[Doc]: Add document of TensorRT and onnxruntime custom ops

.pre-commit-config.yaml

@@ -33,7 +33,8 @@ repos:
     rev: 2.1.4
     hooks:
       - id: markdownlint
-        args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034"]
+        args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034",
+              "-t", "allow_different_nesting"]
   - repo: https://github.com/myint/docformatter
     rev: v1.3.1
     hooks:

docs/deployment.rst

@@ -0,0 +1,11 @@
+Deployment
+========
+
+.. toctree::
+    :maxdepth: 2
+
+    onnx.md
+    onnxruntime_op.md
+    onnxruntime_custom_ops.md
+    tensorrt_plugin.md
+    tensorrt_custom_ops.md

docs/index.rst

@@ -17,6 +17,7 @@ Contents
    cnn.md
    ops.md
    build.md
+   deployment.rst
    trouble_shooting.md
    api.rst
 

docs/onnxruntime_custom_ops.md

@@ -0,0 +1,173 @@
+# Onnxruntime Custom Ops
+
+<!-- TOC -->
+
+- [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)
+
+<!-- TOC -->
+
+## 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
+
+<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.
+
+### 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
+
+<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.
+
+### 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
+
+<dl>
+<dt><tt>bboxes</tt>: T</dt>
+<dd>Input boxes. 2-D tensor of shape (num_boxes, 4). num_boxes is the number of input boxes.</dd>
+<dt><tt>scores</tt>: T</dt>
+<dd>Input scores. 1-D tensor of shape (num_boxes, ).</dd>
+</dl>
+
+### Outputs
+
+<dl>
+<dt><tt>indices</tt>: tensor(int32, Linear)</dt>
+<dd>Selected indices. 1-D tensor of shape (num_valid_boxes, ). num_valid_boxes is the number of valid boxes.</dd>
+</dl>
+
+### 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
+
+<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>
+</dl>
+
+### Type Constraints
+
+- T:tensor(float32, Linear)

docs/onnxruntime_op.md

@@ -15,10 +15,12 @@
 
 ## List of operators for ONNX Runtime supported in MMCV
 
-| Operator |  CPU  |  GPU  |                                                Note                                                 |
-| :------: | :---: | :---: | :-------------------------------------------------------------------------------------------------: |
-| SoftNMS  |   Y   |   N   | commit [94810f](https://github.com/open-mmlab/mmcv/commit/94810f2297871d0ea3ca650dcb2e842f5374d998) |
-| RoiAlign |   Y   |   N   |                                                None                                                 |
+|                        Operator                        |  CPU  |  GPU  | MMCV Releases |
+| :----------------------------------------------------: | :---: | :---: | :-----------: |
+|      [SoftNMS](onnxruntime_custom_ops.md#softnms)      |   Y   |   N   |     1.2.3     |
+|     [RoIAlign](onnxruntime_custom_ops.md#roialign)     |   Y   |   N   |     1.2.5     |
+|          [NMS](onnxruntime_custom_ops.md#nms)          |   Y   |   N   |     1.2.7     |
+| [grid_sampler](onnxruntime_custom_ops.md#grid_sampler) |   Y   |   N   |    master     |
 
 ## How to build custom operators for ONNX Runtime