def CreateModel(args):
if args.model == 'DeepLab':
model = Deeplab(num_classes=args.num_classes, init_weights=args.init_weights, restore_from=args.restore_from, phase=args.set)
if args.set == 'train':
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay)
optimizer.zero_grad()
return model, optimizer
else:
return model
if args.model == 'VGG':
model = VGG16_FCN8s(num_classes=19, init_weights=args.init_weights, restore_from=args.restore_from)
if args.set == 'train':
optimizer = optim.Adam(
[
{'params': model.get_parameters(bias=False)},
{'params': model.get_parameters(bias=True),
'lr': args.learning_rate * 2}
],
lr=args.learning_rate,
betas=(0.9, 0.99))
optimizer.zero_grad()
return model, optimizer
else:
return model
def CreateSSLModel(args):
if args.model == 'DeepLab':
model = Deeplab(num_classes=args.num_classes, init_weights=args.init_weights, restore_from=args.restore_from, phase=args.set)
elif args.model == 'VGG':
model = VGG16_FCN8s(num_classes=19, init_weights=args.init_weights, restore_from=args.restore_from)
else:
raise ValueError('The model mush be either deeplab-101 or vgg16-fcn')
return model
def CreateModel(args):
"""
Returns:
model: based on the command line arguments
"""
if args.model == 'DeepLab':
phase = 'test'
if args.set == 'train' or args.set == 'trainval':
phase = 'train'
model = Deeplab(num_classes=args.num_classes,
init_weights=args.init_weights,
restore_from=args.restore_from,
phase=phase)
if args.set == 'train' or args.set == 'trainval':
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer.zero_grad()
return model, optimizer
else:
return model
if args.model == 'VGG':
model = VGG16_FCN8s(num_classes=19,
init_weights=args.init_weights,
restore_from=args.restore_from)
if args.set == 'train' or args.set == 'trainval':
optimizer = optim.Adam([{
'params': model.get_parameters(bias=False)
}, {
'params': model.get_parameters(bias=True),
'lr': args.learning_rate * 2
}],
lr=args.learning_rate,
betas=(0.9, 0.99))
optimizer.zero_grad()
return model, optimizer
else:
return model
if args.model == 'CLS':
model = CLSNet(restore_from=args.restore_from)
if args.set == 'train' or args.set == 'trainval':
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer.zero_grad()
return model, optimizer
else:
return model
def CreateSSLModel(args):
"""
Raises:
ValueError: args.model should be either deeplab-101 or vgg16-fcn
Returns:
model: based on command line arguments
"""
if args.model == 'DeepLab':
model = Deeplab(num_classes=args.num_classes,
init_weights=args.init_weights,
restore_from=args.restore_from,
phase=args.set)
elif args.model == 'VGG':
model = VGG16_FCN8s(num_classes=19,
init_weights=args.init_weights,
restore_from=args.restore_from)
else:
raise ValueError('The model mush be either deeplab-101 or vgg16-fcn')
return model
def CreateModel(args):
if args.model == 'enet':
model = ENet(num_classes=args.num_classes).cuda()
optimizer = optim.Adam(
model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)
elif args.model == 'deeplab':
model = Deeplab(num_classes=args.num_classes).cuda()
optimizer = None
elif args.model == 'frrnet':
model = FRRNet(out_channels=args.num_classes).cuda()
optimizer = optim.Adam(
model.parameters(), lr=args.learning_rate)
"""
elif args.model == 'fcn8s':
model = FCN8s(pretrained_net=None, n_class=args.num_classes).cuda()
optimizer = optim.SGD(
model.parameters(), lr=args.learning_rate)
"""
else:
import torch
from torchsummaryX import summary
from model.utils.block import Block
from model.encoder import _Entry, _Middle, _Exit, Encoder
from model.deeplab import Deeplab
from model.decoder import Decoder
if __name__ == "__main__":
# model = Block(728, 728)
# model = _Entry()
# model = _Middle()
# model = _Exit()
# model = Encoder()
# model = Decoder(128,20)
model = Deeplab()
model.to('cuda')
model.eval()
x = torch.Tensor(1, 3, 512, 512).cuda()
output = model(x)
print(output.shape)
# print(model)