Metadata-Version: 2.1
Name: memcnn
Version: 0.3.2
Summary: A PyTorch framework for developing memory efficient deep invertible networks.
Home-page: http://pypi.python.org/pypi/memcnn/
Author: S.C. van de Leemput
Author-email: silvandeleemput@gmail.com
License: LICENSE.txt
Keywords: memcnn invertible PyTorch
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries
Classifier: Operating System :: OS Independent
Description-Content-Type: text/x-rst
Requires-Dist: Pillow
Requires-Dist: numpy
Requires-Dist: SimpleITK
Requires-Dist: tensorboardX (==1.4)
Requires-Dist: tensorflow (>=1.11.0)
Requires-Dist: torch (>=0.4.0)
Requires-Dist: torchvision
Requires-Dist: tqdm

======
MemCNN
======

.. image:: https://img.shields.io/circleci/build/github/silvandeleemput/memcnn/master.svg        
        :alt: CircleCI - Status master branch
        :target: https://circleci.com/gh/silvandeleemput/memcnn/tree/master

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        :alt: Docker - Status
        :target: https://hub.docker.com/r/silvandeleemput/memcnn

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        :alt: Documentation - Status master branch
        :target: https://memcnn.readthedocs.io/en/latest/?badge=latest

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        :alt: Codecov - Status master branch
        :target: https://codecov.io/gh/silvandeleemput/memcnn

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        :alt: PyPI - Latest release
        :target: https://pypi.python.org/pypi/memcnn

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        :alt: PyPI - Implementation
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        :alt: GitHub - Repository license
        :target: https://github.com/silvandeleemput/memcnn/blob/master/LICENSE.txt

A `PyTorch <http://pytorch.org/>`__ framework for developing memory-efficient invertible neural networks.

* Free software: `MIT license <https://github.com/silvandeleemput/memcnn/blob/master/LICENSE.txt>`__ (please cite our work if you use it)
* Documentation: https://memcnn.readthedocs.io.
* Installation: https://memcnn.readthedocs.io/en/latest/installation.html

Features
--------

* Simple `ReversibleBlock` wrapper class to wrap and convert arbitrary PyTorch Modules into invertible versions.
* Simple switching between `additive` and `affine` invertible coupling schemes and different implementations.
* Simple toggling of memory saving by setting the `keep_input` property of the `ReversibleBlock`.
* Training and evaluation code for reproducing RevNet experiments using MemCNN.
* CI tests for Python v2.7 and v3.6 and torch v0.4, v1.0, and v1.1 with good code coverage.

Example usage: ReversibleBlock
------------------------------

.. code:: python

    import torch
    import torch.nn as nn
    import memcnn


    # define a new torch Module with a sequence of operations: Relu o BatchNorm2d o Conv2d
    class ExampleOperation(nn.Module):
        def __init__(self, channels):
            super(ExampleOperation, self).__init__()
            self.seq = nn.Sequential(
                                        nn.Conv2d(in_channels=channels, out_channels=channels,
                                                  kernel_size=(3, 3), padding=1),
                                        nn.BatchNorm2d(num_features=channels),
                                        nn.ReLU(inplace=True)
                                    )

        def forward(self, x):
            return self.seq(x)

    # generate some random input data (batch_size, num_channels, y_elements, x_elements)
    X = torch.rand(2, 10, 8, 8)

    # application of the operation(s) the normal way
    model_normal = ExampleOperation(channels=10)
    Y = model_normal(X)

    # application of the operation(s) turned invertible using the reversible block
    F = ExampleOperation(channels=10 // 2)
    model_invertible = memcnn.ReversibleBlock(F, coupling='additive', keep_input=True, keep_input_inverse=True)
    Y2 = model_invertible(X)

    # The input (X) can be approximated (X2) by applying the inverse method of the reversible block on Y2
    X2 = model_invertible.inverse(Y2)

Run PyTorch Experiments
-----------------------

After installing MemCNN run:

.. code:: bash

    python -m memcnn.train [MODEL] [DATASET] [--fresh] [--no-cuda]

* Available values for ``DATASET`` are ``cifar10`` and ``cifar100``.
* Available values for ``MODEL`` are ``resnet32``, ``resnet110``, ``resnet164``, ``revnet38``, ``revnet110``, ``revnet164``
* Use the ``--fresh`` flag to remove earlier experiment results.
* Use the ``--no-cuda`` flag to train on the CPU rather than the GPU through CUDA.

Datasets are automatically downloaded if they are not available.

When using Python 3.* replace the ``python`` directive with the appropriate Python 3 directive. For example when using the MemCNN docker image use ``python3.6``.




