54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
import torch
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import torch.nn.functional as F
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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 MultiLabelClsHead(BaseHead):
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"""Classification head for multilabel task.
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Args:
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loss (dict): Config of classification loss.
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"""
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def __init__(self,
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loss=dict(
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type='CrossEntropyLoss',
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use_sigmoid=True,
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reduction='mean',
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loss_weight=1.0),
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init_cfg=None):
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super(MultiLabelClsHead, self).__init__(init_cfg=init_cfg)
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assert isinstance(loss, dict)
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self.compute_loss = build_loss(loss)
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def loss(self, cls_score, gt_label):
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gt_label = gt_label.type_as(cls_score)
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num_samples = len(cls_score)
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losses = dict()
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# map difficult examples to positive ones
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_gt_label = torch.abs(gt_label)
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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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losses['loss'] = loss
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return losses
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def forward_train(self, cls_score, gt_label):
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gt_label = gt_label.type_as(cls_score)
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losses = self.loss(cls_score, gt_label)
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return losses
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def simple_test(self, cls_score):
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if isinstance(cls_score, list):
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cls_score = sum(cls_score) / float(len(cls_score))
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pred = F.sigmoid(cls_score) if cls_score is not None else None
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if torch.onnx.is_in_onnx_export():
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return pred
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pred = list(pred.detach().cpu().numpy())
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return pred
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