mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-22 08:16:08 +00:00
Brandon Thomas
69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
# ! /usr/bin/python
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from nemo.core.classes import Loss, Typing, typecheck
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from nemo.core.neural_types import LabelsType, LogitsType, LossType, NeuralType
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__all__ = ['AngularSoftmaxLoss']
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class AngularSoftmaxLoss(Loss, Typing):
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"""
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Computes ArcFace Angular softmax angle loss
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reference: https://openaccess.thecvf.com/content_CVPR_2019/papers/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.pdf
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args:
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scale: scale value for cosine angle
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margin: margin value added to cosine angle
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"""
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@property
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def input_types(self):
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"""Input types definitions for AnguarLoss.
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"""
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return {
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"logits": NeuralType(('B', 'D'), LogitsType()),
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"labels": NeuralType(('B',), LabelsType()),
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}
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@property
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def output_types(self):
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"""Output types definitions for AngularLoss.
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loss:
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NeuralType(None)
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"""
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return {"loss": NeuralType(elements_type=LossType())}
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def __init__(self, scale=20.0, margin=1.35):
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super().__init__()
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self.eps = 1e-7
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self.scale = scale
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self.margin = margin
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@typecheck()
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def forward(self, logits, labels):
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numerator = self.scale * torch.cos(
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torch.acos(torch.clamp(torch.diagonal(logits.transpose(0, 1)[labels]), -1.0 + self.eps, 1 - self.eps))
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+ self.margin
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)
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excl = torch.cat(
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[torch.cat((logits[i, :y], logits[i, y + 1 :])).unsqueeze(0) for i, y in enumerate(labels)], dim=0
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)
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denominator = torch.exp(numerator) + torch.sum(torch.exp(self.scale * excl), dim=1)
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L = numerator - torch.log(denominator)
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return -torch.mean(L)
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