Shortcuts

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.

LinearClsHead

class mmcls.models.LinearClsHead(num_classes, in_channels, init_cfg={'layer': 'Linear', 'std': 0.01, 'type': 'Normal'}, *args, **kwargs)[source]

Linear classifier head.

Parameters
  • num_classes (int) – Number of categories excluding the background category.

  • in_channels (int) – Number of channels in the input feature map.

  • init_cfg (dict | optional) – The extra init config of layers. Defaults to use dict(type=’Normal’, layer=’Linear’, std=0.01).

simple_test(x, softmax=True, post_process=True)[source]

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).

  • softmax (bool) – Whether to softmax 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

Read the Docs v: master
Versions
master
latest
1.x
dev-1.x
Downloads
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.