MNNInferSession.__call__ extracted session output via
np.array(out_tensor.getData()).reshape(out_shape), which marshals
tensor contents through a slow per-element Python path. For a
PP-OCRv6 medium recognizer output (~17MB), this cost ~454ms versus
~210ms for the actual GPU inference that produced it.
Switch to getNumpyData(), which exposes the buffer directly. An
explicit copy is required since getNumpyData() returns a view into
memory owned by out_tensor; without it, out_tensor going out of
scope or being reused across shape changes caused intermittent
segfaults.
On a 39-box real-world image, total recognition time dropped from
~22s to ~8.4s. Per-crop output extraction dropped from ~454ms to
~10ms.
Fixes intermittent segfault risk from unmanaged zero-copy tensor
lifetime as a side effect.
