chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# isort: skip_file
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# 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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"""VISION operators."""
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from .multibox_transform_loc import *
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from .nms import *
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from .roi_align import *
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from .roi_pool import *
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# 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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"""Constructor APIs"""
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import tvm_ffi
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tvm_ffi.init_ffi_api("relax.op.vision", __name__)
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@@ -0,0 +1,85 @@
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# Licensed to the Apache Software Foundation (ASF) under one
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# 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
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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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# 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
|
||||
# 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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"""Non-maximum suppression operators."""
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from . import _ffi_api
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def all_class_non_max_suppression(
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boxes,
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scores,
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max_output_boxes_per_class,
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iou_threshold,
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score_threshold,
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output_format="onnx",
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):
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"""Non-maximum suppression operator for object detection, corresponding to ONNX
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NonMaxSuppression and TensorFlow combined_non_max_suppression.
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NMS is performed for each class separately.
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Parameters
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----------
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boxes : relax.Expr
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3-D tensor with shape (batch_size, num_boxes, 4)
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scores: relax.Expr
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3-D tensor with shape (batch_size, num_classes, num_boxes)
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max_output_boxes_per_class : relax.Expr
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The maxinum number of output selected boxes per class
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iou_threshold : relax.Expr
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IoU test threshold
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score_threshold : relax.Expr
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Score threshold to filter out low score boxes early
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output_format : str, optional
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"onnx" or "tensorflow", see below.
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Returns
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-------
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out : relax.Expr
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If `output_format` is "onnx", the output is two tensors. The first is `indices` of size
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`(batch_size * num_class* num_boxes , 3)` and the second is a scalar tensor
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`num_total_detection` of shape `(1,)` representing the total number of selected
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boxes. The three values in `indices` encode batch, class, and box indices.
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Rows of `indices` are ordered such that selected boxes from batch 0, class 0 come
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first, in descending of scores, followed by boxes from batch 0, class 1 etc.
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The output uses dynamic_strided_slice to trim to only valid detections,
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so the first tensor has shape (num_total_detection, 3) containing only valid rows.
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If `output_format` is "tensorflow", the output is three tensors, the first
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is `indices` of size `(batch_size, num_class * num_boxes , 2)`, the second is `scores` of
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size `(batch_size, num_class * num_boxes)`, and the third is `num_total_detection` of size
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`(batch_size,)` representing the total number of selected boxes per batch. The two values
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in `indices` encode class and box indices. Of num_class * num_boxes boxes in `indices` at
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batch b, only the first `num_total_detection[b]` entries are valid. The second axis of
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`indices` and `scores` are sorted within each class by box scores, but not across classes.
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So the box indices and scores for the class 0 come first in a sorted order, followed by
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the class 1 etc.
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"""
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return _ffi_api.all_class_non_max_suppression(
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boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, output_format
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)
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def get_valid_counts(data, score_threshold=0, id_index=0, score_index=1):
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"""Get valid count of bounding boxes given a score threshold.
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Also moves valid boxes to the top of input data.
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Parameters
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----------
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data : relax.Expr
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3-D tensor with shape [batch_size, num_anchors, elem_length].
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score_threshold : float, optional
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Lower limit of score for valid bounding boxes.
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id_index : int, optional
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Index of the class categories. Set to ``-1`` to disable the class-id check.
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score_index : int, optional
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Index of the scores/confidence of boxes.
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Returns
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-------
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out : relax.Expr
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A tuple ``(valid_count, out_tensor, out_indices)`` where ``valid_count``
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has shape ``[batch_size]``, ``out_tensor`` has shape
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``[batch_size, num_anchors, elem_length]``, and ``out_indices`` has shape
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``[batch_size, num_anchors]``.
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"""
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return _ffi_api.get_valid_counts(data, score_threshold, id_index, score_index)
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def non_max_suppression(
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data,
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valid_count,
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indices,
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max_output_size=-1,
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iou_threshold=0.5,
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force_suppress=False,
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top_k=-1,
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coord_start=2,
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score_index=1,
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id_index=0,
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return_indices=True,
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invalid_to_bottom=False,
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soft_nms_sigma=0.0,
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score_threshold=0.0,
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):
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"""Non-maximum suppression operator for object detection.
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Parameters
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----------
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data : relax.Expr
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3-D tensor with shape [batch_size, num_anchors, elem_length].
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valid_count : relax.Expr
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1-D tensor for valid number of boxes.
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indices : relax.Expr
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2-D tensor with shape [batch_size, num_anchors].
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max_output_size : int, optional
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Max number of output valid boxes, -1 for no limit.
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iou_threshold : float, optional
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Non-maximum suppression IoU threshold.
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force_suppress : bool, optional
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Whether to suppress all detections regardless of class_id. When
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``id_index`` is ``-1``, all valid boxes are treated as belonging to the
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same class, so this flag has the same effect as ``True``.
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top_k : int, optional
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Keep maximum top k detections before nms, -1 for no limit.
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coord_start : int, optional
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Start index of the consecutive 4 coordinates.
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score_index : int, optional
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Index of the scores/confidence of boxes.
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id_index : int, optional
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Index of the class categories. Set to ``-1`` to suppress boxes across
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all classes.
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return_indices : bool, optional
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Whether to return box indices in input data.
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invalid_to_bottom : bool, optional
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Whether to move valid bounding boxes to the top of the returned tensor.
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This option only affects the ``return_indices=False`` path.
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soft_nms_sigma : float, optional
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Sigma for soft-NMS Gaussian penalty. When ``0.0`` (default), standard
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hard NMS is used. Positive values decay overlapping box scores instead
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of suppressing them outright.
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score_threshold : float, optional
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Post-decay minimum score for a box to remain eligible during soft-NMS.
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Only used when ``soft_nms_sigma > 0``. This is distinct from
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``get_valid_counts.score_threshold``, which filters boxes before NMS.
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Defaults to ``0.0``.
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Returns
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-------
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out : relax.Expr
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The return tuple shape depends on ``soft_nms_sigma``.
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If ``return_indices`` is ``True`` and ``soft_nms_sigma`` is ``0.0``,
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returns a 2-tuple ``(box_indices, valid_box_count)`` with shapes
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``[batch_size, num_anchors]`` and ``[batch_size, 1]``.
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If ``return_indices`` is ``True`` and ``soft_nms_sigma > 0``,
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returns a 3-tuple ``(out_data, box_indices, valid_box_count)`` where
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decayed ``out_data`` is prepended and has the same shape as the input
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data.
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Otherwise returns the modified data tensor.
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"""
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return _ffi_api.non_max_suppression(
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data,
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valid_count,
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indices,
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max_output_size,
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iou_threshold,
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force_suppress,
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top_k,
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coord_start,
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score_index,
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id_index,
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return_indices,
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invalid_to_bottom,
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soft_nms_sigma,
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score_threshold,
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)
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@@ -0,0 +1,78 @@
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# 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.
|
||||
"""ROI Align operator"""
|
||||
|
||||
from ..base import Expr
|
||||
from . import _ffi_api
|
||||
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||||
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def roi_align(
|
||||
data: Expr,
|
||||
rois: Expr,
|
||||
pooled_size: int | tuple[int, int] | list[int],
|
||||
spatial_scale: float,
|
||||
sample_ratio: int = -1,
|
||||
aligned: bool = False,
|
||||
layout: str = "NCHW",
|
||||
mode: str = "avg",
|
||||
):
|
||||
"""ROI Align operator.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data : relax.Expr
|
||||
4-D input tensor.
|
||||
|
||||
rois : relax.Expr
|
||||
2-D input tensor with shape `(num_roi, 5)` in
|
||||
`[batch_idx, x1, y1, x2, y2]` format.
|
||||
|
||||
pooled_size : Union[int, Tuple[int, int], List[int]]
|
||||
Output pooled size.
|
||||
|
||||
spatial_scale : float
|
||||
Ratio of input feature map height (or width) to raw image height (or width).
|
||||
|
||||
sample_ratio : int, optional
|
||||
Sampling ratio for ROI align. Non-positive values use adaptive sampling.
|
||||
|
||||
aligned : bool, optional
|
||||
Whether to use aligned ROIAlign semantics without the legacy 1-pixel clamp.
|
||||
|
||||
layout : str, optional
|
||||
Layout of the input data. Supported values are `NCHW` and `NHWC`.
|
||||
|
||||
mode : str, optional
|
||||
Mode for ROI align. Supported values are `avg` and `max`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
result : relax.Expr
|
||||
The computed result.
|
||||
"""
|
||||
if isinstance(pooled_size, int):
|
||||
pooled_size = (pooled_size, pooled_size)
|
||||
return _ffi_api.roi_align(
|
||||
data,
|
||||
rois,
|
||||
pooled_size,
|
||||
spatial_scale,
|
||||
sample_ratio,
|
||||
aligned,
|
||||
layout,
|
||||
mode,
|
||||
)
|
||||
@@ -0,0 +1,57 @@
|
||||
# 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.
|
||||
"""ROI Pool operator"""
|
||||
|
||||
from ..base import Expr
|
||||
from . import _ffi_api
|
||||
|
||||
|
||||
def roi_pool(
|
||||
data: Expr,
|
||||
rois: Expr,
|
||||
pooled_size: int | tuple[int, int] | list[int],
|
||||
spatial_scale: float,
|
||||
layout: str = "NCHW",
|
||||
):
|
||||
"""ROI Pool operator.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data : relax.Expr
|
||||
4-D input tensor.
|
||||
|
||||
rois : relax.Expr
|
||||
2-D input tensor with shape `(num_roi, 5)` in
|
||||
`[batch_idx, x1, y1, x2, y2]` format.
|
||||
|
||||
pooled_size : Union[int, Tuple[int, int], List[int]]
|
||||
Output pooled size.
|
||||
|
||||
spatial_scale : float
|
||||
Ratio of input feature map height (or width) to raw image height (or width).
|
||||
|
||||
layout : str, optional
|
||||
Layout of the input data. Currently only `NCHW` is supported.
|
||||
|
||||
Returns
|
||||
-------
|
||||
result : relax.Expr
|
||||
The computed result.
|
||||
"""
|
||||
if isinstance(pooled_size, int):
|
||||
pooled_size = (pooled_size, pooled_size)
|
||||
return _ffi_api.roi_pool(data, rois, pooled_size, spatial_scale, layout)
|
||||
Reference in New Issue
Block a user