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mmselfsup.models.necks.swav_neck 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch
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 SwAVNeck(BaseModule):
    """The non-linear neck of SwAV: fc-bn-relu-fc-normalization.

    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.
        with_l2norm (bool): whether to normalize the output after projection.
            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,
                 with_l2norm=True,
                 norm_cfg=dict(type='SyncBN'),
                 init_cfg=[
                     dict(
                         type='Constant',
                         val=1,
                         layer=['_BatchNorm', 'GroupNorm'])
                 ]):
        super(SwAVNeck, self).__init__(init_cfg)
        self.with_avg_pool = with_avg_pool
        self.with_l2norm = with_l2norm
        if with_avg_pool:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        if out_channels == 0:
            self.projection_neck = None
        elif hid_channels == 0:
            self.projection_neck = nn.Linear(in_channels, out_channels)
        else:
            self.bn = build_norm_layer(norm_cfg, hid_channels)[1]
            self.projection_neck = nn.Sequential(
                nn.Linear(in_channels, hid_channels), self.bn,
                nn.ReLU(inplace=True), nn.Linear(hid_channels, out_channels))

    def forward_projection(self, x):
        if self.projection_neck is not None:
            x = self.projection_neck(x)
        if self.with_l2norm:
            x = nn.functional.normalize(x, dim=1, p=2)
        return x

    def forward(self, x):
        # forward computing
        # x: list of feature maps, len(x) according to len(num_crops)
        avg_out = []
        for _x in x:
            _x = _x[0]
            if self.with_avg_pool:
                _out = self.avgpool(_x)
                avg_out.append(_out)
        feat_vec = torch.cat(avg_out)  # [sum(num_crops) * N, C]
        feat_vec = feat_vec.view(feat_vec.size(0), -1)
        output = self.forward_projection(feat_vec)
        return [output]