#-*- coding: utf-8 -*-
import os
import sys
import shutil
import struct

from google.protobuf import text_format

import caffe
from caffe.proto import caffe_pb2


LAYER_PARAM = {'Convolution', 'InnerProduct'}


class CalFlop():
    def __init__(self, model, deploy):
        self.model = model
        self.deploy = deploy
        
        self.net = caffe.Net(deploy, model, caffe.TEST)       
        self.transformer = caffe.io.Transformer({'data': self.net.blobs['data'].data.shape})
        self.transformer.set_transpose('data', (2,0,1))
        self.netlist = caffe_pb2.NetParameter()
        text_format.Merge(open(deploy).read(), self.netlist)


    def GetLayerList(self):
        LayerList = list()

        for layername in self.netlist.layer:
            LayerList.append(layername.name)
            print layername

        return LayerList


    def CalFlops(self):
        LayerList = self.GetLayerList()
        ALL_FLOPS = 0
        for Layer in LayerList:
            idx = LayerList.index(Layer)
            layerparam = self.netlist.layer._values[idx]

            if layerparam.type in LAYER_PARAM:
                H = self.net._blobs_dict[Layer].height
                W = self.net._blobs_dict[Layer].width

                blobs = self.net.params[layerparam.name]
                batch = blobs[0].num
                chns = blobs[0].channels
                kh = blobs[0].height
                kw = blobs[0].width
                FLOPS = batch * chns * kh * kw * H * W
                ALL_FLOPS += FLOPS
             #   print "{} FLOPS is {}".format(Layer, FLOPS)
        print "Net FLOPS is {}".format(ALL_FLOPS)
    def CalParams(self):
        params = 0
        for layername in self.netlist.layer:
            if layername.type == 'Convolution':
                botName = layername.bottom[0]

                C = self.net._blobs_dict[botName].channels

                chns =  layername.convolution_param.num_output
                kw   =  layername.convolution_param.kernel_size[0]
                kh   =  layername.convolution_param.kernel_size[0]                 
                params = params +  kw * kh * chns * C
            if layername.type == 'InnerProduct':
                botName = layername.bottom[0]
                H = self.net._blobs_dict[botName].height
                W = self.net._blobs_dict[botName].width
                C = self.net._blobs_dict[botName].channels
                params = params + layername.inner_product_param.num_output * H * W * C
        print "params is {}".format(params)




if __name__ =='__main__':

    MODEL_FILE = r'/home/ssd/deploy.prototxt'
    PRETRAINED = r'/home/ssd/VGG_SSD_300x300.caffemodel'


    trans = CalFlop(PRETRAINED, MODEL_FILE)
    trans.CalFlops()
    trans.CalParams()
 

   寫了個統計模型運算量 和 餐數量的指令碼，主要用於模型優化後更加直觀些。VGG 可用 ，VGG-SSD 可用，沒有統計更多，可能有bug ，希望支出。

  轉載請名出處。