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removed ipdb unused import #7

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removed ipdb unused import #7

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1 change: 0 additions & 1 deletion dfme/network/gan.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,6 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
import ipdb

class Flatten(nn.Module):
def __init__(self):
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2 changes: 1 addition & 1 deletion dfme/train.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
from __future__ import print_function
import argparse, ipdb, json
import argparse, json
import torch
import torch.nn.functional as F
import torch.nn as nn
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1 change: 0 additions & 1 deletion surrogate_benchmark/dataloader.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,6 @@
from torchvision import datasets, transforms
from torch.utils.data import DataLoader, Dataset
import PIL
import ipdb
import torch

class RandDataset(Dataset):
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2 changes: 0 additions & 2 deletions surrogate_benchmark/train.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
import torch
import ipdb
import torch.nn as nn
import torch.optim as optim
import sys, os, json
Expand Down Expand Up @@ -38,7 +37,6 @@ def epoch(args, loader, model, teacher = None, lr_schedule = None, epoch_i = Non
func = tqdm if stop == False else lambda x:x
criterion_kl = nn.KLDivLoss(reduction = "batchmean")
alpha, T = 1.0, args.temp
# ipdb.set_trace()
for batch in func(loader):
X,y = batch[0].to(args.device), batch[1].to(args.device)
if args.surrogate == "mnist":
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