Add ASPP for KerasCV.

PiperOrigin-RevId: 333644426

official/vision/keras_cv/__init__.py

@@ -14,5 +14,6 @@
 # ==============================================================================
 """Keras-CV package definition."""
 # pylint: disable=wildcard-import
+from official.vision.keras_cv import layers
 from official.vision.keras_cv import losses
 from official.vision.keras_cv import ops

official/vision/keras_cv/layers/__init__.py

+# Copyright 2020 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.
+# ==============================================================================
+"""Keras-CV layers package definition."""
+from official.vision.keras_cv.layers.deeplab import ASPP

official/vision/keras_cv/layers/deeplab.py

+# Copyright 2020 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.
+# ==============================================================================
+"""Layers for DeepLabV3."""
+
+import tensorflow as tf
+
+
+@tf.keras.utils.register_keras_serializable(package='keras_cv')
+class ASPP(tf.keras.layers.Layer):
+  """Implements the Atrous Spatial Pyramid Pooling.
+
+  Reference:
+    [Rethinking Atrous Convolution for Semantic Image Segmentation](
+      https://arxiv.org/pdf/1706.05587.pdf)
+  """
+
+  def __init__(
+      self,
+      output_channels,
+      dilation_rates,
+      batchnorm_momentum=0.99,
+      dropout=0.5,
+      kernel_initializer='glorot_uniform',
+      kernel_regularizer=None,
+      interpolation='bilinear',
+      **kwargs):
+    """Initializes `ASPP`.
+
+    Arguments:
+      output_channels: Number of channels produced by ASPP.
+      dilation_rates: A list of integers for parallel dilated conv.
+      batchnorm_momentum: A float for the momentum in BatchNorm. Defaults to
+        0.99.
+      dropout: A float for the dropout rate before output. Defaults to 0.5.
+      kernel_initializer: Kernel initializer for conv layers. Defaults to
+        `glorot_uniform`.
+      kernel_regularizer: Kernel regularizer for conv layers. Defaults to None.
+      interpolation: The interpolation method for upsampling. Defaults to
+        `bilinear`.
+      **kwargs: Other keyword arguments for the layer.
+    """
+    super(ASPP, self).__init__(**kwargs)
+
+    self.output_channels = output_channels
+    self.dilation_rates = dilation_rates
+    self.batchnorm_momentum = batchnorm_momentum
+    self.dropout = dropout
+    self.kernel_initializer = tf.keras.initializers.get(kernel_initializer)
+    self.kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
+    self.interpolation = interpolation
+    self.input_spec = tf.keras.layers.InputSpec(ndim=4)
+
+  def build(self, input_shape):
+    height = input_shape[1]
+    width = input_shape[2]
+    channels = input_shape[3]
+
+    self.aspp_layers = []
+
+    conv_sequential = tf.keras.Sequential([
+        tf.keras.layers.Conv2D(
+            filters=self.output_channels, kernel_size=(1, 1),
+            kernel_initializer=self.kernel_initializer,
+            kernel_regularizer=self.kernel_regularizer, use_bias=False),
+        tf.keras.layers.BatchNormalization(momentum=self.batchnorm_momentum),
+        tf.keras.layers.Activation('relu')])
+    self.aspp_layers.append(conv_sequential)
+
+    for dilation_rate in self.dilation_rates:
+      conv_sequential = tf.keras.Sequential([
+          tf.keras.layers.Conv2D(
+              filters=self.output_channels, kernel_size=(3, 3),
+              padding='same', kernel_regularizer=self.kernel_regularizer,
+              kernel_initializer=self.kernel_initializer,
+              dilation_rate=dilation_rate, use_bias=False),
+          tf.keras.layers.BatchNormalization(momentum=self.batchnorm_momentum),
+          tf.keras.layers.Activation('relu')])
+      self.aspp_layers.append(conv_sequential)
+
+    pool_sequential = tf.keras.Sequential([
+        tf.keras.layers.GlobalAveragePooling2D(),
+        tf.keras.layers.Reshape((1, 1, channels)),
+        tf.keras.layers.Conv2D(
+            filters=self.output_channels, kernel_size=(1, 1),
+            kernel_initializer=self.kernel_initializer,
+            kernel_regularizer=self.kernel_regularizer, use_bias=False),
+        tf.keras.layers.BatchNormalization(momentum=self.batchnorm_momentum),
+        tf.keras.layers.Activation('relu'),
+        tf.keras.layers.experimental.preprocessing.Resizing(
+            height, width, interpolation=self.interpolation)])
+    self.aspp_layers.append(pool_sequential)
+
+    self.projection = tf.keras.Sequential([
+        tf.keras.layers.Conv2D(
+            filters=self.output_channels, kernel_size=(1, 1),
+            kernel_initializer=self.kernel_initializer,
+            kernel_regularizer=self.kernel_regularizer, use_bias=False),
+        tf.keras.layers.BatchNormalization(momentum=self.batchnorm_momentum),
+        tf.keras.layers.Activation('relu'),
+        tf.keras.layers.Dropout(rate=self.dropout)])
+
+  def call(self, inputs, training=None):
+    if training is None:
+      training = tf.keras.backend.learning_phase()
+    result = []
+    for layer in self.aspp_layers:
+      result.append(layer(inputs, training=training))
+    result = tf.concat(result, axis=-1)
+    result = self.projection(result, training=training)
+    return result
+
+  def get_config(self):
+    config = {
+        'output_channels': self.output_channels,
+        'dilation_rates': self.dilation_rates,
+        'batchnorm_momentum': self.batchnorm_momentum,
+        'dropout': self.dropout,
+        'kernel_initializer': tf.keras.initializers.serialize(
+            self.kernel_initializer),
+        'kernel_regularizer': tf.keras.regularizers.serialize(
+            self.kernel_regularizer),
+        'interpolation': self.interpolation,
+    }
+    base_config = super(ASPP, self).get_config()
+    return dict(list(base_config.items()) + list(config.items()))

official/vision/keras_cv/layers/deeplab_test.py

+# Copyright 2020 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 ASPP."""
+
+import tensorflow as tf
+
+from tensorflow.python.keras import keras_parameterized
+from official.vision.keras_cv.layers import deeplab
+
+
+@keras_parameterized.run_all_keras_modes
+class DeeplabTest(keras_parameterized.TestCase):
+
+  def test_aspp(self):
+    inputs = tf.keras.Input(shape=(64, 64, 128), dtype=tf.float32)
+    layer = deeplab.ASPP(output_channels=256, dilation_rates=[6, 12, 18])
+    output = layer(inputs)
+    self.assertAllEqual([None, 64, 64, 256], output.shape)
+
+  def test_aspp_invalid_shape(self):
+    inputs = tf.keras.Input(shape=(64, 64), dtype=tf.float32)
+    layer = deeplab.ASPP(output_channels=256, dilation_rates=[6, 12, 18])
+    with self.assertRaises(ValueError):
+      _ = layer(inputs)
+
+  def test_config_with_custom_name(self):
+    layer = deeplab.ASPP(256, [5], name='aspp')
+    config = layer.get_config()
+    layer_1 = deeplab.ASPP.from_config(config)
+    self.assertEqual(layer_1.name, layer.name)
+
+
+if __name__ == '__main__':
+  tf.test.main()