Python utils.spectral_norm方法代码示例

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self, in_ch, out_ch, h_ch=None, ksize=3, pad=1,
                 activation=F.relu, downsample=False):
        super(Block, self).__init__()

        self.activation = activation
        self.downsample = downsample

        self.learnable_sc = (in_ch != out_ch) or downsample
        if h_ch is None:
            h_ch = in_ch
        else:
            h_ch = out_ch

        self.c1 = utils.spectral_norm(nn.Conv2d(in_ch, h_ch, ksize, 1, pad))
        self.c2 = utils.spectral_norm(nn.Conv2d(h_ch, out_ch, ksize, 1, pad))
        if self.learnable_sc:
            self.c_sc = utils.spectral_norm(nn.Conv2d(in_ch, out_ch, 1, 1, 0))

        self._initialize() 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self, num_features, num_classes=0, activation=F.relu):
        super(SNResNetProjectionDiscriminator, self).__init__()
        self.num_features = num_features
        self.num_classes = num_classes
        self.activation = activation

        self.block1 = OptimizedBlock(3, num_features)
        self.block2 = Block(num_features, num_features * 2,
                            activation=activation, downsample=True)
        self.block3 = Block(num_features * 2, num_features * 4,
                            activation=activation, downsample=True)
        self.block4 = Block(num_features * 4, num_features * 8,
                            activation=activation, downsample=True)
        self.block5 = Block(num_features * 8, num_features * 16,
                            activation=activation, downsample=True)
        self.block6 = Block(num_features * 16, num_features * 16,
                            activation=activation, downsample=True)
        self.l7 = utils.spectral_norm(nn.Linear(num_features * 16, 1))
        if num_classes > 0:
            self.l_y = utils.spectral_norm(
                nn.Embedding(num_classes, num_features * 16))

        self._initialize() 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self, num_features=64, num_classes=0, activation=F.relu):
        super(SNResNetProjectionDiscriminator, self).__init__()
        self.num_features = num_features
        self.num_classes = num_classes
        self.activation = activation

        self.block1 = OptimizedBlock(3, num_features)
        self.block2 = Block(num_features, num_features * 2,
                            activation=activation, downsample=True)
        self.block3 = Block(num_features * 2, num_features * 4,
                            activation=activation, downsample=True)
        self.block4 = Block(num_features * 4, num_features * 8,
                            activation=activation, downsample=True)
        self.block5 = Block(num_features * 8, num_features * 16,
                            activation=activation, downsample=True)
        self.l6 = utils.spectral_norm(nn.Linear(num_features * 16, 1))
        if num_classes > 0:
            self.l_y = utils.spectral_norm(
                nn.Embedding(num_classes, num_features * 16))

        self._initialize() 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self, depth=4):
    super(ConvEnergy, self).__init__()
    self.preprocess = nn.Conv2d(3, _next(0), 1)
    self.blocks = nn.ModuleList([
      spectral_norm(nn.Conv2d(_next(idx), _next(idx + 1), 3, padding=1))
      for idx in range(depth)
    ])
    self.project = [
      upscale(_next(idx + 1))
      for idx in range(depth)
    ]
    self.bn = nn.ModuleList([
      nn.ReLU()
      for idx in range(depth)
    ])
    self.postprocess = spectral_norm(nn.Conv2d(_next(depth), 128, 1))
    self.predict = spectral_norm(nn.Linear(128, 10)) 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self, in_size, out_size, latent_size,
               hidden_size=None, upsample=1,
               normalization=tsn.AdaptiveBatchNorm,
               activation=func.relu):
    super(BigGANBlock, self).__init__()
    if hidden_size is None:
      hidden_size = in_size // 4
    self.in_size = in_size
    self.out_size = out_size
    self.upsample = upsample
    self.bn = nn.ModuleList([
      normalization(in_size, latent_size),
      normalization(hidden_size, latent_size),
      normalization(hidden_size, latent_size),
      normalization(hidden_size, latent_size)
    ])
    self.blocks = nn.ModuleList([
      spectral_norm(nn.Conv2d(in_size, hidden_size, 1)),
      spectral_norm(nn.Conv2d(hidden_size, hidden_size, 3, padding=1)),
      spectral_norm(nn.Conv2d(hidden_size, hidden_size, 3, padding=1)),
      spectral_norm(nn.Conv2d(hidden_size, out_size, 1))
    ])
    self.activation = activation 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self, fin, fout, fhidden=None, is_bias=True):
        super(GatedResnetBlock,self).__init__()
        # Attributes
        self.is_bias = is_bias
        self.learned_shortcut = (fin != fout)
        self.fin = fin
        self.fout = fout
        if fhidden is None:
            self.fhidden = min(fin, fout)
        else:
            self.fhidden = fhidden

        norm_layer='instance'
        # Submodules
        self.conv_0 = spectral_norm(nn.Conv2d(self.fin, self.fhidden, 3, stride=1, padding=1))
        self.conv_1 = spectral_norm(nn.Conv2d(self.fhidden, self.fout, 3, stride=1, padding=1, bias=is_bias))

        if self.learned_shortcut:
            self.conv_s = spectral_norm( nn.Conv2d(self.fin, self.fout, 1, stride=1, padding=0, bias=False)) 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def turn_on_spectral_norm(self):
        """
        private helper for turning on the spectral normalization
        :return: None (has side effect)
        """
        from torch.nn.utils import spectral_norm

        if self.spectral_norm_mode is not None:
            assert self.spectral_norm_mode is False, \
                "can't apply spectral_norm. It is already applied"

        # apply the same to the remaining relevant blocks
        for module in self.layers:
            module.conv_1 = spectral_norm(module.conv_1)
            module.conv_2 = spectral_norm(module.conv_2)

        # toggle the state variable:
        self.spectral_norm_mode = True 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def turn_off_spectral_norm(self):
        """
        private helper for turning off the spectral normalization
        :return: None (has side effect)
        """
        from torch.nn.utils import remove_spectral_norm

        if self.spectral_norm_mode is not None:
            assert self.spectral_norm_mode is True, \
                "can't remove spectral_norm. It is not applied"

        # remove the applied spectral norm
        for module in self.layers:
            remove_spectral_norm(module.conv_1)
            remove_spectral_norm(module.conv_2)

        # toggle the state variable:
        self.spectral_norm_mode = False 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self,in_channels,out_channels,kernel_size,padding,stride,num_classes=0,activation=nn.LeakyReLU(0.2),conv_groups=1):
        """

        :param in_channels:
        :param out_channels:
        :param kernel_size:
        :param padding:
        :param stride:
        :param num_classes:
        :param activation:
        :param conv_groups:
        """

        super(StandardGeneratorBlock,self).__init__()

        self.activation = activation
        self.num_classes = num_classes


        self.conv = spectral_norm(ConvTranspose2d(in_channels,out_channels,kernel_size=kernel_size,padding=padding,stride=stride,weight_init=Xavier_Uniform(),groups=conv_groups))
        if num_classes > 0:
            self.bn = ConditionalBatchNorm2d(out_channels,num_classes)
        else:
            self.bn = BatchNorm2d(out_channels) 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=None,
                 bias=True,
                 spectral_norm=False,
                 residual_init=True):
        super(CustomConv2d, self).__init__()
        self.residual_init = residual_init
        if padding is None:
            padding = int((kernel_size - 1) / 2)

        self.conv = nn.Conv2d(in_channels,
                              out_channels,
                              kernel_size,
                              stride=stride,
                              padding=padding,
                              bias=bias)
        if spectral_norm:
            self.conv = utils.spectral_norm(self.conv) 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self):
        super(Discriminator_VGG_128_SN, self).__init__()
        # features
        # hxw, c
        # 128, 64
        self.lrelu = nn.LeakyReLU(0.2, True)

        self.conv0 = spectral_norm(nn.Conv2d(3, 64, 3, 1, 1))
        self.conv1 = spectral_norm(nn.Conv2d(64, 64, 4, 2, 1))
        # 64, 64
        self.conv2 = spectral_norm(nn.Conv2d(64, 128, 3, 1, 1))
        self.conv3 = spectral_norm(nn.Conv2d(128, 128, 4, 2, 1))
        # 32, 128
        self.conv4 = spectral_norm(nn.Conv2d(128, 256, 3, 1, 1))
        self.conv5 = spectral_norm(nn.Conv2d(256, 256, 4, 2, 1))
        # 16, 256
        self.conv6 = spectral_norm(nn.Conv2d(256, 512, 3, 1, 1))
        self.conv7 = spectral_norm(nn.Conv2d(512, 512, 4, 2, 1))
        # 8, 512
        self.conv8 = spectral_norm(nn.Conv2d(512, 512, 3, 1, 1))
        self.conv9 = spectral_norm(nn.Conv2d(512, 512, 4, 2, 1))
        # 4, 512

        # classifier
        self.linear0 = spectral_norm(nn.Linear(512 * 4 * 4, 100))
        self.linear1 = spectral_norm(nn.Linear(100, 1)) 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self, size=64, z=32):
    super().__init__()
    self.z = z
    self.preprocess = spectral_norm(nn.Conv2d(5 + 3, size, 3, padding=1))
    self.noise = nn.Parameter(torch.rand(6, 1, size, 1, 1))
    self.post_noise = nn.Parameter(torch.rand(3, 1, size, 1, 1))
    self.bg = nn.Parameter(torch.randn(1, 64, 8, 8))
    self.color = nn.Conv2d(size, 3, 1)
    self.encoder = nn.ModuleList([
      spectral_norm(nn.Conv2d(size, size, 3, dilation=idx + 1, padding=idx + 1))
      for idx in range(6)
    ])
    self.encoder_norm = nn.ModuleList([
      nn.InstanceNorm2d(size)
      for idx in range(6)
    ])
    self.decoder = nn.ModuleList([
      nn.Conv2d(2 * size, size, 3, dilation=idx + 1, padding=idx + 1)
      for idx in reversed(range(6))
    ])
    self.decoder_norm = nn.ModuleList([
      AdaptiveInstanceNormPP(size, z)
      for idx in reversed(range(6))
    ])
    self.post = nn.ModuleList([
      nn.Conv2d(size, size, 3, dilation=1, padding=1)
      for idx in range(3)
    ])
    self.post_norm = nn.ModuleList([
      AdaptiveInstanceNormPP(size, z)
      for idx in reversed(range(3))
    ]) 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self):
    super(Energy, self).__init__()
    self.input = MLP(28 * 28, 128, hidden_size=128, depth=3, batch_norm=False, normalization=spectral_norm)
    self.condition = MLP(10, 128, depth=3, batch_norm=False, normalization=spectral_norm)
    self.combine = MLP(128, 1, hidden_size=64, depth=3, batch_norm=False, normalization=spectral_norm) 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self, single, size=5, latents=64):
    super(Encoder, self).__init__()
    self.size = size
    self.single = single
    self.weight = spectral_norm(nn.Linear(128, 1))
    self.combine = MLP(
      128, 128, 64,
      depth=3, batch_norm=False,
      normalization=spectral_norm,
      activation=func.leaky_relu
    )
    self.mean = spectral_norm(nn.Linear(128, latents))
    self.logvar = spectral_norm(nn.Linear(128, latents)) 

# 需要导入模块: from torch.nn import utils [as 别名]
# 或者: from torch.nn.utils import spectral_norm [as 别名]
def __init__(self, latents=32):
    super(SingleEncoder, self).__init__()
    self.block = MLP(28 * 28, latents, hidden_size=64, depth=4, batch_norm=False, normalization=spectral_norm)