No public description

PiperOrigin-RevId: 712590121
Change-Id: I5998e535ee492df506ca81a777c52d39a4453cab

qkeras/autoqkeras/forgiving_metrics/forgiving_bits.py

@@ -35,7 +35,7 @@ def __init__(
     self.ref_size = {}
     self.config = config if config else {}
 
-    super(ForgivingFactorBits, self).__init__(delta_p, delta_n, rate)
+    super().__init__(delta_p, delta_n, rate)
 
   def _param_size(self, layer):
     """Computes size of parameters of a layer in bits."""

qkeras/autoqkeras/forgiving_metrics/forgiving_energy.py

@@ -50,7 +50,7 @@ def __init__(self, delta_p, delta_n, rate, stress=1.0, **kwargs):
     #   energy calculation
     # keras_layer_quantizer: quantizer for keras layers in hybrid models
 
-    super(ForgivingFactorPower, self).__init__(delta_p, delta_n, rate)
+    super().__init__(delta_p, delta_n, rate)
 
     self.stress = stress
     # process: horowitz... - must be present in config_json

qkeras/callbacks.py

@@ -58,7 +58,7 @@ def __init__(self,
         not.
       log_dir: Str. log directory to save qnoise_factor every epoch end.
     """
-    super(QNoiseScheduler, self).__init__()
+    super().__init__()
 
     self.start = start
     self.finish = finish

qkeras/qconv2d_batchnorm.py

@@ -109,7 +109,7 @@ def __init__(
     """
 
     # intialization the qconv2d part of the composite layer
-    super(QConv2DBatchnorm, self).__init__(
+    super().__init__(
         filters=filters,
         kernel_size=kernel_size,
         strides=strides,
@@ -126,7 +126,8 @@ def __init__(
         bias_constraint=bias_constraint,
         kernel_quantizer=kernel_quantizer,
         bias_quantizer=bias_quantizer,
-        **kwargs)
+        **kwargs
+    )
 
     # initialization of batchnorm part of the composite layer
     self.batchnorm = layers.BatchNormalization(

qkeras/qconvolutional.py

@@ -170,7 +170,7 @@ def __init__(self,
     if activation is not None:
       activation = get_quantizer(activation)
 
-    super(QConv1D, self).__init__(
+    super().__init__(
         filters=filters,
         kernel_size=kernel_size,
         strides=strides,
@@ -185,7 +185,8 @@ def __init__(self,
         activity_regularizer=activity_regularizer,
         kernel_constraint=kernel_constraint,
         bias_constraint=bias_constraint,
-        **kwargs)
+        **kwargs
+    )
 
   def call(self, inputs):
     if self.kernel_quantizer:
@@ -323,7 +324,7 @@ def __init__(self,
     if activation is not None:
       activation = get_quantizer(activation)
 
-    super(QConv2D, self).__init__(
+    super().__init__(
         filters=filters,
         kernel_size=kernel_size,
         strides=strides,
@@ -339,7 +340,8 @@ def __init__(self,
         activity_regularizer=activity_regularizer,
         kernel_constraint=kernel_constraint,
         bias_constraint=bias_constraint,
-        **kwargs)
+        **kwargs
+    )
 
   def call(self, inputs):
     if self.kernel_quantizer:
@@ -463,7 +465,7 @@ def __init__(self,
     if activation is not None:
       activation = get_quantizer(activation)
 
-    super(QConv2DTranspose, self).__init__(
+    super().__init__(
         filters=filters,
         kernel_size=kernel_size,
         strides=strides,
@@ -480,7 +482,8 @@ def __init__(self,
         activity_regularizer=activity_regularizer,
         kernel_constraint=kernel_constraint,
         bias_constraint=bias_constraint,
-        **kwargs)
+        **kwargs
+    )
 
   def call(self, inputs):
     inputs_shape = array_ops.shape(inputs)
@@ -650,7 +653,7 @@ def __init__(self,
     if activation is not None:
       activation = get_quantizer(activation)
 
-    super(QSeparableConv1D, self).__init__(
+    super().__init__(
         filters=filters,
         kernel_size=kernel_size,
         strides=strides,
@@ -670,7 +673,8 @@ def __init__(self,
         depthwise_constraint=constraints.get(depthwise_constraint),
         pointwise_constraint=constraints.get(pointwise_constraint),
         bias_constraint=constraints.get(bias_constraint),
-        **kwargs)
+        **kwargs
+    )
 
   def call(self, inputs):
     if self.padding == 'causal':
@@ -828,7 +832,7 @@ def __init__(self,
     if activation is not None:
       activation = get_quantizer(activation)
 
-    super(QSeparableConv2D, self).__init__(
+    super().__init__(
         filters=filters,
         kernel_size=kernel_size,
         strides=strides,
@@ -848,7 +852,8 @@ def __init__(self,
         depthwise_constraint=constraints.get(depthwise_constraint),
         pointwise_constraint=constraints.get(pointwise_constraint),
         bias_constraint=constraints.get(bias_constraint),
-        **kwargs)
+        **kwargs
+    )
 
   def call(self, inputs):
     # Apply the actual ops.
@@ -986,7 +991,7 @@ def __init__(self,
     if activation is not None:
       activation = get_quantizer(activation)
 
-    super(QDepthwiseConv2D, self).__init__(
+    super().__init__(
         kernel_size=kernel_size,
         strides=strides,
         padding=padding,
@@ -1002,7 +1007,8 @@ def __init__(self,
         depthwise_constraint=depthwise_constraint,
         bias_constraint=bias_constraint,
         dilation_rate=dilation_rate,
-        **kwargs)
+        **kwargs
+    )
 
   def build(self, input_shape):
     if len(input_shape) < 4:

qkeras/qdepthwiseconv2d_batchnorm.py

@@ -107,7 +107,7 @@ def __init__(
     """
 
     # intialization the QDepthwiseConv2d part of the composite layer
-    super(QDepthwiseConv2DBatchnorm, self).__init__(
+    super().__init__(
         kernel_size=kernel_size,
         strides=strides,
         padding=padding,
@@ -127,7 +127,8 @@ def __init__(
         bias_quantizer=bias_quantizer,
         depthwise_range=depthwise_range,
         bias_range=bias_range,
-        **kwargs)
+        **kwargs
+    )
 
     # initialization of batchnorm part of the composite layer
     self.batchnorm = layers.BatchNormalization(

qkeras/qlayers.py

@@ -155,7 +155,7 @@ class QActivation(Layer, PrunableLayer):
   # object if string is given as activation.
   def __init__(self, activation, **kwargs):
 
-    super(QActivation, self).__init__(**kwargs)
+    super().__init__(**kwargs)
 
     self.activation = activation
 
@@ -267,7 +267,7 @@ def __init__(self,
         that this param is ignored if the activation is not quantized_relu
       **kwargs: Args passed to the Layer class.
     """
-    super(QAdaptiveActivation, self).__init__(**kwargs)
+    super().__init__(**kwargs)
 
     self.total_bits = total_bits
     self.symmetric = symmetric
@@ -630,7 +630,7 @@ def __init__(self,
     if activation is not None:
       activation = get_quantizer(activation)
 
-    super(QDense, self).__init__(
+    super().__init__(
         units=units,
         activation=activation,
         use_bias=use_bias,
@@ -641,7 +641,8 @@ def __init__(self,
         activity_regularizer=activity_regularizer,
         kernel_constraint=kernel_constraint,
         bias_constraint=bias_constraint,
-        **kwargs)
+        **kwargs,
+    )
 
   def call(self, inputs):
     if self.kernel_quantizer:

qkeras/qnormalization.py

@@ -154,7 +154,7 @@ def __init__(
                     'in qkeras qnormalization.py.')
       del kwargs['adjustment']
 
-    super(QBatchNormalization, self).__init__(
+    super().__init__(
         axis=axis,
         momentum=momentum,
         epsilon=epsilon,
@@ -172,7 +172,8 @@ def __init__(
         renorm=False,
         virtual_batch_size=None,
         adjustment=None,
-        **kwargs)
+        **kwargs
+    )
 
   def call(self, inputs, training=None):
     if self.scale and self.gamma_quantizer:

qkeras/qpooling.py

@@ -45,9 +45,13 @@ def __init__(self, pool_size=(2, 2),
     else:
       self.activation = activation
 
-    super(QAveragePooling2D, self).__init__(
-        pool_size=pool_size, strides=strides, padding=padding,
-        data_format=data_format, **kwargs)
+    super().__init__(
+        pool_size=pool_size,
+        strides=strides,
+        padding=padding,
+        data_format=data_format,
+        **kwargs
+    )
 
   def call(self, inputs):
     """Performs quantized AveragePooling followed by QActivation.
@@ -142,8 +146,7 @@ def __init__(self, data_format=None,
     else:
       self.activation = activation
 
-    super(QGlobalAveragePooling2D, self).__init__(
-        data_format=data_format, **kwargs)
+    super().__init__(data_format=data_format, **kwargs)
 
   def compute_pooling_area(self, input_shape):
     if not isinstance(input_shape, tuple):