class mmcls.models.ClsHead(loss={'loss_weight': 1.0, 'type': 'CrossEntropyLoss'}, topk=(1,), cal_acc=False, init_cfg=None)[source]

Parameters
• loss (dict) – Config of classification loss.

• topk (int | tuple) – Top-k accuracy.

• cal_acc (bool) – Whether to calculate accuracy during training. If you use Mixup/CutMix or something like that during training, it is not reasonable to calculate accuracy. Defaults to False.

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

Inference without augmentation.

Parameters
• cls_score (tuple[Tensor]) – The input classification score logits. Multi-stage inputs are acceptable but only the last stage will be used to classify. The shape of every item should be (num_samples, num_classes).

• 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