ExperimentalDetectronDetectionOutput#
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:
1. Applies deltas to boxes sizes [x 1, y 1, x 2, y 2] 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
andbox_h
are width and height of box, respectively:
box_w = x1 - x0 + 1.0
box_h = y1 - y0 + 1.0
ctr_x
andctr_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
andd_log_h
are deltas calculated according to the formulas below, anddeltas_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 is a flag that specifies whether to delete background classes or not.
Range of values:
true
means background classes should be deletedfalse
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: [x 1, y 1, x 2, y 2]. 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
orint32
.
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>