I use mxnet on [insightface](https://github.com/deepinsight/insightface) to
extract features.
The output of the model is a feature, but insightface sets the label for
forward().
> db = mx.io.DataBatch(data=(_data,), label=(_label,))
> model.forward(db, is_train=False)
and did not enter a label when loading the model.
> model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0],
> image_size[1]))])
https://github.com/deepinsight/insightface/blob/836db1b89677b566fe80575e6b694079b3c6f299/src/eval/verification.py#L229
I don't think you should enter a label in the feature extraction to avoid
errors.
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