Merge pull request #952 from zsdonghao/master

TensorLayer 2.0.0-alpha Release

Author: zsdonghao

.gitignore

@@ -128,4 +128,4 @@ update_tl.py
 
 # Data Files and ByteCode files
 *.gz
-*.npz
+*.npz

.travis.yml

@@ -13,27 +13,28 @@ addons:
 branches:
   only:
     - master
-    - TensorLayer-1.x
+    - TensorLayer-2.x
     - /^\d+\.\d+(\.\d+)?(\S*)?$/
 
 python:
   - "3.6"
   - "3.5"
-  - "2.7"
+#  - "2.7"  # TensorLayer 2.0 does not support python2 now
 
 env:
 
   # Backward Compatibility in insured for release less than 1 year old.
   # https://pypi.org/project/tensorflow/#history
   matrix:
-    - _TF_VERSION=1.12.0 # Remove on Oct 22, 2019
-    - _TF_VERSION=1.11.0 # Remove on Sep 28, 2019
-    - _TF_VERSION=1.10.1 # Remove on Aug 24, 2019
-    - _TF_VERSION=1.9.0  # Remove on Jul 10, 2019
-    - _TF_VERSION=1.8.0  # Remove on Apr 28, 2019
-    - _TF_VERSION=1.7.1  # Remove on May 08, 2019
-    - _TF_VERSION=1.7.0  # Remove on Mar 29, 2019
-    - _TF_VERSION=1.6.0  # Remove on Mar 01, 2019
+    - _TF_VERSION=2.0.0a0
+#     - _TF_VERSION=1.12.0 # Remove on Oct 22, 2019
+#     - _TF_VERSION=1.11.0 # Remove on Sep 28, 2019
+#     - _TF_VERSION=1.10.1 # Remove on Aug 24, 2019
+#     - _TF_VERSION=1.9.0  # Remove on Jul 10, 2019
+#     - _TF_VERSION=1.8.0  # Remove on Apr 28, 2019
+#     - _TF_VERSION=1.7.1  # Remove on May 08, 2019
+#     - _TF_VERSION=1.7.0  # Remove on Mar 29, 2019
+#     - _TF_VERSION=1.6.0  # Remove on Mar 01, 2019
 
   global:
 
@@ -62,7 +63,7 @@ matrix:
 install:
   - |
     if [[ -v _DOC_AND_YAPF_TEST ]]; then
-        pip install tensorflow
+        pip install tensorflow==2.0.0a0
         pip install yapf
         pip install -e .[doc]
     else
@@ -100,7 +101,8 @@ deploy:
   on:
     tags: true
     python: '3.6'
-    condition: '$_TF_VERSION = 1.11.0'
+    condition: '$_TF_VERSION = 2.0.0a0'
+#     condition: '$_TF_VERSION = 1.11.0'
 
 # Documentation: https://docs.travis-ci.com/user/deployment/releases/
 - provider: releases
@@ -113,4 +115,6 @@ deploy:
   on:
     tags: true
     python: '3.6'
-    condition: '$_TF_VERSION = 1.11.0'
+    condition: '$_TF_VERSION = 2.0.0a0'
+#     condition: '$_TF_VERSION = 1.11.0'
+

CHANGELOG.md

@@ -90,6 +90,26 @@ To release a new version, please update the changelog as followed:
 ### Contributors
 @zsdonghao: #931
 
+
+## [2.0.0-alpha] - 2019-05-04
+
+### Changed
+* update for TensorLayer 2.0.0 alpha version (PR #952)
+* support TensorFlow 2.0.0-alpha
+* support both static and dynamic model building
+
+### Dependencies Update
+- tensorflow>=1.6,<1.13 => tensorflow>=2.0.0-alpha (PR #952)
+- h5py>=2.9 (PR #952)
+- cloudpickle>=0.8.1 (PR #952)
+- remove matplotlib
+
+### Contributors
+- @zsdonghao
+- @JingqingZ
+- @ChrisWu1997
+- @warshallrho
+
 ## [1.11.1] - 2018-11-15
 
 ### Changed

CONTRIBUTING.md

@@ -1,5 +1,18 @@
 # TensorLayer Contributor Guideline
 
+## Welcome to contribute!
+You are more than welcome to contribute to TensorLayer! If you have any improvement, please send us your [pull requests](https://help.github.com/en/articles/about-pull-requests). You may implement your improvement on your [fork](https://help.github.com/en/articles/working-with-forks).
+
+## Checklist
+* Continuous integration
+* Build from sources
+* Unittest
+* Documentation
+* General intro to TensorLayer2
+* How to contribute a new `Layer`
+* How to contribute a new `Model`
+* How to contribute a new example/tutorial
+
 ## Continuous integration
 
 We appreciate contributions
@@ -24,8 +37,8 @@ to apply those tools before submitting your PR.
 
 ```bash
 # First clone the repository and change the current directory to the newly cloned repository
-git clone https://github.com/tensorlayer/tensorlayer.git
-cd tensorlayer
+git clone https://github.com/zsdonghao/tensorlayer2.git
+cd tensorlayer2
 
 # Install virtualenv if necessary
 pip install virtualenv
@@ -43,15 +56,144 @@ venv\Scripts\activate.bat
 
 # ============= IF TENSORFLOW IS NOT ALREADY INSTALLED ============= #
 
-# for a machine **without** an NVIDIA GPU
-pip install -e .[all_cpu_dev] --upgrade
+# basic installation
+pip install .
+
+# advanced: for a machine **without** an NVIDIA GPU
+pip install -e ".[all_cpu_dev]"
 
-# for a machine **with** an NVIDIA GPU
-pip install -e .[all_gpu_dev] --upgrade
+# advanced: for a machine **with** an NVIDIA GPU
+pip install -e ".[all_gpu_dev]"
 ```
 
-Launching the unittest:
+## Unittest
+
+Launching the unittest for the whole repo:
 
 ```bash
+# install pytest
+pip install pytest
+
+# run pytest
 pytest
 ```
+
+Running your unittest code on your implemented module only:
+
+```bash
+# install coverage
+pip install coverage
+
+cd /path/to/your/unittest/code
+# For example: cd tests/layers/
+
+# run unittest
+coverage run --source myproject.module -m unittest discover
+# For example: coverage run --source tensorlayer.layers -m unittest discover
+
+# generate html report
+coverage html
+```
+
+## Documentation
+Even though you follow [numpydoc](https://numpydoc.readthedocs.io/en/latest/) document style when writing your code, 
+this does not ensure those lines appear on TensorLayer online documentation. 
+You need further modify corresponding RST files in `docs/modules`.
+
+For example, to add your implemented new pooling layer into documentation, modify `docs/modules/layer.rst`. First, insert layer name under Layer list
+```rst
+Layer list
+----------
+
+.. autosummary::
+
+    NewPoolingLayer
+```
+
+Second, find pooling layer part and add:
+```rst
+.. -----------------------------------------------------------
+..                     Pooling Layers
+.. -----------------------------------------------------------
+
+Pooling Layers
+------------------------
+
+New Pooling Layer
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: NewPoolingLayer
+```
+
+Finally, test with local documentation:
+```bash
+cd ./docs
+
+make clean
+make html  
+# then view generated local documentation by ./html/index.html
+``` 
+
+## General intro to TensorLayer2
+* TensorLayer2 is built on [TensorFlow2](https://www.tensorflow.org/alpha), so TensorLayer2 is purely eager, no sessions, no globals.
+* TensorLayer2 supports APIs to build static models and dynamic models. Therefore, all `Layers` should be compatible with the two modes.
+```python
+# An example of a static model
+# A static model has inputs and outputs with fixed shape.
+inputs = tl.layers.Input([32, 784])
+dense1 = tl.layers.Dense(n_units=800, act=tf.nn.relu, in_channels=784, name='dense1')(inputs)
+dense2 = tl.layers.Dense(n_units=10,  act=tf.nn.relu, in_channels=800, name='dense2')(dense1)
+model = tl.models.Model(inputs=inputs, outputs=dense2)
+
+# An example of a dynamic model
+# A dynamic model has more flexibility. The inputs and outputs may be different in different runs.
+class CustomizeModel(tl.models.Model):
+    def __init__(self):
+        super(CustomizeModel, self).__init__()
+        self.dense1 = tl.layers.Dense(n_units=800, act=tf.nn.relu, in_channels=784, name='dense1')
+        self.dense2 = tl.layers.Dense(n_units=10,  act=tf.nn.relu, in_channels=800, name='dense2')
+
+    # a dynamic model allows more flexibility by customising forwarding.
+    def forward(self, x, bar=None):
+        d1 = self.dense1(x)
+        if bar:
+            return d1
+        else:
+            d2 = self.dense2(d1)
+            return d1, d2
+
+model = CustomizeModel()
+```
+* More examples can be found in [examples](examples/) and [tests/layers](tests/layers/). Note that not all of them are completed.
+
+## How to contribute a new `Layer`
+* A `NewLayer` should be a derived from the base class [`Layer`](tensorlayer/layers/core.py).
+* Member methods to be overrided:
+  - `__init__(self, args1, args2, inputs_shape=None, name=None)`: The constructor of the `NewLayer`, which should
+    - Call `super(NewLayer, self).__init__(name)` to construct the base.
+    - Define member variables based on the args1, args2 (or even more).
+    - If the `inputs_shape` is provided, call `self.build(inputs_shape)` and set `self._built=True`. Note that sometimes only `in_channels` should be enough to build the layer like [`Dense`](tensorlayer/layers/dense/base_dense.py).
+    - Logging by `logging.info(...)`.
+  - `__repr__(self)`: Return a printable representation of the `NewLayer`.
+  - `build(self, inputs_shape)`: Build the `NewLayer` by defining weights.
+  - `forward(self, inputs, **kwargs)`: Forward feeding the `NewLayer`. Note that the forward feeding of some `Layers` may be different during training and testing like [`Dropout`](tensorlayer/layers/dropout.py).
+* Unittest:
+  - Unittest should be done before a pull request. Unittest code can be written in [tests/](tests/)
+* Documents:
+  - Please write a description for each class and method in RST format. The description may include the functionality, arguments, references, examples of the `NewLayer`.
+* Examples: [`Dense`](tensorlayer/layers/dense/base_dense.py), [`Dropout`](tensorlayer/layers/dropout.py), [`Conv`](tensorlayer/layers/convolution/simplified_conv.py).
+
+## How to contribute a new `Model`
+* A `NewModel` should be derived from the base class [`Model`](tensorlayer/models/core.py) (if dynamic) or an instance of [`Model`](tensorlayer/models/core.py) (if static).
+* A static `NewModel` should have fixed inputs and outputs. Please check the example [`VGG_Static`](tensorlayer/models/vgg.py)
+* A dynamic `NewModel` has more flexiblility. Please check the example [`VGG16`](tensorlayer/models/vgg16.py)
+
+## How to contribute a new example/tutorial
+* A new example/tutorial should implement a complete workflow of deep learning which includes (but not limited)
+  - `Models` construction based on `Layers`.
+  - Data processing and loading.
+  - Training and testing.
+  - Forward feeding by calling the models.
+  - Loss function.
+  - Back propagation by `tf.GradientTape()`.
+  - Model saving and restoring.
+* Examples: [MNIST](examples/basic_tutorials/tutorial_mnist_mlp_static.py), [CIFAR10](examples/basic_tutorials/tutorial_cifar10_cnn_static.py), [FastText](examples/text_classification/tutorial_imdb_fasttext.py)

Makefile

@@ -5,40 +5,23 @@ default:
 	@echo "\tmake install3  # install tensorlayer in current workspace with pip3"
 
 lint:
-	pylint example/*.py
+	pylint examples/*.py
 	pylint tensorlayer
 
 test:
-	python3 tests/test_yapf_format.py
-	# python3 tests/test_pydocstyle.py
-	python3 tests/test_mnist_simple.py
-	python3 tests/test_reuse_mlp.py
-	python3 tests/test_layers_basic.py
-	python3 tests/test_layers_convolution.py
-	python3 tests/test_layers_core.py
-	python3 tests/test_layers_extend.py
-	python3 tests/test_layers_flow_control.py
-	python3 tests/test_layers_importer.py
-	python3 tests/test_layers_merge.py
-	python3 tests/test_layers_normalization.py
-	python3 tests/test_layers_pooling.py
-	python3 tests/test_layers_recurrent.py
-	python3 tests/test_layers_shape.py
-	python3 tests/test_layers_spatial_transformer.py
-	python3 tests/test_layers_special_activation.py
-	python3 tests/test_layers_stack.py
-	python3 tests/test_layers_super_resolution.py
-	python3 tests/test_layers_time_distributed.py
+	python3 tests/models/test_model_core.py
+	python3 tests/layers/test_layernode.py
+	python3 tests/files/test_utils_saveload.py
 
 format:
-	autoflake -i example/*.py
+	autoflake -i examples/*.py
 	autoflake -i tensorlayer/*.py
 	autoflake -i tensorlayer/**/*.py
 
-	isort -rc example
+	isort -rc examples
 	isort -rc tensorlayer
 
-	yapf -i example/*.py
+	yapf -i examples/*.py
 	yapf -i tensorlayer/*.py
 	yapf -i tensorlayer/**/*.py