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# Copyright (c) OpenMMLab. All rights reserved.
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().__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, list):
outs = [self.gap(x) for x in inputs]
outs = [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
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