def train(args):
experiment_dir = mkdir('./experiment/')
checkpoints_dir = mkdir('./experiment/semseg/%s/'%(args.model_name))
train_data, train_label, test_data, test_label = _load()
dataset = S3DISDataLoader(train_data, train_label, data_augmentation = args.augment)
dataloader = DataLoader(dataset, batch_size=args.batch_size,shuffle=True, num_workers=args.workers)
test_dataset = S3DISDataLoader(test_data, test_label)
testdataloader = DataLoader(test_dataset, batch_size=args.batch_size,shuffle=True, num_workers=args.workers)
num_classes = 13
if args.model_name == 'pointnet':
model = PointNetSeg(num_classes, feature_transform=True, input_dims = 9)
else:
model = PointNet2SemSeg(num_classes, feature_dims = 6)
torch.backends.cudnn.benchmark = True
model = torch.nn.DataParallel(model).cuda()
log.debug('Using gpu:',args.gpu)
if args.pretrain is not None:
log.debug('Use pretrain model...')
model.load_state_dict(torch.load(args.pretrain))
init_epoch = int(args.pretrain[:-4].split('-')[-1])
log.debug('start epoch from', init_epoch)
else:
log.debug('Training from scratch')
init_epoch = 0
if args.optimizer == 'SGD':
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
elif args.optimizer == 'Adam':
optimizer = torch.optim.Adam(
model.parameters(),
lr=args.learning_rate,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=args.decay_rate)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
LEARNING_RATE_CLIP = 1e-5
history = {'loss':[]}
best_acc = 0
best_meaniou = 0
for epoch in range(init_epoch,args.epoch):
scheduler.step()
lr = max(optimizer.param_groups[0]['lr'],LEARNING_RATE_CLIP)
log.info(job='semseg',model=args.model_name,gpu=args.gpu,epoch='%d/%s' % (epoch, args.epoch),lr=lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
for points, target in tqdm(dataloader, total=len(dataloader), smoothing=0.9, dynamic_ncols=True):
points, target = points.float(), target.long()
points = points.transpose(2, 1)
points, target = points.cuda(), target.cuda()
optimizer.zero_grad()
model = model.train()
if args.model_name == 'pointnet':
pred, trans_feat = model(points)
else:
pred = model(points)
pred = pred.contiguous().view(-1, num_classes)
target = target.view(-1, 1)[:, 0]
loss = F.nll_loss(pred, target)
if args.model_name == 'pointnet':
loss += feature_transform_reguliarzer(trans_feat) * 0.001
history['loss'].append(loss.cpu().data.numpy())
loss.backward()
optimizer.step()
log.debug('clear cuda cache')
torch.cuda.empty_cache()
test_metrics, cat_mean_iou = test_semseg(
model.eval(),
testdataloader,
label_id_to_name,
args.model_name,
num_classes,
)
mean_iou = np.mean(cat_mean_iou)
save_model = False
if test_metrics['accuracy'] > best_acc:
best_acc = test_metrics['accuracy']
if mean_iou > best_meaniou:
best_meaniou = mean_iou
save_model = True
if save_model:
fn_pth = 'semseg-%s-%.5f-%04d.pth' % (args.model_name, best_meaniou, epoch)
log.info('Save model...',fn = fn_pth)
torch.save(model.state_dict(), os.path.join(checkpoints_dir, fn_pth))
log.warn(cat_mean_iou)
else:
log.info('No need to save model')
log.warn(cat_mean_iou)
log.warn('Curr',accuracy=test_metrics['accuracy'], meanIOU=mean_iou)
log.warn('Best',accuracy=best_acc, meanIOU=best_meaniou)
def train(args):
experiment_dir = mkdir('experiment/')
checkpoints_dir = mkdir('experiment/%s/' % (args.model_name))
kitti_utils = Semantic_KITTI_Utils(KITTI_ROOT, subset=args.subset)
class_names = kitti_utils.class_names
num_classes = kitti_utils.num_classes
if args.subset == 'inview':
train_npts = 8000
test_npts = 24000
if args.subset == 'all':
train_npts = 50000
test_npts = 100000
log.info(subset=args.subset, train_npts=train_npts, test_npts=test_npts)
dataset = SemKITTI_Loader(KITTI_ROOT,
train_npts,
train=True,
subset=args.subset)
dataloader = DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
test_dataset = SemKITTI_Loader(KITTI_ROOT,
test_npts,
train=False,
subset=args.subset)
testdataloader = DataLoader(test_dataset,
batch_size=int(args.batch_size / 2),
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.model_name == 'pointnet':
model = PointNetSeg(num_classes, input_dims=4, feature_transform=True)
else:
model = PointNet2SemSeg(num_classes, feature_dims=1)
if args.optimizer == 'SGD':
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
elif args.optimizer == 'Adam':
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=1e-4)
torch.backends.cudnn.benchmark = True
model = torch.nn.DataParallel(model)
model.cuda()
log.info('Using gpu:', args.gpu)
if args.pretrain is not None:
log.info('Use pretrain model...')
model.load_state_dict(torch.load(args.pretrain))
init_epoch = int(args.pretrain[:-4].split('-')[-1])
log.info('Restart training', epoch=init_epoch)
else:
log.msg('Training from scratch')
init_epoch = 0
best_acc = 0
best_miou = 0
for epoch in range(init_epoch, args.epoch):
model.train()
lr = calc_decay(args.learning_rate, epoch)
log.info(subset=args.subset,
model=args.model_name,
gpu=args.gpu,
epoch=epoch,
lr=lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
for points, target in tqdm(dataloader,
total=len(dataloader),
smoothing=0.9,
dynamic_ncols=True):
points = points.float().transpose(2, 1).cuda()
target = target.long().cuda()
if args.model_name == 'pointnet':
logits, trans_feat = model(points)
else:
logits = model(points)
#logits = logits.contiguous().view(-1, num_classes)
#target = target.view(-1, 1)[:, 0]
#loss = F.nll_loss(logits, target)
logits = logits.transpose(2, 1)
loss = nn.CrossEntropyLoss()(logits, target)
if args.model_name == 'pointnet':
loss += feature_transform_reguliarzer(trans_feat) * 0.001
optimizer.zero_grad()
loss.backward()
optimizer.step()
torch.cuda.empty_cache()
acc, miou = test_kitti_semseg(model.eval(), testdataloader,
args.model_name, num_classes,
class_names)
save_model = False
if acc > best_acc:
best_acc = acc
if miou > best_miou:
best_miou = miou
save_model = True
if save_model:
fn_pth = '%s-%s-%.5f-%04d.pth' % (args.model_name, args.subset,
best_miou, epoch)
log.info('Save model...', fn=fn_pth)
torch.save(model.state_dict(),
os.path.join(checkpoints_dir, fn_pth))
else:
log.info('No need to save model')
log.warn('Curr', accuracy=acc, mIOU=miou)
log.warn('Best', accuracy=best_acc, mIOU=best_miou)