gfcs_leba_comparison.diff

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..8df9e1d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,6 @@
+
+.idea/
+
+*.pyc
+
+note_log_all/
diff --git a/LeBA10.py b/LeBA10.py
index d938653..08f0f3a 100644
--- a/LeBA10.py
+++ b/LeBA10.py
@@ -1,65 +1,68 @@
 # coding: utf-8
-import matplotlib.pyplot as plt
-from matplotlib import cm
+
+# NOTE: It's assumed, as of this writing, that inception_v3 is the only net being used that takes 299x299 images as
+#   input. This assumption is hardcoded into the resolution handling in both this file and data_utils.py. Find those
+#   sections and adjust them if this assumption is ever undermined.
+
+
 import torch.optim as optim
 import argparse
 
-import torch
-import numpy as np
 import torch.nn as nn
-import torch.nn.functional as F
-import pandas as pd
 from data_utils import *
-#from defense import *
-#from task import imagenet
-import cv2
+
 from skimage.io import imread, imsave
+
 from torch.distributions import Categorical
 import torch.autograd as autograd
 from defense.defense import get_defense 
 import random
 
-class QueryModel():
-    '''  Query Model Class
+import eval_sets
+from pathlib import Path
+
+
+class QueryModel:
+    """  Query Model Class
     Args: defense_method (str): defense name, 
           model (nn.module): basic victim model 
-    '''
+    """
     def __init__(self, defense_method='', model=None):
-        if defense_method!='':
+        if defense_method != '':
             # If using defensive method, Get defense net 
             self.defense_net = get_defense(args.defense_method, model)
         else:
             self.model = model
-        self.defense_method=defense_method
+        self.defense_method = defense_method
 
     def get_query(self, out, labels):
-        #return query results: score, cw loss and cross_entropy_loss
-        if out.shape[1]==1001:
+        # return query results: score, cw loss and cross_entropy_loss
+        if out.shape[1] == 1001:
             c_labels = labels.clone()
-        elif out.shape[1]==1000:
+        elif out.shape[1] == 1000:
             c_labels = labels.clone() - 1
         with torch.no_grad():
-            prob = F.softmax(out,dim=1)
+            prob = F.softmax(out, dim=1)
             loss = nn.CrossEntropyLoss(reduction='none')(out, c_labels)
-        score = prob.gather(1, c_labels.reshape([-1,1]))
-        correct = prob.argmax(dim=1)==c_labels
+        score = prob.gather(1, c_labels.reshape([-1, 1]))
+        correct = prob.argmax(dim=1) == c_labels
         top2 = prob.topk(2)
-        delta_score = torch.log(top2.values[:,0])-torch.log(top2.values[:,1])
+        delta_score = torch.log(top2.values[:, 0])-torch.log(top2.values[:, 1])
         return score, delta_score, loss, correct
 
     def query(self, imgs, model, preprocess, labels):   
         # Query for no defense case
         with torch.no_grad():
             out = model(preprocess(imgs))
-            return self.get_query(out,labels)
+            return self.get_query(out, labels)
 
     def __call__(self, imgs,  preprocess, labels):
-        if self.defense_method=='':
+        if self.defense_method == '':
             return self.query(imgs, self.model, preprocess, labels)
         elif self.defense_method in ['jpeg', 'GD']:
             with torch.no_grad():
                 out = self.defense_net(imgs, preprocess)
-            return self.get_query(out,labels)
+            return self.get_query(out, labels)
         else:
             raise NameError('False defense method')
 
@@ -74,6 +77,7 @@ def gkern(kernlen=21, nsig=3):
     kernel = kernel_raw / kernel_raw.sum()
     return kernel.astype(np.float32)
 
+
 def gauss_conv(img, k_size):
     kernel = gkern(k_size, 3).astype(np.float32)
     stack_kernel = np.stack([kernel, kernel, kernel])
@@ -82,39 +86,43 @@ def gauss_conv(img, k_size):
     out = F.conv2d(img, stack_kernel, padding=(k_size-1)//2, groups=3)
     return out
 
+
 def distance(imgs1, imgs2=None, norm=2):
-    #Compute L2 or L_inf distance between imgs1 and imgs2
-    if imgs1.dim()==3:
+    # Compute L2 or L_inf distance between imgs1 and imgs2
+    if imgs1.dim() == 3:
         imgs1 = imgs1.unsqueeze(0)
         imgs2 = imgs2.unsqueeze(0)
     img_num = imgs1.shape[0]
     if imgs2 is None:
-        if norm==2:
-            distance = (imgs1.view([img_num,-1])).norm(2,dim=1)
+        if norm == 2:
+            distance = (imgs1.view([img_num, -1])).norm(2, dim=1)
             return distance   
-    if norm==2:
+    if norm == 2:
         try:
-            distance = (imgs1.view([img_num,-1])-imgs2.view([img_num,-1])).norm(2, dim=1)
+            distance = (imgs1.view([img_num, -1])-imgs2.view([img_num, -1])).norm(2, dim=1)
         except:
             print(img_num, imgs1.shape, imgs2.shape)
-    elif norm=='inf':
-        distance = (imgs1.view([img_num,-1])-imgs2.view([img_num,-1])).norm(float('inf'), dim=1)
+    elif norm == 'inf':
+        distance = (imgs1.view([img_num, -1])-imgs2.view([img_num, -1])).norm(float('inf'), dim=1)
     return distance  
 
+
 def update_img(imgs, raw_imgs, diff, max_distance):
-    #update imgs: clip(imgs+diff), clip new_imgs to constrain the noise within max_distace
-    if imgs.dim()==3:
+    # update imgs: clip(imgs+diff), clip new_imgs to constrain the noise within max_distace
+    if imgs.dim() == 3:
         imgs = imgs.unsqueeze(0)
         raw_imgs = raw_imgs.unsqueeze(0)
         diff = diff.unsqueeze(0)
-    diff_norm = distance( torch.clamp(imgs+diff,0,1), raw_imgs)
-    factor =  (max_distance / diff_norm).clamp(0,1.0).reshape((-1,1,1,1))
-    adv_diff = (torch.clamp(imgs+diff,0,1) - raw_imgs)*factor 
-    adv_imgs = torch.clamp(raw_imgs+adv_diff,0,1)
+    diff_norm = distance(torch.clamp(imgs+diff, 0, 1), raw_imgs)
+    factor = (max_distance / diff_norm).clamp(0, 1.0).reshape((-1, 1, 1, 1))
+    adv_diff = (torch.clamp(imgs+diff, 0, 1) - raw_imgs) * factor
+    adv_imgs = torch.clamp(raw_imgs+adv_diff, 0, 1)
     return adv_imgs
 
+
 def normalize(input_):
-    return input_ / input_.view([input_.shape[0],-1]).pow(2).mean(-1).sqrt().view([-1,1,1,1]).clamp(1e-12,1e6)
+    return input_ / input_.view([input_.shape[0], -1]).pow(2).mean(-1).sqrt().view([-1, 1, 1, 1]).clamp(1e-12, 1e6)
+
 
 def update_slice(value, slice1, slice2, target):
     temp = value[slice1]
@@ -122,7 +130,6 @@ def update_slice(value, slice1, slice2, target):
     value[slice1] = temp
 
 
-
 '''def get_diff(select, reference):
     diff_map = torch.zeros(reference.shape).to(device)
     diff = torch.zeros(select.shape[0]).to(device)
@@ -131,209 +138,224 @@ def update_slice(value, slice1, slice2, target):
         diff[i] =  reference[i, select[i,0, 0], select[i,0,1], select[i,0,2]]
     return diff_map,diff'''
 
+
 def get_gauss_diff(shape, select, k_size, epsilon=1.0):
-    diff = torch.zeros([shape[0],shape[1],shape[2]+k_size-1,shape[3]+k_size-1])#.to(device)
-    diff_kernel = torch.zeros([shape[0],k_size,k_size])
+    diff = torch.zeros([shape[0], shape[1], shape[2] + k_size - 1, shape[3] + k_size - 1])  # .to(device)
+    diff_kernel = torch.zeros([shape[0], k_size, k_size])
     for i in range(shape[0]):
-        gauss_kernel = torch.tensor(torch.tensor(gkern(k_size,3))*epsilon)#.to(device)
-        diff_kernel[i] = gauss_kernel  + torch.randn(gauss_kernel.shape)*gauss_kernel*0.1
-        diff[i, select[i,0], select[i,1]:select[i,1]+k_size, select[i,2]:select[i,2]+k_size] += diff_kernel[i]                             
-    if k_size!=1:
-        diff = diff[:,:,k_size//2:-(k_size//2), k_size//2:-(k_size//2)]
-    return diff,diff_kernel
-
-def get_diff_gauss(selects, shape, reference,k_size):
-    #Return Gaussian diff 
-    diff,diff_kernel = get_gauss_diff(shape, selects[:,0,:], k_size)
+        gauss_kernel = torch.tensor(torch.tensor(gkern(k_size, 3)) * epsilon)  # .to(device)
+        diff_kernel[i] = gauss_kernel + torch.randn(gauss_kernel.shape) * gauss_kernel * 0.1
+        diff[i, select[i, 0], select[i, 1]:(select[i, 1] + k_size), select[i, 2]:(select[i, 2] + k_size)] += diff_kernel[i]
+    if k_size != 1:
+        diff = diff[:, :, k_size//2:-(k_size//2), k_size//2:-(k_size//2)]
+    return diff, diff_kernel
+
+
+def get_diff_gauss(selects, shape, reference, k_size):
+    # Return Gaussian diff
+    diff, diff_kernel = get_gauss_diff(shape, selects[:, 0, :], k_size)
     diff = diff.to(device)
     for i in range(diff.shape[0]):
-        diff[i] = diff[i]/diff[i].max()
-        diff[i]*=reference[i]
-        diff_kernel[i] = diff_kernel[i]/diff_kernel[i].max()
-        diff_kernel[i]*=reference[i]
-    return diff,diff_kernel
+        diff[i] = diff[i] / diff[i].max()
+        diff[i] *= reference[i]
+        diff_kernel[i] = diff_kernel[i] / diff_kernel[i].max()
+        diff_kernel[i] *= reference[i]
+    return diff, diff_kernel
+
 
 def sample_byprob( probs, shape):
-    #Sample one pixel per image according to probs
+    # Sample one pixel per image according to probs
     with torch.no_grad():
         m = Categorical(probs)
         select = m.sample()
-        c = select//(shape[2]*shape[3])
+        c = select // (shape[2] * shape[3])
         w = select % shape[3]
-        h = (select-c*shape[2]*shape[3])//shape[3]
-        select = torch.stack([c,h,w]).transpose(1,0).long()
+        h = (select - (c * shape[2] * shape[3])) // shape[3]
+        select = torch.stack([c, h, w]).transpose(1, 0).long()
     return select
 
+
 def select_points(mode='by_prob', probs=None, select_num=1):
-    #Args: mode: 'by_prob': select pixel by prob map
-    #		 	or 'max': select top k prob pixel
+    # Args: mode: 'by_prob': select pixel by prob map or 'max': select top k prob pixel
     # Sample Multi pixels.
     shape = probs.shape
-    if mode=='by_prob':
-        probs = probs.reshape([probs.shape[0],-1])
+    if mode == 'by_prob':
+        probs = probs.reshape([probs.shape[0], -1])
         selects = []
         for n in range(select_num):
-            select = sample_byprob(probs,shape)
+            select = sample_byprob(probs, shape)
             selects.append(select)
-        selects = torch.stack(selects).permute(1,0,2)
-    elif mode=='max':
-        probs = probs.reshape([probs.shape[0],-1])
+        selects = torch.stack(selects).permute(1, 0, 2)
+    elif mode == 'max':
+        probs = probs.reshape([probs.shape[0], -1])
         a, select = torch.topk(probs, select_num, dim=-1)
-        c = select//(shape[2]*shape[3])
+        c = select // (shape[2] * shape[3])
         w = select % shape[3]
-        h = (select-c*shape[2]*shape[3])//shape[3]
-        selects = torch.stack([c,h,w]).permute([1,2,0]).long() 
+        h = (select - (c * shape[2] * shape[3])) // shape[3]
+        selects = torch.stack([c, h, w]).permute([1, 2, 0]).long()
     return selects
 
+
 def attack_black(images, labels, model, model2, preprocess1, preprocess2, counts, correct, last_query):
-    ''' Black-box attack TIMI, run in the first iteration in LeBA Attack
+    """ Black-box attack TIMI, run in the first iteration in LeBA Attack
         Args: preprocess1, preprocess2: preprocess function for model, model2
               counts, correct, last_query: Init records
-    '''
+    """
     raw_imgs = images
     adv_img = images.clone()
-    adv_img.requires_grad=True
-    diff=0
-    momentum=0.9
-    epsilon= args.max_distance/16.37
+    adv_img.requires_grad = True
+    diff = 0
+    momentum = 0.9
+    epsilon = args.max_distance / 16.37
     max_distance = args.max_distance
     img_num = images.shape[0]
     best_advimg = images.clone()
-    def proj(imgs,diff, index, mask=None):
+
+    def proj(imgs, diff, index, mask=None):
         return update_img(imgs, raw_imgs[index], diff, max_distance)
     
     for it in range(10):
         out = model2(preprocess2(adv_img))
-        if out.dim()==1:
+        if out.dim() == 1:
             out = out.unsqueeze(0)
-        if out.shape[1]==1001:
+        if out.shape[1] == 1001:
             c_labels = labels
-        elif out.shape[1]==1000:
-            c_labels = labels-1
-        loss = nn.CrossEntropyLoss()(out,c_labels)
+        elif out.shape[1] == 1000:
+            c_labels = labels - 1
+        loss = nn.CrossEntropyLoss()(out, c_labels)
         loss.backward()
         grad = adv_img.grad.data
-        grad = gauss_conv(grad,9)
-        diff_norm = (diff*momentum + grad).view(img_num,-1).norm(2,dim=1).clamp(1e-12, 1e12).reshape([img_num,1,1,1])
-        diff = epsilon*(diff*momentum + grad)/diff_norm
+        grad = gauss_conv(grad, 9)
+        diff_norm = ((diff * momentum) + grad).view(img_num, -1).norm(2, dim=1).clamp(1e-12, 1e12).reshape(
+            [img_num, 1, 1, 1])
+        diff = epsilon * ((diff * momentum) + grad) / diff_norm
         adv_img.data[correct] = proj(adv_img.data[correct], diff[correct], correct)
         adv_img.grad.zero_()
         model2.zero_grad()
-        if it>2 and it%1==0: # TIMI in first iteration will query model during iterations, 
-                             # it will early stop some query success sample, and won't update some no improve perturbation
+        if it > 2 and it % 1 == 0:  # TIMI in first iteration will query model during iterations, it will early stop
+            # some query success sample, and won't update some no improve perturbation
             c1 = correct.clone()
             score1, q1, loss1, c1[correct] = query(adv_img.data[correct], preprocess1, labels[correct])
-            counts[correct] +=1
-            update_index = (q1<last_query[correct]).reshape([-1]) |(~c1[correct])
+            counts[correct] += 1
+            update_index = (q1 < last_query[correct]).reshape([-1]) | (~c1[correct])
             update_slice(last_query, correct, update_index, q1[update_index])
             update_slice(best_advimg, correct, update_index, adv_img.data[correct][update_index])
-            correct *=c1
-            if correct.sum()==0:
+            correct *= c1
+            if correct.sum() == 0:
                 break
     adv_img = adv_img.detach()
-    adv_img.requires_grad=False
-    log.print('black_attack,distance: ',end='')
+    adv_img.requires_grad = False
+    log.print('black_attack,distance: ', end='')
     log.print(distance(images, best_advimg))
     return best_advimg, adv_img
 
 
-
 def get_trans_advimg(imgs, model2, labels, raw_imgs, ba_num):
-    # TIMI for following iterations in LeBA, similar to attack_black function, but it won't query victim model during iteration
+    # TIMI for following iterations in LeBA, similar to attack_black function, but it won't query victim model during
+    # iteration
     # Args: ba_num: iteration num in TIMI
     adv_img = imgs.detach().clone()
-    adv_img.requires_grad=True
-    diff=0
+    adv_img.requires_grad = True
+    diff = 0
     momentum = 0.9
-    epsilon = args.max_distance/16.37
+    epsilon = args.max_distance / 16.37
     max_distance = args.max_distance
     img_num = imgs.shape[0]
-    def proj(img,diff, mask=None):
+
+    def proj(img, diff, mask=None):
         return update_img(img, raw_imgs, diff, max_distance)
-    for i in range(ba_num):   
+
+    for i in range(ba_num):
         out = model2(preprocess2(adv_img))
-        if out.dim()==1:
+        if out.dim() == 1:
             out = out.unsqueeze(0)
-        if out.shape[1]==1001:
+        if out.shape[1] == 1001:
             c_labels = labels
-        elif out.shape[1]==1000:
+        elif out.shape[1] == 1000:
             c_labels = labels-1
-        loss = nn.CrossEntropyLoss()(out,c_labels)
+        loss = nn.CrossEntropyLoss()(out, c_labels)
         loss.backward()
         grad = adv_img.grad.data
-        grad = gauss_conv(grad,9)
-        diff_norm = (diff*momentum + grad).view(img_num,-1).norm(2,dim=1).clamp(1e-8, 1e8).reshape([img_num,1,1,1])
-        diff = epsilon*(diff*momentum + grad)/diff_norm
+        grad = gauss_conv(grad, 9)
+        diff_norm = ((diff * momentum) + grad).view(img_num, -1).norm(2, dim=1).clamp(1e-8, 1e8).reshape(
+            [img_num, 1, 1, 1])
+        diff = epsilon * ((diff * momentum) + grad) / diff_norm
         adv_img.data = proj(adv_img.data, diff)
         adv_img.grad.zero_()
         model2.zero_grad()
-    adv_img.requires_grad=False
+    adv_img.requires_grad = False
     return adv_img.detach()
 
 
-def adjust_learning_rate(optimizer,lr):
-    #lr = args.lr * (0.1 ** (epoch // 30))
+def adjust_learning_rate(optimizer, lr):
+    # lr = args.lr * (0.1 ** (epoch // 30))
     for param_group in optimizer.param_groups:
         param_group['lr'] = lr
 
+
 class TrainModelS():
-    #HOGA: Class Method to train surrogate model 
+    # HOGA: Class Method to train surrogate model
     def __init__(self):
-        self.train_num=0
-        self.lamda_dict={}
-        self.d_loss_record={}
-        self.s_loss_record={}
-        self.d_loss_sum=0
-        self.s_loss_sum=0
+        self.train_num = 0
+        self.lamda_dict = {}
+        self.d_loss_record = {}
+        self.s_loss_record = {}
+        self.d_loss_sum = 0
+        self.s_loss_sum = 0
         
     def get_lamda(self, filenames):
-        #Adaptive gamma in paper, Here is to get adaptive lamda
+        # Adaptive gamma in paper, Here is to get adaptive lamda
         for filename in filenames:
             if filename in self.d_loss_record and filename in self.s_loss_record:
                 d_loss_list = self.d_loss_record[filename]
                 s_loss_list = self.s_loss_record[filename]
-                if self.train_num>50:
-                    lamda2 = self.s_loss_sum / self.d_loss_sum    # Use history s_loss sum and d_loss sum, compute lamda2
-                    self.lamda_dict[filename] = self.lamda_dict[filename]*0.9 + lamda2*0.1   #Update lamda with lamda2 using momentum 
+                if self.train_num > 50:
+                    lamda2 = self.s_loss_sum / self.d_loss_sum  # Use history s_loss sum and d_loss sum, compute lamda2
+                    self.lamda_dict[filename] = self.lamda_dict[filename]*0.9 + lamda2*0.1
+                    # ^ Update lamda with lamda2 using momentum
                 else:
                     self.lamda_dict[filename] = 3.0
             else:
                 self.lamda_dict[filename] = 3.0
 
     def __call__(self, filenames, imgs, model2, labels, diff, query_score, query_loss, last_loss, optimizer):
-        #Call HOGA, train model2 
-        '''Args:
+        # Call HOGA, train model2
+        """Args:
             diff: Current query perturbation
             query_score: Current query score with (imgs+diff)
             query_loss: Current query loss with (imgs+diff)
             last_loss: History query loss with (imgs)
             model2: surrogate model
             optimizer: optimizer for model2
-        '''
+        """
         self.get_lamda(filenames)
         lamda = torch.tensor([self.lamda_dict[filename] for filename in filenames]).to(device)
-        self.train_num+=1
-        d_loss = query_loss-last_loss    #Get Query delta loss
+        self.train_num += 1
+        d_loss = query_loss-last_loss  # Get Query delta loss
         adv_imgs = imgs.detach().clone()
-        adv_imgs.requires_grad=True
+        adv_imgs.requires_grad = True
         out = model2(preprocess2(adv_imgs))
-       # print(out.shape,labels.shape)
-        if out.dim()==1:
+        # print(out.shape,labels.shape)
+        if out.dim() == 1:
             out = out.unsqueeze(0)
-        if out.shape[1]==1001:
+        if out.shape[1] == 1001:
             c_labels = labels
-        elif out.shape[1]==1000:
-            c_labels = labels-1
-        prob = F.softmax(out,dim=1)
-        s_score = prob.gather(1, c_labels.reshape([-1,1]))
-        loss = nn.CrossEntropyLoss(reduction='none')(out, c_labels)   #Note that using cross entropy loss to train surrogate model here
-        grad = autograd.grad(loss.sum(), adv_imgs,create_graph = True)   # Create High Order Compute Graph
+        elif out.shape[1] == 1000:
+            c_labels = labels - 1
+        prob = F.softmax(out, dim=1)
+        s_score = prob.gather(1, c_labels.reshape([-1, 1]))
+        loss = nn.CrossEntropyLoss(reduction='none')(out, c_labels)   # Note that using cross entropy loss to train
+        # surrogate model here
+        grad = autograd.grad(loss.sum(), adv_imgs, create_graph=True)   # Create High Order Compute Graph
         grad = grad[0]
-        s_loss = (diff.detach()*grad).view([imgs.shape[0],-1]).sum(dim=1)  #diff*s_grad: surrogate model loss with diff.
-        forward_loss = nn.MSELoss()(s_score, query_score.detach())    #Forward Loss: approximate forward-pass score number 
-        backward_loss = nn.MSELoss()(s_loss/lamda, d_loss.detach())   
-                    #Backward Loss: Minimize difference between surrogate model loss and query loss. equal to high-order gradient approximation.
-        loss2 =backward_loss + forward_loss*args.FL_rate
+        s_loss = (diff.detach()*grad).view([imgs.shape[0], -1]).sum(dim=1)
+        # ^ diff*s_grad: surrogate model loss with diff.
+        forward_loss = nn.MSELoss()(s_score, query_score.detach())
+        # ^ Forward Loss: approximate forward-pass score number
+        backward_loss = nn.MSELoss()(s_loss / lamda, d_loss.detach())
+        # ^ Backward Loss: Minimize difference between surrogate model loss and query loss. equal to high-order gradient
+        # approximation.
+        loss2 = backward_loss + (forward_loss * args.FL_rate)
 
         model2.zero_grad()
         loss2.backward()
@@ -341,9 +363,9 @@ class TrainModelS():
         model2.zero_grad()
         optimizer.zero_grad()
         del adv_imgs
-        with open(train_log_file,'a')  as f:
+        with open(train_log_file, 'a') as f:
             for i in range(s_loss.shape[0]):
-                f.write("(%f,%f,%f), "%(s_loss[i], d_loss[i],lamda[i]))
+                f.write("(%f,%f,%f), " % (s_loss[i], d_loss[i], lamda[i]))
             f.write('\n')
         for i in range(len(filenames)):
             filename = filenames[i]
@@ -353,14 +375,14 @@ class TrainModelS():
                 self.s_loss_record[filename] = []  
             self.d_loss_record[filename].append(d_loss[i].detach().cpu())
             self.s_loss_record[filename].append(s_loss[i].detach().cpu())
-            self.d_loss_sum+=d_loss[i].detach().cpu().abs()
-            self.s_loss_sum+=s_loss[i].detach().cpu().abs()
+            self.d_loss_sum += d_loss[i].detach().cpu().abs()
+            self.s_loss_sum += s_loss[i].detach().cpu().abs()
 
 
 def get_data(data_iter, num):
-    #Get Data from data_loader
-    #Args:
-    #	num: get data number.
+    # Get Data from data_loader
+    # Args:
+    #   num: get data number.
     filenames = []
     imgs = []
     labels = []
@@ -370,58 +392,60 @@ def get_data(data_iter, num):
             imgs.append(data['image'].to(device))
             labels.append(data['label'].to(device))
             filenames.append(data['filename'][0])
-            #data_end=False
+            # data_end=False
         except:
             log.print("Data Iterater finished")
             break
     return imgs, labels, filenames
 
 
-
-def before_query_iter(imgs, labels,  model, model2, preprocess1, preprocess2, with_TIMI, with_s_prior, log):
-    #First iteration in LeBA
+def before_query_iter(imgs, labels, model, model2, preprocess1, preprocess2, with_TIMI, with_s_prior, log):
+    # First iteration in LeBA
     raw_imgs = imgs.clone()
-    #First query victim model. 
-    #Get last_score, last_query(cw_loss:delta log score for simbda) and last_loss(cross entropy loss for TIMI), 
-    #correct:correctly classified: Not correct = Success
+    # First query victim model.
+    # Get last_score, last_query(cw_loss:delta log score for simbda) and last_loss(cross entropy loss for TIMI),
+    # correct:correctly classified: Not correct = Success
     last_score, last_query, last_loss, correct = query(imgs,  preprocess1, labels)
     _, a, b, correct_s = query2(imgs, model2, preprocess2, labels)
-    log.print("Init correct rate, model %f, model_s %f"%(correct.float().mean(), correct_s.float().mean()))
+    log.print("Init correct rate, model %f, model_s %f" % (correct.float().mean(), correct_s.float().mean()))
     img_num = imgs.shape[0]
     counts = torch.ones([img_num]).to(device)
     end_type = torch.zeros([img_num]).to(device)
     prior_prob = torch.ones(imgs.shape).to(device)
-    if correct.sum()>0:
-        #RUN TIMI, and update counts, correct, last_query status
+    if correct.sum() > 0:
+        # RUN TIMI, and update counts, correct, last_query status
         if with_s_prior:
-            best_advimg, adv_img = attack_black(imgs, labels, model, model2, preprocess1, preprocess2, counts, correct, last_query)
-            #Update prior prob according to  accumulative gradient in TIMI, accumulative gradient is more stable.
-            prior_prob = (best_advimg-raw_imgs).abs().clamp(1e-6,1e6)  #修改： best_advimg to adv_img
-            prior_norm = prior_prob.view(img_num,-1).norm(2,dim=1).clamp(1e-12, 1e12).reshape([img_num,1,1,1])
+            best_advimg, adv_img = attack_black(imgs, labels, model, model2, preprocess1, preprocess2, counts, correct,
+                                                last_query)
+            # Update prior prob according to  accumulative gradient in TIMI, accumulative gradient is more stable.
+            prior_prob = (best_advimg - raw_imgs).abs().clamp(1e-6, 1e6)  # 修改： best_advimg to adv_img
+            prior_norm = prior_prob.view(img_num, -1).norm(2, dim=1).clamp(1e-12, 1e12).reshape([img_num, 1, 1, 1])
             prior_prob = prior_prob/prior_norm 
         if with_TIMI and with_s_prior:
             imgs = best_advimg
-    last_score,last_query, last_loss, correct = query(imgs,  preprocess1, labels)
-    counts+=correct.float()
+    last_score, last_query, last_loss, correct = query(imgs,  preprocess1, labels)
+    counts += correct.float()
     end_type[~correct] = 1
-    return imgs, counts, last_score, last_query, last_loss, correct,prior_prob, end_type
+    return imgs, counts, last_score, last_query, last_loss, correct, prior_prob, end_type
+
 
 def index_(list1, index):
     new_list = []
     for i in range(index.shape[0]):
-        if index[i].data==True:
+        if index[i].data:
             new_list.append(list1[i])
     return new_list
 
+
 def normalizer(tensor):
     img_num = tensor.shape[0]
-    norm = tensor.view(img_num,-1).norm(2,dim=1).clamp(1e-12, 1e12).reshape([img_num,1,1,1])
-    return tensor/norm
+    norm = tensor.view(img_num, -1).norm(2, dim=1).clamp(1e-12, 1e12).reshape([img_num, 1, 1, 1])
+    return tensor / norm
 
-def run_attack_train(model, model2, data_loader, minibatch,
-                        preprocess1, preprocess2, log, optimizer, log_name,
-                        if_train=True, with_TIMI=True, with_s_prior=True):
-    '''
+
+def run_attack_train(model, model2, data_loader, minibatch, preprocess1, preprocess2, log, optimizer, log_name,
+                     if_train=True, with_TIMI=True, with_s_prior=True):
+    """
     Main function to run LeBA algorithm.
     We use batch for attack, and to accelerate speed, we introduce pipeline attack
     Pipeline attack means if one image has been breached, we add a new image to attack.
@@ -436,214 +460,252 @@ def run_attack_train(model, model2, data_loader, minibatch,
         optimizer:  optimizer for model2(srrogate model)
         log_name: name of result file
         if_train: Flag of if train surrogate model, if 'if_train' off, function degrade to SimBA++
-    '''
+    """
     data_iter = iter(data_loader)
     img_nums = len(data_loader)
 
-    minibatch = minibatch if minibatch<=img_nums else img_nums
-    correct_all = torch.ones([img_nums]).bool().to(device)  #record all correct(not success) flag
-    counts_all = torch.zeros([img_nums]).to(device)         #Record all query numbers 
-    end_type_all = torch.zeros([img_nums]).to(device).float()  #for debug
+    minibatch = minibatch if minibatch <= img_nums else img_nums
+    correct_all = torch.ones([img_nums]).bool().to(device)  # record all correct(not success) flag
+    counts_all = torch.zeros([img_nums]).to(device)         # Record all query numbers
+    end_type_all = torch.zeros([img_nums]).to(device).float()  # for debug
     L2_all = torch.zeros([img_nums]).to(device)         # Record final perturbation amount
-    it=0
-    img_id=0
-    indices=torch.zeros([img_nums]).bool().to(device)   #Record indices of all has been attacked images
+    it = 0
+    img_id = 0
+    indices = torch.zeros([img_nums]).bool().to(device)   # Record indices of all has been attacked images
     indices[:minibatch] = True
     
-    correct = torch.zeros([minibatch]).bool().to(device)  #Minibatch correct(not success) flag
+    correct = torch.zeros([minibatch]).bool().to(device)  # Minibatch correct(not success) flag
     counts = torch.zeros([minibatch]).to(device)      # Record minibatch query numbers 
-    end_type = torch.zeros([minibatch]).to(device).float()  #for debug
-    max_query=10000        # max query budget
-    epsilon= args.epsilon     #epsilon for SimBA part
-    max_distance = args.max_distance #Max perturb budget  (L2 distance)
-    b_num=0    
-    #data_end=False
+    end_type = torch.zeros([minibatch]).to(device).float()  # for debug
+    max_query = 10000  # max query budget
+    epsilon = args.epsilon  # epsilon for SimBA part
+    max_distance = args.max_distance  # Max perturb budget  (L2 distance)
+    b_num = 0
+    # data_end=False
     get_new_flag = False
-    def proj(imgs,diff, raw_imgs):  #Clip function 
+
+    def proj(imgs, diff, raw_imgs):  # Clip function
         return update_img(imgs, raw_imgs, diff, max_distance)
+
     while True:          
-        it+=1
-        if it%50==1 or get_new_flag:  # Per 50 iteration, add new input data, and save success samples.
+        it += 1
+        if it % 50 == 1 or get_new_flag:  # Per 50 iteration, add new input data, and save success samples.
             get_new_flag = False 
-            b_num+=1
-            if b_num!=1:
+            b_num += 1
+            if b_num != 1:
                 L2 = distance(imgs, raw_imgs)
                 end_type_all[indices] = end_type
                 L2_all[indices] = L2
-                with open(out_dir+'/'+log_name,'a')  as f:
+                with open(out_dir + '/' + log_name, 'a') as f:
                     for i in range(len(imgs)):
-                        if correct[i]==False or counts[i]>max_query:
-                            #Write attack result to result file
-                            f.write(filenames[i]+' Success:%d'%(~correct[i])+' counts:%d, L2:%.5f, end_type:%d \n'%(counts[i], L2[i], end_type[i]))
-                            adv_img = imgs[i].cpu().detach().numpy().clip(0, 1).transpose((1,2,0))
-                            imsave(out_dir+'/images/'+filenames[i], adv_img)  #Save adversarial example
-                            correct[i]=False  
+                        if ~correct[i] or counts[i] > max_query:
+                            # Write attack result to result file
+                            f.write(filenames[i] + ' Success:%d' % (~correct[i]) +
+                                    ' counts:%d, L2:%.5f, end_type:%d \n' % (counts[i], L2[i], end_type[i]))
+                            adv_img = imgs[i].cpu().detach().numpy().clip(0, 1).transpose((1, 2, 0))
+                            imsave(out_dir + '/images/' + filenames[i], adv_img)  # Save adversarial example
+                            correct[i] = False
             correct_all[indices] = correct
             counts_all[indices] = counts
-            if img_id==img_nums and correct.sum()==0 and get_new_flag==False: #Attack finish
+            if img_id == img_nums and correct.sum() == 0 and not get_new_flag:  # Attack finish
                 break
-            if correct.sum()<minibatch:
-                indices *=correct_all
-                new_imgs, new_labels,  new_filenames = get_data(data_iter, minibatch-(correct).sum()) #Get new data to attack
-                get_new = (new_labels!=[])  #New attack is available
+            if correct.sum() < minibatch:
+                indices *= correct_all
+                new_imgs, new_labels, new_filenames = get_data(data_iter, minibatch-correct.sum())
+                # ^ Get new data to attack
+                get_new = (new_labels != [])  # New attack is available
                 if get_new:
                     new_labels = torch.cat(new_labels) 
-                    indices[img_id:img_id+new_labels.shape[0]] = True
-                    img_id+=new_labels.shape[0]
+                    indices[img_id:(img_id + new_labels.shape[0])] = True
+                    img_id += new_labels.shape[0]
                     new_raw_imgs = torch.cat(new_imgs).clone()
-                    #Run TIMI first
-                    #Get new_imgs and several update properties
+                    # Run TIMI first
+                    # Get new_imgs and several update properties
                     new_imgs, counts0, last_score0, last_query0, last_loss0, correct0, prior_prob0, end_type0 = \
-                                        before_query_iter(torch.cat(new_imgs), new_labels,  model, model2,preprocess1, preprocess2, with_TIMI, with_s_prior, log)
+                        before_query_iter(torch.cat(new_imgs), new_labels,  model, model2,preprocess1, preprocess2,
+                                          with_TIMI, with_s_prior, log)
                     
                     last_improve0 = torch.zeros([new_imgs.shape[0]]).to(device)
      
-                if b_num==1:
-                    correct=correct0
-                #Update all the propertities in pipeline
-                last_score = last_score0 if b_num==1 else torch.cat([last_score[correct], last_score0]) if get_new else last_score[correct]
-                last_query = last_query0 if b_num==1 else torch.cat([last_query[correct], last_query0]) if get_new else last_query[correct]
-                last_loss = last_loss0 if b_num==1 else  torch.cat([last_loss[correct], last_loss0]) if get_new else last_loss[correct] 
-                imgs =  new_imgs if b_num==1 else torch.cat([imgs[correct], new_imgs]) if get_new else imgs[correct]
-                raw_imgs =  new_raw_imgs if b_num==1 else torch.cat([raw_imgs[correct], new_raw_imgs]) if get_new else raw_imgs[correct]
+                if b_num == 1:
+                    correct = correct0
+                # Update all the propertities in pipeline
+                last_score = last_score0 if b_num == 1 else torch.cat([last_score[correct], last_score0]) if get_new else last_score[correct]
+                last_query = last_query0 if b_num == 1 else torch.cat([last_query[correct], last_query0]) if get_new else last_query[correct]
+                last_loss = last_loss0 if b_num == 1 else torch.cat([last_loss[correct], last_loss0]) if get_new else last_loss[correct]
+                imgs = new_imgs if b_num == 1 else torch.cat([imgs[correct], new_imgs]) if get_new else imgs[correct]
+                raw_imgs = new_raw_imgs if b_num == 1 else torch.cat([raw_imgs[correct], new_raw_imgs]) if get_new else raw_imgs[correct]
                 
-                filenames = new_filenames if b_num==1 else index_(filenames,correct) + new_filenames if get_new else index_(filenames,correct)
-                labels = new_labels if b_num==1 else torch.cat([labels[correct], new_labels]) if get_new else labels[correct]
-                prior_prob = prior_prob0 if b_num==1 else torch.cat([prior_prob[correct], prior_prob0])  if get_new else prior_prob[correct]
-                counts = counts0 if b_num==1 else torch.cat([counts[correct],counts0]).to(device) if get_new else counts[correct]
-                end_type = end_type0 if b_num==1 else torch.cat([end_type[correct],end_type0]).to(device) if get_new else end_type[correct]
-                last_improve = last_improve0 if b_num==1 else torch.cat([last_improve[correct],last_improve0]).to(device) if get_new else last_improve[correct]
-                correct = correct0 if b_num==1 else torch.cat([correct[correct],correct0]).to(device) if get_new else correct[correct]
+                filenames = new_filenames if b_num == 1 else index_(filenames,correct) + new_filenames if get_new else index_(filenames,correct)
+                labels = new_labels if b_num == 1 else torch.cat([labels[correct], new_labels]) if get_new else labels[correct]
+                prior_prob = prior_prob0 if b_num == 1 else torch.cat([prior_prob[correct], prior_prob0]) if get_new else prior_prob[correct]
+                counts = counts0 if b_num == 1 else torch.cat([counts[correct], counts0]).to(device) if get_new else counts[correct]
+                end_type = end_type0 if b_num == 1 else torch.cat([end_type[correct], end_type0]).to(device) if get_new else end_type[correct]
+                last_improve = last_improve0 if b_num==1 else torch.cat([last_improve[correct], last_improve0]).to(device) if get_new else last_improve[correct]
+                correct = correct0 if b_num == 1 else torch.cat([correct[correct], correct0]).to(device) if get_new else correct[correct]
                 print(b_num, correct)
                 print("Init last_query:", last_query)
                 log.print(filenames)
         
-        if it%args.ba_interval==(args.ba_interval-1) and with_s_prior:
-            #Run TIMI 
-            adv_imgs = get_trans_advimg(imgs[correct], model2, labels[correct], raw_imgs[correct],args.ba_num)
+        if it % args.ba_interval == (args.ba_interval-1) and with_s_prior:
+            # Run TIMI
+            adv_imgs = get_trans_advimg(imgs[correct], model2, labels[correct], raw_imgs[correct], args.ba_num)
             score3, d_score3, loss3, c3 = query(adv_imgs,  preprocess1, labels[correct])
-            #Update prior_prob
-            prior_prob[correct] =normalizer((adv_imgs-raw_imgs[correct]).abs().clamp(1e-6,1e6)) #+ torch.rand(imgs[correct].shape).to(device)*0.2
-            update_index = (d_score3<last_query[correct]) | (~c3) #| ((last_query[correct]==1.0) & (last_improve[correct]>=80)) 
+            # Update prior_prob
+            prior_prob[correct] = normalizer((adv_imgs-raw_imgs[correct]).abs().clamp(1e-6, 1e6))
+            # ^+ torch.rand(imgs[correct].shape).to(device)*0.2
+            update_index = (d_score3 < last_query[correct]) | (~c3)
+            # ^| ((last_query[correct]==1.0) & (last_improve[correct]>=80))
             # If TIMI attack improve query result(cw_loss: delta log score), update images and properties.
-            if update_index.sum()>0:
+            if update_index.sum() > 0:
                 new_prior = (adv_imgs-imgs[correct])[update_index]
                 if with_TIMI:
                     update_slice(imgs, correct, update_index, adv_imgs[update_index])
                     update_slice(last_score, correct, update_index, score3[update_index])
                     update_slice(last_query, correct, update_index, d_score3[update_index])
                     update_slice(last_loss, correct, update_index, loss3[update_index])
-            counts+=correct.float()  # update counts record
-            correct[correct]*=c3  #update correct flags
-            end_type[(end_type==0)*(~correct)] = 2
-            if correct.sum()==0:
-                get_new_flag=True
+            counts += correct.float()  # update counts record
+            correct[correct] *= c3  # update correct flags
+            end_type[(end_type == 0)*(~correct)] = 2
+            if correct.sum() == 0:
+                get_new_flag = True
                 continue 
-        if it%10==0: # log
-            score, d_score, loss, c = query(imgs, preprocess1, labels)  #(Only for log)
+        if it % 10 == 0:  # log
+            score, d_score, loss, c = query(imgs, preprocess1, labels)  # (Only for log)
             L2 = distance(imgs, raw_imgs)
-            log.print('It%d, Query:%d, d_score:%f, loss1:%f,  correct: %f, L2: %.4f'%(it, counts.mean(), last_query.mean(), last_loss.mean(), correct.float().mean(), L2.mean()))
-            logs_str="Counts: "
-            logs_L2="L2: "
-            logs_score="score: "
-            logs_loss="loss: "
+            log.print('It%d, Query:%d, d_score:%f, loss1:%f,  correct: %f, L2: %.4f' %
+                      (it, counts.mean(), last_query.mean(), last_loss.mean(), correct.float().mean(), L2.mean()))
+            logs_str = "Counts: "
+            logs_L2 = "L2: "
+            logs_score = "score: "
+            logs_loss = "loss: "
             for i in range(imgs.shape[0]):
-                logs_str+="%d, "%counts[i]
-                logs_L2+="%.3f, "%L2[i]
-                logs_score+="%.3f, "%last_query[i]
-                logs_loss+="%.3f, "%last_loss[i]
+                logs_str += "%d, "% counts[i]
+                logs_L2 += "%.3f, "% L2[i]
+                logs_score += "%.3f, "% last_query[i]
+                logs_loss += "%.3f, "% last_loss[i]
             log.print(logs_str)
             log.print(logs_L2)
             log.print(logs_score)
         
-        #Run SimBA+: 
+        # Run SimBA+:
         reference = torch.ones(imgs.shape[0])*epsilon
         if not with_s_prior:
             prior_prob = torch.ones(imgs.shape).to(device)
-        selects = select_points(mode='by_prob', probs=prior_prob, select_num=1) #Select point according to prior prob got by TIMI.
-        k_size = int( (args.max_distance*25/16.38 +1)//2*2+1 )
-        diff,diff_kernel = get_diff_gauss(selects, imgs.shape, reference, k_size=k_size)   #Add gaussian noise on select pixel.
+        selects = select_points(mode='by_prob', probs=prior_prob, select_num=1)
+        # ^ Select point according to prior prob got by TIMI.
+        k_size = int((args.max_distance * 25 / 16.38 + 1) // 2 * 2 + 1)
+        diff, diff_kernel = get_diff_gauss(selects, imgs.shape, reference, k_size=k_size)
+        # ^ Add gaussian noise on select pixel.
 
         c1 = correct.clone()      
         adv_imgs = proj(imgs[correct], diff[correct], raw_imgs[correct])     
-        score1, d_score1, loss1, c1[correct] = query(adv_imgs,  preprocess1, labels[correct]) #Query model1 with +diff noise
-        update_index = (d_score1<last_query[correct]) | (~c1[correct])  
-        if if_train: #Use query information to train surrogate model (HOGA)
-            train_model_s(index_(filenames,correct), imgs[correct], model2, labels[correct], adv_imgs-imgs[correct], score1, loss1, last_loss[correct], optimizer)
+        score1, d_score1, loss1, c1[correct] = query(adv_imgs,  preprocess1, labels[correct])
+        # ^ Query model1 with +diff noise
+        update_index = (d_score1 < last_query[correct]) | (~c1[correct])
+        if if_train:  # Use query information to train surrogate model (HOGA)
+            train_model_s(index_(filenames, correct), imgs[correct], model2, labels[correct], adv_imgs-imgs[correct],
+                          score1, loss1, last_loss[correct], optimizer)
         
-        last_improve[correct]+=1
-        #If query result improve update imgs and properties
+        last_improve[correct] += 1
+        # If query result improve update imgs and properties
         update_slice(imgs, correct, update_index, adv_imgs[update_index])
         update_slice(last_score, correct, update_index, score1[update_index])
         update_slice(last_query, correct, update_index, d_score1[update_index])
         update_slice(last_loss, correct, update_index, loss1[update_index])
         update_slice(last_improve, correct, update_index, 0)
-        counts+=correct.float()
-        #record not correct and not update with +diff indices
+        counts += correct.float()
+        # record not correct and not update with +diff indices
         remain = correct.clone() 
         update_slice(remain, correct, update_index, False)
-        correct*=c1
-        end_type[(end_type==0)*(~correct)] = 3
-        if correct.sum()==0:
-            get_new_flag=True
+        correct *= c1
+        end_type[(end_type == 0)*(~correct)] = 3
+        if correct.sum() == 0:
+            get_new_flag = True
             continue   
-        if remain.sum()>0:  #For not correct and not update with +diff samples
+        if remain.sum() > 0:  # For not correct and not update with +diff samples
             c2 = correct.clone()
-            adv_imgs = proj(imgs[remain], -diff[remain], raw_imgs[remain])  #Query model1 with -diff noise
+            adv_imgs = proj(imgs[remain], -diff[remain], raw_imgs[remain])  # Query model1 with -diff noise
             score2, d_score2, loss2, c2[remain] = query(adv_imgs,  preprocess1, labels[remain])
-            if if_train:  #HOGA
-                train_model_s(index_(filenames,remain), imgs[remain], model2, labels[remain], adv_imgs-imgs[remain], score2, loss2, last_loss[remain], optimizer)
-            counts+=remain.float()
-            update_index2 = (d_score2<last_query[remain]) | (~c2[remain])
-
-            #If query result improve update imgs and properties
-            last_improve[remain]+=1
+            if if_train:  # HOGA
+                train_model_s(index_(filenames, remain), imgs[remain], model2, labels[remain], adv_imgs-imgs[remain],
+                              score2, loss2, last_loss[remain], optimizer)
+            counts += remain.float()
+            update_index2 = (d_score2 < last_query[remain]) | (~c2[remain])
+
+            # If query result improve update imgs and properties
+            last_improve[remain] += 1
             update_slice(imgs, remain, update_index2, adv_imgs[update_index2])
             update_slice(last_score, remain, update_index2, score2[update_index2])
             update_slice(last_query, remain, update_index2, d_score2[update_index2])
             update_slice(last_loss, remain, update_index2, loss2[update_index2])
             update_slice(last_improve, remain, update_index2, 0)
-            correct*=c2
-            end_type[(end_type==0)*(~correct)] = 3
-        #score, d_score, loss, c = query(imgs, preprocess1, labels)
+            correct *= c2
+            end_type[(end_type == 0)*(~correct)] = 3
+        # score, d_score, loss, c = query(imgs, preprocess1, labels)
         
-        if correct.sum()==0:
-            get_new_flag=True
+        if correct.sum() == 0:
+            get_new_flag = True
             continue 
         
-    if if_train:  #Save train weight of surrogate model
-        torch.save(model2.state_dict(),out_dir+'/snapshot/%s_final.pth'%args.model2)
+    if if_train:  # Save train weight of surrogate model
+        torch.save(model2.state_dict(), out_dir + '/snapshot/%s_final.pth' % args.model2)
     return counts_all, correct_all, end_type_all, L2_all
-                
-
 
 
 def parse_args():
     parser = argparse.ArgumentParser(description='BA&SA L3 Query Attack')
-    parser.add_argument('--task_id',default=0, help='task id for log dir name', type=int)   
-    parser.add_argument('--input_dir',default='./images', help='input dir of images', type=str)
-    parser.add_argument('--label_file',default='old_labels', help='label file name in input dir', type=str)
-    parser.add_argument('--model1',default='inception_v3', help="Name of victim Model", type=str)
-    parser.add_argument('--model2',default='resnet152', help="Name of substitute Model",type=str)
+    parser.add_argument('--task_id', default=0, help='task id for log dir name', type=int)
+    parser.add_argument('--input_dir', default='./images', help='input dir of images', type=str)
+    parser.add_argument('--label_file', default='old_labels', help='label file name in input dir', type=str)
+    parser.add_argument('--model1', default='inception_v3', help="Name of victim Model", type=str)
+    parser.add_argument('--model2', default='resnet152', help="Name of substitute Model", type=str)
     parser.add_argument('--gpu_id', default="0,1,2", help='using gpu id', type=str)
     parser.add_argument('--epsilon', default=0.1, help="Epsilon in Simba Attack part", type=float)
     parser.add_argument('--seed', default=1, help="Random number generate seed", type=int)
     parser.add_argument('--lr', default=0.005, help="Learning rate for train s_model.", type=float)
     parser.add_argument('--FL_rate', default=0.01, help="rate for forward loss", type=float)
     parser.add_argument('--defense_method', default='',  help="jpeg or GD supported for defense name", type=str)
-    parser.add_argument('--pretrain_weight',default='', help="pretrained weight path for surrogate model", type=str)
-    parser.add_argument('--mode', default="train", help="train(LeBA) / test(LeBA test mode(SimBA++)) / SimBA++ / SimBA+ / SimBA", type=str)
-    parser.add_argument('--batch_size', default=0, help="batch_size, if = 0, compute batch_size with gpu number", type=int)
+    parser.add_argument('--pretrain_weight', default='', help="pretrained weight path for surrogate model", type=str)
+    parser.add_argument('--mode', default="train",
+                        choices=['train', 'test', 'SimBA', 'SimBA+', 'SimBA++'],
+                        help="train(LeBA) / test(LeBA test mode(SimBA++)) / SimBA++ / SimBA+ / SimBA", type=str)
+    parser.add_argument('--batch_size', default=0, help="batch_size, if = 0, compute batch_size with gpu number",
+                        type=int)
     parser.add_argument('--ba_num', default=10, help="iterations for TIMI attack", type=int)
     parser.add_argument('--ba_interval', default=20, help="interval for TIMI attack", type=int)
     parser.add_argument('--max_distance', default=16.37, help="max perturbation (L2 norm)", type=float)
     parser.add_argument('--out_dir', default='out', help="output dir", type=str)
+
+    # Additions to get the custom ImageNet val set lists to work in LeBA, for standardisation vs. other methods.
+    # See GFCS codebase.
+    parser.add_argument('--num_sample', default=1000, type=int, help='number of image samples')
+    parser.add_argument('--data_index_set', type=str,
+                        choices=['vgg16_bn_mstr', 'vgg16_bn_batch0', 'vgg16_bn_batch1',
+                                 'vgg16_bn_batch2', 'vgg16_bn_batch3', 'vgg16_bn_batch4', 'vgg16_bn_batch0_2',
+                                 'resnet50_mstr', 'resnet50_batch0', 'resnet50_batch1',
+                                 'resnet50_batch2', 'resnet50_batch3', 'resnet50_batch4', 'resnet50_batch0_2', 'resnet50_batch0_3',
+                                 'inceptionv3_mstr', 'inceptionv3_batch0', 'inceptionv3_batch1',
+                                 'inceptionv3_batch2', 'inceptionv3_batch3', 'inceptionv3_batch4', 'inceptionv3_batch0_2',
+                                 'imagenet_val_random'],
+                        default='imagenet_val_random',
+                        help='The indices from the ImageNet val set to use as inputs. Most options represent '
+                             'predefined randomly sampled batches. imagenet_val_random samples from the val set '
+                             'randomly, and may not necessarily give images that are correctly classified by the '
+                             'target net.')
+    parser.add_argument('--corrected_normalisation_and_interp', action='store_true',
+                        help='If True, uses standard ImageNet normalisation instead of the strange functions provided '
+                             'in the reference code, matching the native resolution of the target network as well as '
+                             'including differentiable interpolation to match the native resolution of the surrogate '
+                             'network.')
+
     return parser.parse_args()    
 
-args =  parse_args()
 
-#Set random seed 
-seed=args.seed
+args = parse_args()
+
+# Set random seed
+seed = args.seed
 np.random.seed(seed)
 torch.manual_seed(seed)            
 torch.cuda.manual_seed(seed)       
@@ -651,87 +713,107 @@ torch.cuda.manual_seed_all(seed)
 torch.backends.cudnn.deterministic = True
 random.seed = seed
 
+# It's assumed we're using ImageNet val as the master input set here, so:
+#imagenet_dir = Path("/mnt/linux_disk/dataset/ilsvrc2012")
+imagenet_dir = Path("images")
+# ^ Can be a bit annoying to always have to pass the data directory, so, hardcoded here.
+
+if args.data_index_set == 'imagenet_val_random':
+    input_index_list = torch.randperm(len(50000))[:args.num_sample]
+else:
+    input_index_list = getattr(eval_sets, args.data_index_set)[:args.num_sample]
 
-#Get victim model(model1) and surrogate model(model2) and wrap them for multi gpus.
+# Get victim model(model1) and surrogate model(model2) and wrap them for multi gpus.
 cpu_model = get_model(args.model1)
 cpu_model2 = get_model(args.model2)
 
-data_loader = load_images_data(args.input_dir, 1, False, args.label_file)
+# NL: Subbing in the loader that takes our custom lists of Imagenet val images.
+# data_loader = load_images_data(args.input_dir, 1, False, args.label_file)
+if args.corrected_normalisation_and_interp:
+    data_loader = load_images_data_custom_imagenet_val_list(imagenet_dir, args.model1, input_index_list)
+else:  # If not, you always get 299x299x3 images as in the original. Passing 'inception_v3' gets this behaviour.
+    data_loader = load_images_data_custom_imagenet_val_list(imagenet_dir, 'inception_v3', input_index_list)
+
 os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
 gpu_num = len(args.gpu_id.split(',')) 
-if gpu_num==0:
-    gpu_num=1
+if gpu_num == 0:
+    gpu_num = 1
 device = torch.device("cuda")
 model = cpu_model.to(device)
 model2 = cpu_model2.to(device)
-model = nn.DataParallel(cpu_model.to(device),device_ids=[i for i in range(gpu_num)])
+model = nn.DataParallel(cpu_model.to(device), device_ids=[i for i in range(gpu_num)])
 model2 = nn.DataParallel(cpu_model2.to(device), device_ids=[i for i in range(gpu_num)])
 
 model.eval()
 model2.eval()
 
-#Set output dir 
-out_dir = args.out_dir  #  used to be try20
+# Set output dir
+out_dir = args.out_dir  # used to be try20
 check_mkdir(out_dir+'/images')
 check_mkdir(out_dir+'/snapshot')
 check_mkdir(out_dir+'/logs')
 check_mkdir(out_dir+'/gauss_images')
 
-#preprocess functions for model1, model2
-preprocess1 = get_preprocess(args.model1)
-preprocess2 = get_preprocess(args.model2)
+# preprocess functions for model1, model2
+if args.corrected_normalisation_and_interp:
+    preprocess1 = get_preprocess_standard()
+    surrogate_resolution = 299 if args.model2 == 'inception_v3' else 224
+    preprocess2 = get_preprocess_standard_w_interp(surrogate_resolution)
+else:  # The reference behaviour:
+    preprocess1 = get_preprocess(args.model1)
+    preprocess2 = get_preprocess(args.model2)
 
-#query functions for model1, model2
+# query functions for model1, model2
 query = QueryModel(args.defense_method, model)
 query2 = QueryModel('', model2).query
 
-optimizer=0
-b=0
-log = Logger(out_dir+'/logs/') #log file
+optimizer = 0
+b = 0
+log = Logger(out_dir+'/logs/')  # log file
 train_log_file = out_dir+'/train_log0'
 
 log.print("Args:")
 log.print(args)
 
-if_train=False
+if_train = False
 with_TIMI = True
 with_s_prior = True
 
-if args.mode=='train': #LeBA
-    if_train=True
-    minibatch = gpu_num *8
-elif args.mode=='test':  #LeBA test mode
-    minibatch = gpu_num *8 
-    if args.pretrain_weight=='':
-        args.pretrain_weight=='this_weight'
-elif args.mode=='SimBA++':  #SimBA++
-    minibatch = gpu_num *8
+if args.mode == 'train':  # LeBA
+    if_train = True
+    minibatch = gpu_num * 8
+elif args.mode == 'test':  # LeBA test mode
+    minibatch = gpu_num * 8
+    if args.pretrain_weight == '':
+        args.pretrain_weight == 'this_weight'
+elif args.mode == 'SimBA++':  # SimBA++
+    minibatch = gpu_num * 8
     args.pretrain_weight = ''
-elif args.mode=='SimBA+':
-    minibatch = gpu_num *8
+elif args.mode == 'SimBA+':
+    minibatch = gpu_num * 8
     args.pretrain_weight = ''
     with_TIMI = False
-elif args.mode=='SimBA':
-    minibatch = gpu_num *16
+elif args.mode == 'SimBA':
+    minibatch = gpu_num * 16
     with_TIMI = False
     with_s_prior = False
 
-
-if args.batch_size!=0:
+if args.batch_size != 0:
     minibatch = args.batch_size
 
-if args.mode[:3]!='all':
-    log_name = "log_"+args.mode+'_'+args.input_dir.split('/')[-1]+'_idx%d_0'%(args.task_id)  #result file name
+if args.mode[:3] != 'all':
+    log_name = "log_" + args.mode + '_' + args.input_dir.split('/')[-1] + '_idx%d_0' % args.task_id  # result file name
 
-    if args.pretrain_weight=='this_weight':  #Load last trained surrogate weight
-        model2.load_state_dict(torch.load(args.out_dir+'/snapshot/'+args.model2+'_final.pth'))
-    elif args.pretrain_weight!='':
+    if args.pretrain_weight == 'this_weight':  # Load last trained surrogate weight
+        model2.load_state_dict(torch.load(args.out_dir + '/snapshot/' + args.model2 + '_final.pth'))
+    elif args.pretrain_weight != '':
         model2.load_state_dict(torch.load(args.pretrain_weight))
 
     data_iter = iter(data_loader)
-    optimizer = optim.SGD(model2.parameters(), lr = args.lr, momentum=0.9)
-    train_model_s = TrainModelS()  #function for train surrogate model
+    optimizer = optim.SGD(model2.parameters(), lr=args.lr, momentum=0.9)
+    train_model_s = TrainModelS()  # function for train surrogate model
     
-    #Run LeBA
-    counts_all, correct_all,  end_type_all, L2_all = run_attack_train(model, model2, data_loader, minibatch, 
-                    preprocess1, preprocess2, log, optimizer, log_name, if_train, with_TIMI, with_s_prior)
+    # Run LeBA
+    counts_all, correct_all,  end_type_all, L2_all = run_attack_train(
+        model, model2, data_loader, minibatch, preprocess1, preprocess2, log, optimizer, log_name, if_train, with_TIMI,
+        with_s_prior)
diff --git a/data_utils.py b/data_utils.py
index 46498d8..79e19a9 100644
--- a/data_utils.py
+++ b/data_utils.py
@@ -4,13 +4,17 @@ import time
 import sys
 import pandas as pd
 import numpy as np
-import imp
 import torch.nn.functional as F
 from torch.utils.data import Dataset, DataLoader
 from torchvision import transforms
 from PIL import Image
 import imagenet
 
+import torchvision.datasets
+
+
+imagenet_mean = [0.485, 0.456, 0.406]
+imagenet_std = [0.229, 0.224, 0.225]
 
 def check_mkdir(path):
     if not os.path.exists(path):
@@ -24,20 +28,21 @@ class Logger:
         self.file += local_time
         self.All_file = 'logs/All.log'
         
-    def print(self, content='', end = '\n', file=None):
+    def print(self, content='', end='\n', file=None):
         if file is None:
             file = self.file
         with open(file, 'a') as f:
             if isinstance(content, str):
                 f.write(content+end)
             else:
-                old=sys.stdout 
+                old = sys.stdout
                 sys.stdout = f
                 print(content)
                 sys.stdout = old
         if file is None:
             self.print(content, file=self.All_file)
-        print(content,end=end)
+        print(content, end=end)
+
 
 class ImageSet(Dataset):
     def __init__(self, df, input_dir, transformer):
@@ -50,8 +55,8 @@ class ImageSet(Dataset):
 
     def __getitem__(self, item):
         image_name = self.df.iloc[item]['image_path']
-        image_path = os.path.join(self.input_dir,image_name)
-        image = torch.tensor(np.array(Image.open(image_path)).astype(np.float32).transpose((2,0,1)))/255.0
+        image_path = os.path.join(self.input_dir, image_name)
+        image = torch.tensor(np.array(Image.open(image_path)).astype(np.float32).transpose((2, 0, 1))) / 255.0
         label_idx = self.df.iloc[item]['label_idx']
         target_idx = self.df.iloc[item]['target_idx']
         sample = {
@@ -63,23 +68,66 @@ class ImageSet(Dataset):
         }
         return sample
 
-def load_images_data(input_dir,  batch_size=16, shuffle=False, label_file='old_labels'):   #Only forward
-    dev_data = pd.read_csv(input_dir+'/'+label_file,header=None, sep=' ',
-                            names=['image_path','label_idx','target_idx'])
+
+class ImageSetCustomImagenetValList(Dataset):
+    def __init__(self, imagenet_dir, transform, custom_ind_list):
+        self.dataset = torchvision.datasets.ImageNet(imagenet_dir, split='val', transform=transform, download=True)
+        self.custom_ind_list = custom_ind_list
+
+    def __len__(self):
+        return len(self.custom_ind_list)
+
+    def __getitem__(self, item):
+        imagenet_val_ind = self.custom_ind_list[item]
+        sample = {
+            # 'dataset_idx': imagenet_val_ind,
+            # ^ Note that since this is never used in the project, its value is arbitrary.
+            'image': self.dataset[imagenet_val_ind][0],
+            'label': self.dataset[imagenet_val_ind][1] + 1,
+            # ^ Note that this +1 will then be subtracted back out elsewhere in the code. This is to
+            #   do with using TensorFlow nets as a reference point, even though none are actually ever used.
+            # 'target': -1,  # This value is never valid. It is also never used in the code regardless.
+            'filename': os.path.basename(self.dataset.samples[imagenet_val_ind][0])
+        }
+        return sample
+
+
+def load_images_data(input_dir, batch_size=16, shuffle=False, label_file='old_labels'):  # Only forward
+    dev_data = pd.read_csv(input_dir + '/' + label_file, header=None, sep=' ',
+                           names=['image_path', 'label_idx', 'target_idx'])
     transformer = transforms.Compose([
         transforms.ToTensor(),
-        #transforms.Normalize(mean=[0.5, 0.5, 0.5],
+        # transforms.Normalize(mean=[0.5, 0.5, 0.5],
         #                    std=[0.5, 0.5, 0.5]),
     ])
     datasets = ImageSet(dev_data, input_dir, transformer)
-    dataloader =  DataLoader(datasets,
-                       batch_size=batch_size,
-                       num_workers=0,
-                       shuffle=shuffle)
+    dataloader = DataLoader(datasets, batch_size=batch_size, num_workers=0, shuffle=shuffle)
     return dataloader
 
+
+def load_images_data_custom_imagenet_val_list(imagenet_dir, target_net_name, custom_ind_list):
+    # Note: In the original code, images were input as 299x299x3 crops. In this version, that is only done for target
+    #   networks which expect such a resolution, otherwise, they are resized and centre-cropped to 224x224x3 in the
+    #   standard manner. (They are then returned as Torch tensors.)
+    if target_net_name == 'inception_v3':
+        transformer = transforms.Compose([
+            transforms.Resize(342),
+            transforms.CenterCrop(299),
+            transforms.ToTensor()
+        ])
+    else:
+        transformer = transforms.Compose([
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor()
+        ])
+    custom_image_set = ImageSetCustomImagenetValList(imagenet_dir, transformer, custom_ind_list)
+    dataloader = DataLoader(custom_image_set, batch_size=1, num_workers=0, shuffle=False)
+    return dataloader
+
+
 def get_model(model_type):
-    '''if model_type=='inception_v3':
+    """if model_type=='inception_v3':
         MainModel = imp.load_source('MainModel', "./pth_weights/tf_inception_v3.py")
         model = torch.load('./pth_weights/tf_inception_v3.pth')
     elif model_type=='adv_inception_v3':
@@ -99,28 +147,51 @@ def get_model(model_type):
         model = torch.load('./pth_weights/vgg16.pth')    
     elif model_type=="resnet_50":   #resnet_v2 from tensorflow
         MainModel = imp.load_source('MainModel', "./pth_weights/resnet50.py")
-        model = torch.load('./pth_weights/resnet50.pth') '''
+        model = torch.load('./pth_weights/resnet50.pth') """
     return imagenet.get_model(model_type)
 
-def normalize(x, mean=[0.5,0.5,0.5], std=[0.5,0.5,0.5]):
-    mean_t = torch.Tensor([0.5, 0.5, 0.5]).reshape([1,3,1,1]).to(x.device)
-    std_t = torch.Tensor([0.5, 0.5, 0.5]).reshape([1,3,1,1]).to(x.device)
+
+# This is the normalisation function from the original code, which is substituted out for the versions provided below.
+def normalize(x, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
+    mean_t = torch.Tensor([0.5, 0.5, 0.5]).reshape([1, 3, 1, 1]).to(x.device)
+    std_t = torch.Tensor([0.5, 0.5, 0.5]).reshape([1, 3, 1, 1]).to(x.device)
     y = (x-mean_t)/std_t
     return y
 
 
+# See below for other version.
 def get_preprocess(model):
     def preprocess(images):
         return normalize(images)
+
     def preprocess2(images):
         images = images*255.0
         VGG_MEAN = [123.68, 116.78, 103.94] 
         for i in range(3):
-            images[:, i,:, :] = images[:, i,:, :] - VGG_MEAN[i]
-        new_images = F.interpolate(images,224)
+            images[:, i, :, :] = images[:, i, :, :] - VGG_MEAN[i]
+        new_images = F.interpolate(images, 224)
         return normalize(new_images)
-    if model=='vgg16':
+
+    if model == 'vgg16':
         return preprocess2
     else:
         return preprocess
 
+
+# We use the below functions instead of the above from the original.
+def normalise_imagenet_standard(images):
+    mean = torch.as_tensor(imagenet_mean).reshape([1, 3, 1, 1]).to(images.device)
+    std = torch.as_tensor(imagenet_std).reshape([1, 3, 1, 1]).to(images.device)
+    y = (images - mean) / std
+    return y
+
+
+def get_preprocess_standard():
+    return normalise_imagenet_standard
+
+
+def get_preprocess_standard_w_interp(resolution):
+    def normalise_imagenet_standard_w_interp(images):
+        interp_images = torch.nn.functional.interpolate(images, size=resolution, mode='bilinear', align_corners=False)
+        return normalise_imagenet_standard(interp_images)
+    return normalise_imagenet_standard_w_interp
diff --git a/environment.yml b/environment.yml
new file mode 100644
index 0000000..27b61ec
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,19 @@
+name: Leba
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - cudatoolkit
+  - pillow=5.4.1
+    #- opencv-python=4.1.1.26
+  - matplotlib
+  - numpy
+  - pip
+  - python=3.7
+  - pytorch=1.2.0
+  - torchvision
+  - pandas=0.25.1
+  - scikit-image 
+  - pip:
+    - configargparse
diff --git a/eval_sets.py b/eval_sets.py
new file mode 100644
index 0000000..76d6e24
--- /dev/null
+++ b/eval_sets.py
@@ -0,0 +1,32 @@
+
+# These are index lists from the ImageNet val set, representing randomly sampled images that are correctly classified by
+#   each named net. Each master list of 5000 images is sampled with a different seed, so each net sees a generally
+#   different batch of images. Each list of 5000 is split equally into 5 lists of 1000 each, for doing different runs.
+
+# This is the main system for standardising inputs across competing methods. Other methods will require their own glue
+#   code depending on how their reference implementations were done, but this provides the definition.
+
+vgg16_bn_mstr = [3906, 26500, 19887, 37577, 33250, 47859, 6312, 19239, 44880, 48038, 36054, 43084, 2825, 21052, 35656, 29478, 6091, 38958, 25001, 614, 8838, 34243, 18197, 7694, 25934, 988, 40188, 27288, 573, 3959, 25832, 11228, 28270, 16026, 48587, 43332, 29347, 38939, 24237, 18066, 5328, 13306, 19133, 32395, 2427, 48651, 10289, 30818, 29661, 20899, 48756, 16838, 18937, 45980, 18275, 2846, 13840, 11891, 23282, 11422, 25349, 19580, 17246, 41002, 28951, 23015, 35206, 15173, 6491, 27972, 35081, 4805, 19428, 16791, 29782, 10055, 20037, 6155, 43809, 25134, 49411, 17003, 39157, 33493, 32970, 13129, 21883, 39558, 10622, 49650, 49346, 21186, 10928, 16236, 11898, 11231, 21763, 41123, 4511, 19316, 6762, 22288, 22249, 32013, 47097, 24450, 47979, 13149, 19601, 7160, 10793, 47504, 18578, 19431, 27387, 18380, 42627, 44771, 30484, 25037, 35820, 26324, 3065, 39646, 15851, 38782, 45006, 43303, 29844, 17738, 20311, 42375, 27550, 29530, 19145, 23092, 31594, 30243, 33015, 30831, 48432, 34841, 27219, 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+
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+
+inceptionv3_mstr = [34830, 40381, 48426, 12743, 49074, 25551, 19612, 4290, 5502, 7320, 23192, 48063, 14235, 22349, 18326, 11450, 40242, 16969, 37324, 12593, 8797, 48976, 49266, 25746, 24682, 24821, 47402, 19815, 35214, 41572, 9005, 47901, 38308, 16344, 4053, 15162, 3620, 3973, 39834, 4928, 38902, 41418, 34280, 37673, 33740, 5139, 29612, 35895, 43423, 42106, 38560, 16535, 15973, 30159, 23717, 34921, 6555, 42452, 36689, 37954, 3854, 5160, 6581, 13232, 7133, 22830, 44045, 46546, 13245, 48669, 42687, 40835, 28161, 41328, 11656, 22899, 779, 31454, 1546, 39541, 11184, 28548, 45729, 38403, 6424, 27397, 38034, 5252, 39066, 45512, 35398, 29883, 24983, 7731, 47159, 19357, 7554, 45127, 47160, 49748, 19035, 33270, 10968, 932, 25002, 41619, 27047, 28175, 34623, 19074, 4920, 40109, 33068, 12954, 27685, 12658, 35782, 1013, 20369, 8349, 40746, 40779, 13054, 39212, 1721, 26190, 44625, 3217, 29686, 27567, 9143, 11057, 14003, 8984, 24627, 28755, 660, 18781, 8872, 21171, 34936, 37624, 8196, 29053, 17064, 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+inceptionv3_batch0 = [34830, 40381, 48426, 12743, 49074, 25551, 19612, 4290, 5502, 7320, 23192, 48063, 14235, 22349, 18326, 11450, 40242, 16969, 37324, 12593, 8797, 48976, 49266, 25746, 24682, 24821, 47402, 19815, 35214, 41572, 9005, 47901, 38308, 16344, 4053, 15162, 3620, 3973, 39834, 4928, 38902, 41418, 34280, 37673, 33740, 5139, 29612, 35895, 43423, 42106, 38560, 16535, 15973, 30159, 23717, 34921, 6555, 42452, 36689, 37954, 3854, 5160, 6581, 13232, 7133, 22830, 44045, 46546, 13245, 48669, 42687, 40835, 28161, 41328, 11656, 22899, 779, 31454, 1546, 39541, 11184, 28548, 45729, 38403, 6424, 27397, 38034, 5252, 39066, 45512, 35398, 29883, 24983, 7731, 47159, 19357, 7554, 45127, 47160, 49748, 19035, 33270, 10968, 932, 25002, 41619, 27047, 28175, 34623, 19074, 4920, 40109, 33068, 12954, 27685, 12658, 35782, 1013, 20369, 8349, 40746, 40779, 13054, 39212, 1721, 26190, 44625, 3217, 29686, 27567, 9143, 11057, 14003, 8984, 24627, 28755, 660, 18781, 8872, 21171, 34936, 37624, 8196, 29053, 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+inceptionv3_batch1 = [9375, 37040, 4783, 9753, 4347, 6498, 34968, 40218, 25222, 13199, 30144, 25297, 7008, 28886, 45149, 47545, 3753, 19189, 126, 9959, 8121, 21900, 4905, 7673, 35574, 36338, 15822, 14979, 39749, 49528, 41662, 15505, 1896, 10711, 28485, 46625, 39524, 36146, 26001, 38427, 28495, 29330, 8884, 44224, 20887, 8960, 1185, 30290, 30965, 2103, 17659, 26283, 18606, 46146, 25308, 42646, 44184, 38099, 35526, 40082, 35000, 48894, 42712, 16594, 48583, 9667, 14921, 29135, 18462, 43737, 36968, 6773, 21384, 19705, 32477, 33167, 45912, 46780, 34372, 29134, 34093, 15084, 23447, 5365, 29244, 27668, 30649, 18097, 45760, 44536, 15062, 34390, 35952, 36621, 12699, 21202, 44843, 3212, 41987, 37219, 15143, 11756, 35930, 5164, 19828, 40671, 22355, 3913, 28796, 42944, 3125, 3500, 9247, 9511, 16095, 14928, 15323, 47565, 12917, 24763, 13360, 43469, 43354, 6259, 15560, 10802, 46334, 27645, 25343, 33427, 37461, 28211, 27726, 1494, 49833, 37517, 10860, 30266, 35994, 3918, 21488, 15280, 4382, 47906, 2515, 37583, 39888, 3199, 9119, 14512, 3723, 15700, 8743, 15348, 1317, 11069, 21164, 18497, 22177, 22180, 16941, 11471, 23880, 3398, 22793, 9016, 37610, 30089, 6311, 32302, 7185, 15191, 33544, 12195, 15961, 43726, 549, 29076, 19515, 31557, 8507, 30197, 22154, 38851, 14197, 41365, 35660, 7502, 27588, 26914, 7200, 38339, 36925, 21178, 7245, 47886, 14526, 43350, 21606, 28467, 16067, 7028, 32234, 6474, 15694, 39563, 11636, 29909, 42385, 3289, 34286, 5091, 44700, 43717, 25618, 40853, 18981, 34225, 1588, 27563, 19226, 28315, 25673, 31510, 15427, 46293, 12636, 18924, 9727, 18850, 7305, 4515, 47796, 40177, 30847, 46658, 33723, 27353, 438, 8874, 4672, 43950, 5732, 23711, 1085, 43967, 44975, 45591, 41486, 18810, 48205, 9019, 29820, 23796, 44486, 8338, 20157, 6530, 36928, 2513, 34207, 26142, 36150, 22812, 13273, 15823, 37323, 14389, 3615, 23534, 36653, 13531, 22620, 39084, 24526, 637, 44934, 7436, 40335, 14937, 43481, 46620, 11022, 47650, 26888, 32974, 14527, 20115, 44485, 20162, 35940, 9925, 29971, 5840, 11177, 33586, 43213, 24856, 23605, 19565, 16004, 8584, 12097, 47588, 22327, 19151, 20889, 42076, 38508, 42945, 35628, 40887, 40262, 14586, 21114, 41932, 26430, 13844, 18199, 31651, 35896, 24865, 4206, 10827, 46033, 40585, 10396, 11811, 25203, 40501, 34724, 12475, 6015, 2273, 47265, 17589, 18338, 25869, 33620, 10914, 47519, 26127, 44368, 36387, 12465, 34880, 29490, 49658, 21654, 21450, 35367, 15047, 11449, 24848, 24753, 40050, 44519, 13504, 46748, 19708, 41656, 33892, 1219, 9332, 27742, 28894, 11341, 47576, 25516, 8414, 6839, 1680, 12339, 27483, 37857, 8229, 4258, 17666, 25047, 29573, 39004, 980, 36617, 14621, 19315, 37917, 11838, 17033, 7333, 11178, 8115, 37415, 39502, 21746, 10865, 5876, 24096, 24453, 32423, 9817, 34975, 4628, 41638, 9443, 6879, 48774, 3960, 20471, 4261, 7407, 6915, 28102, 35731, 3628, 13549, 16966, 15554, 35717, 42911, 41743, 26556, 24065, 2691, 27504, 23420, 5939, 5163, 42575, 38930, 13772, 37645, 36167, 46562, 7503, 28207, 17330, 41173, 21412, 1399, 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+inceptionv3_batch2 = [10312, 15642, 13272, 41579, 19381, 24793, 29269, 19908, 9449, 40837, 13834, 20850, 31456, 22723, 3869, 11456, 16687, 47964, 38818, 46232, 6918, 47837, 47936, 568, 26587, 18769, 28535, 42717, 1576, 27402, 9642, 6255, 35985, 8654, 30203, 34700, 22070, 16332, 45767, 48033, 7786, 22261, 41514, 352, 35682, 31818, 31644, 6312, 6991, 459, 21119, 22491, 30427, 46524, 7310, 16991, 40005, 31432, 7278, 48573, 31529, 15853, 41019, 47753, 16934, 23729, 15800, 7882, 944, 37669, 25908, 1197, 29480, 47818, 28084, 28876, 10053, 15354, 9087, 18154, 17348, 28293, 49120, 26887, 47396, 28740, 32768, 35836, 34427, 8673, 14596, 35677, 3443, 7186, 39757, 27606, 16190, 2121, 16764, 16629, 15682, 1440, 3383, 9552, 49866, 11897, 11759, 16774, 18375, 27737, 11256, 45255, 12980, 42198, 30565, 1445, 45879, 18339, 22484, 14549, 39545, 276, 44344, 35890, 2868, 8566, 38959, 35922, 19081, 41235, 30092, 7528, 2780, 16958, 5390, 6737, 46255, 31275, 35179, 46490, 22691, 33594, 6147, 38554, 3672, 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32686, 44864, 16480, 42702, 7510, 42394, 34763, 28756, 34903, 28976, 49354, 49982, 26994, 23638, 8615, 35292, 35297, 8302, 1099, 40146, 36347, 3956, 31817, 15543, 11745, 14125, 577, 12567, 22645, 48493, 25426, 8645, 15901, 35981, 22366, 23429, 19182, 15236, 12369, 27442, 29735, 46836, 11056, 35037, 24497, 4081, 47000, 27878, 429, 38100, 9349, 17126, 32736, 41402, 25640, 16169, 25478, 46162, 482, 27561, 3560, 31486, 39994, 36531, 23083, 11358, 5003, 4255, 24575, 22891, 13203, 35171, 16784, 28586, 17874, 34125, 12334, 24029, 34108, 29999, 28145, 20936, 7733, 27030, 48128, 20319, 48688, 32386, 4312, 38778, 41225, 8085, 40182, 6343, 4043, 6030, 27150, 248, 7058, 8134, 48629, 10152, 17394, 22513, 37061, 40365, 3224, 43184, 16093, 22034, 14878, 40122, 25346, 5475, 30729, 15579, 26432, 36972, 19326, 22875, 10917, 35827, 27467, 13265, 16530, 11571, 15947, 2932, 20513, 14623, 28188, 4926, 26546, 46256, 47776, 39464, 3163, 21469, 20719, 27735, 23961, 1904, 6297, 28789, 11113, 27612, 36993, 5734, 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+inceptionv3_batch3 = [47889, 16408, 2514, 5405, 8629, 6582, 10527, 18404, 15503, 42298, 37582, 21637, 22842, 2940, 19614, 10375, 531, 12942, 20536, 16107, 19855, 17277, 10814, 2065, 27359, 25445, 40191, 30541, 28798, 22828, 12317, 19978, 38156, 20726, 38263, 10868, 799, 29453, 27535, 39820, 12571, 951, 49739, 17358, 46996, 43963, 38231, 38649, 11987, 2006, 31060, 35602, 42864, 6973, 13222, 32460, 29200, 27347, 36215, 47112, 25177, 43249, 18991, 6678, 45436, 26217, 45867, 15712, 26564, 32446, 48840, 27808, 19621, 15395, 39149, 9902, 866, 13304, 33573, 34256, 12853, 19135, 19380, 2140, 40222, 15469, 7547, 49302, 15720, 11195, 33438, 9767, 3819, 22520, 5819, 47809, 44226, 46696, 22638, 6613, 38087, 32175, 11338, 8945, 48572, 20672, 25325, 24522, 27549, 36485, 15925, 13843, 48868, 30658, 44750, 49003, 41097, 36143, 39981, 43176, 41276, 8844, 8563, 35756, 2818, 45153, 11875, 45787, 2477, 49226, 46129, 46115, 16504, 22587, 22781, 49066, 2519, 48225, 6117, 35845, 4178, 38465, 40977, 32164, 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+inceptionv3_batch4 = [430, 24470, 3670, 36003, 42526, 10519, 25510, 19587, 18131, 38577, 16860, 45344, 30574, 6541, 32005, 43592, 25998, 698, 34560, 20878, 24113, 27815, 18632, 5177, 20440, 18642, 5561, 3167, 2436, 19140, 3919, 23653, 46838, 43752, 46025, 31334, 26647, 34653, 28668, 38635, 27528, 41989, 41031, 2814, 14053, 12913, 48403, 3624, 22653, 1378, 21528, 38184, 1953, 7807, 1397, 20834, 8152, 9229, 45849, 47555, 25041, 49239, 48554, 5081, 43355, 176, 23333, 34364, 48358, 44124, 16710, 6275, 19000, 35040, 14432, 35525, 33266, 12371, 16667, 22952, 12397, 44892, 1511, 19787, 15999, 38088, 36524, 42508, 26358, 3465, 7506, 37178, 11417, 14946, 35359, 16494, 47029, 47390, 17982, 21036, 49029, 20711, 4066, 33445, 25383, 29870, 991, 37822, 22224, 21961, 38730, 34137, 48018, 22669, 35117, 30856, 43050, 6181, 47317, 29721, 29829, 44570, 48893, 19961, 47130, 8974, 5497, 1876, 253, 48505, 49402, 2989, 47281, 46776, 5107, 23046, 11115, 518, 41133, 28088, 17448, 21864, 49191, 37456, 46471, 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9004, 13190, 33081, 1179, 21962, 35904, 47573, 8791, 22791, 20504, 19466, 17610, 16179, 47306, 35627, 19349, 41340, 16973, 26922, 27802, 7300, 22331, 2110, 22202, 45043, 44391, 30671, 11118, 42439, 34236, 21979, 10486, 45483, 24747, 8832, 2387, 13187, 14971, 19111, 17077, 4444, 8792, 24398, 9348, 2615, 16330, 47823, 3824, 39551, 31897, 27039, 10199, 6685, 12373, 34868, 2671, 7229, 47855, 47674, 46960, 20527, 16158, 28669, 27600, 3127, 40825, 47598, 16771, 46784, 23734, 28178, 8060, 23676, 19075, 1887, 40268, 17717, 6859, 40325, 23776, 21237, 10469, 20048, 21482, 35129, 4845, 483, 21338, 44579, 14847, 21227]
diff --git a/get_result.py b/get_result.py
index dc769d3..b49cfd8 100644
--- a/get_result.py
+++ b/get_result.py
@@ -1,30 +1,81 @@
 from test_tool import test_new
 import torch
-from collections import OrderedDict
-import os
-
-
-out_dir = 'note_log_all/vgg16_Dec26_f8'
-
-with open(out_dir+'/test2.res','w') as f1:
-    for idx in range(3):
-        result = OrderedDict()
-        for ty in os.listdir(out_dir):
-            print(ty)
-            #f = 'log_inception_v3_'+ty+'_idx%d_0'%idx
-            f = ty
-            #pic_list, res = test_new(out_dir+'/'+f)
-            try:
-                pic_list, res = test_new(out_dir+'/'+f,2001)
-            except:
-                continue
-            counts = [res[r][0] for r in res]
-            L2 =  [res[r][1] for r in res]
-            success = [res[r][2] for r in res]
-            result[ty] = [torch.tensor(counts).float().mean(), torch.tensor(L2).mean(), torch.tensor(success).float().mean(),len(L2)]
-            print()
-        f1.write('\n'+out_dir+'_idx%d\n'%idx)
-        for r in result:
-            f1.write('%s: Counts:%f, L2:%f, success:%f, Num:%d\n'%(r, result[r][0], result[r][1], result[r][2],result[r][3]))
-            pass
+import os.path
+import configargparse
 
+# hardcoded names used in LeBA10.py
+# log_names = ['log_SimBA++_images_idx0_0', 'log_train_images_idx0_0', 'log_test_images_idx0_0']
+# method_names = ['SimBA++', 'LeBAtrain', 'LeBAtest']
+# 
+# log_names = ['log_test_images_idx0_0']
+# method_names = ['LeBAtest']
+
+
+def get_result():
+
+    parser = configargparse.ArgumentParser(
+        description="Read the logs output by LeBA10.py and save results in compatible format",
+        add_help=False
+    )
+
+    required_named_arguments = parser.add_argument_group("required named arguments")
+
+
+    required_named_arguments.add_argument(
+        '--method', type=str,
+        choices=['SimBA++', 'LeBA'],
+        help="Method used to produce the result file to be parsed"
+    )
+
+    required_named_arguments.add_argument(
+        '--log_path', type=str,
+        help="Local path to the folder containing raw result file"
+    )
+
+    required_named_arguments.add_argument(
+        '--save_to_dir', type=str,
+        default="../ODS-mirror/experimental_results/2022/untargeted/1surr/",
+        help="Where to save the results."
+    )
+
+    required_named_arguments.add_argument(
+        '--out_name', type=str,
+        help="What name to use when saving out the results."
+    )
+
+    optional_arguments = parser.add_argument_group("optional arguments")
+    optional_arguments.add_argument(
+        '-h', '--help', action='help', default=configargparse.SUPPRESS,
+        help="show this help message and exit"
+    )
+
+    args = parser.parse_args()
+
+    # these are the hardcoded names used in LeBA10.py
+    if args.method == 'SimBA++':
+        log_name = 'log_SimBA++_images_idx0_0'
+    elif args.method == 'LeBA':
+        log_name = 'TODO'
+
+    full_out_name = os.path.join(args.save_to_dir, args.out_name)
+
+    try:
+        t = os.path.join(args.log_path, log_name)
+        _, res = test_new(t, 2001)
+    except:
+        print('ERROR: Log saved in an unexpected format.')
+        raise
+
+    queries = [res[r][0] for r in res]
+    l2_norms = [res[r][1] for r in res]
+    succs = [res[r][2] for r in res]
+
+    torch.save({
+        "succs": torch.BoolTensor(succs),
+        "queries": torch.IntTensor(queries),
+        "l2_norms": torch.as_tensor(l2_norms)
+    }, full_out_name)
+
+
+if __name__ == "__main__":
+    get_result()
diff --git a/run_attack.py b/run_attack.py
index 9dacc6a..bf1f2fe 100644
--- a/run_attack.py
+++ b/run_attack.py
@@ -8,12 +8,12 @@ def check_mkdir(path):
         os.makedirs(path)
 
 #script = "saba_attack_exp.py"
-def get_cmd(task_id, gpu_id, mode, model1, model2,input_dir, label_file, out_dir,pretrain_weight, seed=0):
+def get_cmd(task_id, gpu_id, batch_size, mode, model1, model2,input_dir, label_file, out_dir,pretrain_weight, seed=0):
     FL_rate=args.FL_rate
-    cmd_temp = '''python %s --task_id=%d --gpu_id=%s --mode=%s --model1=%s --model2=%s  \
+    cmd_temp = '''python %s --task_id=%d --gpu_id=%s --batch_size=%d --mode=%s --model1=%s --model2=%s  \
             --input_dir=%s --label_file=%s --out_dir=%s --pretrain_weight=%s --seed=%d --FL_rate=%s \
             --defense_method=%s --lr=%s --ba_num=%d --ba_interval=%d
-            '''% (script, task_id, gpu_id, mode,  model1, model2, input_dir, label_file,
+            '''% (script, task_id, gpu_id, batch_size, mode,  model1, model2, input_dir, label_file,
                  out_dir, pretrain_weight, seed, FL_rate,  args.defense_method, args.lr, args.ba_num, args.ba_interval)
     return cmd_temp
 
@@ -27,11 +27,12 @@ def run_cmd(cmd, out_dir_al, type_):
 import argparse
 
 parser = argparse.ArgumentParser(description='RUN BA&SA L3 Query Attack')
-parser.add_argument('--task_id',default=1, help='task idx for log dir name', type=int)
+parser.add_argument('--task_id', default=1, help='task idx for log dir name', type=int)
 #parser.add_argument('--input_dir',default='./images', type=str)
 parser.add_argument('--model1', default='inception_v3', help='victim model name', type=str)
 parser.add_argument('--model2', default='resnet152', help='surrogate model name', type=str)
 parser.add_argument('--gpu_id', default='0,1,2', type=str)
+parser.add_argument('--batch_size', default=0, help="batch_size, if = 0, compute batch_size with gpu number", type=int)
 #parser.add_argument('--mode', default='train', type=str)
 parser.add_argument('--FL_rate', default=0.01, help='index control forward loss', type=float)
 parser.add_argument('--lr', default=0.005, help='learning rate', type=float)
@@ -56,6 +57,7 @@ args = parser.parse_args()
 script = args.script
 
 gpu_id=args.gpu_id
+batch_size = args.batch_size
 task_id=args.task_id
 model1 = args.model1
 
@@ -72,19 +74,21 @@ subprocess.run('cp %s note_log_all/%s/'%(__file__, out_dir), shell=True)
 
 for idx in range(1):
     seed = idx
-    cmd = get_cmd(idx, gpu_id, 'train', model1, model2, "./images",'labels', out_dir_al, pretrain_weight, seed)
+    cmd = get_cmd(idx, gpu_id, batch_size, 'train', model1, model2, "./images", 'labels', out_dir_al, pretrain_weight,
+                  seed)
     run_cmd(cmd, out_dir_al, 'train_images')
     
-    cmd = get_cmd(idx, gpu_id, 'test', model1,  model2, "./images",'labels',out_dir_al, 'this_weight', seed)
-    run_cmd(cmd, out_dir_al,'test_images')
+    cmd = get_cmd(idx, gpu_id, batch_size, 'test', model1, model2, "./images", 'labels', out_dir_al, 'this_weight',
+                  seed)
+    run_cmd(cmd, out_dir_al, 'test_images')
 
-    cmd = get_cmd(idx, gpu_id, 'SimBA++', model1, model2,  "./images",'labels', out_dir_al, '', seed)
-    run_cmd(cmd, out_dir_al,'SimBA++_images')
-    
-    cmd = get_cmd(idx, gpu_id, 'SimBA+', model1, model2,  "./images",'labels', out_dir_al, '', seed)
-    run_cmd(cmd, out_dir_al,'SimBA+_images')
+    cmd = get_cmd(idx, gpu_id, batch_size, 'SimBA++', model1, model2, "./images", 'labels', out_dir_al, '', seed)
+    run_cmd(cmd, out_dir_al, 'SimBA++_images')
+
+    cmd = get_cmd(idx, gpu_id, batch_size, 'SimBA+', model1, model2, "./images", 'labels', out_dir_al, '', seed)
+    run_cmd(cmd, out_dir_al, 'SimBA+_images')
     
-    cmd = get_cmd(idx, gpu_id, 'SimBA', model1, model2,  "./images",'labels', out_dir_al, '', seed)
-    run_cmd(cmd, out_dir_al,'SimBA_images')
+    cmd = get_cmd(idx, gpu_id, batch_size, 'SimBA', model1, model2, "./images", 'labels', out_dir_al, '', seed)
+    run_cmd(cmd, out_dir_al, 'SimBA_images')
 
 
diff --git a/test_tool.py b/test_tool.py
index 4628af6..93891e8 100644
--- a/test_tool.py
+++ b/test_tool.py
@@ -51,11 +51,11 @@ def test_new(log_file, times=2501):
                 pass
                 #print(pic_name)
 
-    print(np.array(L2_list).mean())    
-    print(np.array(counts_list).mean())
-    print(np.sum(np.array(counts_list)<300))
-    print(np.array(success_list).sum()/len(success_list))
-    print(len(counts_list))
+    # print(np.array(L2_list).mean())    
+    # print(np.array(counts_list).mean())
+    # print(np.sum(np.array(counts_list)<300))
+    # print(np.array(success_list).sum()/len(success_list))
+    # print(len(counts_list))
     return pic_list, results