Python init.constant方法代码示例

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def conv(in_planes, out_planes, kernel_size=3, stride=1, dilation=1, bias=False, transposed=False):
  if transposed:
    layer = nn.ConvTranspose2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=1, output_padding=1, dilation=dilation, bias=bias)
    # Bilinear interpolation init
    w = torch.Tensor(kernel_size, kernel_size)
    centre = kernel_size % 2 == 1 and stride - 1 or stride - 0.5
    for y in range(kernel_size):
      for x in range(kernel_size):
        w[y, x] = (1 - abs((x - centre) / stride)) * (1 - abs((y - centre) / stride))
    layer.weight.data.copy_(w.div(in_planes).repeat(in_planes, out_planes, 1, 1))
  else:
    padding = (kernel_size + 2 * (dilation - 1)) // 2
    layer = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias)
  if bias:
    init.constant(layer.bias, 0)
  return layer


# Returns 2D batch normalisation layer 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def __init__(self, num_classes, pretrained_net):
    super().__init__()
    self.pretrained_net = pretrained_net
    self.relu = nn.ReLU(inplace=True)
    self.conv5 = conv(512, 256, stride=2, transposed=True)
    self.bn5 = bn(256)
    self.conv6 = conv(256, 128, stride=2, transposed=True)
    self.bn6 = bn(128)
    self.conv7 = conv(128, 64, stride=2, transposed=True)
    self.bn7 = bn(64)
    self.conv8 = conv(64, 64, stride=2, transposed=True)
    self.bn8 = bn(64)
    self.conv9 = conv(64, 32, stride=2, transposed=True)
    self.bn9 = bn(32)
    self.conv10 = conv(32, num_classes, kernel_size=7)
    init.constant(self.conv10.weight, 0)  # Zero init 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def __init__(self, dim, dropout=0.2, slope=0.0):
        super(SDAE, self).__init__()
        self.in_dim = dim[0]
        self.nlayers = len(dim)-1
        self.reluslope = slope
        self.enc, self.dec = [], []
        for i in range(self.nlayers):
            self.enc.append(nn.Linear(dim[i], dim[i+1]))
            setattr(self, 'enc_{}'.format(i), self.enc[-1])
            self.dec.append(nn.Linear(dim[i+1], dim[i]))
            setattr(self, 'dec_{}'.format(i), self.dec[-1])
        self.base = []
        for i in range(self.nlayers):
            self.base.append(nn.Sequential(*self.enc[:i]))
        self.dropmodule1 = nn.Dropout(p=dropout)
        self.dropmodule2 = nn.Dropout(p=dropout)
        self.loss = nn.MSELoss(size_average=True)

        # initialization
        for m in self.modules():
            if isinstance(m, nn.Linear):
                init.normal(m.weight, std=1e-2)
                if m.bias.data is not None:
                    init.constant(m.bias, 0) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def init_params(net):
    '''Init layer parameters.'''
    for m in net.modules():
        if isinstance(m, nn.Conv2d):
            init.kaiming_normal(m.weight, mode='fan_out')
            if m.bias:
                init.constant(m.bias, 0)
        elif isinstance(m, nn.BatchNorm2d):
            init.constant(m.weight, 1)
            init.constant(m.bias, 0)
        elif isinstance(m, nn.Linear):
            init.normal(m.weight, std=1e-3)
            if m.bias:
                init.constant(m.bias, 0)


#_, term_width = os.popen('stty size', 'r').read().split()
# term_width = int(term_width) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def reset_parameters(self):
        """
        Initialize parameters following the way proposed in the paper.
        """

        # The input-to-hidden weight matrix is initialized orthogonally.
        init.orthogonal(self.weight_ih.data)
        # The hidden-to-hidden weight matrix is initialized as an identity
        # matrix.
        weight_hh_data = torch.eye(self.hidden_size)
        weight_hh_data = weight_hh_data.repeat(1, 4)
        self.weight_hh.data.set_(weight_hh_data)
        # The bias is just set to zero vectors.
        init.constant(self.bias.data, val=0)
        # Initialization of BN parameters.
        self.bn_ih.reset_parameters()
        self.bn_hh.reset_parameters()
        self.bn_c.reset_parameters()
        self.bn_ih.bias.data.fill_(0)
        self.bn_hh.bias.data.fill_(0)
        self.bn_ih.weight.data.fill_(0.1)
        self.bn_hh.weight.data.fill_(0.1)
        self.bn_c.weight.data.fill_(0.1) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def __init__(self, dim, slope=0.0):
        super(extractSDAE, self).__init__()
        self.in_dim = dim[0]
        self.nlayers = len(dim)-1
        self.reluslope = slope
        self.enc, self.dec = [], []
        for i in range(self.nlayers):
            self.enc.append(nn.Linear(dim[i], dim[i+1]))
            setattr(self, 'enc_{}'.format(i), self.enc[-1])
            self.dec.append(nn.Linear(dim[i+1], dim[i]))
            setattr(self, 'dec_{}'.format(i), self.dec[-1])
        self.base = []
        for i in range(self.nlayers):
            self.base.append(nn.Sequential(*self.enc[:i]))

        # initialization
        for m in self.modules():
            if isinstance(m, nn.Linear):
                init.normal(m.weight, std=1e-2)
                if m.bias.data is not None:
                    init.constant(m.bias, 0) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def reset_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal(m.weight, mode='fan_in')
                if m.bias is not None:
                    init.constant(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant(m.weight, 1)
                init.constant(m.bias, 0)
            elif isinstance(m, nn.BatchNorm1d):
                init.constant(m.weight, 1)
                init.constant(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.kaiming_normal(m.weight, mode='fan_in')
                if m.bias is not None:
                    init.constant(m.bias, 0) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def weights_init(init_type='xavier'):
    def init_fun(m):
        classname = m.__class__.__name__
        if (classname.find('Conv') == 0 or classname.find('Linear') == 0) and hasattr(m, 'weight'):
            if init_type == 'normal':
                init.normal(m.weight.data, 0.0, 0.02)
            elif init_type == 'xavier':
                init.xavier_normal(m.weight.data, gain=math.sqrt(2))
            elif init_type == 'kaiming':
                init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
            elif init_type == 'orthogonal':
                init.orthogonal(m.weight.data, gain=math.sqrt(2))
            elif init_type == 'default':
                pass
            else:
                assert 0, "Unsupported initialization: {}".format(init_type)
            if hasattr(m, 'bias') and m.bias is not None:
                init.constant(m.bias.data, 0.0)
        elif (classname.find('Norm') == 0):
            if hasattr(m, 'weight') and m.weight is not None:
                init.constant(m.weight.data, 1.0)
            if hasattr(m, 'bias') and m.bias is not None:
                init.constant(m.bias.data, 0.0)
    return init_fun 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def init_params(net):
    '''Init layer parameters.'''
    for m in net.modules():
        if isinstance(m, nn.Conv2d):
            init.kaiming_normal(m.weight, mode='fan_out')
            if m.bias:
                init.constant(m.bias, 0)
        elif isinstance(m, nn.BatchNorm2d):
            init.constant(m.weight, 1)
            init.constant(m.bias, 0)
        elif isinstance(m, nn.Linear):
            init.normal(m.weight, std=1e-3)
            if m.bias:
                init.constant(m.bias, 0) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def conv(in_planes, out_planes, kernel_size=3, stride=1, dilation=1, bias=False,
         transposed=False):
    """
    Returns 2D convolutional layer with space-preserving padding
    """
    if transposed:
        layer = nn.ConvTranspose2d(
            in_planes, out_planes, kernel_size=kernel_size, stride=stride,
            padding=1, output_padding=1, dilation=dilation, bias=bias)
        # Bilinear interpolation init
        w = torch.Tensor(kernel_size, kernel_size)
        centre = kernel_size % 2 == 1 and stride - 1 or stride - 0.5
        for y in range(kernel_size):
            for x in range(kernel_size):
                w[y, x] = (1 - abs((x - centre) / stride)) * (1 - abs(
                    (y - centre) / stride))
        layer.weight.data.copy_(
            w.div(in_planes).repeat(in_planes, out_planes, 1, 1))
    else:
        padding = (kernel_size + 2 * (dilation - 1)) // 2
        layer = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size,
                          stride=stride, padding=padding, dilation=dilation,
                          bias=bias)
    if bias:
        init.constant(layer.bias, 0)
    return layer 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def conv_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        init.xavier_uniform(m.weight, gain=np.sqrt(2))
        init.constant(m.bias, 0)
    elif classname.find('BatchNorm') != -1:
        init.constant(m.weight, 1)
        init.constant(m.bias, 0) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def init_weights_xavier(model):
    if isinstance(model, nn.Conv2d):
        init.xavier_normal(model.weight)
        init.constant(model.bias, 0) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def init_weights_he(model):
    if isinstance(model, nn.Conv2d):
        init.kaiming_normal(model.weight)
        init.constant(model.bias, 0) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def reset_parameters(self):
        init.constant(self.weight,self.gamma) 

# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def init_weights(net, init_type='normal', gain=0.02):
    def init_func(m):
        classname = m.__class__.__name__
        if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
            init.normal(m.weight.data, 0.0, gain)
            if hasattr(m, 'bias') and m.bias is not None:
                init.constant(m.bias.data, 0.0)
        elif classname.find('BatchNorm2d') != -1:
            init.normal(m.weight.data, 1.0, gain)
            init.constant(m.bias.data, 0.0)

    print('Network initialized with weights sampled from N(0,0.02).')
    net.apply(init_func)