## ONNX Runtime Ops
- [ONNX Runtime Ops](#onnx-runtime-ops)
- [grid_sampler](#grid_sampler)
- [Description](#description)
- [Parameters](#parameters)
- [Inputs](#inputs)
- [Outputs](#outputs)
- [Type Constraints](#type-constraints)
- [MMCVModulatedDeformConv2d](#mmcvmodulateddeformconv2d)
- [Description](#description-1)
- [Parameters](#parameters-1)
- [Inputs](#inputs-1)
- [Outputs](#outputs-1)
- [Type Constraints](#type-constraints-1)
- [NMSRotated](#nmsrotated)
- [Description](#description-2)
- [Parameters](#parameters-2)
- [Inputs](#inputs-2)
- [Outputs](#outputs-2)
- [Type Constraints](#type-constraints-2)
- [RoIAlignRotated](#roialignrotated)
- [Description](#description-3)
- [Parameters](#parameters-3)
- [Inputs](#inputs-3)
- [Outputs](#outputs-3)
- [Type Constraints](#type-constraints-3)
### 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 are 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)
### MMCVModulatedDeformConv2d
#### Description
Perform Modulated Deformable Convolution on input feature, read [Deformable ConvNets v2: More Deformable, Better Results](https://arxiv.org/abs/1811.11168?from=timeline) for detail.
#### Parameters
| Type | Parameter | Description |
| -------------- | ------------------- | ------------------------------------------------------------------------------------- |
| `list of ints` | `stride` | The stride of the convolving kernel. (sH, sW) |
| `list of ints` | `padding` | Paddings on both sides of the input. (padH, padW) |
| `list of ints` | `dilation` | The spacing between kernel elements. (dH, dW) |
| `int` | `deformable_groups` | Groups of deformable offset. |
| `int` | `groups` | Split input into groups. `input_channel` should be divisible by the number of groups. |
#### Inputs
- inputs[0]: T
- Input feature; 4-D tensor of shape (N, C, inH, inW), where N is the batch size, C is the number of channels, inH and inW are the height and width of the data.
- inputs[1]: T
- Input offset; 4-D tensor of shape (N, deformable_group* 2* kH* kW, outH, outW), where kH and kW are the height and width of weight, outH and outW are the height and width of offset and output.
- inputs[2]: T
- Input mask; 4-D tensor of shape (N, deformable_group* kH* kW, outH, outW), where kH and kW are the height and width of weight, outH and outW are the height and width of offset and output.
- inputs[3]: T
- Input weight; 4-D tensor of shape (output_channel, input_channel, kH, kW).
- inputs[4]: T, optional
- Input bias; 1-D tensor of shape (output_channel).
#### Outputs
- outputs[0]: T
- Output feature; 4-D tensor of shape (N, output_channel, outH, outW).
#### Type Constraints
- T:tensor(float32, Linear)
### NMSRotated
#### Description
Non Max Suppression for rotated bboxes.
#### Parameters
| Type | Parameter | Description |
| ------- | --------------- | -------------------------- |
| `float` | `iou_threshold` | The IoU threshold for NMS. |
#### Inputs
- inputs[0]: T
- Input feature; 2-D tensor of shape (N, 5), where N is the number of rotated bboxes, .
- inputs[1]: T
- Input offset; 1-D tensor of shape (N, ), where N is the number of rotated bboxes.
#### Outputs
- outputs[0]: T
- Output feature; 1-D tensor of shape (K, ), where K is the number of keep bboxes.
#### Type Constraints
- T:tensor(float32, Linear)
### RoIAlignRotated
#### Description
Perform RoIAlignRotated on output feature, used in bbox_head of most two-stage rotated object 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. |
| `int` | `aligned` | If `aligned=0`, use the legacy implementation in MMDetection. Else, align the results more perfectly. |
| `int` | `clockwise` | If True, the angle in each proposal follows a clockwise fashion in image space, otherwise, the angle is counterclockwise. Default: False. |
#### 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, 6) given as [[batch_index, cx, cy, w, h, theta], ...]. 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)