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new_nosoft_cl_train.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from models import Encoder, ClassClassifier, DomainClassifier
from dataset import get_dataloader
from utils import gen_soft_labels, ret_soft_label
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "7"
# Parameters
epochs = 10000
temperature = 2
batch_size = 15
lr = 1e-4
momentum = 0.9
interval = 100
data_dir = '/home/lucliu/dataset/domain_adaptation/office31'
src_dir = 'amazon'
tgt_train_dir = 'dslr_tgt'
tgt_dir = 'webcam'
test_dir = 'test'
cuda = torch.cuda.is_available()
test_loader = get_dataloader(data_dir, tgt_dir, batch_size=15, train=False)
# lam for confusion
lam = 0.01
# nu for soft
nu = 0.1
# load the pretrained and fine-tuned alex model
encoder = Encoder()
cl_classifier = ClassClassifier(num_classes=31)
dm_classifier = DomainClassifier()
encoder.load_state_dict(torch.load('./checkpoints/a2w/src_encoder_final.pth'))
cl_classifier.load_state_dict(torch.load('./checkpoints/a2w/src_classifier_final.pth'))
src_train_loader = get_dataloader(data_dir, src_dir, batch_size, train=True)
tgt_train_loader = get_dataloader(data_dir, tgt_train_dir, batch_size, train=True)
criterion = nn.CrossEntropyLoss()
# criterion_kl = nn.KLDivLoss()
if cuda:
criterion = criterion.cuda()
cl_classifier = cl_classifier.cuda()
dm_classifier = dm_classifier.cuda()
encoder = encoder.cuda()
#soft_labels = gen_soft_labels(31, src_train_loader, encoder, cl_classifier)
# optimizer
optimizer = optim.SGD(
list(encoder.parameters()) + list(cl_classifier.parameters()),
lr=lr,
momentum=momentum)
# optimizer_conf = optim.SGD(
# encoder.parameters(),
# lr=lr,
# momentum=momentum)
# optimizer_dm = optim.SGD(
# dm_classifier.parameters(),
# lr=lr,
# momentum=momentum)
# begin training
encoder.train()
cl_classifier.train()
#dm_classifier.train()
for epoch in range(1, epochs+1):
correct = 0
for batch_idx, ((src_data, src_label_cl), (tgt_data, tgt_label_cl)) in enumerate(zip(src_train_loader, tgt_train_loader)):
src_label_dm = torch.ones(src_label_cl.size()).long()
tgt_label_dm = torch.zeros(tgt_label_cl.size()).long()
if cuda:
src_data, src_label_cl, src_label_dm = src_data.cuda(), src_label_cl.cuda(), src_label_dm.cuda()
tgt_data, tgt_label_cl, tgt_label_dm = tgt_data.cuda(), tgt_label_cl.cuda(), tgt_label_dm.cuda()
src_data, src_label_cl, src_label_dm = Variable(src_data), Variable(src_label_cl), Variable(src_label_dm)
tgt_data, tgt_label_cl, tgt_label_dm = Variable(tgt_data), Variable(tgt_label_cl), Variable(tgt_label_dm)
#soft_label_for_batch = ret_soft_label(tgt_label_cl, soft_labels)
# update encoder & class classifier
optimizer.zero_grad()
src_feature = encoder(src_data)
tgt_feature = encoder(tgt_data)
# class output
src_output_cl = cl_classifier(src_feature)
tgt_output_cl = cl_classifier(tgt_feature)
#soft_label_for_batch = ret_soft_label(tgt_label_cl, soft_labels)
#if cuda:
#soft_label_for_batch = soft_label_for_batch.cuda()
#soft_label_for_batch = Variable(soft_label_for_batch)
#output_cl_score = F.softmax(tgt_output_cl/temperature, dim=1)
loss_cl = criterion(tgt_output_cl, tgt_label_cl)
#loss_soft = - (torch.sum(soft_label_for_batch * torch.log(output_cl_score)))/float(output_cl_score.size(0))
# loss_soft = criterion_kl(tgt_output_cl, soft_label_for_batch)
# loss = loss_cl + nu * loss_soft
loss = loss_cl
loss.backward()
optimizer.step()
# update domain classifier only
# optimizer_dm.zero_grad()
# # domain output
# src_output_dm = dm_classifier(src_feature.detach())
# tgt_output_dm = dm_classifier(tgt_feature.detach())
# loss_dm_src = criterion(src_output_dm, src_label_dm)
# loss_dm_tgt = criterion(tgt_output_dm, tgt_label_dm)
# loss_dm = loss_dm_src + loss_dm_tgt
# loss_dm.backward()
# optimizer_dm.step()
# # update encoder only using domain loss
# optimizer_conf.zero_grad()
# feature_concat = torch.cat((src_feature, tgt_feature), 0)
# # src_output_dm_conf = dm_classifier(src_feature)
# # tgt_output_dm_conf = dm_classifier(tgt_feature)
# output_dm_conf = dm_classifier(feature_concat)
# uni_distrib = torch.FloatTensor(output_dm_conf.size()).uniform_(0, 1)
# if cuda:
# uni_distrib = uni_distrib.cuda()
# uni_distrib = Variable(uni_distrib)
# # loss_conf = lam * criterion_kl(tgt_output_dm_conf, uni_distrib)
# loss_conf = - lam * (torch.sum(uni_distrib * torch.log(output_dm_conf)))/float(output_dm_conf.size(0))
# loss_conf.backward()
# optimizer_conf.step()
# acc
tgt_output_cl_score = F.softmax(tgt_output_cl, dim=1) # softmax first
pred = tgt_output_cl_score.data.max(1, keepdim=True)[1]
correct += pred.eq(tgt_label_cl.data.view_as(pred)).cpu().sum()
acc = correct / len(tgt_train_loader.dataset)
print("epoch: %d, class loss: %f, acc: %f"%(epoch, loss.data[0], acc))
# save parameters
if (epoch % interval == 0):
torch.save(encoder.state_dict(), "./checkpoints/a2d/no_soft_encoder{}.pth".format(epoch))
torch.save(cl_classifier.state_dict(), "./checkpoints/a2d/no_soft_class_classifier{}.pth".format(epoch))
torch.save(encoder.state_dict(), "./checkpoints/a2w/no_soft_encoder_final.pth")
torch.save(cl_classifier.state_dict(), "./checkpoints/a2d/no_soft_class_classifier_final.pth")