wehub-resource-sync
86 lines
3.3 KiB
Python
86 lines
3.3 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""Multibox location transform for object detection."""
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from . import _ffi_api
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def multibox_transform_loc(
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cls_pred,
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loc_pred,
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anchor,
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clip=False,
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threshold=0.0,
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variances=(1.0, 1.0, 1.0, 1.0),
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keep_background=True,
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):
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"""SSD / TFLite-style decode: priors + offsets → boxes; logits → softmax scores.
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Box decode follows TFLite ``DecodeCenterSizeBoxes``; expected tensor layout matches
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``tflite_frontend.convert_detection_postprocess`` (loc reorder yxhw→xywh, anchor ltrb).
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Parameters
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----------
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cls_pred : relax.Expr
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``[B, C, N]`` class logits (pre-softmax).
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loc_pred : relax.Expr
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``[B, 4*N]`` per-anchor encodings as ``(x,y,w,h)`` after reorder (see above).
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anchor : relax.Expr
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``[1, N, 4]`` priors: ``(left, top, right, bottom)``.
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clip : bool
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If True, clip ``ymin,xmin,ymax,xmax`` to ``[0, 1]``.
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threshold : float
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After softmax, multiply scores by mask ``(score >= threshold)``.
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variances : tuple of 4 floats
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``(x,y,w,h)`` = TFLite ``1/x_scale, 1/y_scale, 1/w_scale, 1/h_scale``.
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Use magnitudes consistent with the model: very large ``w``/``h`` entries scale the
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encoded height/width terms inside ``exp(...)`` and can overflow in float32/float16.
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keep_background : bool
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If False, set output scores at class index 0 to zero.
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Returns
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-------
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result : relax.Expr
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Tuple ``(boxes, scores)``: ``boxes`` is ``[B, N, 4]`` as ``(ymin,xmin,ymax,xmax)``;
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``scores`` is ``[B, C, N]`` softmax, post-processed like the implementation.
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Notes
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-----
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**Shape/dtype (checked in ``FInferType`` when static):**
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- ``cls_pred``: 3-D; ``loc_pred``: 2-D; ``anchor``: 3-D.
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- ``cls_pred``, ``loc_pred``, ``anchor`` dtypes must match.
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- ``N = cls_pred.shape[2]``; ``loc_pred.shape[1] == 4*N``; ``anchor.shape == [1,N,4]``.
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- ``loc_pred.shape[1]`` must be divisible by 4.
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- ``cls_pred.shape[0]`` must equal ``loc_pred.shape[0]`` (batch).
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If ``cls_pred`` has **unknown** shape, inference only returns generic rank-3 tensor
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type for the two outputs; it does **not** verify ``4*N`` vs ``loc_pred`` or
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``anchor.shape[1]`` vs ``N``, because ``N`` is not available statically. Other checks
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(ranks, dtypes, ``loc_pred.shape[1] % 4 == 0`` when known, batch match when both batch
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axes are known, etc.) still run where applicable.
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"""
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return _ffi_api.multibox_transform_loc(
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cls_pred,
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loc_pred,
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anchor,
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clip,
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threshold,
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variances,
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keep_background,
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)
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