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bulk change to unify **Required.** and **Optional.** (#6503)

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Patryk Elszkowski authored and GitHub committed 2021-07-15 09:17:23 +02:00
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docs/ops/detection/DeformablePSROIPooling_1.md
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@@ -81,12 +81,12 @@ This operation is compatible with [MXNet DeformablePSROIPooling](https://mxnet.a
**Inputs**:
* **1**: 4D input tensor of type *T* and shape `[N_in, C_in, H_in, W_in]` with position sensitive score maps. Required.
* **1**: 4D input tensor of type *T* and shape `[N_in, C_in, H_in, W_in]` with position sensitive score maps. **Required.**
* **2**: 2D input tensor of type *T* and shape `[NUM_ROIS, 5]`. It contains a list of five element tuples describing a single ROI (region of interest): `[batch_id, x_1, y_1, x_2, y_2]`. Required.
* **2**: 2D input tensor of type *T* and shape `[NUM_ROIS, 5]`. It contains a list of five element tuples describing a single ROI (region of interest): `[batch_id, x_1, y_1, x_2, y_2]`. **Required.**
Batch indices must be in the range of `[0, N_in-1]`.
* **3**: 4D input tensor of type *T* and shape `[NUM_ROIS, 2*NUM_CLASSES, group_size, group_size]` with transformation values. It contains normalized `[0, 1]` offsets for each ROI bin left top corner coordinates. Channel dimension is multiplied by `2` because of encoding two `(x, y)` coordinates. Optional.
* **3**: 4D input tensor of type *T* and shape `[NUM_ROIS, 2*NUM_CLASSES, group_size, group_size]` with transformation values. It contains normalized `[0, 1]` offsets for each ROI bin left top corner coordinates. Channel dimension is multiplied by `2` because of encoding two `(x, y)` coordinates. **Optional.**
**Outputs**:
* **1**: 4D output tensor of type *T* shape `[NUM_ROIS, output_dim, group_size, group_size]` with ROIs score maps.
docs/ops/detection/DetectionOutput_1.md
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@@ -133,12 +133,11 @@ At each feature map cell, *DetectionOutput* predicts the offsets relative to the
**Inputs**
* **1**: 2D input tensor with box logits with shape `[N, num_prior_boxes * num_loc_classes * 4]` and type *T*. `num_loc_classes` is equal to `num_classes` when `share_location` is 0 or it's equal to 1 otherwise. Required.
* **2**: 2D input tensor with class predictions with shape `[N, num_prior_boxes * num_classes]` and type *T*. Required.
* **3**: 3D input tensor with proposals with shape `[priors_batch_size, 1, num_prior_boxes * prior_box_size]` or `[priors_batch_size, 2, num_prior_boxes * prior_box_size]`. `priors_batch_size` is either 1 or `N`. Size of the second dimension depends on `variance_encoded_in_target`. If `variance_encoded_in_target` is equal to 0, the second dimension equals to 2 and variance values are provided for each boxes coordinates. If `variance_encoded_in_target` is equal to 1, the second dimension equals to 1 and this tensor contains proposals boxes only. `prior_box_size` is equal to 4 when `normalized` is set to 1 or it's equal to 5 otherwise. Required.
Required.
* **4**: 2D input tensor with additional class predictions information described in the [article](https://arxiv.org/pdf/1711.06897.pdf). Its shape must be equal to `[N, num_prior_boxes * 2]`. Optional.
* **5**: 2D input tensor with additional box predictions information described in the [article](https://arxiv.org/pdf/1711.06897.pdf). Its shape must be equal to first input tensor shape. Optional.
* **1**: 2D input tensor with box logits with shape `[N, num_prior_boxes * num_loc_classes * 4]` and type *T*. `num_loc_classes` is equal to `num_classes` when `share_location` is 0 or it's equal to 1 otherwise. **Required.**
* **2**: 2D input tensor with class predictions with shape `[N, num_prior_boxes * num_classes]` and type *T*. **Required.**
* **3**: 3D input tensor with proposals with shape `[priors_batch_size, 1, num_prior_boxes * prior_box_size]` or `[priors_batch_size, 2, num_prior_boxes * prior_box_size]`. `priors_batch_size` is either 1 or `N`. Size of the second dimension depends on `variance_encoded_in_target`. If `variance_encoded_in_target` is equal to 0, the second dimension equals to 2 and variance values are provided for each boxes coordinates. If `variance_encoded_in_target` is equal to 1, the second dimension equals to 1 and this tensor contains proposals boxes only. `prior_box_size` is equal to 4 when `normalized` is set to 1 or it's equal to 5 otherwise. **Required.**
* **4**: 2D input tensor with additional class predictions information described in the [article](https://arxiv.org/pdf/1711.06897.pdf). Its shape must be equal to `[N, num_prior_boxes * 2]`. **Optional.**
* **5**: 2D input tensor with additional box predictions information described in the [article](https://arxiv.org/pdf/1711.06897.pdf). Its shape must be equal to first input tensor shape. **Optional.**
**Outputs**
docs/ops/detection/ExperimentalDetectronDetectionOutput_6.md
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@@ -4,12 +4,12 @@
**Category**: Object detection
**Short description**: The *ExperimentalDetectronDetectionOutput* operation performs non-maximum suppression to generate
**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<sub>1</sub>, y<sub>1</sub>, x<sub>2</sub>, y<sub>2</sub>] and takes coordinates of
1. Applies deltas to boxes sizes [x<sub>1</sub>, y<sub>1</sub>, x<sub>2</sub>, y<sub>2</sub>] 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`
@@ -19,7 +19,7 @@ refined boxes according to the formulas:
`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`
@@ -31,8 +31,8 @@ refined boxes according to the formulas:
`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
* `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]`
@@ -46,17 +46,17 @@ second input:
2. If *class_agnostic_box_regression* is `true` removes predictions for background classes.
3. Clips boxes to the image.
4. Applies *score_threshold* on detection scores.
5. Applies non-maximum suppression class-wise with *nms_threshold* and returns *post_nms_count* or less detections per
5. Applies non-maximum suppression class-wise with *nms_threshold* and returns *post_nms_count* or less detections per
class.
6. 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
6. 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
* **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
@@ -97,7 +97,7 @@ tensor elements.
* *class_agnostic_box_regression*
* **Description**: *class_agnostic_box_regression* attribute is a flag that specifies whether to delete background
* **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 deleted
@@ -124,8 +124,8 @@ tensor elements.
**Inputs**
* **1**: A 2D tensor of type *T* with input ROIs, with shape `[number_of_ROIs, 4]` providing the ROIs as 4-tuples:
[x<sub>1</sub>, y<sub>1</sub>, x<sub>2</sub>, y<sub>2</sub>]. The batch dimension of first, second, and third inputs
* **1**: A 2D tensor of type *T* with input ROIs, with shape `[number_of_ROIs, 4]` providing the ROIs as 4-tuples:
[x<sub>1</sub>, y<sub>1</sub>, x<sub>2</sub>, y<sub>2</sub>]. 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.
docs/ops/detection/ExperimentalDetectronGenerateProposalsSingleImage_6.md
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@@ -4,7 +4,7 @@
**Category**: Object detection
**Short description**: The *ExperimentalDetectronGenerateProposalsSingleImage* operation computes ROIs and their scores
**Short description**: The *ExperimentalDetectronGenerateProposalsSingleImage* operation computes ROIs and their scores
based on input data.
**Detailed description**: The operation performs the following steps:
@@ -15,7 +15,7 @@ based on input data.
4. Sorts all `(proposal, score)` pairs by score from highest to lowest; order of pairs with equal scores is undefined.
5. Takes top *pre_nms_count* proposals, if total number of proposals is less than *pre_nms_count* takes all proposals.
6. Applies non-maximum suppression with *nms_threshold*.
7. Takes top *post_nms_count* proposals and returns these top proposals and their scores. If total number of proposals
7. 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**:
@@ -54,15 +54,15 @@ is less than *post_nms_count* returns output tensors filled with zeroes.
**Inputs**
* **1**: A 1D tensor of type *T* with 3 elements `[image_height, image_width, scale_height_and_width]` providing input
* **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.
* **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.
* **4**: A 3D tensor of type *T* with shape `[number_of_channels, height, width]` providing proposals scores.
**Required.**
**Outputs**
docs/ops/detection/ExperimentalDetectronPriorGridGenerator_6.md
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@@ -6,14 +6,14 @@
**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
**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
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
`featmap_height`, `featmap_width`, `image_height` and `image_width` are spatial dimensions values from second and third
inputs, respectively.
**Attributes**:
@@ -35,7 +35,7 @@ inputs, respectively.
* **Type**: int
* **Default value**: 0
* **Required**: *no*
* *w*
* **Description**: The *w* attribute specifies number of cells of the generated grid with respect to width.
@@ -51,7 +51,7 @@ inputs, respectively.
* **Type**: float
* **Default value**: 0.0
* **Required**: *no*
* *stride_y*
* **Description**: The *stride_y* attribute specifies the step of generated grid with respect to y coordinate.
@@ -64,19 +64,19 @@ inputs, respectively.
* **1**: A 2D tensor of type *T* with shape `[number_of_priors, 4]` contains priors. **Required.**
* **2**: A 4D tensor of type *T* with input feature map `[1, number_of_channels, featmap_height, featmap_width]`. This
* **2**: A 4D tensor of type *T* with input feature map `[1, number_of_channels, featmap_height, featmap_width]`. This
operation uses only sizes of this input tensor, not its data.**Required.**
* **3**: A 4D tensor of type *T* with input image `[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,
* **3**: A 4D tensor of type *T* with input image `[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**
* **1**: A tensor of type *T* with priors grid with shape `[featmap_height * featmap_width * number_of_priors, 4]`
* **1**: A tensor of type *T* with priors grid with shape `[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
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**
docs/ops/detection/ExperimentalDetectronROIFeatureExtractor_6.md
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@@ -4,11 +4,11 @@
**Category**: Object detection
**Short description**: *ExperimentalDetectronROIFeatureExtractor* is the [ROIAlign](ROIAlign_3.md) operation applied
**Short description**: *ExperimentalDetectronROIFeatureExtractor* is the [ROIAlign](ROIAlign_3.md) operation applied
over a feature pyramid.
**Detailed description**: *ExperimentalDetectronROIFeatureExtractor* maps input ROIs to the levels of the pyramid
depending on the sizes of ROIs and parameters of the operation, and then extracts features via ROIAlign from
**Detailed description**: *ExperimentalDetectronROIFeatureExtractor* maps input ROIs to the levels of the pyramid
depending on the sizes of ROIs and parameters of the operation, and then extracts features via ROIAlign from
corresponding pyramid levels.
Operation applies the *ROIAlign* algorithm to the pyramid layers:
@@ -23,7 +23,7 @@ PyramidLevelMapper maps the ROI to the pyramid level using the following formula
Here 224 is the canonical ImageNet pre-training size, 2 is the pyramid starting level, and `w`, `h` are the ROI width and height.
For more details please see the following source:
For more details please see the following source:
[Feature Pyramid Networks for Object Detection](https://arxiv.org/pdf/1612.03144.pdf).
**Attributes**:
@@ -38,7 +38,7 @@ For more details please see the following source:
* *sampling_ratio*
* **Description**: The *sampling_ratio* attribute specifies the number of sampling points per the output value. If 0,
* **Description**: The *sampling_ratio* attribute specifies the number of sampling points per the output value. If 0,
then use adaptive number computed as `ceil(roi_width / output_width)`, and likewise for height.
* **Range of values**: a non-negative integer number
* **Type**: int
@@ -47,8 +47,8 @@ For more details please see the following source:
* *pyramid_scales*
* **Description**: The *pyramid_scales* enlists `image_size / layer_size[l]` ratios for pyramid layers `l=1,...,L`,
where `L` is the number of pyramid layers, and `image_size` refers to network's input image. Note that pyramid's
* **Description**: The *pyramid_scales* enlists `image_size / layer_size[l]` ratios for pyramid layers `l=1,...,L`,
where `L` is the number of pyramid layers, and `image_size` refers to network's input image. Note that pyramid's
largest layer may have smaller size than input image, e.g. `image_size` is `800 x 1344` in the XML example below.
* **Range of values**: a list of positive integer numbers
* **Type**: int[]
@@ -67,21 +67,21 @@ For more details please see the following source:
**Inputs**:
* **1**: 2D input tensor of type *T* with shape `[number_of_ROIs, 4]` providing the ROIs as 4-tuples:
[x<sub>1</sub>, y<sub>1</sub>, x<sub>2</sub>, y<sub>2</sub>]. Coordinates *x* and *y* are refer to the network's input
*image_size*. **Required**.
* **1**: 2D input tensor of type *T* with shape `[number_of_ROIs, 4]` providing the ROIs as 4-tuples:
[x<sub>1</sub>, y<sub>1</sub>, x<sub>2</sub>, y<sub>2</sub>]. Coordinates *x* and *y* are refer to the network's input
*image_size*. **Required.**
* **2**, ..., **L**: Pyramid of 4D input tensors with feature maps. Shape must be
`[1, number_of_channels, layer_size[l], layer_size[l]]`. The number of channels must be the same for all layers of the
pyramid. The layer width and height must equal to the `layer_size[l] = image_size / pyramid_scales[l]`. **Required**.
* **2**, ..., **L**: Pyramid of 4D input tensors with feature maps. Shape must be
`[1, number_of_channels, layer_size[l], layer_size[l]]`. The number of channels must be the same for all layers of the
pyramid. The layer width and height must equal to the `layer_size[l] = image_size / pyramid_scales[l]`. **Required.**
**Outputs**:
* **1**: 4D output tensor of type *T* with ROIs features. Shape must be
`[number_of_ROIs, number_of_channels, output_size, output_size]`. Channels number is the same as for all images in the
* **1**: 4D output tensor of type *T* with ROIs features. Shape must be
`[number_of_ROIs, number_of_channels, output_size, output_size]`. Channels number is the same as for all images in the
input pyramid.
* **2**: 2D output tensor of type *T* with reordered ROIs according to their mapping to the pyramid levels. Shape
* **2**: 2D output tensor of type *T* with reordered ROIs according to their mapping to the pyramid levels. Shape
must be the same as for 1 input: `[number_of_ROIs, 4]`.
**Types**
docs/ops/detection/PSROIPooling_1.md
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@@ -61,9 +61,9 @@ ROIs coordinates are specified in absolute values for the average mode and in no
**Inputs**:
* **1**: 4D input tensor with shape `[N, C, H, W]` and type *T* with feature maps. Required.
* **1**: 4D input tensor with shape `[N, C, H, W]` and type *T* with feature maps. **Required.**
* **2**: 2D input tensor with shape `[num_boxes, 5]`. It contains a list of five element tuples that describe a region of interest: `[batch_id, x_1, y_1, x_2, y_2]`. Required.
* **2**: 2D input tensor with shape `[num_boxes, 5]`. It contains a list of five element tuples that describe a region of interest: `[batch_id, x_1, y_1, x_2, y_2]`. **Required.**
Batch indices must be in the range of `[0, N-1]`.
**Outputs**:
docs/ops/detection/PriorBoxClustered_1.md
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@@ -59,9 +59,9 @@
**Inputs**:
* **1**: `output_size` - 1D tensor with two integer elements `[height, width]`. Specifies the spatial size of generated grid with boxes. Required.
* **1**: `output_size` - 1D tensor with two integer elements `[height, width]`. Specifies the spatial size of generated grid with boxes. **Required.**
* **2**: `image_size` - 1D tensor with two integer elements `[image_height, image_width]` that specifies shape of the image for which boxes are generated. Optional.
* **2**: `image_size` - 1D tensor with two integer elements `[image_height, image_width]` that specifies shape of the image for which boxes are generated. **Optional.**
**Outputs**:
docs/ops/detection/PriorBox_1.md
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@@ -138,9 +138,9 @@
**Inputs**:
* **1**: `output_size` - 1D tensor of type *T_INT* with two elements `[height, width]`. Specifies the spatial size of generated grid with boxes. **Required**.
* **1**: `output_size` - 1D tensor of type *T_INT* with two elements `[height, width]`. Specifies the spatial size of generated grid with boxes. **Required.**
* **2**: `image_size` - 1D tensor of type *T_INT* with two elements `[image_height, image_width]` that specifies shape of the image for which boxes are generated. **Required**.
* **2**: `image_size` - 1D tensor of type *T_INT* with two elements `[image_height, image_width]` that specifies shape of the image for which boxes are generated. **Required.**
**Outputs**:
docs/ops/detection/Proposal_1.md
+3 -3
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@@ -129,11 +129,11 @@
**Inputs**:
* **1**: 4D tensor of type *T* and shape `[batch_size, 2*K, H, W]` with class prediction scores. Required.
* **1**: 4D tensor of type *T* and shape `[batch_size, 2*K, H, W]` with class prediction scores. **Required.**
* **2**: 4D tensor of type *T* and shape `[batch_size, 4*K, H, W]` with deltas for each bounding box. Required.
* **2**: 4D tensor of type *T* and shape `[batch_size, 4*K, H, W]` with deltas for each bounding box. **Required.**
* **3**: 1D tensor of type *T* with 3 or 4 elements: `[image_height, image_width, scale_height_and_width]` or `[image_height, image_width, scale_height, scale_width]`. Required.
* **3**: 1D tensor of type *T* with 3 or 4 elements: `[image_height, image_width, scale_height_and_width]` or `[image_height, image_width, scale_height, scale_width]`. **Required.**
**Outputs**:
docs/ops/detection/Proposal_4.md
+3 -3
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@@ -138,11 +138,11 @@ the second optional tensor of shape `[batch_size * post_nms_topn]` with probabil
**Inputs**:
* **1**: 4D tensor of type *T* and shape `[batch_size, 2*K, H, W]` with class prediction scores. Required.
* **1**: 4D tensor of type *T* and shape `[batch_size, 2*K, H, W]` with class prediction scores. **Required.**
* **2**: 4D tensor of type *T* and shape `[batch_size, 4*K, H, W]` with deltas for each bounding box. Required.
* **2**: 4D tensor of type *T* and shape `[batch_size, 4*K, H, W]` with deltas for each bounding box. **Required.**
* **3**: 1D tensor of type *T* with 3 or 4 elements: `[image_height, image_width, scale_height_and_width]` or `[image_height, image_width, scale_height, scale_width]`. Required.
* **3**: 1D tensor of type *T* with 3 or 4 elements: `[image_height, image_width, scale_height_and_width]` or `[image_height, image_width, scale_height, scale_width]`. **Required.**
**Outputs**
docs/ops/detection/ROIAlign_3.md
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@@ -55,13 +55,13 @@
**Inputs**:
* **1**: 4D input tensor of shape `[N, C, H, W]` with feature maps of type *T*. Required.
* **1**: 4D input tensor of shape `[N, C, H, W]` with feature maps of type *T*. **Required.**
* **2**: 2D input tensor of shape `[NUM_ROIS, 4]` describing box consisting of 4 element tuples: `[x_1, y_1, x_2, y_2]` in relative coordinates of type *T*.
The box height and width are calculated the following way: `roi_width = max(spatial_scale * (x_2 - x_1), 1.0)`,
`roi_height = max(spatial_scale * (y_2 - y_1), 1.0)`, so the malformed boxes are expressed as a box of size `1 x 1`. Required.
`roi_height = max(spatial_scale * (y_2 - y_1), 1.0)`, so the malformed boxes are expressed as a box of size `1 x 1`. **Required.**
* **3**: 1D input tensor of shape `[NUM_ROIS]` with batch indices of type *IND_T*. Required.
* **3**: 1D input tensor of shape `[NUM_ROIS]` with batch indices of type *IND_T*. **Required.**
**Outputs**:
docs/ops/detection/ROIPooling_1.md
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@@ -52,9 +52,9 @@ The box height and width have different representation based on **method** attri
**Inputs**:
* **1**: 4D input tensor of shape `[N, C, H, W]` with feature maps of type *T*. Required.
* **1**: 4D input tensor of shape `[N, C, H, W]` with feature maps of type *T*. **Required.**
* **2**: 2D input tensor of shape `[NUM_ROIS, 5]` describing region of interest box consisting of 5 element tuples of type *T*: `[batch_id, x_1, y_1, x_2, y_2]`. Required.
* **2**: 2D input tensor of shape `[NUM_ROIS, 5]` describing region of interest box consisting of 5 element tuples of type *T*: `[batch_id, x_1, y_1, x_2, y_2]`. **Required.**
Batch indices must be in the range of `[0, N-1]`.