mmpretrain.models.necks.gem 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch import Tensor, nn
from torch.nn import functional as F
from torch.nn.parameter import Parameter

from mmpretrain.registry import MODELS

def gem(x: Tensor, p: Parameter, eps: float = 1e-6, clamp=True) -> Tensor:
    if clamp:
        x = x.clamp(min=eps)
    return F.avg_pool2d(x.pow(p), (x.size(-2), x.size(-1))).pow(1. / p)

[文档]@MODELS.register_module() class GeneralizedMeanPooling(nn.Module): """Generalized Mean Pooling neck. Note that we use `view` to remove extra channel after pooling. We do not use `squeeze` as it will also remove the batch dimension when the tensor has a batch dimension of size 1, which can lead to unexpected errors. Args: p (float): Parameter value. Defaults to 3. eps (float): epsilon. Defaults to 1e-6. clamp (bool): Use clamp before pooling. Defaults to True p_trainable (bool): Toggle whether Parameter p is trainable or not. Defaults to True. """ def __init__(self, p=3., eps=1e-6, clamp=True, p_trainable=True): assert p >= 1, "'p' must be a value greater than 1" super(GeneralizedMeanPooling, self).__init__() self.p = Parameter(torch.ones(1) * p, requires_grad=p_trainable) self.eps = eps self.clamp = clamp self.p_trainable = p_trainable def forward(self, inputs): if isinstance(inputs, tuple): outs = tuple([ gem(x, p=self.p, eps=self.eps, clamp=self.clamp) for x in inputs ]) outs = tuple( [out.view(x.size(0), -1) for out, x in zip(outs, inputs)]) elif isinstance(inputs, torch.Tensor): outs = gem(inputs, p=self.p, eps=self.eps, clamp=self.clamp) outs = outs.view(inputs.size(0), -1) else: raise TypeError('neck inputs should be tuple or torch.tensor') return outs
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