Source code for catalyst.contrib.nn.modules.arcmargin

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

[docs]class ArcMarginProduct(nn.Module): """Implementation of Arc Margin Product. Args: in_features: size of each input sample. out_features: size of each output sample. 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 = ArcMarginProduct(5, 10) >>> loss_fn = nn.CrosEntropyLoss() >>> embedding = torch.randn(3, 5, requires_grad=True) >>> target = torch.empty(3, dtype=torch.long).random_(10) >>> output = layer(embedding) >>> loss = loss_fn(output, target) >>> loss.backward() """ def __init__(self, in_features: int, out_features: int): # noqa: D107 super(ArcMarginProduct, self).__init__() self.in_features = in_features self.out_features = out_features self.weight = nn.Parameter(torch.Tensor(out_features, in_features)) nn.init.xavier_uniform_(self.weight) def __repr__(self) -> str: """Object representation.""" rep = ( "ArcMarginProduct(" f"in_features={self.in_features}," f"out_features={self.out_features}" ")" ) return rep
[docs] def forward(self, input: torch.Tensor) -> torch.Tensor: """ Args: input: input features, expected shapes ``BxF`` where ``B`` is batch dimension and ``F`` is an input feature dimension. Returns: tensor (logits) with shapes ``BxC`` where ``C`` is a number of classes (out_features). """ cosine = F.linear(F.normalize(input), F.normalize(self.weight)) return cosine
__all__ = ["ArcMarginProduct"]