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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.

VisionTransformerClsHead

class mmcls.models.VisionTransformerClsHead(num_classes, in_channels, hidden_dim=None, act_cfg={'type': 'Tanh'}, init_cfg={'layer': 'Linear', 'type': 'Constant', 'val': 0}, *args, **kwargs)[source]

Vision Transformer classifier head.

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

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

  • hidden_dim (int) – Number of the dimensions for hidden layer. Defaults to None, which means no extra hidden layer.

  • act_cfg (dict) – The activation config. Only available during pre-training. Defaults to dict(type='Tanh').

  • init_cfg (dict) – The extra initialization configs. Defaults to dict(type='Constant', layer='Linear', val=0).

init_weights()[source]

Initialize the weights.

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

Inference without augmentation.

Parameters
  • x (tuple[tuple[tensor, tensor]]) – The input features. Multi-stage inputs are acceptable but only the last stage will be used to classify. Every item should be a tuple which includes patch token and cls token. The cls token will be used to classify and the shape of it 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

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