Making keras-contrib compatible with tf.keras

.travis.yml

@@ -12,6 +12,10 @@ matrix:
           env: KERAS_BACKEND=tensorflow
         - python: 3.6
           env: KERAS_BACKEND=tensorflow
+        - python: 2.7
+          env: KERAS_BACKEND=tensorflow USE_TF_KERAS=1 PYTEST_IGNORE='--ignore=tests/test_doc_auto_generation.py --ignore=tests/keras_contrib/backend --ignore=tests/keras_contrib/utils/save_load_utils_test.py'
+        - python: 3.6
+          env: KERAS_BACKEND=tensorflow USE_TF_KERAS=1 PYTEST_IGNORE='--ignore=tests/test_doc_auto_generation.py --ignore=tests/keras_contrib/backend --ignore=tests/keras_contrib/utils/save_load_utils_test.py'
         - python: 2.7
           env: KERAS_BACKEND=theano THEANO_FLAGS=optimizer=fast_compile
         - python: 3.6
@@ -41,7 +45,9 @@ install:
   - source activate test-environment
   # TODO: remove the fixed numpy version once the next version of theano is out.
   - travis_retry pip install --only-binary=numpy,scipy,pandas numpy==1.15.4 nose scipy h5py theano pytest pytest-pep8 pandas --progress-bar off
-  - pip install git+https://github.com/keras-team/keras.git --progress-bar off
+  - if [[ "$USE_TF_KERAS" == "" ]]; then
+      pip install git+https://github.com/keras-team/keras.git --progress-bar off;
+    fi
 
   # set library path
   - export LD_LIBRARY_PATH=$HOME/miniconda/envs/test-environment/lib/:$LD_LIBRARY_PATH
@@ -50,6 +56,9 @@ install:
       travis_retry conda install -q mkl mkl-service;
     fi
 
+  - if [[ "$USE_TF_KERAS" == "1" ]]; then
+      python convert_to_tf_keras.py;
+    fi
   - pip install -e .[tests] --progress-bar off
 
   # install TensorFlow (CPU version).
@@ -66,7 +75,7 @@ install:
 script:
   - export MKL_THREADING_LAYER="GNU"
   # run keras backend init to initialize backend config
-  - python -c "import keras.backend"
+  - python -c "import keras_contrib.backend"
   # create dataset directory to avoid concurrent directory creation at runtime
   - mkdir ~/.keras/datasets
   # set up keras backend
@@ -75,5 +84,5 @@ script:
   - if [[ "$TEST_MODE" == "PEP8_DOC" ]]; then
       PYTHONPATH=$PWD:$PYTHONPATH py.test --pep8 -m pep8 -n0 && py.test tests/test_documentation.py;
     else
-      PYTHONPATH=$PWD:$PYTHONPATH py.test tests/ --cov-config .coveragerc --cov=keras_contrib tests/;
+      PYTHONPATH=$PWD:$PYTHONPATH py.test tests/ $PYTEST_IGNORE --cov-config .coveragerc --cov=keras_contrib tests/;
     fi

CONTRIBUTING.md

@@ -61,6 +61,44 @@ We love pull requests. Here's a quick guide:
 
 11. Submit your PR. If your changes have been approved in a previous discussion, and if you have complete (and passing) unit tests, your PR is likely to be merged promptly. Otherwise, well...
 
+## About keras-team/keras and tensorflow.keras
+
+This repo supports both keras-team/keras and tensorflow.keras. The way this is done is by changing all the imports in the code by parsing it. This is checked with travis.ci every time you push a commit in a pull request. 
+
+There are a number of reasons why your code would work with keras-team/keras but not with tf.keras. The most common is that you use keras' private API. Since both keras are only similar in behavior with respect to their public API, you should only use this. Otherwise it's likely that the function you are using is not in the same place in tf.keras.
+
+Another gotcha is that when creating custome layers and implementing the `build` function, keras-team/keras expects as `input_shape` a tuple of ints. With tf.keras, `input_shape` is a tuple with `Dimensions` objects. This is likely to make the code incompatible. To solve this
+problem, you should do:
+
+```python
+from keras.layers import Layer
+from keras_contrib.utils.test_utils import to_tuple
+
+
+class MyLayer(Layer):
+    ...
+
+    def build(self, input_shape):
+        input_shape = to_tuple(input_shape)
+        # now `input_shape` is a tuple of ints or None like in keras-team/keras
+        ...
+```
+
+To change all the imports in your code to tf.keras to test compatibility, you can do:
+```
+python convert_to_tf_keras.py
+```
+
+To convert your codebase back to keras-team/keras, do:
+```
+python convert_to_tf_keras.py --revert
+```
+
+Not that you are strongly encouraged to commit your code before in case the parsing would go wrong. To discard all the changes you made since the previous commit:
+```
+git reset --hard
+```
+
 ## A Note for Contributors
 
 Both Keras-Contrib and Keras operate under the [MIT License](LICENSE). At the discretion of the maintainers of both repositories, code may be moved from Keras-Contrib to Keras and vice versa.

README.md

@@ -9,6 +9,7 @@ As the community contributions in Keras-Contrib are tested, used, validated, and
 ---
 ## Installation
 
+#### Install keras_contrib for keras-team/keras
 For instructions on how to install Keras, 
 see [the Keras installation page](https://keras.io/#installation).
 
@@ -24,6 +25,25 @@ Alternatively, using pip:
 sudo pip install git+https://www.github.com/keras-team/keras-contrib.git
 ```
 
+to uninstall:
+```pip
+pip uninstall keras_contrib
+```
+
+#### Install keras_contrib for tensorflow.keras
+
+```shell
+git clone https://www.github.com/keras-team/keras-contrib.git
+cd keras-contrib
+python convert_to_tf_keras.py
+USE_TF_KERAS=1 python setup.py install
+```
+
+to uninstall:
+```shell
+pip uninstall tf_keras_contrib
+```
+
 For contributor guidelines see [CONTRIBUTING.md](https://github.com/keras-team/keras-contrib/blob/master/CONTRIBUTING.md)
 
 ---

convert_to_tf_keras.py

@@ -0,0 +1,59 @@
+import os
+import sys
+
+list_conversions = [('import keras.', 'import tensorflow.keras.'),
+                    ('import keras ', 'from tensorflow import keras '),
+                    ('import keras\n', 'from tensorflow import keras\n'),
+                    ('from keras.', 'from tensorflow.keras.'),
+                    ('from keras ', 'from tensorflow.keras ')]
+
+
+def replace_imports(file_path, revert):
+    if not file_path.endswith('.py'):
+        return False
+    if os.path.abspath(file_path) == os.path.abspath(__file__):
+        return False
+    with open(file_path, 'r') as f:
+        text = f.read()
+
+    if revert:
+        list_imports_to_change = [x[::-1] for x in list_conversions]
+    else:
+        list_imports_to_change = list_conversions
+
+    text_updated = text
+    for old_str, new_str in list_imports_to_change:
+        text_updated = text_updated.replace(old_str, new_str)
+
+    with open(file_path, 'w+') as f:
+        f.write(text_updated)
+
+    return text_updated != text
+
+
+def convert_codebase(revert):
+    nb_of_files_changed = 0
+    keras_dir = os.path.dirname(os.path.abspath(__file__))
+    for root, dirs, files in os.walk(keras_dir):
+        for name in files:
+            if replace_imports(os.path.join(root, name), revert):
+                nb_of_files_changed += 1
+    print('Changed imports in ' + str(nb_of_files_changed) + ' files.')
+    print('Those files were found in the directory ' + keras_dir)
+
+
+def convert_to_tf_keras():
+    """Convert the codebase to tf.keras"""
+    convert_codebase(False)
+
+
+def convert_to_keras_team_keras():
+    """Convert the codebase from tf.keras to keras-team/keras"""
+    convert_codebase(True)
+
+
+if __name__ == '__main__':
+    if '--revert' in sys.argv:
+        convert_to_keras_team_keras()
+    else:
+        convert_to_tf_keras()

keras_contrib/layers/advanced_activations.py

@@ -5,6 +5,7 @@
 from keras.layers import InputSpec
 from keras import backend as K
 from keras.utils import get_custom_objects
+from keras_contrib.utils.test_utils import to_tuple
 
 
 class PELU(Layer):
@@ -65,6 +66,7 @@ def __init__(self, alpha_initializer='ones',
             self.shared_axes = list(shared_axes)
 
     def build(self, input_shape):
+        input_shape = to_tuple(input_shape)
         param_shape = list(input_shape[1:])
         self.param_broadcast = [False] * len(param_shape)
         if self.shared_axes is not None:
@@ -181,6 +183,7 @@ def __init__(self, t_left_initializer='zeros',
             self.shared_axes = list(shared_axes)
 
     def build(self, input_shape):
+        input_shape = to_tuple(input_shape)
         param_shape = list(input_shape[1:])
         self.param_broadcast = [False] * len(param_shape)
         if self.shared_axes is not None:
@@ -284,6 +287,7 @@ def __init__(self, beta=1.0, trainable=False, **kwargs):
         self.trainable = trainable
 
     def build(self, input_shape):
+        input_shape = to_tuple(input_shape)
         self.scaling_factor = K.variable(self.beta,
                                          dtype=K.floatx(),
                                          name='scaling_factor')

keras_contrib/layers/convolutional.py

@@ -13,6 +13,7 @@
 from keras.utils import get_custom_objects
 from keras_contrib.utils.conv_utils import conv_output_length
 from keras_contrib.utils.conv_utils import normalize_data_format
+from keras_contrib.utils.test_utils import to_tuple
 import numpy as np
 
 
@@ -129,6 +130,7 @@ def __init__(self, filters, kernel_size,
         super(CosineConvolution2D, self).__init__(**kwargs)
 
     def build(self, input_shape):
+        input_shape = to_tuple(input_shape)
         if self.data_format == 'channels_first':
             stack_size = input_shape[1]
             self.kernel_shape = (self.filters, stack_size, self.nb_row, self.nb_col)

keras_contrib/layers/core.py

@@ -10,6 +10,7 @@
 from keras.layers import InputSpec
 from keras.layers import Layer
 from keras.utils import get_custom_objects
+from keras_contrib.utils.test_utils import to_tuple
 
 
 class CosineDense(Layer):
@@ -108,6 +109,7 @@ def __init__(self, units, kernel_initializer='glorot_uniform',
         super(CosineDense, self).__init__(**kwargs)
 
     def build(self, input_shape):
+        input_shape = to_tuple(input_shape)
         ndim = len(input_shape)
         assert ndim >= 2
         input_dim = input_shape[-1]

keras_contrib/layers/crf.py

@@ -14,6 +14,7 @@
 from keras_contrib.losses import crf_loss
 from keras_contrib.metrics import crf_marginal_accuracy
 from keras_contrib.metrics import crf_viterbi_accuracy
+from keras_contrib.utils.test_utils import to_tuple
 
 
 class CRF(Layer):
@@ -247,6 +248,7 @@ def __init__(self, units,
         self.unroll = unroll
 
     def build(self, input_shape):
+        input_shape = to_tuple(input_shape)
         self.input_spec = [InputSpec(shape=input_shape)]
         self.input_dim = input_shape[-1]
 

keras_contrib/layers/normalization.py

@@ -3,6 +3,7 @@
 from keras import backend as K
 from keras_contrib import backend as KC
 from keras.utils import get_custom_objects
+from keras_contrib.utils.test_utils import to_tuple
 
 
 class InstanceNormalization(Layer):
@@ -75,6 +76,7 @@ def __init__(self,
         self.gamma_constraint = constraints.get(gamma_constraint)
 
     def build(self, input_shape):
+        input_shape = to_tuple(input_shape)
         ndim = len(input_shape)
         if self.axis == 0:
             raise ValueError('Axis cannot be zero')

keras_contrib/utils/test_utils.py

@@ -4,6 +4,7 @@
 from numpy.testing import assert_allclose
 import inspect
 
+import keras
 from keras.layers import Input
 from keras import Model
 from keras import backend as K
@@ -176,3 +177,28 @@ def unpack_singleton(x):
     if len(x) == 1:
         return x[0]
     return x
+
+
+if keras.__name__ == 'keras':
+    is_tf_keras = False
+elif keras.__name__ == 'tensorflow.keras':
+    is_tf_keras = True
+else:
+    raise KeyError('Cannot detect if using keras or tf.keras.')
+
+
+def to_tuple(shape):
+    """This functions is here to fix an inconsistency between keras and tf.keras.
+
+    In tf.keras, the input_shape argument is an tuple with `Dimensions` objects.
+    In keras, the input_shape is a simple tuple of ints or `None`.
+
+    We'll work with tuples of ints or `None` to be consistent
+    with keras-team/keras. So we must apply this function to
+    all input_shapes of the build methods in custom layers.
+    """
+    if is_tf_keras:
+        import tensorflow as tf
+        return tuple(tf.TensorShape(shape).as_list())
+    else:
+        return shape

setup.py

@@ -1,15 +1,23 @@
 from setuptools import setup
 from setuptools import find_packages
+import os
 
 
-setup(name='keras_contrib',
+if os.environ.get('USE_TF_KERAS', None) == '1':
+    name = 'tf_keras_contrib'
+    install_requires = []
+else:
+    name = 'keras_contrib'
+    install_requires = ['keras']
+
+setup(name=name,
       version='2.0.8',
       description='Keras Deep Learning for Python, Community Contributions',
       author='Fariz Rahman',
       author_email='[email protected]',
       url='https://github.com/farizrahman4u/keras-contrib',
       license='MIT',
-      install_requires=['keras'],
+      install_requires=install_requires,
       extras_require={
           'h5py': ['h5py'],
           'visualize': ['pydot>=1.2.0'],

tests/keras_contrib/layers/test_crf.py

@@ -12,13 +12,17 @@
 from keras_contrib.metrics import crf_marginal_accuracy
 from keras_contrib.metrics import crf_viterbi_accuracy
 from keras_contrib.layers import CRF
+from keras_contrib.utils.test_utils import is_tf_keras
 
 nb_samples, timesteps, embedding_dim, output_dim = 2, 10, 4, 5
 embedding_num = 12
 
 MODEL_PERSISTENCE_PATH = './test_saving_crf_model.h5'
 
 
+@pytest.mark.xfail(is_tf_keras,
+                   reason='TODO: fix it. Using K.tf which is bad.',
+                   strict=True)
 def test_CRF():
     # data
     x = np.random.randint(1, embedding_num, nb_samples * timesteps)