adding option to select specific frame index

research/object_detection/inputs.py

@@ -1094,8 +1094,12 @@ def get_reduce_to_frame_fn(input_reader_config, is_training):
           num_frames = tf.cast(
               tf.shape(tensor_dict[fields.InputDataFields.source_id])[0],
               dtype=tf.int32)
-          frame_index = tf.random.uniform((), minval=0, maxval=num_frames,
-                                          dtype=tf.int32)
+          if input_reader_config.frame_index == -1:
+            frame_index = tf.random.uniform((), minval=0, maxval=num_frames,
+                                            dtype=tf.int32)
+          else:
+            frame_index = tf.constant(input_reader_config.frame_index,
+                                      dtype=tf.int32)
           out_tensor_dict = {}
           for key in tensor_dict:
             if key in fields.SEQUENCE_FIELDS:

research/object_detection/inputs_test.py

@@ -312,6 +312,46 @@ class InputFnTest(test_case.TestCase, parameterized.TestCase):
         tf.float32,
         labels[fields.InputDataFields.groundtruth_weights].dtype)
 
+  def test_context_rcnn_resnet50_train_input_with_sequence_example_frame_index(
+    self, train_batch_size=8):
+    """Tests the training input function for FasterRcnnResnet50."""
+    configs = _get_configs_for_model_sequence_example(
+        'context_rcnn_camera_trap', frame_index=2)
+    model_config = configs['model']
+    train_config = configs['train_config']
+    train_config.batch_size = train_batch_size
+    train_input_fn = inputs.create_train_input_fn(
+        train_config, configs['train_input_config'], model_config)
+    features, labels = _make_initializable_iterator(train_input_fn()).get_next()
+
+    self.assertAllEqual([train_batch_size, 640, 640, 3],
+                        features[fields.InputDataFields.image].shape.as_list())
+    self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
+    self.assertAllEqual([train_batch_size],
+                        features[inputs.HASH_KEY].shape.as_list())
+    self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
+    self.assertAllEqual(
+        [train_batch_size, 100, 4],
+        labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
+    self.assertEqual(tf.float32,
+                    labels[fields.InputDataFields.groundtruth_boxes].dtype)
+    self.assertAllEqual(
+        [train_batch_size, 100, model_config.faster_rcnn.num_classes],
+        labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
+    self.assertEqual(tf.float32,
+                    labels[fields.InputDataFields.groundtruth_classes].dtype)
+    self.assertAllEqual(
+        [train_batch_size, 100],
+        labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
+    self.assertEqual(tf.float32,
+                    labels[fields.InputDataFields.groundtruth_weights].dtype)
+    self.assertAllEqual(
+        [train_batch_size, 100, model_config.faster_rcnn.num_classes],
+        labels[fields.InputDataFields.groundtruth_confidences].shape.as_list())
+    self.assertEqual(
+        tf.float32,
+        labels[fields.InputDataFields.groundtruth_confidences].dtype)
+
   def test_ssd_inceptionV2_train_input(self):
     """Tests the training input function for SSDInceptionV2."""
     configs = _get_configs_for_model('ssd_inception_v2_pets')

research/object_detection/protos/input_reader.proto

+
 syntax = "proto2";
 
 package object_detection.protos;
@@ -31,7 +32,7 @@ enum InputType {
   TF_SEQUENCE_EXAMPLE = 2;        // TfSequenceExample Input
 }
 
-// Next id: 32
+// Next id: 33
 message InputReader {
   // Name of input reader. Typically used to describe the dataset that is read
   // by this input reader.
@@ -133,6 +134,9 @@ message InputReader {
   // Whether input data type is tf.Examples or tf.SequenceExamples
   optional InputType input_type = 30 [default = TF_EXAMPLE];
 
+  // Which frame to choose from the input if Sequence Example. -1 indicates random choice.
+  optional int32 frame_index = 32 [default = -1];
+
   oneof input_reader {
     TFRecordInputReader tf_record_input_reader = 8;
     ExternalInputReader external_input_reader = 9;