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main.py
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main.py
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from __future__ import print_function
import argparse
import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from model.resnet_model import MetaResnet34
from utils.lr_schedule import inv_lr_scheduler
from utils.return_dataset import return_dataset
parser = argparse.ArgumentParser(description='DeCoTa, previously MiCo, for Semi-supervised Domain Adaptation')
parser.add_argument('--steps', type=int, default=20000, metavar='N',
help='maximum number of iterations '
'to train (default: 20000)')
parser.add_argument('--method', type=str, default='mico',
choices=['mico', 'mist'])
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
help='learning rate (default: 0.001)')
parser.add_argument('--multi', type=float, default=0.1, metavar='MLT',
help='learning rate multiplication')
parser.add_argument('--save_check', action='store_true', default=False,
help='save checkpoint or not')
parser.add_argument('--checkpath', type=str, default='./checkpoint',
help='dir to save checkpoint')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=100, metavar='N',
help='how many batches to wait before logging '
'training status')
parser.add_argument('--save_interval', type=int, default=500, metavar='N',
help='how many batches to wait before saving a model')
parser.add_argument('--net', type=str, default='resnet34',
help='which network to use')
parser.add_argument('--source', type=str, default='real',
help='source domain')
parser.add_argument('--target', type=str, default='sketch',
help='target domain')
parser.add_argument('--st',type=int, default=0)
parser.add_argument('--dataset', type=str, default='multi')
parser.add_argument('--num', type=int, default=3,
help='3-shot/1-shot')
parser.add_argument('--th', type=float, default=0.5)
parser.add_argument('--root', type=str, default='./')
parser.add_argument('--net_resume', type=str, default='')
parser.add_argument('--start', type=int, default=0)
parser.add_argument('--runs', type=int, default=999)
parser.add_argument('--eval', action='store_true', default=False)
args = parser.parse_args()
"""DomainNet-subset 7 adaptation scenarios"""
multi = [['real', 'clipart'], ['real', 'painting'],
['painting', 'clipart'], ['clipart','sketch'],
['sketch', 'painting'], ['real', 'sketch'],
['painting', 'real']]
if args.st != 0 and args.dataset == 'multi':
args.source, args.target = multi[args.st-1]
print('Dataset %s Source %s Target %s Labeled num perclass %s Network %s' %
(args.dataset, args.source, args.target, args.num, args.net))
source_loader, target_loader, target_loader_unl, target_loader_val, \
target_loader_test, class_list = return_dataset(args=args, return_idx=False)
""" net: w_f; twin: w_g """
use_gpu = torch.cuda.is_available()
torch.cuda.manual_seed(args.seed)
if args.net == 'resnet34':
net = MetaResnet34(num_class=len(class_list))
twin = MetaResnet34(num_class=len(class_list))
params = []
for value in net.G.params():
if value.requires_grad:
params += [{'params': [value], 'lr': args.multi,
'weight_decay': 0.0005}]
params_F1 = []
for value in net.F1.params():
if value.requires_grad:
params_F1 += [{'params': [value], 'lr': args.multi,
'weight_decay': 0.0005}]
params_2 = []
for value in twin.G.params():
if value.requires_grad:
params_2 += [{'params': [value], 'lr': args.multi,
'weight_decay': 0.0005}]
params_F2 = []
for value in twin.F1.params():
if value.requires_grad:
params_F2 += [{'params': [value], 'lr': args.multi,
'weight_decay': 0.0005}]
""" record & resume path """
args.checkpath = os.path.join(args.checkpath, 'runs_{}'.format(args.runs))
if not os.path.exists(args.checkpath):
os.makedirs(args.checkpath)
record_dir = './record/%s/mico' % args.dataset
if not os.path.exists(record_dir):
os.makedirs(record_dir)
record_file = os.path.join(record_dir,
'exp_net_%s_%s_to_%s_num_%s_%d' %
(args.net, args.source,
args.target, args.num, args.runs))
""" pre-train & resume """
pretrain_src_checkpoint = './pretrained_models/pretrained_src_{}_to_{}.pth.tar'.format(args.source, args.target)
pretrain_t_checkpoint = './pretrained_models/pretrained_tgt_{}_to_{}.pth.tar'.format(args.source, args.target)
if args.net_resume:
p1 = os.path.join(args.checkpath, args.net_resume)
p2 = os.path.join(args.checkpath, 'Twin' + args.net_resume[3:])
net.load_state_dict(
torch.load(p1)
)
twin.load_state_dict(
torch.load(p2)
)
else:
net.load_state_dict(
torch.load(pretrain_src_checkpoint)
)
twin.load_state_dict(
torch.load(pretrain_t_checkpoint)
)
lr = args.lr
net.cuda()
twin.cuda()
im_data_s = torch.FloatTensor(1)
im_data_t = torch.FloatTensor(1)
im_data_tu = torch.FloatTensor(1)
im_data_tu_2 = torch.FloatTensor(1)
gt_labels_s = torch.LongTensor(1)
gt_labels_t = torch.LongTensor(1)
im_data_s = im_data_s.cuda()
im_data_t = im_data_t.cuda()
im_data_tu = im_data_tu.cuda()
im_data_tu_2 = im_data_tu_2.cuda()
gt_labels_s = gt_labels_s.cuda()
gt_labels_t = gt_labels_t.cuda()
im_data_s = Variable(im_data_s)
im_data_t = Variable(im_data_t)
im_data_tu = Variable(im_data_tu)
im_data_tu_2 = Variable(im_data_tu_2)
sgt_labels_s = Variable(gt_labels_s)
gt_labels_t = Variable(gt_labels_t)
def train():
net.train()
twin.train()
optimizer_g = optim.SGD(params, momentum=0.9,
weight_decay=0.0005, nesterov=True)
optimizer_f = optim.SGD(params_F1, lr=1.0, momentum=0.9,
weight_decay=0.0005, nesterov=True)
optimizer_g_2 = optim.SGD(params_2, momentum=0.9,
weight_decay=0.0005, nesterov=True)
optimizer_f_2 = optim.SGD(params_F2, lr=1.0, momentum=0.9,
weight_decay=0.0005, nesterov=True)
def zero_grad_all():
optimizer_g.zero_grad()
optimizer_f.zero_grad()
optimizer_g_2.zero_grad()
optimizer_f_2.zero_grad()
param_lr_g = []
param_lr_g_2 = []
param_lr_f = []
param_lr_f_2 = []
for param_group in optimizer_g.param_groups:
param_lr_g.append(param_group["lr"])
for param_group in optimizer_g_2.param_groups:
param_lr_g_2.append(param_group["lr"])
for param_group in optimizer_f.param_groups:
param_lr_f.append(param_group["lr"])
for param_group in optimizer_f_2.param_groups:
param_lr_f_2.append(param_group["lr"])
criterion = nn.CrossEntropyLoss().cuda()
criterion_no_reduce = nn.CrossEntropyLoss(reduction='none').cuda()
all_step = args.steps
data_iter_s = iter(source_loader)
data_iter_t = iter(target_loader)
data_iter_t_unl = iter(target_loader_unl)
len_train_source = len(source_loader)
len_train_target = len(target_loader)
len_train_target_semi = len(target_loader_unl)
best_acc = 0
counter = 0
for step in range(args.start, all_step):
optimizer_g = inv_lr_scheduler(param_lr_g, optimizer_g, step,
init_lr=args.lr)
optimizer_g_2 = inv_lr_scheduler(param_lr_g_2, optimizer_g_2, step,
init_lr=args.lr)
optimizer_f = inv_lr_scheduler(param_lr_f, optimizer_f, step,
init_lr=args.lr)
optimizer_f_2 = inv_lr_scheduler(param_lr_f_2, optimizer_f_2, step,
init_lr=args.lr)
lr = optimizer_f.param_groups[0]['lr']
if step % len_train_target == 0:
data_iter_t = iter(target_loader)
if step % len_train_target_semi == 0:
data_iter_t_unl = iter(target_loader_unl)
if step % len_train_source == 0:
data_iter_s = iter(source_loader)
data_t = next(data_iter_t)
data_t_unl = next(data_iter_t_unl)
data_s = next(data_iter_s)
with torch.no_grad():
im_data_s.resize_(data_s[0].size()).copy_(data_s[0])
gt_labels_s.resize_(data_s[1].size()).copy_(data_s[1])
im_data_t.resize_(data_t[0].size()).copy_(data_t[0])
gt_labels_t.resize_(data_t[1].size()).copy_(data_t[1])
im_data_tu.resize_(data_t_unl[0].size()).copy_(data_t_unl[0])
"""stream 1"""
zero_grad_all()
data = im_data_t
target = gt_labels_t
out_1 = net(data)
loss_1 = criterion_no_reduce(out_1, target).mean()
zero_grad_all()
"""stream 2"""
data = im_data_s
target = gt_labels_s
out_2 = twin(data)
loss_2 = criterion_no_reduce(out_2, target).mean()
zero_grad_all()
"""pseudo-label"""
u_1_prob = torch.softmax(net(im_data_tu), dim=1)
u_1_pred = u_1_prob.max(1)
u_2_prob = torch.softmax(twin(im_data_tu), dim=1)
u_2_pred = u_2_prob.max(1)
u_1_mask = u_1_pred[0] >= args.th
u_2_mask = u_2_pred[0] >= args.th
im_u_1 = im_data_tu[u_2_mask]
psl_u_1 = u_2_pred[1][u_2_mask]
im_u_2 = im_data_tu[u_1_mask]
psl_u_2 = u_1_pred[1][u_1_mask]
"""mix_up"""
alpha = 1
lam = np.random.beta(alpha, alpha)
# stream 1
if im_u_1.size(0) > 0:
size_1 = im_u_1.size(0)
# print('stream 1: {}'.format(size_1))
t_idx = torch.randperm(im_data_t.size(0))[0:size_1]
mixed_x = lam * im_data_t[t_idx] + (1-lam) * im_u_1
y_a, y_b = gt_labels_t[t_idx], psl_u_1
out_mix = net(mixed_x)
loss_mix_1 = lam * criterion(out_mix, y_a) + (1-lam) * criterion(out_mix, y_b)
loss_1 += loss_mix_1
zero_grad_all()
loss_1.backward(retain_graph=True)
optimizer_f.step()
optimizer_g.step()
else:
zero_grad_all()
loss_1.backward(retain_graph=True)
optimizer_f.step()
optimizer_g.step()
zero_grad_all()
# stream 2
if im_u_2.size(0) > 0:
size_2 = im_u_2.size(0)
# print('stream 2: {}'.format(size_2))
s_idx = torch.randperm(im_data_s.size(0))[0:size_2]
mixed_x = (1-lam) * im_data_s[s_idx] + lam * im_u_2
y_a, y_b = gt_labels_s[s_idx], psl_u_2
out_mix = twin(mixed_x)
loss_mix_2 = (1-lam) * criterion(out_mix, y_a) + lam * criterion(out_mix, y_b)
loss_2 += loss_mix_2
zero_grad_all()
loss_2.backward()
optimizer_f_2.step()
optimizer_g_2.step()
else:
zero_grad_all()
loss_2.backward()
optimizer_f_2.step()
optimizer_g_2.step()
zero_grad_all()
log_train = 'S {} T {} Train Ep: {} lr{} \t ' \
'Method {}\n'.\
format(args.source, args.target,
step, lr,
args.method)
net.zero_grad()
twin.zero_grad()
zero_grad_all()
if step % args.log_interval == 0:
print(log_train)
if step % args.save_interval == 0:
acc_test_net, acc_test_twin, acc_test = test_ensemble(target_loader_test)
acc_val_net, acc_val_twin, acc_val = test_ensemble(target_loader_val)
net.train()
twin.train()
if acc_val >= best_acc:
best_acc = acc_val
best_acc_test = acc_test
counter = 0
else:
counter += 1
print('test acc %f best acc test %f best acc val %f' % (acc_test,
best_acc_test,
best_acc))
print('record %s' % record_file)
with open(record_file, 'a') as f:
f.write('step %d wf %f wg %f mico %f best mico %f best val %f \n' % (step,
acc_test_net,
acc_test_twin,
acc_test,
best_acc_test,
best_acc))
net.train()
twin.train()
if args.save_check:
print('saving model')
torch.save(net.state_dict(),
os.path.join(args.checkpath,
"Net_iter_model_{}_{}_"
"to_{}_step_{}.pth.tar".
format(args.method, args.source,
args.target, step)))
torch.save(twin.state_dict(),
os.path.join(args.checkpath,
"Twin_iter_model_{}_{}_"
"to_{}_step_{}.pth.tar".
format(args.method, args.source,
args.target, step)))
def test_ensemble(loader):
net.eval()
twin.eval()
correct = 0
correct_test_1 = 0
correct_test_2 = 0
total = 0
with torch.no_grad():
for batch_idx, data_t in enumerate(loader):
im_data_t.resize_(data_t[0].size()).copy_(data_t[0])
gt_labels_t.resize_(data_t[1].size()).copy_(data_t[1])
output1 = net(im_data_t)
output2 = twin(im_data_t)
"""test 1 and 2"""
pred_test_1 = output1.max(1)[1]
pred_test_2 = output2.max(1)[1]
correct_test_1 += pred_test_1.eq(gt_labels_t).sum().item()
correct_test_2 += pred_test_2.eq(gt_labels_t).sum().item()
"""ensemble results"""
output = torch.softmax(output1, dim=1) + torch.softmax(output2, dim=1)
pred = output.max(1)[1]
total += gt_labels_t.size(0)
correct += pred.eq(gt_labels_t).sum().item()
acc_test_1 = 100. * (float(correct_test_1)/total)
acc_test_2 = 100. * (float(correct_test_2)/total)
acc = 100. * (float(correct)/total)
return acc_test_1, acc_test_2, acc
if args.eval:
print('eval mode...')
acc_test_net, acc_test_twin, acc_test = test_ensemble(target_loader_test)
print('net acc: {}, twin acc: {}, mico acc: {}'.format(acc_test_net, acc_test_twin, acc_test))
else:
train()