Spaces:
Runtime error
Runtime error
File size: 1,402 Bytes
3b96cb1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmengine.model import BaseModule
from mmdet.registry import MODELS
@MODELS.register_module()
class GlobalAveragePooling(BaseModule):
"""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, kernel_size=None, stride=None):
super(GlobalAveragePooling, self).__init__()
if kernel_size is None and stride is None:
self.gap = nn.AdaptiveAvgPool2d((1, 1))
else:
self.gap = nn.AvgPool2d(kernel_size, stride)
def forward(self, inputs):
if isinstance(inputs, tuple):
outs = tuple([self.gap(x) for x in inputs])
outs = tuple([
out.view(x.size(0),
torch.tensor(out.size()[1:]).prod())
for out, x in zip(outs, inputs)
])
elif isinstance(inputs, torch.Tensor):
outs = self.gap(inputs)
outs = outs.view(
inputs.size(0),
torch.tensor(outs.size()[1:]).prod())
else:
raise TypeError('neck inputs should be tuple or torch.tensor')
return outs
|