chore: import upstream snapshot with attribution

python/tvm/relax/op/vision/multibox_transform_loc.py

@@ -0,0 +1,85 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Multibox location transform for object detection."""
+
+from . import _ffi_api
+
+
+def multibox_transform_loc(
+    cls_pred,
+    loc_pred,
+    anchor,
+    clip=False,
+    threshold=0.0,
+    variances=(1.0, 1.0, 1.0, 1.0),
+    keep_background=True,
+):
+    """SSD / TFLite-style decode: priors + offsets → boxes; logits → softmax scores.
+
+    Box decode follows TFLite ``DecodeCenterSizeBoxes``; expected tensor layout matches
+    ``tflite_frontend.convert_detection_postprocess`` (loc reorder yxhw→xywh, anchor ltrb).
+
+    Parameters
+    ----------
+    cls_pred : relax.Expr
+        ``[B, C, N]`` class logits (pre-softmax).
+    loc_pred : relax.Expr
+        ``[B, 4*N]`` per-anchor encodings as ``(x,y,w,h)`` after reorder (see above).
+    anchor : relax.Expr
+        ``[1, N, 4]`` priors: ``(left, top, right, bottom)``.
+    clip : bool
+        If True, clip ``ymin,xmin,ymax,xmax`` to ``[0, 1]``.
+    threshold : float
+        After softmax, multiply scores by mask ``(score >= threshold)``.
+    variances : tuple of 4 floats
+        ``(x,y,w,h)`` = TFLite ``1/x_scale, 1/y_scale, 1/w_scale, 1/h_scale``.
+        Use magnitudes consistent with the model: very large ``w``/``h`` entries scale the
+        encoded height/width terms inside ``exp(...)`` and can overflow in float32/float16.
+    keep_background : bool
+        If False, set output scores at class index 0 to zero.
+
+    Returns
+    -------
+    result : relax.Expr
+        Tuple ``(boxes, scores)``: ``boxes`` is ``[B, N, 4]`` as ``(ymin,xmin,ymax,xmax)``;
+        ``scores`` is ``[B, C, N]`` softmax, post-processed like the implementation.
+
+    Notes
+    -----
+    **Shape/dtype (checked in ``FInferType`` when static):**
+
+    - ``cls_pred``: 3-D; ``loc_pred``: 2-D; ``anchor``: 3-D.
+    - ``cls_pred``, ``loc_pred``, ``anchor`` dtypes must match.
+    - ``N = cls_pred.shape[2]``; ``loc_pred.shape[1] == 4*N``; ``anchor.shape == [1,N,4]``.
+    - ``loc_pred.shape[1]`` must be divisible by 4.
+    - ``cls_pred.shape[0]`` must equal ``loc_pred.shape[0]`` (batch).
+
+    If ``cls_pred`` has **unknown** shape, inference only returns generic rank-3 tensor
+    type for the two outputs; it does **not** verify ``4*N`` vs ``loc_pred`` or
+    ``anchor.shape[1]`` vs ``N``, because ``N`` is not available statically. Other checks
+    (ranks, dtypes, ``loc_pred.shape[1] % 4 == 0`` when known, batch match when both batch
+    axes are known, etc.) still run where applicable.
+    """
+    return _ffi_api.multibox_transform_loc(
+        cls_pred,
+        loc_pred,
+        anchor,
+        clip,
+        threshold,
+        variances,
+        keep_background,
+    )