num_workers=4)
# ------------ preparation ------------
if exp_cfg['model'] == "scnn":
net = SCNN(resize_shape, pretrained=True)
elif exp_cfg['model'] == "enet_sad":
net = ENet_SAD(resize_shape, sad=True, dataset=dataset_name)
else:
raise Exception(
"Model not match. 'model' in 'cfg.json' should be 'scnn' or 'enet_sad'."
)
net = net.to(device)
net = torch.nn.DataParallel(net)
optimizer = optim.SGD(net.parameters(), **exp_cfg['optim'])
lr_scheduler = PolyLR(optimizer, 0.9, **exp_cfg['lr_scheduler'])
best_val_loss = 1e6
def train(epoch):
print("Train Epoch: {}".format(epoch))
net.train()
train_loss = 0
train_loss_seg = 0
train_loss_exist = 0
progressbar = tqdm(range(len(train_loader)))
for batch_idx, sample in enumerate(train_loader):
img = sample['img'].to(device)
segLabel = sample['segLabel'].to(device)
collate_fn=dataset.collate,
num_workers=4)
# ------------ preparation ------------
seg_classes = 5
if hasattr(dataset, 'seg_classes'):
seg_classes = getattr(dataset, 'seg_classes')
net = SCNN(resize_shape,
pretrained=True,
seg_classes=seg_classes,
weights=Dataset_Type.get_weights(
exp_cfg['dataset']['other']['seg_mode']))
net = net.to(device)
#net = torch.nn.DataParallel(net)
optimizer = optim.Adam(net.parameters(), **exp_cfg['optim'])
lr_scheduler = PolyLR(optimizer, 0.9, **exp_cfg['lr_scheduler'])
best_val_loss = 1e6
def train(epoch):
print("Train Epoch: {}".format(epoch))
net.train()
train_loss = 0
train_loss_seg = 0
train_loss_exist = 0
progressbar = tqdm(range(len(train_loader)))
for batch_idx, sample in enumerate(train_loader):
img = sample['img'].to(device)
segLabel = sample['segLabel'].to(device)
net = SCNN(resize_shape, pretrained=True)
lr_scaler = 1
if torch.cuda.is_available():
net.cuda()
# Horovod: Scale learning rate as per number of devices
if hvd.nccl_built():
lr_scaler = hvd.local_size()
net = torch.nn.DataParallel(net)
lr = exp_cfg['optim']['lr']
momentum = exp_cfg['optim']['momentum']
weight_decay = exp_cfg['optim']['weight_decay']
nesterov = exp_cfg['optim']['nesterov']
# Horovod: scale learning rate by lr_scaler.
optimizer = optim.SGD(net.parameters(),
lr=lr * lr_scaler,
momentum=momentum,
weight_decay=weight_decay,
nesterov=nesterov)
# Horovod: broadcast parameters & optimizer state.
hvd.broadcast_parameters(net.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
# Horovod: (optional) compression algorithm.
#compression = hvd.Compression.fp16
# Horovod: wrap optimizer with DistributedOptimizer.
gradient_predivide_factor = 1.0
optimizer = hvd.DistributedOptimizer(
val_bdd100k = BDDDataset(image_path=bdd100k_val_img_path,
drivable_path=bdd100k_val_dl_path)
val_bdd100k_dataset_loader = DataLoader(dataset=val_bdd100k, **params)
#Declare model & optimizers
net = SCNN(resize_shape, pretrained=True)
net = net.to(device)
#torch.distributed.init_process_group("gloo", rank=rank, world_size=world_size)
#torch.cuda.set_device()
#net = torch.nn.parallel.DistributedDataParallel(net)
#net = torch.nn.DataParallel(net)
#
#net.eval()
tensorboard = SummaryWriter(exp_dir + "tb/")
optimizer = optim.SGD(net.parameters(), **optim_set)
lr_scheduler = PolyLR(optimizer, 0.9, **lr_set)
best_val_loss = 1000
#@profile
def train(epoch):
print("Train Epoch: {}".format(epoch))
net.train()
train_loss = 0
train_loss_seg = 0
##train_loss_exist = 0
epoch_accuracy = 0
progressbar = tqdm(range(len(train_bdd100k_dataset_loader)))
#Training loop
