shuida/pyfunt

Pythonic Deep Learning Framework Inspired by Torch's Neural Network package

PyFunt (/paɪfʊnt/)

Pythonic Deep Learning Framework (WIP and CPU only) inspired by Torch's Neural Network package.

Requirements

• Python 2.7
• Cython
• numpy
• torchfile

Installation

Get pip and run:

pip install git+git://github.com/dnlcrl/PyFunt.git

Usage

Check the examples folder

Example: Parametric Residual Model

Parametric models can be built easily thanks to the module structure:

from pyfunt import (SpatialConvolution, SpatialBatchNormalization,
                SpatialAveragePooling, Sequential, ReLU, Linear,
                Reshape, LogSoftMax, Padding, Identity, ConcatTable,
                CAddTable)

def residual_layer(n_channels, n_out_channels=None, stride=None):
    n_out_channels = n_out_channels or n_channels
    stride = stride or 1

    convs = Sequential()
    add = convs.add
    add(SpatialConvolution(
	n_channels, n_out_channels, 3, 3, stride, stride, 1, 1))
    add(SpatialBatchNormalization(n_out_channels))
    add(SpatialConvolution(n_out_channels, n_out_channels, 3, 3, 1, 1, 1, 1))
    add(SpatialBatchNormalization(n_out_channels))

    if stride > 1:
	shortcut = Sequential()
	shortcut.add(SpatialAveragePooling(2, 2, stride, stride))
	shortcut.add(Padding(1, (n_out_channels - n_channels)/2, 3))
    else:
	shortcut = Identity()

    res = Sequential()
    res.add(ConcatTable().add(convs).add(shortcut)).add(CAddTable())
    res.add(ReLU(True))
    return res


def resnet(n_size, num_starting_filters, reg):
    nfs = num_starting_filters
    model = Sequential()
    add = model.add
    add(SpatialConvolution(3, nfs, 3, 3, 1, 1, 1, 1))
    add(SpatialBatchNormalization(nfs))
    add(ReLU())

    for i in xrange(1, n_size):
	add(residual_layer(nfs))
    add(residual_layer(nfs, 2*nfs, 2))

    for i in xrange(1, n_size-1):
	add(residual_layer(2*nfs))
    add(residual_layer(2*nfs, 4*nfs, 2))

    for i in xrange(1, n_size-1):
	add(residual_layer(4*nfs))

    add(SpatialAveragePooling(8, 8))
    add(Reshape(nfs*4))
    add(Linear(nfs*4, 10))
    add(LogSoftMax())
    return model

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Check the Torch documentation for more informations about the implemented layers (pyfunt is more or less a python port of torch/nn): https://github.com/torch/nn/blob/master/doc/index.md