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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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