Python utils.print_network方法代码示例

# 需要导入模块: import utils [as 别名]
# 或者: from utils import print_network [as 别名]
def __init__(self, test_dataloader):

        self.test_dataloader = test_dataloader
        self.logger = logging.getLogger("Logger")

        self.model_config = CONFIG.model
        self.test_config = CONFIG.test
        self.log_config = CONFIG.log
        self.data_config = CONFIG.data

        self.build_model()
        self.resume_step = None

        utils.print_network(self.G, CONFIG.version)

        if self.test_config.checkpoint:
            self.logger.info('Resume checkpoint: {}'.format(self.test_config.checkpoint))
            self.restore_model(self.test_config.checkpoint) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import print_network [as 别名]
def __init__(self,
                 train_dataloader,
                 test_dataloader,
                 logger,
                 tb_logger):

        # Save GPU memory.
        cudnn.benchmark = False

        self.train_dataloader = train_dataloader
        self.test_dataloader = test_dataloader
        self.logger = logger
        self.tb_logger = tb_logger

        self.model_config = CONFIG.model
        self.train_config = CONFIG.train
        self.log_config = CONFIG.log
        self.loss_dict = {'rec': None,
                          'comp': None,
                          'smooth_l1':None,
                          'grad':None,
                          'gabor':None}
        self.test_loss_dict = {'rec': None,
                               'smooth_l1':None,
                               'mse':None,
                               'sad':None,
                               'grad':None,
                               'gabor':None}

        self.grad_filter = torch.tensor(utils.get_gradfilter()).cuda()
        self.gabor_filter = torch.tensor(utils.get_gaborfilter(16)).cuda()

        self.build_model()
        self.resume_step = None
        self.best_loss = 1e+8

        utils.print_network(self.G, CONFIG.version)
        if self.train_config.resume_checkpoint:
            self.logger.info('Resume checkpoint: {}'.format(self.train_config.resume_checkpoint))
            self.restore_model(self.train_config.resume_checkpoint)

        if self.model_config.imagenet_pretrain and self.train_config.resume_checkpoint is None:
            self.logger.info('Load Imagenet Pretrained: {}'.format(self.model_config.imagenet_pretrain_path))
            if self.model_config.arch.encoder == "vgg_encoder":
                utils.load_VGG_pretrain(self.G, self.model_config.imagenet_pretrain_path)
            else:
                utils.load_imagenet_pretrain(self.G, self.model_config.imagenet_pretrain_path) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import print_network [as 别名]
def __init__(self, args):
        # parameters
        self.epoch = args.epoch
        self.sample_num = 64
        self.batch_size = args.batch_size
        self.save_dir = args.save_dir
        self.result_dir = args.result_dir
        self.dataset = args.dataset
        self.log_dir = args.log_dir
        self.gpu_mode = args.gpu_mode
        self.model_name = args.gan_type

        # EBGAN parameters
        self.pt_loss_weight = 0.1
        self.margin = max(1, self.batch_size / 64.)  # margin for loss function
        # usually margin of 1 is enough, but for large batch size it must be larger than 1

        # networks init
        self.G = generator(self.dataset)
        self.D = discriminator(self.dataset)
        self.G_optimizer = optim.Adam(self.G.parameters(), lr=args.lrG, betas=(args.beta1, args.beta2))
        self.D_optimizer = optim.Adam(self.D.parameters(), lr=args.lrD, betas=(args.beta1, args.beta2))

        if self.gpu_mode:
            self.G.cuda()
            self.D.cuda()
            self.MSE_loss = nn.MSELoss().cuda()
        else:
            self.MSE_loss = nn.MSELoss()

        print('---------- Networks architecture -------------')
        utils.print_network(self.G)
        utils.print_network(self.D)
        print('-----------------------------------------------')

        # load dataset
        if self.dataset == 'mnist':
            self.data_loader = DataLoader(datasets.MNIST('data/mnist', train=True, download=True,
                                                         transform=transforms.Compose(
                                                             [transforms.ToTensor()])),
                                          batch_size=self.batch_size, shuffle=True)
        elif self.dataset == 'fashion-mnist':
            self.data_loader = DataLoader(
                datasets.FashionMNIST('data/fashion-mnist', train=True, download=True, transform=transforms.Compose(
                    [transforms.ToTensor()])),
                batch_size=self.batch_size, shuffle=True)
        elif self.dataset == 'celebA':
            self.data_loader = utils.load_celebA('data/celebA', transform=transforms.Compose(
                [transforms.CenterCrop(160), transforms.Scale(64), transforms.ToTensor()]), batch_size=self.batch_size,
                                                 shuffle=True)
        self.z_dim = 62

        # fixed noise
        if self.gpu_mode:
            self.sample_z_ = Variable(torch.rand((self.batch_size, self.z_dim)).cuda(), volatile=True)
        else:
            self.sample_z_ = Variable(torch.rand((self.batch_size, self.z_dim)), volatile=True) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import print_network [as 别名]
def __init__(self, args):
        # parameters
        self.epoch = args.epoch
        self.sample_num = 100
        self.batch_size = args.batch_size
        self.save_dir = args.save_dir
        self.result_dir = args.result_dir
        self.dataset = args.dataset
        self.log_dir = args.log_dir
        self.gpu_mode = args.gpu_mode
        self.model_name = args.gan_type

        # networks init
        self.G = generator(self.dataset)
        self.D = discriminator(self.dataset)
        self.G_optimizer = optim.Adam(self.G.parameters(), lr=args.lrG, betas=(args.beta1, args.beta2))
        self.D_optimizer = optim.Adam(self.D.parameters(), lr=args.lrD, betas=(args.beta1, args.beta2))

        if self.gpu_mode:
            self.G.cuda()
            self.D.cuda()
            self.BCE_loss = nn.BCELoss().cuda()
        else:
            self.BCE_loss = nn.BCELoss()

        print('---------- Networks architecture -------------')
        utils.print_network(self.G)
        utils.print_network(self.D)
        print('-----------------------------------------------')

        # load mnist
        self.data_X, self.data_Y = utils.load_mnist(args.dataset)
        self.z_dim = 62
        self.y_dim = 10

        # fixed noise & condition
        self.sample_z_ = torch.zeros((self.sample_num, self.z_dim))
        for i in range(10):
            self.sample_z_[i*self.y_dim] = torch.rand(1, self.z_dim)
            for j in range(1, self.y_dim):
                self.sample_z_[i*self.y_dim + j] = self.sample_z_[i*self.y_dim]

        temp = torch.zeros((10, 1))
        for i in range(self.y_dim):
            temp[i, 0] = i

        temp_y = torch.zeros((self.sample_num, 1))
        for i in range(10):
            temp_y[i*self.y_dim: (i+1)*self.y_dim] = temp

        self.sample_y_ = torch.zeros((self.sample_num, self.y_dim))
        self.sample_y_.scatter_(1, temp_y.type(torch.LongTensor), 1)
        if self.gpu_mode:
            self.sample_z_, self.sample_y_ = Variable(self.sample_z_.cuda(), volatile=True), Variable(self.sample_y_.cuda(), volatile=True)
        else:
            self.sample_z_, self.sample_y_ = Variable(self.sample_z_, volatile=True), Variable(self.sample_y_, volatile=True) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import print_network [as 别名]
def __init__(self, args):
        # parameters
        self.epoch = args.epoch
        self.sample_num = 64
        self.batch_size = args.batch_size
        self.save_dir = args.save_dir
        self.result_dir = args.result_dir
        self.dataset = args.dataset
        self.log_dir = args.log_dir
        self.gpu_mode = args.gpu_mode
        self.model_name = args.gan_type
        self.c = 0.01                   # clipping value
        self.n_critic = 5               # the number of iterations of the critic per generator iteration

        # networks init
        self.G = generator(self.dataset)
        self.D = discriminator(self.dataset)
        self.G_optimizer = optim.Adam(self.G.parameters(), lr=args.lrG, betas=(args.beta1, args.beta2))
        self.D_optimizer = optim.Adam(self.D.parameters(), lr=args.lrD, betas=(args.beta1, args.beta2))

        if self.gpu_mode:
            self.G.cuda()
            self.D.cuda()

        print('---------- Networks architecture -------------')
        utils.print_network(self.G)
        utils.print_network(self.D)
        print('-----------------------------------------------')

        # load dataset
        if self.dataset == 'mnist':
            self.data_loader = DataLoader(datasets.MNIST('data/mnist', train=True, download=True,
                                                         transform=transforms.Compose(
                                                             [transforms.ToTensor()])),
                                          batch_size=self.batch_size, shuffle=True)
        elif self.dataset == 'fashion-mnist':
            self.data_loader = DataLoader(
                datasets.FashionMNIST('data/fashion-mnist', train=True, download=True, transform=transforms.Compose(
                    [transforms.ToTensor()])),
                batch_size=self.batch_size, shuffle=True)
        elif self.dataset == 'celebA':
            self.data_loader = utils.load_celebA('data/celebA', transform=transforms.Compose(
                [transforms.CenterCrop(160), transforms.Scale(64), transforms.ToTensor()]), batch_size=self.batch_size,
                                                 shuffle=True)
        self.z_dim = 62

        # fixed noise
        if self.gpu_mode:
            self.sample_z_ = Variable(torch.rand((self.batch_size, self.z_dim)).cuda(), volatile=True)
        else:
            self.sample_z_ = Variable(torch.rand((self.batch_size, self.z_dim)), volatile=True) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import print_network [as 别名]
def __init__(self, args):
        # parameters
        self.epoch = args.epoch
        self.sample_num = 64
        self.batch_size = args.batch_size
        self.save_dir = args.save_dir
        self.result_dir = args.result_dir
        self.dataset = args.dataset
        self.log_dir = args.log_dir
        self.gpu_mode = args.gpu_mode
        self.model_name = args.gan_type

        # networks init
        self.G = generator(self.dataset)
        self.D = discriminator(self.dataset)
        self.G_optimizer = optim.Adam(self.G.parameters(), lr=args.lrG, betas=(args.beta1, args.beta2))
        self.D_optimizer = optim.Adam(self.D.parameters(), lr=args.lrD, betas=(args.beta1, args.beta2))

        if self.gpu_mode:
            self.G.cuda()
            self.D.cuda()
            self.MSE_loss = nn.MSELoss().cuda()
        else:
            self.MSE_loss = nn.MSELoss()

        print('---------- Networks architecture -------------')
        utils.print_network(self.G)
        utils.print_network(self.D)
        print('-----------------------------------------------')

        # load dataset
        if self.dataset == 'mnist':
            self.data_loader = DataLoader(datasets.MNIST('data/mnist', train=True, download=True,
                                                         transform=transforms.Compose(
                                                             [transforms.ToTensor()])),
                                          batch_size=self.batch_size, shuffle=True)
        elif self.dataset == 'fashion-mnist':
            self.data_loader = DataLoader(
                datasets.FashionMNIST('data/fashion-mnist', train=True, download=True, transform=transforms.Compose(
                    [transforms.ToTensor()])),
                batch_size=self.batch_size, shuffle=True)
        elif self.dataset == 'celebA':
            self.data_loader = utils.load_celebA('data/celebA', transform=transforms.Compose(
                [transforms.CenterCrop(160), transforms.Scale(64), transforms.ToTensor()]), batch_size=self.batch_size,
                                                 shuffle=True)
        self.z_dim = 62

        # fixed noise
        if self.gpu_mode:
            self.sample_z_ = Variable(torch.rand((self.batch_size, self.z_dim)).cuda(), volatile=True)
        else:
            self.sample_z_ = Variable(torch.rand((self.batch_size, self.z_dim)), volatile=True) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import print_network [as 别名]
def __init__(self, args):
        # parameters
        self.epoch = args.epoch
        self.sample_num = 16
        self.batch_size = args.batch_size
        self.save_dir = args.save_dir
        self.result_dir = args.result_dir
        self.dataset = args.dataset
        self.log_dir = args.log_dir
        self.gpu_mode = args.gpu_mode
        self.model_name = args.gan_type

        # networks init
        self.G = generator(self.dataset)
        self.D = discriminator(self.dataset)
        self.G_optimizer = optim.Adam(self.G.parameters(), lr=args.lrG, betas=(args.beta1, args.beta2))
        self.D_optimizer = optim.Adam(self.D.parameters(), lr=args.lrD, betas=(args.beta1, args.beta2))

        if self.gpu_mode:
            self.G.cuda()
            self.D.cuda()
            self.BCE_loss = nn.BCELoss().cuda()
        else:
            self.BCE_loss = nn.BCELoss()

        print('---------- Networks architecture -------------')
        utils.print_network(self.G)
        utils.print_network(self.D)
        print('-----------------------------------------------')

        # load dataset
        if self.dataset == 'mnist':
            self.data_loader = DataLoader(datasets.MNIST('data/mnist', train=True, download=True,
                                                                          transform=transforms.Compose(
                                                                              [transforms.ToTensor()])),
                                                           batch_size=self.batch_size, shuffle=True)
        elif self.dataset == 'fashion-mnist':
            self.data_loader = DataLoader(
                datasets.FashionMNIST('data/fashion-mnist', train=True, download=True, transform=transforms.Compose(
                    [transforms.ToTensor()])),
                batch_size=self.batch_size, shuffle=True)
        elif self.dataset == 'celebA':
            self.data_loader = utils.load_celebA('data/celebA', transform=transforms.Compose(
                [transforms.CenterCrop(160), transforms.Scale(64), transforms.ToTensor()]), batch_size=self.batch_size,
                                                 shuffle=True)
        self.z_dim = 62

        # fixed noise
        if self.gpu_mode:
            self.sample_z_ = Variable(torch.rand((self.batch_size, self.z_dim)).cuda(), volatile=True)
        else:
            self.sample_z_ = Variable(torch.rand((self.batch_size, self.z_dim)), volatile=True) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import print_network [as 别名]
def __init__(self, args):
        # parameters
        self.epoch = args.epoch
        self.sample_num = 64
        self.batch_size = args.batch_size
        self.save_dir = args.save_dir
        self.result_dir = args.result_dir
        self.dataset = args.dataset
        self.log_dir = args.log_dir
        self.gpu_mode = args.gpu_mode
        self.model_name = args.gan_type
        self.lambda_ = 0.25

        # networks init
        self.G = generator(self.dataset)
        self.D = discriminator(self.dataset)
        self.G_optimizer = optim.Adam(self.G.parameters(), lr=args.lrG, betas=(args.beta1, args.beta2))
        self.D_optimizer = optim.Adam(self.D.parameters(), lr=args.lrD, betas=(args.beta1, args.beta2))

        if self.gpu_mode:
            self.G.cuda()
            self.D.cuda()
            self.BCE_loss = nn.BCELoss().cuda()
        else:
            self.BCE_loss = nn.BCELoss()

        print('---------- Networks architecture -------------')
        utils.print_network(self.G)
        utils.print_network(self.D)
        print('-----------------------------------------------')

        # load dataset
        if self.dataset == 'mnist':
            self.data_loader = DataLoader(datasets.MNIST('data/mnist', train=True, download=True,
                                                         transform=transforms.Compose(
                                                             [transforms.ToTensor()])),
                                          batch_size=self.batch_size, shuffle=True)
        elif self.dataset == 'fashion-mnist':
            self.data_loader = DataLoader(
                datasets.FashionMNIST('data/fashion-mnist', train=True, download=True, transform=transforms.Compose(
                    [transforms.ToTensor()])),
                batch_size=self.batch_size, shuffle=True)
        elif self.dataset == 'celebA':
            self.data_loader = utils.load_celebA('data/celebA', transform=transforms.Compose(
                [transforms.CenterCrop(160), transforms.Scale(64), transforms.ToTensor()]), batch_size=self.batch_size,
                                                 shuffle=True)
        self.z_dim = 62

        # fixed noise
        if self.gpu_mode:
            self.sample_z_ = Variable(torch.rand((self.batch_size, self.z_dim)).cuda(), volatile=True)
        else:
            self.sample_z_ = Variable(torch.rand((self.batch_size, self.z_dim)), volatile=True) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import print_network [as 别名]
def __init__(self, args):
        # parameters
        self.epoch = args.epoch
        self.sample_num = 100
        self.batch_size = args.batch_size
        self.save_dir = args.save_dir
        self.result_dir = args.result_dir
        self.dataset = args.dataset
        self.log_dir = args.log_dir
        self.gpu_mode = args.gpu_mode
        self.model_name = args.gan_type

        # networks init
        self.G = generator(self.dataset)
        self.D = discriminator(self.dataset)
        self.G_optimizer = optim.Adam(self.G.parameters(), lr=args.lrG, betas=(args.beta1, args.beta2))
        self.D_optimizer = optim.Adam(self.D.parameters(), lr=args.lrD, betas=(args.beta1, args.beta2))

        if self.gpu_mode:
            self.G.cuda()
            self.D.cuda()
            self.BCE_loss = nn.BCELoss().cuda()
            self.CE_loss = nn.CrossEntropyLoss().cuda()
        else:
            self.BCE_loss = nn.BCELoss()
            self.CE_loss = nn.CrossEntropyLoss()

        print('---------- Networks architecture -------------')
        utils.print_network(self.G)
        utils.print_network(self.D)
        print('-----------------------------------------------')

        # load mnist
        self.data_X, self.data_Y = utils.load_mnist(args.dataset)
        self.z_dim = 62
        self.y_dim = 10

        # fixed noise & condition
        self.sample_z_ = torch.zeros((self.sample_num, self.z_dim))
        for i in range(10):
            self.sample_z_[i*self.y_dim] = torch.rand(1, self.z_dim)
            for j in range(1, self.y_dim):
                self.sample_z_[i*self.y_dim + j] = self.sample_z_[i*self.y_dim]

        temp = torch.zeros((10, 1))
        for i in range(self.y_dim):
            temp[i, 0] = i

        temp_y = torch.zeros((self.sample_num, 1))
        for i in range(10):
            temp_y[i*self.y_dim: (i+1)*self.y_dim] = temp

        self.sample_y_ = torch.zeros((self.sample_num, self.y_dim))
        self.sample_y_.scatter_(1, temp_y.type(torch.LongTensor), 1)
        if self.gpu_mode:
            self.sample_z_, self.sample_y_ = Variable(self.sample_z_.cuda(), volatile=True), Variable(self.sample_y_.cuda(), volatile=True)
        else:
            self.sample_z_, self.sample_y_ = Variable(self.sample_z_, volatile=True), Variable(self.sample_y_, volatile=True) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import print_network [as 别名]
def __init__(self, args):
        # parameters
        self.epoch = args.epoch
        self.sample_num = 64
        self.batch_size = args.batch_size
        self.save_dir = args.save_dir
        self.result_dir = args.result_dir
        self.dataset = args.dataset
        self.log_dir = args.log_dir
        self.gpu_mode = args.gpu_mode
        self.model_name = args.gan_type
        self.lambda_ = 0.25
        self.n_critic = 5               # the number of iterations of the critic per generator iteration

        # networks init
        self.G = generator(self.dataset)
        self.D = discriminator(self.dataset)
        self.G_optimizer = optim.Adam(self.G.parameters(), lr=args.lrG, betas=(args.beta1, args.beta2))
        self.D_optimizer = optim.Adam(self.D.parameters(), lr=args.lrD, betas=(args.beta1, args.beta2))

        if self.gpu_mode:
            self.G.cuda()
            self.D.cuda()

        print('---------- Networks architecture -------------')
        utils.print_network(self.G)
        utils.print_network(self.D)
        print('-----------------------------------------------')

        # load dataset
        if self.dataset == 'mnist':
            self.data_loader = DataLoader(datasets.MNIST('data/mnist', train=True, download=True,
                                                         transform=transforms.Compose(
                                                             [transforms.ToTensor()])),
                                          batch_size=self.batch_size, shuffle=True)
        elif self.dataset == 'fashion-mnist':
            self.data_loader = DataLoader(
                datasets.FashionMNIST('data/fashion-mnist', train=True, download=True, transform=transforms.Compose(
                    [transforms.ToTensor()])),
                batch_size=self.batch_size, shuffle=True)
        elif self.dataset == 'celebA':
            self.data_loader = utils.load_celebA('data/celebA', transform=transforms.Compose(
                [transforms.CenterCrop(160), transforms.Scale(64), transforms.ToTensor()]), batch_size=self.batch_size,
                                                 shuffle=True)
        self.z_dim = 62

        # fixed noise
        if self.gpu_mode:
            self.sample_z_ = Variable(torch.rand((self.batch_size, self.z_dim)).cuda(), volatile=True)
        else:
            self.sample_z_ = Variable(torch.rand((self.batch_size, self.z_dim)), volatile=True)