Move _standardize_args to recurrent, remove duplication

keras/layers/convolutional_recurrent.py

@@ -11,6 +11,7 @@
 from .. import regularizers
 from .. import constraints
 from .recurrent import _generate_dropout_mask
+from .recurrent import _standardize_args
 
 import numpy as np
 import warnings
@@ -270,8 +271,8 @@ def get_initial_state(self, inputs):
             return [initial_state]
 
     def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
-        inputs, initial_state, constants = self._standardize_args(
-            inputs, initial_state, constants)
+        inputs, initial_state, constants = _standardize_args(
+            inputs, initial_state, constants, self._num_constants)
 
         if initial_state is None and constants is None:
             return super(ConvRNN2D, self).__call__(inputs, **kwargs)

keras/layers/recurrent.py

@@ -493,8 +493,8 @@ def get_initial_state(self, inputs):
             return [K.tile(initial_state, [1, self.cell.state_size])]
 
     def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
-        inputs, initial_state, constants = self._standardize_args(
-            inputs, initial_state, constants)
+        inputs, initial_state, constants = _standardize_args(
+            inputs, initial_state, constants, self._num_constants)
 
         if initial_state is None and constants is None:
             return super(RNN, self).__call__(inputs, **kwargs)
@@ -633,46 +633,6 @@ def step(inputs, states):
         else:
             return output
 
-    def _standardize_args(self, inputs, initial_state, constants):
-        """Standardize `__call__` to a single list of tensor inputs.
-
-        When running a model loaded from file, the input tensors
-        `initial_state` and `constants` can be passed to `RNN.__call__` as part
-        of `inputs` instead of by the dedicated keyword arguments. This method
-        makes sure the arguments are separated and that `initial_state` and
-        `constants` are lists of tensors (or None).
-
-        # Arguments
-            inputs: tensor or list/tuple of tensors
-            initial_state: tensor or list of tensors or None
-            constants: tensor or list of tensors or None
-
-        # Returns
-            inputs: tensor
-            initial_state: list of tensors or None
-            constants: list of tensors or None
-        """
-        if isinstance(inputs, list):
-            assert initial_state is None and constants is None
-            if self._num_constants is not None:
-                constants = inputs[-self._num_constants:]
-                inputs = inputs[:-self._num_constants]
-            if len(inputs) > 1:
-                initial_state = inputs[1:]
-            inputs = inputs[0]
-
-        def to_list_or_none(x):
-            if x is None or isinstance(x, list):
-                return x
-            if isinstance(x, tuple):
-                return list(x)
-            return [x]
-
-        initial_state = to_list_or_none(initial_state)
-        constants = to_list_or_none(constants)
-
-        return inputs, initial_state, constants
-
     def reset_states(self, states=None):
         if not self.stateful:
             raise AttributeError('Layer must be stateful.')
@@ -2272,3 +2232,44 @@ def dropped_inputs():
         dropped_inputs,
         ones,
         training=training)
+
+
+def _standardize_args(inputs, initial_state, constants, num_constants):
+    """Standardize `__call__` to a single list of tensor inputs.
+
+    When running a model loaded from file, the input tensors
+    `initial_state` and `constants` can be passed to `RNN.__call__` as part
+    of `inputs` instead of by the dedicated keyword arguments. This method
+    makes sure the arguments are separated and that `initial_state` and
+    `constants` are lists of tensors (or None).
+
+    # Arguments
+        inputs: tensor or list/tuple of tensors
+        initial_state: tensor or list of tensors or None
+        constants: tensor or list of tensors or None
+
+    # Returns
+        inputs: tensor
+        initial_state: list of tensors or None
+        constants: list of tensors or None
+    """
+    if isinstance(inputs, list):
+        assert initial_state is None and constants is None
+        if num_constants is not None:
+            constants = inputs[-num_constants:]
+            inputs = inputs[:-num_constants]
+        if len(inputs) > 1:
+            initial_state = inputs[1:]
+        inputs = inputs[0]
+
+    def to_list_or_none(x):
+        if x is None or isinstance(x, list):
+            return x
+        if isinstance(x, tuple):
+            return list(x)
+        return [x]
+
+    initial_state = to_list_or_none(initial_state)
+    constants = to_list_or_none(constants)
+
+    return inputs, initial_state, constants

keras/layers/wrappers.py

@@ -12,6 +12,8 @@
 from ..utils.generic_utils import has_arg
 from .. import backend as K
 
+from . import recurrent
+
 
 class Wrapper(Layer):
     """Abstract wrapper base class.
@@ -316,8 +318,8 @@ def compute_output_shape(self, input_shape):
         return output_shape
 
     def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
-        inputs, initial_state, constants = self._standardize_args(
-            inputs, initial_state, constants)
+        inputs, initial_state, constants = recurrent._standardize_args(
+            inputs, initial_state, constants, self._num_constants)
 
         if initial_state is None and constants is None:
             return super(Bidirectional, self).__call__(inputs, **kwargs)
@@ -434,48 +436,6 @@ def call(self,
             return [output] + states
         return output
 
-    def _standardize_args(self, inputs, initial_state, constants):
-        """Standardize `__call__` to a single list of tensor inputs.
-
-        This method is taken from `RNN` as it is.
-
-        When running a model loaded from file, the input tensors
-        `initial_state` and `constants` can be passed to `RNN.__call__` as part
-        of `inputs` instead of by the dedicated keyword arguments. This method
-        makes sure the arguments are separated and that `initial_state` and
-        `constants` are lists of tensors (or None).
-
-        # Arguments
-            inputs: tensor or list/tuple of tensors
-            initial_state: tensor or list of tensors or None
-            constants: tensor or list of tensors or None
-
-        # Returns
-            inputs: tensor
-            initial_state: list of tensors or None
-            constants: list of tensors or None
-        """
-        if isinstance(inputs, list):
-            assert initial_state is None and constants is None
-            if self._num_constants is not None:
-                constants = inputs[-self._num_constants:]
-                inputs = inputs[:-self._num_constants]
-            if len(inputs) > 1:
-                initial_state = inputs[1:]
-            inputs = inputs[0]
-
-        def to_list_or_none(x):
-            if x is None or isinstance(x, list):
-                return x
-            if isinstance(x, tuple):
-                return list(x)
-            return [x]
-
-        initial_state = to_list_or_none(initial_state)
-        constants = to_list_or_none(constants)
-
-        return inputs, initial_state, constants
-
     def reset_states(self):
         self.forward_layer.reset_states()
         self.backward_layer.reset_states()