Versioned name: ExperimentalDetectronGenerateProposalsSingleImage-6
Category: Object detection
Short description: The ExperimentalDetectronGenerateProposalsSingleImage operation computes ROIs and their scores based on input data.
Detailed description: The operation performs the following steps:
- Transposes and reshapes predicted bounding boxes deltas and scores to get them into the same order as the anchors.
- Transforms anchors into proposals using deltas and clips proposals to an image.
- Removes predicted boxes with either height or width < min_size.
- Sorts all
(proposal, score)
pairs by score from highest to lowest; order of pairs with equal scores is undefined.
- Takes top pre_nms_count proposals, if total number of proposals is less than pre_nms_count takes all proposals.
- Applies non-maximum suppression with nms_threshold.
- Takes top post_nms_count proposals and returns these top proposals and their scores. If total number of proposals is less than post_nms_count returns output tensors filled with zeroes.
Attributes:
- min_size
- Description: The min_size attribute specifies minimum box width and height.
- Range of values: non-negative floating point number
- Type: float
- Default value: None
- Required: yes
- nms_threshold
- Description: The nms_threshold attribute specifies threshold to be used in the NMS stage.
- Range of values: non-negative floating point number
- Type: float
- Default value: None
- Required: yes
- pre_nms_count
- Description: The pre_nms_count attribute specifies number of top-n proposals before NMS.
- Range of values: non-negative integer number
- Type: int
- Default value: None
- Required: yes
- post_nms_count
- Description: The post_nms_count attribute specifies number of top-n proposals after NMS.
- Range of values: non-negative integer number
- Type: int
- Default value: None
- Required: yes
Inputs
- 1: A 1D tensor of type T with 3 elements
[image_height, image_width, scale_height_and_width]
providing input image size info. Required.
- 2: A 2D tensor of type T with shape
[height * width * number_of_channels, 4]
providing anchors. Required.
- 3: A 3D tensor of type T with shape
[number_of_channels * 4, height, width]
providing deltas for anchors. Height and width for third and fourth inputs should be equal. Required.
- 4: A 3D tensor of type T with shape
[number_of_channels, height, width]
providing proposals scores. Required.
Outputs
- 1: A 2D tensor of type T with shape
[post_nms_count, 4]
providing ROIs.
- 2: A 1D tensor of type T with shape
[post_nms_count]
providing ROIs scores.
Types
- T: any supported floating point type.
Example
<layer ... type="ExperimentalDetectronGenerateProposalsSingleImage" version="opset6">
<data min_size="0.0" nms_threshold="0.699999988079071" post_nms_count="1000" pre_nms_count="1000"/>
<input>
<port id="0">
<dim>3</dim>
</port>
<port id="1">
<dim>12600</dim>
<dim>4</dim>
</port>
<port id="2">
<dim>12</dim>
<dim>50</dim>
<dim>84</dim>
</port>
<port id="3">
<dim>3</dim>
<dim>50</dim>
<dim>84</dim>
</port>
</input>
<output>
<port id="4" precision="FP32">
<dim>1000</dim>
<dim>4</dim>
</port>
<port id="5" precision="FP32">
<dim>1000</dim>
</port>
</output>
</layer>