Internal change

PiperOrigin-RevId: 339559679

official/vision/keras_cv/layers/deeplab.py

@@ -30,6 +30,7 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
       self,
       output_channels,
       dilation_rates,
+      pool_kernel_size=None,
       use_sync_bn=False,
       batchnorm_momentum=0.99,
       batchnorm_epsilon=0.001,
@@ -44,6 +45,9 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
     Arguments:
       output_channels: Number of channels produced by SpatialPyramidPooling.
       dilation_rates: A list of integers for parallel dilated conv.
+      pool_kernel_size: A list of integers or None. If None, global average
+        pooling is applied, otherwise an average pooling of pool_kernel_size
+        is applied.
       use_sync_bn: A bool, whether or not to use sync batch normalization.
       batchnorm_momentum: A float for the momentum in BatchNorm. Defaults to
         0.99.
@@ -71,6 +75,7 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
     self.kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
     self.interpolation = interpolation
     self.input_spec = tf.keras.layers.InputSpec(ndim=4)
+    self.pool_kernel_size = pool_kernel_size
 
   def build(self, input_shape):
     height = input_shape[1]
@@ -115,11 +120,19 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
           tf.keras.layers.Activation(self.activation)])
       self.aspp_layers.append(conv_sequential)
 
+    if self.pool_kernel_size is None:
       pool_sequential = tf.keras.Sequential([
           tf.keras.layers.GlobalAveragePooling2D(),
-        tf.keras.layers.Reshape((1, 1, channels)),
+          tf.keras.layers.Reshape((1, 1, channels))])
+    else:
+      pool_sequential = tf.keras.Sequential([
+          tf.keras.layers.AveragePooling2D(self.pool_kernel_size)])
+
+    pool_sequential.add(
+        tf.keras.Sequential([
             tf.keras.layers.Conv2D(
-            filters=self.output_channels, kernel_size=(1, 1),
+                filters=self.output_channels,
+                kernel_size=(1, 1),
                 kernel_initializer=self.kernel_initializer,
                 kernel_regularizer=self.kernel_regularizer,
                 use_bias=False),
@@ -129,7 +142,9 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
                 epsilon=self.batchnorm_epsilon),
             tf.keras.layers.Activation(self.activation),
             tf.keras.layers.experimental.preprocessing.Resizing(
-            height, width, interpolation=self.interpolation)])
+                height, width, interpolation=self.interpolation)
+        ]))
+
     self.aspp_layers.append(pool_sequential)
 
     self.projection = tf.keras.Sequential([
@@ -159,6 +174,7 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
     config = {
         'output_channels': self.output_channels,
         'dilation_rates': self.dilation_rates,
+        'pool_kernel_size': self.pool_kernel_size,
         'use_sync_bn': self.use_sync_bn,
         'batchnorm_momentum': self.batchnorm_momentum,
         'batchnorm_epsilon': self.batchnorm_epsilon,

official/vision/keras_cv/layers/deeplab_test.py

@@ -23,10 +23,15 @@ from official.vision.keras_cv.layers import deeplab
 @keras_parameterized.run_all_keras_modes
 class DeeplabTest(keras_parameterized.TestCase):
 
-  def test_aspp(self):
+  @keras_parameterized.parameterized.parameters(
+      (None,),
+      ([32, 32],),
+      )
+  def test_aspp(self, pool_kernel_size):
     inputs = tf.keras.Input(shape=(64, 64, 128), dtype=tf.float32)
     layer = deeplab.SpatialPyramidPooling(output_channels=256,
-                                          dilation_rates=[6, 12, 18])
+                                          dilation_rates=[6, 12, 18],
+                                          pool_kernel_size=None)
     output = layer(inputs)
     self.assertAllEqual([None, 64, 64, 256], output.shape)