def create_MyDNN(opt):
# Initialize the network
MyDNN = network.MyDNN(opt)
# Init the network
network.weights_init(MyDNN, init_type = opt.init_type, init_gain = opt.init_gain)
print('MyDNN is created!')
return MyDNN
def create_discriminator(opt):
# Initialize the networks
discriminator = network.PatchDiscriminator(opt)
print('Discriminator is created!')
network.weights_init(discriminator, init_type = opt.init_type, init_gain = opt.init_gain)
print('Initialize discriminator with %s type' % opt.init_type)
return discriminator
def create_generator(opt):
# Initialize the networks
generator = network.GatedGenerator(opt)
print('Generator is created!')
network.weights_init(generator, init_type = opt.init_type, init_gain = opt.init_gain)
print('Initialize generator with %s type' % opt.init_type)
return generator
def create_discriminator(opt):
# Initialize the network
discriminator = network.AdversarialDiscriminator(opt)
# Init the network
network.weights_init(discriminator, init_type = opt.init_type, init_gain = opt.init_gain)
print('Discriminators is created!')
return discriminator
def create_CBD(opt):
# Initialize the network
CBD = network.CBD(opt)
# Init the network
network.weights_init(CBD, init_type = opt.init_type, init_gain = opt.init_gain)
print('CBD is created!')
return CBD
def create_DnCNN(opt):
# Initialize the network
DnCNN = network.DnCNN(opt)
# Init the network
network.weights_init(DnCNN, init_type = opt.init_type, init_gain = opt.init_gain)
print('DnCNN is created!')
return DnCNN
def create_UresNet(opt):
# Initialize the network
UresNet = network.UresNet(opt)
# Init the network
network.weights_init(UresNet, init_type = opt.init_type, init_gain = opt.init_gain)
print('UresNet is created!')
return UresNet
def create_CBD_generator(opt):
# Initialize the network
CBD_generator = network.CBD_Generator(opt)
# Init the network
network.weights_init(CBD_generator, init_type = opt.init_type, init_gain = opt.init_gain)
print('CBD_generator is created!')
return CBD_generator
def create_FaceDNN(opt):
# Initialize the network
FaceDNN = network.FaceDNN(opt)
# Init the network
network.weights_init(FaceDNN, init_type = opt.init_type, init_gain = opt.init_gain)
print('FaceDNN is created!')
return FaceDNN
def create_discriminator(opt):
# Initialize the networks
discriminator = network.PatchDiscriminator70(opt)
# Init the networks
network.weights_init(discriminator,
init_type=opt.init_type,
init_gain=opt.init_gain)
return discriminator
def create_discriminator(opt):
# Initialize the network
discriminator_a = network.PatchDiscriminator70(opt)
discriminator_b = network.PatchDiscriminator70(opt)
# Init the network
network.weights_init(discriminator_a, init_type = opt.init_type, init_gain = opt.init_gain)
network.weights_init(discriminator_b, init_type = opt.init_type, init_gain = opt.init_gain)
print('Discriminators is created!')
return discriminator_a, discriminator_b
def create_encoder(opt):
# Initialize the network
encoder = network.Encoder(opt)
# Init the network
network.weights_init(encoder,
init_type=opt.init_type,
init_gain=opt.init_gain)
print('Encoder is created!')
return encoder
def create_ESRGAN_discriminator(opt):
# Initialize the network
discriminator = network.SR_VGG128_Discriminator(in_nc=3, base_nf=64)
# Init the network
network.weights_init(discriminator,
init_type=opt.init_type,
init_gain=opt.init_gain)
print('ESRGAN discriminator is created!')
return discriminator
def create_generator(opt):
# Initialize the network
generator = network.Generator(opt)
# Init or Load value for the network
network.weights_init(generator,
init_type=opt.init_type,
init_gain=opt.init_gain)
print('Generator is created!')
if opt.finetune_path != "":
pretrained_net = torch.load(opt.finetune_path)
generator = load_dict(generator, pretrained_net)
print('Generator is loaded!')
return generator
def create_generator(opt):
# Initialize the networks
generator = network.GatedGenerator(opt)
print('Generator is created!')
if opt.load_name:
generator = load_dict(generator, opt.load_name)
else:
# Init the networks
network.weights_init(generator,
init_type=opt.init_type,
init_gain=opt.init_gain)
print('Initialize generator with %s type' % opt.init_type)
return generator
def create_ESRGAN_generator(opt):
# Initialize the network
esrgan = network.RRDBNet(3, 3, 64, 23, gc=32)
# Init the network
if opt.ESRGAN_name:
pretrained_net = torch.load(opt.ESRGAN_name)
load_dict(esrgan, pretrained_net)
else:
network.weights_init(esrgan,
init_type=opt.init_type,
init_gain=opt.init_gain)
print('ESRGAN generator is created!')
return esrgan
def create_generator(opt):
generator = network.Generator(opt)
if opt.load_pre_train:
pretrained_net = torch.load(opt.load_name + '.pth')
load_dict(generator, pretrained_net)
print('Generator is loaded!')
else:
# Init the network
network.weights_init(generator,
init_type=opt.init_type,
init_gain=opt.init_gain)
print('Generator is created!')
return generator
def create_generator(opt):
# Initialize the network
generator = network.Generator(opt)
if opt.pre_train:
# Init the network
network.weights_init(generator,
init_type=opt.init_type,
init_gain=opt.init_gain)
print('Generator is created!')
else:
# Load a pre-trained network
pretrained_net = torch.load(opt.load_name)
load_dict(generator, pretrained_net)
print('Generator is loaded!')
return generator
def create_generator(opt):
# Initialize the networks
colorizationnet = network.SCGAN(opt)
if opt.load_name == '':
print('Generator is created!')
# Init the networks
network.weights_init(colorizationnet, init_type = opt.init_type, init_gain = opt.init_gain)
pretrained_dict = torch.load(opt.global_feature_network_path)
load_dict(colorizationnet.global_feature_network, pretrained_dict)
print('Generator is loaded with %s!' % (opt.global_feature_network_path))
else:
pretrained_dict = torch.load(opt.load_name)
load_dict(colorizationnet, pretrained_dict)
print('Generator is loaded!')
return colorizationnet
def create_generator(opt):
if opt.pre_train == True:
# Initialize the network
generator_a = network.Generator(opt)
generator_b = network.Generator(opt)
# Init the network
network.weights_init(generator_a, init_type = opt.init_type, init_gain = opt.init_gain)
network.weights_init(generator_b, init_type = opt.init_type, init_gain = opt.init_gain)
print('Generator is created!')
else:
# Load the weights
generator_a = torch.load(opt.load_name + '_a.pth')
generator_b = torch.load(opt.load_name + '_b.pth')
print('Generator is loaded!')
return generator_a, generator_b
def create_generator(opt):
# Initialize the networks
generator = network.GrayInpaintingNet(opt)
print('Generator is created!')
# Init the networks
if opt.finetune_path:
pretrained_net = torch.load(opt.finetune_path)
generator = load_dict(generator, pretrained_net)
print('Load generator with %s' % opt.finetune_path)
else:
network.weights_init(generator,
init_type=opt.init_type,
init_gain=opt.init_gain)
print('Initialize generator with %s type' % opt.init_type)
return generator
def create_generator(opt):
# Initialize the network
generator = network.KPN(opt.color, opt.burst_length, opt.blind_est, opt.kernel_size, opt.sep_conv, \
opt.channel_att, opt.spatial_att, opt.upMode, opt.core_bias)
if opt.load_name == '':
# Init the network
network.weights_init(generator,
init_type=opt.init_type,
init_gain=opt.init_gain)
print('Generator is created!')
else:
# Load a pre-trained network
pretrained_net = torch.load(opt.load_name)
load_dict(generator, pretrained_net)
print('Generator is loaded!')
return generator
def create_generator(opt):
generator = network.Net(num_channels=opt.num_channels, scale_factor=opt.scale_factor, d=32, s=5, m=1)
if opt.load_pre_train:
if '.pkl' in opt.load_name:
generator.load_state_dict(torch.load(opt.load_name))
else:
pretrained_net = torch.load(opt.load_name)
load_dict(generator, pretrained_net)
print('Generator is loaded!')
else:
# Init the network
network.weights_init(generator, init_type = opt.init_type, init_gain = opt.init_gain)
print('Generator is created!')
return generator
