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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Roi_align.
"""
from aitemplate.compiler.ops.vision_ops.roi_ops.roi_ops import roi_ops_base
# pylint: disable=C0103
[docs]class roi_align(roi_ops_base):
"""
Performs Region of Interest (RoI) Align operator with average pooling, as described in Mask R-CNN.
* :attr:`num_rois` identifies the number of RoIs in the input.
* :attr:`pooled_size` identifies the size of the pooling section, i.e., the size of the output (in bins or pixels) after the pooling
is performed, as (height, width).
* :attr:`sampling_ratio` is the number of sampling points in the interpolation grid
used to compute the output value of each pooled output bin. If > 0,
then exactly ``sampling_ratio x sampling_ratio`` sampling points per bin are used. If
<= 0, then an adaptive number of grid points are used (computed as
``ceil(roi_width / output_width)``, and likewise for height).
* :attr:`spatial_scale` is a scaling factor that maps the box coordinates to
the input coordinates. For example, if your boxes are defined on the scale
of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of
the original image), you'll want to set this to 0.5.
* :attr:`position_sensitive`, a bool value.
* :attr:`continuous_coordinate`. a bool value.
Args:
x (Tensor[N, H, W, C]): the feature map, i.e. a batch with ``N`` elements. Each element contains ``C`` feature maps of dimensions ``H x W``.
rois (Tensor[roi_batch, 5]): the list of RoIs and each ROI contains the index of the corresponding element in the batch, i.e. a number in ``[0, N - 1]``, and the box coordinates in (x1, y1, x2, y2) format where the regions will be taken from. The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
Return:
Tensor[roi_batch, pooled_size, pooled_size, C]: the fixed-size feature maps, i.e., the pooled RoIs.
"""
def __init__(
self,
num_rois,
pooled_size,
sampling_ratio,
spatial_scale,
position_sensitive,
continuous_coordinate,
) -> None:
super().__init__(
num_rois,
pooled_size,
sampling_ratio,
spatial_scale,
position_sensitive,
continuous_coordinate,
)
self._attrs["op"] = "roi_align"