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dann.py
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dann.py
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# -*- coding: utf-8 -*-
"""
@ project: WDGRL
@ author: lzx
@ file: dann.py
@ time: 2019/6/19 20:29
"""
# -*- coding: utf-8 -*-
from data import Data_Mnist,Data_Mnist_M
from models import Classifier,Extractor,Discriminator,optimizer_scheduler,mmd_rbf_
import numpy as np
import torch.nn as nn
import torch
import torch.optim as optim
from uitls import save
from torch.autograd import Variable
'''params'''
batch_size = 128
lr = 0.01
momentum = 0.9
total_epochs = 100
source_dataset_train, source_dataset_test = Data_Mnist()
target_dataset_train, target_dataser_test = Data_Mnist_M()
source_loader = torch.utils.data.DataLoader(source_dataset_train, batch_size = batch_size, shuffle = True)
target_loader = torch.utils.data.DataLoader(target_dataset_train, batch_size = batch_size, shuffle = True)
s_test_loader = torch.utils.data.DataLoader(source_dataset_test, batch_size = batch_size, shuffle = True)
t_test_loader = torch.utils.data.DataLoader(target_dataser_test, batch_size = batch_size, shuffle = True)
total_steps = total_epochs*len(source_loader)
'''定义网络框架'''
feature_extrator = Extractor()
class_classifier = Classifier()
class_criterion = nn.NLLLoss()
domain_classifier = Discriminator()
domain_criterion = nn.NLLLoss()
optimizer = optim.SGD([{'params': feature_extrator.parameters()},
{'params': domain_classifier.parameters()},
{'params': class_classifier.parameters()}], lr= lr, momentum= momentum)
if torch.cuda.is_available():
feature_extrator = feature_extrator.cuda()
class_classifier = class_classifier.cuda()
domain_classifier = domain_classifier.cuda()
class_criterion = class_criterion.cuda()
domain_criterion = domain_criterion.cuda()
def train(f,c,d,source,target,optimizer,step):
result = []
source_data, source_label = source
target_data, target_label = target
# torchvision.utils.save_image(source_data,'mnist.png')
# torchvision.utils.save_image(target_data, 'mnist_M.png')
size = min((source_data.shape[0], target_data.shape[0]))
# print(size)
source_data, source_label = source_data[0:size, :, :, :], source_label[0:size]
target_data, target_label = target_data[0:size, :, :, :], target_label[0:size]
p = float(step)/total_steps
constant = 2 / (1 + np.exp(-10 * p)) - 1
if torch.cuda.is_available():
src_data = source_data.cuda()
src_label = source_label.cuda()
tgt_data = target_data.cuda()
S_labels = Variable(torch.zeros(source_data.size()[0])).type(torch.LongTensor).cuda()
T_labels = Variable(torch.ones(target_data.size()[0])).type(torch.LongTensor).cuda()
optimizer = optimizer_scheduler(optimizer,p)
optimizer.zero_grad()
source_Z = f(src_data)
target_Z = f(tgt_data)
class_pred = c(source_Z)
source_pred = d(source_Z, constant)
target_pred = d(target_Z, constant)
source_loss = domain_criterion(source_pred, S_labels)
target_loss = domain_criterion(target_pred, T_labels)
domain_loss = source_loss + target_loss
class_loss = class_criterion(class_pred, src_label)
# mmd_loss = mmd_rbf_(source_Z,target_Z,[1,5,10])
loss = class_loss+domain_loss
loss.backward()
optimizer.step()
result.append({
'step': step,
'total_steps': total_steps,
'classification_loss': class_loss.item(),
'domain loss': domain_loss.item()
})
if (step + 1) % 100 == 0:
print('Train step: [{:2d}/{:2d}]\t'
' classification_loss: {:.6f} domain_loss: {:.6f}'.format(
step,
total_steps,
class_loss.item(),
domain_loss.item()
))
return result
def test(f,c, dataset_loader, every_epoch):
f.eval()
c.eval()
with torch.no_grad():
test_loss = 0
corrcet = 0
for tgt_data,tgt_label in dataset_loader:
if torch.cuda.is_available():
tgt_data = tgt_data.cuda()
tgt_label = tgt_label.cuda()
tgt_out= f(tgt_data)
tgt_out = c(tgt_out)
test_loss += nn.NLLLoss()(tgt_out,tgt_label).item()
pred = tgt_out.data.max(1,keepdim=True)[1]
# print(pred)
# print(tgt_label)
corrcet += pred.eq(tgt_label.data.view_as(pred)).cpu().sum()
# print(corrcet)
test_loss /= len(dataset_loader)
return {
'epoch': every_epoch,
'average_loss': test_loss,
'correct': corrcet,
'total': len(dataset_loader.dataset),
'accuracy': 100. * float(corrcet) / len(dataset_loader.dataset)
}
if __name__ == '__main__':
training_sta = []
test_s_sta = []
test_t_sta = []
for epoch in range(total_epochs):
feature_extrator.train()
class_classifier.train()
start_steps = epoch * len(source_loader)
for index, (source, target) in enumerate(zip(source_loader, target_loader)):
p = float(index + start_steps) / total_steps
res = train(feature_extrator, class_classifier,domain_classifier, source,target, optimizer, index + start_steps)
training_sta.append(res)
test_source = test(feature_extrator,class_classifier, s_test_loader, epoch)
test_target = test(feature_extrator, class_classifier, t_test_loader, epoch)
test_s_sta.append(test_source)
test_t_sta.append(test_target)
print('###Test Source: Epoch: {}, avg_loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'.format(
epoch + 1,
test_source['average_loss'],
test_source['correct'],
test_source['total'],
test_source['accuracy'],
))
print('###Test Target: Epoch: {}, avg_loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'.format(
epoch + 1,
test_target['average_loss'],
test_target['correct'],
test_target['total'],
test_target['accuracy'],
))
result_path = 'result_norm_dann'
import os
os.makedirs(result_path, exist_ok=True)
torch.save([domain_classifier.state_dict(),feature_extrator.state_dict(),class_classifier.state_dict()], result_path + '/checkpoint.tar')
save(training_sta, result_path + '/training_state.pkl')
save(test_s_sta, result_path + '/test_s_sta.pkl')
save(test_t_sta, result_path + '/test_t_sta.pkl')