use tensorflow slim to realize Class Activation Map in Googlenet or other cnn
Based on Learning Deep Features for Discriminative Localization（https://arxiv.org/pdf/1512.04150.pdf）
We can find some packaged code of caffe model to use CAM, but hard to find some code of specifically modified net to use CAM.
This CAM(Class Activation Map) need to be changed on some net structure，I explicitly wrote how to modify it for use on any net.
This code based on Inception v3.
The most important change is to modify the part behind Convolution, which means the CAM need a Global Average Pooling and a fully connected layer.
For example:
last_conv_net = slim.avg_pool2d(net, [8, 8], padding='VALID', scope='AvgPool_1a_8x8') #1x1x2048
logits = slim.fully_connected(net, num_classes, activation_fn=None, normalizer_fn=None, scope='fc')
w_variables = slim.get_model_variables()[-2]#10*2048
When you finish training the net and want to generate the thermal piture, you need to use two parameter(last_conv_net & w_variables). as following:
import numpy as np
def py_returnCAMmap(last_conv_net, w_variables):
n_feat, w, h, n= activation.shape
act_vec = np.reshape(activation, [n_feat, w*h])
n_top = weights_LR.shape[0]
out = np.zeros([w, h, n_top])
for t in range(n_top):
weights_vec = np.reshape(weights_LR[t], [1, weights_LR[t].shape[0]])
heatmap_vec = np.dot(weights_vec,act_vec)
heatmap = np.reshape( np.squeeze(heatmap_vec) , [w, h])
out[:,:,t] = heatmap
return out