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train_classification.py
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train_classification.py
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import argparse, os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from torch.distributions import kl_divergence, Normal
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms
from MAD_VAE import *
from utils.loss_function import *
from utils.classifier import *
from utils.scheduler import *
from utils.dataset import *
# argument parser
def parse_args():
desc = "MAD-VAE for adversarial defense"
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('--batch_size', type=int, default=512, help='Training batch size')
parser.add_argument('--epochs', type=int, default=10, help='Training epoch numbers')
parser.add_argument('--h_dim', type=int, default=4096, help='Hidden dimensions')
parser.add_argument('--z_dim', type=int, default=128, help='Latent dimensions for images')
parser.add_argument('--image_channels', type=int, default=1, help='Image channels')
parser.add_argument('--image_size', type=int, default=28, help='Image size (default to be squared images)')
parser.add_argument('--num_classes', type=int, default=10, help='Number of image classes')
parser.add_argument('--log_dir', type=str, default='c_logs', help='Logs directory')
parser.add_argument('--lr', type=float, default=0.001, help='Learning rate for the Adam optimizer')
parser.add_argument('--closs_weight', type=float, default=0.1, help='Weight for classification loss functions')
parser.add_argument('--data_root', type=str, default='data', help='Data directory')
parser.add_argument('--model_dir', type=str, default='pretrained_model', help='Pretrained model directory')
parser.add_argument('--use_gpu', type=bool, default=True, help='If use GPU for training')
parser.add_argument('--gpu_num', type=int, default=2, choices=range(0,5), help='GPU numbers available for parallel training')
return parser.parse_args()
# main function
def main():
args = parse_args()
# make directories for pretrained models
if not os.path.exists(args.model_dir):
os.mkdir(args.model_dir)
# prepare dataset
data = np.load('data/xs_mnist.npy') # image data in npy file
labels = np.load('data/ys_mnist.npy') # labels data in npy file
adv_data = np.load('data/advs_mnist.npy') # adversarial image data in npy file
dataset = Dataset(data, labels, adv_data)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True, num_workers=6)
# summary writer for tensorboard
writer1 = SummaryWriter(args.log_dir+'/recon_loss')
writer2 = SummaryWriter(args.log_dir+'/img_loss')
writer3 = SummaryWriter(args.log_dir+'/kl_loss')
writer4 = SummaryWriter(args.log_dir+'/c_loss')
# create modules needed
model, classifier, optimizer, scheduler = init_models(args)
# tratinig steps
step = 0
for epoch in range(1, args.epochs+1):
print('Epoch: {}'.format(epoch))
recon_losses, img_losses, kl_losses, c_losses, datas, adv_datas, outputs, step = \
train(args, dataloader, model, classifier, optimizer, step, epoch)
# write to tensorboard
writer1.add_scalar('recon_loss', np.sum(recon_losses)/len(recon_losses), step)
writer2.add_scalar('img_loss', np.sum(img_losses)/len(img_losses), step)
writer3.add_scalar('kl_loss', np.sum(kl_losses)/len(kl_losses), step)
writer4.add_scalar('c_loss', np.sum(c_losses)/len(c_losses), step)
for i in range(len(datas)):
writer1.add_image('original data', datas[i][0], step)
writer1.add_image('adv data', adv_datas[i][0], step)
writer1.add_image("reconstruct data", outputs[i][0], step)
# print out loss
print("batch {}'s img_recon loss: {:.5f}, recon loss: {:.5f}, kl loss: {:.5f}"\
.format(step, np.sum(img_losses)/len(img_losses), np.sum(recon_losses)/len(recon_losses),\
np.sum(kl_losses)/len(kl_losses)))
# step scheduler
scheduler.step()
# save model parameters
if epoch % 5 == 0:
torch.save(model.state_dict(), '{}/classification/params_{}.pt'.format(args.model_dir, epoch))
torch.save(model.state_dict(), '{}/classification/params.pt'.format(args.model_dir))
# training function
def train(args, dataloader, model, classifier, optimizer, step, epoch):
# init output lists
recon_losses = list()
img_losses = list()
kl_losses = list()
c_losses = list()
datas = list()
adv_datas = list()
outputs = list()
# loop for each data pairs
for data, label, adv_data in dataloader:
# initialize
step += 1
if torch.cuda.is_available():
model = model.cuda()
data = data.cuda()
label = label.cuda()
adv_data = adv_data.cuda()
# zero grad for optimizer
optimizer.zero_grad()
# get data and run model
output, dsm, dss, z = model(adv_data)
distribution = Normal(dsm, dss)
# calculate losses
r_loss, img_recon, kld = recon_loss_function(output, data, distribution, step, 0.1)
c_loss = classification_loss(output, label, classifier)
loss = r_loss + args.closs_weight * c_loss
loss.backward()
# clip for gradient
torch.nn.utils.clip_grad_norm_(model.parameters(), 1)
# step optimizer
optimizer.step()
# record results
recon_losses.append(loss.cpu().item())
img_losses.append(img_recon.cpu().item())
kl_losses.append(kld.cpu().item())
c_losses.append(c_loss.cpu().item())
outputs.append(output.cpu())
datas.append(data.cpu())
adv_datas.append(adv_data.cpu())
return recon_losses, img_losses, kl_losses, c_losses, datas, adv_datas, outputs, step
# init models to be used
def init_models(args):
# construct model, classifier and loss module
model = MADVAE(args)
classifier = Classifier(args)
if args.use_gpu and torch.cuda.is_available():
# multi gpu training
if args.gpu_num > 1:
model = torch.nn.DataParallel(model, device_ids=range(args.gpu_num))
classifier = torch.nn.DataParallel(classifier, device_ids=range(args.gpu_num))
model = model.module
classifier = classifier.module
# move to cuda
model.apply(weights_init)
model = model.cuda()
classifier = classifier.cuda()
print('Using: ', torch.cuda.get_device_name(torch.cuda.current_device()))
# training module
model.train()
# evaluation module
classifier.load_state_dict(torch.load('pretrained_model/classifier_mnist.pt'))
classifier.eval()
# construct optimizer
optimizer = optim.Adam(model.parameters(), lr=args.lr)
scheduler = MinExponentialLR(optimizer, gamma=0.998, minimum=1e-5)
return model, classifier, optimizer, scheduler
# initialize model weights
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1 and classname.find('Block') == -1:
m.weight.data.normal_(0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1, 0.02)
m.bias.data.fill_(0)
if __name__ == '__main__':
main()