点击下方卡片，关注“机器视觉与AI深度学习”
视觉/图像重磅干货，第一时间送达!
def generata_grad_cam(self):# weights by averaging the gradientslayer_weights=F.adaptive_avg_pool2d(self.backward_gradients,(1,1))# Combining forward features with weightsgrad_cam=torch.mul(self.forward_features,layer_weights).sum(dim=1,keepdim=True)grad_cam=F.relu(grad_cam)# resizing GradCAM to the input imagegrad_cam_image=F.interpolate(grad_cam, size=(224,224), mode='bilinear', align_corners=False)# normalizing GradCAMgrad_cam_image=grad_cam_image-torch.min(grad_cam_image)grad_cam_image=grad_cam_image/torch.max(grad_cam_image)return grad_cam_image
实际操作：
def forward_features_hook(self,module,input,output):# save forward pass outputself.forward_features=outputdef backward_gradients_hook(self,module,grad_input,grad_output):# save backward pass gradientsself.backward_gradients=grad_output[0]
def __init__(self,model,layer_name):# Initializing variables and registering hooksself.model=modelself.forward_features=Noneself.backward_gradients=Noneself.cam_layer=getattr(self.model,layer_name)self.cam_layer[-1].register_forward_hook(self.forward_features_hook)self.cam_layer[-1].register_full_backward_hook(self.backward_gradients_hook)
(layer4): Sequential((0): Bottleneck((conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)(downsample): Sequential((0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)))(1): Bottleneck((conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True))(2): Bottleneck((conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu): ReLU(inplace=True)))
Prediction: 207, 'golden retriever', 8.249414Prediction: 250, 'Siberian husky', 8.082574https://github.com/dev-essbee/gradcam-pytorch.git?source=post_page-----e678a848ad44--------------------------------—THE END—
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