mirror of https://github.com/open-mmlab/mmcv.git
[Doc]: Add document of TensorRT and onnxruntime custom ops (#920)
* update tensorrt plugin document * add onnxruntime custom ops document * format document * add release note to onnxruntime_op and tensorrt_plugin * update document * add deployment.rst * add grid_sampler onnxruntime document * fix lint * add allow_different_nesting tag * add allow_different_nesting tag Co-authored-by: maningsheng <maningsheng@sensetime.com>pull/942/head
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rev: 2.1.4
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hooks:
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- id: markdownlint
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args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034"]
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args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034",
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"-t", "allow_different_nesting"]
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- repo: https://github.com/myint/docformatter
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rev: v1.3.1
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hooks:
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Deployment
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========
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.. toctree::
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:maxdepth: 2
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onnx.md
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onnxruntime_op.md
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onnxruntime_custom_ops.md
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tensorrt_plugin.md
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tensorrt_custom_ops.md
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@ -17,6 +17,7 @@ Contents
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cnn.md
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ops.md
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build.md
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deployment.rst
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trouble_shooting.md
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api.rst
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# Onnxruntime Custom Ops
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<!-- TOC -->
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- [Onnxruntime Custom Ops](#onnxruntime-custom-ops)
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- [SoftNMS](#softnms)
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- [Description](#description)
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- [Parameters](#parameters)
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- [Inputs](#inputs)
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- [Outputs](#outputs)
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- [Type Constraints](#type-constraints)
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- [RoIAlign](#roialign)
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- [Description](#description-1)
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- [Parameters](#parameters-1)
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- [Inputs](#inputs-1)
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- [Outputs](#outputs-1)
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- [Type Constraints](#type-constraints-1)
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- [NMS](#nms)
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- [Description](#description-2)
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- [Parameters](#parameters-2)
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- [Inputs](#inputs-2)
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- [Outputs](#outputs-2)
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- [Type Constraints](#type-constraints-2)
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- [grid_sampler](#grid_sampler)
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- [Description](#description-3)
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- [Parameters](#parameters-3)
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- [Inputs](#inputs-3)
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- [Outputs](#outputs-3)
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- [Type Constraints](#type-constraints-3)
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<!-- TOC -->
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## SoftNMS
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### Description
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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.
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### Parameters
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| Type | Parameter | Description |
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| ------- | --------------- | -------------------------------------------------------------- |
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| `float` | `iou_threshold` | IoU threshold for NMS |
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| `float` | `sigma` | hyperparameter for gaussian method |
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| `float` | `min_score` | score filter threshold |
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| `int` | `method` | method to do the nms, (0: `naive`, 1: `linear`, 2: `gaussian`) |
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| `int` | `offset` | `boxes` width or height is (x2 - x1 + offset). (0 or 1) |
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### Inputs
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<dl>
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<dt><tt>boxes</tt>: T</dt>
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<dd>Input boxes. 2-D tensor of shape (N, 4). N is the number of boxes.</dd>
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<dt><tt>scores</tt>: T</dt>
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<dd>Input scores. 1-D tensor of shape (N, ).</dd>
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</dl>
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### Outputs
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<dl>
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<dt><tt>dets</tt>: tensor(int64)</dt>
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<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>
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<dt><tt>indices</tt>: T</dt>
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<dd>Output indices. 1-D tensor of shape (num_valid_boxes, ).</dd>
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</dl>
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### Type Constraints
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- T:tensor(float32)
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## RoIAlign
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### Description
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Perform RoIAlign on output feature, used in bbox_head of most two-stage detectors.
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### Parameters
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| Type | Parameter | Description |
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| ------- | ---------------- | ------------------------------------------------------------------------------------------------------------- |
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| `int` | `output_height` | height of output roi |
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| `int` | `output_width` | width of output roi |
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| `float` | `spatial_scale` | used to scale the input boxes |
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| `int` | `sampling_ratio` | number of input samples to take for each output sample. `0` means to take samples densely for current models. |
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| `str` | `mode` | pooling mode in each bin. `avg` or `max` |
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| `int` | `aligned` | If `aligned=0`, use the legacy implementation in MMDetection. Else, align the results more perfectly. |
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### Inputs
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<dl>
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<dt><tt>input</tt>: T</dt>
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<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>
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<dt><tt>rois</tt>: T</dt>
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<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>
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</dl>
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### Outputs
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<dl>
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<dt><tt>feat</tt>: T</dt>
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<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>
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</dl>
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### Type Constraints
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- T:tensor(float32)
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## NMS
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### Description
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Filter out boxes has high IoU overlap with previously selected boxes.
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### Parameters
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| Type | Parameter | Description |
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| ------- | --------------- | ---------------------------------------------------------------------------------------------------------------- |
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| `float` | `iou_threshold` | The threshold for deciding whether boxes overlap too much with respect to IoU. Value range [0, 1]. Default to 0. |
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| `int` | `offset` | 0 or 1, boxes' width or height is (x2 - x1 + offset). |
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### Inputs
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<dl>
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<dt><tt>bboxes</tt>: T</dt>
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<dd>Input boxes. 2-D tensor of shape (num_boxes, 4). num_boxes is the number of input boxes.</dd>
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<dt><tt>scores</tt>: T</dt>
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<dd>Input scores. 1-D tensor of shape (num_boxes, ).</dd>
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</dl>
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### Outputs
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<dl>
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<dt><tt>indices</tt>: tensor(int32, Linear)</dt>
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<dd>Selected indices. 1-D tensor of shape (num_valid_boxes, ). num_valid_boxes is the number of valid boxes.</dd>
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</dl>
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### Type Constraints
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- T:tensor(float32)
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## grid_sampler
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### Description
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Perform sample from `input` with pixel locations from `grid`.
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### Parameters
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| Type | Parameter | Description |
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| ----- | -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `int` | `interpolation_mode` | Interpolation mode to calculate output values. (0: `bilinear` , 1: `nearest`) |
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| `int` | `padding_mode` | Padding mode for outside grid values. (0: `zeros`, 1: `border`, 2: `reflection`) |
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| `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. |
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### Inputs
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<dl>
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<dt><tt>input</tt>: T</dt>
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<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>
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<dt><tt>grid</tt>: T</dt>
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<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>
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</dl>
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### Outputs
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<dl>
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<dt><tt>output</tt>: T</dt>
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<dd>Output feature; 4-D tensor of shape (N, C, outH, outW).</dd>
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</dl>
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### Type Constraints
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- T:tensor(float32, Linear)
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## List of operators for ONNX Runtime supported in MMCV
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| Operator | CPU | GPU | Note |
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| :------: | :---: | :---: | :-------------------------------------------------------------------------------------------------: |
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| SoftNMS | Y | N | commit [94810f](https://github.com/open-mmlab/mmcv/commit/94810f2297871d0ea3ca650dcb2e842f5374d998) |
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| RoiAlign | Y | N | None |
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| Operator | CPU | GPU | MMCV Releases |
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| :----------------------------------------------------: | :---: | :---: | :-----------: |
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| [SoftNMS](onnxruntime_custom_ops.md#softnms) | Y | N | 1.2.3 |
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| [RoIAlign](onnxruntime_custom_ops.md#roialign) | Y | N | 1.2.5 |
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| [NMS](onnxruntime_custom_ops.md#nms) | Y | N | 1.2.7 |
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| [grid_sampler](onnxruntime_custom_ops.md#grid_sampler) | Y | N | master |
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## How to build custom operators for ONNX Runtime
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# TensorRT Custom Ops
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<!-- TOC -->
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- [TensorRT Custom Ops](#tensorrt-custom-ops)
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- [MMCVRoIAlign](#mmcvroialign)
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- [Description](#description)
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- [Parameters](#parameters)
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- [Inputs](#inputs)
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- [Outputs](#outputs)
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- [Type Constraints](#type-constraints)
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- [ScatterND](#scatternd)
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- [Description](#description-1)
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- [Parameters](#parameters-1)
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- [Inputs](#inputs-1)
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- [Outputs](#outputs-1)
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- [Type Constraints](#type-constraints-1)
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- [NonMaxSuppression](#nonmaxsuppression)
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- [Description](#description-2)
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- [Parameters](#parameters-2)
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- [Inputs](#inputs-2)
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- [Outputs](#outputs-2)
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- [Type Constraints](#type-constraints-2)
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- [MMCVDeformConv2d](#mmcvdeformconv2d)
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- [Description](#description-3)
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- [Parameters](#parameters-3)
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- [Inputs](#inputs-3)
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- [Outputs](#outputs-3)
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- [Type Constraints](#type-constraints-3)
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- [grid_sampler](#grid_sampler)
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- [Description](#description-4)
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- [Parameters](#parameters-4)
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- [Inputs](#inputs-4)
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- [Outputs](#outputs-4)
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- [Type Constraints](#type-constraints-4)
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<!-- TOC -->
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## MMCVRoIAlign
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### Description
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Perform RoIAlign on output feature, used in bbox_head of most two stage
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detectors.
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### Parameters
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| Type | Parameter | Description |
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| ------- | ---------------- | ------------------------------------------------------------------------------------------------------------- |
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| `int` | `output_height` | height of output roi |
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| `int` | `output_width` | width of output roi |
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| `float` | `spatial_scale` | used to scale the input boxes |
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| `int` | `sampling_ratio` | number of input samples to take for each output sample. `0` means to take samples densely for current models. |
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| `str` | `mode` | pooling mode in each bin. `avg` or `max` |
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| `int` | `aligned` | If `aligned=0`, use the legacy implementation in MMDetection. Else, align the results more perfectly. |
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### Inputs
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<dl>
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<dt><tt>inputs[0]</tt>: T</dt>
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<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>
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<dt><tt>inputs[1]</tt>: T</dt>
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<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 inputs[0].</dd>
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</dl>
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### Outputs
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<dl>
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<dt><tt>outputs[0]</tt>: T</dt>
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<dd>RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element output[0][r-1] is a pooled feature map corresponding to the r-th RoI inputs[1][r-1].<dd>
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</dl>
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### Type Constraints
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- T:tensor(float32, Linear)
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## ScatterND
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### Description
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ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by updates at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. Note that `indices` should not have duplicate entries. That is, two or more updates for the same index-location is not supported.
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The `output` is calculated via the following equation:
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```python
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output = np.copy(data)
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update_indices = indices.shape[:-1]
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for idx in np.ndindex(update_indices):
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output[indices[idx]] = updates[idx]
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```
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### Parameters
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None
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### Inputs
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<dl>
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<dt><tt>inputs[0]</tt>: T</dt>
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<dd>Tensor of rank r>=1.</dd>
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<dt><tt>inputs[1]</tt>: tensor(int32, Linear)</dt>
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<dd>Tensor of rank q>=1.</dd>
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<dt><tt>inputs[2]</tt>: T</dt>
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<dd>Tensor of rank q + r - indices_shape[-1] - 1.</dd>
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</dl>
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### Outputs
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<dl>
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<dt><tt>outputs[0]</tt>: T</dt>
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<dd>Tensor of rank r >= 1.</dd>
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</dl>
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### Type Constraints
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- T:tensor(float32, Linear), tensor(int32, Linear)
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## NonMaxSuppression
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### Description
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Filter out boxes has high IoU overlap with previously selected boxes or low score. Output the indices of valid boxes. Indices of invalid boxes will be filled with -1.
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### Parameters
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| Type | Parameter | Description |
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| ------- | ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
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| `int` | `center_point_box` | 0 - the box data is supplied as [y1, x1, y2, x2], 1-the box data is supplied as [x_center, y_center, width, height]. |
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| `int` | `max_output_boxes_per_class` | The maximum number of boxes to be selected per batch per class. Default to 0, number of output boxes equal to number of input boxes. |
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| `float` | `iou_threshold` | The threshold for deciding whether boxes overlap too much with respect to IoU. Value range [0, 1]. Default to 0. |
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| `float` | `score_threshold` | The threshold for deciding when to remove boxes based on score. |
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| `int` | `offset` | 0 or 1, boxes' width or height is (x2 - x1 + offset). |
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### Inputs
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<dl>
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<dt><tt>inputs[0]</tt>: T</dt>
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<dd>Input boxes. 3-D tensor of shape (num_batches, spatial_dimension, 4).</dd>
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<dt><tt>inputs[1]</tt>: T</dt>
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<dd>Input scores. 3-D tensor of shape (num_batches, num_classes, spatial_dimension).</dd>
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</dl>
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### Outputs
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<dl>
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<dt><tt>outputs[0]</tt>: tensor(int32, Linear)</dt>
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<dd>Selected indices. 2-D tensor of shape (num_selected_indices, 3) as [[batch_index, class_index, box_index], ...].</dd>
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<dd>num_selected_indices=num_batches* num_classes* min(max_output_boxes_per_class, spatial_dimension).</dd>
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<dd>All invalid indices will be filled with -1.</dd>
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</dl>
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### Type Constraints
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- T:tensor(float32, Linear)
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## MMCVDeformConv2d
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### Description
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Perform Deformable Convolution on input feature, read [Deformable Convolutional Network](https://arxiv.org/abs/1703.06211) for detail.
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### Parameters
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| Type | Parameter | Description |
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| -------------- | ------------------ | --------------------------------------------------------------------------------------------------------------------------------- |
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| `list of ints` | `stride` | The stride of the convolving kernel. (sH, sW) |
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| `list of ints` | `padding` | Paddings on both sides of the input. (padH, padW) |
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| `list of ints` | `dilation` | The spacing between kernel elements. (dH, dW) |
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| `int` | `deformable_group` | Groups of deformable offset. |
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| `int` | `group` | Split input into groups. `input_channel` should be divisible by the number of groups. |
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| `int` | `im2col_step` | DeformableConv2d use im2col to compute convolution. im2col_step is used to split input and offset, reduce memory usage of column. |
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### Inputs
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<dl>
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<dt><tt>inputs[0]</tt>: T</dt>
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<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>
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<dt><tt>inputs[1]</tt>: T</dt>
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<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>
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<dt><tt>inputs[2]</tt>: T</dt>
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<dd>Input weight; 4-D tensor of shape (output_channel, input_channel, kH, kW).</dd>
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</dl>
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### Outputs
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<dl>
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<dt><tt>outputs[0]</tt>: T</dt>
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<dd>Output feature; 4-D tensor of shape (N, output_channel, outH, outW).</dd>
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</dl>
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### Type Constraints
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- T:tensor(float32, Linear)
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## grid_sampler
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|
||||
### 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>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 numbers 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, outH, outW, 2), where outH and outW is the height and width of offset and output. </dd>
|
||||
</dl>
|
||||
|
||||
### Outputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>outputs[0]</tt>: T</dt>
|
||||
<dd>Output feature; 4-D tensor of shape (N, C, outH, outW).</dd>
|
||||
</dl>
|
||||
|
||||
### Type Constraints
|
||||
|
||||
- T:tensor(float32, Linear)
|
||||
|
|
@ -24,11 +24,13 @@ To ease the deployment of trained models with custom operators from `mmcv.ops` u
|
|||
|
||||
## List of TensorRT plugins supported in MMCV
|
||||
|
||||
| ONNX Operator | TensorRT Plugin | Note |
|
||||
| :---------------: | :-------------------: | :---: |
|
||||
| RoiAlign | MMCVRoiAlign | Y |
|
||||
| ScatterND | ScatterND | Y |
|
||||
| NonMaxSuppression | MMCVNonMaxSuppression | WIP |
|
||||
| ONNX Operator | TensorRT Plugin | MMCV Releases |
|
||||
| :---------------: | :-------------------------------------------------------------: | :-----------: |
|
||||
| MMCVRoiAlign | [MMCVRoiAlign](./tensorrt_custom_ops.md#mmcvroialign) | 1.2.6 |
|
||||
| ScatterND | [ScatterND](./tensorrt_custom_ops.md#scatternd) | 1.2.6 |
|
||||
| NonMaxSuppression | [NonMaxSuppression](./tensorrt_custom_ops.md#nonmaxsuppression) | 1.3.0 |
|
||||
| MMCVDeformConv2d | [MMCVDeformConv2d](./tensorrt_custom_ops.md#mmcvdeformconv2d) | 1.3.0 |
|
||||
| grid_sampler | [grid_sampler](./tensorrt_custom_ops.md#grid-sampler) | master |
|
||||
|
||||
Notes
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue