Add image_and_boxes_tensor to helper doc string.

PiperOrigin-RevId: 364895608

Author: Vighnesh Birodkar

research/object_detection/exporter_lib_v2.py

@@ -208,14 +208,6 @@ def __call__(self, input_tensor):
                                                  _decode_tf_example)
     return self._run_inference_on_images(images, true_shapes)
 
-DETECTION_MODULE_MAP = {
-    'image_tensor': DetectionFromImageModule,
-    'encoded_image_string_tensor':
-    DetectionFromEncodedImageModule,
-    'tf_example': DetectionFromTFExampleModule,
-    'float_image_tensor': DetectionFromFloatImageModule
-}
-
 
 def export_inference_graph(input_type,
                            pipeline_config,
@@ -355,6 +347,11 @@ def __call__(self, input_tensor, boxes):
     return self._run_segmentation_on_images(input_tensor, boxes)
 
 
-DETECTION_MODULE_MAP.update({
+DETECTION_MODULE_MAP = {
+    'image_tensor': DetectionFromImageModule,
+    'encoded_image_string_tensor':
+    DetectionFromEncodedImageModule,
+    'tf_example': DetectionFromTFExampleModule,
+    'float_image_tensor': DetectionFromFloatImageModule,
     'image_and_boxes_tensor': DetectionFromImageAndBoxModule,
-})
+}

research/object_detection/exporter_main_v2.py

@@ -31,6 +31,11 @@
   * `tf_example`: Accepts a 1-D string tensor of shape [None] containing
     serialized TFExample protos. Image resolutions are expected to be the same
     if more than 1 image is provided.
+  * `image_and_boxes_tensor`: Accepts a 4-D image tensor of size
+    [1, None, None, 3] and a boxes tensor of size [1, None, 4] of normalized
+    bounding boxes. To be able to support this option, the model needs
+    to implement a predict_masks_from_boxes method. See the documentation
+    for DetectionFromImageAndBoxModule for details.
 
 and the following output nodes returned by the model.postprocess(..):
   * `num_detections`: Outputs float32 tensors of the form [batch]
@@ -107,7 +112,8 @@
 
 flags.DEFINE_string('input_type', 'image_tensor', 'Type of input node. Can be '
                     'one of [`image_tensor`, `encoded_image_string_tensor`, '
-                    '`tf_example`, `float_image_tensor`]')
+                    '`tf_example`, `float_image_tensor`, '
+                    '`image_and_boxes_tensor`]')
 flags.DEFINE_string('pipeline_config_path', None,
                     'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
                     'file.')