Metadata-Version: 2.1
Name: memcnn
Version: 0.3.0
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
Description: ======
        MemCNN
        ======
        
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        A `PyTorch <http://pytorch.org/>`__ framework for developing memory
        efficient deep invertible 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 and good test coverage.
        
        Example usage: ReversibleBlock
        ------------------------------
        
        .. code:: python
        
            # some required imports
            import torch
            import torch.nn as nn
            import numpy as np
            import memcnn.models.revop
        
        
            # define a new class of operation(s) PyTorch style
            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 (b, c, y, x)
            data = np.random.random((2, 10, 8, 8)).astype(np.float32)
            X = torch.from_numpy(data)
        
            # application of the operation(s) the normal way
            Y = ExampleOperation(channels=10)(X)
        
            # application of the operation(s) using the reversible block
            F, G = ExampleOperation(channels=10 // 2), ExampleOperation(channels=10 // 2)
            Y = memcnn.models.revop.ReversibleBlock(F, G, coupling='additive')(X)
        
        Run PyTorch Experiments
        -----------------------
        
        .. code:: bash
        
            ./train.py [MODEL] [DATASET] --fresh
        
        Available values for ``DATASET`` are ``cifar10`` and ``cifar100``.
        
        Available values for ``MODEL`` are ``resnet32``, ``resnet110``,
        ``resnet164``, ``revnet38``, ``revnet110``, ``revnet164``
        
        If not available datasets are automatically downloaded.
        
        
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
