mirror of
https://github.com/open-mmlab/mmpretrain.git
synced 2025-06-03 14:59:18 +08:00
35 lines
1.0 KiB
Python
35 lines
1.0 KiB
Python
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
|
||
|
|
from ..builder import NECKS
|
||
|
|
|
||
|
|
|
||
|
|
@NECKS.register_module()
|
||
|
|
class GlobalAveragePooling(nn.Module):
|
||
|
|
"""Global Average 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.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
super(GlobalAveragePooling, self).__init__()
|
||
|
|
self.gap = nn.AdaptiveAvgPool2d((1, 1))
|
||
|
|
|
||
|
|
def init_weights(self):
|
||
|
|
pass
|
||
|
|
|
||
|
|
def forward(self, inputs):
|
||
|
|
if isinstance(inputs, tuple):
|
||
|
|
outs = tuple([self.gap(x) 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 = self.gap(inputs)
|
||
|
|
outs = outs.view(inputs.size(0), -1)
|
||
|
|
else:
|
||
|
|
raise TypeError('neck inputs should be tuple or torch.tensor')
|
||
|
|
return outs
|