生成基于自己创建的CNN回归模型的Grad-CAM PyTorch实现代码
时间: 2024-01-24 21:16:47 浏览: 36
以下是一个基于自己创建的CNN回归模型的Grad-CAM PyTorch实现代码的示例:
```python
import torch
import torch.nn as nn
from torch.autograd import Function
import cv2
import numpy as np
class GradCAM:
def __init__(self, model, target_layer):
self.model = model
self.target_layer = target_layer
self.feature_maps = None
self.gradient = None
self.activations = []
def save_gradient(self, grad):
self.gradient = grad
def forward(self, x):
self.feature_maps = []
self.activations = [x]
for idx, layer in enumerate(self.model.features):
x = layer(x)
self.activations.append(x)
if idx == self.target_layer:
self.feature_maps.append(x)
x.register_hook(self.save_gradient)
x = self.model.avgpool(x)
x = x.view(x.size(0), -1)
x = self.model.classifier(x)
return x
def backward(self):
gradient = self.gradient[0]
weights = torch.mean(gradient, axis=(2, 3), keepdims=True)
activations = self.feature_maps[0]
grad_CAM = torch.sum(weights * activations, axis=1, keepdims=True)
grad_CAM = nn.functional.relu(grad_CAM)
return grad_CAM.squeeze().cpu().numpy()
def get_cam(img_path, model, target_layer):
model.eval()
img = cv2.imread(img_path)
img = cv2.resize(img, (224, 224))
img = np.float32(img) / 255
x = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0)
grad_cam = GradCAM(model, target_layer)
output = grad_cam.forward(x)
output.backward()
cam = grad_cam.backward()
cam = cv2.resize(cam, (224, 224))
cam = cam - np.min(cam)
cam = cam / np.max(cam)
return cam
# Example usage
model = MyCustomCNN() # Replace with your own custom CNN model
target_layer = 5 # Replace with the index of the target layer in your model
img_path = "example.jpg" # Replace with the path to your input image
cam = get_cam(img_path, model, target_layer)
```
请注意,这个示例代码仅适用于输入尺寸为 224x224 的图像,如果你的模型和输入具有不同的尺寸,需要相应地修改代码。此外,Grad-CAM的原理是基于卷积层的特征图,因此在选择目标层时应选择最后一个卷积层。
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