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