最近在研究模型的计算量，发现Pytorch有库可以直接计算模型的计算量，所以需要一个一个Keras和Tensorflow可以用的，直接把Model接入到函数中，print一下就可以计算出FLOPs
FLOPS：注意全大写，是floating point operations per second的缩写，意指每秒浮点运算次数，理解为计算速度。是一个衡量硬件性能的指标。
FLOPs：注意s小写，是floating point operations的缩写（s表复数），意指浮点运算数，理解为计算量。可以用来衡量算法/模型的复杂度。
对于计算量主要有Madds和MFlops两个概念。shufflenet的论文用的是Flops，Mobilenet用的是Madds，Flops应该是Madds的两倍，具体可参考
https://blog.csdn.net/shwan_ma/article/details/84924142
https://www.zhihu.com/question/65305385/answer/451060549
计算函数如下：
import tensorflow as tf
import keras.backend as K
def get_flops(model):
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 ....
print(get_flops(model))
贴一个Mask_RCNN的计算结果
Profile: node name | # float_ops Mul 98.06m float_ops (100.00%, 44.68%) Sum 57.48m float_ops (55.32%, 26.19%) Square 45.05m float_ops (29.14%, 20.52%) AddN 9.37m float_ops (8.61%, 4.27%) Sub 3.13m float_ops (4.34%, 1.43%) AssignSub 3.13m float_ops (2.91%, 1.43%) Add 3.12m float_ops (1.49%, 1.42%) Rsqrt 61.25k float_ops (0.06%, 0.03%) Maximum 30.78k float_ops (0.03%, 0.01%) RealDiv 24.98k float_ops (0.02%, 0.01%) RsqrtGrad 16.38k float_ops (0.01%, 0.01%) GreaterEqual 4.10k float_ops (0.00%, 0.00%) Neg 108 float_ops (0.00%, 0.00%) AssignAdd 17 float_ops (0.00%, 0.00%) Equal 10 float_ops (0.00%, 0.00%) Log 6 float_ops (0.00%, 0.00%) Greater 3 float_ops (0.00%, 0.00%) Pow 3 float_ops (0.00%, 0.00%) Less 2 float_ops (0.00%, 0.00%) ======================End of Report========================== flops = 219.492156MFlops