Delete data_analysis_mmd.py

mzh/data_analysis_mmd.py

-# -*- coding: utf-8 -*-
-import numpy
-import numpy as np
-import torch
-import sys
-from mmd_loss import *
-from collections import OrderedDict
-
-d1 = sys.argv[1] # bit
-d2 = sys.argv[2] # epoch
-# d1=4
-# d2=5
-
-
-sum=0
-flag=0
-total_quan_list=list()
-total_base_list=list()
-
-
-# CNN FLOPs = Cout * Hout * Wout * (2 * Cin * K * K )  是考虑bias  否则-1
-# FCN FLOPs = Cout * Cin  是考虑bias 否则-1
-
-
-# 把相关的relu，pool也考虑进去了
-# MAdd
-# weight0 =np.array( [ 705600.0+4704.0+ 3528.0 ,  480000.0+ 1600.0 + 1200.0 , 95880.0 + 120.0,
-#            20076.0 + 84.0 , 1670.0 ])
-# weight1=np.array([705,600.0 ,  480,000.0,+  95,880.0 ,
-#            20,076.0  , 1,670.0 ])
-
-# flops
-weight_f0= np.array([357504+4704+4704, 241600+1600+1600,48000+120,10080+84,840])
-weight_f1=np.array([357504, 241600,48000,10080,840])
-
-
-
-summary_quan_dict=OrderedDict()
-summary_base_dict=OrderedDict()
-losses=[]
-# 最外层：不同epoch的字典 内层：各个网络层的grads
-for i in range(int(d2)):
-    total_quan_list.append(torch.load('./project/p/checkpoint/cifar-10_lenet_bn_quant/' + str(d1) + '/ckpt_cifar-10_lenet_bn_quant_'+str(i+1)+'.pth'))
-    #total_quan_list.append(torch.load('checkpoint/cifar-10_lenet_bn/full' + '/ckpt_cifar-10_lenet_bn_' + str(d2) + '.pth'))
-    total_base_list.append(torch.load('./project/p/checkpoint/cifar-10_lenet_bn/full' + '/ckpt_cifar-10_lenet_bn_' + str(i+1) + '.pth'))
-    for k, _ in total_base_list[i]['grads'].items():
-        if flag == 0:
-            summary_quan_dict[k] = total_quan_list[i]['grads'][k].reshape(1,-1)
-            summary_base_dict[k] = total_base_list[i]['grads'][k].reshape(1,-1)
-
-        else :
-            # 字典里的数据不能直接改，需要重新赋值
-            a=summary_quan_dict[k]
-            b=total_quan_list[i]['grads'][k].reshape(1,-1)
-            c=np.vstack((a,b))
-            summary_quan_dict[k] = c
-
-            a = summary_base_dict[k]
-            b = total_base_list[i]['grads'][k].reshape(1,-1)
-            c = np.vstack((a, b))
-            summary_base_dict[k] = c
-
-    flag = 1
-
-cnt = 0
-flag = 0
-for k, _ in summary_quan_dict.items():
-    if flag == 0:
-        sum += 0.99*weight_f1[cnt] * MK_MMD(source=summary_base_dict[k], target=summary_quan_dict[k])  # weight
-    else:
-        sum += 0.01*weight_f1[cnt] * MK_MMD(source=summary_base_dict[k], target=summary_quan_dict[k])  #bias
-
-
-    if flag == 1:
-        cnt = cnt + 1
-        flag = 0
-    else:
-        flag=1
-sum=sum/(weight_f0.sum()*2)
-print(sum)
-
-f = open('./project/p/lenet_ptq_similarity.txt','a')
-f.write('bit:' + str(d1) + ' epoch_num:' + str(d2) +': '+str(sum)+'\n')
-f.close()
-# for k,v in summary_base_dict.items():
-#     if k== 'conv_layers.conv1.weight':
-#         print(v)
-#         print('===========')
-#         print(summary_quan_dict[k])
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