mmpretrain.engine.hooks.margin_head_hooks 源代码

# Copyright (c) OpenMMLab. All rights reserved
import numpy as np
from mmengine.hooks import Hook
from mmengine.model import is_model_wrapper

from mmpretrain.models.heads import ArcFaceClsHead
from mmpretrain.registry import HOOKS

[文档]@HOOKS.register_module() class SetAdaptiveMarginsHook(Hook): r"""Set adaptive-margins in ArcFaceClsHead based on the power of category-wise count. A PyTorch implementation of paper `Google Landmark Recognition 2020 Competition Third Place Solution <>`_. The margins will be :math:`\text{f}(n) = (marginMax - marginMin) · norm(n^p) + marginMin`. The `n` indicates the number of occurrences of a category. Args: margin_min (float): Lower bound of margins. Defaults to 0.05. margin_max (float): Upper bound of margins. Defaults to 0.5. power (float): The power of category freqercy. Defaults to -0.25. """ def __init__(self, margin_min=0.05, margin_max=0.5, power=-0.25) -> None: self.margin_min = margin_min self.margin_max = margin_max self.margin_range = margin_max - margin_min self.p = power
[文档] def before_train(self, runner): """change the margins in ArcFaceClsHead. Args: runner (obj: `Runner`): Runner. """ model = runner.model if is_model_wrapper(model): model = model.module if (hasattr(model, 'head') and not isinstance(model.head, ArcFaceClsHead)): raise ValueError( 'Hook ``SetFreqPowAdvMarginsHook`` could only be used ' f'for ``ArcFaceClsHead``, but get {type(model.head)}') # generate margins base on the dataset. gt_labels = runner.train_dataloader.dataset.get_gt_labels() label_count = np.bincount(gt_labels) label_count[label_count == 0] = 1 # At least one occurrence pow_freq = np.power(label_count, self.p) min_f, max_f = pow_freq.min(), pow_freq.max() normized_pow_freq = (pow_freq - min_f) / (max_f - min_f) margins = normized_pow_freq * self.margin_range + self.margin_min assert len(margins) == runner.model.head.num_classes model.head.set_margins(margins)
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