# network
kwargs = {}
module_list = []
if args.use_fpn:
module_list.append('fpn')
if args.norm_layer is not None:
module_list.append(args.norm_layer)
net_name = '_'.join(('faster_rcnn', *module_list, args.network, args.dataset))
args.save_prefix += net_name
if args.pretrained.lower() in ['true', '1', 'yes', 't']:
net = model_zoo.get_model(net_name, pretrained=True, root=args.root, **kwargs)
else:
net = model_zoo.get_model(net_name, pretrained=False, **kwargs)
net.load_state_dict(args.pretrained.strip())
net.to(device)
# testing data
val_dataset, val_metric = get_dataset(net.short, net.max_size, args.dataset)
val_data = get_dataloader(val_dataset, args.batch_size, args.num_workers,
distributed, args.dataset == 'coco')
classes = val_dataset.classes # class names
# testing
val_metric = validate(net, val_data, device, val_metric, args.dataset == 'coco')
synchronize()
names, values = accumulate_metric(val_metric)
if is_main_process():
for k, v in zip(names, values):
print(k, v)
def train(self):
train_dataset = CIFAR10(root=os.path.join(self.cfg.data_root,
'cifar10'),
train=True,
transform=self.transform_train,
download=True)
train_sampler = make_data_sampler(train_dataset, True,
self.distributed)
train_batch_sampler = data.sampler.BatchSampler(
train_sampler, self.cfg.batch_size, True)
train_data = data.DataLoader(train_dataset,
num_workers=self.cfg.num_workers,
batch_sampler=train_batch_sampler)
val_dataset = CIFAR10(root=os.path.join(self.cfg.data_root, 'cifar10'),
train=False,
transform=self.transform_test)
val_sampler = make_data_sampler(val_dataset, False, self.distributed)
val_batch_sampler = data.sampler.BatchSampler(val_sampler,
self.cfg.batch_size,
False)
val_data = data.DataLoader(val_dataset,
num_workers=self.cfg.num_workers,
batch_sampler=val_batch_sampler)
optimizer = optim.SGD(self.net.parameters(),
nesterov=True,
lr=self.cfg.lr,
weight_decay=self.cfg.wd,
momentum=self.cfg.momentum)
metric = Accuracy()
train_metric = Accuracy()
loss_fn = nn.CrossEntropyLoss()
if is_main_process():
train_history = TrainingHistory(
['training-error', 'validation-error'])
iteration = 0
lr_decay_count = 0
best_val_score = 0
for epoch in range(self.cfg.num_epochs):
tic = time.time()
train_metric.reset()
metric.reset()
train_loss = 0
num_batch = len(train_data)
if epoch == self.lr_decay_epoch[lr_decay_count]:
set_learning_rate(
optimizer,
get_learning_rate(optimizer) * self.cfg.lr_decay)
lr_decay_count += 1
for i, batch in enumerate(train_data):
image = batch[0].to(self.device)
label = batch[1].to(self.device)
output = self.net(image)
loss = loss_fn(output, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
train_metric.update(label, output)
iteration += 1
metric = self.validate(val_data, metric)
synchronize()
train_loss /= num_batch
train_loss = reduce_list(all_gather(train_loss))
name, acc = accumulate_metric(train_metric)
name, val_acc = accumulate_metric(metric)
if is_main_process():
train_history.update([1 - acc, 1 - val_acc])
train_history.plot(save_path='%s/%s_history.png' %
(self.plot_path, self.cfg.model))
if val_acc > best_val_score:
best_val_score = val_acc
torch.save(
self.net.state_dict(), '%s/%.4f-cifar-%s-%d-best.pth' %
(self.save_dir, best_val_score, self.cfg.model, epoch))
logging.info(
'[Epoch %d] train=%f val=%f loss=%f time: %f' %
(epoch, acc, val_acc, train_loss, time.time() - tic))
if self.save_period and self.cfg.save_dir and (
epoch + 1) % self.save_period == 0:
torch.save(
self.net.module.state_dict() if self.distributed else
self.net.state_dict(), '%s/cifar10-%s-%d.pth' %
(self.save_dir, self.cfg.model, epoch))
if is_main_process() and self.save_period and self.save_dir:
torch.save(
self.net.module.state_dict() if self.distributed else
self.net.state_dict(), '%s/cifar10-%s-%d.pth' %
(self.save_dir, self.cfg.model, self.cfg.num_epochs - 1))