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# 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)
@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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