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# 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
@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))
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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