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PCA 可视化 - Feature Space Visualization
对比 DeCLIP 解耦蒸馏和 Integrated 集成蒸馏的特征质量
使用方法：
1. 加载训练好的模型
2. 提取特征
3. PCA 降维到 3 维
4. 映射为 RGB 图像
"""
import torch
import torch.nn.functional as F
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from typing import Tuple, Optional, Dict
import os
class PCAFeatureVisualizer:
"""
PCA 特征可视化器
用于对比不同方法的特征质量
"""
def __init__(self, save_dir: str = "pca_visualization_results"):
self.save_dir = save_dir
os.makedirs(save_dir, exist_ok=True)
def extract_and_visualize(
self,
model: torch.nn.Module,
image: torch.Tensor,
mode: str = "vanilla",
title: str = "Feature PCA",
save_name: Optional[str] = None
) -> np.ndarray:
"""
提取特征并进行 PCA 可视化
Args:
model: CLIP 模型
image: 输入图像 (1, 3, H, W)
mode: 特征提取模式 ("vanilla" for integrated, "csa_vfm_distill" for decoupled)
title: 图像标题
save_name: 保存文件名
Returns:
PCA RGB 图像 (H, W, 3)
"""
model.eval()
with torch.no_grad():
if mode == "vanilla":
# Integrated: 使用完整 forward 的最终输出
features = model.encode_dense(image, normalize=False, keep_shape=True, mode="vanilla")
elif mode in ["csa_vfm_distill", "qq_vfm_distill", "vv_vfm_distill"]:
# Decoupled: 使用解耦模式的输出
features, context = model.encode_dense(image, normalize=False, keep_shape=True, mode=mode)
else:
features = model.encode_dense(image, normalize=False, keep_shape=True, mode=mode)
# features: (B, C, H, W)
B, C, H, W = features.shape
# 转换为 (H*W, C) 用于 PCA
features_flat = features[0].permute(1, 2, 0).reshape(-1, C).cpu().numpy()
# PCA 降维到 3 维
pca = PCA(n_components=3)
features_pca = pca.fit_transform(features_flat)
# 归一化到 [0, 1]
features_pca = (features_pca - features_pca.min()) / (features_pca.max() - features_pca.min() + 1e-8)
# 重塑为图像
pca_image = features_pca.reshape(H, W, 3)
# 可视化
if save_name:
self._save_visualization(pca_image, title, save_name)
return pca_image
def compare_decoupled_vs_integrated(
self,
decoupled_model: torch.nn.Module,
integrated_model: torch.nn.Module,
image: torch.Tensor,
original_image: Optional[np.ndarray] = None,
save_name: str = "comparison.png"
):
"""
对比 DeCLIP 解耦蒸馏和 Integrated 集成蒸馏的特征
Args:
decoupled_model: 解耦蒸馏模型
integrated_model: 集成蒸馏模型
image: 输入图像 (1, 3, H, W)
original_image: 原始图像 (H, W, 3) 用于对比显示
save_name: 保存文件名
"""
# 提取解耦特征
decoupled_pca = self.extract_and_visualize(
decoupled_model, image, mode="csa_vfm_distill", title="DeCLIP (Decoupled)"
)
# 提取集成特征
integrated_pca = self.extract_and_visualize(
integrated_model, image, mode="vanilla", title="Integrated"
)
# 创建对比图
fig, axes = plt.subplots(1, 3 if original_image is not None else 2, figsize=(15, 5))
idx = 0
if original_image is not None:
axes[idx].imshow(original_image)
axes[idx].set_title("Original Image", fontsize=14)
axes[idx].axis("off")
idx += 1
axes[idx].imshow(decoupled_pca)
axes[idx].set_title("DeCLIP (Decoupled)\nExpected: Clear boundaries, semantic coherence", fontsize=12)
axes[idx].axis("off")
idx += 1
axes[idx].imshow(integrated_pca)
axes[idx].set_title("Integrated\nExpected: Blurry, mixed features", fontsize=12)
axes[idx].axis("off")
plt.tight_layout()
save_path = os.path.join(self.save_dir, save_name)
plt.savefig(save_path, dpi=150, bbox_inches="tight")
plt.close()
print(f"Comparison saved to {save_path}")
def extract_qv_features(
self,
model: torch.nn.Module,
image: torch.Tensor,
save_name: str = "qv_features.png"
):
"""
提取并可视化 DeCLIP 的 Q 和 V 特征
用于展示解耦效果
"""
model.eval()
with torch.no_grad():
# 使用 csa_vfm_distill 模式提取 Q 和 K（实际上是 Q 和 V 的 self-attention）
features, context = model.encode_dense(image, normalize=False, keep_shape=True, mode="csa_vfm_distill")
# context 应该是 (q_feature, k_feature) 或类似结构
if isinstance(context, tuple) and len(context) >= 2:
q_feature, v_feature = context[0], context[1]
# 可视化 Q 特征
B, N, nHead, dim = q_feature.shape
H = W = int(np.sqrt(N - 1)) # 减去 cls token
q_flat = q_feature[0, 1:].reshape(H, W, -1).cpu().numpy()
v_flat = v_feature[0, 1:].reshape(H, W, -1).cpu().numpy()
# PCA
pca_q = PCA(n_components=3)
pca_v = PCA(n_components=3)
q_pca = pca_q.fit_transform(q_flat.reshape(-1, q_flat.shape[-1]))
v_pca = pca_v.fit_transform(v_flat.reshape(-1, v_flat.shape[-1]))
q_pca = (q_pca - q_pca.min()) / (q_pca.max() - q_pca.min() + 1e-8)
v_pca = (v_pca - v_pca.min()) / (v_pca.max() - v_pca.min() + 1e-8)
q_image = q_pca.reshape(H, W, 3)
v_image = v_pca.reshape(H, W, 3)
# 可视化
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
axes[0].imshow(q_image)
axes[0].set_title("Q Features (Content)", fontsize=14)
axes[0].axis("off")
axes[1].imshow(v_image)
axes[1].set_title("V Features (Context)", fontsize=14)
axes[1].axis("off")
plt.tight_layout()
save_path = os.path.join(self.save_dir, save_name)
plt.savefig(save_path, dpi=150, bbox_inches="tight")
plt.close()
print(f"Q/V features saved to {save_path}")
return q_image, v_image
else:
print("Could not extract Q and V features. Context format unexpected.")
return None, None
def _save_visualization(self, pca_image: np.ndarray, title: str, save_name: str):
"""保存可视化结果"""
plt.figure(figsize=(8, 8))
plt.imshow(pca_image)
plt.title(title, fontsize=14)
plt.axis("off")
save_path = os.path.join(self.save_dir, save_name)
plt.savefig(save_path, dpi=150, bbox_inches="tight")
plt.close()
print(f"Visualization saved to {save_path}")
def visualize_feature_comparison(
decoupled_checkpoint: str,
integrated_checkpoint: str,
image_path: str,
model_name: str = "EVA02-CLIP-B-16",
save_dir: str = "pca_visualization_results"
):
"""
便捷函数：加载模型并进行特征对比可视化
Args:
decoupled_checkpoint: 解耦蒸馏模型的 checkpoint 路径
integrated_checkpoint: 集成蒸馏模型的 checkpoint 路径
image_path: 测试图像路径
model_name: 模型名称
save_dir: 保存目录
"""
from open_clip import create_model
from torchvision import transforms
from PIL import Image
# 加载模型
print("Loading models...")
device = "cuda" if torch.cuda.is_available() else "cpu"
decoupled_model = create_model(model_name, pretrained="eva", device=device)
decoupled_model.load_state_dict(torch.load(decoupled_checkpoint, map_location=device)["state_dict"])
decoupled_model.eval()
integrated_model = create_model(model_name, pretrained="eva", device=device)
integrated_model.load_state_dict(torch.load(integrated_checkpoint, map_location=device)["state_dict"])
integrated_model.eval()
# 加载图像
print(f"Loading image: {image_path}")
image = Image.open(image_path).convert("RGB")
original_image = np.array(image)
# 预处理
transform = transforms.Compose([
transforms.Resize((560, 560)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
image_tensor = transform(image).unsqueeze(0).to(device)
# 可视化
visualizer = PCAFeatureVisualizer(save_dir)
visualizer.compare_decoupled_vs_integrated(
decoupled_model, integrated_model, image_tensor,
original_image=original_image,
save_name="decoupled_vs_integrated.png"
)
print("Done!")
if __name__ == "__main__":
print("PCA Feature Visualizer")
print("Usage:")
print(" from decoupling_analysis.pca_visualization import PCAFeatureVisualizer")
print(" visualizer = PCAFeatureVisualizer('save_dir')")
print(" visualizer.compare_decoupled_vs_integrated(decoupled_model, integrated_model, image)")
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