如何在Keras中计算Mobilenet FLOP [英] how to calculate a Mobilenet FLOPs in Keras

问题描述

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


with tf.device('/cpu:0'):
    base_model = MobileNet(alpha=1, weights=None, input_tensor=tf.placeholder('float32', shape=(1,224,224,3)))




opts = tf.profiler.ProfileOptionBuilder.float_operation()    
flops = tf.profiler.profile(sess.graph, run_meta=run_meta, cmd='op', options=opts)

opts = tf.profiler.ProfileOptionBuilder.trainable_variables_parameter()    
params = tf.profiler.profile(sess.graph, run_meta=run_meta, cmd='op', options=opts)

print("{:,} --- {:,}".format(flops.total_float_ops, params.total_parameters))

当我运行上面的代码时，我得到了下面的结果

When I run above code, I got a below result

1,137,481,704 --- 4,253,864

这与本文所述的触发器不同.

This is different from the flops described in the paper.

移动网络: https://arxiv.org/pdf/1704.04861.pdf

ShuffleNet: https://arxiv.org/pdf/1707.01083.pdf

ShuffleNet: https://arxiv.org/pdf/1707.01083.pdf

如何计算论文中所述的确切触发器?

How to calculate exact flops described in the paper?

推荐答案

tl; dr您实际上得到了正确的答案！您只是在比较翻倍数和翻倍数(从本文中得出)，因此需要除以二.

如果您使用的是Keras，那么您列出的代码会稍微使事情复杂化...

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

让model是任何已编译的Keras模型.我们可以使用以下代码获得模型的触发器.

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())

但是，当我在计算机上查看自己的示例(不是Mobilenet)时，打印出的 total_float_ops为2115 ，当我我只是打印了flops变量:

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%)

很明显，total_float_ops属性考虑了乘法，加法和减法.

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

然后，我回头看一下MobileNets的示例，简要浏览一下论文，我发现MobileNet的实现是基于参数数量的默认Keras实现:

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:

表中的第一个模型与您得到的结果(4,253,864)相匹配，并且多加"结果大约是您得到的flops结果的一半.因此，您有正确的答案，只是您误以为是Mult-Adds(又名乘累加或MAC)的翻牌.

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).

如果要计算MAC的数量，只需要将以上代码的结果除以2.

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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