def main(args):
# Network Builders
builder = ModelBuilder()
net_encoder = builder.build_encoder(weights=args.weights_encoder)
net_decoder_1 = builder.build_decoder(weights=args.weights_decoder_1)
net_decoder_2 = builder.build_decoder(arch='c1',weights=args.weights_decoder_2)
if args.weighted_class:
crit = nn.NLLLoss(ignore_index=-1, weight=args.class_weight)
else:
crit = nn.NLLLoss(ignore_index=-1)
# Dataset and Loader
dataset_train = GTA(root=args.root_gta, cropSize=args.imgSize, is_train=1)
dataset_val = CityScapes('val', root=args.root_cityscapes, cropSize=args.imgSize,
max_sample=args.num_val, is_train=0)
dataset_val_2 = BDD('val', root=args.root_bdd, cropSize=args.imgSize,
max_sample=args.num_val, is_train=0)
loader_val = torch.utils.data.DataLoader(
dataset_val,
batch_size=args.batch_size_eval,
shuffle=False,
num_workers=int(args.workers),
drop_last=True)
loader_val_2 = torch.utils.data.DataLoader(
dataset_val_2,
batch_size=args.batch_size_eval,
shuffle=False,
num_workers=int(args.workers),
drop_last=True)
args.epoch_iters = int(len(dataset_train) / args.batch_size)
print('1 Epoch = {} iters'.format(args.epoch_iters))
# load nets into gpu
if args.num_gpus > 1:
net_encoder = nn.DataParallel(net_encoder,
device_ids=range(args.num_gpus))
net_decoder_1 = nn.DataParallel(net_decoder_1,
device_ids=range(args.num_gpus))
net_decoder_2 = nn.DataParallel(net_decoder_2,
device_ids=range(args.num_gpus))
nets = (net_encoder, net_decoder_1, net_decoder_2, crit)
for net in nets:
net.cuda()
history = {split: {'epoch': [], 'err': [], 'acc': [], 'mIoU': []}
for split in ('train', 'val', 'val_2')}
# eval
evaluate(nets, loader_val, loader_val_2, history, 0, args)
print('Evaluation Done!')
def main(args):
# Network Builders
builder = ModelBuilder()
net_encoder = builder.build_encoder(arch=args.arch_encoder,
fc_dim=args.fc_dim,
weights=args.weights_encoder)
net_decoder_1 = builder.build_decoder(arch=args.arch_decoder,
fc_dim=args.fc_dim,
num_class=args.num_class,
weights=args.weights_decoder_1)
net_decoder_2 = builder.build_decoder(arch=args.arch_decoder,
fc_dim=args.fc_dim,
num_class=args.num_class,
weights=args.weights_decoder_2)
net_syn = builder.build_syn(weights=args.weights_syn)
crit = nn.NLLLoss2d(ignore_index=-1)
# Dataset and Loader
# dataset_val = CityScapes('val', root=args.root_cityscapes, max_sample=args.num_val, is_train=0)
dataset_val = BDD('val',
root=args.root_unlabeled,
cropSize=args.imgSize,
max_sample=args.num_val,
is_train=0)
loader_val = torch.utils.data.DataLoader(dataset_val,
batch_size=args.batch_size,
shuffle=False,
num_workers=2,
drop_last=True)
nets = (net_encoder, net_decoder_1, net_decoder_2, net_syn, crit)
for net in nets:
net.cuda()
# Main loop
evaluate(nets, loader_val, args)
print('Evaluation Done!')
def main(args):
# Network Builders
builder = ModelBuilder()
net_encoder = builder.build_encoder(weights=args.weights_encoder)
net_decoder_1 = builder.build_decoder(weights=args.weights_decoder_1)
net_decoder_2 = builder.build_decoder(arch='c1',
weights=args.weights_decoder_2)
if args.weighted_class:
crit = nn.NLLLoss(ignore_index=-1, weight=args.class_weight)
else:
crit = nn.NLLLoss(ignore_index=-1)
# Dataset and Loader
dataset_train = GTA(root=args.root_gta,
cropSize=args.imgSize,
is_train=1,
random_mask=args.mask)
dataset_val = CityScapes('val',
root=args.root_cityscapes,
cropSize=args.imgSize,
max_sample=args.num_val,
is_train=0)
dataset_val_2 = BDD('val',
root=args.root_bdd,
cropSize=args.imgSize,
max_sample=args.num_val,
is_train=0)
loader_train = torch.utils.data.DataLoader(dataset_train,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers),
drop_last=True)
loader_val = torch.utils.data.DataLoader(dataset_val,
batch_size=args.batch_size_eval,
shuffle=False,
num_workers=int(args.workers),
drop_last=True)
loader_val_2 = torch.utils.data.DataLoader(dataset_val_2,
batch_size=args.batch_size_eval,
shuffle=False,
num_workers=int(args.workers),
drop_last=True)
args.epoch_iters = int(len(dataset_train) / args.batch_size)
print('1 Epoch = {} iters'.format(args.epoch_iters))
# load nets into gpu
if args.num_gpus > 1:
net_encoder = nn.DataParallel(net_encoder,
device_ids=range(args.num_gpus))
net_decoder_1 = nn.DataParallel(net_decoder_1,
device_ids=range(args.num_gpus))
net_decoder_2 = nn.DataParallel(net_decoder_2,
device_ids=range(args.num_gpus))
nets = (net_encoder, net_decoder_1, net_decoder_2, crit)
for net in nets:
net.cuda()
# Set up optimizers
optimizers = create_optimizers(nets, args)
# Main loop
history = {
split: {
'epoch': [],
'err': [],
'acc': [],
'mIoU': []
}
for split in ('train', 'val', 'val_2')
}
# optional initial eval
# evaluate(nets, loader_val, loader_val_2, history, 0, args)
for epoch in range(1, args.num_epoch + 1):
train(nets, loader_train, optimizers, history, epoch, args)
# Evaluation
if epoch % args.eval_epoch == 0:
evaluate(nets, loader_val, loader_val_2, history, epoch, args)
# checkpointing
checkpoint(nets, history, args)
# adjust learning rate
adjust_learning_rate(optimizers, epoch, args)
print('Training Done!')
def main(args):
# Network Builders
builder = ModelBuilder()
net_encoder = builder.build_encoder(arch=args.arch_encoder,
fc_dim=args.fc_dim,
weights=args.weights_encoder)
net_decoder = builder.build_decoder(arch=args.arch_decoder,
fc_dim=args.fc_dim,
num_class=args.num_class,
weights=args.weights_decoder)
crit = nn.NLLLoss2d(ignore_index=-1)
# Dataset and Loader
#dataset_train = GTA(cropSize=args.imgSize, root=args.root_labeled)
#dataset_train = CityScapes('train', root=args.root_unlabeled, cropSize=args.imgSize, is_train=1)
dataset_train = BDD('train',
root=args.root_labeled,
cropSize=args.imgSize,
is_train=1)
#dataset_val = CityScapes('val', root=args.root_unlabeled, cropSize=args.imgSize, max_sample=args.num_val, is_train=0)
dataset_val = BDD('val',
root=args.root_unlabeled,
cropSize=args.imgSize,
max_sample=args.num_val,
is_train=0)
loader_train = torch.utils.data.DataLoader(dataset_train,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers),
drop_last=True)
loader_val = torch.utils.data.DataLoader(dataset_val,
batch_size=args.batch_size,
shuffle=False,
num_workers=2,
drop_last=True)
args.epoch_iters = int(len(dataset_train) / args.batch_size)
print('1 Epoch = {} iters'.format(args.epoch_iters))
# load nets into gpu
if args.num_gpus > 1:
net_encoder = nn.DataParallel(net_encoder,
device_ids=range(args.num_gpus))
net_decoder = nn.DataParallel(net_decoder,
device_ids=range(args.num_gpus))
nets = (net_encoder, net_decoder, crit)
for net in nets:
net.cuda()
# Set up optimizers
optimizers = create_optimizers(nets, args)
# Main loop
history = {
split: {
'epoch': [],
'err': [],
'acc': []
}
for split in ('train', 'val')
}
# optional initial eval
evaluate(nets, loader_val, history, 0, args)
for epoch in range(1, args.num_epoch + 1):
train(nets, loader_train, optimizers, history, epoch, args)
# checkpointing
checkpoint(nets, history, args)
# adjust learning rate
adjust_learning_rate(optimizers, epoch, args)
# Evaluation and visualization
if epoch % args.eval_epoch == 0:
evaluate(nets, loader_val, history, epoch, args)
print('Training Done!')