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mmpretrain.models.necks.mocov2_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 mmengine.model import BaseModule

from mmpretrain.registry import MODELS


[文档]@MODELS.register_module() class MoCoV2Neck(BaseModule): """The non-linear neck of MoCo v2: fc-relu-fc. 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. init_cfg (dict or list[dict], optional): Initialization config dict. Defaults to None. """ def __init__(self, in_channels: int, hid_channels: int, out_channels: int, with_avg_pool: bool = True, init_cfg: Optional[Union[dict, List[dict]]] = None) -> None: super().__init__(init_cfg) self.with_avg_pool = with_avg_pool if with_avg_pool: self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.mlp = nn.Sequential( nn.Linear(in_channels, hid_channels), nn.ReLU(inplace=True), nn.Linear(hid_channels, out_channels))
[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) return (self.mlp(x.view(x.size(0), -1)), )
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