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mmpretrain.models.necks.nonlinear_neck 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmengine.model import BaseModule

from mmpretrain.registry import MODELS


[文档]@MODELS.register_module() class NonLinearNeck(BaseModule): """The non-linear neck. Structure: fc-bn-[relu-fc-bn] where the substructure in [] can be repeated. For the default setting, the repeated time is 1. The neck can be used in many algorithms, e.g., SimCLR, BYOL, SimSiam. Args: in_channels (int): Number of input channels. hid_channels (int): Number of hidden channels. out_channels (int): Number of output channels. num_layers (int): Number of fc layers. Defaults to 2. with_bias (bool): Whether to use bias in fc layers (except for the last). Defaults to False. with_last_bn (bool): Whether to add the last BN layer. Defaults to True. with_last_bn_affine (bool): Whether to have learnable affine parameters in the last BN layer (set False for SimSiam). Defaults to True. with_last_bias (bool): Whether to use bias in the last fc layer. Defaults to False. 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, num_layers: int = 2, with_bias: bool = False, with_last_bn: bool = True, with_last_bn_affine: bool = True, with_last_bias: bool = False, 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(NonLinearNeck, self).__init__(init_cfg) self.with_avg_pool = with_avg_pool if with_avg_pool: self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.relu = nn.ReLU(inplace=True) self.fc0 = nn.Linear(in_channels, hid_channels, bias=with_bias) self.bn0 = build_norm_layer(norm_cfg, hid_channels)[1] self.fc_names = [] self.bn_names = [] for i in range(1, num_layers): this_channels = out_channels if i == num_layers - 1 \ else hid_channels if i != num_layers - 1: self.add_module( f'fc{i}', nn.Linear(hid_channels, this_channels, bias=with_bias)) self.add_module(f'bn{i}', build_norm_layer(norm_cfg, this_channels)[1]) self.bn_names.append(f'bn{i}') else: self.add_module( f'fc{i}', nn.Linear( hid_channels, this_channels, bias=with_last_bias)) if with_last_bn: self.add_module( f'bn{i}', build_norm_layer( dict(**norm_cfg, affine=with_last_bn_affine), this_channels)[1]) self.bn_names.append(f'bn{i}') else: self.bn_names.append(None) self.fc_names.append(f'fc{i}')
[文档] def forward(self, x: Tuple[torch.Tensor]) -> Tuple[torch.Tensor]: """Forward function. Args: x (Tuple[torch.Tensor]): The feature map of backbone. Returns: Tuple[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) for fc_name, bn_name in zip(self.fc_names, self.bn_names): fc = getattr(self, fc_name) x = self.relu(x) x = fc(x) if bn_name is not None: bn = getattr(self, bn_name) x = bn(x) return (x, )
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