def load_data(args, train=False):
torch.manual_seed(args.manual_seed)
dataroot = join(os.getcwd(), args.data_dir)
transform = transforms.Compose([
# transforms.RandomCrop(args.img_size, padding=None, pad_if_needed=True, fill=0, padding_mode='edge'),
transforms.Resize((args.img_size, args.img_size)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
test_dataset = dataset_public(
root=dataroot,
transform=transform,
train=train,
domains=args.target,
real_test=args.real_test,
)
test_loader = torch.utils.data.DataLoader(
dataset=test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False,
)
return test_loader
def test_model(args):
torch.manual_seed(args.manual_seed)
dataroot = join(os.getcwd(), args.data_dir)
transform = transforms.Compose([
# transforms.RandomCrop(args.img_size, padding=None, pad_if_needed=True, fill=0, padding_mode='edge'),
transforms.Resize((args.img_size, args.img_size)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
target_dataset = dataset_public(
root=dataroot,
transform=transform,
train=False,
domain=args.target,
)
target_loader = torch.utils.data.DataLoader(
dataset=target_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False,
)
model = resnet_345(args).cuda()
model = load_model(args.model_path, model)
preds = []
labels = []
model.eval()
test_pbar = tqdm(total=len(target_loader), ncols=100, leave=True)
for batch_idx, (x_inputs, y_labels) in enumerate(target_loader):
x_inputs = x_inputs.cuda()
with torch.no_grad():
output = model.predict(x_inputs)
_, pred = torch.max(output, 1)
preds += pred.cpu().tolist()
labels += y_labels.tolist()
test_pbar.update()
test_pbar.close()
preds = np.array(preds)
labels = np.array(labels)
acc = float(sum(preds == labels)) / float(len(preds))
print('valid acc = {:4f}'.format(acc))
return acc
def test_model_create_output(args):
torch.manual_seed(args.manual_seed)
dataroot = args.data_dir
transform = transforms.Compose([
# transforms.RandomCrop(args.img_size, padding=None, pad_if_needed=True, fill=0, padding_mode='edge'),
transforms.Resize((args.img_size, args.img_size)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
target_dataset = dataset_public(
root=dataroot,
transform=transform,
train=False,
domain=args.target,
)
target_loader = torch.utils.data.DataLoader(
dataset=target_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False,
)
model = resnet_345(args).cuda()
model = load_model(args.model_path, model)
preds = []
file_names = []
model.eval()
test_pbar = tqdm(total=len(target_loader), ncols=100, leave=True)
for batch_idx, (x_inputs, y_file_names) in enumerate(target_loader):
x_inputs = x_inputs.cuda()
with torch.no_grad():
output = model.predict(x_inputs)
_, pred = torch.max(output, 1)
preds += pred.cpu().tolist()
file_names += y_file_names
test_pbar.update()
test_pbar.close()
with open(join(args.pred_dir, 'output_{}.csv'.format(args.target)),
'w') as f:
f.write('image_name,label\n')
for i, pred in enumerate(preds):
f.write('{}/{},{}\n'.format(args.title, file_names[i], pred))
def main():
args = parse_args()
torch.manual_seed(args.manual_seed)
dataroot = join(os.getcwd(), args.data_dir)
transform=transforms.Compose([
# transforms.RandomCrop(args.img_size, padding=None, pad_if_needed=True, fill=0, padding_mode='constant'),
transforms.Resize((args.img_size,args.img_size)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
train_dataset = dataset_public(root=dataroot,
transform=transform,
train=True,
domains=args.source,
)
train_loader = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False,
)
if args.test:
transform=transforms.Compose([
# transforms.RandomCrop(args.img_size, padding=None, pad_if_needed=True, fill=0, padding_mode='edge'),
transforms.Resize((args.img_size,args.img_size)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
test_dataset = dataset_public(root=dataroot,
transform=transform,
train=False,
domains=args.target,
real_test=args.real_test,
)
test_loader = torch.utils.data.DataLoader(
dataset=test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False,
)
# models
if args.inception:
cls = inception_345().cuda()
else:
cls = resnet_345(args).cuda()
if args.fixed_pretrained:
fixed_pretrained(cls)
params_to_update = []
for name, param in cls.named_parameters():
if param.requires_grad:
params_to_update.append(param)
print('Number of parameters to update: {}'.format(len(params_to_update)))
# optimizer
if args.SGD:
optimizer = optim.SGD(params_to_update, lr=args.lr, momentum=0.9)
elif args.Adam:
optimizer = optim.Adam(params_to_update, lr=args.lr)
if args.resume:
checkpoint_path = join(os.getcwd(), args.model_dir, args.model_name)
load_checkpoint(checkpoint_path, cls, optimizer)
best_acc = 0
patience = 0
if len(args.source) > 1:
err_log_path = join(os.getcwd(), 'models_inception', 'src_combine_tar_{}_err.txt'.format(args.target[0]))
else:
err_log_path = join(os.getcwd(), 'models_inception', 'src_{}_err.txt'.format(args.source[0]))
err_log = open(err_log_path, 'w')
for epoch in range(args.start_epoch, args.max_epochs+1):
criterion = nn.CrossEntropyLoss()
cls.train()
print ('\nEpoch = {}'.format(epoch))
err_log.write('Epoch = {}, '.format(epoch))
losses, train_acc = AverageMeter(), AverageMeter()
train_pbar = tqdm(total=len(train_loader), ncols=100, leave=True)
for i, (images, labels) in enumerate(train_loader):
images, labels = images.cuda(), labels.cuda()
cls.zero_grad()
if args.inception:
class_output, class_aux_output = cls(images)
loss1 = criterion(class_output, labels)
loss2 = criterion(class_aux_output, labels)
loss = loss1 + 0.4*loss2
loss.backward()
else:
class_output = cls(images)
loss = criterion(class_output, labels)
loss.backward()
pred = class_output.max(1, keepdim=True)[1]
correct = pred.eq(labels.view_as(pred)).sum().item()
optimizer.step()
losses.update(loss.data.item(), args.batch_size)
train_acc.update(correct, args.batch_size)
train_pbar.update()
train_pbar.set_postfix({'loss':'{:.4f}'.format(losses.avg),
'acc':'{:.4f}'.format(train_acc.acc),
})
train_pbar.close()
if args.test:
test_acc, test_loss = test(cls, test_loader, args)
if test_acc.acc > best_acc:
best_acc = test_acc.acc
patience = 0
if len(args.source) > 1:
checkpoint_path = join(os.getcwd(), 'models_inception', 'src_combine_tar_{}.pth'.format(args.target[0]))
else:
checkpoint_path = join(os.getcwd(), 'models_inception', 'src_{}.pth'.format(args.source[0]))
save_checkpoint(checkpoint_path, cls, optimizer)
else:
patience += 1
err_log.write('Loss: {:.4f}/{:.4f}, Accuracy: {:.4f}/{:.4f}\n'.format(losses.avg, test_loss.avg,
train_acc.acc, test_acc.acc))
else:
if train_acc.acc > best_acc:
best_acc = train_acc.acc
if len(args.source) > 1:
checkpoint_path = join(os.getcwd(), 'models', 'src_combine_tar_{}.pth'.format(args.target[0]))
else:
checkpoint_path = join(os.getcwd(), 'models', 'src_{}.pth'.format(args.source[0]))
save_checkpoint(checkpoint_path, cls, optimizer)
err_log.write('Loss: {:.4f}, Accuracy: {:.4f}\n'.format(losses.avg, train_acc.acc))
err_log.flush()
if patience == args.early_stopping:
break
err_log.write('Best test_acc: {:.4f}\n'.format(best_acc))
err_log.close()
def main():
args = parse_args()
torch.manual_seed(args.manual_seed)
dataroot = join(os.getcwd(), args.data_dir)
transform = transforms.Compose([
# transforms.RandomCrop(args.img_size, padding=None, pad_if_needed=True, fill=0, padding_mode='edge'),
transforms.Resize((args.img_size, args.img_size)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
src_dataset = dataset_public(
root=dataroot,
transform=transform,
train=True,
domains=args.source,
)
src_loader = torch.utils.data.DataLoader(
dataset=src_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True,
)
tar_dataset = dataset_public(
root=dataroot,
transform=transform,
train=True,
domains=args.target,
)
tar_loader = torch.utils.data.DataLoader(
dataset=tar_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True,
)
# models
src_CNN = resnet_345(args).cuda()
tar_CNN = resnet_345(args).cuda()
D_cls = Domain_classifier().cuda()
# load and fix pretrained models
resume_path = join(os.getcwd(), args.model_dir, args.model_name)
resume_pretrained(resume_path, src_CNN)
resume_pretrained(resume_path, tar_CNN)
fixed_pretrained(src_CNN, 'CNN')
fixed_pretrained(src_CNN, 'fc')
fixed_pretrained(tar_CNN, 'fc')
# optimizer
if args.SGD:
optimizer_tar_CNN = optim.SGD(tar_CNN.parameters(),
lr=args.lr,
momentum=0.9)
optimizer_D_cls = optim.SGD(D_cls.parameters(),
lr=args.lr,
momentum=0.9)
elif args.Adam:
optimizer_tar_CNN = optim.Adam(tar_CNN.parameters(), lr=args.lr)
optimizer_D_cls = optim.Adam(D_cls.parameters(), lr=args.lr)
# domain labels
src_label = torch.full((args.batch_size, ), 0).long().cuda()
tar_label = torch.full((args.batch_size, ), 1).long().cuda()
min_len = min(len(src_loader), len(tar_loader))
best_acc = 0
patience = 0
err_log_path = join(os.getcwd(), 'models',
'adda_{}_err.txt'.format(args.target[0]))
err_log = open(err_log_path, 'w')
for epoch in range(args.start_epoch, args.max_epochs + 1):
criterion = nn.CrossEntropyLoss()
src_CNN.train()
tar_CNN.eval()
D_cls.train()
print('\nEpoch = {}'.format(epoch))
err_log.write('Epoch = {}, '.format(epoch))
losses, train_acc, D_losses, D_acc = AverageMeter(), AverageMeter(
), AverageMeter(), AverageMeter()
train_pbar = tqdm(total=min_len, ncols=100, leave=True)
for i, (src_data, tar_data) in enumerate(zip(src_loader, tar_loader)):
src_imgs, _ = src_data
tar_imgs, tar_labels = tar_data
src_imgs, tar_imgs, tar_labels = src_imgs.cuda(), tar_imgs.cuda(
), tar_labels.cuda()
# train D_cls
D_cls.zero_grad()
src_feature = src_CNN(src_imgs)
tar_feature = tar_CNN(tar_imgs)
pred_src_domain = D_cls(src_feature.detach())
pred_tar_domain = D_cls(tar_feature.detach())
domain_output = torch.cat((pred_src_domain, pred_tar_domain), 0)
label = torch.cat((src_label, tar_label), 0)
domain_output = torch.squeeze(domain_output)
D_loss = criterion(domain_output, label)
D_loss.backward()
optimizer_D_cls.step()
# domain accuracy
_, pred = torch.max(domain_output, 1)
D_correct = sum(pred == label).float() / float(pred.shape[0])
# train tar_CNN
tar_CNN.zero_grad()
tar_feature = tar_CNN(tar_imgs)
pred_tar_domain = D_cls(tar_feature)
pred_tar_domain = torch.squeeze(pred_tar_domain)
loss = criterion(pred_tar_domain, src_label)
loss.backward()
optimizer_tar_CNN.step()
# predict accuracy
class_output = tar_CNN.predict(tar_imgs)
_, pred = torch.max(class_output, 1)
cls_correct = sum(pred == tar_labels).float() / float(
pred.shape[0])
# update losses, accuracy and pbar
D_losses.update(D_loss.data.item(), args.batch_size * 2)
D_acc.update(D_correct, args.batch_size * 2)
losses.update(loss.data.item(), args.batch_size)
train_acc.update(cls_correct, args.batch_size)
train_pbar.update()
train_pbar.set_postfix({
'D_loss': '{:.3f}'.format(D_losses.avg),
'D_acc': '{:.3f}'.format(D_acc.avg),
'loss': '{:.3f}'.format(losses.avg),
'acc': '{:.3f}'.format(train_acc.avg),
})
train_pbar.close()
test_acc, test_loss = test(tar_CNN, tar_loader, args.batch_size)
if test_acc.avg > best_acc:
best_acc = test_acc.avg
patience = 0
checkpoint_path = join(os.getcwd(), 'models',
'adda_{}.pth'.format(args.target[0]))
save_checkpoint(checkpoint_path, tar_CNN, D_cls, optimizer_tar_CNN,
optimizer_D_cls)
else:
patience += 1
err_log.write('Loss: {:.4f}, Accuracy: {:.4f}\n'.format(
losses.avg, test_acc.avg))
err_log.flush()
if patience >= args.early_stopping:
print('Early stopping...')
break
err_log.write('Best test_acc: {:.4f}\n'.format(best_acc))
err_log.close()
def main():
args = parse_args()
torch.manual_seed(args.manual_seed)
dataroot = join(os.getcwd(), args.data_dir)
transform = transforms.Compose([
# transforms.RandomCrop(args.img_size, padding=None, pad_if_needed=True, fill=0, padding_mode='edge'),
transforms.Resize((args.img_size, args.img_size)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
src_dataset = dataset_public(
root=dataroot,
transform=transform,
train=True,
domains=args.source,
)
src_loader = torch.utils.data.DataLoader(
dataset=src_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False,
)
tar_dataset = dataset_public(
root=dataroot,
transform=transform,
train=True,
domains=args.target,
)
tar_loader = torch.utils.data.DataLoader(
dataset=tar_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False,
)
# models
src_CNN = resnet_345(args).cuda()
tar_CNN = resnet_345(args).cuda()
cls = classifier().cuda()
checkpoint_path = join(os.getcwd(), args.model_dir, args.model_name)
load_checkpoint(checkpoint_path, src_CNN)
load_checkpoint(checkpoint_path, tar_CNN)
load_checkpoint(checkpoint_path, cls)
fixed_pretrained(src_CNN)
fixed_pretrained(cls)
# optimizer
if args.SGD:
optimizer = optim.SGD(tar_CNN.parameters(), lr=args.lr, momentum=0.9)
elif args.Adam:
optimizer = optim.Adam(tar_CNN.parameters(), lr=args.lr)
min_len = min(len(src_loader), len(tar_loader))
best_acc = 0
stop_count = 0
err_log_path = join(os.getcwd(), 'models',
'adda_{}_err.txt'.format(args.target[0]))
err_log = open(err_log_path, 'w')
for epoch in range(args.start_epoch, args.max_epochs + 1):
src_CNN.eval()
tar_CNN.train()
cls.eval()
print('\nEpoch = {}'.format(epoch))
err_log.write('Epoch = {}, '.format(epoch))
losses, train_acc = AverageMeter(), AverageMeter()
train_pbar = tqdm(total=min_len, ncols=100, leave=True)
for i, (src_data, tar_data) in enumerate(zip(src_loader, tar_loader)):
src_imgs, _ = src_data
tar_imgs, tar_labels = tar_data
# src_imgs, src_labels = src_data
src_imgs, tar_imgs, tar_labels = src_imgs.cuda(), tar_imgs.cuda(
), tar_labels.cuda()
# src_imgs, tar_imgs, tar_labels, src_labels = src_imgs.cuda(), tar_imgs.cuda(), tar_labels.cuda(), src_labels.cuda()
tar_CNN.zero_grad()
src_feature = src_CNN(src_imgs)
tar_feature = tar_CNN(tar_imgs)
loss = F.mse_loss(src_feature, tar_feature, reduction='mean')
loss.backward()
class_output = cls(tar_feature)
pred = class_output.max(1, keepdim=True)[1]
correct = pred.eq(tar_labels.view_as(pred)).sum().item()
optimizer.step()
losses.update(loss.data.item(), args.batch_size)
train_acc.update(correct, args.batch_size)
train_pbar.update()
train_pbar.set_postfix({
'loss': '{:.4f}'.format(losses.avg),
'acc': '{:.4f}'.format(train_acc.acc),
})
train_pbar.close()
if train_acc.acc > best_acc:
best_acc = train_acc.acc
stop_count = 0
checkpoint_path = join(os.getcwd(), 'models',
'adda_{}.pth'.format(args.target[0]))
save_checkpoint(checkpoint_path, tar_CNN, cls, optimizer)
else:
stop_count += 1
err_log.write('Loss: {:.4f}, Accuracy: {:.4f}\n'.format(
losses.avg, train_acc.acc))
err_log.flush()
if stop_count == args.early_stopping: break
err_log.write('Best test_acc: {:.4f}\n'.format(best_acc))
err_log.close()
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
# create YOLO model
model = resnet_345()
if args.use_cuda:
model = model.cuda()
checkpoint_path = 'models/baseline/src_{}.pth'.format(args.target)
state = torch.load(checkpoint_path)
model.load_state_dict(state['state_dict'])
print('model loaded from %s' % checkpoint_path)
target_dataset = dataset_public(
root=dataroot,
transform=transform,
train=False,
domain='real',
)
target_loader = DataLoader(
dataset=target_dataset,
batch_size=256,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False,
)
predictions = []
labels = []
vectors = []
model.eval()
def train_model(args):
torch.manual_seed(args.manual_seed)
dataroot = join(os.getcwd(), args.data_dir)
transform = transforms.Compose([
# transforms.RandomCrop(args.img_size, padding=None, pad_if_needed=True, fill=0, padding_mode='edge'),
transforms.Resize((args.img_size, args.img_size)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
source_dataset, source_loader = [], []
for source in args.source:
dataset = dataset_public(
root=dataroot,
transform=transform,
train=True,
domain=source,
)
loader = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True,
)
source_dataset.append(dataset)
source_loader.append(loader)
target_dataset = dataset_public(
root=dataroot,
transform=transform,
train=True,
domain=args.target,
)
target_loader = torch.utils.data.DataLoader(
dataset=target_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True,
)
# models
cls = resnet_345(args).cuda()
resume_path = join(os.getcwd(), args.model_dir, args.model_name)
resume_pretrained(resume_path, cls)
if args.fixed_pretrained:
fixed_pretrained(cls)
# print('Training parameters')
params_to_update = []
for name, param in cls.named_parameters():
if param.requires_grad:
params_to_update.append(param)
# print(name)
print('Number of parameters to update: {}'.format(len(params_to_update)))
# optimizer
if args.SGD:
optimizer = optim.SGD(params_to_update, lr=args.lr, momentum=0.9)
elif args.Adam:
optimizer = optim.Adam(params_to_update, lr=args.lr)
best_acc = 0.0
patience = 0
f = open(join('./logs', 'log_{}.txt'.format(args.target)), 'w')
f.write('cls_loss,md_loss,acc\n')
for epoch in range(args.start_epoch, args.max_epochs + 1):
criterion = M3SDA_Loss()
cls.train()
print('\nEpoch = {}'.format(epoch))
cls_losses, md_losses, train_acc = AverageMeter(), AverageMeter(
), AverageMeter()
len_train_loader = min(
[len(loader) for loader in (source_loader + [target_loader])])
train_pbar = tqdm(total=len_train_loader, ncols=100, leave=True)
for i, batch_data in enumerate(
zip(source_loader[0], source_loader[1], source_loader[2],
target_loader)):
data_1, data_2, data_3, data_t = batch_data
input_1, label_1 = data_1
input_2, label_2 = data_2
input_3, label_3 = data_3
input_t, label_t = data_t
input_1, input_2, input_3, input_t = input_1.cuda(), input_2.cuda(
), input_3.cuda(), input_t.cuda()
label_1, label_2, label_3, label_t = label_1.cuda(), label_2.cuda(
), label_3.cuda(), label_t.cuda()
cls.zero_grad()
e1, e2, e3, et, out_1, out_2, out_3 = cls(input_1, input_2,
input_3, input_t)
cls_loss, md_loss = criterion(e1, e2, e3, et, out_1, out_2, out_3,
label_1, label_2, label_3)
loss = cls_loss + md_loss
loss.backward()
optimizer.step()
output = torch.cat((out_1, out_2, out_3), 0)
labels = torch.cat((label_1, label_2, label_3))
_, pred = torch.max(output, 1)
acc = sum(pred == labels).float() / float(pred.shape[0])
cls_losses.update(cls_loss.data.item(), input_1.shape[0])
md_losses.update(md_loss.data.item(), input_1.shape[0])
train_acc.update(acc, 1)
train_pbar.update()
train_pbar.set_postfix({
'cls_loss': '{:.4f}'.format(cls_losses.avg),
'md_loss': '{:.4f}'.format(md_losses.avg),
'acc': '{:.4f}'.format(train_acc.avg)
})
train_pbar.close()
if epoch % 1 == 0:
acc, _ = eval_model(cls, target_loader)
f.write('{:4f},{:4f},{:4f}\n'.format(cls_losses.avg, md_losses.avg,
acc))
f.flush()
if acc > best_acc:
best_acc = acc
save_model(
join('./models',
'm3sda_{}_{}_{}.pth'.format(args.target, epoch, acc)),
cls)
patience = 0
else:
patience += 1
if patience >= args.early_stop:
print('early stopping...')
break