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main_single.py
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main_single.py
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# -*- coding:utf-8 -*-
# Created Time: Thu 05 Jul 2018 10:00:41 PM CST
# Author: Taihong Xiao <[email protected]>
from config import cfg
import torch
import torchvision
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import torch.nn.functional as F
from torchvision import transforms
import numpy as np
import os
import argparse
from tqdm import tqdm, trange
class Adversarial_Reprogramming(object):
def __init__(self, args, cfg=cfg):
self.mode = args.mode
self.gpu = args.gpu
self.restore = args.restore
self.cfg = cfg
self.init_net()
self.init_dataset()
self.init_mask()
self.init_weight()
self.set_mode_and_gpu()
def init_net(self):
if self.cfg.net == 'resnet50':
self.net = torchvision.models.resnet50(pretrained=False)
self.net.load_state_dict(torch.load(os.path.join(self.cfg.models_dir, 'resnet50-19c8e357.pth')))
# mean and std for input
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
mean = mean[..., np.newaxis, np.newaxis]
std = np.array([0.229, 0.224, 0.225],dtype=np.float32)
std = std[..., np.newaxis, np.newaxis]
self.mean = self.tensor2var(torch.from_numpy(mean))
self.std = self.tensor2var(torch.from_numpy(std))
elif self.cfg.net == 'vgg16':
self.net = torchvision.models.vgg16(pretrained=False)
self.net.load_state_dict(torch.load(os.path.join(self.cfg.models_dir, 'vgg16-397923af.pth')))
# mean and std for input
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
mean = mean[..., np.newaxis, np.newaxis]
std = np.array([0.229, 0.224, 0.225],dtype=np.float32)
std = std[..., np.newaxis, np.newaxis]
self.mean = Parameter(torch.from_numpy(mean), requires_grad=False)
self.std = Parameter(torch.from_numpy(std), requires_grad=False)
else:
raise NotImplementationError()
self.net.eval()
def init_dataset(self):
if self.cfg.dataset == 'mnist':
train_set = torchvision.datasets.MNIST(os.path.join(self.cfg.data_dir, 'mnist'), train=True, transform=transforms.ToTensor(), download=True)
test_set = torchvision.datasets.MNIST(os.path.join(self.cfg.data_dir, 'mnist'), train=False, transform=transforms.ToTensor(), download=True)
kwargs = {'num_workers': 1, 'pin_memory': True, 'drop_last': True}
self.train_loader = torch.utils.data.DataLoader(train_set, batch_size=self.cfg.batch_size_per_gpu, shuffle=True, **kwargs)
self.test_loader = torch.utils.data.DataLoader(test_set, batch_size=self.cfg.batch_size_per_gpu, shuffle=True, **kwargs)
else:
raise NotImplementationError()
def init_mask(self):
M = np.ones((3, self.cfg.h1, self.cfg.w1), dtype=np.float32)
c_w, c_h = int(np.ceil(self.cfg.w1/2.)), int(np.ceil(self.cfg.h1/2.))
M[:,c_h-self.cfg.h2//2:c_h+self.cfg.h2//2, c_w-self.cfg.w2//2:c_w+self.cfg.w2//2] = 0
self.M = self.tensor2var(torch.from_numpy(M))
def init_weight(self):
# Learnable parameter W
if self.restore:
W = torch.load(os.path.join(self.cfg.train_dir, 'W_{:03d}.pt'.format(self.restore))).data
self.start_epoch = self.restore + 1
else:
W = torch.randn(self.M.shape)
self.start_epoch = 1
self.W = self.tensor2var(W, requires_grad=True)
def set_mode_and_gpu(self):
if self.mode == 'train':
# optimizer
self.BCE = torch.nn.BCELoss()
self.optimizer = torch.optim.Adam([self.W], lr=self.cfg.lr, betas=(0.5, 0.999))
self.lr_scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=2, gamma=self.cfg.decay)
if self.gpu:
with torch.cuda.device(0):
self.BCE.cuda()
self.net.cuda()
elif self.mode == 'validate' or self.mode == 'test':
if self.gpu:
with torch.cuda.device(0):
self.net.cuda()
else:
raise NotImplementationError()
def imagenet_label2_mnist_label(self, imagenet_label):
return imagenet_label[:,:10]
def tensor2var(self, tensor, requires_grad=False, volatile=False):
if self.gpu:
with torch.cuda.device(0):
tensor = tensor.cuda()
return Variable(tensor, requires_grad=requires_grad, volatile=volatile)
def forward(self, image):
image = np.tile(image, (1,3,1,1))
X = np.zeros((self.cfg.batch_size_per_gpu, 3, self.cfg.h1, self.cfg.w1), dtype=np.float32)
X[:,:,(self.cfg.h1-self.cfg.h2)//2:(self.cfg.h1+self.cfg.h2)//2, (self.cfg.w1-self.cfg.w2)//2:(self.cfg.w1+self.cfg.w2)//2] = image
X = self.tensor2var(torch.from_numpy(X))
P = torch.sigmoid(self.W * self.M)
X_adv = X + P # range [0, 1]
X_adv = (X_adv - self.mean) / self.std
Y_adv = self.net(X_adv)
Y_adv = F.softmax(Y_adv, 1)
return self.imagenet_label2_mnist_label(Y_adv)
def compute_loss(self, out, label):
label = torch.zeros(self.cfg.batch_size_per_gpu, 10).scatter_(1, label.view(-1,1), 1)
label = self.tensor2var(label)
return self.BCE(out, label) + self.cfg.lmd * torch.norm(self.W) ** 2
def validate(self):
acc = 0.0
for i, (image, label) in enumerate(self.test_loader):
out = self.forward(image)
pred = out.data.cpu().numpy().argmax(1)
acc += sum(label.numpy() == pred) / float(len(label) * len(self.test_loader))
print('test accuracy: %.6f' % acc)
def train(self):
for i in range(self.start_epoch, self.cfg.max_epoch + 1):
self.epoch = i
self.lr_scheduler.step()
for j, (image, label) in tqdm(enumerate(self.train_loader)):
if j > 100: continue;
self.out = self.forward(image)
self.loss = self.compute_loss(self.out, label)
self.optimizer.zero_grad()
self.loss.backward()
self.optimizer.step()
print('epoch: %03d/%03d, loss: %.6f' % (self.epoch, self.cfg.max_epoch, self.loss.data.cpu().numpy()))
torch.save(self.W.cpu(), os.path.join(self.cfg.train_dir, 'W_%03d.pt' % self.epoch))
self.validate()
def test(self):
pass
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--mode', default='train', type=str, choices=['train', 'validate', 'test'])
parser.add_argument('-r', '--restore', default=None, action='store', type=int, help='Specify checkpoint id to restore.')
parser.add_argument('-g', '--gpu', default=[], nargs='+', type=str, help='Specify GPU ids.')
# test params
args = parser.parse_args()
# print(args)
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(args.gpu)
AR = Adversarial_Reprogramming(args)
if args.mode == 'train':
AR.train()
elif args.mode == 'validate':
AR.validate()
elif args.mode == 'test':
AR.test()
else:
raise NotImplementationError()
if __name__ == "__main__":
main()