Refactor decoder factory to allow registering other decoders.

PiperOrigin-RevId: 383944185

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

@@ -12,49 +12,105 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
-"""factory method."""
+"""Decoder registers and factory method.
+
+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 Union, Mapping, Optional
+
+# Import libraries
 
-from typing import Mapping
 import tensorflow as tf
 
-from official.vision.beta.projects.volumetric_models.modeling import decoders
+from official.core import registry
+from official.modeling import hyperparams
+
+_REGISTERED_DECODER_CLS = {}
+
+
+def register_decoder_builder(key: str):
+  """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:
+  del input_specs, model_config, l2_regularizer  # Unused by identity decoder.
+  return None
 
 
 def build_decoder(
     input_specs: Mapping[str, tf.TensorShape],
-    model_config,
-    l2_regularizer: tf.keras.regularizers.Regularizer = None) -> tf.keras.Model:
+    model_config: hyperparams.Config,
+    l2_regularizer: tf.keras.regularizers.Regularizer = None,
+    **kwargs) -> Union[None, tf.keras.Model, tf.keras.layers.Layer]:
   """Builds decoder from a config.
 
   Args:
-    input_specs: `dict` input specifications. A dictionary consists of
+    input_specs: A `dict` of input specifications. A dictionary consists of
       {level: TensorShape} from a backbone.
-    model_config: A OneOfConfig. Model config.
-    l2_regularizer: tf.keras.regularizers.Regularizer instance. Default to None.
+    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 == 'unet_3d_decoder':
-    decoder = decoders.UNet3DDecoder(
-        model_id=decoder_cfg.model_id,
+  decoder_builder = registry.lookup(_REGISTERED_DECODER_CLS,
+                                    model_config.decoder.type)
+
+  return decoder_builder(
       input_specs=input_specs,
-        pool_size=decoder_cfg.pool_size,
-        kernel_regularizer=l2_regularizer,
-        activation=norm_activation_config.activation,
-        norm_momentum=norm_activation_config.norm_momentum,
-        norm_epsilon=norm_activation_config.norm_epsilon,
-        use_sync_bn=norm_activation_config.use_sync_bn,
-        use_batch_normalization=decoder_cfg.use_batch_normalization,
-        use_deconvolution=decoder_cfg.use_deconvolution)
-  else:
-    raise ValueError('Decoder {!r} not implement'.format(decoder_type))
-
-  return decoder
+      model_config=model_config,
+      l2_regularizer=l2_regularizer,
+      **kwargs)

official/vision/beta/projects/volumetric_models/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 factory functions."""
+
+from absl.testing import parameterized
+import tensorflow as tf
+
+from tensorflow.python.distribute import combinations
+from official.vision.beta.projects.volumetric_models.configs import decoders as decoders_cfg
+from official.vision.beta.projects.volumetric_models.configs import semantic_segmentation_3d as semantic_segmentation_3d_exp
+from official.vision.beta.projects.volumetric_models.modeling import decoders
+from official.vision.beta.projects.volumetric_models.modeling.decoders import factory
+
+
+class FactoryTest(tf.test.TestCase, parameterized.TestCase):
+
+  @combinations.generate(combinations.combine(model_id=[2, 3],))
+  def test_unet_3d_decoder_creation(self, model_id):
+    """Test creation of UNet 3D decoder."""
+    # Create test input for decoders based on input model_id.
+    input_specs = {}
+    for level in range(model_id):
+      input_specs[str(level + 1)] = tf.TensorShape(
+          [1, 128 // (2**level), 128 // (2**level), 128 // (2**level), 1])
+
+    network = decoders.UNet3DDecoder(
+        model_id=model_id,
+        input_specs=input_specs,
+        use_sync_bn=True,
+        use_batch_normalization=True,
+        use_deconvolution=True)
+
+    model_config = semantic_segmentation_3d_exp.SemanticSegmentationModel3D()
+    model_config.num_classes = 2
+    model_config.num_channels = 1
+    model_config.input_size = [None, None, None]
+    model_config.decoder = decoders_cfg.Decoder(
+        type='unet_3d_decoder',
+        unet_3d_decoder=decoders_cfg.UNet3DDecoder(model_id=model_id))
+
+    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()
+    print(network_config)
+    print(factory_network_config)
+
+    self.assertEqual(network_config, factory_network_config)
+
+  def test_identity_creation(self):
+    """Test creation of identity decoder."""
+    model_config = semantic_segmentation_3d_exp.SemanticSegmentationModel3D()
+    model_config.num_classes = 2
+    model_config.num_channels = 3
+    model_config.input_size = [None, None, None]
+
+    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/projects/volumetric_models/modeling/decoders/unet_3d_decoder.py

@@ -19,10 +19,13 @@ Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse
 Annotation. arXiv:1606.06650.
 """
 
-from typing import Any, Sequence, Dict, Mapping
+from typing import Any, Dict, Mapping, Optional, Sequence
 
 import tensorflow as tf
+
+from official.modeling import hyperparams
 from official.vision.beta.projects.volumetric_models.modeling import nn_blocks_3d
+from official.vision.beta.projects.volumetric_models.modeling.decoders import factory
 
 layers = tf.keras.layers
 
@@ -152,3 +155,39 @@ class UNet3DDecoder(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('unet_3d_decoder')
+def build_unet_3d_decoder(
+    input_specs: Mapping[str, tf.TensorShape],
+    model_config: hyperparams.Config,
+    l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None
+) -> tf.keras.Model:
+  """Builds UNet3D 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 UNet3D decoder.
+  """
+  decoder_type = model_config.decoder.type
+  decoder_cfg = model_config.decoder.get()
+  assert decoder_type == 'unet_3d_decoder', (f'Inconsistent decoder type '
+                                             f'{decoder_type}')
+  norm_activation_config = model_config.norm_activation
+  return UNet3DDecoder(
+      model_id=decoder_cfg.model_id,
+      input_specs=input_specs,
+      pool_size=decoder_cfg.pool_size,
+      kernel_regularizer=l2_regularizer,
+      activation=norm_activation_config.activation,
+      norm_momentum=norm_activation_config.norm_momentum,
+      norm_epsilon=norm_activation_config.norm_epsilon,
+      use_sync_bn=norm_activation_config.use_sync_bn,
+      use_batch_normalization=decoder_cfg.use_batch_normalization,
+      use_deconvolution=decoder_cfg.use_deconvolution)

official/vision/beta/projects/volumetric_models/modeling/factory_test.py

@@ -17,9 +17,11 @@
 from absl.testing import parameterized
 import tensorflow as tf
 
+# pylint: disable=unused-import
 from official.vision.beta.projects.volumetric_models.configs import semantic_segmentation_3d as exp_cfg
+from official.vision.beta.projects.volumetric_models.modeling import backbones
+from official.vision.beta.projects.volumetric_models.modeling import decoders
 from official.vision.beta.projects.volumetric_models.modeling import factory
-from official.vision.beta.projects.volumetric_models.modeling.backbones import unet_3d  # pylint: disable=unused-import
 
 
 class SegmentationModelBuilderTest(parameterized.TestCase, tf.test.TestCase):

official/vision/beta/projects/volumetric_models/registry_imports.py

@@ -17,4 +17,5 @@
 # pylint: disable=unused-import
 from official.common import registry_imports
 from official.vision.beta.projects.volumetric_models.modeling import backbones
+from official.vision.beta.projects.volumetric_models.modeling import decoders
 from official.vision.beta.projects.volumetric_models.tasks import semantic_segmentation_3d

official/vision/beta/projects/volumetric_models/serving/semantic_segmentation_3d.py

@@ -18,8 +18,10 @@ from typing import Mapping
 
 import tensorflow as tf
 
+# pylint: disable=unused-import
+from official.vision.beta.projects.volumetric_models.modeling import backbones
+from official.vision.beta.projects.volumetric_models.modeling import decoders
 from official.vision.beta.projects.volumetric_models.modeling import factory
-from official.vision.beta.projects.volumetric_models.modeling.backbones import unet_3d  # pylint: disable=unused-import
 from official.vision.beta.serving import export_base
 
 

official/vision/beta/projects/volumetric_models/serving/semantic_segmentation_3d_test.py

@@ -20,9 +20,11 @@ from absl.testing import parameterized
 import numpy as np
 import tensorflow as tf
 
+# pylint: disable=unused-import
 from official.core import exp_factory
-from official.vision.beta.projects.volumetric_models.configs import semantic_segmentation_3d as exp_cfg  # pylint: disable=unused-import
-from official.vision.beta.projects.volumetric_models.modeling.backbones import unet_3d  # pylint: disable=unused-import
+from official.vision.beta.projects.volumetric_models.configs import semantic_segmentation_3d as exp_cfg
+from official.vision.beta.projects.volumetric_models.modeling import backbones
+from official.vision.beta.projects.volumetric_models.modeling import decoders
 from official.vision.beta.projects.volumetric_models.serving import semantic_segmentation_3d
 
 

official/vision/beta/projects/volumetric_models/tasks/semantic_segmentation_3d_test.py

@@ -28,7 +28,8 @@ from official.core import exp_factory
 from official.modeling import optimization
 from official.vision.beta.dataloaders import tfexample_utils
 from official.vision.beta.projects.volumetric_models.evaluation import segmentation_metrics
-from official.vision.beta.projects.volumetric_models.modeling.backbones import unet_3d
+from official.vision.beta.projects.volumetric_models.modeling import backbones
+from official.vision.beta.projects.volumetric_models.modeling import decoders
 from official.vision.beta.projects.volumetric_models.tasks import semantic_segmentation_3d as img_seg_task