Updating model to infer when labels are padded, rather than relying solely on...

Updating model to infer when labels are padded, rather than relying solely on the mode. This is necessary for evaluating on train data.

PiperOrigin-RevId: 190960173

research/object_detection/inputs.py

@@ -234,7 +234,7 @@ def create_train_input_fn(train_config, train_input_config,
         features[fields.InputDataFields.true_image_shape] is a [batch_size, 3]
           int32 tensor representing the true image shapes, as preprocessed
           images could be padded.
-        features[fields.InputDataFields.image] (optional) is a
+        features[fields.InputDataFields.original_image] (optional) is a
           [batch_size, H, W, C] float32 tensor with original images.
       labels: Dictionary of groundtruth tensors.
         labels[fields.InputDataFields.num_groundtruth_boxes] is a [batch_size]

research/object_detection/model.py

@@ -225,7 +225,14 @@ def create_model_fn(detection_model_fn, configs, hparams, use_tpu=False):
           labels,
           unpad_groundtruth_tensors=train_config.unpad_groundtruth_tensors)
     elif mode == tf.estimator.ModeKeys.EVAL:
-      labels = unstack_batch(labels, unpad_groundtruth_tensors=False)
+      # For evaling on train data, it is necessary to check whether groundtruth
+      # must be unpadded.
+      boxes_shape = (
+          labels[fields.InputDataFields.groundtruth_boxes].get_shape()
+          .as_list())
+      unpad_groundtruth_tensors = True if boxes_shape[1] is not None else False
+      labels = unstack_batch(
+          labels, unpad_groundtruth_tensors=unpad_groundtruth_tensors)
 
     if mode in (tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL):
       gt_boxes_list = labels[fields.InputDataFields.groundtruth_boxes]