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.
| `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
<dl>
<dt><tt>boxes</tt>: T</dt>
<dd>Input boxes. 2-D tensor of shape (N, 4). N is the number of boxes.</dd>
<dt><tt>scores</tt>: T</dt>
<dd>Input scores. 1-D tensor of shape (N, ).</dd>
</dl>
### Outputs
<dl>
<dt><tt>dets</tt>: tensor(int64)</dt>
<dd>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.</dd>
<dt><tt>indices</tt>: T</dt>
<dd>Output indices. 1-D tensor of shape (num_valid_boxes, ).</dd>
</dl>
### Type Constraints
- T:tensor(float32)
## RoIAlign
### Description
Perform RoIAlign on output feature, used in bbox_head of most two-stage detectors.
| `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
<dl>
<dt><tt>input</tt>: T</dt>
<dd>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.</dd>
<dt><tt>rois</tt>: T</dt>
<dd>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.</dd>
</dl>
### Outputs
<dl>
<dt><tt>feat</tt>: T</dt>
<dd>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].<dd>
</dl>
### Type Constraints
- T:tensor(float32)
## NMS
### Description
Filter out boxes has high IoU overlap with previously selected boxes.
| `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
<dl>
<dt><tt>input</tt>: T</dt>
<dd>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.</dd>
<dt><tt>grid</tt>: T</dt>
<dd>Input offset; 4-D tensor of shape (N, outH, outW, 2), where outH and outW is the height and width of offset and output. </dd>
</dl>
### Outputs
<dl>
<dt><tt>output</tt>: T</dt>
<dd>Output feature; 4-D tensor of shape (N, C, outH, outW).</dd>
Returns a tuple (`values`, `indices`) where `values` is the cumulative maximum elements of `input` in the dimension `dim`. And `indices` is the index location of each maximum value found in the dimension `dim`. Read [torch.cummax](https://pytorch.org/docs/stable/generated/torch.cummax.html) for more details.
| `int` | `dim` | the dimension to do the operation over |
### Inputs
<dl>
<dt><tt>input</tt>: T</dt>
<dd>The input tensor with various shapes. Tensor with empty element is also supported.</dd>
</dl>
### Outputs
<dl>
<dt><tt>output</tt>: T</dt>
<dd>Output the cumulative maximum elements of `input` in the dimension `dim`, with the same shape and dtype as `input`.</dd>
<dt><tt>indices</tt>: tensor(int64)</dt>
<dd>Output the index location of each cumulative maximum value found in the dimension `dim`, with the same shape as `input`.</dd>
</dl>
### Type Constraints
- T:tensor(float32)
## cummin
### Description
Returns a tuple (`values`, `indices`) where `values` is the cumulative minimum elements of `input` in the dimension `dim`. And `indices` is the index location of each minimum value found in the dimension `dim`. Read [torch.cummin](https://pytorch.org/docs/stable/generated/torch.cummin.html) for more details.
| `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
<dl>
<dt><tt>inputs[0]</tt>: T</dt>
<dd>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.</dd>
<dt><tt>inputs[1]</tt>: T</dt>
<dd>Input offset; 4-D tensor of shape (N, deformable_group* 2* kH* kW, outH, outW), where kH and kW is the height and width of weight, outH and outW is the height and width of offset and output.</dd>
<dt><tt>inputs[2]</tt>: T</dt>
<dd>Input mask; 4-D tensor of shape (N, deformable_group* kH* kW, outH, outW), where kH and kW is the height and width of weight, outH and outW is the height and width of offset and output.</dd>
<dt><tt>inputs[3]</tt>: T</dt>
<dd>Input weight; 4-D tensor of shape (output_channel, input_channel, kH, kW).</dd>
<dt><tt>inputs[4]</tt>: T, optional</dt>
<dd>Input bias; 1-D tensor of shape (output_channel).</dd>
</dl>
### Outputs
<dl>
<dt><tt>outputs[0]</tt>: T</dt>
<dd>Output feature; 4-D tensor of shape (N, output_channel, outH, outW).</dd>
