在上一篇文章中，我们探讨了模型压缩与量化部署技术。本文将深入可解释性AI与特征可视化领域，揭示深度学习模型的决策机制，帮助开发者理解和解释模型的内部工作原理。
一、可解释性AI基础
1. 核心概念
- 特征重要性：识别输入特征对预测结果的贡献度
- 决策归因：追溯模型决策的关键依据
- 概念激活：识别模型学习的高级语义概念
2. 主要方法对比
方法类型 | 代表技术 | 适用场景 | 可视化粒度 |
基于梯度 | Saliency Map | 分类任务 | 像素级 |
基于扰动 | LIME | 任意模型 | 特征块级 |
类激活映射 | Grad-CAM | CNN模型 | 空间区域级 |
概念分析 | TCAV | 概念解释 | 语义概念级 |
二、PyTorch特征可视化实战
1. 特征重要性分析（Saliency Map）
import torch
import matplotlib.pyplot as plt
from torchvision import models, transforms
from PIL import Image
# 加载预训练模型
model = models.resnet50(weights='IMAGENET1K_V1').eval()
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# 加载图片
def load_image(image_path):
image = Image.open(image_path)
image_tensor = preprocess(image)
image_tensor = image_tensor.unsqueeze(0)
return image_tensor
# 生成Saliency Map
def generate_saliency(img_tensor, model):
img_tensor.requires_grad_()
output = model(img_tensor)
output.max().backward()
saliency = img_tensor.grad.data.abs().max(dim=1)[0]
return saliency.squeeze().cpu().numpy()
# 可视化
img = load_image('cat.jpeg') # 自定义图像加载函数
saliency = generate_saliency(img, model)
plt.imshow(saliency, cmap='hot')
plt.colorbar()
plt.show()原图：
输出：
2. 类激活映射（Grad-CAM）
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
from torchvision import models, transforms
from PIL import Image
import cv2
import matplotlib.font_manager as fm
# 设置中文字体
plt.rcParams['font.sans-serif'] = ['SimHei'] # 使用黑体
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
class GradCAM:
def __init__(self, model, target_layer):
"""GradCAM类初始化
Args:
model: 目标模型
target_layer: 要计算CAM的目标层
"""
self.model = model
self.gradients = None # 保存梯度
self.activations = None # 保存激活值
self.target_layer = target_layer
# 注册前向和反向钩子
self.target_layer.register_forward_hook(self.save_activations)
self.target_layer.register_full_backward_hook(self.save_gradients)
def save_activations(self, module, input, output):
"""保存前向传播的激活值"""
self.activations = output.detach() # 不计算梯度
def save_gradients(self, module, grad_input, grad_output):
"""保存反向传播的梯度"""
self.gradients = grad_output[0].detach() # 不计算梯度
def forward(self, x):
"""前向传播"""
return self.model(x)
def __call__(self, x, class_idx=None):
"""生成GradCAM热力图
Args:
x: 输入张量
class_idx: 目标类别索引，None表示自动选择最高概率类别
Returns:
cam: 归一化的热力图
class_idx: 使用的类别索引
"""
# 前向传播获取预测结果
logits = self.forward(x)
if class_idx is None:
class_idx = logits.argmax() # 自动选择最高概率类别
# 反向传播计算梯度
self.model.zero_grad()
logits[0, class_idx].backward(retain_graph=True)
# 计算权重（全局平均池化梯度）
weights = self.gradients.mean(dim=(2, 3), keepdim=True)
# 计算类激活图
cam = (weights * self.activations).sum(dim=1, keepdim=True)
cam = F.relu(cam) # 只保留正激活
cam = F.interpolate(cam, x.shape[2:], mode='bilinear', align_corners=False) # 上采样到输入尺寸
# 归一化到[0,1]范围
cam_min, cam_max = cam.min(), cam.max()
cam = (cam - cam_min) / (cam_max - cam_min + 1e-8) # 避免除以零
return cam.squeeze().cpu().numpy(), class_idx.item()
# 图像预处理流程
preprocess = transforms.Compose([
transforms.Resize(256), # 调整大小
transforms.CenterCrop(224), # 中心裁剪
transforms.ToTensor(), # 转为张量
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), # ImageNet标准化
])
def load_image(image_path):
"""加载并预处理图像
Args:
image_path: 图像路径
Returns:
image_tensor: 预处理后的张量
original_image: 原始PIL图像
"""
image = Image.open(image_path).convert('RGB') # 确保RGB格式
image_tensor = preprocess(image)
image_tensor = image_tensor.unsqueeze(0) # 添加批次维度
return image_tensor, image
def visualize_gradcam(image, heatmap, alpha=0.5):
"""可视化GradCAM结果
Args:
image: 原始PIL图像
heatmap: GradCAM生成的热力图
alpha: 热力图透明度
Returns:
superimposed_img: 叠加后的图像
"""
# 转换图像为numpy数组并调整大小
img = np.array(image)
img = cv2.resize(img, (224, 224))
# 调整热力图大小并应用颜色映射
heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
heatmap = np.uint8(255 * heatmap) # 转为0-255范围
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) # 应用Jet颜色
# 叠加热力图和原始图像
superimposed_img = heatmap * alpha + img * (1 - alpha)
superimposed_img = np.clip(superimposed_img, 0, 255).astype(np.uint8) # 限制范围
return superimposed_img
# 主程序
if __name__ == "__main__":
# 加载预训练ResNet50模型
model = models.resnet50(weights='IMAGENET1K_V1').eval()
try:
# 加载图像
img_tensor, original_image = load_image('cat.jpeg')
# 设置目标层（ResNet50最后一个卷积层）
target_layer = model.layer4[-1].conv3
grad_cam = GradCAM(model, target_layer)
# 生成热力图
heatmap, class_idx = grad_cam(img_tensor)
# 可视化结果
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
# 原始图像
ax[0].imshow(original_image)
ax[0].set_title('原始图像')
ax[0].axis('off')
# 热力图
ax[1].imshow(heatmap, cmap='jet')
ax[1].set_title('GradCAM热力图')
ax[1].axis('off')
# 叠加效果
superimposed = visualize_gradcam(original_image, heatmap)
ax[2].imshow(superimposed)
ax[2].set_title(f'可视化结果 (类别: {class_idx})')
ax[2].axis('off')
plt.tight_layout()
plt.savefig('gradcam_results.png', bbox_inches='tight', dpi=300)
plt.show()
except FileNotFoundError:
print("错误：未找到'cat.jpeg'文件，请确保图像存在")
except Exception as e:
print(f"发生错误: {e}")输出为：
三、高级可视化技术
1. 特征反演（Feature Inversion）
import torch
import torch.nn.functional as F
from torch import optim
from torchvision import models
import matplotlib.pyplot as plt
def invert_features(model, target_features, input_size=(3, 224, 224),
num_iterations=300, lr=0.1, show_progress=True):
"""
通过优化随机输入来反演生成与目标特征匹配的图像
参数:
model (nn.Module): 特征提取模型(需要返回目标层的特征)
target_features (Tensor): 目标特征图(形状需与model输出一致)
input_size (tuple): 生成图像的尺寸(channels, height, width)
num_iterations (int): 优化迭代次数
lr (float): 学习率
show_progress (bool): 是否显示优化过程
返回:
Tensor: 反演生成的图像(1, C, H, W)
"""
# 1. 初始化随机输入(使用正态分布)
synthetic_img = torch.randn(1, *input_size,
requires_grad=True,
device=target_features.device)
# 2. 设置优化器(Adam + 学习率衰减)
optimizer = optim.Adam([synthetic_img], lr=lr)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.5)
# 3. 优化循环
losses = []
best_img = None
best_loss = float('inf')
for i in range(num_iterations):
optimizer.zero_grad()
# 前向传播获取当前特征
current_features = model(synthetic_img)
# 计算MSE损失 + 正则项(图像总变分正则化)
mse_loss = F.mse_loss(current_features, target_features)
tv_loss = total_variation(synthetic_img) * 1e-6 # 总变分正则化
loss = mse_loss + tv_loss
# 反向传播
loss.backward()
optimizer.step()
scheduler.step()
# 数值截断(保持合理范围)
synthetic_img.data = torch.clamp(synthetic_img, -2.5, 2.5)
# 记录最佳结果
if loss.item() < best_loss:
best_loss = loss.item()
best_img = synthetic_img.detach().clone()
losses.append(loss.item())
# 打印进度
if show_progress and (i % 50 == 0 or i == num_iterations - 1):
print(f'Iter [{i + 1}/{num_iterations}], Loss: {loss.item():.4f}')
# 4. 后处理并返回最佳结果
final_img = post_process(best_img)
# 可视化结果
if show_progress:
visualize_results(losses, final_img)
return final_img
def total_variation(img):
"""计算图像的总变分(平滑性正则化)"""
batch_size = img.size(0)
height_var = torch.sum(torch.abs(img[:, :, 1:, :] - img[:, :, :-1, :]))
width_var = torch.sum(torch.abs(img[:, :, :, 1:] - img[:, :, :, :-1]))
return (height_var + width_var) / batch_size
def post_process(img_tensor):
"""后处理生成图像"""
# 1. 反归一化(假设输入是ImageNet标准化后的)
mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(img_tensor.device)
std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(img_tensor.device)
img = img_tensor * std + mean
# 2. 裁剪到[0,1]范围
img = torch.clamp(img, 0, 1)
return img
def visualize_results(losses, img_tensor):
"""可视化优化过程和结果"""
plt.figure(figsize=(12, 4))
# 1. 绘制损失曲线
plt.subplot(1, 2, 1)
plt.plot(losses)
plt.title('Optimization Loss')
plt.xlabel('Iteration')
plt.ylabel('Loss')
# 2. 绘制生成图像
plt.subplot(1, 2, 2)
img = img_tensor.squeeze().permute(1, 2, 0).cpu().numpy()
plt.imshow(img)
plt.title('Generated Image')
plt.axis('off')
plt.tight_layout()
plt.show()
# 使用示例
if __name__ == "__main__":
# 示例用法(需要实际模型和目标特征)
model = models.resnet50(pretrained=True).eval()
target_features = torch.randn(1, 2048, 7, 7) # 模拟ResNet50最后一层特征
# 提取中间层特征(需要修改模型forward)
class FeatureExtractor(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
self.features = None
# 注册钩子获取layer4输出
model.layer4.register_forward_hook(self.save_features)
def save_features(self, module, input, output):
self.features = output.detach()
def forward(self, x):
_ = self.model(x) # 标准前向传播
return self.features
feature_model = FeatureExtractor(model)
# 运行特征反演
generated_img = invert_features(
model=feature_model,
target_features=target_features,
num_iterations=300,
lr=0.05,
show_progress=True
)输出为：
Iter [1/300], Loss: 1.6159
Iter [51/300], Loss: 1.2094
Iter [101/300], Loss: 1.2376
Iter [151/300], Loss: 1.1462
Iter [201/300], Loss: 1.1278
Iter [251/300], Loss: 1.1206
Iter [300/300], Loss: 1.11942. 概念激活向量（TCAV）
import torch
import numpy as np
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
from typing import List, Tuple
from torchvision.transforms import functional as F # 直接使用functional接口
class TCAV:
def __init__(self, model: torch.nn.Module, layer_name: str):
"""TCAV初始化
Args:
model: 目标神经网络模型
layer_name: 要分析的目标层名称(如'layer4')
"""
self.model = model
self.layer_output = None
self.hook = self._register_hook(layer_name)
self.device = next(model.parameters()).device
def _register_hook(self, layer_name: str):
"""注册前向钩子到指定层"""
def hook_fn(module, input, output):
self.layer_output = output.detach()
return self.model._modules[layer_name].register_forward_hook(hook_fn)
def compute_concept_sensitivity(self,
concept_imgs: List[torch.Tensor],
random_imgs: List[torch.Tensor],
n_splits: int = 5) -> Tuple[float, np.ndarray]:
"""计算概念敏感度"""
concept_features = self._extract_features(concept_imgs)
random_features = self._extract_features(random_imgs)
X = torch.cat([concept_features, random_features]).cpu().numpy()
y = np.concatenate([np.ones(len(concept_features)),
np.zeros(len(random_features))])
accuracies, cavs = [], []
for _ in range(n_splits):
idx = np.random.permutation(len(X))
lr = LogisticRegression(penalty='l2', C=0.01, max_iter=1000)
lr.fit(X[idx], y[idx])
accuracies.append(lr.score(X[idx], y[idx]))
cavs.append(lr.coef_[0])
return np.mean(accuracies), np.mean(cavs, axis=0)
def _extract_features(self, imgs: List[torch.Tensor]) -> torch.Tensor:
"""直接处理张量输入"""
features = []
for img in tqdm(imgs, desc='Extracting features'):
if img.dim() == 3:
img = img.unsqueeze(0).to(self.device)
self.model(img)
features.append(self.layer_output.flatten())
return torch.stack(features)
def __del__(self):
self.hook.remove()
# 正确的预处理函数
def preprocess_tensor(img_tensor: torch.Tensor) -> torch.Tensor:
"""直接对张量进行预处理"""
# 1. 调整大小和裁剪
img_tensor = F.resize(img_tensor, [256])
img_tensor = F.center_crop(img_tensor, [224, 224])
# 2. 标准化 (使用ImageNet统计量)
mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)
std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
return (img_tensor - mean) / std
if __name__ == "__main__":
import torchvision
# 1. 加载模型
model = torchvision.models.resnet18(weights='IMAGENET1K_V1').eval()
# 2. 生成模拟数据 (直接创建预处理后的张量)
def create_mock_batch(batch_size=10):
imgs = torch.clamp(torch.randn(batch_size, 3, 256, 256)*0.5 + 0.5, 0, 1)
return torch.stack([preprocess_tensor(img) for img in imgs])
concept_imgs = create_mock_batch() # 10张概念图像
random_imgs = create_mock_batch() # 10张随机图像
# 3. 运行TCAV
tcav = TCAV(model, 'layer4')
accuracy, cav = tcav.compute_concept_sensitivity(
concept_imgs=concept_imgs,
random_imgs=random_imgs
)
print(f"分类准确率: {accuracy:.3f}")
print(f"CAV维度: {cav.shape}, 范数: {np.linalg.norm(cav):.3f}")输出为：
Extracting features: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 45.63it/s]
Extracting features: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 48.76it/s]
分类准确率: 1.000
CAV维度: (25088,), 范数: 0.220四、可视化工具集成
1. Captum库实践
import torch
import numpy as np
import matplotlib.pyplot as plt
from captum.attr import IntegratedGradients, LayerGradCam, visualization as viz
from torchvision import models, transforms
from PIL import Image
import cv2 # 用于调整热力图尺寸
# 1. 加载预训练模型
model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1).eval()
# 2. 图像预处理
def preprocess_image(image_path):
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
img = Image.open(image_path).convert('RGB')
return preprocess(img).unsqueeze(0) # 添加batch维度
# 3. 可解释性分析函数
def explain_model(image_path, target_class=None):
# 预处理输入
input_tensor = preprocess_image(image_path)
input_tensor.requires_grad = True
# 获取预测类别
with torch.no_grad():
output = model(input_tensor)
pred_class_idx = output.argmax().item() if target_class is None else target_class
# 综合梯度分析
ig = IntegratedGradients(model)
ig_attr = ig.attribute(input_tensor,
target=pred_class_idx,
n_steps=50,
return_convergence_delta=False)
# 分层GradCAM
target_layer = model.layer4[-1].conv3
layer_gc = LayerGradCam(model, target_layer)
gc_attr = layer_gc.attribute(input_tensor,
target=pred_class_idx,
relu_attributions=True)
# 转换为适合可视化的格式
input_img = np.transpose(input_tensor.squeeze().cpu().detach().numpy(), (1, 2, 0))
ig_attr = np.transpose(ig_attr.squeeze().cpu().detach().numpy(), (1, 2, 0))
# 处理Grad-CAM属性
gc_attr = gc_attr.squeeze().cpu().detach().numpy()
gc_attr = np.maximum(gc_attr, 0) # 只保留正激活
gc_attr = (gc_attr - gc_attr.min()) / (gc_attr.max() - gc_attr.min() + 1e-8) # 归一化
return input_img, ig_attr, gc_attr, pred_class_idx
# 4. 可视化函数
def visualize_attributions(input_img, ig_attr, gc_attr, class_idx, class_names=None):
fig, ax = plt.subplots(1, 3, figsize=(18, 6))
# 原始图像
orig_img = input_img * np.array([0.229, 0.224, 0.225]) + np.array([0.485, 0.456, 0.406])
orig_img = np.clip(orig_img, 0, 1)
ax[0].imshow(orig_img)
# 安全获取类别名称
class_name = "unknown"
if class_names is not None and class_idx in class_names:
class_name = class_names[class_idx]
elif class_names is None:
class_name = str(class_idx)
ax[0].set_title(f'Original Image\nPredicted: {class_name}')
ax[0].axis('off')
# 综合梯度
viz.visualize_image_attr(ig_attr,
orig_img,
method='blended_heat_map',
sign='absolute_value',
show_colorbar=True,
title='Integrated Gradients',
plt_fig_axis=(fig, ax[1]))
# Grad-CAM 可视化
# 将7x7的热力图放大到224x224
heatmap = cv2.resize(gc_attr, (orig_img.shape[1], orig_img.shape[0]))
heatmap = np.uint8(255 * heatmap)
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB) / 255.0 # 转换为RGB并归一化
# 叠加热力图和原始图像
superimposed = heatmap * 0.5 + orig_img * 0.5
ax[2].imshow(superimposed)
ax[2].set_title('Layer Grad-CAM')
ax[2].axis('off')
plt.tight_layout()
plt.savefig('model_explanations.png', bbox_inches='tight', dpi=300)
plt.show()
# 5. 使用示例
if __name__ == "__main__":
# 定义猫类别（可选）
cat_classes = {
281: "Egyptian_cat",
282: "tabby_cat",
283: "tiger_cat",
284: "Persian_cat",
285: "Siamese_cat"
}
# 分析示例图像
image_path = 'cat.jpeg' # 替换为你的图像路径
input_img, ig_attr, gc_attr, pred_class = explain_model(image_path)
# 可视化结果
visualize_attributions(input_img, ig_attr, gc_attr, pred_class, cat_classes)输出为：
2. 交互式可视化（PyTorch+Plotly）
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from torchvision import models, transforms
from PIL import Image
def interactive_heatmap(attr, original_img):
"""
生成交互式热力图可视化
参数:
attr: 归因图 (H,W)或(H,W,1)
original_img: 原始图像 (H,W,3)
"""
# 确保attr是2维的
if attr.ndim == 3:
attr = attr.squeeze()
# 创建热力图
fig = px.imshow(
attr,
color_continuous_scale='RdBu',
range_color=[-np.abs(attr).max(), np.abs(attr).max()],
zmin=-3, # 标准化显示范围
zmax=3,
title="Interactive Attribution Heatmap"
)
# 添加原始图像半透明叠加
fig.add_trace(
go.Image(
z=(original_img * 255).astype(np.uint8),
opacity=0.5
)
)
# 优化布局
fig.update_layout(
width=800,
height=600,
coloraxis_showscale=True,
hovermode='closest',
xaxis_showgrid=False,
yaxis_showgrid=False
)
# 添加滑块控制透明度
fig.update_layout(
sliders=[{
'steps': [{
'method': 'restyle',
'args': ['opacity', [val]],
'label': f'{val:.1f}'
} for val in np.arange(0.1, 1.1, 0.1)],
'active': 4,
'currentvalue': {'prefix': 'Opacity: '}
}]
)
# 添加热力图/原始图切换按钮
fig.update_layout(
updatemenus=[{
'buttons': [
{'method': 'update',
'args': [{'visible': [True, False]},
{'title': 'Heatmap Only'}],
'label': 'Heatmap'},
{'method': 'update',
'args': [{'visible': [False, True]},
{'title': 'Original Only'}],
'label': 'Original'},
{'method': 'update',
'args': [{'visible': [True, True]},
{'title': 'Combined View'}],
'label': 'Combined'}
],
'direction': 'down',
'showactive': True,
}]
)
import os
print(os.getcwd())
fig.write_html("interactive_heatmap.html")
fig.show()
# 使用示例
if __name__ == "__main__":
# 1. 加载模型和图像
model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1).eval()
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
# 2. 加载示例图像
img_path = 'cat.jpeg'
img = Image.open(img_path).convert('RGB')
img_tensor = preprocess(img).unsqueeze(0)
# 3. 生成归因图 (示例使用随机数据)
original_img = np.array(img.resize((224, 224))) / 255.0
attr = np.random.randn(224, 224) * 2 # 模拟归因图
# 4. 交互式可视化
interactive_heatmap(attr, original_img)输出为：
五、多模态解释实践
1. 视觉-语言联合解释
import torch
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from transformers import ViTImageProcessor, ViTModel
import torch.nn.functional as F
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# 初始化ViT模型和特征提取器
feature_extractor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
vit = ViTModel.from_pretrained('google/vit-base-patch16-224', output_attentions=True)
patch_size = 16 # ViT-base的默认patch大小
def visualize_attention(img_path, layer_index=-1, head_index=None):
"""
可视化ViT的注意力图
参数:
img_path: 图像路径
layer_index: 要可视化的层索引(-1表示最后一层)
head_index: 特定注意力头索引(None表示平均所有头)
"""
# 1. 图像预处理
img = Image.open(img_path).convert('RGB')
inputs = feature_extractor(images=img, return_tensors="pt")
# 2. 前向传播获取注意力
with torch.no_grad():
outputs = vit(**inputs)
# 3. 处理注意力图
attentions = outputs.attentions[layer_index] # 获取指定层的注意力
if head_index is not None:
attn = attentions[0, head_index] # 选择特定头 [1, num_heads, num_patches, num_patches]
else:
attn = attentions.mean(dim=1)[0] # 平均所有头 [1, num_patches, num_patches]
# 聚焦于CLS token对其他patch的关注 (位置0是CLS token)
cls_attn = attn[0, 1:] # 忽略CLS token自身的注意力
# 4. 重塑并上采样注意力图
grid_size = int(np.sqrt(cls_attn.shape[-1]))
cls_attn = cls_attn.reshape(grid_size, grid_size)
cls_attn = F.interpolate(
cls_attn[None, None],
size=img.size[1], # 假设是正方形图像
mode='bicubic'
)[0, 0].numpy()
# 5. 可视化
fig = make_subplots(rows=1, cols=2, subplot_titles=("Original Image", "Attention Heatmap"))
# 原始图像
fig.add_trace(
go.Image(z=np.array(img)),
row=1, col=1
)
# 注意力热力图
fig.add_trace(
go.Heatmap(
z=cls_attn,
colorscale='jet',
showscale=True,
opacity=0.7
),
row=1, col=2
)
# 叠加图
fig.add_trace(
go.Heatmap(
z=cls_attn,
colorscale='jet',
showscale=False,
opacity=0.5
),
row=1, col=1
)
# 布局调整
fig.update_layout(
title_text=f"ViT Attention Visualization (Layer {layer_index})",
height=500,
width=1000,
hovermode='closest'
)
# 添加滑块选择不同层
if len(outputs.attentions) > 1:
steps = []
for i in range(len(outputs.attentions)):
steps.append({
'method': 'update',
'args': [{'z': [None, process_attn(outputs.attentions[i])]},
{'title': f"ViT Attention (Layer {i})"}],
'label': f'Layer {i}'
})
fig.update_layout(
sliders=[{
'active': layer_index if layer_index >= 0 else len(outputs.attentions) - 1,
'steps': steps,
'currentvalue': {'prefix': 'Selected Layer: '}
}]
)
fig.show()
return cls_attn
def process_attn(attention, head_index=None):
"""处理注意力矩阵的辅助函数"""
if head_index is not None:
attn = attention[0, head_index]
else:
attn = attention.mean(dim=1)[0]
cls_attn = attn[0, 1:]
grid_size = int(np.sqrt(cls_attn.shape[-1]))
cls_attn = cls_attn.reshape(grid_size, grid_size)
cls_attn = F.interpolate(
cls_attn[None, None],
size=224,
mode='bicubic'
)[0, 0].numpy()
return cls_attn
# 使用示例
if __name__ == "__main__":
# 可视化最后一层的平均注意力
attention_map = visualize_attention(
img_path="cat.jpeg", # 替换为你的图片路径
layer_index=-1, # 最后一层
head_index=None # 平均所有头
)
# 也可以保存注意力图
plt.imsave("attention_heatmap.png", attention_map, cmap='jet')六、总结与展望
本文介绍了以下核心技术：
- 基础可视化方法：Saliency Map、Grad-CAM
- 高级解释技术：特征反演、概念激活分析
- 工具链集成：Captum、Plotly交互可视化
- 多模态解释：视觉-语言联合注意力机制
在下一篇文章《多模态学习与CLIP模型》中，我们将探索如何联合理解视觉和语言信息。
关键工具推荐：
pip install captum torchcam tf-explain应用建议：
- 模型调试阶段使用Grad-CAM定位错误原因
- 产品部署时集成LIME生成局部解释
- 伦理审查时采用TCAV验证公平性