Function for computing CAM

CAM.py

def get_class_activation_map(model, img):
    ''' 
    this function computes the class activation map
    
    Inputs:
        1) model (tensorflow model) : trained model
        2) img (numpy array of shape (224, 224, 3)) : input image
    '''
    
    # expand dimension to fit the image to a network accepted input size
    img = np.expand_dims(img, axis=0)

    # predict to get the winning class
    predictions = model.predict(img)
    label_index = np.argmax(predictions)

    # Get the 2048 input weights to the softmax of the winning class.
    class_weights = model.layers[-1].get_weights()[0]
    class_weights_winner = class_weights[:, label_index]
    
    # get the final conv layer
    final_conv_layer = model.get_layer("conv5_block3_out")
    
    # create a function to fetch the final conv layer output maps (should be shape (1, 7, 7, 2048)) 
    get_output = K.function([model.layers[0].input],[final_conv_layer.output, model.layers[-1].output])
    [conv_outputs, predictions] = get_output([img])
    
    # squeeze conv map to shape image to size (7, 7, 2048)
    conv_outputs = np.squeeze(conv_outputs)
    
    # bilinear upsampling to resize each filtered image to size of original image 
    mat_for_mult = scipy.ndimage.zoom(conv_outputs, (32, 32, 1), order=1) # dim: 224 x 224 x 2048

    # get class activation map for object class that is predicted to be in the image
    final_output = np.dot(mat_for_mult.reshape((224*224, 2048)), class_weights_winner).reshape(224,224) # dim: 224 x 224
    
    # return class activation map
    return final_output, label_index