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CIFARTrainSnapshot.py
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CIFARTrainSnapshot.py
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from __future__ import print_function
import torch
from torchvision import datasets, transforms
import numpy as np
from WongBasedTraining import WongBasedTrainingCIFAR10
from PGDBasedTraining import PGDBasedTraining
from TradesBasedTraining import TradesBasedTrainingCIFAR10
from Architectures import PreActResNet18, PreActResNet18_100, WideResNet, WideResNet34_10_10, WideResNet34_100_10
import matplotlib.pyplot as plt
import os
from datetime import datetime
import json
from utils import applyDSTrans
from AdversarialAttacks import attack_fgsm, attack_pgd
import utils
import argparse
cuda = torch.device('cuda:0')
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config_file", default="Configs/wongCIFAR10Train.json")
return parser.parse_args()
if __name__ == '__main__':
args = get_args()
train_config = {}
with open(args.config_file) as f:
train_config = json.load(f)
if train_config['dataset_name'] == 'cifar10':
train_config['dataset'] = datasets.CIFAR10
train_config['model_base'] = PreActResNet18
elif train_config['dataset_name'] == 'cifar100':
train_config['dataset'] = datasets.CIFAR100
train_config['model_base'] = PreActResNet18_100
if train_config["training_method"] == "trades" and train_config["dataset_name"]=="cifar10":
train_config["model_base"] = WideResNet34_10_10
elif train_config["training_method"] == "trades" and train_config["dataset_name"] == "cifar100":
train_config["model_base"] = WideResNet34_100_10
if train_config['training_method'] == 'wong':
train_config['weak_learner_type'] = WongBasedTrainingCIFAR10
elif train_config['training_method'] == 'pgd':
train_config['weak_learner_type'] = PGDBasedTraining
elif train_config['training_method'] == 'trades':
train_config['weak_learner_type'] = TradesBasedTrainingCIFAR10
train_config['val_attacks'] = [attack_pgd]
wl = train_config['weak_learner_type'](train_config['model_base'], train_config['attack_eps_wl'])
train_ds, test_ds = applyDSTrans(train_config)
train_ds.targets = torch.tensor(np.array(train_ds.targets))
test_ds.targets = torch.tensor(np.array(test_ds.targets))
train_loader = torch.utils.data.DataLoader(train_ds, batch_size=train_config['batch_size_wl'], shuffle=True)
test_loader = torch.utils.data.DataLoader(test_ds, batch_size=train_config['batch_size_wl'], shuffle=True)
wl.fit(train_loader, test_loader, train_config)