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mmselfsup.models.necks.odc_neck 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmcv.runner import BaseModule

from ..builder import NECKS


@NECKS.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,
                 hid_channels,
                 out_channels,
                 with_avg_pool=True,
                 norm_cfg=dict(type='SyncBN'),
                 init_cfg=[
                     dict(
                         type='Constant',
                         val=1,
                         layer=['_BatchNorm', 'GroupNorm'])
                 ]):
        super(ODCNeck, self).__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()

    def forward(self, x):
        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]