def load_pretrained_weights(args):
model = Network(args.classes)
model_path = args.model_path.replace('RealWorld', args.domain)
pre = torch.load(args.model_path)
new_pre = OrderedDict()
for p in pre:
if ('classifier' in p):
# print('----', p)
continue
else:
new_pre[p] = pre[p]
model.load_state_dict(new_pre, strict=False)
for name, p in model.state_dict().items():
if ('classifier' in name):
continue
else:
p.requires_grad = False
torch.nn.init.xavier_uniform_(model.classifier.fc8.weight)
del new_pre
return model
def main():
save_dir = join(save_root, args.save_dir)
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
if args.gpu is not None:
print(('Using GPU %d' % args.gpu))
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
else:
print('CPU mode')
print('Process number: %d' % (os.getpid()))
## DataLoader initialize ILSVRC2012_train_processed
trainpath = join(args.data_path, args.domain)
train_data = DataLoader(trainpath,
split='train',
classes=args.classes,
ssl=True)
train_loader = torch.utils.data.DataLoader(dataset=train_data,
batch_size=args.batch,
shuffle=True,
num_workers=args.cores)
valpath = join(args.data_path, args.domain)
val_data = DataLoader(valpath, split='validation', classes=args.classes)
val_loader = torch.utils.data.DataLoader(dataset=val_data,
batch_size=args.batch,
shuffle=True,
num_workers=args.cores)
iter_per_epoch = train_data.N / args.batch
print('Images: train %d, validation %d' % (train_data.N, val_data.N))
# Network initialize
net = Network(args.classes)
if args.gpu is not None:
net.cuda()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=5e-4)
logger = Logger(join(save_root, args.save_dir, 'train'))
logger_test = Logger(join(save_root, args.save_dir, 'test'))
############## TESTING ###############
if args.evaluate:
test(net, criterion, None, val_loader, 0)
return
############## TRAINING ###############
print(('Start training: lr %f, batch size %d, classes %d' %
(args.lr, args.batch, args.classes)))
print(('Checkpoint: ' + args.save_dir))
# Train the Model
batch_time, net_time = [], []
steps = args.iter_start
best_acc = -1
for epoch in range(args.epochs):
if epoch % 10 == 0 and epoch > 0:
net, acc = test(net, criterion, logger_test, val_loader, steps)
if (best_acc < acc):
net.save(join(save_dir, 'best_model.pth'))
lr = adjust_learning_rate(optimizer,
epoch,
init_lr=args.lr,
step=20,
decay=0.1)
end = time()
for i, (images, labels, original) in enumerate(train_loader):
batch_time.append(time() - end)
if len(batch_time) > 100:
del batch_time[0]
images = Variable(images)
labels = Variable(labels)
if args.gpu is not None:
images = images.cuda()
labels = labels.cuda()
# Forward + Backward + Optimize
optimizer.zero_grad()
t = time()
outputs = net(images)
net_time.append(time() - t)
if len(net_time) > 100:
del net_time[0]
prec1, prec5 = compute_accuracy(outputs.cpu().data,
labels.cpu().data,
topk=(1, 5))
# acc = prec1[0]
acc = prec1
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
loss = float(loss.cpu().data.numpy())
steps += 1
if steps % 1000 == 0:
filename = join(save_dir, ('%06d.pth.tar' % (steps)))
net.save(filename)
print('Saved: ' + args.save_dir)
end = time()
###########################################################################################################
# classifier finetune #
###########################################################################################################
finetune_model = Network(65)
pretrained_dict = {
k: v
for k, v in net.state_dict().items()
if k in finetune_model.state_dict()
}
finetune_model.state_dict().update(pretrained_dict)
