Adding kwargs for rpn feature extractor. Temp: force running keras model using tf1

research/object_detection/builders/model_builder.py

@@ -72,6 +72,7 @@ if tf_version.is_tf1():
   from object_detection.models import faster_rcnn_resnet_v1_feature_extractor as frcnn_resnet_v1
   from object_detection.models import ssd_resnet_v1_fpn_feature_extractor as ssd_resnet_v1_fpn
   from object_detection.models import ssd_resnet_v1_ppn_feature_extractor as ssd_resnet_v1_ppn
+  from object_detection.models import faster_rcnn_resnet_v1_fpn_keras_feature_extractor as frcnn_resnet_fpn_keras
   from object_detection.models.embedded_ssd_mobilenet_v1_feature_extractor import EmbeddedSSDMobileNetV1FeatureExtractor
   from object_detection.models.ssd_inception_v2_feature_extractor import SSDInceptionV2FeatureExtractor
   from object_detection.models.ssd_mobilenet_v2_fpn_feature_extractor import SSDMobileNetV2FpnFeatureExtractor
@@ -229,9 +230,19 @@ if tf_version.is_tf1():
       frcnn_resnet_v1.FasterRCNNResnet152FeatureExtractor,
   }
 
+  FASTER_RCNN_KERAS_FEATURE_EXTRACTOR_CLASS_MAP = {
+      'faster_rcnn_resnet50_fpn_keras':
+          frcnn_resnet_fpn_keras.FasterRCNNResnet50FpnKerasFeatureExtractor,
+      'faster_rcnn_resnet101_fpn_keras':
+          frcnn_resnet_fpn_keras.FasterRCNNResnet101FpnKerasFeatureExtractor,
+      'faster_rcnn_resnet152_fpn_keras':
+          frcnn_resnet_fpn_keras.FasterRCNNResnet152FpnKerasFeatureExtractor,
+  }
+
   FEATURE_EXTRACTOR_MAPS = [
       SSD_FEATURE_EXTRACTOR_CLASS_MAP,
-      FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP
+      FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP,
+      FASTER_RCNN_KERAS_FEATURE_EXTRACTOR_CLASS_MAP
   ]
 
 
@@ -480,6 +491,7 @@ def _build_faster_rcnn_feature_extractor(
   first_stage_features_stride = (
       feature_extractor_config.first_stage_features_stride)
   batch_norm_trainable = feature_extractor_config.batch_norm_trainable
+  print(feature_type)
 
   if feature_type not in FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP:
     raise ValueError('Unknown Faster R-CNN feature_extractor: {}'.format(
@@ -524,9 +536,31 @@ def _build_faster_rcnn_keras_feature_extractor(
         feature_type))
   feature_extractor_class = FASTER_RCNN_KERAS_FEATURE_EXTRACTOR_CLASS_MAP[
       feature_type]
+
+  kwargs = {}
+
+  if feature_extractor_config.HasField('conv_hyperparams'):
+    kwargs.update({
+        'conv_hyperparams':
+            hyperparams_builder.KerasLayerHyperparams(
+                feature_extractor_config.conv_hyperparams),
+        'override_base_feature_extractor_hyperparams':
+            feature_extractor_config.override_base_feature_extractor_hyperparams
+    })
+
+  if feature_extractor_config.HasField('fpn'):
+    kwargs.update({
+        'fpn_min_level':
+            feature_extractor_config.fpn.min_level,
+        'fpn_max_level':
+            feature_extractor_config.fpn.max_level,
+        'additional_layer_depth':
+            feature_extractor_config.fpn.additional_layer_depth,
+    })
+
   return feature_extractor_class(
       is_training, first_stage_features_stride,
-      batch_norm_trainable)
+      batch_norm_trainable, **kwargs)
 
 
 def _build_faster_rcnn_model(frcnn_config, is_training, add_summaries):
@@ -553,7 +587,7 @@ def _build_faster_rcnn_model(frcnn_config, is_training, add_summaries):
   _check_feature_extractor_exists(frcnn_config.feature_extractor.type)
   is_keras = tf_version.is_tf2()
 
-  if is_keras:
+  if is_keras or True:
     feature_extractor = _build_faster_rcnn_keras_feature_extractor(
         frcnn_config.feature_extractor, is_training,
         inplace_batchnorm_update=frcnn_config.inplace_batchnorm_update)