Better error message when loading a wrong checkpoint type in CenterNet.

PiperOrigin-RevId: 322967458

research/object_detection/meta_architectures/center_net_meta_arch.py

@@ -118,6 +118,19 @@ class CenterNetFeatureExtractor(tf.keras.Model):
     """Ther number of feature outputs returned by the feature extractor."""
     pass
 
+  @abc.abstractmethod
+  def get_sub_model(self, sub_model_type):
+    """Returns the underlying keras model for the given sub_model_type.
+
+    This function is useful when we only want to get a subset of weights to
+    be restored from a checkpoint.
+
+    Args:
+      sub_model_type: string, the type of sub model. Currently, CenterNet
+        feature extractors support 'detection' and 'classification'.
+    """
+    pass
+
 
 def make_prediction_net(num_out_channels, kernel_size=3, num_filters=256,
                         bias_fill=None):
@@ -2762,20 +2775,8 @@ class CenterNetMetaArch(model.DetectionModel):
       A dict mapping keys to Trackable objects (tf.Module or Checkpoint).
     """
 
-    if fine_tune_checkpoint_type == 'classification':
-      return {'feature_extractor': self._feature_extractor.get_base_model()}
-
-    elif fine_tune_checkpoint_type == 'detection':
-      return {'feature_extractor': self._feature_extractor.get_model()}
-
-    elif fine_tune_checkpoint_type == 'fine_tune':
-      feature_extractor_model = tf.train.Checkpoint(
-          _feature_extractor=self._feature_extractor)
-      return {'model': feature_extractor_model}
-
-    else:
-      raise ValueError('Not supported  fine tune checkpoint type - {}'.format(
-          fine_tune_checkpoint_type))
+    sub_model = self._feature_extractor.get_sub_model(fine_tune_checkpoint_type)
+    return {'feature_extractor': sub_model}
 
   def updates(self):
     raise RuntimeError('This model is intended to be used with model_lib_v2 '

research/object_detection/meta_architectures/center_net_meta_arch_tf2_test.py

@@ -17,7 +17,9 @@
 from __future__ import division
 
 import functools
+import re
 import unittest
+
 from absl.testing import parameterized
 import numpy as np
 import tensorflow.compat.v1 as tf
@@ -1788,6 +1790,15 @@ class CenterNetMetaArchRestoreTest(test_case.TestCase):
     self.assertIsInstance(restore_from_objects_map['feature_extractor'],
                           tf.keras.Model)
 
+  def test_retore_map_error(self):
+    """Test that restoring unsupported checkpoint type raises an error."""
+
+    model = build_center_net_meta_arch(build_resnet=True)
+    msg = ("Sub model detection is not defined for ResNet."
+           "Supported types are ['classification'].")
+    with self.assertRaisesRegex(ValueError, re.escape(msg)):
+      model.restore_from_objects('detection')
+
 
 class DummyFeatureExtractor(cnma.CenterNetFeatureExtractor):
 

research/object_detection/models/center_net_hourglass_feature_extractor.py

@@ -62,8 +62,14 @@ class CenterNetHourglassFeatureExtractor(
     """Ther number of feature outputs returned by the feature extractor."""
     return self._network.num_hourglasses
 
-  def get_model(self):
+  def get_sub_model(self, sub_model_type):
+    if sub_model_type == 'detection':
       return self._network
+    else:
+      supported_types = ['detection']
+      raise ValueError(
+          ('Sub model {} is not defined for Hourglass.'.format(sub_model_type) +
+           'Supported types are {}.'.format(supported_types)))
 
 
 def hourglass_104(channel_means, channel_stds, bgr_ordering):

research/object_detection/models/center_net_mobilenet_v2_feature_extractor.py

@@ -101,8 +101,14 @@ class CenterNetMobileNetV2FeatureExtractor(
     """The number of feature outputs returned by the feature extractor."""
     return 1
 
-  def get_model(self):
+  def get_sub_model(self, sub_model_type):
+    if sub_model_type == 'detection':
       return self._network
+    else:
+      supported_types = ['detection']
+      raise ValueError(
+          ('Sub model {} is not defined for MobileNet.'.format(sub_model_type) +
+           'Supported types are {}.'.format(supported_types)))
 
 
 def mobilenet_v2(channel_means, channel_stds, bgr_ordering):

research/object_detection/models/center_net_resnet_feature_extractor.py

@@ -101,10 +101,6 @@ class CenterNetResnetFeatureExtractor(CenterNetFeatureExtractor):
   def load_feature_extractor_weights(self, path):
     self._base_model.load_weights(path)
 
-  def get_base_model(self):
-    """Get base resnet model for inspection and testing."""
-    return self._base_model
-
   def call(self, inputs):
     """Returns image features extracted by the backbone.
 
@@ -127,6 +123,15 @@ class CenterNetResnetFeatureExtractor(CenterNetFeatureExtractor):
   def out_stride(self):
     return 4
 
+  def get_sub_model(self, sub_model_type):
+    if sub_model_type == 'classification':
+      return self._base_model
+    else:
+      supported_types = ['classification']
+      raise ValueError(
+          ('Sub model {} is not defined for ResNet.'.format(sub_model_type) +
+           'Supported types are {}.'.format(supported_types)))
+
 
 def resnet_v2_101(channel_means, channel_stds, bgr_ordering):
   """The ResNet v2 101 feature extractor."""

research/object_detection/models/center_net_resnet_v1_fpn_feature_extractor.py

@@ -137,10 +137,6 @@ class CenterNetResnetV1FpnFeatureExtractor(CenterNetFeatureExtractor):
   def load_feature_extractor_weights(self, path):
     self._base_model.load_weights(path)
 
-  def get_base_model(self):
-    """Get base resnet model for inspection and testing."""
-    return self._base_model
-
   def call(self, inputs):
     """Returns image features extracted by the backbone.
 
@@ -163,6 +159,15 @@ class CenterNetResnetV1FpnFeatureExtractor(CenterNetFeatureExtractor):
   def out_stride(self):
     return 4
 
+  def get_sub_model(self, sub_model_type):
+    if sub_model_type == 'classification':
+      return self._base_model
+    else:
+      supported_types = ['classification']
+      raise ValueError(
+          ('Sub model {} is not defined for ResNet FPN.'.format(sub_model_type)
+           + 'Supported types are {}.'.format(supported_types)))
+
 
 def resnet_v1_101_fpn(channel_means, channel_stds, bgr_ordering):
   """The ResNet v1 101 FPN feature extractor."""