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You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Source code for mmcls.models.heads.multi_label_linear_head

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

from ..builder import HEADS
from .multi_label_head import MultiLabelClsHead


[docs]@HEADS.register_module() class MultiLabelLinearClsHead(MultiLabelClsHead): """Linear classification head for multilabel task. Args: num_classes (int): Number of categories. in_channels (int): Number of channels in the input feature map. loss (dict): Config of classification loss. init_cfg (dict | optional): The extra init config of layers. Defaults to use dict(type='Normal', layer='Linear', std=0.01). """ def __init__(self, num_classes, in_channels, loss=dict( type='CrossEntropyLoss', use_sigmoid=True, reduction='mean', loss_weight=1.0), init_cfg=dict(type='Normal', layer='Linear', std=0.01)): super(MultiLabelLinearClsHead, self).__init__( loss=loss, init_cfg=init_cfg) if num_classes <= 0: raise ValueError( f'num_classes={num_classes} must be a positive integer') self.in_channels = in_channels self.num_classes = num_classes self.fc = nn.Linear(self.in_channels, self.num_classes) def pre_logits(self, x): if isinstance(x, tuple): x = x[-1] return x def forward_train(self, x, gt_label, **kwargs): x = self.pre_logits(x) gt_label = gt_label.type_as(x) cls_score = self.fc(x) losses = self.loss(cls_score, gt_label, **kwargs) return losses
[docs] def simple_test(self, x, sigmoid=True, post_process=True): """Inference without augmentation. Args: x (tuple[Tensor]): The input features. Multi-stage inputs are acceptable but only the last stage will be used to classify. The shape of every item should be ``(num_samples, in_channels)``. 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)``. """ x = self.pre_logits(x) cls_score = self.fc(x) if sigmoid: pred = torch.sigmoid(cls_score) if cls_score is not None else None else: pred = cls_score if post_process: return self.post_process(pred) else: return pred
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