how to calculate a Mobilenet FLOPs in Keras

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借酒劲吻你
借酒劲吻你 2020-12-16 18:14

run_meta = tf.RunMetadata()
enter codwith tf.Session(graph=tf.Graph()) as sess:
K.set_session(sess)


with tf.dev         


        
4条回答
  •  南方客
    南方客 (楼主)
    2020-12-16 18:31

    tl;dr You've actually got the right answer! You are simply comparing flops with multiply accumulates (from the paper) and therefore need to divide by two.

    If you're using Keras, then the code you listed is slightly over-complicating things...

    Let model be any compiled Keras model. We can arrive at the flops of the model with the following code.

    import tensorflow as tf
    import keras.backend as K
    
    
    def get_flops():
        run_meta = tf.RunMetadata()
        opts = tf.profiler.ProfileOptionBuilder.float_operation()
    
        # We use the Keras session graph in the call to the profiler.
        flops = tf.profiler.profile(graph=K.get_session().graph,
                                    run_meta=run_meta, cmd='op', options=opts)
    
        return flops.total_float_ops  # Prints the "flops" of the model.
    
    
    # .... Define your model here ....
    # You need to have compiled your model before calling this.
    print(get_flops())
    

    However, when I look at my own example (not Mobilenet) that I did on my computer, the printed out total_float_ops was 2115 and I had the following results when I simply printed the flops variable:

    [...]
    Mul                      1.06k float_ops (100.00%, 49.98%)
    Add                      1.06k float_ops (50.02%, 49.93%)
    Sub                          2 float_ops (0.09%, 0.09%)
    

    It's pretty clear that the total_float_ops property takes into consideration multiplication, addition and subtraction.

    I then looked back at the MobileNets example, looking through the paper briefly, I found the implementation of MobileNet that is the default Keras implementation based on the number of parameters:

    image

    The first model in the table matches the result you have (4,253,864) and the Mult-Adds are approximately half of the flops result that you have. Therefore you have the correct answer, it's just you were mistaking flops for Mult-Adds (aka multiply accumulates or MACs).

    If you want to compute the number of MACs you simply have to divide the result from the above code by two.

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