIR}")"
+FVIT_DIR="${ABLATION_DIR}/../CLIPSelf/F-ViT"
+RESULT_DIR="${ABLATION_DIR}/results"
+RESULT_FILE="${RESULT_DIR}/declip_ablation_results.txt"
+
+# 权重路径
+EVAB_CKPT="/opt/tiger/xiaomoguhzz/declip2_ovcoco_detector/EVAB_dinov2B_epoch2.pth"
+EVAL_CKPT="/opt/tiger/xiaomoguhzz/declip2_ovcoco_detector/EVAL_dinov2L_epoch3.pth"
+
+# 配置文件路径
+CONFIG_B_SINGLE="${ABLATION_DIR}/configs/fvit_vitb16_ovcoco_declip_single_prompt.py"
+CONFIG_B_ENSEMBLE="${ABLATION_DIR}/configs/fvit_vitb16_ovcoco_declip_ensemble.py"
+CONFIG_L_SINGLE="${ABLATION_DIR}/configs/fvit_vitl14_ovcoco_declip_single_prompt.py"
+CONFIG_L_ENSEMBLE="${ABLATION_DIR}/configs/fvit_vitl14_ovcoco_declip_ensemble.py"
+
+# 创建结果目录
+mkdir -p "${RESULT_DIR}"
+
+# 初始化结果文件
+echo "================================================" > "${RESULT_FILE}"
+echo "DeCLIP Prompt Ensemble Ablation Results" >> "${RESULT_FILE}"
+echo "Date: $(date)" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "" >> "${RESULT_FILE}"
+
+# 检查权重文件
+echo "检查权重文件..."
+if [ ! -f "${EVAB_CKPT}" ]; then
+    echo "错误: EVA-B 权重不存在: ${EVAB_CKPT}"
+    exit 1
+fi
+if [ ! -f "${EVAL_CKPT}" ]; then
+    echo "错误: EVA-L 权重不存在: ${EVAL_CKPT}"
+    exit 1
+fi
+echo "权重文件检查通过"
+
+cd "${FVIT_DIR}"
+export PYTHONPATH="$(pwd)/..":$PYTHONPATH
+
+# 运行单个测试的函数
+run_test() {
+    local name=$1
+    local config=$2
+    local checkpoint=$3
+    local tmpdir=$4
+    local log_file="${RESULT_DIR}/${name}.log"
+    
+    echo ""
+    echo "================================================"
+    echo "Running: ${name}"
+    echo "Config: ${config}"
+    echo "Checkpoint: ${checkpoint}"
+    echo "================================================"
+    
+    echo "## ${name}" >> "${RESULT_FILE}"
+    echo "Config: $(basename ${config})" >> "${RESULT_FILE}"
+    echo "Checkpoint: $(basename ${checkpoint})" >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+    
+    # 运行测试并保存日志
+    torchrun --nproc_per_node=8 --master_port=12346 \
+        test.py \
+        "${config}" \
+        "${checkpoint}" \
+        --launcher pytorch \
+        --eval bbox \
+        --tmpdir "${tmpdir}" \
+        2>&1 | tee "${log_file}"
+    
+    # 提取结果
+    echo "Results:" >> "${RESULT_FILE}"
+    grep -E "(base_ap50|novel_ap50|all_ap50|bbox_mAP)" "${log_file}" | tail -1 >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+    echo "----------------------------------------" >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+    
+    echo "Completed: ${name}"
+}
+
+# 运行 4 个测试
+echo ""
+echo "开始运行 DeCLIP 消融实验 (共 4 个测试)..."
+echo ""
+
+# 1. DeCLIP EVA-B Single Prompt
+run_test "declip_evab_single_prompt" \
+    "${CONFIG_B_SINGLE}" \
+    "${EVAB_CKPT}" \
+    "/tmp/dist_test_declip_b_single"
+
+# 2. DeCLIP EVA-B Ensemble
+run_test "declip_evab_ensemble" \
+    "${CONFIG_B_ENSEMBLE}" \
+    "${EVAB_CKPT}" \
+    "/tmp/dist_test_declip_b_ensemble"
+
+# 3. DeCLIP EVA-L Single Prompt
+run_test "declip_eval_single_prompt" \
+    "${CONFIG_L_SINGLE}" \
+    "${EVAL_CKPT}" \
+    "/tmp/dist_test_declip_l_single"
+
+# 4. DeCLIP EVA-L Ensemble
+run_test "declip_eval_ensemble" \
+    "${CONFIG_L_ENSEMBLE}" \
+    "${EVAL_CKPT}" \
+    "/tmp/dist_test_declip_l_ensemble"
+
+# 汇总结果
+echo "" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "Summary" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "" >> "${RESULT_FILE}"
+echo "| Model | Method | base_ap50 | novel_ap50 | all_ap50 | bbox_mAP |" >> "${RESULT_FILE}"
+echo "|-------|--------|-----------|------------|----------|----------|" >> "${RESULT_FILE}"
+
+# 从日志中提取并格式化结果
+for log in declip_evab_single_prompt declip_evab_ensemble declip_eval_single_prompt declip_eval_ensemble; do
+    if [ -f "${RESULT_DIR}/${log}.log" ]; then
+        # 提取模型和方法名
+        if [[ "${log}" == *"evab"* ]]; then
+            model="DeCLIP-B"
+        else
+            model="DeCLIP-L"
+        fi
+        if [[ "${log}" == *"single"* ]]; then
+            method="Single"
+        else
+            method="Ensemble"
+        fi
+        
+        # 提取指标
+        result_line=$(grep "OrderedDict" "${RESULT_DIR}/${log}.log" | tail -1)
+        if [ -n "${result_line}" ]; then
+            base_ap50=$(echo "${result_line}" | grep -oP "base_ap50', \K[0-9.]+" || echo "N/A")
+            novel_ap50=$(echo "${result_line}" | grep -oP "novel_ap50', \K[0-9.]+" || echo "N/A")
+            all_ap50=$(echo "${result_line}" | grep -oP "all_ap50', \K[0-9.]+" || echo "N/A")
+            bbox_mAP=$(echo "${result_line}" | grep -oP "bbox_mAP', \K[0-9.]+" || echo "N/A")
+            echo "| ${model} | ${method} | ${base_ap50} | ${novel_ap50} | ${all_ap50} | ${bbox_mAP} |" >> "${RESULT_FILE}"
+        fi
+    fi
+done
+
+echo "" >> "${RESULT_FILE}"
+echo "Completed at: $(date)" >> "${RESULT_FILE}"
+
+echo ""
+echo "================================================"
+echo "所有测试完成!"
+echo "结果保存在: ${RESULT_FILE}"
+echo "================================================"
+cat "${RESULT_FILE}"

analysis/prompt_ensemble_ablation/scripts/run_maskclip_b_clipself_l.sh

@@ -0,0 +1,140 @@
+#!/bin/bash
+# 测试 MaskCLIP Base + CLIPSelf Large 的 Prompt Ensemble 消融
+# 共 4 个测试
+
+set -e
+
+# 路径配置
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ABLATION_DIR="$(dirname "${SCRIPT_DIR}")"
+FVIT_DIR="${ABLATION_DIR}/../CLIPSelf/F-ViT"
+RESULT_DIR="${ABLATION_DIR}/results"
+RESULT_FILE="${RESULT_DIR}/maskclip_b_clipself_l_results.txt"
+
+# 权重路径
+MASKCLIP_B_CKPT="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/work_dirs/fvit_vitb16_ovcoco_maskclip/epoch_3.pth"
+CLIPSELF_L_CKPT="/opt/tiger/xiaomoguhzz/fvit_eva_vitl14_ovcoco_clipself_proposals.pth"
+
+# 配置文件
+CONFIG_DIR="${ABLATION_DIR}/configs"
+MASKCLIP_B_SINGLE="${CONFIG_DIR}/fvit_vitb16_ovcoco_maskclip_single_prompt.py"
+MASKCLIP_B_ENSEMBLE="${CONFIG_DIR}/fvit_vitb16_ovcoco_maskclip_ensemble.py"
+CLIPSELF_L_SINGLE="${CONFIG_DIR}/fvit_vitl14_ovcoco_single_prompt.py"
+CLIPSELF_L_ENSEMBLE="${CONFIG_DIR}/fvit_vitl14_ovcoco_ensemble.py"
+
+# 创建结果目录
+mkdir -p "${RESULT_DIR}"
+
+# 初始化结果文件
+echo "================================================" > "${RESULT_FILE}"
+echo "MaskCLIP-B + CLIPSelf-L Ablation Results" >> "${RESULT_FILE}"
+echo "Date: $(date)" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "" >> "${RESULT_FILE}"
+
+# 检查权重文件
+echo "检查权重文件..."
+for ckpt in "${MASKCLIP_B_CKPT}" "${CLIPSELF_L_CKPT}"; do
+    if [ ! -f "${ckpt}" ]; then
+        echo "错误: 权重不存在: ${ckpt}"
+        exit 1
+    fi
+done
+echo "权重文件检查通过"
+
+cd "${FVIT_DIR}"
+export PYTHONPATH="$(pwd)/..":$PYTHONPATH
+
+# 运行测试函数
+run_test() {
+    local name=$1
+    local config=$2
+    local checkpoint=$3
+    local tmpdir=$4
+    local log_file="${RESULT_DIR}/${name}.log"
+    
+    echo ""
+    echo "================================================"
+    echo "Running: ${name}"
+    echo "Config: $(basename ${config})"
+    echo "Checkpoint: $(basename ${checkpoint})"
+    echo "================================================"
+    
+    echo "## ${name}" >> "${RESULT_FILE}"
+    echo "Config: $(basename ${config})" >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+    
+    torchrun --nproc_per_node=8 --master_port=12346 \
+        test.py \
+        "${config}" \
+        "${checkpoint}" \
+        --launcher pytorch \
+        --eval bbox \
+        --tmpdir "${tmpdir}" \
+        2>&1 | tee "${log_file}"
+    
+    echo "Results:" >> "${RESULT_FILE}"
+    grep -E "OrderedDict" "${log_file}" | tail -1 >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+    echo "----------------------------------------" >> "${RESULT_FILE}"
+    echo "" >> "${RESULT_FILE}"
+}
+
+echo ""
+echo "开始测试 (共 4 个)..."
+
+# 1. MaskCLIP Base Single
+run_test "maskclip_evab_single" "${MASKCLIP_B_SINGLE}" "${MASKCLIP_B_CKPT}" "/tmp/maskclip_b_single"
+
+# 2. MaskCLIP Base Ensemble
+run_test "maskclip_evab_ensemble" "${MASKCLIP_B_ENSEMBLE}" "${MASKCLIP_B_CKPT}" "/tmp/maskclip_b_ensemble"
+
+# 3. CLIPSelf Large Single
+run_test "clipself_eval_single" "${CLIPSELF_L_SINGLE}" "${CLIPSELF_L_CKPT}" "/tmp/clipself_l_single"
+
+# 4. CLIPSelf Large Ensemble
+run_test "clipself_eval_ensemble" "${CLIPSELF_L_ENSEMBLE}" "${CLIPSELF_L_CKPT}" "/tmp/clipself_l_ensemble"
+
+# 汇总结果
+echo "" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "Summary" >> "${RESULT_FILE}"
+echo "================================================" >> "${RESULT_FILE}"
+echo "" >> "${RESULT_FILE}"
+echo "| Model | Method | base_ap50 | novel_ap50 | all_ap50 | bbox_mAP |" >> "${RESULT_FILE}"
+echo "|-------|--------|-----------|------------|----------|----------|" >> "${RESULT_FILE}"
+
+for log in maskclip_evab_single maskclip_evab_ensemble clipself_eval_single clipself_eval_ensemble; do
+    if [ -f "${RESULT_DIR}/${log}.log" ]; then
+        if [[ "${log}" == *"maskclip"* ]]; then
+            model="MaskCLIP-B"
+        else
+            model="CLIPSelf-L"
+        fi
+        if [[ "${log}" == *"single"* ]]; then
+            method="Single"
+        else
+            method="Ensemble"
+        fi
+        
+        result_line=$(grep "OrderedDict" "${RESULT_DIR}/${log}.log" | tail -1)
+        if [ -n "${result_line}" ]; then
+            base_ap50=$(echo "${result_line}" | grep -oP "base_ap50', \K[0-9.]+" || echo "N/A")
+            novel_ap50=$(echo "${result_line}" | grep -oP "novel_ap50', \K[0-9.]+" || echo "N/A")
+            all_ap50=$(echo "${result_line}" | grep -oP "all_ap50', \K[0-9.]+" || echo "N/A")
+            bbox_mAP=$(echo "${result_line}" | grep -oP "bbox_mAP', \K[0-9.]+" || echo "N/A")
+            echo "| ${model} | ${method} | ${base_ap50} | ${novel_ap50} | ${all_ap50} | ${bbox_mAP} |" >> "${RESULT_FILE}"
+        fi
+    fi
+done
+
+echo "" >> "${RESULT_FILE}"
+echo "Completed at: $(date)" >> "${RESULT_FILE}"
+
+echo ""
+echo "================================================"
+echo "所有测试完成!"
+echo "结果���存在: ${RESULT_FILE}"
+echo "================================================"
+echo ""
+cat "${RESULT_FILE}"

analysis/robustness_eval/README.md

@@ -0,0 +1,131 @@
+# OV-COCO 鲁棒性评估 (Robustness Evaluation)
+
+评估目标检测模型在图像退化条件下的性能，**专为 OV-COCO 数据集设计**，支持 `base_ap50`、`novel_ap50`、`all_ap50` 指标。
+
+## 目录结构
+
+```
+robustness_eval/
+├── test_robustness_ovcoco.py    # OV-COCO 鲁棒性测试脚本
+├── merge_robustness_results.py  # 结果合并与报告生成
+├── run_clearclip_robustness.sh  # ClearCLIP 8-GPU 并行测试
+├── run_clipself_robustness.sh   # CLIPSelf 8-GPU 并行测试
+├── logs/                        # 运行日志
+└── results/                     # 测试结果
+    ├── clearclip/
+    └── clipself/
+```
+
+## 退化类型 (15 种 benchmark)
+
+| 类别 | 退化类型 |
+|------|----------|
+| Noise | gaussian_noise, shot_noise, impulse_noise |
+| Blur | defocus_blur, glass_blur, motion_blur, zoom_blur |
+| Weather | snow, frost, fog, brightness |
+| Digital | contrast, elastic_transform, pixelate, jpeg_compression |
+
+## 严重程度
+
+1-5 级，数字越大退化越严重。共 75 个场景 (15 类型 × 5 级别)。
+
+## 评估指标
+
+### OV-COCO 特有指标
+- **base_ap50**: 已知类别 (48 类) 的 AP@IoU=0.50
+- **novel_ap50**: 新类别 (17 类) 的 AP@IoU=0.50
+- **all_ap50**: 所有类别 (65 类) 的 AP@IoU=0.50
+
+### 鲁棒性指标
+- **P (Performance)**: 原始图像上的性能
+- **mPC (mean Performance under Corruption)**: 所有退化条件下的平均性能
+- **rPC (relative Performance under Corruption)**: mPC / P，衡量鲁棒性
+
+## 使用方法
+
+### 1. 运行鲁棒性测试
+
+```bash
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval
+
+# ClearCLIP (8 GPU 并行，后台运行)
+bash run_clearclip_robustness.sh --nohup
+
+# CLIPSelf (8 GPU 并行，后台运行)
+bash run_clipself_robustness.sh --nohup
+
+# 两个可以同时运行（GPU 显存足够时）
+bash run_clearclip_robustness.sh --nohup && bash run_clipself_robustness.sh --nohup
+```
+
+### 2. 监控进度
+
+```bash
+# 查看日志
+tail -f logs/gpu0_clearclip.log
+
+# 检查完成数量 (预期 75 个)
+ls results/clearclip/*_results.pkl 2>/dev/null | wc -l
+ls results/clipself/*_results.pkl 2>/dev/null | wc -l
+```
+
+### 3. 合并结果并生成报告
+
+```bash
+# ClearCLIP
+python3 merge_robustness_results.py \
+    --results-dir results/clearclip \
+    --model-name ClearCLIP
+
+# CLIPSelf
+python3 merge_robustness_results.py \
+    --results-dir results/clipself \
+    --model-name CLIPSelf
+```
+
+### 4. 输出文件
+
+- **日志报告**: 控制台输出 base_ap50、novel_ap50、all_ap50 等汇总
+- **Excel 报告**: `results/<model>/robustness_report.xlsx`
+  - Summary: P、mPC、rPC 汇总
+  - Category mPC: 按退化类别 (noise/blur/weather/digital) 统计
+  - Corruption Avg: 每种退化类型的平均值
+  - Base Ap50 / Novel Ap50 / All Ap50: 详细 15×5 矩阵
+  - Full Matrix: 完整结果（便于复制到论文）
+
+## 单场景测试
+
+如需测试单个场景：
+
+```bash
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT
+export PYTHONPATH=$PWD:/opt/tiger/xiaomoguhzz/mmdetection:$PYTHONPATH
+
+python3 /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/test_robustness_ovcoco.py \
+    configs/declip/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_clearclip.py \
+    work_dirs/clearclip_ovcoco/epoch_3.pth \
+    --out /tmp/test.pkl \
+    --corruptions gaussian_noise \
+    --severities 1 \
+    --eval bbox
+```
+
+## 注意事项
+
+1. **依赖安装**:
+   ```bash
+   pip install imagecorruptions openpyxl pandas
+   ```
+
+2. **运行时间**: 75 个场景 × 4836 图像，8 GPU 并行约需 2-3 小时
+
+3. **与旧脚本的区别**: 
+   - 旧脚本使用 `mmdet/test_robustness.py`，只输出标准 COCO 指标
+   - 新脚本 `test_robustness_ovcoco.py` 调用 `CocoDatasetOV.evaluate()`，输出 OV-COCO 特有的 base/novel AP50
+
+## 模型路径
+
+| 模型 | Config | Checkpoint |
+|------|--------|------------|
+| ClearCLIP | `configs/declip/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_clearclip.py` | `work_dirs/clearclip_ovcoco/epoch_3.pth` |
+| CLIPSelf | `configs/ov_coco/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_eva_clipself_proposals.py` | `/opt/tiger/xiaomoguhzz/fvit_eva_vitb16_ovcoco_clipself_proposals.pth` |

analysis/robustness_eval/compare_models.py

@@ -0,0 +1,225 @@
+#!/usr/bin/env python3
+"""
+比较多个模型的 OV-COCO 鲁棒性结果
+
+生成格式:
+                    ClearCLIP                     CLIPSelf
+              P_clean   mPC   rPC (%)    P_clean   mPC   rPC (%)
+novel_ap50    26.74    13.84   51.76      37.51    29.27   78.05
+base_ap50     44.00    26.21   59.57      54.94    40.81   74.27
+all_ap50      39.49    22.97   58.17      50.38    37.79   75.01
+"""
+
+import os
+import json
+import argparse
+import pickle
+
+try:
+    import pandas as pd
+    HAS_PANDAS = True
+except ImportError:
+    HAS_PANDAS = False
+    print("Warning: pandas not installed, Excel output disabled")
+
+# 预定义的 Clean 数据性能 (P_clean)
+PREDEFINED_CLEAN_METRICS = {
+    'clearclip': {
+        'base_ap50': 44.00,
+        'novel_ap50': 26.74,
+        'all_ap50': 39.49,
+        'bbox_mAP': 20.30,
+        'bbox_mAP_50': 39.50,
+    },
+    'clipself': {
+        'base_ap50': 54.94,
+        'novel_ap50': 37.51,
+        'all_ap50': 50.38,
+        'bbox_mAP': 27.70,
+        'bbox_mAP_50': 50.00,
+    }
+}
+
+
+def load_model_results(results_dir, model_name):
+    """加载单个模型的结果"""
+    # 尝试加载 JSON 汇总
+    json_path = os.path.join(results_dir, 'robustness_summary.json')
+    if os.path.exists(json_path):
+        with open(json_path, 'r') as f:
+            return json.load(f)
+    
+    # 尝试加载 pkl
+    pkl_path = os.path.join(results_dir, 'merged_results.pkl')
+    if os.path.exists(pkl_path):
+        with open(pkl_path, 'rb') as f:
+            data = pickle.load(f)
+        robustness = data.get('robustness_results', {})
+        return {
+            'model': model_name,
+            'P_clean': robustness.get('P', {}),
+            'mPC': robustness.get('mPC', {}),
+            'rPC': {k: v * 100 for k, v in robustness.get('rPC', {}).items()},
+            'category_mPC': robustness.get('category_mPC', {})
+        }
+    
+    return None
+
+
+def print_comparison_table(models_data):
+    """打印多模型比较表格"""
+    model_names = list(models_data.keys())
+    metrics = ['novel_ap50', 'base_ap50', 'all_ap50']
+    
+    # 计算列宽
+    col_width = 10
+    
+    # 打印表头
+    print("\n" + "=" * 80)
+    print("OV-COCO Robustness Comparison")
+    print("=" * 80)
+    
+    # 打印模型名称行
+    print(f"{'Metric':<12}", end="")
+    for model in model_names:
+        print(f" | {model:^{col_width * 3 + 4}}", end="")
+    print()
+    
+    # 打印子表头
+    print(f"{'':12}", end="")
+    for _ in model_names:
+        print(f" | {'P_clean':>{col_width}} {'mPC':>{col_width}} {'rPC(%)':>{col_width}}", end="")
+    print()
+    print("-" * (12 + (col_width * 3 + 5) * len(model_names)))
+    
+    # 打印数据行
+    for metric in metrics:
+        print(f"{metric:<12}", end="")
+        for model in model_names:
+            data = models_data[model]
+            p_val = data.get('P_clean', {}).get(metric, None)
+            mpc_val = data.get('mPC', {}).get(metric, None)
+            rpc_val = data.get('rPC', {}).get(metric, None)
+            
+            p_str = f"{p_val:.2f}" if p_val is not None else "N/A"
+            mpc_str = f"{mpc_val:.2f}" if mpc_val is not None else "N/A"
+            rpc_str = f"{rpc_val:.2f}" if rpc_val is not None else "N/A"
+            
+            print(f" | {p_str:>{col_width}} {mpc_str:>{col_width}} {rpc_str:>{col_width}}", end="")
+        print()
+    
+    print("=" * 80)
+
+
+def save_comparison_excel(models_data, output_path):
+    """保存多模型比较到 Excel"""
+    if not HAS_PANDAS:
+        print("ERROR: pandas not installed, cannot save Excel")
+        return
+    
+    model_names = list(models_data.keys())
+    metrics = ['novel_ap50', 'base_ap50', 'all_ap50', 'bbox_mAP', 'bbox_mAP_50']
+    
+    with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
+        # Sheet 1: Core Comparison
+        rows = []
+        for metric in metrics[:3]:  # 只取核心指标
+            row = {'Metric': metric}
+            for model in model_names:
+                data = models_data[model]
+                row[f'{model}_P_clean'] = data.get('P_clean', {}).get(metric)
+                row[f'{model}_mPC'] = data.get('mPC', {}).get(metric)
+                row[f'{model}_rPC(%)'] = data.get('rPC', {}).get(metric)
+            rows.append(row)
+        
+        df_core = pd.DataFrame(rows)
+        df_core.to_excel(writer, sheet_name='Core Comparison', index=False)
+        
+        # Sheet 2: Extended Comparison
+        rows = []
+        for metric in metrics:
+            row = {'Metric': metric}
+            for model in model_names:
+                data = models_data[model]
+                row[f'{model}_P_clean'] = data.get('P_clean', {}).get(metric)
+                row[f'{model}_mPC'] = data.get('mPC', {}).get(metric)
+                row[f'{model}_rPC(%)'] = data.get('rPC', {}).get(metric)
+            rows.append(row)
+        
+        df_ext = pd.DataFrame(rows)
+        df_ext.to_excel(writer, sheet_name='Extended Comparison', index=False)
+        
+        # Sheet 3: Category Comparison
+        categories = ['noise', 'blur', 'weather', 'digital']
+        for metric in ['base_ap50', 'novel_ap50', 'all_ap50']:
+            rows = []
+            for cat in categories:
+                row = {'Category': cat}
+                for model in model_names:
+                    data = models_data[model]
+                    val = data.get('category_mPC', {}).get(cat, {}).get(metric)
+                    row[model] = round(val, 2) if val is not None else None
+                rows.append(row)
+            
+            df_cat = pd.DataFrame(rows)
+            df_cat.to_excel(writer, sheet_name=f'Category {metric}', index=False)
+    
+    print(f"Comparison Excel saved to: {output_path}")
+
+
+def save_comparison_json(models_data, output_path):
+    """保存比较结果到 JSON"""
+    with open(output_path, 'w') as f:
+        json.dump(models_data, f, indent=2)
+    print(f"Comparison JSON saved to: {output_path}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Compare OV-COCO robustness results across models')
+    parser.add_argument('--results-dirs', type=str, nargs='+', required=True,
+                        help='Directories containing model results')
+    parser.add_argument('--model-names', type=str, nargs='+', default=None,
+                        help='Model names (default: infer from directory names)')
+    parser.add_argument('--output-dir', type=str, default='.',
+                        help='Output directory for comparison files')
+    parser.add_argument('--output-prefix', type=str, default='robustness_comparison',
+                        help='Output file prefix')
+    args = parser.parse_args()
+    
+    # 推断模型名称
+    if args.model_names is None:
+        args.model_names = [os.path.basename(d.rstrip('/')) for d in args.results_dirs]
+    
+    if len(args.model_names) != len(args.results_dirs):
+        print("ERROR: Number of model names must match number of results directories")
+        return
+    
+    # 加载所有模型结果
+    models_data = {}
+    for results_dir, model_name in zip(args.results_dirs, args.model_names):
+        print(f"Loading results for {model_name} from {results_dir}...")
+        data = load_model_results(results_dir, model_name)
+        if data:
+            models_data[model_name] = data
+        else:
+            print(f"Warning: Could not load results for {model_name}")
+    
+    if not models_data:
+        print("ERROR: No valid results found!")
+        return
+    
+    # 打印比较表格
+    print_comparison_table(models_data)
+    
+    # 保存结果
+    os.makedirs(args.output_dir, exist_ok=True)
+    
+    excel_path = os.path.join(args.output_dir, f'{args.output_prefix}.xlsx')
+    save_comparison_excel(models_data, excel_path)
+    
+    json_path = os.path.join(args.output_dir, f'{args.output_prefix}.json')
+    save_comparison_json(models_data, json_path)
+
+
+if __name__ == '__main__':
+    main()

analysis/robustness_eval/generate_corruption_table.py

@@ -0,0 +1,291 @@
+#!/usr/bin/env python3
+"""
+生成按退化类型分类的详细表格
+
+输出格式:
+Corruption          Category  novel_ap50_PC_c  novel_ap50_rPC(%)  base_ap50_PC_c  base_ap50_rPC(%)  all_ap50_PC_c  all_ap50_rPC(%)
+gaussian_noise      noise     27.32            72.83              38.18           69.50             35.34          70.15
+shot_noise          noise     27.17            72.43              37.67           68.57             34.92          69.32
+...
+"""
+
+import os
+import json
+import argparse
+import pickle
+
+try:
+    import pandas as pd
+    HAS_PANDAS = True
+except ImportError:
+    HAS_PANDAS = False
+    print("Warning: pandas not installed, Excel output disabled")
+
+# 15 种 benchmark 退化类型
+BENCHMARK_CORRUPTIONS = [
+    'gaussian_noise', 'shot_noise', 'impulse_noise',
+    'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur',
+    'snow', 'frost', 'fog', 'brightness',
+    'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression'
+]
+
+# 退化类别
+CORRUPTION_CATEGORIES = {
+    'gaussian_noise': 'noise',
+    'shot_noise': 'noise',
+    'impulse_noise': 'noise',
+    'defocus_blur': 'blur',
+    'glass_blur': 'blur',
+    'motion_blur': 'blur',
+    'zoom_blur': 'blur',
+    'snow': 'weather',
+    'frost': 'weather',
+    'fog': 'weather',
+    'brightness': 'weather',
+    'contrast': 'digital',
+    'elastic_transform': 'digital',
+    'pixelate': 'digital',
+    'jpeg_compression': 'digital'
+}
+
+# 预定义的 Clean 数据性能 (P_clean)
+PREDEFINED_CLEAN_METRICS = {
+    'clearclip': {
+        'base_ap50': 44.00,
+        'novel_ap50': 26.74,
+        'all_ap50': 39.49,
+    },
+    'clipself': {
+        'base_ap50': 54.94,
+        'novel_ap50': 37.51,
+        'all_ap50': 50.38,
+    }
+}
+
+
+def load_model_results(results_dir, model_name):
+    """加载单个模型的结果"""
+    # 尝试加载 merged_results.pkl (包含详细的 corruption_avg)
+    pkl_path = os.path.join(results_dir, 'merged_results.pkl')
+    if os.path.exists(pkl_path):
+        with open(pkl_path, 'rb') as f:
+            data = pickle.load(f)
+        return data
+    
+    # 尝试加载 JSON 汇总
+    json_path = os.path.join(results_dir, 'robustness_summary.json')
+    if os.path.exists(json_path):
+        with open(json_path, 'r') as f:
+            return {'robustness_results': json.load(f)}
+    
+    return None
+
+
+def get_clean_metrics(model_name):
+    """获取模型的 clean metrics"""
+    model_key = model_name.lower().replace('-', '').replace('_', '')
+    for key, metrics in PREDEFINED_CLEAN_METRICS.items():
+        if key in model_key or model_key in key:
+            return metrics.copy()
+    return {}
+
+
+def generate_corruption_table(results_dir, model_name):
+    """生成按退化类型的详细表格"""
+    data = load_model_results(results_dir, model_name)
+    if data is None:
+        print(f"ERROR: Could not load results from {results_dir}")
+        return None
+    
+    robustness = data.get('robustness_results', data)
+    corruption_avg = robustness.get('corruption_avg', {})
+    
+    # 获取 P_clean
+    p_clean = robustness.get('P', robustness.get('P_clean', {}))
+    if not p_clean:
+        p_clean = get_clean_metrics(model_name)
+    
+    rows = []
+    for corr in BENCHMARK_CORRUPTIONS:
+        category = CORRUPTION_CATEGORIES.get(corr, 'unknown')
+        corr_data = corruption_avg.get(corr, {})
+        
+        row = {
+            'Corruption': corr,
+            'Category': category,
+        }
+        
+        for metric in ['novel_ap50', 'base_ap50', 'all_ap50']:
+            pc_c = corr_data.get(metric)
+            p_clean_val = p_clean.get(metric)
+            
+            # PC_c (Performance under Corruption for this corruption type)
+            row[f'{metric}_PC_c'] = round(pc_c, 2) if pc_c is not None else None
+            
+            # rPC(%) = PC_c / P_clean * 100
+            if pc_c is not None and p_clean_val is not None and p_clean_val > 0:
+                rpc = (pc_c / p_clean_val) * 100
+                row[f'{metric}_rPC(%)'] = round(rpc, 2)
+            else:
+                row[f'{metric}_rPC(%)'] = None
+        
+        rows.append(row)
+    
+    return rows, p_clean
+
+
+def print_corruption_table(rows, model_name, p_clean):
+    """打印退化类型表格"""
+    print(f"\n{'='*120}")
+    print(f"Corruption-level Performance: {model_name}")
+    print(f"P_clean: novel_ap50={p_clean.get('novel_ap50')}, base_ap50={p_clean.get('base_ap50')}, all_ap50={p_clean.get('all_ap50')}")
+    print(f"{'='*120}")
+    
+    # 表头
+    header = f"{'Corruption':<20} {'Category':<10}"
+    for metric in ['novel_ap50', 'base_ap50', 'all_ap50']:
+        header += f" {metric+'_PC_c':>14} {metric+'_rPC(%)':>14}"
+    print(header)
+    print("-" * 120)
+    
+    # 数据行
+    for row in rows:
+        line = f"{row['Corruption']:<20} {row['Category']:<10}"
+        for metric in ['novel_ap50', 'base_ap50', 'all_ap50']:
+            pc_c = row.get(f'{metric}_PC_c')
+            rpc = row.get(f'{metric}_rPC(%)')
+            pc_c_str = f"{pc_c:.2f}" if pc_c is not None else "N/A"
+            rpc_str = f"{rpc:.2f}" if rpc is not None else "N/A"
+            line += f" {pc_c_str:>14} {rpc_str:>14}"
+        print(line)
+    
+    print("=" * 120)
+
+
+def save_corruption_table_excel(all_tables, output_path):
+    """保存所有模型的退化类型表格到 Excel"""
+    if not HAS_PANDAS:
+        print("ERROR: pandas not installed, cannot save Excel")
+        return
+    
+    columns = ['Corruption', 'Category',
+               'novel_ap50_PC_c', 'novel_ap50_rPC(%)',
+               'base_ap50_PC_c', 'base_ap50_rPC(%)',
+               'all_ap50_PC_c', 'all_ap50_rPC(%)']
+    
+    with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
+        for model_name, (rows, p_clean) in all_tables.items():
+            # 创建 DataFrame
+            df = pd.DataFrame(rows)
+            df = df[columns]
+            
+            # 计算 mPC
+            mpc = {}
+            for metric in ['novel_ap50', 'base_ap50', 'all_ap50']:
+                values = [row.get(f'{metric}_PC_c') for row in rows if row.get(f'{metric}_PC_c') is not None]
+                if values:
+                    mpc[metric] = sum(values) / len(values)
+            
+            # 计算整体 rPC
+            rpc = {}
+            for metric in ['novel_ap50', 'base_ap50', 'all_ap50']:
+                if mpc.get(metric) and p_clean.get(metric):
+                    rpc[metric] = (mpc[metric] / p_clean[metric]) * 100
+            
+            # 创建汇总行 (确保每行都有完整的列)
+            summary_rows = [
+                # 空行
+                {col: None for col in columns},
+                # P_clean 行
+                {
+                    'Corruption': 'P_clean',
+                    'Category': None,
+                    'novel_ap50_PC_c': p_clean.get('novel_ap50'),
+                    'novel_ap50_rPC(%)': None,
+                    'base_ap50_PC_c': p_clean.get('base_ap50'),
+                    'base_ap50_rPC(%)': None,
+                    'all_ap50_PC_c': p_clean.get('all_ap50'),
+                    'all_ap50_rPC(%)': None,
+                },
+                # mPC 行
+                {
+                    'Corruption': 'mPC',
+                    'Category': None,
+                    'novel_ap50_PC_c': round(mpc.get('novel_ap50', 0), 2),
+                    'novel_ap50_rPC(%)': round(rpc.get('novel_ap50', 0), 2),
+                    'base_ap50_PC_c': round(mpc.get('base_ap50', 0), 2),
+                    'base_ap50_rPC(%)': round(rpc.get('base_ap50', 0), 2),
+                    'all_ap50_PC_c': round(mpc.get('all_ap50', 0), 2),
+                    'all_ap50_rPC(%)': round(rpc.get('all_ap50', 0), 2),
+                },
+            ]
+            
+            df_summary = pd.DataFrame(summary_rows, columns=columns)
+            
+            # 合并到主 DataFrame
+            df_full = pd.concat([df, df_summary], ignore_index=True)
+            df_full.to_excel(writer, sheet_name=model_name, index=False)
+    
+    print(f"Corruption table Excel saved to: {output_path}")
+
+
+def save_corruption_table_json(all_tables, output_path):
+    """保存所有模型的退化类型表格到 JSON"""
+    result = {}
+    for model_name, (rows, p_clean) in all_tables.items():
+        result[model_name] = {
+            'P_clean': p_clean,
+            'corruption_details': rows
+        }
+    
+    with open(output_path, 'w') as f:
+        json.dump(result, f, indent=2)
+    print(f"Corruption table JSON saved to: {output_path}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Generate corruption-level performance table')
+    parser.add_argument('--results-dirs', type=str, nargs='+', required=True,
+                        help='Directories containing model results')
+    parser.add_argument('--model-names', type=str, nargs='+', default=None,
+                        help='Model names (default: infer from directory names)')
+    parser.add_argument('--output-dir', type=str, default='.',
+                        help='Output directory')
+    parser.add_argument('--output-prefix', type=str, default='corruption_table',
+                        help='Output file prefix')
+    args = parser.parse_args()
+    
+    # 推断模型名称
+    if args.model_names is None:
+        args.model_names = [os.path.basename(d.rstrip('/')) for d in args.results_dirs]
+    
+    if len(args.model_names) != len(args.results_dirs):
+        print("ERROR: Number of model names must match number of results directories")
+        return
+    
+    # 生成所有模型的表格
+    all_tables = {}
+    for results_dir, model_name in zip(args.results_dirs, args.model_names):
+        print(f"Generating table for {model_name} from {results_dir}...")
+        result = generate_corruption_table(results_dir, model_name)
+        if result:
+            rows, p_clean = result
+            all_tables[model_name] = (rows, p_clean)
+            print_corruption_table(rows, model_name, p_clean)
+    
+    if not all_tables:
+        print("ERROR: No valid results found!")
+        return
+    
+    # 保存结果
+    os.makedirs(args.output_dir, exist_ok=True)
+    
+    excel_path = os.path.join(args.output_dir, f'{args.output_prefix}.xlsx')
+    save_corruption_table_excel(all_tables, excel_path)
+    
+    json_path = os.path.join(args.output_dir, f'{args.output_prefix}.json')
+    save_corruption_table_json(all_tables, json_path)
+
+
+if __name__ == '__main__':
+    main()

analysis/robustness_eval/merge_robustness_results.py

@@ -0,0 +1,605 @@
+#!/usr/bin/env python3
+"""
+合并 OV-COCO 鲁棒性测试结果
+
+功能:
+1. 加载所有单独的 pkl 结果文件
+2. 提取 base_ap50, novel_ap50, all_ap50 以及标准 COCO 指标
+3. 计算 P_clean、mPC、rPC 指标
+4. 输出到控制台和 Excel 表格
+
+输出格式示例:
+              P_clean   mPC    rPC (%)
+novel_ap50    34.56    29.27   84.69
+base_ap50     55.47    40.81   73.57
+all_ap50      50.00    37.79   75.58
+"""
+
+import os
+import pickle
+import argparse
+import json
+from collections import defaultdict
+import numpy as np
+
+try:
+    import pandas as pd
+    HAS_PANDAS = True
+except ImportError:
+    HAS_PANDAS = False
+    print("Warning: pandas not installed, Excel output disabled")
+
+
+# 15 种 benchmark 退化类型
+BENCHMARK_CORRUPTIONS = [
+    'gaussian_noise', 'shot_noise', 'impulse_noise',
+    'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur',
+    'snow', 'frost', 'fog', 'brightness',
+    'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression'
+]
+
+# 退化类别
+CORRUPTION_CATEGORIES = {
+    'noise': ['gaussian_noise', 'shot_noise', 'impulse_noise'],
+    'blur': ['defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur'],
+    'weather': ['snow', 'frost', 'fog', 'brightness'],
+    'digital': ['contrast', 'elastic_transform', 'pixelate', 'jpeg_compression']
+}
+
+# 要提取的指标
+METRICS_TO_EXTRACT = [
+    'base_ap50', 'novel_ap50', 'all_ap50',
+    'bbox_mAP', 'bbox_mAP_50', 'bbox_mAP_75',
+    'bbox_mAP_s', 'bbox_mAP_m', 'bbox_mAP_l'
+]
+
+# 预定义的 Clean 数据性能 (P_clean)
+# 来自 OV-COCO 标准测试 (无扰动)
+PREDEFINED_CLEAN_METRICS = {
+    'clearclip': {
+        'base_ap50': 44.00,
+        'novel_ap50': 26.74,
+        'all_ap50': 39.49,
+        'bbox_mAP': 20.30,
+        'bbox_mAP_50': 39.50,
+        'bbox_mAP_75': 19.30,
+    },
+    'clipself': {
+        'base_ap50': 54.94,
+        'novel_ap50': 37.51,
+        'all_ap50': 50.38,
+        'bbox_mAP': 27.70,
+        'bbox_mAP_50': 50.00,
+        'bbox_mAP_75': 27.80,
+    }
+}
+
+
+def load_results(results_dir):
+    """加载所有单独的 pkl 结果文件"""
+    all_results = {}
+    
+    for corr in BENCHMARK_CORRUPTIONS:
+        for sev in range(1, 6):
+            scenario = f"{corr}_{sev}"
+            
+            # 尝试多种文件名格式
+            possible_files = [
+                f"{scenario}_results.pkl",  # 新格式
+                f"{scenario}.pkl",           # 旧格式
+            ]
+            
+            for fname in possible_files:
+                pkl_path = os.path.join(results_dir, fname)
+                if os.path.exists(pkl_path):
+                    try:
+                        with open(pkl_path, 'rb') as f:
+                            data = pickle.load(f)
+                        all_results[scenario] = data
+                        print(f"Loaded: {scenario} from {fname}")
+                    except Exception as e:
+                        print(f"Error loading {pkl_path}: {e}")
+                    break
+            else:
+                print(f"Missing: {scenario}")
+    
+    return all_results
+
+
+def extract_metrics_from_result(result_data):
+    """从单个结果中提取指标"""
+    metrics = {}
+    
+    # 结果可能是嵌套的 dict (corruption -> severity -> metrics)
+    # 或者直接是 metrics dict
+    if isinstance(result_data, dict):
+        # 检查是否是嵌套格式 (来自 aggregated results)
+        first_key = next(iter(result_data.keys()), None)
+        if first_key and isinstance(result_data.get(first_key), dict):
+            # 可能是 {corruption: {severity: metrics}} 格式
+            # 或者是 {severity: metrics} 格式
+            first_val = result_data[first_key]
+            if isinstance(first_val, dict):
+                # 检查是否是 severity -> metrics
+                if isinstance(next(iter(first_val.values()), None), dict):
+                    # {corruption: {severity: metrics}} 格式
+                    # 取第一个 corruption 的第一个 severity
+                    for corr_data in result_data.values():
+                        for sev_data in corr_data.values():
+                            if isinstance(sev_data, dict):
+                                return extract_metrics_from_flat_dict(sev_data)
+                else:
+                    # {severity: metrics} 或 {metric_name: value} 格式
+                    return extract_metrics_from_flat_dict(result_data)
+        else:
+            # 直接是 metrics dict
+            return extract_metrics_from_flat_dict(result_data)
+    
+    return metrics
+
+
+def extract_metrics_from_flat_dict(data):
+    """从扁平的 metrics dict 中提取指标"""
+    metrics = {}
+    
+    for metric_name in METRICS_TO_EXTRACT:
+        if metric_name in data:
+            val = data[metric_name]
+            # 转换为百分比（如果需要）
+            if metric_name in ['base_ap50', 'novel_ap50', 'all_ap50']:
+                # 这些已经是百分比格式
+                metrics[metric_name] = float(val)
+            elif 'mAP' in metric_name:
+                # mAP 是 0-1 格式，转为百分比
+                metrics[metric_name] = float(val) * 100
+            else:
+                metrics[metric_name] = float(val)
+    
+    return metrics
+
+
+def load_and_extract_all_metrics(results_dir):
+    """加载并提取所有结果的指标"""
+    all_metrics = {}
+    
+    for corr in BENCHMARK_CORRUPTIONS:
+        all_metrics[corr] = {}
+        for sev in range(1, 6):
+            scenario = f"{corr}_{sev}"
+            
+            # 尝试加载 _results.pkl 文件
+            results_pkl = os.path.join(results_dir, f"{scenario}_results.pkl")
+            if os.path.exists(results_pkl):
+                try:
+                    with open(results_pkl, 'rb') as f:
+                        data = pickle.load(f)
+                    
+                    # 从 aggregated results 中提取
+                    if corr in data and sev in data[corr]:
+                        metrics = extract_metrics_from_flat_dict(data[corr][sev])
+                        if metrics:
+                            all_metrics[corr][sev] = metrics
+                            print(f"Extracted from {scenario}_results.pkl: {list(metrics.keys())}")
+                            continue
+                except Exception as e:
+                    print(f"Error reading {results_pkl}: {e}")
+            
+            # 尝试从单独的 pkl 文件加载（旧格式，可能只有 outputs）
+            pkl_path = os.path.join(results_dir, f"{scenario}.pkl")
+            if os.path.exists(pkl_path):
+                print(f"Note: {scenario}.pkl exists but needs re-evaluation for OV-COCO metrics")
+    
+    return all_metrics
+
+
+def compute_robustness_metrics(metrics_dict, clean_metrics=None):
+    """
+    计算鲁棒性指标
+    
+    Args:
+        metrics_dict: dict, {corruption: {severity: {metric: value}}}
+        clean_metrics: dict, 原始图像的指标 (P)
+    
+    Returns:
+        dict with P, mPC, rPC for each metric
+    """
+    results = {
+        'P': clean_metrics or {},
+        'mPC': {},
+        'rPC': {},
+        'corruption_avg': {},
+        'category_mPC': {},
+        'detailed': metrics_dict
+    }
+    
+    # 收集每个指标在所有 corruption/severity 下的值
+    metric_values = defaultdict(list)
+    corruption_metric_values = defaultdict(lambda: defaultdict(list))
+    
+    for corr in BENCHMARK_CORRUPTIONS:
+        if corr not in metrics_dict:
+            continue
+        for sev in range(1, 6):
+            if sev not in metrics_dict[corr]:
+                continue
+            for metric_name, value in metrics_dict[corr][sev].items():
+                if not np.isnan(value):
+                    metric_values[metric_name].append(value)
+                    corruption_metric_values[corr][metric_name].append(value)
+    
+    # 计算 mPC (所有 corruption 的平均)
+    for metric_name, values in metric_values.items():
+        if values:
+            results['mPC'][metric_name] = np.mean(values)
+    
+    # 计算每个 corruption 的平均
+    for corr in corruption_metric_values:
+        results['corruption_avg'][corr] = {}
+        for metric_name, values in corruption_metric_values[corr].items():
+            if values:
+                results['corruption_avg'][corr][metric_name] = np.mean(values)
+    
+    # 计算每个类别的 mPC
+    for cat, corrs in CORRUPTION_CATEGORIES.items():
+        results['category_mPC'][cat] = {}
+        for metric_name in METRICS_TO_EXTRACT:
+            cat_values = []
+            for corr in corrs:
+                if corr in results['corruption_avg'] and metric_name in results['corruption_avg'][corr]:
+                    cat_values.append(results['corruption_avg'][corr][metric_name])
+            if cat_values:
+                results['category_mPC'][cat][metric_name] = np.mean(cat_values)
+    
+    # 计算 rPC (相对于 clean performance)
+    if clean_metrics:
+        for metric_name in results['mPC']:
+            if metric_name in clean_metrics and clean_metrics[metric_name] > 0:
+                results['rPC'][metric_name] = results['mPC'][metric_name] / clean_metrics[metric_name]
+    
+    return results
+
+
+def print_report(robustness_results, model_name="Model"):
+    """打印鲁棒性报告"""
+    print("\n" + "=" * 80)
+    print(f"OV-COCO Robustness Evaluation Report: {model_name}")
+    print("=" * 80)
+    
+    # ===== 核心汇总表 (用户期望的格式) =====
+    print("\n" + "-" * 50)
+    print("Core Summary (P_clean, mPC, rPC)")
+    print("-" * 50)
+    print(f"{'Metric':<15} {'P_clean':>10} {'mPC':>10} {'rPC (%)':>10}")
+    print("-" * 50)
+    
+    core_metrics = ['novel_ap50', 'base_ap50', 'all_ap50']
+    for metric in core_metrics:
+        p_val = robustness_results['P'].get(metric, float('nan'))
+        mpc_val = robustness_results['mPC'].get(metric, float('nan'))
+        rpc_val = robustness_results['rPC'].get(metric, float('nan'))
+        
+        p_str = f"{p_val:.2f}" if not np.isnan(p_val) else "N/A"
+        mpc_str = f"{mpc_val:.2f}" if not np.isnan(mpc_val) else "N/A"
+        rpc_str = f"{rpc_val*100:.2f}" if not np.isnan(rpc_val) else "N/A"
+        
+        print(f"{metric:<15} {p_str:>10} {mpc_str:>10} {rpc_str:>10}")
+    print("-" * 50)
+    
+    # ===== 扩展指标 =====
+    print("\nExtended Metrics:")
+    print(f"{'Metric':<15} {'P_clean':>10} {'mPC':>10} {'rPC (%)':>10}")
+    print("-" * 50)
+    
+    extended_metrics = ['bbox_mAP', 'bbox_mAP_50', 'bbox_mAP_75']
+    for metric in extended_metrics:
+        p_val = robustness_results['P'].get(metric, float('nan'))
+        mpc_val = robustness_results['mPC'].get(metric, float('nan'))
+        rpc_val = robustness_results['rPC'].get(metric, float('nan'))
+        
+        p_str = f"{p_val:.2f}" if not np.isnan(p_val) else "N/A"
+        mpc_str = f"{mpc_val:.2f}" if not np.isnan(mpc_val) else "N/A"
+        rpc_str = f"{rpc_val*100:.2f}" if not np.isnan(rpc_val) else "N/A"
+        
+        print(f"{metric:<15} {p_str:>10} {mpc_str:>10} {rpc_str:>10}")
+    
+    # ===== 按类别统计 =====
+    print("\n" + "-" * 60)
+    print("Category mPC Breakdown")
+    print("-" * 60)
+    print(f"{'Category':<12}", end="")
+    for metric in ['base_ap50', 'novel_ap50', 'all_ap50']:
+        print(f" {metric:>12}", end="")
+    print()
+    print("-" * 60)
+    
+    for cat in ['noise', 'blur', 'weather', 'digital']:
+        print(f"{cat:<12}", end="")
+        for metric in ['base_ap50', 'novel_ap50', 'all_ap50']:
+            val = robustness_results['category_mPC'].get(cat, {}).get(metric, float('nan'))
+            val_str = f"{val:.2f}" if not np.isnan(val) else "N/A"
+            print(f" {val_str:>12}", end="")
+        print()
+    
+    # ===== 详细结果（每个场景）=====
+    print("\n" + "-" * 70)
+    print("Detailed Results (per corruption & severity)")
+    print("-" * 70)
+    print(f"{'Corruption':<20} {'Sev':>4}", end="")
+    for metric in ['base_ap50', 'novel_ap50', 'all_ap50']:
+        print(f" {metric:>11}", end="")
+    print()
+    print("-" * 70)
+    
+    detailed = robustness_results['detailed']
+    for corr in BENCHMARK_CORRUPTIONS:
+        if corr not in detailed:
+            continue
+        for sev in range(1, 6):
+            if sev not in detailed[corr]:
+                continue
+            print(f"{corr:<20} {sev:>4}", end="")
+            for metric in ['base_ap50', 'novel_ap50', 'all_ap50']:
+                val = detailed[corr][sev].get(metric, float('nan'))
+                val_str = f"{val:.2f}" if not np.isnan(val) else "N/A"
+                print(f" {val_str:>11}", end="")
+            print()
+    
+    print("=" * 80)
+
+
+def save_to_excel(robustness_results, output_path, model_name="Model"):
+    """
+    将鲁棒性结果保存为 Excel 表格
+    
+    Sheet 结构:
+    1. Core Summary - P_clean, mPC, rPC (%) 核心指标汇总
+    2. Category mPC - 按类别的 mPC 分解
+    3. Corruption Avg - 每种扰动的平均值
+    4-6. base_ap50/novel_ap50/all_ap50 详细结果
+    7. Full Matrix - 完整矩阵
+    """
+    if not HAS_PANDAS:
+        print("ERROR: pandas not installed, cannot save Excel")
+        return
+    
+    with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
+        # ===== Sheet 1: Core Summary (用户期望的格式) =====
+        core_metrics = ['novel_ap50', 'base_ap50', 'all_ap50']
+        core_rows = []
+        for metric in core_metrics:
+            p_val = robustness_results['P'].get(metric, None)
+            mpc_val = robustness_results['mPC'].get(metric, None)
+            rpc_val = robustness_results['rPC'].get(metric, None)
+            core_rows.append({
+                'Metric': metric,
+                'P_clean': round(p_val, 2) if p_val is not None else None,
+                'mPC': round(mpc_val, 2) if mpc_val is not None else None,
+                'rPC (%)': round(rpc_val * 100, 2) if rpc_val is not None else None
+            })
+        df_core = pd.DataFrame(core_rows)
+        df_core.to_excel(writer, sheet_name='Core Summary', index=False)
+        
+        # ===== Sheet 2: Extended Summary =====
+        key_metrics = ['base_ap50', 'novel_ap50', 'all_ap50', 'bbox_mAP', 'bbox_mAP_50', 'bbox_mAP_75']
+        summary_rows = []
+        for metric in key_metrics:
+            p_val = robustness_results['P'].get(metric, None)
+            mpc_val = robustness_results['mPC'].get(metric, None)
+            rpc_val = robustness_results['rPC'].get(metric, None)
+            summary_rows.append({
+                'Metric': metric,
+                'P_clean': round(p_val, 2) if p_val is not None else None,
+                'mPC': round(mpc_val, 2) if mpc_val is not None else None,
+                'rPC (%)': round(rpc_val * 100, 2) if rpc_val is not None else None
+            })
+        df_summary = pd.DataFrame(summary_rows)
+        df_summary.to_excel(writer, sheet_name='Extended Summary', index=False)
+        
+        # ===== Sheet 3: Category mPC =====
+        category_rows = []
+        for cat in ['noise', 'blur', 'weather', 'digital']:
+            row = {'Category': cat}
+            for metric in key_metrics:
+                val = robustness_results['category_mPC'].get(cat, {}).get(metric, None)
+                row[metric] = round(val, 2) if val is not None else None
+            category_rows.append(row)
+        df_category = pd.DataFrame(category_rows)
+        df_category.to_excel(writer, sheet_name='Category mPC', index=False)
+        
+        # ===== Sheet 4: Corruption Average =====
+        corr_rows = []
+        for corr in BENCHMARK_CORRUPTIONS:
+            if corr not in robustness_results['corruption_avg']:
+                continue
+            row = {'Corruption': corr}
+            for metric in key_metrics:
+                val = robustness_results['corruption_avg'][corr].get(metric, None)
+                row[metric] = round(val, 2) if val is not None else None
+            corr_rows.append(row)
+        df_corr = pd.DataFrame(corr_rows)
+        df_corr.to_excel(writer, sheet_name='Corruption Avg', index=False)
+        
+        # ===== Sheet 5-7: Detailed results for base_ap50, novel_ap50, all_ap50 =====
+        for metric in ['base_ap50', 'novel_ap50', 'all_ap50']:
+            detailed_rows = []
+            for corr in BENCHMARK_CORRUPTIONS:
+                row = {'Corruption': corr}
+                if corr in robustness_results['detailed']:
+                    for sev in range(1, 6):
+                        if sev in robustness_results['detailed'][corr]:
+                            val = robustness_results['detailed'][corr][sev].get(metric, None)
+                            row[f'Sev {sev}'] = round(val, 2) if val is not None else None
+                        else:
+                            row[f'Sev {sev}'] = None
+                    # Average
+                    avg_val = robustness_results['corruption_avg'].get(corr, {}).get(metric, None)
+                    row['Avg'] = round(avg_val, 2) if avg_val is not None else None
+                detailed_rows.append(row)
+            
+            # 添加汇总行
+            detailed_rows.append({})  # 空行
+            mpc_row = {'Corruption': 'mPC'}
+            mpc_val = robustness_results['mPC'].get(metric, None)
+            mpc_row['Avg'] = round(mpc_val, 2) if mpc_val is not None else None
+            detailed_rows.append(mpc_row)
+            
+            # 添加 rPC 行
+            rpc_row = {'Corruption': 'rPC (%)'}
+            rpc_val = robustness_results['rPC'].get(metric, None)
+            rpc_row['Avg'] = round(rpc_val * 100, 2) if rpc_val is not None else None
+            detailed_rows.append(rpc_row)
+            
+            df_detailed = pd.DataFrame(detailed_rows)
+            sheet_name = metric.replace('_', ' ').title()
+            df_detailed.to_excel(writer, sheet_name=sheet_name, index=False)
+        
+        # ===== Sheet 8: Full Matrix (all metrics, for paper) =====
+        matrix_rows = []
+        for corr in BENCHMARK_CORRUPTIONS:
+            if corr not in robustness_results['detailed']:
+                continue
+            for sev in range(1, 6):
+                if sev not in robustness_results['detailed'][corr]:
+                    continue
+                row = {'Corruption': corr, 'Severity': sev}
+                for metric in key_metrics:
+                    val = robustness_results['detailed'][corr][sev].get(metric, None)
+                    row[metric] = round(val, 2) if val is not None else None
+                matrix_rows.append(row)
+        
+        df_matrix = pd.DataFrame(matrix_rows)
+        df_matrix.to_excel(writer, sheet_name='Full Matrix', index=False)
+    
+    print(f"Excel saved to: {output_path}")
+
+
+def get_clean_metrics_for_model(model_name):
+    """
+    根据模型名称获取预定义的 clean metrics
+    
+    Args:
+        model_name: 模型名称 (e.g., 'clearclip', 'clipself', 'ClearCLIP', 'CLIPSelf')
+    
+    Returns:
+        dict or None
+    """
+    model_key = model_name.lower().replace('-', '').replace('_', '')
+    
+    for key, metrics in PREDEFINED_CLEAN_METRICS.items():
+        if key in model_key or model_key in key:
+            return metrics.copy()
+    
+    return None
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Merge OV-COCO robustness results')
+    parser.add_argument('--results-dir', type=str, required=True,
+                        help='Directory containing individual pkl files')
+    parser.add_argument('--clean-results', type=str, default=None,
+                        help='Path to clean (no corruption) results pkl file')
+    parser.add_argument('--output', type=str, default=None,
+                        help='Output pkl file for merged results')
+    parser.add_argument('--excel', type=str, default=None,
+                        help='Output Excel file path')
+    parser.add_argument('--json', type=str, default=None,
+                        help='Output JSON file path for summary')
+    parser.add_argument('--model-name', type=str, default='Model',
+                        help='Model name for report title (also used to auto-detect clean metrics)')
+    parser.add_argument('--clean-base-ap50', type=float, default=None,
+                        help='Clean base_ap50 value (P_clean)')
+    parser.add_argument('--clean-novel-ap50', type=float, default=None,
+                        help='Clean novel_ap50 value (P_clean)')
+    parser.add_argument('--clean-all-ap50', type=float, default=None,
+                        help='Clean all_ap50 value (P_clean)')
+    args = parser.parse_args()
+    
+    # 加载并提取所有指标
+    print("Loading and extracting metrics...")
+    all_metrics = load_and_extract_all_metrics(args.results_dir)
+    
+    # 统计已加载的结果
+    loaded_count = sum(len(sev_dict) for sev_dict in all_metrics.values())
+    print(f"\nExtracted metrics from {loaded_count} / 75 scenarios")
+    
+    if loaded_count == 0:
+        print("ERROR: No valid results found!")
+        print("Make sure you ran test_robustness_ovcoco.py to generate results with OV-COCO metrics.")
+        return
+    
+    # 确定 clean metrics
+    clean_metrics = None
+    
+    # 优先级 1: 从命令行参数
+    if args.clean_base_ap50 is not None or args.clean_novel_ap50 is not None or args.clean_all_ap50 is not None:
+        clean_metrics = {}
+        if args.clean_base_ap50 is not None:
+            clean_metrics['base_ap50'] = args.clean_base_ap50
+        if args.clean_novel_ap50 is not None:
+            clean_metrics['novel_ap50'] = args.clean_novel_ap50
+        if args.clean_all_ap50 is not None:
+            clean_metrics['all_ap50'] = args.clean_all_ap50
+        print(f"Using clean metrics from command line: {clean_metrics}")
+    
+    # 优先级 2: 从 pkl 文件加载
+    elif args.clean_results and os.path.exists(args.clean_results):
+        try:
+            with open(args.clean_results, 'rb') as f:
+                clean_data = pickle.load(f)
+            clean_metrics = extract_metrics_from_result(clean_data)
+            print(f"Loaded clean results from file: {clean_metrics}")
+        except Exception as e:
+            print(f"Warning: Could not load clean results: {e}")
+    
+    # 优先级 3: 使用预定义的 clean metrics (根据模型名称)
+    if clean_metrics is None:
+        clean_metrics = get_clean_metrics_for_model(args.model_name)
+        if clean_metrics:
+            print(f"Using predefined clean metrics for '{args.model_name}': {clean_metrics}")
+        else:
+            print(f"Warning: No clean metrics found for '{args.model_name}'")
+            print("Available models with predefined clean metrics:", list(PREDEFINED_CLEAN_METRICS.keys()))
+            print("You can specify clean metrics via --clean-base-ap50, --clean-novel-ap50, --clean-all-ap50")
+    
+    # 计算鲁棒性指标
+    robustness_results = compute_robustness_metrics(all_metrics, clean_metrics)
+    
+    # 打印报告
+    print_report(robustness_results, args.model_name)
+    
+    # 保存合并结果 (pkl)
+    if args.output is None:
+        args.output = os.path.join(args.results_dir, 'merged_results.pkl')
+    
+    with open(args.output, 'wb') as f:
+        pickle.dump({
+            'metrics': all_metrics,
+            'robustness_results': robustness_results
+        }, f)
+    print(f"\nMerged results saved to: {args.output}")
+    
+    # 保存 Excel 报告
+    if args.excel is None:
+        args.excel = os.path.join(args.results_dir, 'robustness_report.xlsx')
+    
+    save_to_excel(robustness_results, args.excel, args.model_name)
+    
+    # 保存 JSON 汇总 (核心指标)
+    if args.json is None:
+        args.json = os.path.join(args.results_dir, 'robustness_summary.json')
+    
+    summary_data = {
+        'model': args.model_name,
+        'P_clean': robustness_results['P'],
+        'mPC': {k: round(v, 2) for k, v in robustness_results['mPC'].items()},
+        'rPC': {k: round(v * 100, 2) for k, v in robustness_results['rPC'].items()},
+        'category_mPC': robustness_results['category_mPC']
+    }
+    
+    with open(args.json, 'w') as f:
+        json.dump(summary_data, f, indent=2)
+    print(f"JSON summary saved to: {args.json}")
+
+
+if __name__ == '__main__':
+    main()

analysis/robustness_eval/results/clearclip/robustness_summary.json

@@ -0,0 +1,76 @@
+{
+  "model": "ClearCLIP",
+  "P_clean": {
+    "base_ap50": 44.0,
+    "novel_ap50": 26.74,
+    "all_ap50": 39.49,
+    "bbox_mAP": 20.3,
+    "bbox_mAP_50": 39.5,
+    "bbox_mAP_75": 19.3
+  },
+  "mPC": {
+    "base_ap50": 26.21,
+    "novel_ap50": 13.84,
+    "all_ap50": 22.97,
+    "bbox_mAP": 11.37,
+    "bbox_mAP_50": 22.98,
+    "bbox_mAP_75": 10.23,
+    "bbox_mAP_s": 3.9,
+    "bbox_mAP_m": 10.82,
+    "bbox_mAP_l": 19.85
+  },
+  "rPC": {
+    "base_ap50": 59.56,
+    "novel_ap50": 51.76,
+    "all_ap50": 58.17,
+    "bbox_mAP": 56.01,
+    "bbox_mAP_50": 58.17,
+    "bbox_mAP_75": 53.02
+  },
+  "category_mPC": {
+    "noise": {
+      "base_ap50": 19.19353333333333,
+      "novel_ap50": 9.077466666666666,
+      "all_ap50": 16.547866666666668,
+      "bbox_mAP": 8.18,
+      "bbox_mAP_50": 16.55333333333333,
+      "bbox_mAP_75": 7.333333333333333,
+      "bbox_mAP_s": 2.6733333333333333,
+      "bbox_mAP_m": 7.6000000000000005,
+      "bbox_mAP_l": 14.493333333333334
+    },
+    "blur": {
+      "base_ap50": 23.363400000000002,
+      "novel_ap50": 13.38795,
+      "all_ap50": 20.754600000000003,
+      "bbox_mAP": 9.975,
+      "bbox_mAP_50": 20.755,
+      "bbox_mAP_75": 8.635000000000002,
+      "bbox_mAP_s": 2.5549999999999997,
+      "bbox_mAP_m": 8.9,
+      "bbox_mAP_l": 18.85
+    },
+    "weather": {
+      "base_ap50": 28.861250000000002,
+      "novel_ap50": 14.41235,
+      "all_ap50": 25.082250000000002,
+      "bbox_mAP": 12.530000000000001,
+      "bbox_mAP_50": 25.08,
+      "bbox_mAP_75": 11.455000000000002,
+      "bbox_mAP_s": 5.09,
+      "bbox_mAP_m": 12.465,
+      "bbox_mAP_l": 20.595
+    },
+    "digital": {
+      "base_ap50": 31.6556,
+      "novel_ap50": 17.29665,
+      "all_ap50": 27.900150000000004,
+      "bbox_mAP": 13.999999999999998,
+      "bbox_mAP_50": 27.91,
+      "bbox_mAP_75": 12.785000000000002,
+      "bbox_mAP_s": 4.96,
+      "bbox_mAP_m": 13.495,
+      "bbox_mAP_l": 24.134999999999998
+    }
+  }
+}

analysis/robustness_eval/results/clearclip/run_gpu0.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=0
+
+# 切换到 F-ViT 目录（custom_imports 需要从这里找 datasets 和 models）
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT
+export PYTHONPATH=/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT:/opt/tiger/xiaomoguhzz/mmdetection:$PYTHONPATH
+
+for scenario in  gaussian_noise_1 gaussian_noise_2 gaussian_noise_3 gaussian_noise_4 gaussian_noise_5 shot_noise_1 shot_noise_2 shot_noise_3 shot_noise_4 shot_noise_5; do
+    # 解析 corruption 和 severity
+    corr=$(echo $scenario | rev | cut -d'_' -f2- | rev)
+    sev=$(echo $scenario | rev | cut -d'_' -f1 | rev)
+    
+    output_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}.pkl"
+    output_results_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}_results.pkl"
+    
+    # 检查 _results.pkl 是否存在（包含 OV-COCO 指标）
+    if [ -f "$output_results_pkl" ]; then
+        echo "[GPU 0] Skip existing: $scenario"
+        continue
+    fi
+    
+    echo "[GPU 0] Running: $scenario (corruption=$corr, severity=$sev)"
+    
+    # 使用自定义的 OV-COCO 鲁棒性测试脚本
+    python3 /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/test_robustness_ovcoco.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/configs/declip/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_clearclip.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/work_dirs/clearclip_ovcoco/epoch_3.pth \
+        --out $output_pkl \
+        --corruptions $corr \
+        --severities $sev \
+        --eval bbox \
+        --workers 8
+done
+
+echo "[GPU 0] Completed all scenarios"

analysis/robustness_eval/results/clearclip/run_gpu1.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=1
+
+# 切换到 F-ViT 目录（custom_imports 需要从这里找 datasets 和 models）
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT
+export PYTHONPATH=/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT:/opt/tiger/xiaomoguhzz/mmdetection:$PYTHONPATH
+
+for scenario in  impulse_noise_1 impulse_noise_2 impulse_noise_3 impulse_noise_4 impulse_noise_5 defocus_blur_1 defocus_blur_2 defocus_blur_3 defocus_blur_4 defocus_blur_5; do
+    # 解析 corruption 和 severity
+    corr=$(echo $scenario | rev | cut -d'_' -f2- | rev)
+    sev=$(echo $scenario | rev | cut -d'_' -f1 | rev)
+    
+    output_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}.pkl"
+    output_results_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}_results.pkl"
+    
+    # 检查 _results.pkl 是否存在（包含 OV-COCO 指标）
+    if [ -f "$output_results_pkl" ]; then
+        echo "[GPU 1] Skip existing: $scenario"
+        continue
+    fi
+    
+    echo "[GPU 1] Running: $scenario (corruption=$corr, severity=$sev)"
+    
+    # 使用自定义的 OV-COCO 鲁棒性测试脚本
+    python3 /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/test_robustness_ovcoco.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/configs/declip/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_clearclip.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/work_dirs/clearclip_ovcoco/epoch_3.pth \
+        --out $output_pkl \
+        --corruptions $corr \
+        --severities $sev \
+        --eval bbox \
+        --workers 8
+done
+
+echo "[GPU 1] Completed all scenarios"

analysis/robustness_eval/results/clearclip/run_gpu2.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=2
+
+# 切换到 F-ViT 目录（custom_imports 需要从这里找 datasets 和 models）
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT
+export PYTHONPATH=/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT:/opt/tiger/xiaomoguhzz/mmdetection:$PYTHONPATH
+
+for scenario in  glass_blur_1 glass_blur_2 glass_blur_3 glass_blur_4 glass_blur_5 motion_blur_1 motion_blur_2 motion_blur_3 motion_blur_4 motion_blur_5; do
+    # 解析 corruption 和 severity
+    corr=$(echo $scenario | rev | cut -d'_' -f2- | rev)
+    sev=$(echo $scenario | rev | cut -d'_' -f1 | rev)
+    
+    output_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}.pkl"
+    output_results_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}_results.pkl"
+    
+    # 检查 _results.pkl 是否存在（包含 OV-COCO 指标）
+    if [ -f "$output_results_pkl" ]; then
+        echo "[GPU 2] Skip existing: $scenario"
+        continue
+    fi
+    
+    echo "[GPU 2] Running: $scenario (corruption=$corr, severity=$sev)"
+    
+    # 使用自定义的 OV-COCO 鲁棒性测试脚本
+    python3 /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/test_robustness_ovcoco.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/configs/declip/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_clearclip.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/work_dirs/clearclip_ovcoco/epoch_3.pth \
+        --out $output_pkl \
+        --corruptions $corr \
+        --severities $sev \
+        --eval bbox \
+        --workers 8
+done
+
+echo "[GPU 2] Completed all scenarios"

analysis/robustness_eval/results/clearclip/run_gpu3.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=3
+
+# 切换到 F-ViT 目录（custom_imports 需要从这里找 datasets 和 models）
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT
+export PYTHONPATH=/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT:/opt/tiger/xiaomoguhzz/mmdetection:$PYTHONPATH
+
+for scenario in  zoom_blur_1 zoom_blur_2 zoom_blur_3 zoom_blur_4 zoom_blur_5 snow_1 snow_2 snow_3 snow_4; do
+    # 解析 corruption 和 severity
+    corr=$(echo $scenario | rev | cut -d'_' -f2- | rev)
+    sev=$(echo $scenario | rev | cut -d'_' -f1 | rev)
+    
+    output_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}.pkl"
+    output_results_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}_results.pkl"
+    
+    # 检查 _results.pkl 是否存在（包含 OV-COCO 指标）
+    if [ -f "$output_results_pkl" ]; then
+        echo "[GPU 3] Skip existing: $scenario"
+        continue
+    fi
+    
+    echo "[GPU 3] Running: $scenario (corruption=$corr, severity=$sev)"
+    
+    # 使用自定义的 OV-COCO 鲁棒性测试脚本
+    python3 /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/test_robustness_ovcoco.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/configs/declip/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_clearclip.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/work_dirs/clearclip_ovcoco/epoch_3.pth \
+        --out $output_pkl \
+        --corruptions $corr \
+        --severities $sev \
+        --eval bbox \
+        --workers 8
+done
+
+echo "[GPU 3] Completed all scenarios"

analysis/robustness_eval/results/clearclip/run_gpu4.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=4
+
+# 切换到 F-ViT 目录（custom_imports 需要从这里找 datasets 和 models）
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT
+export PYTHONPATH=/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT:/opt/tiger/xiaomoguhzz/mmdetection:$PYTHONPATH
+
+for scenario in  snow_5 frost_1 frost_2 frost_3 frost_4 frost_5 fog_1 fog_2 fog_3; do
+    # 解析 corruption 和 severity
+    corr=$(echo $scenario | rev | cut -d'_' -f2- | rev)
+    sev=$(echo $scenario | rev | cut -d'_' -f1 | rev)
+    
+    output_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}.pkl"
+    output_results_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}_results.pkl"
+    
+    # 检查 _results.pkl 是否存在（包含 OV-COCO 指标）
+    if [ -f "$output_results_pkl" ]; then
+        echo "[GPU 4] Skip existing: $scenario"
+        continue
+    fi
+    
+    echo "[GPU 4] Running: $scenario (corruption=$corr, severity=$sev)"
+    
+    # 使用自定义的 OV-COCO 鲁棒性测试脚本
+    python3 /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/test_robustness_ovcoco.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/configs/declip/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_clearclip.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/work_dirs/clearclip_ovcoco/epoch_3.pth \
+        --out $output_pkl \
+        --corruptions $corr \
+        --severities $sev \
+        --eval bbox \
+        --workers 8
+done
+
+echo "[GPU 4] Completed all scenarios"

analysis/robustness_eval/results/clearclip/run_gpu5.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=5
+
+# 切换到 F-ViT 目录（custom_imports 需要从这里找 datasets 和 models）
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT
+export PYTHONPATH=/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT:/opt/tiger/xiaomoguhzz/mmdetection:$PYTHONPATH
+
+for scenario in  fog_4 fog_5 brightness_1 brightness_2 brightness_3 brightness_4 brightness_5 contrast_1 contrast_2; do
+    # 解析 corruption 和 severity
+    corr=$(echo $scenario | rev | cut -d'_' -f2- | rev)
+    sev=$(echo $scenario | rev | cut -d'_' -f1 | rev)
+    
+    output_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}.pkl"
+    output_results_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}_results.pkl"
+    
+    # 检查 _results.pkl 是否存在（包含 OV-COCO 指标）
+    if [ -f "$output_results_pkl" ]; then
+        echo "[GPU 5] Skip existing: $scenario"
+        continue
+    fi
+    
+    echo "[GPU 5] Running: $scenario (corruption=$corr, severity=$sev)"
+    
+    # 使用自定义的 OV-COCO 鲁棒性测试脚本
+    python3 /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/test_robustness_ovcoco.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/configs/declip/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_clearclip.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/work_dirs/clearclip_ovcoco/epoch_3.pth \
+        --out $output_pkl \
+        --corruptions $corr \
+        --severities $sev \
+        --eval bbox \
+        --workers 8
+done
+
+echo "[GPU 5] Completed all scenarios"

analysis/robustness_eval/results/clearclip/run_gpu6.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=6
+
+# 切换到 F-ViT 目录（custom_imports 需要从这里找 datasets 和 models）
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT
+export PYTHONPATH=/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT:/opt/tiger/xiaomoguhzz/mmdetection:$PYTHONPATH
+
+for scenario in  contrast_3 contrast_4 contrast_5 elastic_transform_1 elastic_transform_2 elastic_transform_3 elastic_transform_4 elastic_transform_5 pixelate_1; do
+    # 解析 corruption 和 severity
+    corr=$(echo $scenario | rev | cut -d'_' -f2- | rev)
+    sev=$(echo $scenario | rev | cut -d'_' -f1 | rev)
+    
+    output_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}.pkl"
+    output_results_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}_results.pkl"
+    
+    # 检查 _results.pkl 是否存在（包含 OV-COCO 指标）
+    if [ -f "$output_results_pkl" ]; then
+        echo "[GPU 6] Skip existing: $scenario"
+        continue
+    fi
+    
+    echo "[GPU 6] Running: $scenario (corruption=$corr, severity=$sev)"
+    
+    # 使用自定义的 OV-COCO 鲁棒性测试脚本
+    python3 /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/test_robustness_ovcoco.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/configs/declip/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_clearclip.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/work_dirs/clearclip_ovcoco/epoch_3.pth \
+        --out $output_pkl \
+        --corruptions $corr \
+        --severities $sev \
+        --eval bbox \
+        --workers 8
+done
+
+echo "[GPU 6] Completed all scenarios"

analysis/robustness_eval/results/clearclip/run_gpu7.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=7
+
+# 切换到 F-ViT 目录（custom_imports 需要从这里找 datasets 和 models）
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT
+export PYTHONPATH=/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT:/opt/tiger/xiaomoguhzz/mmdetection:$PYTHONPATH
+
+for scenario in  pixelate_2 pixelate_3 pixelate_4 pixelate_5 jpeg_compression_1 jpeg_compression_2 jpeg_compression_3 jpeg_compression_4 jpeg_compression_5; do
+    # 解析 corruption 和 severity
+    corr=$(echo $scenario | rev | cut -d'_' -f2- | rev)
+    sev=$(echo $scenario | rev | cut -d'_' -f1 | rev)
+    
+    output_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}.pkl"
+    output_results_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clearclip/${scenario}_results.pkl"
+    
+    # 检查 _results.pkl 是否存在（包含 OV-COCO 指标）
+    if [ -f "$output_results_pkl" ]; then
+        echo "[GPU 7] Skip existing: $scenario"
+        continue
+    fi
+    
+    echo "[GPU 7] Running: $scenario (corruption=$corr, severity=$sev)"
+    
+    # 使用自定义的 OV-COCO 鲁棒性测试脚本
+    python3 /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/test_robustness_ovcoco.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/configs/declip/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_clearclip.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/work_dirs/clearclip_ovcoco/epoch_3.pth \
+        --out $output_pkl \
+        --corruptions $corr \
+        --severities $sev \
+        --eval bbox \
+        --workers 8
+done
+
+echo "[GPU 7] Completed all scenarios"

analysis/robustness_eval/results/clipself/robustness_summary.json

@@ -0,0 +1,76 @@
+{
+  "model": "CLIPSelf",
+  "P_clean": {
+    "base_ap50": 54.94,
+    "novel_ap50": 37.51,
+    "all_ap50": 50.38,
+    "bbox_mAP": 27.7,
+    "bbox_mAP_50": 50.0,
+    "bbox_mAP_75": 27.8
+  },
+  "mPC": {
+    "base_ap50": 40.81,
+    "novel_ap50": 29.27,
+    "all_ap50": 37.79,
+    "bbox_mAP": 19.27,
+    "bbox_mAP_50": 37.79,
+    "bbox_mAP_75": 17.99,
+    "bbox_mAP_s": 7.29,
+    "bbox_mAP_m": 19.82,
+    "bbox_mAP_l": 31.79
+  },
+  "rPC": {
+    "base_ap50": 74.29,
+    "novel_ap50": 78.02,
+    "all_ap50": 75.02,
+    "bbox_mAP": 69.58,
+    "bbox_mAP_50": 75.59,
+    "bbox_mAP_75": 64.72
+  },
+  "category_mPC": {
+    "noise": {
+      "base_ap50": 37.54313333333334,
+      "novel_ap50": 27.0374,
+      "all_ap50": 34.79533333333333,
+      "bbox_mAP": 17.753333333333334,
+      "bbox_mAP_50": 34.800000000000004,
+      "bbox_mAP_75": 16.513333333333332,
+      "bbox_mAP_s": 5.933333333333333,
+      "bbox_mAP_m": 17.906666666666666,
+      "bbox_mAP_l": 29.933333333333337
+    },
+    "blur": {
+      "base_ap50": 35.2237,
+      "novel_ap50": 26.4242,
+      "all_ap50": 32.92235,
+      "bbox_mAP": 16.23,
+      "bbox_mAP_50": 32.915000000000006,
+      "bbox_mAP_75": 14.58,
+      "bbox_mAP_s": 4.420000000000001,
+      "bbox_mAP_m": 15.530000000000001,
+      "bbox_mAP_l": 29.595
+    },
+    "weather": {
+      "base_ap50": 44.5319,
+      "novel_ap50": 31.022299999999998,
+      "all_ap50": 40.998850000000004,
+      "bbox_mAP": 21.240000000000002,
+      "bbox_mAP_50": 41.0,
+      "bbox_mAP_75": 20.175,
+      "bbox_mAP_s": 9.690000000000001,
+      "bbox_mAP_m": 22.71,
+      "bbox_mAP_l": 32.735
+    },
+    "digital": {
+      "base_ap50": 45.1416,
+      "novel_ap50": 32.0268,
+      "all_ap50": 41.711650000000006,
+      "bbox_mAP": 21.495,
+      "bbox_mAP_50": 41.715,
+      "bbox_mAP_75": 20.33,
+      "bbox_mAP_s": 8.775,
+      "bbox_mAP_m": 22.655,
+      "bbox_mAP_l": 34.43
+    }
+  }
+}

analysis/robustness_eval/results/clipself/run_gpu0.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=0
+
+# 切换到 F-ViT 目录（custom_imports 需要从这里找 datasets 和 models）
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT
+export PYTHONPATH=/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT:/opt/tiger/xiaomoguhzz/mmdetection:$PYTHONPATH
+
+for scenario in  gaussian_noise_1 gaussian_noise_2 gaussian_noise_3 gaussian_noise_4 gaussian_noise_5 shot_noise_1 shot_noise_2 shot_noise_3 shot_noise_4 shot_noise_5; do
+    # 解析 corruption 和 severity
+    corr=$(echo $scenario | rev | cut -d'_' -f2- | rev)
+    sev=$(echo $scenario | rev | cut -d'_' -f1 | rev)
+    
+    output_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clipself/${scenario}.pkl"
+    output_results_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clipself/${scenario}_results.pkl"
+    
+    # 检查 _results.pkl 是否存在（包含 OV-COCO 指标）
+    if [ -f "$output_results_pkl" ]; then
+        echo "[GPU 0] Skip existing: $scenario"
+        continue
+    fi
+    
+    echo "[GPU 0] Running: $scenario (corruption=$corr, severity=$sev)"
+    
+    # 使用自定义的 OV-COCO 鲁棒性测试脚本
+    python3 /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/test_robustness_ovcoco.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/configs/ov_coco/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_eva_clipself_proposals.py \
+        /opt/tiger/xiaomoguhzz/fvit_eva_vitb16_ovcoco_clipself_proposals.pth \
+        --out $output_pkl \
+        --corruptions $corr \
+        --severities $sev \
+        --eval bbox \
+        --workers 8
+done
+
+echo "[GPU 0] Completed all scenarios"

analysis/robustness_eval/results/clipself/run_gpu1.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=1
+
+# 切换到 F-ViT 目录（custom_imports 需要从这里找 datasets 和 models）
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT
+export PYTHONPATH=/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT:/opt/tiger/xiaomoguhzz/mmdetection:$PYTHONPATH
+
+for scenario in  impulse_noise_1 impulse_noise_2 impulse_noise_3 impulse_noise_4 impulse_noise_5 defocus_blur_1 defocus_blur_2 defocus_blur_3 defocus_blur_4 defocus_blur_5; do
+    # 解析 corruption 和 severity
+    corr=$(echo $scenario | rev | cut -d'_' -f2- | rev)
+    sev=$(echo $scenario | rev | cut -d'_' -f1 | rev)
+    
+    output_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clipself/${scenario}.pkl"
+    output_results_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clipself/${scenario}_results.pkl"
+    
+    # 检查 _results.pkl 是否存在（包含 OV-COCO 指标）
+    if [ -f "$output_results_pkl" ]; then
+        echo "[GPU 1] Skip existing: $scenario"
+        continue
+    fi
+    
+    echo "[GPU 1] Running: $scenario (corruption=$corr, severity=$sev)"
+    
+    # 使用自定义的 OV-COCO 鲁棒性测试脚本
+    python3 /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/test_robustness_ovcoco.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/configs/ov_coco/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_eva_clipself_proposals.py \
+        /opt/tiger/xiaomoguhzz/fvit_eva_vitb16_ovcoco_clipself_proposals.pth \
+        --out $output_pkl \
+        --corruptions $corr \
+        --severities $sev \
+        --eval bbox \
+        --workers 8
+done
+
+echo "[GPU 1] Completed all scenarios"

analysis/robustness_eval/results/clipself/run_gpu2.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=2
+
+# 切换到 F-ViT 目录（custom_imports 需要从这里找 datasets 和 models）
+cd /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT
+export PYTHONPATH=/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT:/opt/tiger/xiaomoguhzz/mmdetection:$PYTHONPATH
+
+for scenario in  glass_blur_1 glass_blur_2 glass_blur_3 glass_blur_4 glass_blur_5 motion_blur_1 motion_blur_2 motion_blur_3 motion_blur_4 motion_blur_5; do
+    # 解析 corruption 和 severity
+    corr=$(echo $scenario | rev | cut -d'_' -f2- | rev)
+    sev=$(echo $scenario | rev | cut -d'_' -f1 | rev)
+    
+    output_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clipself/${scenario}.pkl"
+    output_results_pkl="/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/results/clipself/${scenario}_results.pkl"
+    
+    # 检查 _results.pkl 是否存在（包含 OV-COCO 指标）
+    if [ -f "$output_results_pkl" ]; then
+        echo "[GPU 2] Skip existing: $scenario"
+        continue
+    fi
+    
+    echo "[GPU 2] Running: $scenario (corruption=$corr, severity=$sev)"
+    
+    # 使用自定义的 OV-COCO 鲁棒性测试脚本
+    python3 /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/robustness_eval/test_robustness_ovcoco.py \
+        /mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/CLIPSelf/F-ViT/configs/ov_coco/fvit_vitb16_upsample_fpn_bs64_3e_ovcoco_eva_clipself_proposals.py \
+        /opt/tiger/xiaomoguhzz/fvit_eva_vitb16_ovcoco_clipself_proposals.pth \
+        --out $output_pkl \
+        --corruptions $corr \
+        --severities $sev \
+        --eval bbox \
+        --workers 8
+done
+
+echo "[GPU 2] Completed all scenarios"