(l-onnx-doc-MaxRoiPool)=

# MaxRoiPool


(l-onnx-op-maxroipool-22)=

## MaxRoiPool - 22

### Version

- **name**: [MaxRoiPool (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#MaxRoiPool)
- **domain**: `main`
- **since_version**: `22`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 22**.

### Summary

ROI max pool consumes an input tensor X and region of interests (RoIs) to
apply max pooling across each RoI, to produce output 4-D tensor of shape
(num_rois, channels, pooled_shape[0], pooled_shape[1]).

### Attributes

* **pooled_shape - INTS** (required) :

  ROI pool output shape (height, width).

* **spatial_scale - FLOAT** (default is `'1.0'`):

  Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.

### Inputs

- **X** (heterogeneous) - **T**:

  Input data tensor from the previous operator; dimensions for image case are (N x C x H x 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** (heterogeneous) - **T**:

  RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].

### Outputs

- **Y** (heterogeneous) - **T**:

  RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.

```{toctree}
text_diff_MaxRoiPool_1_22
```

(l-onnx-op-maxroipool-1)=

## MaxRoiPool - 1

### Version

- **name**: [MaxRoiPool (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#MaxRoiPool)
- **domain**: `main`
- **since_version**: `1`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 1**.

### Summary

ROI max pool consumes an input tensor X and region of interests (RoIs) to
apply max pooling across each RoI, to produce output 4-D tensor of shape
(num_rois, channels, pooled_shape[0], pooled_shape[1]).

### Attributes

* **pooled_shape - INTS** (required) :

  ROI pool output shape (height, width).

* **spatial_scale - FLOAT** (default is `'1.0'`):

  Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.

### Inputs

- **X** (heterogeneous) - **T**:

  Input data tensor from the previous operator; dimensions for image case are (N x C x H x 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** (heterogeneous) - **T**:

  RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].

### Outputs

- **Y** (heterogeneous) - **T**:

  RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.