.. _op_ai_onnx_RoiAlign-16:

RoiAlign - version 16
=====================

This page documents version **16** of operator **RoiAlign**. See :doc:`RoiAlign` for the latest version (since version 22).

- **Domain**: ``ai.onnx``
- **Since version**: 16

Region of Interest (RoI) align operation described in the
`Mask R-CNN paper <https://arxiv.org/abs/1703.06870>`_.
RoiAlign consumes an input tensor X and region of interests (rois)
to apply pooling across each RoI; it produces a 4-D tensor of shape
(num_rois, C, output_height, output_width).

RoiAlign is proposed to avoid the misalignment by removing
quantizations while converting from original image into feature
map and from feature map into RoI feature; in each ROI bin,
the value of the sampled locations are computed directly
through bilinear interpolation.

**Inputs**

- **X** (*T1*): Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.
- **rois** (*T1*): RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.
- **batch_indices** (*T2*): 1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.

**Outputs**

- **Y** (*T1*): RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].

**Type Constraints**

- **T1**: Constrain types to float tensors.
  Allowed types: tensor(bfloat16), tensor(double), tensor(float), tensor(float16).
- **T2**: Constrain types to int tensors.
  Allowed types: tensor(int64).

Examples
--------

**test_cc_roialign**

.. code-block:: text

    Attributes:
      mode = "avg"
      output_height = 5
      output_width = 5
      sampling_ratio = 2
      spatial_scale = 1.0

.. code-block:: text

    Inputs:
      X: shape=(1, 1, 10, 10), dtype=float32
        [[[[0.  , 0.01, 0.02, ..., 0.07, 0.08, 0.09],
           [0.1 , 0.11, 0.12, ..., 0.17, 0.18, 0.19],
           [0.2 , 0.21, 0.22, ..., 0.27, 0.28, 0.29],
           ...,
           [0.7 , 0.71, 0.72, ..., 0.77, 0.78, 0.79],
           [0.8 , 0.81, 0.82, ..., 0.87, 0.88, 0.89],
           [0.9 , 0.91, 0.92, ..., 0.97, 0.98, 0.99]]]]
      rois: shape=(2, 4), dtype=float32
        [[0., 0., 9., 9.],
         [2., 2., 7., 7.]]
      batch_indices: shape=(2,), dtype=int64
        [0, 0]

    Outputs:
      Y: shape=(2, 1, 5, 5), dtype=float32
        [[[[0.04674999, 0.0645    , 0.0825    , 0.10049999, 0.11849999],
           [0.22424999, 0.24199998, 0.26      , 0.278     , 0.296     ],
           [0.40425003, 0.422     , 0.44      , 0.458     , 0.47599998],
           [0.58425   , 0.60199994, 0.61999995, 0.63799995, 0.6559999 ],
           [0.7642499 , 0.78199995, 0.7999999 , 0.81799996, 0.8359999 ]]],
        
        
         [[[0.22      , 0.22999999, 0.24      , 0.25      , 0.26      ],
           [0.32      , 0.32999998, 0.33999997, 0.35000002, 0.36      ],
           [0.42      , 0.43      , 0.44      , 0.45      , 0.45999998],
           [0.52      , 0.53      , 0.53999996, 0.5500001 , 0.56      ],
           [0.62      , 0.63      , 0.64      , 0.65      , 0.65999997]]]]

**test_cc_roialign_max**

.. code-block:: text

    Attributes:
      mode = "max"
      coordinate_transformation_mode = "output_half_pixel"
      output_height = 5
      output_width = 5
      sampling_ratio = 2
      spatial_scale = 1.0

.. code-block:: text

    Inputs:
      X: shape=(1, 1, 10, 10), dtype=float32
        [[[[0.  , 0.01, 0.02, ..., 0.07, 0.08, 0.09],
           [0.1 , 0.11, 0.12, ..., 0.17, 0.18, 0.19],
           [0.2 , 0.21, 0.22, ..., 0.27, 0.28, 0.29],
           ...,
           [0.7 , 0.71, 0.72, ..., 0.77, 0.78, 0.79],
           [0.8 , 0.81, 0.82, ..., 0.87, 0.88, 0.89],
           [0.9 , 0.91, 0.92, ..., 0.97, 0.98, 0.99]]]]
      rois: shape=(2, 4), dtype=float32
        [[0., 0., 9., 9.],
         [2., 2., 7., 7.]]
      batch_indices: shape=(2,), dtype=int64
        [0, 0]

    Outputs:
      Y: shape=(2, 1, 5, 5), dtype=float32
        [[[[0.14849998, 0.16649999, 0.18449998, 0.20249999, 0.22049998],
           [0.32849997, 0.34649998, 0.3645    , 0.3825    , 0.40049997],
           [0.5084999 , 0.5264999 , 0.5445    , 0.5625    , 0.5804999 ],
           [0.6884999 , 0.7065    , 0.72449994, 0.74249995, 0.76049995],
           [0.86849993, 0.88649994, 0.9044999 , 0.92249995, 0.9404999 ]]],
        
        
         [[[0.3025    , 0.3125    , 0.3225    , 0.3325    , 0.3425    ],
           [0.4025    , 0.4125    , 0.42249998, 0.4325    , 0.4425    ],
           [0.50249994, 0.5125    , 0.52250004, 0.5325    , 0.5425    ],
           [0.60249996, 0.61249995, 0.6225    , 0.6325    , 0.64250004],
           [0.7025    , 0.7125    , 0.72249997, 0.7325    , 0.74249995]]]]

Differences with previous version (10)
--------------------------------------

**SchemaDiff**: ``RoiAlign`` (domain ``'ai.onnx'``)

* old version: 10
* new version: 16
* breaking: no