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remove roi_align plugin for ORT (#258)
* remove roi_align plugin * remove ut * skip single_roi_extractor UT for ORT in CI * move align to symbolic and update docs * recover UT * resolve comments
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csrc/backend_ops/onnxruntime/roi_align/roi_align.cpp

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csrc/backend_ops/onnxruntime/roi_align/roi_align.h

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docs/en/backends/onnxruntime.md

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| Operator | CPU | GPU | MMDeploy Releases |
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| :--------------------------------------------------------------------------- | :---: | :---: | :---------------- |
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| [RoIAlign](../ops/onnxruntime.md#roialign) | Y | N | master |
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| [grid_sampler](../ops/onnxruntime.md#grid_sampler) | Y | N | master |
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| [MMCVModulatedDeformConv2d](../ops/onnxruntime.md#mmcvmodulateddeformconv2d) | Y | N | master |
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docs/en/ops/onnxruntime.md

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<!-- TOC -->
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- [ONNX Runtime Ops](#onnx-runtime-ops)
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- [RoIAlign](#roialign)
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- [grid_sampler](#grid_sampler)
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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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- [grid_sampler](#grid_sampler)
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- [MMCVModulatedDeformConv2d](#mmcvmodulateddeformconv2d)
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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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- [MMCVModulatedDeformConv2d](#mmcvmodulateddeformconv2d)
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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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<!-- TOC -->
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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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### grid_sampler
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#### Description

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