source /home/hwnam/anaconda3/bin/activateMerge branch 'desktop' of ht…

…tps://github.com/hwnam831/ring-defender into desktop

conda activate base

Models.py

@@ -267,7 +267,7 @@ def forward(self, x):
         out = out + res #N,S,C
         out = self.decoder(out).view(out.size(0),-1)
         #out = out + self.scale*torch.randn_like(out)
-        out = out + noise
+        #out = out + noise
 
         return torch.relu(out)   
 

RingClassifier.py

@@ -9,7 +9,7 @@
 from torch.utils.data import random_split
 import torch.nn as nn
 import re
-
+import time
 
 
 
@@ -171,6 +171,7 @@ def quantizer(arr, std=8):
     for e in range(args.epochs):
         gen.train()
         classifier.train()
+        trainstart = time.time()
         for x,y in trainloader:
             xdata, ydata = x.cuda(), y.cuda()
             shifted = shifter(xdata)
@@ -244,7 +245,7 @@ def quantizer(arr, std=8):
                 totcorrect += (pred==ydata).sum().item()
                 totcount += y.size(0)
             macc = float(totcorrect)/totcount
-            print("epoch {} \t acc {:.6f}\t loss {:.6f}\t Avg perturb {:.6f}\n".format(e+1, macc, mloss, mnorm))
+            print("epoch {} \t acc {:.4f}\t loss {:.4f}\t Avg perturb {:.4f}\t duration {:.4f}\n".format(e+1, macc, mloss, mnorm, time.time()-trainstart))
             if e > (args.epochs*4)//5 and macc - 0.5 < 0.001:
                 break
     gen.eval()