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mmcls.models.heads.multi_label_head 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch

from ..builder import HEADS, build_loss
from ..utils import is_tracing
from .base_head import BaseHead


[文档]@HEADS.register_module() class MultiLabelClsHead(BaseHead): """Classification head for multilabel task. Args: loss (dict): Config of classification loss. """ def __init__(self, loss=dict( type='CrossEntropyLoss', use_sigmoid=True, reduction='mean', loss_weight=1.0), init_cfg=None): super(MultiLabelClsHead, self).__init__(init_cfg=init_cfg) assert isinstance(loss, dict) self.compute_loss = build_loss(loss) def loss(self, cls_score, gt_label): gt_label = gt_label.type_as(cls_score) num_samples = len(cls_score) losses = dict() # map difficult examples to positive ones _gt_label = torch.abs(gt_label) # compute loss loss = self.compute_loss(cls_score, _gt_label, avg_factor=num_samples) losses['loss'] = loss return losses def forward_train(self, cls_score, gt_label, **kwargs): if isinstance(cls_score, tuple): cls_score = cls_score[-1] gt_label = gt_label.type_as(cls_score) losses = self.loss(cls_score, gt_label, **kwargs) return losses def pre_logits(self, x): if isinstance(x, tuple): x = x[-1] from mmcls.utils import get_root_logger logger = get_root_logger() logger.warning( 'The input of MultiLabelClsHead should be already logits. ' 'Please modify the backbone if you want to get pre-logits feature.' ) return x def simple_test(self, x, sigmoid=True, post_process=True): """Inference without augmentation. Args: cls_score (tuple[Tensor]): The input classification score logits. Multi-stage inputs are acceptable but only the last stage will be used to classify. The shape of every item should be ``(num_samples, num_classes)``. sigmoid (bool): Whether to sigmoid the classification score. post_process (bool): Whether to do post processing the inference results. It will convert the output to a list. Returns: Tensor | list: The inference results. - If no post processing, the output is a tensor with shape ``(num_samples, num_classes)``. - If post processing, the output is a multi-dimentional list of float and the dimensions are ``(num_samples, num_classes)``. """ if isinstance(x, tuple): x = x[-1] if sigmoid: # Convert to full precision because sigmoid is sensitive. pred = torch.sigmoid(x.float()) if x is not None else None else: pred = x if post_process: return self.post_process(pred) else: return pred def post_process(self, pred): on_trace = is_tracing() if torch.onnx.is_in_onnx_export() or on_trace: return pred pred = list(pred.detach().cpu().numpy()) return pred
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