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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. You may implement your improvement on your fork.

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 either by adding / improving examples or extending / fixing the core library. To make your contributions, you would need to follow the pep8 coding style and numpydoc document style. We rely on Continuous Integration (CI) for checking push commits. The following tools are used to ensure that your commits can pass through the CI test:

  • yapf (format code), compulsory
  • isort (sort imports), optional
  • autoflake (remove unused imports), optional

You can simply run

make format

to apply those tools before submitting your PR.

Build from sources

# First clone the repository and change the current directory to the newly cloned repository
git clone https://github.com/zsdonghao/tensorlayer2.git
cd tensorlayer2

# Install virtualenv if necessary
pip install virtualenv

# Then create a virtualenv called `venv`
virtualenv venv

# Activate the virtualenv

## Linux:
source venv/bin/activate

## Windows:
venv\Scripts\activate.bat

# ============= IF TENSORFLOW IS NOT ALREADY INSTALLED ============= #

# basic installation
pip install .

# advanced: for a machine **without** an NVIDIA GPU
pip install -e ".[all_cpu_dev]"

# advanced: for a machine **with** an NVIDIA GPU
pip install -e ".[all_gpu_dev]"

Unittest

Launching the unittest for the whole repo:

# install pytest
pip install pytest

# run pytest
pytest

Running your unittest code on your implemented module only:

# 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 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

Layer list
----------

.. autosummary::

    NewPoolingLayer

Second, find pooling layer part and add:

.. -----------------------------------------------------------
..                     Pooling Layers
.. -----------------------------------------------------------

Pooling Layers
------------------------

New Pooling Layer
^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: NewPoolingLayer

Finally, test with local documentation:

cd ./docs

make clean
make html  
# then view generated local documentation by ./html/index.html

General intro to TensorLayer2

  • TensorLayer2 is built on TensorFlow2, 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.
# 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='relu', in_channels=784, name='dense1')(inputs)
dense2 = tl.layers.Dense(n_units=10,  act='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='relu', in_channels=784, name='dense1')
        self.dense2 = tl.layers.Dense(n_units=10,  act='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()

How to contribute a new Layer

  • A NewLayer should be a derived from the base class Layer.
  • 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.
      • 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.
  • Unittest:
    • Unittest should be done before a pull request. Unittest code can be written in 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, Dropout, Conv.

How to contribute a new Model

  • A NewModel should be derived from the base class Model (if dynamic) or an instance of Model (if static).
  • A static NewModel should have fixed inputs and outputs. Please check the example VGG_Static
  • A dynamic NewModel has more flexiblility. Please check the example VGG16

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, CIFAR10, FastText