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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'})[source]

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)[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).

  • 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

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