46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
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from mmcls.models.losses import Accuracy
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from ..builder import HEADS, build_loss
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from .base_head import BaseHead
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@HEADS.register_module()
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class ClsHead(BaseHead):
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"""classification head.
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Args:
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loss (dict): Config of classification loss.
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topk (int | tuple): Top-k accuracy.
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""" # noqa: W605
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def __init__(self,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
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topk=(1, )):
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super(ClsHead, self).__init__()
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assert isinstance(loss, dict)
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assert isinstance(topk, (int, tuple))
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if isinstance(topk, int):
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topk = (topk, )
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for _topk in topk:
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assert _topk > 0, 'Top-k should be larger than 0'
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self.topk = topk
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self.compute_loss = build_loss(loss)
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self.compute_accuracy = Accuracy(topk=self.topk)
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def loss(self, cls_score, gt_label):
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num_samples = len(cls_score)
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losses = dict()
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# compute loss
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loss = self.compute_loss(cls_score, gt_label, avg_factor=num_samples)
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# compute accuracy
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acc = self.compute_accuracy(cls_score, gt_label)
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assert len(acc) == len(self.topk)
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losses['loss'] = loss
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losses['accuracy'] = {f'top-{k}': a for k, a in zip(self.topk, acc)}
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return losses
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def forward_train(self, cls_score, gt_label):
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losses = self.loss(cls_score, gt_label)
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return losses
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