wehub-resource-sync
96 lines
4.6 KiB
Markdown
96 lines
4.6 KiB
Markdown
# regionPlugin [DEPRECATED]
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**This plugin is deprecated since TensorRT 10.12 and will be removed in a future release. No alternatives are planned to be provided.**
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**Table Of Contents**
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- [Description](#description)
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* [Structure](#structure)
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- [Parameters](#parameters)
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- [Additional resources](#additional-resources)
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- [License](#license)
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- [Changelog](#changelog)
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- [Known issues](#known-issues)
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## Description
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The `regionPlugin` is specifically used to generate encoded bounding box predictions, encoded bounding box objectness, and probabilities of bounding box being candidate objects for the YOLOv2 object detection model in TensorRT.
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### Structure
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The `regionPlugin` takes one input and generates one output.
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The input has a shape of `[N, C, H, W]`, where:
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- `N` is the batch size
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- `C` is the number of channels in the input tensor. For example, `C = num * (coords + 1 + classes)`.
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- `H` is the height of feature map
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- `W` is the width of feature map
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The information order of the channels are:
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- `[t_x, t_y, t_w, t_h, t_o, d_1, d_2, ..., d_classes] for bbox_1`
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- `[t_x, t_y, t_w, t_h, t_o, d_1, d_2, ..., d_classes] for bbox_2`, and so on
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- `[t_x, t_y, t_w, t_h, t_o, d_1, d_2, ..., d_classes]` for `bbox_num`, totalling `num * (coords + 1 + classes)` channels.
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Specifically:
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- ` t_x, t_y, t_w, t_h` are the predicted offsets of bounding boxes before sigmoid activation
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- `t_o` is the predicted objectness of the bounding box before sigmoid activation (see [YOLOv2 paper](https://arxiv.org/abs/1612.08242))
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- `d_1, d_2, ..., d_classes` are the digits for each candidate class before the Softmax activation.
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The output has the same shape as the input, in other words, `[N, C, H, W]`. The information order of the channels are:
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- `[sigmoid(t_x), sigmoid(t_y), t_w, t_h, sigmoid(t_o), p_1, p_2, ..., p_classes]` for `bbox_1`
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- `[sigmoid(t_x), sigmoid(t_y), t_w, t_h, sigmoid(t_o), p_1, p_2, ..., p_classes]` for `bbox_2`, and so on
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- `[sigmoid(t_x), sigmoid(t_y), t_w, t_h, sigmoid(t_o), p_1, p_2, ..., p_classes]` for `bbox_num`, totalling `num * (coords + 1 + classes)` channels
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Specifically:
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- `sigmoid(t_x), sigmoid(t_y)`, and `sigmoid(t_o)` are sigmoid activated `t_x, t_y`, and `t_o` from the input
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- `p_1, p_2, ..., p_classes` are the probability for each candidate class after the Softmax activation.
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**Note:** `t_w` and `t_h` from the input remain unchanged.
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## Parameters
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The `regionPlugin` has a plugin creator class `RegionPluginCreator` and plugin class `Region`.
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The following parameters were used to create the `Region` instance.
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| Type | Parameter | Description
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|----------|--------------------------|--------------------------------------------------------
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|`int` |`num` |The number of predicted bounding box for each grid cell.
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|`int` |`coords` |The number of coordinates for the bounding box. This value has to be `4`. Other values for `coords` are not supported currently.
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|`int` |`classes` |The number of candidate classes to be predicted.
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|`smTree` |`softmaxTree` |When performing yolo9000, `softmaxTree` is helping to perform Softmax on confidence scores, for example, to get the precise candidate classes through the word-tree structured candidate classes definition. `softmaxTree` is not required for non-hierarchical classification. The definition of `softmaxTree` can be found in [NvInferPlugin.h](https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/c_api/_nv_infer_plugin_8h_source.html) and [here](https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/c_api/structnvinfer1_1_1plugin_1_1softmax_tree.html).
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|`bool` |`hasSoftmaxTree` |If `softmaxTree` is not `nullptr`, it is `true`; else it is `false`.
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|`int` |`C` |The number of channels in the input tensor. `C = num * (coords + 1 + classes)` has to be satisfied.
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|`int` |`H` |The height of the input tensor (feature map).
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|`int` |`W` |The width of the input tensor (feature map).
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## Additional resources
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The following resources provide a deeper understanding of the `regionPlugin` plugin:
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**Networks**
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- [YOLOv2 paper](https://arxiv.org/abs/1612.08242)
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## License
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For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html)
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documentation.
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## Changelog
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May 2025
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Add deprecation note.
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May 2019
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This is the first release of this `README.md` file.
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## Known issues
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There are no known issues in this plugin.
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