Versioned name: ExperimentalDetectronDetectionOutput-6
Category: Object detection
Short description: The ExperimentalDetectronDetectionOutput operation performs non-maximum suppression to generate the detection output using information on location and score predictions.
Detailed description: The operation performs the following steps:
- Applies deltas to boxes sizes [x1, y1, x2, y2] and takes coordinates of refined boxes according to the formulas:
x1_new = ctr_x + (dx - 0.5 * exp(min(d_log_w, max_delta_log_wh))) * box_w
y0_new = ctr_y + (dy - 0.5 * exp(min(d_log_h, max_delta_log_wh))) * box_h
x1_new = ctr_x + (dx + 0.5 * exp(min(d_log_w, max_delta_log_wh))) * box_w - 1.0
y1_new = ctr_y + (dy + 0.5 * exp(min(d_log_h, max_delta_log_wh))) * box_h - 1.0
box_w
and box_h
are width and height of box, respectively:
box_w = x1 - x0 + 1.0
box_h = y1 - y0 + 1.0
ctr_x
and ctr_y
are center location of a box:
ctr_x = x0 + 0.5f * box_w
ctr_y = y0 + 0.5f * box_h
dx
, dy
, d_log_w
and d_log_h
are deltas calculated according to the formulas below, and deltas_tensor
is a second input:
dx = deltas_tensor[roi_idx, 4 * class_idx + 0] / deltas_weights[0]
dy = deltas_tensor[roi_idx, 4 * class_idx + 1] / deltas_weights[1]
d_log_w = deltas_tensor[roi_idx, 4 * class_idx + 2] / deltas_weights[2]
d_log_h = deltas_tensor[roi_idx, 4 * class_idx + 3] / deltas_weights[3]
- If class_agnostic_box_regression is
true
removes predictions for background classes.
- Clips boxes to the image.
- Applies score_threshold on detection scores.
- Applies non-maximum suppression class-wise with nms_threshold and returns post_nms_count or less detections per class.
- Returns max_detections_per_image detections if total number of detections is more than max_detections_per_image; otherwise, returns total number of detections and the output tensor is filled with undefined values for rest output tensor elements.
Attributes:
- score_threshold
- Description: The score_threshold attribute specifies a threshold to consider only detections whose score are larger than the threshold.
- Range of values: non-negative floating point number
- Type: float
- Default value: None
- Required: yes
- nms_threshold
- Description: The nms_threshold attribute specifies a threshold to be used in the NMS stage.
- Range of values: non-negative floating point number
- Type: float
- Default value: None
- Required: yes
- num_classes
- Description: The num_classes attribute specifies the number of detected classes.
- Range of values: non-negative integer number
- Type: int
- Default value: None
- Required: yes
- post_nms_count
- Description: The post_nms_count attribute specifies the maximal number of detections per class.
- Range of values: non-negative integer number
- Type: int
- Default value: None
- Required: yes
- max_detections_per_image
- Description: The max_detections_per_image attribute specifies maximal number of detections per image.
- Range of values: non-negative integer number
- Type: int
- Default value: None
- Required: yes
- class_agnostic_box_regression
- Description: class_agnostic_box_regression attribute ia a flag specifies whether to delete background classes or not.
- Range of values:
true
means background classes should be deleted
false
means background classes should not be deleted
- Type: boolean
- Default value: false
- Required: no
- max_delta_log_wh
- Description: The max_delta_log_wh attribute specifies maximal delta of logarithms for width and height.
- Range of values: floating point number
- Type: float
- Default value: None
- Required: yes
- deltas_weights
- Description: The deltas_weights attribute specifies weights for bounding boxes sizes deltas.
- Range of values: a list of non-negative floating point numbers
- Type: float[]
- Default value: None
- Required: yes
Inputs
- 1: A 2D tensor of type T with input ROIs, with shape
[number_of_ROIs, 4]
providing the ROIs as 4-tuples: [x1, y1, x2, y2]. The batch dimension of first, second, and third inputs should be the same. Required.
- 2: A 2D tensor of type T with shape
[number_of_ROIs, num_classes * 4]
providing deltas for input boxes. Required.
- 3: A 2D tensor of type T with shape
[number_of_ROIs, num_classes]
providing detections scores. Required.
- 4: A 2D tensor of type T with shape
[1, 3]
contains three elements [image_height, image_width, scale_height_and_width]
providing input image size info. Required.
Outputs
- 1: A 2D tensor of type T with shape
[max_detections_per_image, 4]
providing boxes indices.
- 2: A 1D tensor of type T_IND with shape
[max_detections_per_image]
providing classes indices.
- 3: A 1D tensor of type T with shape
[max_detections_per_image]
providing scores indices.
Types
- T: any supported floating point type.
- T_IND:
int64
or int32
.
Example
<layer ... type="ExperimentalDetectronDetectionOutput" version="opset6">
<data class_agnostic_box_regression="false" deltas_weights="10.0,10.0,5.0,5.0" max_delta_log_wh="4.135166645050049" max_detections_per_image="100" nms_threshold="0.5" num_classes="81" post_nms_count="2000" score_threshold="0.05000000074505806"/>
<input>
<port id="0">
<dim>1000</dim>
<dim>4</dim>
</port>
<port id="1">
<dim>1000</dim>
<dim>324</dim>
</port>
<port id="2">
<dim>1000</dim>
<dim>81</dim>
</port>
<port id="3">
<dim>1</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="4" precision="FP32">
<dim>100</dim>
<dim>4</dim>
</port>
<port id="5" precision="I32">
<dim>100</dim>
</port>
<port id="6" precision="FP32">
<dim>100</dim>
</port>
<port id="7" precision="I32">
<dim>100</dim>
</port>
</output>
</layer>