# Onnxruntime Custom Ops
- [Onnxruntime Custom Ops](#onnxruntime-custom-ops)
- [SoftNMS](#softnms)
- [Description](#description)
- [Parameters](#parameters)
- [Inputs](#inputs)
- [Outputs](#outputs)
- [Type Constraints](#type-constraints)
- [RoIAlign](#roialign)
- [Description](#description-1)
- [Parameters](#parameters-1)
- [Inputs](#inputs-1)
- [Outputs](#outputs-1)
- [Type Constraints](#type-constraints-1)
- [NMS](#nms)
- [Description](#description-2)
- [Parameters](#parameters-2)
- [Inputs](#inputs-2)
- [Outputs](#outputs-2)
- [Type Constraints](#type-constraints-2)
- [grid_sampler](#grid_sampler)
- [Description](#description-3)
- [Parameters](#parameters-3)
- [Inputs](#inputs-3)
- [Outputs](#outputs-3)
- [Type Constraints](#type-constraints-3)
## SoftNMS
### Description
Perform soft NMS on `boxes` with `scores`. Read [Soft-NMS -- Improving Object Detection With One Line of Code](https://arxiv.org/abs/1704.04503) for detail.
### Parameters
| Type | Parameter | Description |
| ------- | --------------- | -------------------------------------------------------------- |
| `float` | `iou_threshold` | IoU threshold for NMS |
| `float` | `sigma` | hyperparameter for gaussian method |
| `float` | `min_score` | score filter threshold |
| `int` | `method` | method to do the nms, (0: `naive`, 1: `linear`, 2: `gaussian`) |
| `int` | `offset` | `boxes` width or height is (x2 - x1 + offset). (0 or 1) |
### Inputs
- boxes: T
- Input boxes. 2-D tensor of shape (N, 4). N is the number of boxes.
- scores: T
- Input scores. 1-D tensor of shape (N, ).
### Outputs
- dets: tensor(int64)
- Output boxes and scores. 2-D tensor of shape (num_valid_boxes, 5), [[x1, y1, x2, y2, score], ...]. num_valid_boxes is the number of valid boxes.
- indices: T
- Output indices. 1-D tensor of shape (num_valid_boxes, ).
### Type Constraints
- T:tensor(float32)
## RoIAlign
### Description
Perform RoIAlign on output feature, used in bbox_head of most two-stage detectors.
### Parameters
| Type | Parameter | Description |
| ------- | ---------------- | ------------------------------------------------------------------------------------------------------------- |
| `int` | `output_height` | height of output roi |
| `int` | `output_width` | width of output roi |
| `float` | `spatial_scale` | used to scale the input boxes |
| `int` | `sampling_ratio` | number of input samples to take for each output sample. `0` means to take samples densely for current models. |
| `str` | `mode` | pooling mode in each bin. `avg` or `max` |
| `int` | `aligned` | If `aligned=0`, use the legacy implementation in MMDetection. Else, align the results more perfectly. |
### Inputs
- input: T
- Input feature map; 4D tensor of shape (N, C, H, W), where N is the batch size, C is the numbers of channels, H and W are the height and width of the data.
- rois: T
- RoIs (Regions of Interest) to pool over; 2-D tensor of shape (num_rois, 5) given as [[batch_index, x1, y1, x2, y2], ...]. The RoIs' coordinates are the coordinate system of input.
### Outputs
- feat: T
- RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element feat[r-1] is a pooled feature map corresponding to the r-th RoI RoIs[r-1].
-
### Type Constraints
- T:tensor(float32)
## NMS
### Description
Filter out boxes has high IoU overlap with previously selected boxes.
### Parameters
| Type | Parameter | Description |
| ------- | --------------- | ---------------------------------------------------------------------------------------------------------------- |
| `float` | `iou_threshold` | The threshold for deciding whether boxes overlap too much with respect to IoU. Value range [0, 1]. Default to 0. |
| `int` | `offset` | 0 or 1, boxes' width or height is (x2 - x1 + offset). |
### Inputs
- bboxes: T
- Input boxes. 2-D tensor of shape (num_boxes, 4). num_boxes is the number of input boxes.
- scores: T
- Input scores. 1-D tensor of shape (num_boxes, ).
### Outputs
- indices: tensor(int32, Linear)
- Selected indices. 1-D tensor of shape (num_valid_boxes, ). num_valid_boxes is the number of valid boxes.
### Type Constraints
- T:tensor(float32)
## grid_sampler
### Description
Perform sample from `input` with pixel locations from `grid`.
### Parameters
| Type | Parameter | Description |
| ----- | -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `int` | `interpolation_mode` | Interpolation mode to calculate output values. (0: `bilinear` , 1: `nearest`) |
| `int` | `padding_mode` | Padding mode for outside grid values. (0: `zeros`, 1: `border`, 2: `reflection`) |
| `int` | `align_corners` | If `align_corners=1`, the extrema (`-1` and `1`) are considered as referring to the center points of the input's corner pixels. If `align_corners=0`, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic. |
### Inputs
- input: T
- Input feature; 4-D tensor of shape (N, C, inH, inW), where N is the batch size, C is the numbers of channels, inH and inW are the height and width of the data.
- grid: T
- Input offset; 4-D tensor of shape (N, outH, outW, 2), where outH and outW is the height and width of offset and output.
### Outputs
- output: T
- Output feature; 4-D tensor of shape (N, C, outH, outW).
### Type Constraints
- T:tensor(float32, Linear)