Merge branch 'master' of https://github.com/tensorflow/models

official/vision/beta/modeling/backbones/spinenet.py

@@ -393,8 +393,10 @@ class SpineNet(tf.keras.Model):
               block_spec.level))
         if (block_spec.level < self._min_level or
             block_spec.level > self._max_level):
-          raise ValueError('Output level is out of range [{}, {}]'.format(
-              self._min_level, self._max_level))
+          logging.warning(
+              'SpineNet output level out of range [min_level, max_level] = '
+              '[%s, %s] will not be used for further processing.',
+              self._min_level, self._max_level)
         endpoints[str(block_spec.level)] = x
 
     return endpoints

official/vision/beta/modeling/backbones/spinenet_mobile.py

@@ -152,6 +152,7 @@ class SpineNetMobile(tf.keras.Model):
       use_sync_bn: bool = False,
       norm_momentum: float = 0.99,
       norm_epsilon: float = 0.001,
+      use_keras_upsampling_2d: bool = False,
       **kwargs):
     """Initializes a Mobile SpineNet model.
 
@@ -181,6 +182,7 @@ class SpineNetMobile(tf.keras.Model):
       use_sync_bn: If True, use synchronized batch normalization.
       norm_momentum: A `float` of normalization momentum for the moving average.
       norm_epsilon: A small `float` added to variance to avoid dividing by zero.
+      use_keras_upsampling_2d: If True, use keras UpSampling2D layer.
       **kwargs: Additional keyword arguments to be passed.
     """
     self._input_specs = input_specs
@@ -200,12 +202,7 @@ class SpineNetMobile(tf.keras.Model):
     self._use_sync_bn = use_sync_bn
     self._norm_momentum = norm_momentum
     self._norm_epsilon = norm_epsilon
-    if activation == 'relu':
-      self._activation_fn = tf.nn.relu
-    elif activation == 'swish':
-      self._activation_fn = tf.nn.swish
-    else:
-      raise ValueError('Activation {} not implemented.'.format(activation))
+    self._use_keras_upsampling_2d = use_keras_upsampling_2d
     self._num_init_blocks = 2
 
     if use_sync_bn:
@@ -271,7 +268,7 @@ class SpineNetMobile(tf.keras.Model):
           norm_momentum=self._norm_momentum,
           norm_epsilon=self._norm_epsilon)(
               inputs)
-    return tf.identity(x, name=name)
+    return tf.keras.layers.Activation('linear', name=name)(x)
 
   def _build_stem(self, inputs):
     """Builds SpineNet stem."""
@@ -290,7 +287,7 @@ class SpineNetMobile(tf.keras.Model):
         momentum=self._norm_momentum,
         epsilon=self._norm_epsilon)(
             x)
-    x = tf_utils.get_activation(self._activation_fn)(x)
+    x = tf_utils.get_activation(self._activation, use_keras_layer=True)(x)
 
     net = []
     stem_strides = [1, 2]
@@ -365,14 +362,15 @@ class SpineNetMobile(tf.keras.Model):
         parent_weights = [
             tf.nn.relu(tf.cast(tf.Variable(1.0, name='block{}_fusion{}'.format(
                 i, j)), dtype=dtype)) for j in range(len(parents))]
-        weights_sum = tf.add_n(parent_weights)
+        weights_sum = layers.Add()(parent_weights)
         parents = [
             parents[i] * parent_weights[i] / (weights_sum + 0.0001)
             for i in range(len(parents))
         ]
 
       # Fuse all parent nodes then build a new block.
-      x = tf_utils.get_activation(self._activation_fn)(tf.add_n(parents))
+      x = tf_utils.get_activation(
+          self._activation, use_keras_layer=True)(layers.Add()(parents))
       x = self._block_group(
           inputs=x,
           in_filters=target_num_filters,
@@ -421,7 +419,7 @@ class SpineNetMobile(tf.keras.Model):
           momentum=self._norm_momentum,
           epsilon=self._norm_epsilon)(
               x)
-      x = tf_utils.get_activation(self._activation_fn)(x)
+      x = tf_utils.get_activation(self._activation, use_keras_layer=True)(x)
       endpoints[str(level)] = x
     return endpoints
 
@@ -446,11 +444,13 @@ class SpineNetMobile(tf.keras.Model):
             momentum=self._norm_momentum,
             epsilon=self._norm_epsilon)(
                 x)
-        x = tf_utils.get_activation(self._activation_fn)(x)
+        x = tf_utils.get_activation(
+            self._activation, use_keras_layer=True)(x)
         input_width /= 2
     elif input_width < target_width:
       scale = target_width // input_width
-      x = spatial_transform_ops.nearest_upsampling(x, scale=scale)
+      x = spatial_transform_ops.nearest_upsampling(
+          x, scale=scale, use_keras_layer=self._use_keras_upsampling_2d)
 
     # Last 1x1 conv to match filter size.
     x = layers.Conv2D(
@@ -485,7 +485,8 @@ class SpineNetMobile(tf.keras.Model):
         'activation': self._activation,
         'use_sync_bn': self._use_sync_bn,
         'norm_momentum': self._norm_momentum,
-        'norm_epsilon': self._norm_epsilon
+        'norm_epsilon': self._norm_epsilon,
+        'use_keras_upsampling_2d': self._use_keras_upsampling_2d,
     }
     return config_dict
 
@@ -531,4 +532,5 @@ def build_spinenet_mobile(
       activation=norm_activation_config.activation,
       use_sync_bn=norm_activation_config.use_sync_bn,
       norm_momentum=norm_activation_config.norm_momentum,
-      norm_epsilon=norm_activation_config.norm_epsilon)
+      norm_epsilon=norm_activation_config.norm_epsilon,
+      use_keras_upsampling_2d=backbone_cfg.use_keras_upsampling_2d)

official/vision/beta/modeling/backbones/spinenet_mobile_test.py

@@ -90,6 +90,7 @@ class SpineNetMobileTest(parameterized.TestCase, tf.test.TestCase):
         kernel_initializer='VarianceScaling',
         kernel_regularizer=None,
         bias_regularizer=None,
+        use_keras_upsampling_2d=False,
     )
     network = spinenet_mobile.SpineNetMobile(**kwargs)
 

official/vision/beta/modeling/backbones/spinenet_test.py

@@ -24,17 +24,16 @@ from official.vision.beta.modeling.backbones import spinenet
 class SpineNetTest(parameterized.TestCase, tf.test.TestCase):
 
   @parameterized.parameters(
-      (128, 0.65, 1, 0.5, 128),
-      (256, 1.0, 1, 0.5, 256),
-      (384, 1.0, 2, 0.5, 256),
-      (512, 1.0, 3, 1.0, 256),
-      (640, 1.3, 4, 1.0, 384),
+      (128, 0.65, 1, 0.5, 128, 4, 6),
+      (256, 1.0, 1, 0.5, 256, 3, 6),
+      (384, 1.0, 2, 0.5, 256, 4, 7),
+      (512, 1.0, 3, 1.0, 256, 3, 7),
+      (640, 1.3, 4, 1.0, 384, 3, 7),
   )
   def test_network_creation(self, input_size, filter_size_scale, block_repeats,
-                            resample_alpha, endpoints_num_filters):
+                            resample_alpha, endpoints_num_filters, min_level,
+                            max_level):
     """Test creation of SpineNet models."""
-    min_level = 3
-    max_level = 7
 
     tf.keras.backend.set_image_data_format('channels_last')
 

official/vision/beta/modeling/decoders/aspp.py

@@ -13,12 +13,15 @@
 # limitations under the License.
 
 """Contains definitions of Atrous Spatial Pyramid Pooling (ASPP) decoder."""
-from typing import Any, List, Optional, Mapping
+from typing import Any, List, Mapping, Optional
 
 # Import libraries
+
 import tensorflow as tf
 
+from official.modeling import hyperparams
 from official.vision import keras_cv
+from official.vision.beta.modeling.decoders import factory
 
 
 @tf.keras.utils.register_keras_serializable(package='Vision')
@@ -128,3 +131,46 @@ class ASPP(tf.keras.layers.Layer):
   @classmethod
   def from_config(cls, config, custom_objects=None):
     return cls(**config)
+
+
+@factory.register_decoder_builder('aspp')
+def build_aspp_decoder(
+    input_specs: Mapping[str, tf.TensorShape],
+    model_config: hyperparams.Config,
+    l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None
+) -> tf.keras.Model:
+  """Builds ASPP decoder from a config.
+
+  Args:
+    input_specs: A `dict` of input specifications. A dictionary consists of
+      {level: TensorShape} from a backbone. Note this is for consistent
+        interface, and is not used by ASPP decoder.
+    model_config: A OneOfConfig. Model config.
+    l2_regularizer: A `tf.keras.regularizers.Regularizer` instance. Default to
+      None.
+
+  Returns:
+    A `tf.keras.Model` instance of the ASPP decoder.
+
+  Raises:
+    ValueError: If the model_config.decoder.type is not `aspp`.
+  """
+  del input_specs  # input_specs is not used by ASPP decoder.
+  decoder_type = model_config.decoder.type
+  decoder_cfg = model_config.decoder.get()
+  if decoder_type != 'aspp':
+    raise ValueError(f'Inconsistent decoder type {decoder_type}. '
+                     'Need to be `aspp`.')
+
+  norm_activation_config = model_config.norm_activation
+  return ASPP(
+      level=decoder_cfg.level,
+      dilation_rates=decoder_cfg.dilation_rates,
+      num_filters=decoder_cfg.num_filters,
+      pool_kernel_size=decoder_cfg.pool_kernel_size,
+      dropout_rate=decoder_cfg.dropout_rate,
+      use_sync_bn=norm_activation_config.use_sync_bn,
+      norm_momentum=norm_activation_config.norm_momentum,
+      norm_epsilon=norm_activation_config.norm_epsilon,
+      activation=norm_activation_config.activation,
+      kernel_regularizer=l2_regularizer)

official/vision/beta/modeling/decoders/factory.py

@@ -12,80 +12,124 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
-"""Contains the factory method to create decoders."""
+"""Decoder registers and factory method.
 
-from typing import Mapping, Optional
+One can register a new decoder model by the following two steps:
+
+1 Import the factory and register the build in the decoder file.
+2 Import the decoder class and add a build in __init__.py.
+
+```
+# my_decoder.py
+
+from modeling.decoders import factory
+
+class MyDecoder():
+  ...
+
+@factory.register_decoder_builder('my_decoder')
+def build_my_decoder():
+  return MyDecoder()
+
+# decoders/__init__.py adds import
+from modeling.decoders.my_decoder import MyDecoder
+```
+
+If one wants the MyDecoder class to be used only by those binary
+then don't imported the decoder module in decoders/__init__.py, but import it
+in place that uses it.
+"""
+from typing import Any, Callable, Mapping, Optional, Union
 
 # Import libraries
 
 import tensorflow as tf
 
+from official.core import registry
 from official.modeling import hyperparams
-from official.vision.beta.modeling import decoders
+
+_REGISTERED_DECODER_CLS = {}
+
+
+def register_decoder_builder(key: str) -> Callable[..., Any]:
+  """Decorates a builder of decoder class.
+
+  The builder should be a Callable (a class or a function).
+  This decorator supports registration of decoder builder as follows:
+
+  ```
+  class MyDecoder(tf.keras.Model):
+    pass
+
+  @register_decoder_builder('mydecoder')
+  def builder(input_specs, config, l2_reg):
+    return MyDecoder(...)
+
+  # Builds a MyDecoder object.
+  my_decoder = build_decoder_3d(input_specs, config, l2_reg)
+  ```
+
+  Args:
+    key: A `str` of key to look up the builder.
+
+  Returns:
+    A callable for using as class decorator that registers the decorated class
+    for creation from an instance of task_config_cls.
+  """
+  return registry.register(_REGISTERED_DECODER_CLS, key)
+
+
+@register_decoder_builder('identity')
+def build_identity(
+    input_specs: Optional[Mapping[str, tf.TensorShape]] = None,
+    model_config: Optional[hyperparams.Config] = None,
+    l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None) -> None:
+  """Builds identity decoder from a config.
+
+  All the input arguments are not used by identity decoder but kept here to
+  ensure the interface is consistent.
+
+  Args:
+    input_specs: A `dict` of input specifications. A dictionary consists of
+      {level: TensorShape} from a backbone.
+    model_config: A `OneOfConfig` of model config.
+    l2_regularizer: A `tf.keras.regularizers.Regularizer` object. Default to
+      None.
+
+  Returns:
+    An instance of the identity decoder.
+  """
+  del input_specs, model_config, l2_regularizer  # Unused by identity decoder.
 
 
 def build_decoder(
     input_specs: Mapping[str, tf.TensorShape],
     model_config: hyperparams.Config,
-    l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None
-) -> tf.keras.Model:
+    l2_regularizer: tf.keras.regularizers.Regularizer = None,
+    **kwargs) -> Union[None, tf.keras.Model, tf.keras.layers.Layer]:
   """Builds decoder from a config.
 
+  A decoder can be a keras.Model, a keras.layers.Layer, or None. If it is not
+  None, the decoder will take features from the backbone as input and generate
+  decoded feature maps. If it is None, such as an identity decoder, the decoder
+  is skipped and features from the backbone are regarded as model output.
+
   Args:
     input_specs: A `dict` of input specifications. A dictionary consists of
       {level: TensorShape} from a backbone.
-    model_config: A OneOfConfig. Model config.
-    l2_regularizer: A `tf.keras.regularizers.Regularizer` instance. Default to
+    model_config: A `OneOfConfig` of model config.
+    l2_regularizer: A `tf.keras.regularizers.Regularizer` object. Default to
       None.
+    **kwargs: Additional keyword args to be passed to decoder builder.
 
   Returns:
-    A `tf.keras.Model` instance of the decoder.
+    An instance of the decoder.
   """
-  decoder_type = model_config.decoder.type
-  decoder_cfg = model_config.decoder.get()
-  norm_activation_config = model_config.norm_activation
-
-  if decoder_type == 'identity':
-    decoder = None
-  elif decoder_type == 'fpn':
-    decoder = decoders.FPN(
-        input_specs=input_specs,
-        min_level=model_config.min_level,
-        max_level=model_config.max_level,
-        num_filters=decoder_cfg.num_filters,
-        use_separable_conv=decoder_cfg.use_separable_conv,
-        activation=norm_activation_config.activation,
-        use_sync_bn=norm_activation_config.use_sync_bn,
-        norm_momentum=norm_activation_config.norm_momentum,
-        norm_epsilon=norm_activation_config.norm_epsilon,
-        kernel_regularizer=l2_regularizer)
-  elif decoder_type == 'nasfpn':
-    decoder = decoders.NASFPN(
+  decoder_builder = registry.lookup(_REGISTERED_DECODER_CLS,
+                                    model_config.decoder.type)
+
+  return decoder_builder(
       input_specs=input_specs,
-        min_level=model_config.min_level,
-        max_level=model_config.max_level,
-        num_filters=decoder_cfg.num_filters,
-        num_repeats=decoder_cfg.num_repeats,
-        use_separable_conv=decoder_cfg.use_separable_conv,
-        activation=norm_activation_config.activation,
-        use_sync_bn=norm_activation_config.use_sync_bn,
-        norm_momentum=norm_activation_config.norm_momentum,
-        norm_epsilon=norm_activation_config.norm_epsilon,
-        kernel_regularizer=l2_regularizer)
-  elif decoder_type == 'aspp':
-    decoder = decoders.ASPP(
-        level=decoder_cfg.level,
-        dilation_rates=decoder_cfg.dilation_rates,
-        num_filters=decoder_cfg.num_filters,
-        pool_kernel_size=decoder_cfg.pool_kernel_size,
-        dropout_rate=decoder_cfg.dropout_rate,
-        use_sync_bn=norm_activation_config.use_sync_bn,
-        norm_momentum=norm_activation_config.norm_momentum,
-        norm_epsilon=norm_activation_config.norm_epsilon,
-        activation=norm_activation_config.activation,
-        kernel_regularizer=l2_regularizer)
-  else:
-    raise ValueError('Decoder {!r} not implement'.format(decoder_type))
-
-  return decoder
+      model_config=model_config,
+      l2_regularizer=l2_regularizer,
+      **kwargs)

official/vision/beta/modeling/decoders/factory_test.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for decoder factory functions."""
+
+from absl.testing import parameterized
+import tensorflow as tf
+
+from tensorflow.python.distribute import combinations
+from official.vision.beta import configs
+from official.vision.beta.configs import decoders as decoders_cfg
+from official.vision.beta.modeling import decoders
+from official.vision.beta.modeling.decoders import factory
+
+
+class FactoryTest(tf.test.TestCase, parameterized.TestCase):
+
+  @combinations.generate(
+      combinations.combine(
+          num_filters=[128, 256], use_separable_conv=[True, False]))
+  def test_fpn_decoder_creation(self, num_filters, use_separable_conv):
+    """Test creation of FPN decoder."""
+    min_level = 3
+    max_level = 7
+    input_specs = {}
+    for level in range(min_level, max_level):
+      input_specs[str(level)] = tf.TensorShape(
+          [1, 128 // (2**level), 128 // (2**level), 3])
+
+    network = decoders.FPN(
+        input_specs=input_specs,
+        num_filters=num_filters,
+        use_separable_conv=use_separable_conv,
+        use_sync_bn=True)
+
+    model_config = configs.retinanet.RetinaNet()
+    model_config.min_level = min_level
+    model_config.max_level = max_level
+    model_config.num_classes = 10
+    model_config.input_size = [None, None, 3]
+    model_config.decoder = decoders_cfg.Decoder(
+        type='fpn',
+        fpn=decoders_cfg.FPN(
+            num_filters=num_filters, use_separable_conv=use_separable_conv))
+
+    factory_network = factory.build_decoder(
+        input_specs=input_specs, model_config=model_config)
+
+    network_config = network.get_config()
+    factory_network_config = factory_network.get_config()
+
+    self.assertEqual(network_config, factory_network_config)
+
+  @combinations.generate(
+      combinations.combine(
+          num_filters=[128, 256],
+          num_repeats=[3, 5],
+          use_separable_conv=[True, False]))
+  def test_nasfpn_decoder_creation(self, num_filters, num_repeats,
+                                   use_separable_conv):
+    """Test creation of NASFPN decoder."""
+    min_level = 3
+    max_level = 7
+    input_specs = {}
+    for level in range(min_level, max_level):
+      input_specs[str(level)] = tf.TensorShape(
+          [1, 128 // (2**level), 128 // (2**level), 3])
+
+    network = decoders.NASFPN(
+        input_specs=input_specs,
+        num_filters=num_filters,
+        num_repeats=num_repeats,
+        use_separable_conv=use_separable_conv,
+        use_sync_bn=True)
+
+    model_config = configs.retinanet.RetinaNet()
+    model_config.min_level = min_level
+    model_config.max_level = max_level
+    model_config.num_classes = 10
+    model_config.input_size = [None, None, 3]
+    model_config.decoder = decoders_cfg.Decoder(
+        type='nasfpn',
+        nasfpn=decoders_cfg.NASFPN(
+            num_filters=num_filters,
+            num_repeats=num_repeats,
+            use_separable_conv=use_separable_conv))
+
+    factory_network = factory.build_decoder(
+        input_specs=input_specs, model_config=model_config)
+
+    network_config = network.get_config()
+    factory_network_config = factory_network.get_config()
+
+    self.assertEqual(network_config, factory_network_config)
+
+  @combinations.generate(
+      combinations.combine(
+          level=[3, 4],
+          dilation_rates=[[6, 12, 18], [6, 12]],
+          num_filters=[128, 256]))
+  def test_aspp_decoder_creation(self, level, dilation_rates, num_filters):
+    """Test creation of ASPP decoder."""
+    input_specs = {'1': tf.TensorShape([1, 128, 128, 3])}
+
+    network = decoders.ASPP(
+        level=level,
+        dilation_rates=dilation_rates,
+        num_filters=num_filters,
+        use_sync_bn=True)
+
+    model_config = configs.semantic_segmentation.SemanticSegmentationModel()
+    model_config.num_classes = 10
+    model_config.input_size = [None, None, 3]
+    model_config.decoder = decoders_cfg.Decoder(
+        type='aspp',
+        aspp=decoders_cfg.ASPP(
+            level=level, dilation_rates=dilation_rates,
+            num_filters=num_filters))
+
+    factory_network = factory.build_decoder(
+        input_specs=input_specs, model_config=model_config)
+
+    network_config = network.get_config()
+    factory_network_config = factory_network.get_config()
+
+    self.assertEqual(network_config, factory_network_config)
+
+  def test_identity_decoder_creation(self):
+    """Test creation of identity decoder."""
+    model_config = configs.retinanet.RetinaNet()
+    model_config.num_classes = 2
+    model_config.input_size = [None, None, 3]
+
+    model_config.decoder = decoders_cfg.Decoder(
+        type='identity', identity=decoders_cfg.Identity())
+
+    factory_network = factory.build_decoder(
+        input_specs=None, model_config=model_config)
+
+    self.assertIsNone(factory_network)
+
+
+if __name__ == '__main__':
+  tf.test.main()

official/vision/beta/modeling/decoders/fpn.py

@@ -16,9 +16,12 @@
 from typing import Any, Mapping, Optional
 
 # Import libraries
+
 import tensorflow as tf
 
+from official.modeling import hyperparams
 from official.modeling import tf_utils
+from official.vision.beta.modeling.decoders import factory
 from official.vision.beta.ops import spatial_transform_ops
 
 
@@ -187,3 +190,43 @@ class FPN(tf.keras.Model):
   def output_specs(self) -> Mapping[str, tf.TensorShape]:
     """A dict of {level: TensorShape} pairs for the model output."""
     return self._output_specs
+
+
+@factory.register_decoder_builder('fpn')
+def build_fpn_decoder(
+    input_specs: Mapping[str, tf.TensorShape],
+    model_config: hyperparams.Config,
+    l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None
+) -> tf.keras.Model:
+  """Builds FPN decoder from a config.
+
+  Args:
+    input_specs: A `dict` of input specifications. A dictionary consists of
+      {level: TensorShape} from a backbone.
+    model_config: A OneOfConfig. Model config.
+    l2_regularizer: A `tf.keras.regularizers.Regularizer` instance. Default to
+      None.
+
+  Returns:
+    A `tf.keras.Model` instance of the FPN decoder.
+
+  Raises:
+    ValueError: If the model_config.decoder.type is not `fpn`.
+  """
+  decoder_type = model_config.decoder.type
+  decoder_cfg = model_config.decoder.get()
+  if decoder_type != 'fpn':
+    raise ValueError(f'Inconsistent decoder type {decoder_type}. '
+                     'Need to be `fpn`.')
+  norm_activation_config = model_config.norm_activation
+  return FPN(
+      input_specs=input_specs,
+      min_level=model_config.min_level,
+      max_level=model_config.max_level,
+      num_filters=decoder_cfg.num_filters,
+      use_separable_conv=decoder_cfg.use_separable_conv,
+      activation=norm_activation_config.activation,
+      use_sync_bn=norm_activation_config.use_sync_bn,
+      norm_momentum=norm_activation_config.norm_momentum,
+      norm_epsilon=norm_activation_config.norm_epsilon,
+      kernel_regularizer=l2_regularizer)

official/vision/beta/modeling/decoders/fpn_test.py

@@ -19,6 +19,7 @@
 from absl.testing import parameterized
 import tensorflow as tf
 
+from official.vision.beta.modeling.backbones import mobilenet
 from official.vision.beta.modeling.backbones import resnet
 from official.vision.beta.modeling.decoders import fpn
 
@@ -52,6 +53,33 @@ class FPNTest(parameterized.TestCase, tf.test.TestCase):
           [1, input_size // 2**level, input_size // 2**level, 256],
           feats[str(level)].shape.as_list())
 
+  @parameterized.parameters(
+      (256, 3, 7, False),
+      (256, 3, 7, True),
+  )
+  def test_network_creation_with_mobilenet(self, input_size, min_level,
+                                           max_level, use_separable_conv):
+    """Test creation of FPN with mobilenet backbone."""
+    tf.keras.backend.set_image_data_format('channels_last')
+
+    inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1)
+
+    backbone = mobilenet.MobileNet(model_id='MobileNetV2')
+    network = fpn.FPN(
+        input_specs=backbone.output_specs,
+        min_level=min_level,
+        max_level=max_level,
+        use_separable_conv=use_separable_conv)
+
+    endpoints = backbone(inputs)
+    feats = network(endpoints)
+
+    for level in range(min_level, max_level + 1):
+      self.assertIn(str(level), feats)
+      self.assertAllEqual(
+          [1, input_size // 2**level, input_size // 2**level, 256],
+          feats[str(level)].shape.as_list())
+
   def test_serialize_deserialize(self):
     # Create a network object that sets all of its config options.
     kwargs = dict(

official/vision/beta/modeling/maskrcnn_model.py

@@ -16,7 +16,6 @@
 
 from typing import Any, List, Mapping, Optional, Union
 
-# Import libraries
 import tensorflow as tf
 
 from official.vision.beta.ops import anchor
@@ -147,14 +146,18 @@ class MaskRCNNModel(tf.keras.Model):
     model_outputs = {}
 
     # Feature extraction.
-    features = self.backbone(images)
+    backbone_features = self.backbone(images)
     if self.decoder:
-      features = self.decoder(features)
+      features = self.decoder(backbone_features)
+    else:
+      features = backbone_features
 
     # Region proposal network.
     rpn_scores, rpn_boxes = self.rpn_head(features)
 
     model_outputs.update({
+        'backbone_features': backbone_features,
+        'decoder_features': features,
         'rpn_boxes': rpn_boxes,
         'rpn_scores': rpn_scores
     })