Unified naming for parameterizer objects.

Author: Johannes Ballé

docs/api_docs/python/tfc/GDN.md

@@ -91,10 +91,10 @@ more flexible, as `beta` and `gamma` are trainable parameters.
     A good default setting is somewhere between 0 and 0.5.
 * <b>`data_format`</b>: Format of input tensor. Currently supports `'channels_first'`
     and `'channels_last'`.
-* <b>`beta_parameterization`</b>: Parameterizer object for beta parameter. Defaults
-    to NonnegativeParameterizer with a minimum value of 1e-6.
-* <b>`gamma_parameterization`</b>: Parameterizer object for gamma parameter.
-    Defaults to NonnegativeParameterizer with a minimum value of 0.
+* <b>`beta_parameterizer`</b>: Reparameterization for beta parameter. Defaults to
+    `NonnegativeParameterizer` with a minimum value of `1e-6`.
+* <b>`gamma_parameterizer`</b>: Reparameterization for gamma parameter. Defaults to
+    `NonnegativeParameterizer` with a minimum value of `0`.
 * <b>`activity_regularizer`</b>: Regularizer function for the output.
 * <b>`trainable`</b>: Boolean, if `True`, also add variables to the graph collection
     `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
@@ -314,8 +314,8 @@ __init__(
     rectify=False,
     gamma_init=0.1,
     data_format='channels_last',
-    beta_parameterization=_default_beta_param,
-    gamma_parameterization=_default_gamma_param,
+    beta_parameterizer=_default_beta_param,
+    gamma_parameterizer=_default_gamma_param,
     activity_regularizer=None,
     trainable=True,
     name=None,

docs/api_docs/python/tfc/SignalConv1D.md

@@ -4,7 +4,7 @@
 <meta itemprop="property" content="activity_regularizer"/>
 <meta itemprop="property" content="bias"/>
 <meta itemprop="property" content="bias_initializer"/>
-<meta itemprop="property" content="bias_parameterization"/>
+<meta itemprop="property" content="bias_parameterizer"/>
 <meta itemprop="property" content="bias_regularizer"/>
 <meta itemprop="property" content="channel_separable"/>
 <meta itemprop="property" content="corr"/>
@@ -19,7 +19,7 @@
 <meta itemprop="property" content="input_shape"/>
 <meta itemprop="property" content="kernel"/>
 <meta itemprop="property" content="kernel_initializer"/>
-<meta itemprop="property" content="kernel_parameterization"/>
+<meta itemprop="property" content="kernel_parameterizer"/>
 <meta itemprop="property" content="kernel_regularizer"/>
 <meta itemprop="property" content="kernel_support"/>
 <meta itemprop="property" content="losses"/>
@@ -245,11 +245,10 @@ will throw an exception.
 * <b>`kernel_regularizer`</b>: Optional regularizer for the filter kernel.
 * <b>`bias_regularizer`</b>: Optional regularizer for the bias vector.
 * <b>`activity_regularizer`</b>: Regularizer function for the output.
-* <b>`kernel_parameterization`</b>: Reparameterization applied to filter kernel. If not
-    `None`, must be a parameterization object. Defaults to RDFT
-    parameterization.
-* <b>`bias_parameterization`</b>: Reparameterization applied to bias. If not `None`,
-    must be a parameterization object.
+* <b>`kernel_parameterizer`</b>: Reparameterization applied to filter kernel. If not
+    `None`, must be a `Parameterizer` object. Defaults to `RDFTParameterizer`.
+* <b>`bias_parameterizer`</b>: Reparameterization applied to bias. If not `None`, must
+    be a `Parameterizer` object.
 * <b>`trainable`</b>: Boolean. Whether the layer should be trained.
 * <b>`name`</b>: String. The name of the layer.
 * <b>`dtype`</b>: Default dtype of the layer's parameters (default of `None` means use
@@ -272,8 +271,8 @@ Read-only properties:
 * <b>`kernel_regularizer`</b>: See above.
 * <b>`bias_regularizer`</b>: See above.
 * <b>`activity_regularizer`</b>: See above.
-* <b>`kernel_parameterization`</b>: See above.
-* <b>`bias_parameterization`</b>: See above.
+* <b>`kernel_parameterizer`</b>: See above.
+* <b>`bias_parameterizer`</b>: See above.
 * <b>`name`</b>: See above.
 * <b>`dtype`</b>: See above.
 * <b>`kernel`</b>: `Tensor`-like object. The convolution kernel as applied to the
@@ -309,7 +308,7 @@ Optional regularizer function for the output of this layer.
 
 
 
-<h3 id="bias_parameterization"><code>bias_parameterization</code></h3>
+<h3 id="bias_parameterizer"><code>bias_parameterizer</code></h3>
 
 
 
@@ -417,7 +416,7 @@ Input shape, as an integer shape tuple
 
 
 
-<h3 id="kernel_parameterization"><code>kernel_parameterization</code></h3>
+<h3 id="kernel_parameterizer"><code>kernel_parameterizer</code></h3>
 
 
 

docs/api_docs/python/tfc/SignalConv2D.md

@@ -4,7 +4,7 @@
 <meta itemprop="property" content="activity_regularizer"/>
 <meta itemprop="property" content="bias"/>
 <meta itemprop="property" content="bias_initializer"/>
-<meta itemprop="property" content="bias_parameterization"/>
+<meta itemprop="property" content="bias_parameterizer"/>
 <meta itemprop="property" content="bias_regularizer"/>
 <meta itemprop="property" content="channel_separable"/>
 <meta itemprop="property" content="corr"/>
@@ -19,7 +19,7 @@
 <meta itemprop="property" content="input_shape"/>
 <meta itemprop="property" content="kernel"/>
 <meta itemprop="property" content="kernel_initializer"/>
-<meta itemprop="property" content="kernel_parameterization"/>
+<meta itemprop="property" content="kernel_parameterizer"/>
 <meta itemprop="property" content="kernel_regularizer"/>
 <meta itemprop="property" content="kernel_support"/>
 <meta itemprop="property" content="losses"/>
@@ -245,11 +245,10 @@ will throw an exception.
 * <b>`kernel_regularizer`</b>: Optional regularizer for the filter kernel.
 * <b>`bias_regularizer`</b>: Optional regularizer for the bias vector.
 * <b>`activity_regularizer`</b>: Regularizer function for the output.
-* <b>`kernel_parameterization`</b>: Reparameterization applied to filter kernel. If not
-    `None`, must be a parameterization object. Defaults to RDFT
-    parameterization.
-* <b>`bias_parameterization`</b>: Reparameterization applied to bias. If not `None`,
-    must be a parameterization object.
+* <b>`kernel_parameterizer`</b>: Reparameterization applied to filter kernel. If not
+    `None`, must be a `Parameterizer` object. Defaults to `RDFTParameterizer`.
+* <b>`bias_parameterizer`</b>: Reparameterization applied to bias. If not `None`, must
+    be a `Parameterizer` object.
 * <b>`trainable`</b>: Boolean. Whether the layer should be trained.
 * <b>`name`</b>: String. The name of the layer.
 * <b>`dtype`</b>: Default dtype of the layer's parameters (default of `None` means use
@@ -272,8 +271,8 @@ Read-only properties:
 * <b>`kernel_regularizer`</b>: See above.
 * <b>`bias_regularizer`</b>: See above.
 * <b>`activity_regularizer`</b>: See above.
-* <b>`kernel_parameterization`</b>: See above.
-* <b>`bias_parameterization`</b>: See above.
+* <b>`kernel_parameterizer`</b>: See above.
+* <b>`bias_parameterizer`</b>: See above.
 * <b>`name`</b>: See above.
 * <b>`dtype`</b>: See above.
 * <b>`kernel`</b>: `Tensor`-like object. The convolution kernel as applied to the
@@ -309,7 +308,7 @@ Optional regularizer function for the output of this layer.
 
 
 
-<h3 id="bias_parameterization"><code>bias_parameterization</code></h3>
+<h3 id="bias_parameterizer"><code>bias_parameterizer</code></h3>
 
 
 
@@ -417,7 +416,7 @@ Input shape, as an integer shape tuple
 
 
 
-<h3 id="kernel_parameterization"><code>kernel_parameterization</code></h3>
+<h3 id="kernel_parameterizer"><code>kernel_parameterizer</code></h3>
 
 
 

docs/api_docs/python/tfc/SignalConv3D.md

@@ -4,7 +4,7 @@
 <meta itemprop="property" content="activity_regularizer"/>
 <meta itemprop="property" content="bias"/>
 <meta itemprop="property" content="bias_initializer"/>
-<meta itemprop="property" content="bias_parameterization"/>
+<meta itemprop="property" content="bias_parameterizer"/>
 <meta itemprop="property" content="bias_regularizer"/>
 <meta itemprop="property" content="channel_separable"/>
 <meta itemprop="property" content="corr"/>
@@ -19,7 +19,7 @@
 <meta itemprop="property" content="input_shape"/>
 <meta itemprop="property" content="kernel"/>
 <meta itemprop="property" content="kernel_initializer"/>
-<meta itemprop="property" content="kernel_parameterization"/>
+<meta itemprop="property" content="kernel_parameterizer"/>
 <meta itemprop="property" content="kernel_regularizer"/>
 <meta itemprop="property" content="kernel_support"/>
 <meta itemprop="property" content="losses"/>
@@ -245,11 +245,10 @@ will throw an exception.
 * <b>`kernel_regularizer`</b>: Optional regularizer for the filter kernel.
 * <b>`bias_regularizer`</b>: Optional regularizer for the bias vector.
 * <b>`activity_regularizer`</b>: Regularizer function for the output.
-* <b>`kernel_parameterization`</b>: Reparameterization applied to filter kernel. If not
-    `None`, must be a parameterization object. Defaults to RDFT
-    parameterization.
-* <b>`bias_parameterization`</b>: Reparameterization applied to bias. If not `None`,
-    must be a parameterization object.
+* <b>`kernel_parameterizer`</b>: Reparameterization applied to filter kernel. If not
+    `None`, must be a `Parameterizer` object. Defaults to `RDFTParameterizer`.
+* <b>`bias_parameterizer`</b>: Reparameterization applied to bias. If not `None`, must
+    be a `Parameterizer` object.
 * <b>`trainable`</b>: Boolean. Whether the layer should be trained.
 * <b>`name`</b>: String. The name of the layer.
 * <b>`dtype`</b>: Default dtype of the layer's parameters (default of `None` means use
@@ -272,8 +271,8 @@ Read-only properties:
 * <b>`kernel_regularizer`</b>: See above.
 * <b>`bias_regularizer`</b>: See above.
 * <b>`activity_regularizer`</b>: See above.
-* <b>`kernel_parameterization`</b>: See above.
-* <b>`bias_parameterization`</b>: See above.
+* <b>`kernel_parameterizer`</b>: See above.
+* <b>`bias_parameterizer`</b>: See above.
 * <b>`name`</b>: See above.
 * <b>`dtype`</b>: See above.
 * <b>`kernel`</b>: `Tensor`-like object. The convolution kernel as applied to the
@@ -309,7 +308,7 @@ Optional regularizer function for the output of this layer.
 
 
 
-<h3 id="bias_parameterization"><code>bias_parameterization</code></h3>
+<h3 id="bias_parameterizer"><code>bias_parameterizer</code></h3>
 
 
 
@@ -417,7 +416,7 @@ Input shape, as an integer shape tuple
 
 
 
-<h3 id="kernel_parameterization"><code>kernel_parameterization</code></h3>
+<h3 id="kernel_parameterizer"><code>kernel_parameterizer</code></h3>
 
 
 

docs/api_docs/python/tfc/irdft_matrix.md

@@ -20,7 +20,7 @@ Example code for kernel creation, assuming 2D kernels:
 
 ```
 def create_kernel(init):
-  shape = init.get_shape().as_list()
+  shape = init.shape.as_list()
   matrix = irdft_matrix(shape[:2])
   init = tf.reshape(init, (shape[0] * shape[1], shape[2] * shape[3]))
   init = tf.matmul(tf.transpose(matrix), init)

tensorflow_compression/python/layers/gdn.py

@@ -75,10 +75,10 @@ class GDN(base.Layer):
       A good default setting is somewhere between 0 and 0.5.
     data_format: Format of input tensor. Currently supports `'channels_first'`
       and `'channels_last'`.
-    beta_parameterization: Parameterizer object for beta parameter. Defaults
-      to NonnegativeParameterizer with a minimum value of 1e-6.
-    gamma_parameterization: Parameterizer object for gamma parameter.
-      Defaults to NonnegativeParameterizer with a minimum value of 0.
+    beta_parameterizer: Reparameterization for beta parameter. Defaults to
+      `NonnegativeParameterizer` with a minimum value of `1e-6`.
+    gamma_parameterizer: Reparameterization for gamma parameter. Defaults to
+      `NonnegativeParameterizer` with a minimum value of `0`.
     activity_regularizer: Regularizer function for the output.
     trainable: Boolean, if `True`, also add variables to the graph collection
       `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
@@ -100,8 +100,8 @@ def __init__(self,
                rectify=False,
                gamma_init=.1,
                data_format="channels_last",
-               beta_parameterization=_default_beta_param,
-               gamma_parameterization=_default_gamma_param,
+               beta_parameterizer=_default_beta_param,
+               gamma_parameterizer=_default_gamma_param,
                activity_regularizer=None,
                trainable=True,
                name=None,
@@ -113,8 +113,8 @@ def __init__(self,
     self.rectify = bool(rectify)
     self._gamma_init = float(gamma_init)
     self.data_format = data_format
-    self._beta_parameterization = beta_parameterization
-    self._gamma_parameterization = gamma_parameterization
+    self._beta_parameterizer = beta_parameterizer
+    self._gamma_parameterizer = gamma_parameterizer
     self._channel_axis()  # trigger ValueError early
     self.input_spec = base.InputSpec(min_ndim=2)
 
@@ -136,11 +136,11 @@ def build(self, input_shape):
     self.input_spec = base.InputSpec(ndim=input_shape.ndims,
                                      axes={channel_axis: num_channels})
 
-    self.beta = self._beta_parameterization(
+    self.beta = self._beta_parameterizer(
         name="beta", shape=[num_channels], dtype=self.dtype,
         getter=self.add_variable, initializer=init_ops.Ones())
 
-    self.gamma = self._gamma_parameterization(
+    self.gamma = self._gamma_parameterizer(
         name="gamma", shape=[num_channels, num_channels], dtype=self.dtype,
         getter=self.add_variable,
         initializer=init_ops.Identity(gain=self._gamma_init))

tensorflow_compression/python/layers/signal_conv.py

@@ -213,11 +213,10 @@ class _SignalConv(base.Layer):
     kernel_regularizer: Optional regularizer for the filter kernel.
     bias_regularizer: Optional regularizer for the bias vector.
     activity_regularizer: Regularizer function for the output.
-    kernel_parameterization: Reparameterization applied to filter kernel. If not
-      `None`, must be a parameterization object. Defaults to RDFT
-      parameterization.
-    bias_parameterization: Reparameterization applied to bias. If not `None`,
-      must be a parameterization object.
+    kernel_parameterizer: Reparameterization applied to filter kernel. If not
+      `None`, must be a `Parameterizer` object. Defaults to `RDFTParameterizer`.
+    bias_parameterizer: Reparameterization applied to bias. If not `None`, must
+      be a `Parameterizer` object.
     trainable: Boolean. Whether the layer should be trained.
     name: String. The name of the layer.
     dtype: Default dtype of the layer's parameters (default of `None` means use
@@ -240,8 +239,8 @@ class _SignalConv(base.Layer):
     kernel_regularizer: See above.
     bias_regularizer: See above.
     activity_regularizer: See above.
-    kernel_parameterization: See above.
-    bias_parameterization: See above.
+    kernel_parameterizer: See above.
+    bias_parameterizer: See above.
     name: See above.
     dtype: See above.
     kernel: `Tensor`-like object. The convolution kernel as applied to the
@@ -268,8 +267,8 @@ def __init__(self, rank, filters, kernel_support,
                kernel_initializer=init_ops.VarianceScaling(),
                bias_initializer=init_ops.Zeros(),
                kernel_regularizer=None, bias_regularizer=None,
-               kernel_parameterization=parameterizers.RDFTParameterizer(),
-               bias_parameterization=None,
+               kernel_parameterizer=parameterizers.RDFTParameterizer(),
+               bias_parameterizer=None,
                **kwargs):
     super(_SignalConv, self).__init__(**kwargs)
     self._rank = int(rank)
@@ -299,8 +298,8 @@ def __init__(self, rank, filters, kernel_support,
     self._bias_initializer = bias_initializer
     self._kernel_regularizer = kernel_regularizer
     self._bias_regularizer = bias_regularizer
-    self._kernel_parameterization = kernel_parameterization
-    self._bias_parameterization = bias_parameterization
+    self._kernel_parameterizer = kernel_parameterizer
+    self._bias_parameterizer = bias_parameterizer
     self.input_spec = base.InputSpec(ndim=self._rank + 2)
 
   @property
@@ -364,12 +363,12 @@ def bias_regularizer(self):
     return self._bias_regularizer
 
   @property
-  def kernel_parameterization(self):
-    return self._kernel_parameterization
+  def kernel_parameterizer(self):
+    return self._kernel_parameterizer
 
   @property
-  def bias_parameterization(self):
-    return self._bias_parameterization
+  def bias_parameterizer(self):
+    return self._bias_parameterizer
 
   @property
   def kernel(self):
@@ -401,21 +400,21 @@ def build(self, input_shape):
     else:
       output_channels = self.filters
 
-    if self.kernel_parameterization is None:
+    if self.kernel_parameterizer is None:
       getter = self.add_variable
     else:
       getter = functools.partial(
-          self.kernel_parameterization, getter=self.add_variable)
+          self.kernel_parameterizer, getter=self.add_variable)
     self._kernel = getter(
         name="kernel", shape=kernel_shape, dtype=self.dtype,
         initializer=self.kernel_initializer,
         regularizer=self.kernel_regularizer)
 
-    if self.bias_parameterization is None:
+    if self.bias_parameterizer is None:
       getter = self.add_variable
     else:
       getter = functools.partial(
-          self.bias_parameterization, getter=self.add_variable)
+          self.bias_parameterizer, getter=self.add_variable)
     self._bias = None if not self.use_bias else getter(
         name="bias", shape=(output_channels,), dtype=self.dtype,
         initializer=self.bias_initializer, regularizer=self.bias_regularizer)

tensorflow_compression/python/layers/signal_conv_test.py

@@ -110,7 +110,7 @@ def run_valid(self, batch, input_support, channels, filters, kernel_support,
         strides_up=strides_up, padding="valid", extra_pad_end=extra_pad_end,
         channel_separable=channel_separable, data_format=data_format,
         activation=activation, use_bias=use_bias,
-        kernel_parameterization=tf_kernel)
+        kernel_parameterizer=tf_kernel)
     tf_outputs = layer(tf_inputs)
     with self.test_session() as sess:
       sess.run(tf.global_variables_initializer())
@@ -159,7 +159,7 @@ def run_same(self, batch, input_support, channels, filters, kernel_support,
         strides_up=strides_up, padding=padding, extra_pad_end=extra_pad_end,
         channel_separable=channel_separable, data_format=data_format,
         activation=activation, use_bias=use_bias,
-        kernel_parameterization=tf_kernel)
+        kernel_parameterizer=tf_kernel)
     tf_outputs = layer(tf_inputs)
     with self.test_session() as sess:
       sess.run(tf.global_variables_initializer())

tensorflow_compression/python/ops/spectral_ops.py

@@ -42,7 +42,7 @@ def irdft_matrix(shape, dtype=dtypes.float32):
 
   ```
   def create_kernel(init):
-    shape = init.get_shape().as_list()
+    shape = init.shape.as_list()
     matrix = irdft_matrix(shape[:2])
     init = tf.reshape(init, (shape[0] * shape[1], shape[2] * shape[3]))
     init = tf.matmul(tf.transpose(matrix), init)