基于只包含全连接层之前的CNN网络,实现Grad-cam可视化,生成pytorch代码
时间: 2024-02-11 17:09:28 浏览: 37
好的,以下是基于只包含全连接层之前的CNN网络实现Grad-cam可视化的PyTorch代码:
```python
import torch
import torch.nn as nn
import torchvision.models as models
from torch.autograd import Function
class FeatureExtractor(nn.Module):
def __init__(self, cnn, layer):
super(FeatureExtractor, self).__init__()
self.features = nn.Sequential(*list(cnn.features.children())[:layer+1])
def forward(self, x):
return self.features(x)
class GradCam:
def __init__(self, cnn, layer):
self.cnn = cnn
self.layer = layer
self.feature_extractor = FeatureExtractor(self.cnn, self.layer)
self.cnn.eval()
self.feature_extractor.eval()
self.gradients = None
def save_gradient(self, grad):
self.gradients = grad
def forward(self, x):
return self.feature_extractor(x)
def backward(self, y):
y.backward(gradient=self.gradients)
def __call__(self, x):
features = self.forward(x)
output = self.cnn(features)
output = output.view(output.size(0), -1)
self.gradients = None
output.register_hook(self.save_gradient)
output = output.cpu()
pred = torch.argmax(output, dim=1)
one_hot_output = torch.zeros((1, output.size()[-1]))
one_hot_output[0][pred] = 1
one_hot_output = one_hot_output.cuda()
output.backward(gradient=one_hot_output, retain_graph=True)
grad_val = self.gradients.cpu().data.numpy()[0]
features_val = features.cpu().data.numpy()[0]
weights = np.mean(grad_val, axis=(1,2))
cam = np.zeros(features_val.shape[1:], dtype=np.float32)
for i, w in enumerate(weights):
cam += w * features_val[i,:,:]
cam = np.maximum(cam, 0)
cam = cv2.resize(cam, (224,224))
cam = cam - np.min(cam)
cam = cam / np.max(cam)
return cam
```
其中,FeatureExtractor类用于提取指定层的特征图,GradCam类用于实现Grad-cam算法。在使用GradCam时,需要先对模型进行实例化,并指定要可视化的层,然后将输入图像通过GradCam实例进行前向传播并返回cam可视化结果。