Simplification of clustering registry.

Added test for DepthwiseConv2D.

Change-Id: Ida1598ac91e2044b3a13fd8fd0cf5a6f3279133c

Author: wwwind

tensorflow_model_optimization/python/core/clustering/keras/cluster_test.py

@@ -65,6 +65,7 @@ def setUp(self):
     self.keras_unsupported_layer = layers.ConvLSTM2D(2, (5, 5))  # Unsupported
     self.custom_clusterable_layer = CustomClusterableLayer(10)
     self.custom_non_clusterable_layer = CustomNonClusterableLayer(10)
+    self.keras_depthwiseconv2d_layer = layers.DepthwiseConv2D((3, 3), (1, 1))
 
     clustering_registry.ClusteringLookupRegistry.register_new_implementation(
         {
@@ -81,10 +82,10 @@ def setUp(self):
             cluster_config.CentroidInitialization.DENSITY_BASED
     }
 
-  def _build_clustered_layer_model(self, layer):
+  def _build_clustered_layer_model(self, layer, input_shape=(10, 1)):
     wrapped_layer = cluster.cluster_weights(layer, **self.params)
     self.model.add(wrapped_layer)
-    self.model.build(input_shape=(10, 1))
+    self.model.build(input_shape=input_shape)
 
     return wrapped_layer
 
@@ -124,13 +125,32 @@ def testClusterKerasNonClusterableLayer(self):
                                    wrapped_layer)
     self.assertEqual([], wrapped_layer.layer.get_clusterable_weights())
 
+  @keras_parameterized.run_all_keras_modes
+  def testDepthwiseConv2DLayerNonClusterable(self):
+     """
+     Verifies that we don't cluster a DepthwiseConv2D layer,
+     because clustering of this type of layer gives
+     big unrecoverable accuracy loss.
+     """
+     wrapped_layer = self._build_clustered_layer_model(
+         self.keras_depthwiseconv2d_layer,
+         input_shape=(1, 10, 10, 10)
+     )
+
+     self._validate_clustered_layer(self.keras_depthwiseconv2d_layer,
+                                    wrapped_layer)
+     self.assertEqual([], wrapped_layer.layer.get_clusterable_weights())
+
   def testClusterKerasUnsupportedLayer(self):
     """
     Verifies that attempting to cluster an unsupported layer raises an
     exception.
     """
+    keras_unsupported_layer = self.keras_unsupported_layer
+    # We need to build weights before check.
+    keras_unsupported_layer.build(input_shape = (10, 10))
     with self.assertRaises(ValueError):
-      cluster.cluster_weights(self.keras_unsupported_layer, **self.params)
+      cluster.cluster_weights(keras_unsupported_layer, **self.params)
 
   @keras_parameterized.run_all_keras_modes
   def testClusterCustomClusterableLayer(self):
@@ -149,8 +169,11 @@ def testClusterCustomNonClusterableLayer(self):
     Verifies that attempting to cluster a custom non-clusterable layer raises
     an exception.
     """
+    custom_non_clusterable_layer = self.custom_non_clusterable_layer
+    # We need to build weights before check.
+    custom_non_clusterable_layer.build(input_shape=(10, 10))
     with self.assertRaises(ValueError):
-      cluster_wrapper.ClusterWeights(self.custom_non_clusterable_layer,
+      cluster_wrapper.ClusterWeights(custom_non_clusterable_layer,
                                      **self.params)
 
   @keras_parameterized.run_all_keras_modes
@@ -206,11 +229,14 @@ def testClusterModelUnsupportedKerasLayerRaisesError(self):
     Verifies that attempting to cluster a model that contains an unsupported
     layer raises an exception.
     """
+    keras_unsupported_layer = self.keras_unsupported_layer
+    # We need to build weights before check.
+    keras_unsupported_layer.build(input_shape = (10, 10))
     with self.assertRaises(ValueError):
       cluster.cluster_weights(
           keras.Sequential([
               self.keras_clusterable_layer, self.keras_non_clusterable_layer,
-              self.custom_clusterable_layer, self.keras_unsupported_layer
+              self.custom_clusterable_layer, keras_unsupported_layer
           ]), **self.params)
 
   def testClusterModelCustomNonClusterableLayerRaisesError(self):
@@ -219,10 +245,13 @@ def testClusterModelCustomNonClusterableLayerRaisesError(self):
     non-clusterable layer raises an exception.
     """
     with self.assertRaises(ValueError):
+      custom_non_clusterable_layer = self.custom_non_clusterable_layer
+      # We need to build weights before check.
+      custom_non_clusterable_layer.build(input_shape = (1, 2))
       cluster.cluster_weights(
           keras.Sequential([
               self.keras_clusterable_layer, self.keras_non_clusterable_layer,
-              self.custom_clusterable_layer, self.custom_non_clusterable_layer
+              self.custom_clusterable_layer, custom_non_clusterable_layer
           ]), **self.params)
 
   @keras_parameterized.run_all_keras_modes

tensorflow_model_optimization/python/core/clustering/keras/cluster_wrapper_test.py

@@ -33,9 +33,16 @@
 CentroidInitialization = cluster_config.CentroidInitialization
 
 
-class NonClusterableLayer(layers.Dense):
-  """A custom layer that is not clusterable."""
-
+class NonClusterableLayer(layers.Layer):
+  """"A custom layer with weights that is not clusterable."""
+  def __init__(self, units=10):
+      super(NonClusterableLayer, self).__init__()
+      self.add_weight(shape=(1, units),
+                      initializer='uniform',
+                      name='kernel')
+
+  def call(self, inputs):
+    return tf.matmul(inputs, self.weights)
 
 class AlreadyClusterableLayer(layers.Dense, clusterable_layer.ClusterableLayer):
   """A custom layer that is clusterable."""

tensorflow_model_optimization/python/core/clustering/keras/clustering_registry.py

@@ -17,7 +17,6 @@
 import abc
 import six
 import tensorflow as tf
-
 from tensorflow.keras import layers
 
 from tensorflow_model_optimization.python.core.clustering.keras import clusterable_layer
@@ -230,69 +229,21 @@ class ClusteringRegistry(object):
   # the variables within the layers which hold the kernel weights. This
   # allows the wrapper to access and modify the weights.
   _LAYERS_WEIGHTS_MAP = {
-      layers.ELU: [],
-      layers.LeakyReLU: [],
-      layers.ReLU: [],
-      layers.Softmax: [],
-      layers.ThresholdedReLU: [],
       layers.Conv1D: ['kernel'],
       layers.Conv2D: ['kernel'],
       layers.Conv2DTranspose: ['kernel'],
       layers.Conv3D: ['kernel'],
       layers.Conv3DTranspose: ['kernel'],
-      layers.Cropping1D: [],
-      layers.Cropping2D: [],
-      layers.Cropping3D: [],
+      # non-clusterable due to big unrecoverable accuracy loss
       layers.DepthwiseConv2D: [],
       layers.SeparableConv1D: ['pointwise_kernel'],
       layers.SeparableConv2D: ['pointwise_kernel'],
-      layers.UpSampling1D: [],
-      layers.UpSampling2D: [],
-      layers.UpSampling3D: [],
-      layers.ZeroPadding1D: [],
-      layers.ZeroPadding2D: [],
-      layers.ZeroPadding3D: [],
-      layers.Activation: [],
-      layers.ActivityRegularization: [],
       layers.Dense: ['kernel'],
-      layers.Dropout: [],
-      layers.Flatten: [],
-      layers.Lambda: [],
-      layers.Masking: [],
-      layers.Permute: [],
-      layers.RepeatVector: [],
-      layers.Reshape: [],
-      layers.SpatialDropout1D: [],
-      layers.SpatialDropout2D: [],
-      layers.SpatialDropout3D: [],
       layers.Embedding: ['embeddings'],
       layers.LocallyConnected1D: ['kernel'],
       layers.LocallyConnected2D: ['kernel'],
-      layers.Add: [],
-      layers.Average: [],
-      layers.Concatenate: [],
-      layers.Dot: [],
-      layers.Maximum: [],
-      layers.Minimum: [],
-      layers.Multiply: [],
-      layers.Subtract: [],
-      layers.AlphaDropout: [],
-      layers.GaussianDropout: [],
-      layers.GaussianNoise: [],
       layers.BatchNormalization: [],
       layers.LayerNormalization: [],
-      layers.AveragePooling1D: [],
-      layers.AveragePooling2D: [],
-      layers.AveragePooling3D: [],
-      layers.GlobalAveragePooling1D: [],
-      layers.GlobalAveragePooling2D: [],
-      layers.GlobalAveragePooling3D: [],
-      layers.GlobalMaxPooling1D: [],
-      layers.GlobalMaxPooling2D: [],
-      layers.GlobalMaxPooling3D: [],
-      layers.MaxPooling1D: [],
-      layers.MaxPooling2D: [],
-      layers.MaxPooling3D: [],
   }
 
   _RNN_CELLS_WEIGHTS_MAP = {
@@ -339,6 +290,11 @@ def supports(cls, layer):
       True/False whether the layer type is supported.
 
     """
+    # Automatically enable layers with zero trainable weights.
+    # Example: Reshape, AveragePooling2D, Maximum/Minimum, etc.
+    if len(layer.trainable_weights) == 0:
+      return True
+
     if layer.__class__ in cls._LAYERS_WEIGHTS_MAP:
       return True
 
@@ -363,6 +319,10 @@ def _is_rnn_layer(cls, layer):
 
   @classmethod
   def _weight_names(cls, layer):
+    # For layers with zero trainable weights, like Reshape, Pooling.
+    if len(layer.trainable_weights) == 0:
+      return []
+
     return cls._LAYERS_WEIGHTS_MAP[layer.__class__]
 
   @classmethod

tensorflow_model_optimization/python/core/clustering/keras/clustering_registry_test.py

@@ -92,7 +92,14 @@ def testConvolutionalWeightsCA(self,
 
 class CustomLayer(layers.Layer):
   """A custom non-clusterable layer class."""
+  def __init__(self, units=10):
+      super(CustomLayer, self).__init__()
+      self.add_weight(shape=(1, units),
+                      initializer='uniform',
+                      name='kernel')
 
+  def call(self, inputs):
+    return tf.matmul(inputs, self.weights)
 
 class ClusteringLookupRegistryTest(test.TestCase, parameterized.TestCase):
   """Unit tests for the ClusteringLookupRegistry class."""
@@ -191,6 +198,11 @@ class CustomLayerFromClusterableLayer(layers.Dense):
     """A custom layer class derived from a built-in clusterable layer."""
     pass
 
+  class CustomLayerFromClusterableLayerNoWeights(layers.Reshape):
+    """A custom layer class derived from a built-in clusterable layer,
+    that does not have any weights."""
+    pass
+
   class MinimalRNNCell(keras.layers.Layer):
     """A minimal RNN cell implementation."""
 
@@ -250,7 +262,10 @@ def testDoesNotSupportKerasUnsupportedLayer(self):
     Verifies that ClusterRegistry does not support an unknown built-in layer.
     """
     # ConvLSTM2D is a built-in keras layer but not supported.
-    self.assertFalse(ClusterRegistry.supports(layers.ConvLSTM2D(2, (5, 5))))
+    l = layers.ConvLSTM2D(2, (5, 5))
+    # We need to build weights
+    l.build(input_shape = (10, 10))
+    self.assertFalse(ClusterRegistry.supports(l))
 
   def testSupportsKerasRNNLayers(self):
     """
@@ -265,8 +280,10 @@ def testDoesNotSupportKerasRNNLayerUnknownCell(self):
     Verifies that ClusterRegistry does not support a custom non-clusterable RNN
     cell.
     """
-    self.assertFalse(ClusterRegistry.supports(
-        keras.layers.RNN(ClusterRegistryTest.MinimalRNNCell(32))))
+    l = keras.layers.RNN(ClusterRegistryTest.MinimalRNNCell(32))
+    # We need to build it to have weights
+    l.build((10,1))
+    self.assertFalse(ClusterRegistry.supports(l))
 
   def testSupportsKerasRNNLayerClusterableCell(self):
     """
@@ -285,19 +302,31 @@ def testDoesNotSupportCustomLayer(self):
   def testDoesNotSupportCustomLayerInheritedFromClusterableLayer(self):
     """
     Verifies that ClusterRegistry does not support a custom layer derived from
-    a clusterable layer.
+    a clusterable layer if there are trainable weights.
+    """
+    custom_layer = ClusterRegistryTest.CustomLayerFromClusterableLayer(10)
+    custom_layer.build(input_shape=(10, 10))
+    self.assertFalse(ClusterRegistry.supports(custom_layer))
+
+  def testSupportsCustomLayerInheritedFromClusterableLayerNoWeights(self):
+    """
+    Verifies that ClusterRegistry supports a custom layer derived from
+    a clusterable layer that does not have trainable weights.
     """
-    self.assertFalse(
-        ClusterRegistry.supports(
-            ClusterRegistryTest.CustomLayerFromClusterableLayer(10)))
+    custom_layer = ClusterRegistryTest.\
+      CustomLayerFromClusterableLayerNoWeights((7, 1))
+    custom_layer.build(input_shape=(3, 4))
+    self.assertTrue(ClusterRegistry.supports(custom_layer))
 
   def testMakeClusterableRaisesErrorForKerasUnsupportedLayer(self):
     """
     Verifies that an unsupported built-in layer cannot be made clusterable by
     calling make_clusterable().
     """
+    l = layers.ConvLSTM2D(2, (5, 5))
+    l.build(input_shape = (10, 10))
     with self.assertRaises(ValueError):
-      ClusterRegistry.make_clusterable(layers.ConvLSTM2D(2, (5, 5)))
+      ClusterRegistry.make_clusterable(l)
 
   def testMakeClusterableRaisesErrorForCustomLayer(self):
     """
@@ -313,9 +342,10 @@ def testMakeClusterableRaisesErrorForCustomLayerInheritedFromClusterableLayer(
     Verifies that a non-clusterable layer derived from a clusterable layer
     cannot be made clusterable by calling make_clusterable().
     """
+    l = ClusterRegistryTest.CustomLayerFromClusterableLayer(10)
+    l.build(input_shape = (10, 10))
     with self.assertRaises(ValueError):
-      ClusterRegistry.make_clusterable(
-          ClusterRegistryTest.CustomLayerFromClusterableLayer(10))
+      ClusterRegistry.make_clusterable(l)
 
   def testMakeClusterableWorksOnKerasClusterableLayer(self):
     """
@@ -413,9 +443,12 @@ def testMakeClusterableRaisesErrorOnRNNLayersUnsupportedCell(self):
     Verifies that make_clusterable() raises an exception when invoked with a
     built-in RNN layer that contains a non-clusterable custom RNN cell.
     """
+    l = ClusterRegistryTest.MinimalRNNCell(5)
+    # we need to build weights
+    l.build(input_shape = (10, 1))
     with self.assertRaises(ValueError):
       ClusterRegistry.make_clusterable(layers.RNN(
-          [layers.LSTMCell(10), ClusterRegistryTest.MinimalRNNCell(5)]))
+          [layers.LSTMCell(10), l]))
 
 
 if __name__ == '__main__':