Source code for mmselfsup.models.necks.odc_neck
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Union
import torch
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmengine.model import BaseModule
from mmselfsup.registry import MODELS
[docs]@MODELS.register_module()
class ODCNeck(BaseModule):
"""The non-linear neck of ODC: fc-bn-relu-dropout-fc-relu.
Args:
in_channels (int): Number of input channels.
hid_channels (int): Number of hidden channels.
out_channels (int): Number of output channels.
with_avg_pool (bool): Whether to apply the global
average pooling after backbone. Defaults to True.
norm_cfg (dict): Dictionary to construct and config norm layer.
Defaults to dict(type='SyncBN').
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
def __init__(
self,
in_channels: int,
hid_channels: int,
out_channels: int,
with_avg_pool: bool = True,
norm_cfg: dict = dict(type='SyncBN'),
init_cfg: Optional[Union[dict, List[dict]]] = [
dict(type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm'])
]
) -> None:
super().__init__(init_cfg)
self.with_avg_pool = with_avg_pool
if with_avg_pool:
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc0 = nn.Linear(in_channels, hid_channels)
self.bn0 = build_norm_layer(
dict(**norm_cfg, momentum=0.001, affine=False), hid_channels)[1]
self.fc1 = nn.Linear(hid_channels, out_channels)
self.relu = nn.ReLU(inplace=True)
self.dropout = nn.Dropout()
[docs] def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
"""Forward function.
Args:
x (List[torch.Tensor]): The feature map of backbone.
Returns:
List[torch.Tensor]: The output features.
"""
assert len(x) == 1
x = x[0]
if self.with_avg_pool:
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc0(x)
x = self.bn0(x)
x = self.relu(x)
x = self.dropout(x)
x = self.fc1(x)
x = self.relu(x)
return [x]