Refactoring: Used the function `normalize_data_format` to remove some code. (#10690)

* Used the function `normalize_data_format` to remove some code. Moved the implementation to keras/backend/common.py.

* Changed all conv_utils.normalize_data_format into K.normalize_data_format

docs/autogen.py

@@ -99,6 +99,7 @@ EXCLUDE = {
     'deserialize',
     'get',
     'set_image_dim_ordering',
+    'normalize_data_format',
     'image_dim_ordering',
     'get_variable_shape',
 }

keras/backend/__init__.py

@@ -11,6 +11,7 @@ from .common import set_floatx
 from .common import cast_to_floatx
 from .common import image_data_format
 from .common import set_image_data_format
+from .common import normalize_data_format
 
 # Set Keras base dir path given KERAS_HOME env variable, if applicable.
 # Otherwise either ~/.keras or /tmp.

keras/backend/cntk_backend.py

@@ -4,7 +4,10 @@ from __future__ import print_function
 
 import cntk as C
 import numpy as np
-from .common import floatx, epsilon, image_dim_ordering, image_data_format
+from .common import floatx
+from .common import epsilon
+from .common import image_data_format
+from .common import normalize_data_format
 from collections import defaultdict
 from contextlib import contextmanager
 import warnings
@@ -184,10 +187,7 @@ def variable(value, dtype=None, name=None, constraint=None):
 
 
 def bias_add(x, bias, data_format=None):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     dims = len(x.shape)
     if dims > 0 and x.shape[0] == C.InferredDimension:
@@ -1480,10 +1480,7 @@ def hard_sigmoid(x):
 
 def conv1d(x, kernel, strides=1, padding='valid',
            data_format=None, dilation_rate=1):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     if padding == 'causal':
         # causal (dilated) convolution:
@@ -1512,10 +1509,7 @@ def conv1d(x, kernel, strides=1, padding='valid',
 
 def conv2d(x, kernel, strides=(1, 1), padding='valid',
            data_format=None, dilation_rate=(1, 1)):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     x = _preprocess_conv2d_input(x, data_format)
     kernel = _preprocess_conv2d_kernel(kernel, data_format)
@@ -1546,10 +1540,7 @@ def conv2d(x, kernel, strides=(1, 1), padding='valid',
 
 def separable_conv1d(x, depthwise_kernel, pointwise_kernel, strides=1,
                      padding='valid', data_format=None, dilation_rate=1):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
     if isinstance(strides, int):
         strides = (strides,)
     if isinstance(dilation_rate, int):
@@ -1599,10 +1590,7 @@ def separable_conv1d(x, depthwise_kernel, pointwise_kernel, strides=1,
 
 def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1),
                      padding='valid', data_format=None, dilation_rate=(1, 1)):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     x = _preprocess_conv2d_input(x, data_format)
     depthwise_kernel = _preprocess_conv2d_kernel(depthwise_kernel, data_format)
@@ -1637,10 +1625,7 @@ def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1),
 
 def depthwise_conv2d(x, depthwise_kernel, strides=(1, 1), padding='valid',
                      data_format=None, dilation_rate=(1, 1)):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     x = _preprocess_conv2d_input(x, data_format)
     depthwise_kernel = _preprocess_conv2d_kernel(depthwise_kernel, data_format)
@@ -1668,10 +1653,7 @@ def depthwise_conv2d(x, depthwise_kernel, strides=(1, 1), padding='valid',
 
 def conv3d(x, kernel, strides=(1, 1, 1), padding='valid',
            data_format=None, dilation_rate=(1, 1, 1)):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     x = _preprocess_conv3d_input(x, data_format)
     kernel = _preprocess_conv3d_kernel(kernel, data_format)
@@ -1692,10 +1674,7 @@ def conv3d(x, kernel, strides=(1, 1, 1), padding='valid',
 
 def conv3d_transpose(x, kernel, output_shape, strides=(1, 1, 1),
                      padding='valid', data_format=None):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     x = _preprocess_conv3d_input(x, data_format)
     kernel = _preprocess_conv3d_kernel(kernel, data_format)
@@ -1728,10 +1707,7 @@ def conv3d_transpose(x, kernel, output_shape, strides=(1, 1, 1),
 def pool2d(x, pool_size, strides=(1, 1),
            padding='valid', data_format=None,
            pool_mode='max'):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     padding = _preprocess_border_mode(padding)
     strides = strides
@@ -1758,10 +1734,7 @@ def pool2d(x, pool_size, strides=(1, 1),
 
 def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
            data_format=None, pool_mode='max'):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     padding = _preprocess_border_mode(padding)
 
@@ -2101,10 +2074,7 @@ def spatial_2d_padding(x, padding=((1, 1), (1, 1)), data_format=None):
     assert len(padding) == 2
     assert len(padding[0]) == 2
     assert len(padding[1]) == 2
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     num_dynamic_axis = _get_dynamic_axis_num(x)
     assert len(x.shape) == 4 - (1 if num_dynamic_axis > 0 else 0)
@@ -2116,10 +2086,7 @@ def spatial_3d_padding(x, padding=((1, 1), (1, 1), (1, 1)), data_format=None):
     assert len(padding[0]) == 2
     assert len(padding[1]) == 2
     assert len(padding[2]) == 2
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     num_dynamic_axis = _get_dynamic_axis_num(x)
     assert len(x.shape) == 5 - (1 if num_dynamic_axis > 0 else 0)
@@ -2224,10 +2191,7 @@ def in_top_k(predictions, targets, k):
 
 def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
                      padding='valid', data_format=None):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     x = _preprocess_conv2d_input(x, data_format)
     kernel = _preprocess_conv2d_kernel(kernel, data_format)
@@ -2357,10 +2321,7 @@ def _reshape_sequence(x, time_step):
 
 
 def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     stride = strides[0]
     kernel_shape = int_shape(kernel)
@@ -2389,10 +2350,7 @@ def local_conv2d(inputs,
                  strides,
                  output_shape,
                  data_format=None):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     stride_row, stride_col = strides
     output_row, output_col = output_shape

keras/backend/common.py

@@ -147,6 +147,37 @@ def set_image_data_format(data_format):
     _IMAGE_DATA_FORMAT = str(data_format)
 
 
+def normalize_data_format(value):
+    """Checks that the value correspond to a valid data format.
+
+    # Arguments
+        value: String or None. `'channels_first'` or `'channels_last'`.
+
+    # Returns
+        A string, either `'channels_first'` or `'channels_last'`
+
+    # Example
+    ```python
+        >>> from keras import backend as K
+        >>> K.normalize_data_format(None)
+        'channels_first'
+        >>> K.normalize_data_format('channels_last')
+        'channels_last'
+    ```
+
+    # Raises
+        ValueError: if `value` or the global `data_format` invalid.
+    """
+    if value is None:
+        value = image_data_format()
+    data_format = value.lower()
+    if data_format not in {'channels_first', 'channels_last'}:
+        raise ValueError('The `data_format` argument must be one of '
+                         '"channels_first", "channels_last". Received: ' +
+                         str(value))
+    return data_format
+
+
 # Legacy methods
 
 def set_image_dim_ordering(dim_ordering):

keras/backend/tensorflow_backend.py

@@ -17,8 +17,9 @@ from collections import defaultdict
 import numpy as np
 import os
 
-from .common import floatx, epsilon
-from .common import image_data_format
+from .common import floatx
+from .common import epsilon
+from .common import normalize_data_format
 from ..utils.generic_utils import has_arg
 
 # Legacy functions
@@ -2235,10 +2236,7 @@ def spatial_2d_padding(x, padding=((1, 1), (1, 1)), data_format=None):
     assert len(padding) == 2
     assert len(padding[0]) == 2
     assert len(padding[1]) == 2
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     if data_format == 'channels_first':
         pattern = [[0, 0],
@@ -2279,10 +2277,7 @@ def spatial_3d_padding(x, padding=((1, 1), (1, 1), (1, 1)), data_format=None):
     assert len(padding[0]) == 2
     assert len(padding[1]) == 2
     assert len(padding[2]) == 2
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     if data_format == 'channels_first':
         pattern = [
@@ -3508,10 +3503,7 @@ def conv1d(x, kernel, strides=1, padding='valid',
         ValueError: If `data_format` is neither
             `"channels_last"` nor `"channels_first"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     kernel_shape = kernel.get_shape().as_list()
     if padding == 'causal':
@@ -3559,10 +3551,7 @@ def conv2d(x, kernel, strides=(1, 1), padding='valid',
         ValueError: If `data_format` is neither
             `"channels_last"` nor `"channels_first"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     x, tf_data_format = _preprocess_conv2d_input(x, data_format)
 
@@ -3601,10 +3590,7 @@ def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
         ValueError: If `data_format` is neither
             `"channels_last"` nor `"channels_first"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
     if isinstance(output_shape, (tuple, list)):
         output_shape = tf.stack(output_shape)
 
@@ -3653,10 +3639,7 @@ def separable_conv1d(x, depthwise_kernel, pointwise_kernel, strides=1,
         ValueError: If `data_format` is neither
             `"channels_last"` nor `"channels_first"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
     if isinstance(strides, int):
         strides = (strides,)
     if isinstance(dilation_rate, int):
@@ -3714,10 +3697,7 @@ def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1),
         ValueError: If `data_format` is neither
             `"channels_last"` nor `"channels_first"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     x, tf_data_format = _preprocess_conv2d_input(x, data_format)
     padding = _preprocess_padding(padding)
@@ -3756,10 +3736,7 @@ def depthwise_conv2d(x, depthwise_kernel, strides=(1, 1), padding='valid',
         ValueError: If `data_format` is neither
             `"channels_last"` nor `"channels_first"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     x, tf_data_format = _preprocess_conv2d_input(x, data_format)
     padding = _preprocess_padding(padding)
@@ -3799,10 +3776,7 @@ def conv3d(x, kernel, strides=(1, 1, 1), padding='valid',
         ValueError: If `data_format` is neither
             `"channels_last"` nor `"channels_first"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     x, tf_data_format = _preprocess_conv3d_input(x, data_format)
     padding = _preprocess_padding(padding)
@@ -3839,10 +3813,7 @@ def conv3d_transpose(x, kernel, output_shape, strides=(1, 1, 1),
         ValueError: If `data_format` is neither
             `"channels_last"` nor `"channels_first"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
     if isinstance(output_shape, (tuple, list)):
         output_shape = tf.stack(output_shape)
 
@@ -3892,10 +3863,7 @@ def pool2d(x, pool_size, strides=(1, 1),
         ValueError: if `data_format` is neither `"channels_last"` or `"channels_first"`.
         ValueError: if `pool_mode` is neither `"max"` or `"avg"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     x, tf_data_format = _preprocess_conv2d_input(x, data_format)
     padding = _preprocess_padding(padding)
@@ -3941,10 +3909,7 @@ def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
         ValueError: if `data_format` is neither `"channels_last"` or `"channels_first"`.
         ValueError: if `pool_mode` is neither `"max"` or `"avg"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     x, tf_data_format = _preprocess_conv3d_input(x, data_format)
     padding = _preprocess_padding(padding)
@@ -3989,10 +3954,7 @@ def bias_add(x, bias, data_format=None):
                        the bias should be either a vector or
                        a tensor with ndim(x) - 1 dimension
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
     bias_shape = int_shape(bias)
     if len(bias_shape) != 1 and len(bias_shape) != ndim(x) - 1:
         raise ValueError('Unexpected bias dimensions %d, expect to be 1 or %d dimensions'
@@ -4321,10 +4283,7 @@ def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None):
         ValueError: If `data_format` is neither
             `"channels_last"` nor `"channels_first"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     stride = strides[0]
     kernel_shape = int_shape(kernel)
@@ -4373,10 +4332,7 @@ def local_conv2d(inputs, kernel, kernel_size, strides, output_shape, data_format
         ValueError: if `data_format` is neither
                     `channels_last` or `channels_first`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format: ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     stride_row, stride_col = strides
     output_row, output_col = output_shape

keras/backend/theano_backend.py

@@ -20,7 +20,9 @@ except ImportError:
     from theano.sandbox.softsign import softsign as T_softsign
 
 import numpy as np
-from .common import floatx, epsilon, image_data_format
+from .common import floatx
+from .common import epsilon
+from .common import normalize_data_format
 from ..utils.generic_utils import has_arg
 # Legacy functions
 from .common import set_image_dim_ordering, image_dim_ordering
@@ -1115,10 +1117,7 @@ def spatial_2d_padding(x, padding=((1, 1), (1, 1)), data_format=None):
     assert len(padding[1]) == 2
     top_pad, bottom_pad = padding[0]
     left_pad, right_pad = padding[1]
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     input_shape = x.shape
     if data_format == 'channels_first':
@@ -1151,10 +1150,7 @@ def spatial_3d_padding(x, padding=((1, 1), (1, 1), (1, 1)), data_format=None):
     """Pad the 2nd, 3rd and 4th dimensions of a 5D tensor
     with "padding[0]", "padding[1]" and "padding[2]" (resp.) zeros left and right.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     input_shape = x.shape
     if data_format == 'channels_first':
@@ -1933,10 +1929,7 @@ def conv1d(x, kernel, strides=1, padding='valid',
         data_format: string, one of "channels_last", "channels_first"
         dilation_rate: integer.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ', data_format)
+    data_format = normalize_data_format(data_format)
 
     kernel_shape = int_shape(kernel)
     if padding == 'causal':
@@ -1990,10 +1983,7 @@ def conv2d(x, kernel, strides=(1, 1), padding='valid',
             Whether to use Theano or TensorFlow data format
         in inputs/kernels/outputs.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ', data_format)
+    data_format = normalize_data_format(data_format)
 
     image_shape = _preprocess_conv2d_image_shape(int_shape(x), data_format)
     kernel_shape = int_shape(kernel)
@@ -2033,10 +2023,7 @@ def conv2d_transpose(x, kernel, output_shape, strides=(1, 1),
         ValueError: if using an even kernel size with padding 'same'.
     """
     flip_filters = False
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + data_format)
+    data_format = normalize_data_format(data_format)
 
     if data_format == 'channels_last':
         output_shape = (output_shape[0],
@@ -2089,10 +2076,7 @@ def separable_conv1d(x, depthwise_kernel, pointwise_kernel, strides=1,
     # Raises
         ValueError: if `data_format` is neither `"channels_last"` or `"channels_first"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ', data_format)
+    data_format = normalize_data_format(data_format)
     if isinstance(strides, int):
         strides = (strides,)
     if isinstance(dilation_rate, int):
@@ -2163,10 +2147,7 @@ def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1),
     # Raises
         ValueError: if `data_format` is neither `"channels_last"` or `"channels_first"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ', data_format)
+    data_format = normalize_data_format(data_format)
 
     image_shape = _preprocess_conv2d_image_shape(int_shape(x), data_format)
     depthwise_kernel_shape = int_shape(depthwise_kernel)
@@ -2221,10 +2202,7 @@ def depthwise_conv2d(x, depthwise_kernel, strides=(1, 1), padding='valid',
     # Raises
         ValueError: if `data_format` is neither `"channels_last"` or `"channels_first"`.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ', data_format)
+    data_format = normalize_data_format(data_format)
 
     image_shape = _preprocess_conv2d_image_shape(int_shape(x), data_format)
     depthwise_kernel_shape = int_shape(depthwise_kernel)
@@ -2261,10 +2239,7 @@ def conv3d(x, kernel, strides=(1, 1, 1),
             Whether to use Theano or TensorFlow data format
         in inputs/kernels/outputs.
     """
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format:', data_format)
+    data_format = normalize_data_format(data_format)
 
     volume_shape = _preprocess_conv3d_volume_shape(int_shape(x), data_format)
     kernel_shape = int_shape(kernel)
@@ -2304,10 +2279,7 @@ def conv3d_transpose(x, kernel, output_shape, strides=(1, 1, 1),
         ValueError: if using an even kernel size with padding 'same'.
     """
     flip_filters = False
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + data_format)
+    data_format = normalize_data_format(data_format)
 
     if data_format == 'channels_last':
         output_shape = (output_shape[0],
@@ -2344,10 +2316,7 @@ def conv3d_transpose(x, kernel, output_shape, strides=(1, 1, 1),
 
 def pool2d(x, pool_size, strides=(1, 1), padding='valid',
            data_format=None, pool_mode='max'):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format:', data_format)
+    data_format = normalize_data_format(data_format)
 
     assert pool_size[0] >= 1 and pool_size[1] >= 1
 
@@ -2389,10 +2358,7 @@ def pool2d(x, pool_size, strides=(1, 1), padding='valid',
 
 def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
            data_format=None, pool_mode='max'):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format:', data_format)
+    data_format = normalize_data_format(data_format)
 
     if padding == 'same':
         w_pad = pool_size[0] - 2 if pool_size[0] % 2 == 1 else pool_size[0] - 1
@@ -2436,10 +2402,7 @@ def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
 
 
 def bias_add(x, bias, data_format=None):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
     if ndim(bias) != 1 and ndim(bias) != ndim(x) - 1:
         raise ValueError('Unexpected bias dimensions %d, '
                          'expect to be 1 or %d dimensions'
@@ -2701,10 +2664,7 @@ def foldr(fn, elems, initializer=None, name=None):
 
 
 def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     stride = strides[0]
     kernel_shape = int_shape(kernel)
@@ -2723,10 +2683,7 @@ def local_conv1d(inputs, kernel, kernel_size, strides, data_format=None):
 
 
 def local_conv2d(inputs, kernel, kernel_size, strides, output_shape, data_format=None):
-    if data_format is None:
-        data_format = image_data_format()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('Unknown data_format ' + str(data_format))
+    data_format = normalize_data_format(data_format)
 
     stride_row, stride_col = strides
     output_row, output_col = output_shape

keras/layers/convolutional.py

@@ -107,7 +107,7 @@ class _Conv(Layer):
         self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
         self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
         self.padding = conv_utils.normalize_padding(padding)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
         self.activation = activations.get(activation)
         self.use_bias = use_bias
@@ -1934,7 +1934,7 @@ class UpSampling2D(Layer):
     @interfaces.legacy_upsampling2d_support
     def __init__(self, size=(2, 2), data_format=None, **kwargs):
         super(UpSampling2D, self).__init__(**kwargs)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         self.size = conv_utils.normalize_tuple(size, 2, 'size')
         self.input_spec = InputSpec(ndim=4)
 
@@ -2002,7 +2002,7 @@ class UpSampling3D(Layer):
 
     @interfaces.legacy_upsampling3d_support
     def __init__(self, size=(2, 2, 2), data_format=None, **kwargs):
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         self.size = conv_utils.normalize_tuple(size, 3, 'size')
         self.input_spec = InputSpec(ndim=5)
         super(UpSampling3D, self).__init__(**kwargs)
@@ -2130,7 +2130,7 @@ class ZeroPadding2D(Layer):
                  data_format=None,
                  **kwargs):
         super(ZeroPadding2D, self).__init__(**kwargs)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         if isinstance(padding, int):
             self.padding = ((padding, padding), (padding, padding))
         elif hasattr(padding, '__len__'):
@@ -2234,7 +2234,7 @@ class ZeroPadding3D(Layer):
     @interfaces.legacy_zeropadding3d_support
     def __init__(self, padding=(1, 1, 1), data_format=None, **kwargs):
         super(ZeroPadding3D, self).__init__(**kwargs)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         if isinstance(padding, int):
             self.padding = ((padding, padding), (padding, padding), (padding, padding))
         elif hasattr(padding, '__len__'):
@@ -2413,7 +2413,7 @@ class Cropping2D(Layer):
     def __init__(self, cropping=((0, 0), (0, 0)),
                  data_format=None, **kwargs):
         super(Cropping2D, self).__init__(**kwargs)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         if isinstance(cropping, int):
             self.cropping = ((cropping, cropping), (cropping, cropping))
         elif hasattr(cropping, '__len__'):
@@ -2541,7 +2541,7 @@ class Cropping3D(Layer):
     def __init__(self, cropping=((1, 1), (1, 1), (1, 1)),
                  data_format=None, **kwargs):
         super(Cropping3D, self).__init__(**kwargs)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         if isinstance(cropping, int):
             self.cropping = ((cropping, cropping),
                              (cropping, cropping),

keras/layers/convolutional_recurrent.py

@@ -563,7 +563,7 @@ class ConvLSTM2DCell(Layer):
         self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
         self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
         self.padding = conv_utils.normalize_padding(padding)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2, 'dilation_rate')
         self.activation = activations.get(activation)
         self.recurrent_activation = activations.get(recurrent_activation)

keras/layers/core.py

@@ -209,12 +209,7 @@ class SpatialDropout2D(Dropout):
     @interfaces.legacy_spatialdropoutNd_support
     def __init__(self, rate, data_format=None, **kwargs):
         super(SpatialDropout2D, self).__init__(rate, **kwargs)
-        if data_format is None:
-            data_format = K.image_data_format()
-        if data_format not in {'channels_last', 'channels_first'}:
-            raise ValueError('`data_format` must be in '
-                             '{`"channels_last"`, `"channels_first"`}')
-        self.data_format = data_format
+        self.data_format = K.normalize_data_format(data_format)
         self.input_spec = InputSpec(ndim=4)
 
     def _get_noise_shape(self, inputs):
@@ -262,12 +257,7 @@ class SpatialDropout3D(Dropout):
     @interfaces.legacy_spatialdropoutNd_support
     def __init__(self, rate, data_format=None, **kwargs):
         super(SpatialDropout3D, self).__init__(rate, **kwargs)
-        if data_format is None:
-            data_format = K.image_data_format()
-        if data_format not in {'channels_last', 'channels_first'}:
-            raise ValueError('`data_format` must be in '
-                             '{`"channels_last"`, `"channels_first"`}')
-        self.data_format = data_format
+        self.data_format = K.normalize_data_format(data_format)
         self.input_spec = InputSpec(ndim=5)
 
     def _get_noise_shape(self, inputs):
@@ -497,7 +487,7 @@ class Flatten(Layer):
     def __init__(self, data_format=None, **kwargs):
         super(Flatten, self).__init__(**kwargs)
         self.input_spec = InputSpec(min_ndim=3)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
 
     def compute_output_shape(self, input_shape):
         if not all(input_shape[1:]):

keras/layers/local.py

@@ -101,7 +101,7 @@ class LocallyConnected1D(Layer):
         if self.padding != 'valid':
             raise ValueError('Invalid border mode for LocallyConnected1D '
                              '(only "valid" is supported): ' + padding)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         self.activation = activations.get(activation)
         self.use_bias = use_bias
         self.kernel_initializer = initializers.get(kernel_initializer)
@@ -283,7 +283,7 @@ class LocallyConnected2D(Layer):
         if self.padding != 'valid':
             raise ValueError('Invalid border mode for LocallyConnected2D '
                              '(only "valid" is supported): ' + padding)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         self.activation = activations.get(activation)
         self.use_bias = use_bias
         self.kernel_initializer = initializers.get(kernel_initializer)

keras/layers/pooling.py

@@ -126,7 +126,7 @@ class _Pooling2D(Layer):
         self.pool_size = conv_utils.normalize_tuple(pool_size, 2, 'pool_size')
         self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
         self.padding = conv_utils.normalize_padding(padding)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         self.input_spec = InputSpec(ndim=4)
 
     def compute_output_shape(self, input_shape):
@@ -287,7 +287,7 @@ class _Pooling3D(Layer):
         self.pool_size = conv_utils.normalize_tuple(pool_size, 3, 'pool_size')
         self.strides = conv_utils.normalize_tuple(strides, 3, 'strides')
         self.padding = conv_utils.normalize_padding(padding)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         self.input_spec = InputSpec(ndim=5)
 
     def compute_output_shape(self, input_shape):
@@ -488,7 +488,7 @@ class _GlobalPooling2D(Layer):
     @interfaces.legacy_global_pooling_support
     def __init__(self, data_format=None, **kwargs):
         super(_GlobalPooling2D, self).__init__(**kwargs)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         self.input_spec = InputSpec(ndim=4)
 
     def compute_output_shape(self, input_shape):
@@ -583,7 +583,7 @@ class _GlobalPooling3D(Layer):
     @interfaces.legacy_global_pooling_support
     def __init__(self, data_format=None, **kwargs):
         super(_GlobalPooling3D, self).__init__(**kwargs)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         self.input_spec = InputSpec(ndim=5)
 
     def compute_output_shape(self, input_shape):

keras/legacy/layers.py

@@ -748,7 +748,7 @@ class ConvRecurrent2D(Recurrent):
         self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
         self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
         self.padding = conv_utils.normalize_padding(padding)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = K.normalize_data_format(data_format)
         self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2, 'dilation_rate')
         self.return_sequences = return_sequences
         self.go_backwards = go_backwards

keras/utils/conv_utils.py

@@ -48,17 +48,6 @@ def normalize_tuple(value, n, name):
     return value_tuple
 
 
-def normalize_data_format(value):
-    if value is None:
-        value = K.image_data_format()
-    data_format = value.lower()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('The `data_format` argument must be one of '
-                         '"channels_first", "channels_last". Received: ' +
-                         str(value))
-    return data_format
-
-
 def normalize_padding(value):
     padding = value.lower()
     allowed = {'valid', 'same', 'causal'}

tests/keras/utils/conv_utils_test.py

 import pytest
 import numpy as np
 from keras.utils import conv_utils
+from keras import backend as K
 
 
 def test_normalize_tuple():
@@ -17,7 +18,7 @@ def test_normalize_tuple():
 
 def test_invalid_data_format():
     with pytest.raises(ValueError):
-        conv_utils.normalize_data_format('channels_middle')
+        K.normalize_data_format('channels_middle')
 
 
 def test_invalid_padding():