Versioned name: ExperimentalDetectronPriorGridGenerator-6
Category: Object detection
Short description: The ExperimentalDetectronPriorGridGenerator operation generates prior grids of specified sizes.
Detailed description: The operation takes coordinates of centres of boxes and adds strides with offset 0.5
to them to calculate coordinates of prior grids.
Numbers of generated cells is featmap_height
and featmap_width
if h and w are zeroes; otherwise, h and w, respectively. Steps of generated grid are image_height
/ layer_height
and image_width
/ layer_width
if stride_h and stride_w are zeroes; otherwise, stride_h and stride_w, respectively.
featmap_height
, featmap_width
, image_height
and image_width
are spatial dimensions values from second and third inputs, respectively.
Attributes:
true
- the output tensor should be a 2D tensorfalse
- the output tensor should be a 4D tensorfeatmap_height
featmap_width
Inputs
[number_of_priors, 4]
contains priors. Required.[1, number_of_channels, featmap_height, featmap_width]
. This operation uses only sizes of this input tensor, not its data.**Required.**[1, number_of_channels, image_height, image_width]
. The number of channels of both feature map and input image tensors must match. This operation uses only sizes of this input tensor, not its data. Required.Outputs
[featmap_height * featmap_width * number_of_priors, 4]
if flatten is true
or [featmap_height, featmap_width, number_of_priors, 4]
, otherwise. If 0 < h < featmap_height
and/or 0 < w < featmap_width
the output data size is less than featmap_height
* featmap_width
* number_of_priors
* 4 and the output tensor is filled with undefined values for rest output tensor elements.Types
Example