Source code for catalyst.contrib.layers.amsoftmax

import torch
import torch.nn as nn
import torch.nn.functional as F

[docs]class AMSoftmax(nn.Module): """Implementation of `AMSoftmax: Additive Margin Softmax for Face Verification`_. .. _AMSoftmax\: Additive Margin Softmax for Face Verification: Args: in_features: size of each input sample. out_features: size of each output sample. s: norm of input feature. Default: ``64.0``. m: margin. Default: ``0.5``. eps: operation accuracy. Default: ``1e-6``. Shape: - Input: :math:`(batch, H_{in})` where :math:`H_{in} = in\_features`. - Output: :math:`(batch, H_{out})` where :math:`H_{out} = out\_features`. Example: >>> layer = AMSoftmax(5, 10, s=1.31, m=0.5) >>> loss_fn = nn.CrossEntropyLoss() >>> embedding = torch.randn(3, 5, requires_grad=True) >>> target = torch.empty(3, dtype=torch.long).random_(10) >>> output = layer(embedding, target) >>> loss = loss_fn(output, target) >>> self.engine.backward(loss) """ def __init__( # noqa: D107 self, in_features: int, out_features: int, s: float = 64.0, m: float = 0.5, eps: float = 1e-6, ): super(AMSoftmax, self).__init__() self.in_features = in_features self.out_features = out_features self.s = s self.m = m self.eps = eps self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features)) nn.init.xavier_uniform_(self.weight) def __repr__(self) -> str: """Object representation.""" rep = ( "ArcFace(" f"in_features={self.in_features}," f"out_features={self.out_features}," f"s={self.s}," f"m={self.m}," f"eps={self.eps}" ")" ) return rep
[docs] def forward( self, input: torch.Tensor, target: torch.LongTensor = None ) -> torch.Tensor: """ Args: input: input features, expected shapes ``BxF`` where ``B`` is batch dimension and ``F`` is an input feature dimension. target: target classes, expected shapes ``B`` where ``B`` is batch dimension. If `None` then will be returned projection on centroids. Default is `None`. Returns: tensor (logits) with shapes ``BxC`` where ``C`` is a number of classes (out_features). """ cos_theta = F.linear(F.normalize(input), F.normalize(self.weight)) if target is None: return cos_theta cos_theta = torch.clamp(cos_theta, -1.0 + self.eps, 1.0 - self.eps) one_hot = torch.zeros_like(cos_theta) one_hot.scatter_(1, target.view(-1, 1).long(), 1) logits = torch.where(one_hot.bool(), cos_theta - self.m, cos_theta) logits *= self.s return logits
__all__ = ["AMSoftmax"]