GradCAM
TensorFlow Network layer attention visualizer
GradCAM Principle
GradCAM visualize the saliency map of feature maps to the prediction class. In details, it cals every feature map's weights and its output
. Then, apply RELU to filter out negative correlation points.
Then we cals its weights:
where Z is the pixel nums of feature map.
GradCAM Usage
Overide the following part for preprocessing and fetching the input tensor and auxiliary tensors
# TODO create your own process
def preprocess(self, x):
x = x / 255.0
x = x - np.array([0.485, 0.456, 0.406])
x = x / np.array([0.229, 0.224, 0.225])
return x
# TODO fetch key tensors
def fetch_key_tensors(self):
"""
X:input tensor
logit:logit output tensor
layer:gradient dependent layer to the logit
... :fetch and set other tensors and their default values for inference
"""
self.X = self.sess.graph.get_tensor_by_name('input:0')
self.logit = self.sess.graph.get_tensor_by_name("finetune_dense1/BiasAdd:0")
self.layer = self.sess.graph.get_tensor_by_name("ResNetnSequentialnlayer4nnBasicBlockn1nnReLUnrelun168:0")
is_training = self.sess.graph.get_tensor_by_name("Placeholder:0")
self.feed_dict = {is_training:False}
After that, simply use the following function to added saliency map to the images
visualizer =GradCam(model_dir,nb_classes)
visualizer.visualize_imgs(imgs_path,out_folder)
Please reference the following for further details: