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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.

StackedLinearClsHead

class mmcls.models.StackedLinearClsHead(num_classes: int, in_channels: int, mid_channels: Sequence, dropout_rate: float = 0.0, norm_cfg: Optional[Dict] = None, act_cfg: Dict = {'type': 'ReLU'}, **kwargs)[source]

Classifier head with several hidden fc layer and a output fc layer.

Parameters
  • num_classes (int) – Number of categories.

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

  • mid_channels (Sequence) – Number of channels in the hidden fc layers.

  • dropout_rate (float) – Dropout rate after each hidden fc layer, except the last layer. Defaults to 0.

  • norm_cfg (dict, optional) – Config dict of normalization layer after each hidden fc layer, except the last layer. Defaults to None.

  • act_cfg (dict, optional) – Config dict of activation function after each hidden layer, except the last layer. Defaults to use “ReLU”.

init_weights()[source]

Initialize the weights.

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

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