Remove tf.keras.utils.convert_all_kernels_in_model as its deprecation date is overdue.

PiperOrigin-RevId: 331570917
Change-Id: I60183861ac4e04acad041c631c541189a6f9bfe8

tensorflow/python/keras/saving/hdf5_format.py

@@ -30,7 +30,6 @@ from tensorflow.python.keras import optimizers
 from tensorflow.python.keras.saving import model_config as model_config_lib
 from tensorflow.python.keras.saving import saving_utils
 from tensorflow.python.keras.saving.saved_model import json_utils
-from tensorflow.python.keras.utils import conv_utils
 from tensorflow.python.keras.utils.generic_utils import LazyLoader
 from tensorflow.python.keras.utils.io_utils import ask_to_proceed_with_overwrite
 from tensorflow.python.ops import variables as variables_module
@@ -402,10 +401,6 @@ def preprocess_weights_for_loading(layer,
 
   conv_layers = ['Conv1D', 'Conv2D', 'Conv3D', 'Conv2DTranspose', 'ConvLSTM2D']
   if layer.__class__.__name__ in conv_layers:
-    if original_backend == 'theano':
-      weights[0] = conv_utils.convert_kernel(weights[0])
-      if layer.__class__.__name__ == 'ConvLSTM2D':
-        weights[1] = conv_utils.convert_kernel(weights[1])
     if K.int_shape(layer.weights[0]) != weights[0].shape:
       weights[0] = np.transpose(weights[0], (3, 2, 0, 1))
       if layer.__class__.__name__ == 'ConvLSTM2D':

tensorflow/python/keras/utils/all_utils.py

@@ -35,7 +35,6 @@ from tensorflow.python.keras.utils.generic_utils import get_custom_objects
 from tensorflow.python.keras.utils.generic_utils import Progbar
 from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
 from tensorflow.python.keras.utils.io_utils import HDF5Matrix
-from tensorflow.python.keras.utils.layer_utils import convert_all_kernels_in_model
 from tensorflow.python.keras.utils.layer_utils import get_source_inputs
 from tensorflow.python.keras.utils.multi_gpu_utils import multi_gpu_model
 from tensorflow.python.keras.utils.np_utils import normalize

tensorflow/python/keras/utils/conv_utils.py

@@ -208,31 +208,6 @@ def normalize_padding(value):
   return padding
 
 
-def convert_kernel(kernel):
-  """Converts a Numpy kernel matrix from Theano format to TensorFlow format.
-
-  Also works reciprocally, since the transformation is its own inverse.
-
-  This is used for converting legacy Theano-saved model files.
-
-  Arguments:
-      kernel: Numpy array (3D, 4D or 5D).
-
-  Returns:
-      The converted kernel.
-
-  Raises:
-      ValueError: in case of invalid kernel shape or invalid data_format.
-  """
-  kernel = np.asarray(kernel)
-  if not 3 <= kernel.ndim <= 5:
-    raise ValueError('Invalid kernel shape:', kernel.shape)
-  slices = [slice(None, None, -1) for _ in range(kernel.ndim)]
-  no_flip = (slice(None, None), slice(None, None))
-  slices[-2:] = no_flip
-  return np.copy(kernel[slices])
-
-
 def conv_kernel_mask(input_shape, kernel_shape, strides, padding):
   """Compute a mask representing the connectivity of a convolution operation.
 

tensorflow/python/keras/utils/layer_utils.py

@@ -25,9 +25,6 @@ import weakref
 import numpy as np
 import six
 
-from tensorflow.python.keras import backend as K
-from tensorflow.python.keras.utils.conv_utils import convert_kernel
-from tensorflow.python.util import deprecation
 from tensorflow.python.util import nest
 from tensorflow.python.util.tf_export import keras_export
 
@@ -328,37 +325,6 @@ def gather_non_trainable_weights(trainable, sub_layers, extra_variables):
   return weights + non_trainable_extra_variables
 
 
-@deprecation.deprecated('2020-06-23',
-                        'The Theano kernel format is legacy; '
-                        'this utility will be removed.')
-@keras_export('keras.utils.convert_all_kernels_in_model')
-def convert_all_kernels_in_model(model):
-  """Converts all convolution kernels in a model from Theano to TensorFlow.
-
-  Also works from TensorFlow to Theano.
-
-  This is used for converting legacy Theano-saved model files.
-
-  Arguments:
-      model: target model for the conversion.
-  """
-  # Note: SeparableConvolution not included
-  # since only supported by TF.
-  conv_classes = {
-      'Conv1D',
-      'Conv2D',
-      'Conv3D',
-      'Conv2DTranspose',
-  }
-  to_assign = []
-  for layer in model.layers:
-    if layer.__class__.__name__ in conv_classes:
-      original_kernel = K.get_value(layer.kernel)
-      converted_kernel = convert_kernel(original_kernel)
-      to_assign.append((layer.kernel, converted_kernel))
-  K.batch_set_value(to_assign)
-
-
 def convert_dense_weights_data_format(dense,
                                       previous_feature_map_shape,
                                       target_data_format='channels_first'):

tensorflow/tools/api/golden/v1/tensorflow.keras.utils.pbtxt

@@ -32,10 +32,6 @@ tf_module {
     name: "custom_object_scope"
     mtype: "<type \'type\'>"
   }
-  member_method {
-    name: "convert_all_kernels_in_model"
-    argspec: "args=[\'model\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "deserialize_keras_object"
     argspec: "args=[\'identifier\', \'module_objects\', \'custom_objects\', \'printable_module_name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'object\'], "

tensorflow/tools/api/golden/v2/tensorflow.keras.utils.pbtxt

@@ -32,10 +32,6 @@ tf_module {
     name: "custom_object_scope"
     mtype: "<type \'type\'>"
   }
-  member_method {
-    name: "convert_all_kernels_in_model"
-    argspec: "args=[\'model\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "deserialize_keras_object"
     argspec: "args=[\'identifier\', \'module_objects\', \'custom_objects\', \'printable_module_name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'object\'], "