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mmpretrain.models.necks.linear_neck 源代码

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

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

from mmpretrain.registry import MODELS


[文档]@MODELS.register_module() class LinearNeck(BaseModule): """Linear neck with Dimension projection. Args: in_channels (int): Number of channels in the input. out_channels (int): Number of channels in the output. gap_dim (int): Dimensions of each sample channel, can be one of {0, 1, 2, 3}. Defaults to 0. norm_cfg (dict, optional): dictionary to construct and config norm layer. Defaults to dict(type='BN1d'). act_cfg (dict, optional): dictionary to construct and config activate layer. Defaults to None. init_cfg (dict, optional): dictionary to initialize weights. Defaults to None. """ def __init__(self, in_channels: int, out_channels: int, gap_dim: int = 0, norm_cfg: Optional[dict] = dict(type='BN1d'), act_cfg: Optional[dict] = None, init_cfg: Optional[dict] = None): super().__init__(init_cfg=init_cfg) self.in_channels = in_channels self.out_channels = out_channels self.norm_cfg = copy.deepcopy(norm_cfg) self.act_cfg = copy.deepcopy(act_cfg) assert gap_dim in [0, 1, 2, 3], 'GlobalAveragePooling dim only ' \ f'support {0, 1, 2, 3}, get {gap_dim} instead.' if gap_dim == 0: self.gap = nn.Identity() elif gap_dim == 1: self.gap = nn.AdaptiveAvgPool1d(1) elif gap_dim == 2: self.gap = nn.AdaptiveAvgPool2d((1, 1)) elif gap_dim == 3: self.gap = nn.AdaptiveAvgPool3d((1, 1, 1)) self.fc = nn.Linear(in_features=in_channels, out_features=out_channels) if norm_cfg: self.norm = build_norm_layer(norm_cfg, out_channels)[1] else: self.norm = nn.Identity() if act_cfg: self.act = build_activation_layer(act_cfg) else: self.act = nn.Identity()
[文档] def forward(self, inputs: Union[Tuple, torch.Tensor]) -> Tuple[torch.Tensor]: """forward function. Args: inputs (Union[Tuple, torch.Tensor]): The features extracted from the backbone. Multiple stage inputs are acceptable but only the last stage will be used. Returns: Tuple[torch.Tensor]: A tuple of output features. """ assert isinstance(inputs, (tuple, torch.Tensor)), ( 'The inputs of `LinearNeck` must be tuple or `torch.Tensor`, ' f'but get {type(inputs)}.') if isinstance(inputs, tuple): inputs = inputs[-1] x = self.gap(inputs) x = x.view(x.size(0), -1) out = self.act(self.norm(self.fc(x))) return (out, )
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