Note

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.

MultiLabelLinearClsHead

class mmcls.models.MultiLabelLinearClsHead(num_classes, in_channels, loss={'loss_weight': 1.0, 'reduction': 'mean', 'type': 'CrossEntropyLoss', 'use_sigmoid': True}, init_cfg={'layer': 'Linear', 'std': 0.01, 'type': 'Normal'})

Linear classification head for multilabel task.

Parameters

• 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).

simple_test(x, sigmoid=True, post_process=True)

Inference without augmentation.

Parameters

• 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

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).

Return type

Tensor | list