mirror of
https://github.com/open-mmlab/mmclassification.git
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44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
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import torch.nn as nn
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from mmcv.cnn import normal_init
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from ..builder import HEADS
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from .cls_head import ClsHead
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@HEADS.register_module()
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class LinearClsHead(ClsHead):
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"""Linear classifier head.
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Args:
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num_classes (int): Number of categories excluding the background
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category.
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in_channels (int): Number of channels in the input feature map.
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loss (dict): Config of classification loss.
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""" # noqa: W605
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def __init__(self,
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num_classes,
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in_channels,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
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topk=(1, )):
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super(LinearClsHead, self).__init__(loss=loss, topk=topk)
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self.in_channels = in_channels
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self.num_classes = num_classes
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if self.num_classes <= 0:
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raise ValueError(
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f'num_classes={num_classes} must be a positive integer')
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self._init_layers()
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def _init_layers(self):
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self.fc = nn.Linear(self.in_channels, self.num_classes)
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def init_weights(self):
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normal_init(self.fc, mean=0, std=0.01, bias=0)
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def forward_train(self, x, gt_label):
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cls_score = self.fc(x)
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losses = self.loss(cls_score, gt_label)
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return losses
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