Normalize(mean=(0.3598, 0.3653, 0.3662), std=(0.2573, 0.2663, 0.2756)))
train_dataset = CULane(CULane_path, "train", transform_train)
train_loader = DataLoader(train_dataset, **exp_cfg['dataset'], shuffle=True, collate_fn=train_dataset.collate, num_workers=4)
# ------------ val data ------------
transform_val = Compose(Resize((800, 288)), ToTensor(),
Normalize(mean=(0.3598, 0.3653, 0.3662), std=(0.2573, 0.2663, 0.2756)))
val_dataset = CULane(CULane_path, "val", transform_val)
val_loader = DataLoader(val_dataset, batch_size=8, collate_fn=train_dataset.collate, num_workers=4)
# ------------ preparation ------------
net = SCNN(pretrained=False).to(device)
net = torch.nn.DataParallel(net)
optimizer = optim.SGD(net.parameters(), **exp_cfg['optim'])
lr_scheduler = PolyLR(optimizer, 0.9, exp_cfg['MAX_ITER'])
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)
exist = sample['exist'].to(device)
train_loader = DataLoader(train_dataset, batch_size=exp_cfg['dataset']['batch_size'], shuffle=True, collate_fn=train_dataset.collate, num_workers=8)
# ------------ val data ------------
transform_val_img = Resize(resize_shape)
transform_val_x = Compose(ToTensor(), Normalize(mean=mean, std=std))
transform_val = Compose(transform_val_img, transform_val_x)
val_dataset = Dataset_Type(Dataset_Path[dataset_name], "val", transform_val)
val_loader = DataLoader(val_dataset, batch_size=8, collate_fn=val_dataset.collate, num_workers=4)
# ------------ preparation ------------
net = SCNN(resize_shape, pretrained=True)
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)
exist = sample['exist'].to(device)
model = deeplabv3plus_resnet50(num_classes=num_class,
output_stride=par.out_stride).cuda()
set_bn_momentum(model.backbone, momentum=0.01)
#=========================================================== Define Optimizer ================================================
import torch.optim as optim
train_params = [{
'params': model.backbone.parameters(),
'lr': par.lr * 0.1
}, {
'params': model.classifier.parameters(),
'lr': par.lr
}]
optimizer = optim.SGD(train_params, lr=par.lr, momentum=0.9, weight_decay=1e-4)
scheduler = PolyLR(optimizer, 10000, power=0.9)
# Define Criterion
# whether to use class balanced weights
weight = None
criterion = SegmentationLosses(weight=weight,
cuda=par.cuda).build_loss(mode=par.loss_type)
# Define Evaluator
evaluator = Evaluator(num_class)
#===================================================== Resuming checkpoint ====================================================
best_pred = 0.0
if par.resume is not None:
if not os.path.isfile(par.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(par.resume))
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
for batch_idx, sample in enumerate(train_bdd100k_dataset_loader):