Skip to content

Cannot apply sparse_switchnorm to 2D input? #6

Description

@Ning5195

When I want to apply sparse_switchnorm to a 2D tensor, it fails at self.var_weight and meets the same problem as #2 ?
I modified the code as follows.

`class SSN(nn.Module):
def init(self, num_features, eps=1e-5, momentum=0.997, using_moving_average=True, last_gamma=False):
super(SSN1d, self).init()
self.eps = eps
self.momentum = momentum
self.using_moving_average = using_moving_average
self.weight = nn.Parameter(torch.ones(1, num_features))
self.bias = nn.Parameter(torch.zeros(1, num_features))

    self.mean_weight = nn.Parameter(torch.ones(2))
    self.var_weight = nn.Parameter(torch.ones(2))
    self.register_buffer('running_mean', torch.zeros(1, num_features))
    self.register_buffer('running_var', torch.zeros(1, num_features))

    # self.rad = 0.
    self.register_buffer('mean_fixed', torch.LongTensor([0]))
    self.register_buffer('var_fixed', torch.LongTensor([0]))
    self.register_buffer('radius', torch.zeros(1))

    self.mean_weight_ = torch.cuda.FloatTensor([1.,1.])
    self.var_weight_ = torch.cuda.FloatTensor([1.,1.])

    self.reset_parameters()

def reset_parameters(self):
    self.running_mean.zero_()
    self.running_var.zero_()
    self.weight.data.fill_(1)
    self.mean_fixed.data.fill_(0)
    self.var_fixed.data.fill_(0)
    self.bias.data.zero_()

def _check_input_dim(self, input):
    if input.dim() != 2:
        raise ValueError('expected 2D input (got {}D input)'
                         .format(input.dim()))

def forward(self, x):
    self._check_input_dim(x)

    mean_ln = x.mean(1, keepdim=True)
    var_ln = x.var(1, keepdim=True)

    if self.training:
        mean_bn = x.mean(0, keepdim=True)
        var_bn = x.var(0, keepdim=True)
        if self.using_moving_average:
            self.running_mean.mul_(self.momentum)
            self.running_mean.add_((1 - self.momentum) * mean_bn.data)
            self.running_var.mul_(self.momentum)
            self.running_var.add_((1 - self.momentum) * var_bn.data)
        else:
            self.running_mean.add_(mean_bn.data)
            self.running_var.add_(mean_bn.data ** 2 + var_bn.data)
    else:
        mean_bn = torch.autograd.Variable(self.running_mean)
        var_bn = torch.autograd.Variable(self.running_var)

    rad = self.radius.item()
    if not self.mean_fixed:
        self.mean_weight_ = sparsestmax(self.mean_weight, rad)
        if max(self.mean_weight_) - min(self.mean_weight_) >= 1:
            self.mean_fixed.data.fill_(1)
            self.mean_weight.data = self.mean_weight_.data
            self.mean_weight_ = self.mean_weight.detach()
    else:
        self.mean_weight_ = self.mean_weight.detach()

    if not self.var_fixed:
        **self.var_weight_ = sparsestmax(self.var_weight, rad)**
        if max(self.var_weight_) - min(self.var_weight_) >= 1:
            self.var_fixed.data.fill_(1)
            self.var_weight.data = self.var_weight_.data
            self.var_weight_ = self.var_weight.detach()
    else:
        self.var_weight_ = self.var_weight.detach()

    mean = self.mean_weight_[0] * mean_ln + self.mean_weight_[1] * mean_bn
    var = self.var_weight_[0] * var_ln + self.var_weight_[1] * var_bn

    x = (x - mean) / (var + self.eps).sqrt()
    return x * self.weight + self.bias

def get_mean(self):
    return self.mean_weight_

def get_var(self):
    return self.var_weight_

def set_rad(self, rad):
    self.radius[0].fill_(rad)
    # self.rad = torch.squeeze(self.radius)

def get_rad(self):
    return torch.squeeze(self.radius)`

`

It seems that the value of self.var_weight in "self.var_weight_ = sparsestmax(self.var_weight, rad)" is nan.
Is it because of an error in the modified code?
Or sparse_switchnorm cannot apply to the 2D input?

Activity

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Metadata

Metadata

Assignees

No one assigned

    Labels

    No labels
    No labels

    Projects

    No projects

      Milestone

      No milestone

      Relationships

      None yet

      Development

      No branches or pull requests

      Issue actions