def init_network(self, opt): Base_Model.init_network(self, opt) self.if_pool = opt.if_pool self.multi_view = opt.multi_view self.conditional_D = opt.conditional_D assert len(self.multi_view) > 0 self.loss_names = ['D', 'G'] self.metrics_names = ['Mse', 'CosineSimilarity', 'PSNR'] self.visual_names = ['G_real', 'G_fake', 'G_input', 'G_Map_fake_F', 'G_Map_real_F', 'G_Map_fake_S', 'G_Map_real_S'] # identity loss if self.opt.idt_lambda > 0: self.loss_names += ['idt'] # feature metric loss if self.opt.fea_m_lambda > 0: self.loss_names += ['fea_m'] # map loss if self.opt.map_m_lambda > 0: self.loss_names += ['map_m'] if self.training: self.model_names = ['G', 'D'] else: # during test time, only load Gs self.model_names = ['G'] self.netG = factory.define_3DG(opt.noise_len, opt.input_shape, opt.output_shape, opt.input_nc_G, opt.output_nc_G, opt.ngf, opt.which_model_netG, opt.n_downsampling, opt.norm_G, not opt.no_dropout, opt.init_type, self.gpu_ids, opt.n_blocks, opt.encoder_input_shape, opt.encoder_input_nc, opt.encoder_norm, opt.encoder_blocks, opt.skip_number, opt.activation_type, opt=opt) if self.training: if opt.ganloss == 'gan': use_sigmoid = True elif opt.ganloss == 'lsgan': use_sigmoid = False else: raise ValueError() # conditional Discriminator if self.conditional_D: d_input_channels = opt.input_nc_D + 1 else: d_input_channels = opt.input_nc_D self.netD = factory.define_D(d_input_channels, opt.ndf, opt.which_model_netD, opt.n_layers_D, opt.norm_D, use_sigmoid, opt.init_type, self.gpu_ids, opt.discriminator_feature, num_D=opt.num_D, n_out_channels=opt.n_out_ChannelsD) if self.if_pool: self.fake_pool = ImagePool(opt.pool_size)
def init_network(self, opt): Base_Model.init_network(self, opt) self.loss_names = ['D', 'G'] self.metrics_names = ['Mse', 'CosineSimilarity'] self.visual_names = [ 'G_real', 'G_fake', 'G_input', 'G_map_fake', 'G_map_real' ] # identity loss if self.opt.idt_lambda > 0: self.loss_names += ['idt'] # feature metric loss if self.opt.fea_m_lambda > 0: self.loss_names += ['fea_m'] # map loss if self.opt.map_m_lambda > 0: self.loss_names += ['map_m'] if self.training: self.model_names = ['G', 'D'] else: # during test time, only load Gs self.model_names = ['G'] self.netG = factory.define_3DG( opt.noise_len, opt.input_shape, opt.output_shape, opt.input_nc_G, opt.output_nc_G, opt.ngf, opt.which_model_netG, opt.n_downsampling, opt.norm_G, not opt.no_dropout, opt.init_type, self.gpu_ids, opt.n_blocks, opt.encoder_input_shape, opt.encoder_input_nc, opt.encoder_norm) if self.training: if opt.ganloss == 'gan': use_sigmoid = True elif opt.ganloss == 'lsgan': use_sigmoid = False elif opt.ganloss == 'wgan': self.loss_names += ['wasserstein'] use_sigmoid = False elif opt.ganloss == 'wgan_gp': self.loss_names += ['wasserstein', 'grad_penalty'] use_sigmoid = False else: raise ValueError() self.netD = factory.define_D(opt.input_nc_D, opt.ndf, opt.which_model_netD, opt.n_layers_D, opt.norm_D, use_sigmoid, opt.init_type, self.gpu_ids, opt.discriminator_feature) self.fake_pool = ImagePool(opt.pool_size)
def init_network(self, opt): Base_Model.init_network(self, opt) self.if_pool = opt.if_pool self.multi_view = opt.multi_view assert len(self.multi_view) > 0 self.loss_names = ['MSE'] self.metrics_names = ['Mse', 'CosineSimilarity', 'PSNR'] self.visual_names = [ 'G_real', 'G_fake', 'G_input', 'G_Map_fake_F', 'G_Map_real_F', 'G_Map_fake_S', 'G_Map_real_S' ] self.model_names = ['G'] self.netG = Xray3DVolumes_2DModel() self.netG = init_net(self.netG, opt.init_type, self.gpu_ids)
def init_network(self, opt): Base_Model.init_network(self, opt) self.if_pool = opt.if_pool self.multi_view = opt.multi_view assert len(self.multi_view) > 0 self.metrics_names = ['Mse', 'CosineSimilarity', 'PSNR'] self.visual_names = [ 'G_real', 'G_fake', 'G_input1', 'G_input2', 'G_Map_fake_F', 'G_Map_real_F', 'G_Map_fake_S', 'G_Map_real_S' ] self.netG = factory.define_3DG(opt.noise_len, opt.input_shape, opt.output_shape, opt.input_nc_G, opt.output_nc_G, opt.ngf, opt.which_model_netG, opt.n_downsampling, opt.norm_G, not opt.no_dropout, opt.init_type, self.gpu_ids, opt.n_blocks, opt.encoder_input_shape, opt.encoder_input_nc, opt.encoder_norm, opt.encoder_blocks, opt.skip_number, opt.activation_type, opt=opt) self.loss_names = ['idt'] self.model_names = ['G'] # map loss if self.opt.map_projection_lambda > 0: self.loss_names += ['map_m']
def init_network(self, opt): Base_Model.init_network(self, opt) self.if_pool = opt.if_pool self.multi_view = opt.multi_view self.conditional_D = opt.conditional_D self.order_map_list = [(0, 1, 2, 3, 4), (0, 1, 3, 2, 4), (0, 1, 4, 2, 3)] assert len(self.multi_view) >= 0 self.loss_names = ['D', 'G'] self.metrics_names = ['Mse', 'CosineSimilarity', 'PSNR'] self.visual_names = [ 'G_real', 'G_fake', 'G_input', 'G_Map_fake_F', 'G_Map_real_F', 'G_Map_fake_S', 'G_Map_real_S' ] self.model_names = ['G'] if self.training: self.model_names += ['D'] # identity loss if self.opt.idt_lambda > 0: self.loss_names += ['idt'] # feature metric loss if self.opt.feature_D_lambda > 0: self.loss_names += ['fea_m'] self.loss_names += ['D_Map', 'G_Map'] # feature metric loss if self.opt.feature_D_map_lambda > 0: self.loss_names += ['fea_m_Map'] # map loss if self.opt.map_projection_lambda > 0: self.loss_names += ['idt_Map'] # models self.model_names += ['D_Map'] self.netG = factory.define_3DG(opt.noise_len, opt.input_shape, opt.output_shape, opt.input_nc_G, opt.output_nc_G, opt.ngf, opt.which_model_netG, opt.n_downsampling, opt.norm_G, not opt.no_dropout, opt.init_type, self.gpu_ids, opt.n_blocks, opt.encoder_input_shape, opt.encoder_input_nc, opt.encoder_norm, opt.encoder_blocks, opt.skip_number, opt.activation_type, opt=opt) if self.training: # out of discriminator is not probability when # GAN loss is LSGAN use_sigmoid = False # conditional Discriminator if self.conditional_D: d_input_channels = opt.input_nc_D + 1 else: d_input_channels = opt.input_nc_D self.netD = factory.define_D(d_input_channels, opt.ndf, opt.which_model_netD, opt.n_layers_D, opt.norm_D, use_sigmoid, opt.init_type, self.gpu_ids, opt.discriminator_feature, num_D=opt.num_D, n_out_channels=opt.n_out_ChannelsD) self.netD_Map = factory.define_D( len(self.multi_view), opt.map_ndf, opt.map_which_model_netD, opt.map_n_layers_D, opt.map_norm_D, use_sigmoid, opt.init_type, self.gpu_ids, opt.discriminator_feature, opt.map_num_D, n_out_channels=opt.map_n_out_ChannelsD) if self.if_pool: self.fake_pool = ImagePool(opt.pool_size) self.fake_pool_Map = ImagePool(opt.map_pool_size)