Copybara import of the project:

--
63719f08

 by Anirudh Vegesana <anirudh.vegesana@gmail.com>:

YOLO Family: Updated model (#9923)

* Update YOLO model

* Fix some docstrings

* Fix docstrings

* Address some of Dr. Davis' changes

* Give descriptive names to the test cases

* Fix bugs

* Fix YOLO head imports

* docstring and variable name updates

* docstring and variable name updates

* docstring and variable name updates
Co-authored-by: vishnubanna <banna3vishnu@gmail.com>
Co-authored-by: Vishnu Banna <43182884+vishnubanna@users.noreply.github.com>
--
725b8c8c

 by Anirudh Vegesana <anirudh.vegesana@gmail.com>:

disclaimer (#10020)
Co-authored-by: Vishnu Banna <43182884+vishnubanna@users.noreply.github.com>
--
404d24b0

 by Anirudh Vegesana <anirudh.vegesana@gmail.com>:

YOLO Family: Linting (#10027)

* YOLO Family: Updated model (#9923)

* Update YOLO model

* Fix some docstrings

* Fix docstrings

* Address some of Dr. Davis' changes

* Give descriptive names to the test cases

* Fix bugs

* Fix YOLO head imports

* docstring and variable name updates

* docstring and variable name updates

* docstring and variable name updates
Co-authored-by: vishnubanna <banna3vishnu@gmail.com>
Co-authored-by: Vishnu Banna <43182884+vishnubanna@users.noreply.github.com>

* disclaimer

* Fix some PyLint errors
Co-authored-by: vishnubanna <banna3vishnu@gmail.com>
Co-authored-by: Vishnu Banna <43182884+vishnubanna@users.noreply.github.com>
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/models/pull/10021 from tensorflow:purdue-yolo 404d24b0
PiperOrigin-RevId: 379372162

official/vision/beta/projects/yolo/README.md

+DISCLAIMER: this YOLO implementation is still under development. No support will
+be provided during the development phase.
+
 # YOLO Object Detectors, You Only Look Once
 
 [![Paper](http://img.shields.io/badge/Paper-arXiv.1804.02767-B3181B?logo=arXiv)](https://arxiv.org/abs/1804.02767)
@@ -74,3 +77,5 @@ head could be connected to a new, more powerful backbone if a person chose to.
 
 [![TensorFlow 2.2](https://img.shields.io/badge/TensorFlow-2.2-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v2.2.0)
 [![Python 3.8](https://img.shields.io/badge/Python-3.8-3776AB)](https://www.python.org/downloads/release/python-380/)
+
+

official/vision/beta/projects/yolo/configs/backbones.py

@@ -24,11 +24,14 @@ from official.vision.beta.configs import backbones
 
 
 @dataclasses.dataclass
-class DarkNet(hyperparams.Config):
-  """DarkNet config."""
-  model_id: str = "darknet53"
+class Darknet(hyperparams.Config):
+  """Darknet config."""
+  model_id: str = 'darknet53'
+  width_scale: float = 1.0
+  depth_scale: float = 1.0
+  dilate: bool = False
 
 
 @dataclasses.dataclass
 class Backbone(backbones.Backbone):
-  darknet: DarkNet = DarkNet()
+  darknet: Darknet = Darknet()

official/vision/beta/projects/yolo/configs/darknet_classification.py

@@ -32,7 +32,7 @@ class ImageClassificationModel(hyperparams.Config):
   num_classes: int = 0
   input_size: List[int] = dataclasses.field(default_factory=list)
   backbone: backbones.Backbone = backbones.Backbone(
-      type='darknet', resnet=backbones.DarkNet())
+      type='darknet', darknet=backbones.Darknet())
   dropout_rate: float = 0.0
   norm_activation: common.NormActivation = common.NormActivation()
   # Adds a BatchNormalization layer pre-GlobalAveragePooling in classification

official/vision/beta/projects/yolo/modeling/backbones/darknet_test.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 # Lint as: python3
-"""Tests for resnet."""
+"""Tests for yolo."""
 
 from absl.testing import parameterized
 import numpy as np
@@ -24,35 +24,48 @@ from tensorflow.python.distribute import strategy_combinations
 from official.vision.beta.projects.yolo.modeling.backbones import darknet
 
 
-class DarkNetTest(parameterized.TestCase, tf.test.TestCase):
+class DarknetTest(parameterized.TestCase, tf.test.TestCase):
 
   @parameterized.parameters(
-      (224, "darknet53", 2, 1),
-      (224, "darknettiny", 1, 2),
-      (224, "cspdarknettiny", 1, 1),
-      (224, "cspdarknet53", 2, 1),
+      (224, 'darknet53', 2, 1, True),
+      (224, 'darknettiny', 1, 2, False),
+      (224, 'cspdarknettiny', 1, 1, False),
+      (224, 'cspdarknet53', 2, 1, True),
   )
-  def test_network_creation(self, input_size, model_id,
-                            endpoint_filter_scale, scale_final):
+  def test_network_creation(self, input_size, model_id, endpoint_filter_scale,
+                            scale_final, dilate):
     """Test creation of ResNet family models."""
-    tf.keras.backend.set_image_data_format("channels_last")
+    tf.keras.backend.set_image_data_format('channels_last')
 
-    network = darknet.Darknet(model_id=model_id, min_level=3, max_level=5)
+    network = darknet.Darknet(
+        model_id=model_id, min_level=3, max_level=5, dilate=dilate)
     self.assertEqual(network.model_id, model_id)
 
     inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1)
     endpoints = network(inputs)
 
-    self.assertAllEqual(
-        [1, input_size / 2**3, input_size / 2**3, 128 * endpoint_filter_scale],
-        endpoints["3"].shape.as_list())
-    self.assertAllEqual(
-        [1, input_size / 2**4, input_size / 2**4, 256 * endpoint_filter_scale],
-        endpoints["4"].shape.as_list())
+    if dilate:
+      self.assertAllEqual([
+          1, input_size / 2**3, input_size / 2**3, 128 * endpoint_filter_scale
+      ], endpoints['3'].shape.as_list())
+      self.assertAllEqual([
+          1, input_size / 2**3, input_size / 2**3, 256 * endpoint_filter_scale
+      ], endpoints['4'].shape.as_list())
+      self.assertAllEqual([
+          1, input_size / 2**3, input_size / 2**3,
+          512 * endpoint_filter_scale * scale_final
+      ], endpoints['5'].shape.as_list())
+    else:
+      self.assertAllEqual([
+          1, input_size / 2**3, input_size / 2**3, 128 * endpoint_filter_scale
+      ], endpoints['3'].shape.as_list())
+      self.assertAllEqual([
+          1, input_size / 2**4, input_size / 2**4, 256 * endpoint_filter_scale
+      ], endpoints['4'].shape.as_list())
       self.assertAllEqual([
           1, input_size / 2**5, input_size / 2**5,
           512 * endpoint_filter_scale * scale_final
-    ], endpoints["5"].shape.as_list())
+      ], endpoints['5'].shape.as_list())
 
   @combinations.generate(
       combinations.combine(
@@ -66,20 +79,20 @@ class DarkNetTest(parameterized.TestCase, tf.test.TestCase):
     """Test for sync bn on TPU and GPU devices."""
     inputs = np.random.rand(1, 224, 224, 3)
 
-    tf.keras.backend.set_image_data_format("channels_last")
+    tf.keras.backend.set_image_data_format('channels_last')
 
     with strategy.scope():
-      network = darknet.Darknet(model_id="darknet53", min_size=3, max_size=5)
+      network = darknet.Darknet(model_id='darknet53', min_size=3, max_size=5)
       _ = network(inputs)
 
   @parameterized.parameters(1, 3, 4)
   def test_input_specs(self, input_dim):
     """Test different input feature dimensions."""
-    tf.keras.backend.set_image_data_format("channels_last")
+    tf.keras.backend.set_image_data_format('channels_last')
 
     input_specs = tf.keras.layers.InputSpec(shape=[None, None, None, input_dim])
     network = darknet.Darknet(
-        model_id="darknet53", min_level=3, max_level=5, input_specs=input_specs)
+        model_id='darknet53', min_level=3, max_level=5, input_specs=input_specs)
 
     inputs = tf.keras.Input(shape=(224, 224, input_dim), batch_size=1)
     _ = network(inputs)
@@ -87,14 +100,14 @@ class DarkNetTest(parameterized.TestCase, tf.test.TestCase):
   def test_serialize_deserialize(self):
     # Create a network object that sets all of its config options.
     kwargs = dict(
-        model_id="darknet53",
+        model_id='darknet53',
         min_level=3,
         max_level=5,
         use_sync_bn=False,
-        activation="relu",
+        activation='relu',
         norm_momentum=0.99,
         norm_epsilon=0.001,
-        kernel_initializer="VarianceScaling",
+        kernel_initializer='VarianceScaling',
         kernel_regularizer=None,
         bias_regularizer=None,
     )
@@ -113,5 +126,5 @@ class DarkNetTest(parameterized.TestCase, tf.test.TestCase):
     self.assertAllEqual(network.get_config(), new_network.get_config())
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
   tf.test.main()

official/vision/beta/projects/yolo/modeling/decoders/__init__.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.
+

official/vision/beta/projects/yolo/modeling/decoders/yolo_decoder.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.
+
+# Lint as: python3
+"""Feature Pyramid Network and Path Aggregation variants used in YOLO."""
+
+import tensorflow as tf
+from official.vision.beta.projects.yolo.modeling.layers import nn_blocks
+
+
+@tf.keras.utils.register_keras_serializable(package='yolo')
+class _IdentityRoute(tf.keras.layers.Layer):
+
+  def call(self, inputs):
+    return None, inputs
+
+
+@tf.keras.utils.register_keras_serializable(package='yolo')
+class YoloFPN(tf.keras.layers.Layer):
+  """YOLO Feature pyramid network."""
+
+  def __init__(self,
+               fpn_depth=4,
+               use_spatial_attention=False,
+               csp_stack=False,
+               activation='leaky',
+               fpn_filter_scale=1,
+               use_sync_bn=False,
+               norm_momentum=0.99,
+               norm_epsilon=0.001,
+               kernel_initializer='glorot_uniform',
+               kernel_regularizer=None,
+               bias_regularizer=None,
+               **kwargs):
+    """Yolo FPN initialization function (Yolo V4).
+
+    Args:
+      fpn_depth: `int`, number of layers to use in each FPN path
+        if you choose to use an FPN.
+      use_spatial_attention: `bool`, use the spatial attention module.
+      csp_stack: `bool`, CSPize the FPN.
+      activation: `str`, the activation function to use typically leaky or mish.
+      fpn_filter_scale: `int`, scaling factor for the FPN filters.
+      use_sync_bn: if True, use synchronized batch normalization.
+      norm_momentum: `float`, normalization momentum for the moving average.
+      norm_epsilon: `float`, small float added to variance to avoid dividing by
+        zero.
+      kernel_initializer: kernel_initializer for convolutional layers.
+      kernel_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
+      bias_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
+      **kwargs: keyword arguments to be passed.
+    """
+
+    super().__init__(**kwargs)
+    self._fpn_depth = fpn_depth
+
+    self._activation = activation
+    self._use_sync_bn = use_sync_bn
+    self._norm_momentum = norm_momentum
+    self._norm_epsilon = norm_epsilon
+    self._kernel_initializer = kernel_initializer
+    self._kernel_regularizer = kernel_regularizer
+    self._bias_regularizer = bias_regularizer
+    self._use_spatial_attention = use_spatial_attention
+    self._filter_scale = fpn_filter_scale
+    self._csp_stack = csp_stack
+
+    self._base_config = dict(
+        activation=self._activation,
+        use_sync_bn=self._use_sync_bn,
+        kernel_regularizer=self._kernel_regularizer,
+        kernel_initializer=self._kernel_initializer,
+        bias_regularizer=self._bias_regularizer,
+        norm_epsilon=self._norm_epsilon,
+        norm_momentum=self._norm_momentum)
+
+  def get_raw_depths(self, minimum_depth, inputs):
+    """Calculates the unscaled depths of the FPN branches.
+
+    Args:
+      minimum_depth (int): depth of the smallest branch of the FPN.
+      inputs (dict): dictionary of the shape of input args as a dictionary of
+        lists.
+
+    Returns:
+      The unscaled depths of the FPN branches.
+    """
+
+    depths = []
+    for i in range(self._min_level, self._max_level + 1):
+      depths.append(inputs[str(i)][-1] / self._filter_scale)
+    return list(reversed(depths))
+
+  def build(self, inputs):
+    """Use config dictionary to generate all important attributes for head.
+
+    Args:
+       inputs: dictionary of the shape of input args as a dictionary of lists.
+    """
+
+    keys = [int(key) for key in inputs.keys()]
+    self._min_level = min(keys)
+    self._max_level = max(keys)
+    self._min_depth = inputs[str(self._min_level)][-1]
+    self._depths = self.get_raw_depths(self._min_depth, inputs)
+
+    # directly connect to an input path and process it
+    self.preprocessors = dict()
+    # resample an input and merge it with the output of another path
+    # inorder to aggregate backbone outputs
+    self.resamples = dict()
+    # set of convoltion layers and upsample layers that are used to
+    # prepare the FPN processors for output
+
+    for level, depth in zip(
+        reversed(range(self._min_level, self._max_level + 1)), self._depths):
+      if level == self._min_level:
+        self.resamples[str(level)] = nn_blocks.PathAggregationBlock(
+            filters=depth // 2,
+            inverted=True,
+            upsample=True,
+            drop_final=self._csp_stack == 0,
+            upsample_size=2,
+            **self._base_config)
+        self.preprocessors[str(level)] = _IdentityRoute()
+      elif level != self._max_level:
+        self.resamples[str(level)] = nn_blocks.PathAggregationBlock(
+            filters=depth // 2,
+            inverted=True,
+            upsample=True,
+            drop_final=False,
+            upsample_size=2,
+            **self._base_config)
+        self.preprocessors[str(level)] = nn_blocks.DarkRouteProcess(
+            filters=depth,
+            repetitions=self._fpn_depth - int(level == self._min_level),
+            block_invert=True,
+            insert_spp=False,
+            csp_stack=self._csp_stack,
+            **self._base_config)
+      else:
+        self.preprocessors[str(level)] = nn_blocks.DarkRouteProcess(
+            filters=depth,
+            repetitions=self._fpn_depth + 1 * int(self._csp_stack == 0),
+            insert_spp=True,
+            block_invert=False,
+            csp_stack=self._csp_stack,
+            **self._base_config)
+
+  def call(self, inputs):
+    outputs = dict()
+    layer_in = inputs[str(self._max_level)]
+    for level in reversed(range(self._min_level, self._max_level + 1)):
+      _, x = self.preprocessors[str(level)](layer_in)
+      outputs[str(level)] = x
+      if level > self._min_level:
+        x_next = inputs[str(level - 1)]
+        _, layer_in = self.resamples[str(level - 1)]([x_next, x])
+    return outputs
+
+
+@tf.keras.utils.register_keras_serializable(package='yolo')
+class YoloPAN(tf.keras.layers.Layer):
+  """YOLO Path Aggregation Network."""
+
+  def __init__(self,
+               path_process_len=6,
+               max_level_process_len=None,
+               embed_spp=False,
+               use_spatial_attention=False,
+               csp_stack=False,
+               activation='leaky',
+               use_sync_bn=False,
+               norm_momentum=0.99,
+               norm_epsilon=0.001,
+               kernel_initializer='glorot_uniform',
+               kernel_regularizer=None,
+               bias_regularizer=None,
+               fpn_input=True,
+               fpn_filter_scale=1.0,
+               **kwargs):
+    """Yolo Path Aggregation Network initialization function (Yolo V3 and V4).
+
+    Args:
+      path_process_len: `int`, number of layers ot use in each Decoder path.
+      max_level_process_len: `int`, number of layers ot use in the largest
+        processing path, or the backbones largest output if it is different.
+      embed_spp: `bool`, use the SPP found in the YoloV3 and V4 model.
+      use_spatial_attention: `bool`, use the spatial attention module.
+      csp_stack: `bool`, CSPize the FPN.
+      activation: `str`, the activation function to use typically leaky or mish.
+      use_sync_bn: if True, use synchronized batch normalization.
+      norm_momentum: `float`, normalization omentum for the moving average.
+      norm_epsilon: `float`, small float added to variance to avoid dividing
+        by zero.
+      kernel_initializer: kernel_initializer for convolutional layers.
+      kernel_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
+      bias_regularizer: tf.keras.regularizers.Regularizer object for Conv2d.
+      fpn_input: `bool`, for whether the input into this fucntion is an FPN or
+        a backbone.
+      fpn_filter_scale: `int`, scaling factor for the FPN filters.
+      **kwargs: keyword arguments to be passed.
+    """
+
+    super().__init__(**kwargs)
+
+    self._path_process_len = path_process_len
+    self._embed_spp = embed_spp
+    self._use_spatial_attention = use_spatial_attention
+
+    self._activation = activation
+    self._use_sync_bn = use_sync_bn
+    self._norm_momentum = norm_momentum
+    self._norm_epsilon = norm_epsilon
+    self._kernel_initializer = kernel_initializer
+    self._kernel_regularizer = kernel_regularizer
+    self._bias_regularizer = bias_regularizer
+    self._fpn_input = fpn_input
+    self._max_level_process_len = max_level_process_len
+    self._csp_stack = csp_stack
+    self._fpn_filter_scale = fpn_filter_scale
+
+    if max_level_process_len is None:
+      self._max_level_process_len = path_process_len
+
+    self._base_config = dict(
+        activation=self._activation,
+        use_sync_bn=self._use_sync_bn,
+        kernel_regularizer=self._kernel_regularizer,
+        kernel_initializer=self._kernel_initializer,
+        bias_regularizer=self._bias_regularizer,
+        norm_epsilon=self._norm_epsilon,
+        norm_momentum=self._norm_momentum)
+
+  def build(self, inputs):
+    """Use config dictionary to generate all important attributes for head.
+
+    Args:
+      inputs: dictionary of the shape of input args as a dictionary of lists.
+    """
+
+    # define the key order
+    keys = [int(key) for key in inputs.keys()]
+    self._min_level = min(keys)
+    self._max_level = max(keys)
+    self._min_depth = inputs[str(self._min_level)][-1]
+    self._depths = self.get_raw_depths(self._min_depth, inputs)
+
+    # directly connect to an input path and process it
+    self.preprocessors = dict()
+    # resample an input and merge it with the output of another path
+    # inorder to aggregate backbone outputs
+    self.resamples = dict()
+
+    # FPN will reverse the key process order for the backbone, so we need
+    # adjust the order that objects are created and processed to adjust for
+    # this. not using an FPN will directly connect the decoder to the backbone
+    # therefore the object creation order needs to be done from the largest
+    # to smallest level.
+    if self._fpn_input:
+      # process order {... 3, 4, 5}
+      self._iterator = range(self._min_level, self._max_level + 1)
+      self._check = lambda x: x < self._max_level
+      self._key_shift = lambda x: x + 1
+      self._input = self._min_level
+      downsample = True
+      upsample = False
+    else:
+      # process order {5, 4, 3, ...}
+      self._iterator = list(
+          reversed(range(self._min_level, self._max_level + 1)))
+      self._check = lambda x: x > self._min_level
+      self._key_shift = lambda x: x - 1
+      self._input = self._max_level
+      downsample = False
+      upsample = True
+
+    if self._csp_stack == 0:
+      proc_filters = lambda x: x
+      resample_filters = lambda x: x // 2
+    else:
+      proc_filters = lambda x: x * 2
+      resample_filters = lambda x: x
+    for level, depth in zip(self._iterator, self._depths):
+      if level == self._input:
+        self.preprocessors[str(level)] = nn_blocks.DarkRouteProcess(
+            filters=proc_filters(depth),
+            repetitions=self._max_level_process_len,
+            insert_spp=self._embed_spp,
+            block_invert=False,
+            insert_sam=self._use_spatial_attention,
+            csp_stack=self._csp_stack,
+            **self._base_config)
+      else:
+        self.resamples[str(level)] = nn_blocks.PathAggregationBlock(
+            filters=resample_filters(depth),
+            upsample=upsample,
+            downsample=downsample,
+            inverted=False,
+            drop_final=self._csp_stack == 0,
+            **self._base_config)
+        self.preprocessors[str(level)] = nn_blocks.DarkRouteProcess(
+            filters=proc_filters(depth),
+            repetitions=self._path_process_len,
+            insert_spp=False,
+            insert_sam=self._use_spatial_attention,
+            csp_stack=self._csp_stack,
+            **self._base_config)
+
+  def get_raw_depths(self, minimum_depth, inputs):
+    """Calculates the unscaled depths of the FPN branches.
+
+    Args:
+      minimum_depth: `int` depth of the smallest branch of the FPN.
+      inputs: `dict[str, tf.InputSpec]` of the shape of input args as a
+        dictionary of lists.
+
+    Returns:
+      The unscaled depths of the FPN branches.
+    """
+
+    depths = []
+    if len(inputs.keys()) > 3 or self._fpn_filter_scale > 1:
+      for i in range(self._min_level, self._max_level + 1):
+        depths.append(inputs[str(i)][-1] * 2)
+    else:
+      for _ in range(self._min_level, self._max_level + 1):
+        depths.append(minimum_depth)
+        minimum_depth *= 2
+    if self._fpn_input:
+      return depths
+    return list(reversed(depths))
+
+  def call(self, inputs):
+    outputs = dict()
+    layer_in = inputs[str(self._input)]
+
+    for level in self._iterator:
+      x_route, x = self.preprocessors[str(level)](layer_in)
+      outputs[str(level)] = x
+      if self._check(level):
+        x_next = inputs[str(self._key_shift(level))]
+        _, layer_in = self.resamples[str(
+            self._key_shift(level))]([x_route, x_next])
+    return outputs
+
+
+@tf.keras.utils.register_keras_serializable(package='yolo')
+class YoloDecoder(tf.keras.Model):
+  """Darknet Backbone Decoder."""
+
+  def __init__(self,
+               input_specs,
+               use_fpn=False,
+               use_spatial_attention=False,
+               csp_stack=False,
+               fpn_depth=4,
+               fpn_filter_scale=1,
+               path_process_len=6,
+               max_level_process_len=None,
+               embed_spp=False,
+               activation='leaky',
+               use_sync_bn=False,
+               norm_momentum=0.99,
+               norm_epsilon=0.001,
+               kernel_initializer='glorot_uniform',
+               kernel_regularizer=None,
+               bias_regularizer=None,
+               **kwargs):
+    """Yolo Decoder initialization function.
+
+    A unified model that ties all decoder components into a conditionally build
+    YOLO decoder.
+
+    Args:
+      input_specs: `dict[str, tf.InputSpec]`: input specs of each of the inputs
+        to the heads.
+      use_fpn: `bool`, use the FPN found in the YoloV4 model.
+      use_spatial_attention: `bool`, use the spatial attention module.
+      csp_stack: `bool`, CSPize the FPN.
+      fpn_depth: `int`, number of layers ot use in each FPN path
+        if you choose to use an FPN.
+      fpn_filter_scale: `int`, scaling factor for the FPN filters.
+      path_process_len: `int`, number of layers ot use in each Decoder path.
+      max_level_process_len: `int`, number of layers ot use in the largest
+        processing path, or the backbones largest output if it is different.
+      embed_spp: `bool`, use the SPP found in the YoloV3 and V4 model.
+      activation: `str`, the activation function to use typically leaky or mish.
+      use_sync_bn: if True, use synchronized batch normalization.
+      norm_momentum: `float`, normalization omentum for the moving average.
+      norm_epsilon: `float`, small float added to variance to avoid dividing by
+        zero.
+      kernel_initializer: kernel_initializer for convolutional layers.
+      kernel_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
+      bias_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
+      **kwargs: keyword arguments to be passed.
+    """
+
+    self._input_specs = input_specs
+    self._use_fpn = use_fpn
+    self._fpn_depth = fpn_depth
+    self._path_process_len = path_process_len
+    self._max_level_process_len = max_level_process_len
+    self._embed_spp = embed_spp
+
+    self._activation = activation
+    self._use_sync_bn = use_sync_bn
+    self._norm_momentum = norm_momentum
+    self._norm_epsilon = norm_epsilon
+    self._kernel_initializer = kernel_initializer
+    self._kernel_regularizer = kernel_regularizer
+    self._bias_regularizer = bias_regularizer
+
+    self._base_config = dict(
+        use_spatial_attention=use_spatial_attention,
+        csp_stack=csp_stack,
+        activation=self._activation,
+        use_sync_bn=self._use_sync_bn,
+        fpn_filter_scale=fpn_filter_scale,
+        norm_momentum=self._norm_momentum,
+        norm_epsilon=self._norm_epsilon,
+        kernel_initializer=self._kernel_initializer,
+        kernel_regularizer=self._kernel_regularizer,
+        bias_regularizer=self._bias_regularizer)
+
+    self._decoder_config = dict(
+        path_process_len=self._path_process_len,
+        max_level_process_len=self._max_level_process_len,
+        embed_spp=self._embed_spp,
+        fpn_input=self._use_fpn,
+        **self._base_config)
+
+    inputs = {
+        key: tf.keras.layers.Input(shape=value[1:])
+        for key, value in input_specs.items()
+    }
+    if self._use_fpn:
+      inter_outs = YoloFPN(
+          fpn_depth=self._fpn_depth, **self._base_config)(
+              inputs)
+      outputs = YoloPAN(**self._decoder_config)(inter_outs)
+    else:
+      inter_outs = None
+      outputs = YoloPAN(**self._decoder_config)(inputs)
+
+    self._output_specs = {key: value.shape for key, value in outputs.items()}
+    super().__init__(inputs=inputs, outputs=outputs, name='YoloDecoder')
+
+  @property
+  def use_fpn(self):
+    return self._use_fpn
+
+  @property
+  def output_specs(self):
+    return self._output_specs
+
+  def get_config(self):
+    config = dict(
+        input_specs=self._input_specs,
+        use_fpn=self._use_fpn,
+        fpn_depth=self._fpn_depth,
+        **self._decoder_config)
+    return config
+
+  @classmethod
+  def from_config(cls, config, custom_objects=None):
+    return cls(**config)

official/vision/beta/projects/yolo/modeling/decoders/yolo_decoder_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.
+
+# Lint as: python3
+"""Tests for YOLO."""
+
+# Import libraries
+from absl.testing import parameterized
+import tensorflow as tf
+
+from tensorflow.python.distribute import combinations
+from tensorflow.python.distribute import strategy_combinations
+from official.vision.beta.projects.yolo.modeling.decoders import yolo_decoder as decoders
+
+
+class YoloDecoderTest(parameterized.TestCase, tf.test.TestCase):
+
+  def _build_yolo_decoder(self, input_specs, name='1'):
+    # Builds 4 different arbitrary decoders.
+    if name == '1':
+      model = decoders.YoloDecoder(
+          input_specs=input_specs,
+          embed_spp=False,
+          use_fpn=False,
+          max_level_process_len=2,
+          path_process_len=1,
+          activation='mish')
+    elif name == '6spp':
+      model = decoders.YoloDecoder(
+          input_specs=input_specs,
+          embed_spp=True,
+          use_fpn=False,
+          max_level_process_len=None,
+          path_process_len=6,
+          activation='mish')
+    elif name == '6sppfpn':
+      model = decoders.YoloDecoder(
+          input_specs=input_specs,
+          embed_spp=True,
+          use_fpn=True,
+          max_level_process_len=None,
+          path_process_len=6,
+          activation='mish')
+    elif name == '6':
+      model = decoders.YoloDecoder(
+          input_specs=input_specs,
+          embed_spp=False,
+          use_fpn=False,
+          max_level_process_len=None,
+          path_process_len=6,
+          activation='mish')
+    else:
+      raise NotImplementedError(f'YOLO decoder test {type} not implemented.')
+    return model
+
+  @parameterized.parameters('1', '6spp', '6sppfpn', '6')
+  def test_network_creation(self, version):
+    """Test creation of ResNet family models."""
+    tf.keras.backend.set_image_data_format('channels_last')
+    input_shape = {
+        '3': [1, 52, 52, 256],
+        '4': [1, 26, 26, 512],
+        '5': [1, 13, 13, 1024]
+    }
+    decoder = self._build_yolo_decoder(input_shape, version)
+
+    inputs = {}
+    for key in input_shape:
+      inputs[key] = tf.ones(input_shape[key], dtype=tf.float32)
+
+    endpoints = decoder.call(inputs)
+
+    for key in endpoints.keys():
+      self.assertAllEqual(endpoints[key].shape.as_list(), input_shape[key])
+
+  @combinations.generate(
+      combinations.combine(
+          strategy=[
+              strategy_combinations.cloud_tpu_strategy,
+              strategy_combinations.one_device_strategy_gpu,
+          ],
+          use_sync_bn=[False, True],
+      ))
+  def test_sync_bn_multiple_devices(self, strategy, use_sync_bn):
+    """Test for sync bn on TPU and GPU devices."""
+
+    tf.keras.backend.set_image_data_format('channels_last')
+
+    with strategy.scope():
+      input_shape = {
+          '3': [1, 52, 52, 256],
+          '4': [1, 26, 26, 512],
+          '5': [1, 13, 13, 1024]
+      }
+      decoder = self._build_yolo_decoder(input_shape, '6')
+
+      inputs = {}
+      for key in input_shape:
+        inputs[key] = tf.ones(input_shape[key], dtype=tf.float32)
+
+      _ = decoder.call(inputs)
+
+  @parameterized.parameters(1, 3, 4)
+  def test_input_specs(self, input_dim):
+    """Test different input feature dimensions."""
+    tf.keras.backend.set_image_data_format('channels_last')
+
+    input_shape = {
+        '3': [1, 52, 52, 256],
+        '4': [1, 26, 26, 512],
+        '5': [1, 13, 13, 1024]
+    }
+    decoder = self._build_yolo_decoder(input_shape, '6')
+
+    inputs = {}
+    for key in input_shape:
+      inputs[key] = tf.ones(input_shape[key], dtype=tf.float32)
+    _ = decoder(inputs)
+
+  def test_serialize_deserialize(self):
+    """Create a network object that sets all of its config options."""
+    tf.keras.backend.set_image_data_format('channels_last')
+
+    input_shape = {
+        '3': [1, 52, 52, 256],
+        '4': [1, 26, 26, 512],
+        '5': [1, 13, 13, 1024]
+    }
+    decoder = self._build_yolo_decoder(input_shape, '6')
+
+    inputs = {}
+    for key in input_shape:
+      inputs[key] = tf.ones(input_shape[key], dtype=tf.float32)
+
+    _ = decoder(inputs)
+    config = decoder.get_config()
+    decoder_from_config = decoders.YoloDecoder.from_config(config)
+    self.assertAllEqual(decoder.get_config(), decoder_from_config.get_config())
+
+
+if __name__ == '__main__':
+  tf.test.main()

official/vision/beta/projects/yolo/modeling/heads/__init__.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.
+

official/vision/beta/projects/yolo/modeling/heads/yolo_head.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.
+
+# Lint as: python3
+"""Yolo heads."""
+
+import tensorflow as tf
+from official.vision.beta.projects.yolo.modeling.layers import nn_blocks
+
+
+class YoloHead(tf.keras.layers.Layer):
+  """YOLO Prediction Head."""
+
+  def __init__(self,
+               min_level,
+               max_level,
+               classes=80,
+               boxes_per_level=3,
+               output_extras=0,
+               norm_momentum=0.99,
+               norm_epsilon=0.001,
+               kernel_initializer='glorot_uniform',
+               kernel_regularizer=None,
+               bias_regularizer=None,
+               activation=None,
+               **kwargs):
+    """Yolo Prediction Head initialization function.
+
+    Args:
+      min_level: `int`, the minimum backbone output level.
+      max_level: `int`, the maximum backbone output level.
+      classes: `int`, number of classes per category.
+      boxes_per_level: `int`, number of boxes to predict per level.
+      output_extras: `int`, number of additional output channels that the head.
+        should predict for non-object detection and non-image classification
+        tasks.
+      norm_momentum: `float`, normalization momentum for the moving average.
+      norm_epsilon: `float`, small float added to variance to avoid dividing by
+        zero.
+      kernel_initializer: kernel_initializer for convolutional layers.
+      kernel_regularizer: tf.keras.regularizers.Regularizer object for Conv2D.
+      bias_regularizer: tf.keras.regularizers.Regularizer object for Conv2d.
+      activation: `str`, the activation function to use typically leaky or mish.
+      **kwargs: keyword arguments to be passed.
+    """
+
+    super().__init__(**kwargs)
+    self._min_level = min_level
+    self._max_level = max_level
+
+    self._key_list = [
+        str(key) for key in range(self._min_level, self._max_level + 1)
+    ]
+
+    self._classes = classes
+    self._boxes_per_level = boxes_per_level
+    self._output_extras = output_extras
+
+    self._output_conv = (classes + output_extras + 5) * boxes_per_level
+
+    self._base_config = dict(
+        activation=activation,
+        norm_momentum=norm_momentum,
+        norm_epsilon=norm_epsilon,
+        kernel_initializer=kernel_initializer,
+        kernel_regularizer=kernel_regularizer,
+        bias_regularizer=bias_regularizer)
+
+    self._conv_config = dict(
+        filters=self._output_conv,
+        kernel_size=(1, 1),
+        strides=(1, 1),
+        padding='same',
+        use_bn=False,
+        **self._base_config)
+
+  def build(self, input_shape):
+    self._head = dict()
+    for key in self._key_list:
+      self._head[key] = nn_blocks.ConvBN(**self._conv_config)
+
+  def call(self, inputs):
+    outputs = dict()
+    for key in self._key_list:
+      outputs[key] = self._head[key](inputs[key])
+    return outputs
+
+  @property
+  def output_depth(self):
+    return (self._classes + self._output_extras + 5) * self._boxes_per_level
+
+  @property
+  def num_boxes(self):
+    if self._min_level is None or self._max_level is None:
+      raise Exception(
+          'Model has to be built before number of boxes can be determined.')
+    return (self._max_level - self._min_level + 1) * self._boxes_per_level
+
+  def get_config(self):
+    config = dict(
+        min_level=self._min_level,
+        max_level=self._max_level,
+        classes=self._classes,
+        boxes_per_level=self._boxes_per_level,
+        output_extras=self._output_extras,
+        **self._base_config)
+    return config
+
+  @classmethod
+  def from_config(cls, config, custom_objects=None):
+    return cls(**config)

official/vision/beta/projects/yolo/modeling/heads/yolo_head_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.
+
+# Lint as: python3
+"""Tests for yolo heads."""
+
+# Import libraries
+from absl.testing import parameterized
+import tensorflow as tf
+
+from official.vision.beta.projects.yolo.modeling.heads import yolo_head as heads
+
+
+class YoloDecoderTest(parameterized.TestCase, tf.test.TestCase):
+
+  def test_network_creation(self):
+    """Test creation of YOLO family models."""
+    tf.keras.backend.set_image_data_format('channels_last')
+    input_shape = {
+        '3': [1, 52, 52, 256],
+        '4': [1, 26, 26, 512],
+        '5': [1, 13, 13, 1024]
+    }
+    classes = 100
+    bps = 3
+    head = heads.YoloHead(3, 5, classes=classes, boxes_per_level=bps)
+
+    inputs = {}
+    for key in input_shape:
+      inputs[key] = tf.ones(input_shape[key], dtype=tf.float32)
+
+    endpoints = head(inputs)
+    # print(endpoints)
+
+    for key in endpoints.keys():
+      expected_input_shape = input_shape[key]
+      expected_input_shape[-1] = (classes + 5) * bps
+      self.assertAllEqual(endpoints[key].shape.as_list(), expected_input_shape)
+
+  def test_serialize_deserialize(self):
+    # Create a network object that sets all of its config options.
+    tf.keras.backend.set_image_data_format('channels_last')
+    input_shape = {
+        '3': [1, 52, 52, 256],
+        '4': [1, 26, 26, 512],
+        '5': [1, 13, 13, 1024]
+    }
+    classes = 100
+    bps = 3
+    head = heads.YoloHead(3, 5, classes=classes, boxes_per_level=bps)
+
+    inputs = {}
+    for key in input_shape:
+      inputs[key] = tf.ones(input_shape[key], dtype=tf.float32)
+
+    _ = head(inputs)
+    configs = head.get_config()
+    head_from_config = heads.YoloHead.from_config(configs)
+    self.assertAllEqual(head.get_config(), head_from_config.get_config())
+
+
+if __name__ == '__main__':
+  tf.test.main()

official/vision/beta/projects/yolo/modeling/layers/nn_blocks_test.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 # Lint as: python3
-
 from absl.testing import parameterized
 import numpy as np
 import tensorflow as tf
@@ -23,8 +22,8 @@ from official.vision.beta.projects.yolo.modeling.layers import nn_blocks
 
 class CSPConnectTest(tf.test.TestCase, parameterized.TestCase):
 
-  @parameterized.named_parameters(("same", 224, 224, 64, 1),
-                                  ("downsample", 224, 224, 64, 2))
+  @parameterized.named_parameters(('same', 224, 224, 64, 1),
+                                  ('downsample', 224, 224, 64, 2))
   def test_pass_through(self, width, height, filters, mod):
     x = tf.keras.Input(shape=(width, height, filters))
     test_layer = nn_blocks.CSPRoute(filters=filters, filter_scale=mod)
@@ -38,8 +37,8 @@ class CSPConnectTest(tf.test.TestCase, parameterized.TestCase):
         [None, np.ceil(width // 2),
          np.ceil(height // 2), (filters)])
 
-  @parameterized.named_parameters(("same", 224, 224, 64, 1),
-                                  ("downsample", 224, 224, 128, 2))
+  @parameterized.named_parameters(('same', 224, 224, 64, 1),
+                                  ('downsample', 224, 224, 128, 2))
   def test_gradient_pass_though(self, filters, width, height, mod):
     loss = tf.keras.losses.MeanSquaredError()
     optimizer = tf.keras.optimizers.SGD()
@@ -49,8 +48,9 @@ class CSPConnectTest(tf.test.TestCase, parameterized.TestCase):
     init = tf.random_normal_initializer()
     x = tf.Variable(
         initial_value=init(shape=(1, width, height, filters), dtype=tf.float32))
-    y = tf.Variable(initial_value=init(shape=(1, int(np.ceil(width // 2)),
-                                              int(np.ceil(height // 2)),
+    y = tf.Variable(
+        initial_value=init(
+            shape=(1, int(np.ceil(width // 2)), int(np.ceil(height // 2)),
                    filters),
             dtype=tf.float32))
 
@@ -66,8 +66,8 @@ class CSPConnectTest(tf.test.TestCase, parameterized.TestCase):
 
 class CSPRouteTest(tf.test.TestCase, parameterized.TestCase):
 
-  @parameterized.named_parameters(("same", 224, 224, 64, 1),
-                                  ("downsample", 224, 224, 64, 2))
+  @parameterized.named_parameters(('same', 224, 224, 64, 1),
+                                  ('downsample', 224, 224, 64, 2))
   def test_pass_through(self, width, height, filters, mod):
     x = tf.keras.Input(shape=(width, height, filters))
     test_layer = nn_blocks.CSPRoute(filters=filters, filter_scale=mod)
@@ -79,8 +79,8 @@ class CSPRouteTest(tf.test.TestCase, parameterized.TestCase):
         [None, np.ceil(width // 2),
          np.ceil(height // 2), (filters / mod)])
 
-  @parameterized.named_parameters(("same", 224, 224, 64, 1),
-                                  ("downsample", 224, 224, 128, 2))
+  @parameterized.named_parameters(('same', 224, 224, 64, 1),
+                                  ('downsample', 224, 224, 128, 2))
   def test_gradient_pass_though(self, filters, width, height, mod):
     loss = tf.keras.losses.MeanSquaredError()
     optimizer = tf.keras.optimizers.SGD()
@@ -90,8 +90,9 @@ class CSPRouteTest(tf.test.TestCase, parameterized.TestCase):
     init = tf.random_normal_initializer()
     x = tf.Variable(
         initial_value=init(shape=(1, width, height, filters), dtype=tf.float32))
-    y = tf.Variable(initial_value=init(shape=(1, int(np.ceil(width // 2)),
-                                              int(np.ceil(height // 2)),
+    y = tf.Variable(
+        initial_value=init(
+            shape=(1, int(np.ceil(width // 2)), int(np.ceil(height // 2)),
                    filters),
             dtype=tf.float32))
 
@@ -107,11 +108,11 @@ class CSPRouteTest(tf.test.TestCase, parameterized.TestCase):
 
 class CSPStackTest(tf.test.TestCase, parameterized.TestCase):
 
-  def build_layer(
-      self, layer_type, filters, filter_scale, count, stack_type, downsample):
+  def build_layer(self, layer_type, filters, filter_scale, count, stack_type,
+                  downsample):
     if stack_type is not None:
       layers = []
-      if layer_type == "residual":
+      if layer_type == 'residual':
         for _ in range(count):
           layers.append(
               nn_blocks.DarkResidual(
@@ -120,7 +121,7 @@ class CSPStackTest(tf.test.TestCase, parameterized.TestCase):
         for _ in range(count):
           layers.append(nn_blocks.ConvBN(filters=filters))
 
-      if stack_type == "model":
+      if stack_type == 'model':
         layers = tf.keras.Sequential(layers=layers)
     else:
       layers = None
@@ -133,10 +134,10 @@ class CSPStackTest(tf.test.TestCase, parameterized.TestCase):
     return stack
 
   @parameterized.named_parameters(
-      ("no_stack", 224, 224, 64, 2, "residual", None, 0, True),
-      ("residual_stack", 224, 224, 64, 2, "residual", "list", 2, True),
-      ("conv_stack", 224, 224, 64, 2, "conv", "list", 3, False),
-      ("callable_no_scale", 224, 224, 64, 1, "residual", "model", 5, False))
+      ('no_stack', 224, 224, 64, 2, 'residual', None, 0, True),
+      ('residual_stack', 224, 224, 64, 2, 'residual', 'list', 2, True),
+      ('conv_stack', 224, 224, 64, 2, 'conv', 'list', 3, False),
+      ('callable_no_scale', 224, 224, 64, 1, 'residual', 'model', 5, False))
   def test_pass_through(self, width, height, filters, mod, layer_type,
                         stack_type, count, downsample):
     x = tf.keras.Input(shape=(width, height, filters))
@@ -152,10 +153,10 @@ class CSPStackTest(tf.test.TestCase, parameterized.TestCase):
       self.assertAllEqual(outx.shape.as_list(), [None, width, height, filters])
 
   @parameterized.named_parameters(
-      ("no_stack", 224, 224, 64, 2, "residual", None, 0, True),
-      ("residual_stack", 224, 224, 64, 2, "residual", "list", 2, True),
-      ("conv_stack", 224, 224, 64, 2, "conv", "list", 3, False),
-      ("callable_no_scale", 224, 224, 64, 1, "residual", "model", 5, False))
+      ('no_stack', 224, 224, 64, 2, 'residual', None, 0, True),
+      ('residual_stack', 224, 224, 64, 2, 'residual', 'list', 2, True),
+      ('conv_stack', 224, 224, 64, 2, 'conv', 'list', 3, False),
+      ('callable_no_scale', 224, 224, 64, 1, 'residual', 'model', 5, False))
   def test_gradient_pass_though(self, width, height, filters, mod, layer_type,
                                 stack_type, count, downsample):
     loss = tf.keras.losses.MeanSquaredError()
@@ -188,10 +189,10 @@ class CSPStackTest(tf.test.TestCase, parameterized.TestCase):
 class ConvBNTest(tf.test.TestCase, parameterized.TestCase):
 
   @parameterized.named_parameters(
-      ("valid", (3, 3), "valid", (1, 1)), ("same", (3, 3), "same", (1, 1)),
-      ("downsample", (3, 3), "same", (2, 2)), ("test", (1, 1), "valid", (1, 1)))
+      ('valid', (3, 3), 'valid', (1, 1)), ('same', (3, 3), 'same', (1, 1)),
+      ('downsample', (3, 3), 'same', (2, 2)), ('test', (1, 1), 'valid', (1, 1)))
   def test_pass_through(self, kernel_size, padding, strides):
-    if padding == "same":
+    if padding == 'same':
       pad_const = 1
     else:
       pad_const = 0
@@ -212,16 +213,16 @@ class ConvBNTest(tf.test.TestCase, parameterized.TestCase):
     print(test)
     self.assertAllEqual(outx.shape.as_list(), test)
 
-  @parameterized.named_parameters(("filters", 3))
+  @parameterized.named_parameters(('filters', 3))
   def test_gradient_pass_though(self, filters):
     loss = tf.keras.losses.MeanSquaredError()
     optimizer = tf.keras.optimizers.SGD()
-    with tf.device("/CPU:0"):
-      test_layer = nn_blocks.ConvBN(filters, kernel_size=(3, 3), padding="same")
+    with tf.device('/CPU:0'):
+      test_layer = nn_blocks.ConvBN(filters, kernel_size=(3, 3), padding='same')
 
     init = tf.random_normal_initializer()
-    x = tf.Variable(initial_value=init(shape=(1, 224, 224,
-                                              3), dtype=tf.float32))
+    x = tf.Variable(
+        initial_value=init(shape=(1, 224, 224, 3), dtype=tf.float32))
     y = tf.Variable(
         initial_value=init(shape=(1, 224, 224, filters), dtype=tf.float32))
 
@@ -235,9 +236,9 @@ class ConvBNTest(tf.test.TestCase, parameterized.TestCase):
 
 class DarkResidualTest(tf.test.TestCase, parameterized.TestCase):
 
-  @parameterized.named_parameters(("same", 224, 224, 64, False),
-                                  ("downsample", 223, 223, 32, True),
-                                  ("oddball", 223, 223, 32, False))
+  @parameterized.named_parameters(('same', 224, 224, 64, False),
+                                  ('downsample', 223, 223, 32, True),
+                                  ('oddball', 223, 223, 32, False))
   def test_pass_through(self, width, height, filters, downsample):
     mod = 1
     if downsample:
@@ -252,9 +253,9 @@ class DarkResidualTest(tf.test.TestCase, parameterized.TestCase):
         [None, np.ceil(width / mod),
          np.ceil(height / mod), filters])
 
-  @parameterized.named_parameters(("same", 64, 224, 224, False),
-                                  ("downsample", 32, 223, 223, True),
-                                  ("oddball", 32, 223, 223, False))
+  @parameterized.named_parameters(('same', 64, 224, 224, False),
+                                  ('downsample', 32, 223, 223, True),
+                                  ('oddball', 32, 223, 223, False))
   def test_gradient_pass_though(self, filters, width, height, downsample):
     loss = tf.keras.losses.MeanSquaredError()
     optimizer = tf.keras.optimizers.SGD()
@@ -268,8 +269,9 @@ class DarkResidualTest(tf.test.TestCase, parameterized.TestCase):
     init = tf.random_normal_initializer()
     x = tf.Variable(
         initial_value=init(shape=(1, width, height, filters), dtype=tf.float32))
-    y = tf.Variable(initial_value=init(shape=(1, int(np.ceil(width / mod)),
-                                              int(np.ceil(height / mod)),
+    y = tf.Variable(
+        initial_value=init(
+            shape=(1, int(np.ceil(width / mod)), int(np.ceil(height / mod)),
                    filters),
             dtype=tf.float32))
 
@@ -281,5 +283,104 @@ class DarkResidualTest(tf.test.TestCase, parameterized.TestCase):
 
     self.assertNotIn(None, grad)
 
-if __name__ == "__main__":
+
+class DarkSppTest(tf.test.TestCase, parameterized.TestCase):
+
+  @parameterized.named_parameters(('RouteProcessSpp', 224, 224, 3, [5, 9, 13]),
+                                  ('test1', 300, 300, 10, [2, 3, 4, 5]),
+                                  ('test2', 256, 256, 5, [10]))
+  def test_pass_through(self, width, height, channels, sizes):
+    x = tf.keras.Input(shape=(width, height, channels))
+    test_layer = nn_blocks.SPP(sizes=sizes)
+    outx = test_layer(x)
+    self.assertAllEqual(outx.shape.as_list(),
+                        [None, width, height, channels * (len(sizes) + 1)])
+    return
+
+  @parameterized.named_parameters(('RouteProcessSpp', 224, 224, 3, [5, 9, 13]),
+                                  ('test1', 300, 300, 10, [2, 3, 4, 5]),
+                                  ('test2', 256, 256, 5, [10]))
+  def test_gradient_pass_though(self, width, height, channels, sizes):
+    loss = tf.keras.losses.MeanSquaredError()
+    optimizer = tf.keras.optimizers.SGD()
+    test_layer = nn_blocks.SPP(sizes=sizes)
+
+    init = tf.random_normal_initializer()
+    x = tf.Variable(
+        initial_value=init(
+            shape=(1, width, height, channels), dtype=tf.float32))
+    y = tf.Variable(
+        initial_value=init(
+            shape=(1, width, height, channels * (len(sizes) + 1)),
+            dtype=tf.float32))
+
+    with tf.GradientTape() as tape:
+      x_hat = test_layer(x)
+      grad_loss = loss(x_hat, y)
+    grad = tape.gradient(grad_loss, test_layer.trainable_variables)
+    optimizer.apply_gradients(zip(grad, test_layer.trainable_variables))
+
+    self.assertNotIn(None, grad)
+    return
+
+
+class DarkRouteProcessTest(tf.test.TestCase, parameterized.TestCase):
+
+  @parameterized.named_parameters(
+      ('test1', 224, 224, 64, 7, False), ('test2', 223, 223, 32, 3, False),
+      ('tiny', 223, 223, 16, 1, False), ('spp', 224, 224, 64, 7, False))
+  def test_pass_through(self, width, height, filters, repetitions, spp):
+    x = tf.keras.Input(shape=(width, height, filters))
+    test_layer = nn_blocks.DarkRouteProcess(
+        filters=filters, repetitions=repetitions, insert_spp=spp)
+    outx = test_layer(x)
+    self.assertLen(outx, 2, msg='len(outx) != 2')
+    if repetitions == 1:
+      filter_y1 = filters
+    else:
+      filter_y1 = filters // 2
+    self.assertAllEqual(
+        outx[1].shape.as_list(), [None, width, height, filter_y1])
+    self.assertAllEqual(
+        filters % 2,
+        0,
+        msg='Output of a DarkRouteProcess layer has an odd number of filters')
+    self.assertAllEqual(outx[0].shape.as_list(), [None, width, height, filters])
+
+  @parameterized.named_parameters(
+      ('test1', 224, 224, 64, 7, False), ('test2', 223, 223, 32, 3, False),
+      ('tiny', 223, 223, 16, 1, False), ('spp', 224, 224, 64, 7, False))
+  def test_gradient_pass_though(self, width, height, filters, repetitions, spp):
+    loss = tf.keras.losses.MeanSquaredError()
+    optimizer = tf.keras.optimizers.SGD()
+    test_layer = nn_blocks.DarkRouteProcess(
+        filters=filters, repetitions=repetitions, insert_spp=spp)
+
+    if repetitions == 1:
+      filter_y1 = filters
+    else:
+      filter_y1 = filters // 2
+
+    init = tf.random_normal_initializer()
+    x = tf.Variable(
+        initial_value=init(shape=(1, width, height, filters), dtype=tf.float32))
+    y_0 = tf.Variable(
+        initial_value=init(shape=(1, width, height, filters), dtype=tf.float32))
+    y_1 = tf.Variable(
+        initial_value=init(
+            shape=(1, width, height, filter_y1), dtype=tf.float32))
+
+    with tf.GradientTape() as tape:
+      x_hat_0, x_hat_1 = test_layer(x)
+      grad_loss_0 = loss(x_hat_0, y_0)
+      grad_loss_1 = loss(x_hat_1, y_1)
+    grad = tape.gradient([grad_loss_0, grad_loss_1],
+                         test_layer.trainable_variables)
+    optimizer.apply_gradients(zip(grad, test_layer.trainable_variables))
+
+    self.assertNotIn(None, grad)
+    return
+
+
+if __name__ == '__main__':
   tf.test.main()