Creates non-breaking changes where necessary in preparation for switching all of Keras to new serialization format.

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

@@ -667,7 +667,11 @@ def testStripClusteringSequentialModel(self):
     stripped_model = cluster.strip_clustering(clustered_model)
 
     self.assertEqual(self._count_clustered_layers(stripped_model), 0)
-    self.assertEqual(model.get_config(), stripped_model.get_config())
+    model_config = model.get_config()
+    for layer in model_config['layers']:
+      # New serialization format includes `build_config` in wrapper
+      layer.pop('build_config', None)
+    self.assertEqual(model_config, stripped_model.get_config())
 
   def testClusterStrippingFunctionalModel(self):
     """Verifies that stripping the clustering wrappers from a functional model produces the expected config."""

tensorflow_model_optimization/python/core/quantization/keras/BUILD

@@ -72,6 +72,7 @@ py_strict_test(
     visibility = ["//visibility:public"],
     deps = [
         ":quantizers",
+        ":utils",
         # absl/testing:parameterized dep1,
         # numpy dep1,
         # tensorflow dep1,
@@ -87,9 +88,10 @@ py_strict_library(
     srcs_version = "PY3",
     visibility = ["//visibility:public"],
     deps = [
+        ":quantizers",
+        ":utils",
         # six dep1,
         # tensorflow dep1,
-        "//tensorflow_model_optimization/python/core/quantization/keras:quantizers",
     ],
 )
 
@@ -125,6 +127,7 @@ py_strict_library(
     srcs_version = "PY3",
     visibility = ["//visibility:public"],
     deps = [
+        ":utils",
         # tensorflow dep1,
     ],
 )
@@ -152,6 +155,7 @@ py_strict_library(
     srcs_version = "PY3",
     visibility = ["//visibility:public"],
     deps = [
+        ":utils",
         # tensorflow dep1,
         "//tensorflow_model_optimization/python/core/keras:utils",
     ],
@@ -167,6 +171,7 @@ py_strict_test(
     deps = [
         ":quantize_aware_activation",
         ":quantizers",
+        ":utils",
         # absl/testing:parameterized dep1,
         # numpy dep1,
         # tensorflow dep1,
@@ -182,6 +187,7 @@ py_strict_library(
     visibility = ["//visibility:public"],
     deps = [
         ":quantizers",
+        ":utils",
         # tensorflow dep1,
         "//tensorflow_model_optimization/python/core/keras:utils",
     ],
@@ -211,6 +217,7 @@ py_strict_library(
     visibility = ["//visibility:public"],
     deps = [
         ":quantize_aware_activation",
+        ":utils",
         # tensorflow dep1,
         # python/util tensorflow dep2,
         "//tensorflow_model_optimization/python/core/keras:metrics",
@@ -249,6 +256,7 @@ py_strict_library(
         ":quantize_layer",
         ":quantize_wrapper",
         ":quantizers",
+        ":utils",
         # tensorflow dep1,
         "//tensorflow_model_optimization/python/core/keras:metrics",
         "//tensorflow_model_optimization/python/core/quantization/keras/default_8bit:default_8bit_quantize_registry",

tensorflow_model_optimization/python/core/quantization/keras/default_8bit/default_8bit_quantize_registry_test.py

@@ -25,14 +25,15 @@
 import tensorflow as tf
 
 from tensorflow_model_optimization.python.core.quantization.keras import quantizers
+from tensorflow_model_optimization.python.core.quantization.keras import utils as quantize_utils
 from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_registry
 
 keras = tf.keras
 K = tf.keras.backend
 l = tf.keras.layers
 
-deserialize_keras_object = tf.keras.utils.deserialize_keras_object
-serialize_keras_object = tf.keras.utils.serialize_keras_object
+deserialize_keras_object = quantize_utils.deserialize_keras_object
+serialize_keras_object = quantize_utils.serialize_keras_object
 
 
 class _TestHelper(object):

tensorflow_model_optimization/python/core/quantization/keras/default_8bit/default_8bit_transforms.py

@@ -24,6 +24,7 @@
 from tensorflow_model_optimization.python.core.quantization.keras import quantize_aware_activation
 from tensorflow_model_optimization.python.core.quantization.keras import quantize_layer
 from tensorflow_model_optimization.python.core.quantization.keras import quantizers
+from tensorflow_model_optimization.python.core.quantization.keras import utils as quantize_utils
 from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_configs
 from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_registry
 from tensorflow_model_optimization.python.core.quantization.keras.graph_transformations import transforms
@@ -67,13 +68,17 @@ def _get_params(conv_layer, bn_layer, relu_layer=None):
       list(conv_layer['config'].items()) + list(bn_layer['config'].items()))
 
   if relu_layer is not None:
-    params['post_activation'] = keras.layers.deserialize(relu_layer)
+    params['post_activation'] = quantize_utils.deserialize_layer(
+        relu_layer, use_legacy_format=True
+    )
 
   return params
 
 
 def _get_layer_node(fused_layer, weights):
-  layer_config = keras.layers.serialize(fused_layer)
+  layer_config = quantize_utils.serialize_layer(
+      fused_layer, use_legacy_format=True
+  )
   layer_config['name'] = layer_config['config']['name']
   # This config tracks which layers get quantized, and whether they have a
   # custom QuantizeConfig.
@@ -118,7 +123,10 @@ def _replace(self, bn_layer_node, conv_layer_node):
       return bn_layer_node
 
     conv_layer_node.layer['config']['activation'] = (
-        keras.activations.serialize(quantize_aware_activation.NoOpActivation()))
+        quantize_utils.serialize_activation(
+            quantize_aware_activation.NoOpActivation(), use_legacy_format=True
+        )
+    )
     bn_layer_node.metadata['quantize_config'] = (
         default_8bit_quantize_configs.Default8BitOutputQuantizeConfig())
 
@@ -180,7 +188,10 @@ def _replace(self, relu_layer_node, bn_layer_node, conv_layer_node):
       return relu_layer_node
 
     conv_layer_node.layer['config']['activation'] = (
-        keras.activations.serialize(quantize_aware_activation.NoOpActivation()))
+        quantize_utils.serialize_activation(
+            quantize_aware_activation.NoOpActivation(), use_legacy_format=True
+        )
+    )
     bn_layer_node.metadata['quantize_config'] = (
         default_8bit_quantize_configs.NoOpQuantizeConfig())
 
@@ -261,7 +272,10 @@ def _replace(self, bn_layer_node, dense_layer_node):
       return bn_layer_node
 
     dense_layer_node.layer['config']['activation'] = (
-        keras.activations.serialize(quantize_aware_activation.NoOpActivation()))
+        quantize_utils.serialize_activation(
+            quantize_aware_activation.NoOpActivation(), use_legacy_format=True
+        )
+    )
     bn_layer_node.metadata['quantize_config'] = (
         default_8bit_quantize_configs.Default8BitOutputQuantizeConfig())
 
@@ -297,7 +311,10 @@ def _replace(self, relu_layer_node, bn_layer_node, dense_layer_node):
       return relu_layer_node
 
     dense_layer_node.layer['config']['activation'] = (
-        keras.activations.serialize(quantize_aware_activation.NoOpActivation()))
+        quantize_utils.serialize_activation(
+            quantize_aware_activation.NoOpActivation(), use_legacy_format=True
+        )
+    )
     bn_layer_node.metadata['quantize_config'] = (
         default_8bit_quantize_configs.NoOpQuantizeConfig())
 
@@ -408,7 +425,9 @@ def replacement(self, match_layer):
     else:
       spatial_dim = 2
 
-    sepconv2d_layer_config = keras.layers.serialize(sepconv2d_layer)
+    sepconv2d_layer_config = quantize_utils.serialize_layer(
+        sepconv2d_layer, use_legacy_format=True
+    )
     sepconv2d_layer_config['name'] = sepconv2d_layer.name
 
     # Needed to ensure these new layers are considered for quantization.
@@ -420,15 +439,19 @@ def replacement(self, match_layer):
     expand_layer = tf.keras.layers.Lambda(
         lambda x: tf.expand_dims(x, spatial_dim),
         name=self._get_name('sepconv1d_expand'))
-    expand_layer_config = keras.layers.serialize(expand_layer)
+    expand_layer_config = quantize_utils.serialize_layer(
+        expand_layer, use_legacy_format=True
+    )
     expand_layer_config['name'] = expand_layer.name
     expand_layer_metadata = {
         'quantize_config': default_8bit_quantize_configs.NoOpQuantizeConfig()}
 
     squeeze_layer = tf.keras.layers.Lambda(
         lambda x: tf.squeeze(x, [spatial_dim]),
         name=self._get_name('sepconv1d_squeeze'))
-    squeeze_layer_config = keras.layers.serialize(squeeze_layer)
+    squeeze_layer_config = quantize_utils.serialize_layer(
+        squeeze_layer, use_legacy_format=True
+    )
     squeeze_layer_config['name'] = squeeze_layer.name
     squeeze_layer_metadata = {
         'quantize_config': default_8bit_quantize_configs.NoOpQuantizeConfig()}
@@ -493,7 +516,9 @@ def replacement(self, match_layer):
     )
     dconv_weights = collections.OrderedDict()
     dconv_weights['depthwise_kernel:0'] = sepconv_weights[0]
-    dconv_layer_config = keras.layers.serialize(dconv_layer)
+    dconv_layer_config = quantize_utils.serialize_layer(
+        dconv_layer, use_legacy_format=True
+    )
     dconv_layer_config['name'] = dconv_layer.name
     # Needed to ensure these new layers are considered for quantization.
     dconv_metadata = {'quantize_config': None}
@@ -521,7 +546,9 @@ def replacement(self, match_layer):
     conv_weights['kernel:0'] = sepconv_weights[1]
     if sepconv_layer['config']['use_bias']:
       conv_weights['bias:0'] = sepconv_weights[2]
-    conv_layer_config = keras.layers.serialize(conv_layer)
+    conv_layer_config = quantize_utils.serialize_layer(
+        conv_layer, use_legacy_format=True
+    )
     conv_layer_config['name'] = conv_layer.name
     # Needed to ensure these new layers are considered for quantization.
     conv_metadata = {'quantize_config': None}
@@ -588,7 +615,9 @@ def replacement(self, match_layer):
     quant_layer = quantize_layer.QuantizeLayer(
         quantizers.AllValuesQuantizer(
             num_bits=8, per_axis=False, symmetric=False, narrow_range=False))
-    layer_config = keras.layers.serialize(quant_layer)
+    layer_config = quantize_utils.serialize_layer(
+        quant_layer, use_legacy_format=True
+    )
     layer_config['name'] = quant_layer.name
 
     quant_layer_node = LayerNode(

tensorflow_model_optimization/python/core/quantization/keras/default_8bit/default_8bit_transforms_test.py

@@ -707,4 +707,6 @@ def testConcatConcatTransformDisablesOutput(self):
 
 
 if __name__ == '__main__':
+  if hasattr(tf.keras.__internal__, 'enable_unsafe_deserialization'):
+    tf.keras.__internal__.enable_unsafe_deserialization()
   tf.test.main()

tensorflow_model_optimization/python/core/quantization/keras/default_8bit/quantize_numerical_test.py

@@ -200,4 +200,6 @@ def testModelEndToEnd(self, model_fn):
 
 
 if __name__ == '__main__':
+  if hasattr(tf.keras.__internal__, 'enable_unsafe_deserialization'):
+    tf.keras.__internal__.enable_unsafe_deserialization()
   tf.test.main()

tensorflow_model_optimization/python/core/quantization/keras/experimental/default_n_bit/default_n_bit_quantize_registry_test.py

@@ -25,14 +25,15 @@
 import tensorflow as tf
 
 from tensorflow_model_optimization.python.core.quantization.keras import quantizers
+from tensorflow_model_optimization.python.core.quantization.keras import utils as quantize_utils
 from tensorflow_model_optimization.python.core.quantization.keras.experimental.default_n_bit import default_n_bit_quantize_registry as n_bit_registry
 
 keras = tf.keras
 K = tf.keras.backend
 l = tf.keras.layers
 
-deserialize_keras_object = tf.keras.utils.deserialize_keras_object
-serialize_keras_object = tf.keras.utils.serialize_keras_object
+deserialize_keras_object = quantize_utils.deserialize_keras_object
+serialize_keras_object = quantize_utils.serialize_keras_object
 
 
 class _TestHelper(object):