plot_cam.py 文件源码

def maps_pred_fun(checkpoint):
    # Load model
    model = load_model(checkpoint)
    x = model.input
    # Get feature maps before GAP
    o = [l for l in model.layers if type(l) == GlobalAveragePooling2D][-1].input

    # Setup CAM
    dense_list = [l for l in model.layers if type(l) == Dense]
    num_dense = len(dense_list)
    if num_dense > 1:
        raise ValueError('Expected only one dense layer, found %d' %num_dense)
    # If there is no dense layer after (NiN), the maps are already class maps
    if num_dense: # Apply CAM if there is a dense layer
        dense_layer = dense_list[0]
        # Get dense layer weights
        W = K.get_value(dense_layer.W)[None, None] # (1, 1, ?, ?)
        b = K.get_value(dense_layer.b)

        # Transform it into a 1x1 conv
        # This convolution will map the feature maps into class 'heatmaps'
        o = Convolution2D(W.shape[-1], 1, 1, border_mode='valid', weights=[W, b])(o)

    # Resize with bilinear method
    maps = tf.image.resize_images(o, K.shape(x)[1:3], method=tf.image.ResizeMethod.BILINEAR)
    return K.function([x, K.learning_phase()], [maps, model.output])