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RegionYolo operation specification refactoring. (#5926)

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* RegionYolo spec refactored against explicit type indication.

* Improve readability.
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Jozef Daniecki 2021-06-07 19:12:57 +02:00 committed by GitHub
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docs/ops/detection/RegionYolo_1.md
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@ -6,7 +6,7 @@
**Short description**: *RegionYolo* computes the coordinates of regions with probability for each class.
**Detailed description**: This operation is directly mapped to the original YOLO layer. [Reference](https://arxiv.org/pdf/1612.08242.pdf)
**Detailed description**: This operation is directly mapped to the [YOLO9000: Better, Faster, Stronger](https://arxiv.org/pdf/1612.08242.pdf) paper.
**Attributes**:
@ -78,14 +78,17 @@
**Inputs**:
* **1**: `data` - 4D input tensor with floating point elements and shape `[N, C, H, W]`. Required.
* **1**: `data` - 4D tensor of type `T` and shape `[N, C, H, W]`. **Required.**
**Outputs**:
* **1**: output tensor of rank 4 or less that codes detected regions. Refer to the original YOLO paper to decode the output as boxes. `anchors` should be used to decode real box coordinates. If `do_softmax` is set to 0, then the output shape is `[N, (classes + coords + 1)*len(mask), H, W]`. If `do_softmax` is set to 1, then output shape is partially flattened and defined in the following way:
* **1**: tensor of type `T` and rank 4 or less that codes detected regions. Refer to the [YOLO9000: Better, Faster, Stronger](https://arxiv.org/pdf/1612.08242.pdf) paper to decode the output as boxes. `anchors` should be used to decode real box coordinates. If `do_softmax` is set to `0`, then the output shape is `[N, (classes + coords + 1) * len(mask), H, W]`. If `do_softmax` is set to `1`, then output shape is partially flattened and defined in the following way:
flat_dim = data.shape[axis] * data.shape[axis+1] * ... * data.shape[end_axis]
output.shape = [data.shape[0], ..., data.shape[axis-1], flat_dim, data.shape[end_axis + 1], ...]
`flat_dim = data.shape[axis] * data.shape[axis+1] * ... * data.shape[end_axis]`
`output.shape = [data.shape[0], ..., data.shape[axis-1], flat_dim, data.shape[end_axis + 1], ...]`
**Types**
* *T*: any supported floating point type.
**Example**