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mmselfsup.models.necks.densecl_neck 源代码
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule
from ..builder import NECKS
[文档]@NECKS.register_module()
class DenseCLNeck(BaseModule):
"""The non-linear neck of DenseCL.
Single and dense neck in parallel: fc-relu-fc, conv-relu-conv.
Borrowed from the authors' code: `<https://github.com/WXinlong/DenseCL`_.
Args:
in_channels (int): Number of input channels.
hid_channels (int): Number of hidden channels.
out_channels (int): Number of output channels.
num_grid (int): The grid size of dense features. Defaults to None.
init_cfg (dict or list[dict], optional): Initialization config dict.
Defaults to None.
"""
def __init__(self,
in_channels,
hid_channels,
out_channels,
num_grid=None,
init_cfg=None):
super(DenseCLNeck, self).__init__(init_cfg)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.mlp = nn.Sequential(
nn.Linear(in_channels, hid_channels), nn.ReLU(inplace=True),
nn.Linear(hid_channels, out_channels))
self.with_pool = True if num_grid is not None else False
if self.with_pool:
self.pool = nn.AdaptiveAvgPool2d((num_grid, num_grid))
self.mlp2 = nn.Sequential(
nn.Conv2d(in_channels, hid_channels, 1), nn.ReLU(inplace=True),
nn.Conv2d(hid_channels, out_channels, 1))
self.avgpool2 = nn.AdaptiveAvgPool2d((1, 1))
[文档] def forward(self, x):
"""Forward function of neck.
Args:
x (list[tensor]): feature map of backbone.
"""
assert len(x) == 1
x = x[0]
avgpooled_x = self.avgpool(x)
avgpooled_x = self.mlp(avgpooled_x.view(avgpooled_x.size(0), -1))
if self.with_pool:
x = self.pool(x) # sxs
x = self.mlp2(x) # sxs: bxdxsxs
avgpooled_x2 = self.avgpool2(x) # 1x1: bxdx1x1
x = x.view(x.size(0), x.size(1), -1) # bxdxs^2
avgpooled_x2 = avgpooled_x2.view(avgpooled_x2.size(0), -1) # bxd
return [avgpooled_x, x, avgpooled_x2]