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main.py
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from __future__ import print_function
import argparse
import data_loader
import torch
import os
import copy
import math
from utils import *
from models.I2Attack import run_I2Attack
from models.MDD import MDDModel
from models.DANN import DANNModel
from models.DAN import DANModel
# Command setting
parser = argparse.ArgumentParser(description='Domain Adaptation')
parser.add_argument('-model', type=str, default='MDD', help='model name')
parser.add_argument('-mode', type=str, default='poison', help='poison|clean')
parser.add_argument('-batch_size', type=int, default=32, help='batch size')
parser.add_argument('-test_batch_size', type=int, default=500, help='test batch size')
parser.add_argument('-cuda', type=int, default=0, help='cuda id')
parser.add_argument('-root_dir', type=str, default='E:/Codes/data/office/')
parser.add_argument('-source', type=str, default='webcam_list.txt')
parser.add_argument('-target', type=str, default='amazon_list.txt')
parser.add_argument('-epochs', type=int, default=2000)
parser.add_argument('-num_classes', type=int, default=31) # 31 for office; 65 for office-home; 12 for image-clef, visda
parser.add_argument('-lr', type=float, default=0.01)
parser.add_argument('-moment', type=float, default=0.9)
parser.add_argument('-l2_decay', type=float, default=5e-4)
args = parser.parse_args()
def train(src_data, tgt_data, tgt_test_data, device):
model_name = args.model
model = None
print(model_name)
if model_name == 'MDD':
model = MDDModel(num_classes=args.num_classes).to(device)
elif model_name == 'DAN':
model = DANModel(num_classes=args.num_classes).to(device)
elif model_name == 'DANN':
model = DANNModel(num_classes=args.num_classes).to(device)
optimizer = get_optimizer(model, args)
if args.mode == 'poison':
src_data = run_I2Attack(copy.deepcopy(model), src_data, tgt_data, device, eps=0.1, step_size=0.01, attack_epochs=25)
print("Poisoning source is done")
src_generator = batch_generator(src_data, batch_size=args.batch_size)
tgt_generator = batch_generator(tgt_data, batch_size=args.batch_size)
for i in range(args.epochs):
model.train()
learning_rate = args.lr / math.pow((1 + 10 * i / args.epochs), 0.75)
adjust_learning_rate(optimizer, learning_rate)
sinputs, slabels = next(src_generator)
tinputs, _ = next(tgt_generator)
sinputs = torch.tensor(sinputs, requires_grad=False, dtype=torch.float).to(device)
slabels = torch.tensor(slabels, requires_grad=False, dtype=torch.long).to(device)
tinputs = torch.tensor(tinputs, requires_grad=False, dtype=torch.float).to(device)
optimizer.zero_grad()
alpha = 2 / (1 + math.exp(-10 * i / args.epochs)) - 1
loss = model(sinputs, slabels, tinputs, alpha)
loss.backward()
optimizer.step()
# print('Epoch: [{:02d}/{:02d}], loss: {:.6f}'.format(i + 1, args.epochs, loss.item()))
if (i + 1) % 200 == 0:
model.eval()
test_acc = 0.
with torch.no_grad():
test_len = tgt_test_data['X'].shape[0] // args.test_batch_size
for j in range(test_len):
outputs = model.inference(torch.tensor(tgt_test_data['X'][args.test_batch_size * j:args.test_batch_size * (j + 1)], requires_grad=False).to(device))
preds = torch.max(outputs, 1)[1]
test_acc += torch.sum(preds == torch.tensor(tgt_test_data['Y'][args.test_batch_size * j:args.test_batch_size * (j + 1)], requires_grad=False, dtype=torch.long).to(device))
if test_len * args.test_batch_size < tgt_test_data['X'].shape[0]:
outputs = model.inference(torch.tensor(tgt_test_data['X'][test_len * args.test_batch_size:], requires_grad=False).to(device))
preds = torch.max(outputs, 1)[1]
test_acc += torch.sum(preds == torch.tensor(tgt_test_data['Y'][test_len * args.test_batch_size:], requires_grad=False, dtype=torch.long).to(device))
test_acc = test_acc.double() / tgt_test_data['X'].shape[0]
with torch.no_grad():
train_acc = 0.
train_len = src_data['X'].shape[0] // args.test_batch_size
for j in range(train_len):
outputs = model.inference(torch.tensor(src_data['X'][args.test_batch_size * j:args.test_batch_size * (j + 1)], requires_grad=False).to(device))
preds = torch.max(outputs, 1)[1]
train_acc += torch.sum(preds == torch.tensor(src_data['Y'][args.test_batch_size * j:args.test_batch_size * (j + 1)], requires_grad=False, dtype=torch.long).to(device))
if train_len * args.test_batch_size < src_data['X'].shape[0]:
outputs = model.inference(torch.tensor(src_data['X'][train_len * args.test_batch_size:], requires_grad=False).to(device))
preds = torch.max(outputs, 1)[1]
train_acc += torch.sum(preds == torch.tensor(src_data['Y'][train_len * args.test_batch_size:], requires_grad=False, dtype=torch.long).to(device))
train_acc = train_acc.double() / src_data['X'].shape[0]
with torch.no_grad():
discrepancy = 0.
num_examples = 32
num_batchs = min(src_data['X'].shape[0], tgt_data['X'].shape[0]) // num_examples
for j in range(num_batchs):
s_val_inputs = torch.tensor(src_data['X'][num_examples * j: num_examples * (j + 1)], requires_grad=False).to(device)
t_val_inputs = torch.tensor(tgt_data['X'][num_examples * j: num_examples * (j + 1)], requires_grad=False).to(device)
discrepancy += model.get_discrepancy(s_val_inputs, t_val_inputs)
discrepancy = discrepancy / num_batchs
print('Epoch: [{:02d}/{:02d}], loss: {:.6f}, train acc: {:.4f}, discrepancy: {:.4f}, test acc: {:.4f}'.format(i + 1, args.epochs, loss.item(), train_acc, discrepancy, test_acc))
return test_acc
if __name__ == '__main__':
device = torch.device('cuda:' + str(args.cuda) if torch.cuda.is_available() else 'cpu')
torch.manual_seed(23)
if not os.path.isfile("data_preprocessed/{}.pkl".format(args.source[:-9])):
raw_src_loader = data_loader.load_training(args.root_dir, args.source, args.batch_size)
save_data(raw_src_loader, name=args.source[:-9])
src_data = pickle.load(open("data_preprocessed/{}.pkl".format(args.source[:-9]), "rb"))
if not os.path.isfile("data_preprocessed/{}.pkl".format(args.target[:-9])):
raw_tgt_loader = data_loader.load_training(args.root_dir, args.target, args.batch_size)
save_data(raw_tgt_loader, name=args.target[:-9])
tgt_data = pickle.load(open("data_preprocessed/{}.pkl".format(args.target[:-9]), "rb"))
if not os.path.isfile("data_preprocessed/{}_test.pkl".format(args.target[:-9])):
raw_tgt_loader = data_loader.load_testing(args.root_dir, args.target, args.batch_size)
save_data(raw_tgt_loader, name=args.target[:-9]+'_test')
tgt_test_data = pickle.load(open("data_preprocessed/{}_test.pkl".format(args.target[:-9]), "rb"))
test_acc = train(src_data, tgt_data, tgt_test_data, device)