注意力机制打印cam-526互联

import cv2
import numpy as np

def get_cam(feature_map, spatial_att):
    # 获取权重
    weights = spatial_att.view(spatial_att.size(0), -1).unsqueeze(-1).unsqueeze(-1)
    
    # 将权重乘以特征图
    cam = (weights * feature_map).sum(dim=1, keepdim=True)
    
    # 使用ReLU函数保留正值部分
    cam = F.relu(cam)
    
    # 将CAM调整到0到1之间
    cam = cam - cam.min()
    cam = cam / cam.max()
    
    # 将CAM调整到图像原始尺寸
    cam = F.interpolate(cam, (224, 224), mode='bilinear', align_corners=False)
    
    # 转换为numpy数组
    cam = cam.detach().cpu().numpy()[0, 0]
    
    return cam

# 获取模型输出和对应的中间特征图
output, feature_maps = model(input_data)

# 获取CAM
cam = get_cam(feature_maps[0], feature_maps[1])

# 将CAM叠加在原始图像上
original_image = input_data.detach().cpu().numpy()[0].transpose((1, 2, 0))
cam_heatmap = cv2.applyColorMap(np.uint8(255 * cam), cv2.COLORMAP_JET)
cam_on_image = np.float32(cam_heatmap) / 255 + original_image
cam_on_image = cam_on_image / np.max(cam_on_image)

# 显示CAM叠加的图像
plt.imshow(cam_on_image)
plt.axis('off')
plt.show()