FLOPs

Auto compute the FLOPs of the nerural network with Pytorch. this Impeletation do not care the batch size.The FLOPs has nothing to do with batch size, it only reletes too the network itself.

How to use

   from torchvision import models
   from FLOPs_counter import print_model_parm_flops
   model = models.resnet50()
   input = torch.randn(1, 3, 224, 224)
   print_model_parm_flops(model, input, detail=True)

output

+ Number of FLOPs: 8.17G
  + Conv FLOPs: 8.11G
  + Linear FLOPs: 0.00G
  + Batch Norm FLOPs: 0.04G
  + Relu FLOPs: 0.01G
  + Pooling FLOPs: 0.00G

you can use make the detail=False to hide the detials, only print the total FLOPs

NOTE

the FLOPs may be diffrent when you use diffrent code to compute because the FLOPs don not have a strict definition. for example, in the Conv2d, somebody may only consider the mutiply operations ,somebody may just think the add operations' number is the same with the mutiply operation. These problem can be ignored if you just to compare the related computations. This impetetations compute all the float oparations strictlly and exactly