Add a new Batched NMS OP

tensorflow/core/api_def/base_api/api_def_CombinedNonMaxSuppression.pbtxt

@@ -0,0 +1,101 @@
+op {
+  graph_op_name: "CombinedNonMaxSuppression"
+  in_arg {
+    name: "boxes"
+    description: <<END
+A 4-D float tensor of shape `[batch_size, num_boxes, q, 4]`. If `q` is 1 then 
+same boxes are used for all classes otherwise, if `q` is equal to number of 
+classes, class-specific boxes are used.
+END
+  }
+  in_arg {
+    name: "scores"
+    description: <<END
+A 3-D float tensor of shape `[batch_size, num_boxes, num_classes]`
+representing a single score corresponding to each box (each row of boxes).
+END
+  }
+  in_arg {
+    name: "max_output_size_per_class"
+    description: <<END
+A scalar integer tensor representing the maximum number of 
+boxes to be selected by non max suppression per class
+END
+  }
+  in_arg {
+    name: "max_total_size"
+    description: <<END
+A scalar representing maximum number of boxes retained over all classes.
+END
+  }
+  in_arg {
+    name: "iou_threshold"
+    description: <<END
+A 0-D float tensor representing the threshold for deciding whether
+boxes overlap too much with respect to IOU.
+END
+  }
+  in_arg {
+    name: "score_threshold"
+    description: <<END
+A 0-D float tensor representing the threshold for deciding when to remove
+boxes based on score.
+END
+  }
+  attr {
+    name: "pad_per_class"
+    description: <<END
+If false, the output nmsed boxes, scores and classes
+are padded/clipped to `max_total_size`. If true, the
+output nmsed boxes, scores and classes are padded to be of length
+`max_size_per_class`*`num_classes`, unless it exceeds `max_total_size` in
+which case it is clipped to `max_total_size`. Defaults to false.
+END
+  }
+  out_arg {
+    name: "nmsed_boxes"
+    description: <<END
+A [batch_size, max_detections, 4] float32 tensor 
+containing the non-max suppressed boxes.
+END
+  }
+  out_arg {
+    name: "nmsed_scores"
+    description: <<END
+A [batch_size, max_detections] float32 tensor 
+containing the scores for the boxes.
+END
+  }
+  out_arg {
+    name: "nmsed_classes"
+    description: <<END
+A [batch_size, max_detections] float32 tensor 
+containing the classes for the boxes.
+END
+  }
+  out_arg {
+    name: "valid_detections"
+    description: <<END
+A [batch_size] int32 tensor indicating the number of
+valid detections per batch item. Only the top num_detections[i] entries in
+nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The rest of the
+entries are zero paddings.
+END
+  }
+  summary: "Greedily selects a subset of bounding boxes in descending order of score,"
+  description: <<END
+This operation performs non_max_suppression on the inputs per batch, across
+all classes.
+Prunes away boxes that have high intersection-over-union (IOU) overlap
+with previously selected boxes.  Bounding boxes are supplied as
+[y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
+diagonal pair of box corners and the coordinates can be provided as normalized
+(i.e., lying in the interval [0, 1]) or absolute.  Note that this algorithm
+is agnostic to where the origin is in the coordinate system. Also note that
+this algorithm is invariant to orthogonal transformations and translations
+of the coordinate system; thus translating or reflections of the coordinate
+system result in the same boxes being selected by the algorithm.
+The output of this operation is the final boxes, scores and classes tensor
+returned after performing non_max_suppression.
+END
+}