Source code for mmselfsup.models.heads.cls_head
# 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 mmselfsup.registry import MODELS
[docs]@MODELS.register_module()
class ClsHead(BaseModule):
"""Simplest classifier head, with only one fc layer.
Args:
loss (dict): Config of the loss.
with_avg_pool (bool): Whether to apply the average pooling
after neck. Defaults to False.
in_channels (int): Number of input channels. Defaults to 2048.
num_classes (int): Number of classes. Defaults to 1000.
init_cfg (Dict or List[Dict], optional): Initialization config dict.
"""
def __init__(
self,
loss: dict,
with_avg_pool: bool = False,
in_channels: int = 2048,
num_classes: int = 1000,
vit_backbone: bool = False,
init_cfg: Optional[Union[dict, List[dict]]] = [
dict(type='Normal', std=0.01, layer='Linear'),
dict(type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm'])
]
) -> None:
super().__init__(init_cfg)
self.loss = MODELS.build(loss)
self.with_avg_pool = with_avg_pool
self.in_channels = in_channels
self.num_classes = num_classes
self.vit_backbone = vit_backbone
if self.with_avg_pool:
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc_cls = nn.Linear(in_channels, num_classes)
[docs] def logits(
self, x: Union[List[torch.Tensor],
Tuple[torch.Tensor]]) -> List[torch.Tensor]:
"""Get the logits before the cross_entropy loss.
This module is used to obtain the logits before the loss.
Args:
x (List[Tensor] | Tuple[Tensor]): Feature maps of backbone,
each tensor has shape (N, C, H, W).
Returns:
List[Tensor]: A list of class scores.
"""
assert isinstance(x, (tuple, list)) and len(x) == 1
x = x[0]
if self.vit_backbone:
x = x[-1]
if self.with_avg_pool:
assert x.dim() == 4, \
f'Tensor must has 4 dims, got: {x.dim()}'
x = self.avg_pool(x)
x = x.view(x.size(0), -1)
cls_score = self.fc_cls(x)
return [cls_score]
[docs] def forward(self, x: Union[List[torch.Tensor], Tuple[torch.Tensor]],
label: torch.Tensor) -> torch.Tensor:
"""Get the loss.
Args:
x (List[Tensor] | Tuple[Tensor]): Feature maps of backbone,
each tensor has shape (N, C, H, W).
label (torch.Tensor): The label for cross entropy loss.
Returns:
torch.Tensor: The cross entropy loss.
"""
outs = self.logits(x)
loss = self.loss(outs[0], label)
return loss