Python models.Generator方法代码示例

# 需要导入模块: import models [as 别名]
# 或者: from models import Generator [as 别名]
def train(args):
    nz = args.nz
    batch_size = args.batch_size
    epochs = args.epochs
    gpu = args.gpu

    # CIFAR-10 images in range [-1, 1] (tanh generator outputs)
    train, _ = datasets.get_cifar10(withlabel=False, ndim=3, scale=2)
    train -= 1.0
    train_iter = iterators.SerialIterator(train, batch_size)

    z_iter = RandomNoiseIterator(GaussianNoiseGenerator(0, 1, args.nz),
                                 batch_size)

    optimizer_generator = optimizers.RMSprop(lr=0.00005)
    optimizer_critic = optimizers.RMSprop(lr=0.00005)
    optimizer_generator.setup(Generator())
    optimizer_critic.setup(Critic())

    updater = WassersteinGANUpdater(
        iterator=train_iter,
        noise_iterator=z_iter,
        optimizer_generator=optimizer_generator,
        optimizer_critic=optimizer_critic,
        device=gpu)

    trainer = training.Trainer(updater, stop_trigger=(epochs, 'epoch'))
    trainer.extend(extensions.ProgressBar())
    trainer.extend(extensions.LogReport(trigger=(1, 'iteration')))
    trainer.extend(GeneratorSample(), trigger=(1, 'epoch'))
    trainer.extend(extensions.PrintReport(['epoch', 'iteration', 'critic/loss',
            'critic/loss/real', 'critic/loss/fake', 'generator/loss']))
    trainer.run() 

# 需要导入模块: import models [as 别名]
# 或者: from models import Generator [as 别名]
def build_models(hps, current_res_w, use_ema_sampling=False, num_classes=None, label_list=None): # todo: fix num_classes
    mapping_network = MappingNetwork() if hps.do_mapping_network else None
    gen_model = Generator(current_res_w, hps.res_w, use_pixel_norm=hps.do_pixel_norm,
                          start_shape=(hps.start_res_h, hps.start_res_w),
                          equalized_lr=hps.do_equalized_lr,
                          traditional_input=hps.do_traditional_input,
                          add_noise=hps.do_add_noise,
                          resize_method=hps.resize_method,
                          use_mapping_network=hps.do_mapping_network,
                          cond_layers=hps.cond_layers,
                          map_cond=hps.map_cond)
    dis_model = Discriminator(current_res_w, equalized_lr=hps.do_equalized_lr,
                              do_minibatch_stddev=hps.do_minibatch_stddev,
                              end_shape=(hps.start_res_h, hps.start_res_w),
                              resize_method=hps.resize_method, cgan_nclasses=num_classes,
                              label_list=label_list)
    if use_ema_sampling:
        sampling_model = Generator(current_res_w, hps.res_w, use_pixel_norm=hps.do_pixel_norm,
                                   start_shape=(hps.start_res_h, hps.start_res_w),
                                   equalized_lr=hps.do_equalized_lr,
                                   traditional_input=hps.do_traditional_input,
                                   add_noise=hps.do_add_noise,
                                   resize_method=hps.resize_method,
                                   use_mapping_network=hps.do_mapping_network,
                                   cond_layers=hps.cond_layers,
                                   map_cond=hps.map_cond)
        return gen_model, mapping_network, dis_model, sampling_model
    else:
        return gen_model, mapping_network, dis_model 

# 需要导入模块: import models [as 别名]
# 或者: from models import Generator [as 别名]
def __init__(self,
                 device,
                 model,
                 model_num_labels,
                 image_nc,
                 box_min,
                 box_max,
                 model_path):
        output_nc = image_nc
        self.device = device
        self.model_num_labels = model_num_labels
        self.model = model
        self.input_nc = image_nc
        self.output_nc = output_nc
        self.box_min = box_min
        self.box_max = box_max
        self.model_path = model_path

        self.gen_input_nc = image_nc
        self.netG = models.Generator(self.gen_input_nc, image_nc).to(device)
        self.netDisc = models.Discriminator(image_nc).to(device)

        # initialize all weights
        self.netG.apply(weights_init)
        self.netDisc.apply(weights_init)

        # initialize optimizers
        self.optimizer_G = torch.optim.Adam(self.netG.parameters(),
                                            lr=0.001)
        self.optimizer_D = torch.optim.Adam(self.netDisc.parameters(),
                                            lr=0.001)