BVLC GoogleNet translated to Lasagne

bvlc_googlenet.py

from lasagne.layers import InputLayer
from lasagne.layers import DenseLayer
from lasagne.layers import ConcatLayer
from lasagne.layers import NonlinearityLayer
from lasagne.layers import GlobalPoolLayer
from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
from lasagne.layers.dnn import MaxPool2DDNNLayer as PoolLayerDNN
from lasagne.layers import MaxPool2DLayer as PoolLayer
from lasagne.layers import LocalResponseNormalization2DLayer as LRNLayer
from lasagne.nonlinearities import softmax, linear


def build_inception_module(name, input_layer, nfilters):
    # nfilters: (pool_proj, 1x1, 3x3_reduce, 3x3, 5x5_reduce, 5x5)
    net = {}
    net['pool'] = PoolLayerDNN(input_layer, pool_size=3, stride=1, pad=1)
    net['pool_proj'] = ConvLayer(net['pool'], nfilters[0], 1)

    net['1x1'] = ConvLayer(input_layer, nfilters[1], 1)

    net['3x3_reduce'] = ConvLayer(input_layer, nfilters[2], 1)
    net['3x3'] = ConvLayer(net['3x3_reduce'], nfilters[3], 3, pad=1)

    net['5x5_reduce'] = ConvLayer(input_layer, nfilters[4], 1)
    net['5x5'] = ConvLayer(net['5x5_reduce'], nfilters[5], 5, pad=2)

    net['output'] = ConcatLayer([
        net['1x1'],
        net['3x3'],
        net['5x5'],
        net['pool_proj'],
        ])

    return {'{}/{}'.format(name, k): v for k, v in net.items()}


def build_model():
    net = {}
    net['input'] = InputLayer((None, 3, None, None))
    net['conv1/7x7_s2'] = ConvLayer(net['input'], 64, 7, stride=2, pad=3)
    net['pool1/3x3_s2'] = PoolLayer(net['conv1/7x7_s2'], 3, stride=2, ignore_border=False)
    net['pool1/norm1'] = LRNLayer(net['pool1/3x3_s2'], alpha=0.00002, k=1)
    net['conv2/3x3_reduce'] = ConvLayer(net['pool1/norm1'], 64, 1)
    net['conv2/3x3'] = ConvLayer(net['conv2/3x3_reduce'], 192, 3, pad=1)
    net['conv2/norm2'] = LRNLayer(net['conv2/3x3'], alpha=0.00002, k=1)
    net['pool2/3x3_s2'] = PoolLayer(net['conv2/norm2'], 3, stride=2)

    net.update(build_inception_module('inception_3a',
                                      net['pool2/3x3_s2'],
                                      [32, 64, 96, 128, 16, 32]))
    net.update(build_inception_module('inception_3b',
                                      net['inception_3a/output'],
                                      [64, 128, 128, 192, 32, 96]))
    net['pool3/3x3_s2'] = PoolLayer(net['inception_3b/output'], 3, stride=2)

    net.update(build_inception_module('inception_4a',
                                      net['pool3/3x3_s2'],
                                      [64, 192, 96, 208, 16, 48]))
    net.update(build_inception_module('inception_4b',
                                      net['inception_4a/output'],
                                      [64, 160, 112, 224, 24, 64]))
    net.update(build_inception_module('inception_4c',
                                      net['inception_4b/output'],
                                      [64, 128, 128, 256, 24, 64]))
    net.update(build_inception_module('inception_4d',
                                      net['inception_4c/output'],
                                      [64, 112, 144, 288, 32, 64]))
    net.update(build_inception_module('inception_4e',
                                      net['inception_4d/output'],
                                      [128, 256, 160, 320, 32, 128]))
    net['pool4/3x3_s2'] = PoolLayer(net['inception_4e/output'], 3, stride=2)

    net.update(build_inception_module('inception_5a',
                                      net['pool4/3x3_s2'],
                                      [128, 256, 160, 320, 32, 128]))
    net.update(build_inception_module('inception_5b',
                                      net['inception_5a/output'],
                                      [128, 384, 192, 384, 48, 128]))

    net['pool5/7x7_s1'] = GlobalPoolLayer(net['inception_5b/output'])
    net['loss3/classifier'] = DenseLayer(net['pool5/7x7_s1'],
                                         num_units=1000,
                                         nonlinearity=linear)
    net['prob'] = NonlinearityLayer(net['loss3/classifier'],
                                    nonlinearity=softmax)
    return net

Why do you use ignore_border=True? This prevent that layer to be on the GPU.