最近在研究模型的計算量，發現Pytorch有庫可以直接計算模型的計算量，所以需要一個一個Keras和Tensorflow可以用的，直接把Model接入到函式中，print一下就可以計算出FLOPs

FLOPS：注意全大寫，是floating point operations per second的縮寫，意指每秒浮點運算次數，理解為計算速度。是一個衡量硬體效能的指標。

FLOPs：注意s小寫，是floating point operations的縮寫（s表複數），意指浮點運算數，理解為計算量。可以用來衡量演算法/模型的複雜度。

對於計算量主要有Madds和MFlops兩個概念。shufflenet的論文用的是Flops，Mobilenet用的是Madds，Flops應該是Madds的兩倍，具體可參考

https://blog.csdn.net/shwan_ma/article/details/84924142

https://www.zhihu.com/question/65305385/answer/451060549

計算函式如下：

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))

貼一個Mask_RCNN的計算結果

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