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add flip_filters, fix import and some pep8
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@ebenolson
ebenolson committed Feb 12, 2016
1 parent 8b78ab8 commit fce42d0
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Showing 5 changed files with 125 additions and 64 deletions.
27 changes: 18 additions & 9 deletions modelzoo/cifar10_nin.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,9 +16,12 @@ def build_model():
net['conv1'] = ConvLayer(net['input'],
num_filters=192,
filter_size=5,
pad=2)
net['cccp1'] = ConvLayer(net['conv1'], num_filters=160, filter_size=1)
net['cccp2'] = ConvLayer(net['cccp1'], num_filters=96, filter_size=1)
pad=2,
flip_filters=False)
net['cccp1'] = ConvLayer(
net['conv1'], num_filters=160, filter_size=1, flip_filters=False)
net['cccp2'] = ConvLayer(
net['cccp1'], num_filters=96, filter_size=1, flip_filters=False)
net['pool1'] = PoolLayer(net['cccp2'],
pool_size=3,
stride=2,
Expand All @@ -28,9 +31,12 @@ def build_model():
net['conv2'] = ConvLayer(net['drop3'],
num_filters=192,
filter_size=5,
pad=2)
net['cccp3'] = ConvLayer(net['conv2'], num_filters=192, filter_size=1)
net['cccp4'] = ConvLayer(net['cccp3'], num_filters=192, filter_size=1)
pad=2,
flip_filters=False)
net['cccp3'] = ConvLayer(
net['conv2'], num_filters=192, filter_size=1, flip_filters=False)
net['cccp4'] = ConvLayer(
net['cccp3'], num_filters=192, filter_size=1, flip_filters=False)
net['pool2'] = PoolLayer(net['cccp4'],
pool_size=3,
stride=2,
Expand All @@ -40,9 +46,12 @@ def build_model():
net['conv3'] = ConvLayer(net['drop6'],
num_filters=192,
filter_size=3,
pad=1)
net['cccp5'] = ConvLayer(net['conv3'], num_filters=192, filter_size=1)
net['cccp6'] = ConvLayer(net['cccp5'], num_filters=10, filter_size=1)
pad=1,
flip_filters=False)
net['cccp5'] = ConvLayer(
net['conv3'], num_filters=192, filter_size=1, flip_filters=False)
net['cccp6'] = ConvLayer(
net['cccp5'], num_filters=10, filter_size=1, flip_filters=False)
net['pool3'] = PoolLayer(net['cccp6'],
pool_size=8,
mode='average_exc_pad',
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32 changes: 19 additions & 13 deletions modelzoo/googlenet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,15 +23,20 @@ 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['pool_proj'] = ConvLayer(
net['pool'], nfilters[0], 1, flip_filters=False)

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

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

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

net['output'] = ConcatLayer([
net['1x1'],
Expand All @@ -46,14 +51,15 @@ def build_inception_module(name, input_layer, nfilters):
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'],
pool_size=3,
stride=2,
ignore_border=False)
net['conv1/7x7_s2'] = ConvLayer(
net['input'], 64, 7, stride=2, pad=3, flip_filters=False)
net['pool1/3x3_s2'] = PoolLayer(
net['conv1/7x7_s2'], pool_size=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['conv2/3x3_reduce'] = ConvLayer(
net['pool1/norm1'], 64, 1, flip_filters=False)
net['conv2/3x3'] = ConvLayer(
net['conv2/3x3_reduce'], 192, 3, pad=1, flip_filters=False)
net['conv2/norm2'] = LRNLayer(net['conv2/3x3'], alpha=0.00002, k=1)
net['pool2/3x3_s2'] = PoolLayer(net['conv2/norm2'], pool_size=3, stride=2)

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49 changes: 33 additions & 16 deletions modelzoo/vgg16.py
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Expand Up @@ -6,38 +6,55 @@
# Download pretrained weights from:
# https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg16.pkl

from lasagne.layers import InputLayer, DenseLayer, NonlinearityLayer, DropoutLayer
from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
from lasagne.layers import InputLayer
from lasagne.layers import DenseLayer
from lasagne.layers import NonlinearityLayer
from lasagne.layers import DropoutLayer
from lasagne.layers import Pool2DLayer as PoolLayer
from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
from lasagne.nonlinearities import softmax


def build_model():
net = {}
net['input'] = InputLayer((None, 3, 224, 224))
net['conv1_1'] = ConvLayer(net['input'], 64, 3, pad=1)
net['conv1_2'] = ConvLayer(net['conv1_1'], 64, 3, pad=1)
net['conv1_1'] = ConvLayer(
net['input'], 64, 3, pad=1, flip_filters=False)
net['conv1_2'] = ConvLayer(
net['conv1_1'], 64, 3, pad=1, flip_filters=False)
net['pool1'] = PoolLayer(net['conv1_2'], 2)
net['conv2_1'] = ConvLayer(net['pool1'], 128, 3, pad=1)
net['conv2_2'] = ConvLayer(net['conv2_1'], 128, 3, pad=1)
net['conv2_1'] = ConvLayer(
net['pool1'], 128, 3, pad=1, flip_filters=False)
net['conv2_2'] = ConvLayer(
net['conv2_1'], 128, 3, pad=1, flip_filters=False)
net['pool2'] = PoolLayer(net['conv2_2'], 2)
net['conv3_1'] = ConvLayer(net['pool2'], 256, 3, pad=1)
net['conv3_2'] = ConvLayer(net['conv3_1'], 256, 3, pad=1)
net['conv3_3'] = ConvLayer(net['conv3_2'], 256, 3, pad=1)
net['conv3_1'] = ConvLayer(
net['pool2'], 256, 3, pad=1, flip_filters=False)
net['conv3_2'] = ConvLayer(
net['conv3_1'], 256, 3, pad=1, flip_filters=False)
net['conv3_3'] = ConvLayer(
net['conv3_2'], 256, 3, pad=1, flip_filters=False)
net['pool3'] = PoolLayer(net['conv3_3'], 2)
net['conv4_1'] = ConvLayer(net['pool3'], 512, 3, pad=1)
net['conv4_2'] = ConvLayer(net['conv4_1'], 512, 3, pad=1)
net['conv4_3'] = ConvLayer(net['conv4_2'], 512, 3, pad=1)
net['conv4_1'] = ConvLayer(
net['pool3'], 512, 3, pad=1, flip_filters=False)
net['conv4_2'] = ConvLayer(
net['conv4_1'], 512, 3, pad=1, flip_filters=False)
net['conv4_3'] = ConvLayer(
net['conv4_2'], 512, 3, pad=1, flip_filters=False)
net['pool4'] = PoolLayer(net['conv4_3'], 2)
net['conv5_1'] = ConvLayer(net['pool4'], 512, 3, pad=1)
net['conv5_2'] = ConvLayer(net['conv5_1'], 512, 3, pad=1)
net['conv5_3'] = ConvLayer(net['conv5_2'], 512, 3, pad=1)
net['conv5_1'] = ConvLayer(
net['pool4'], 512, 3, pad=1, flip_filters=False)
net['conv5_2'] = ConvLayer(
net['conv5_1'], 512, 3, pad=1, flip_filters=False)
net['conv5_3'] = ConvLayer(
net['conv5_2'], 512, 3, pad=1, flip_filters=False)
net['pool5'] = PoolLayer(net['conv5_3'], 2)
net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
net['fc6_dropout'] = DropoutLayer(net['fc6'], p=0.5)
net['fc7'] = DenseLayer(net['fc6_dropout'], num_units=4096)
net['fc7_dropout'] = DropoutLayer(net['fc7'], p=0.5)
net['fc8'] = DenseLayer(net['fc7_dropout'], num_units=1000, nonlinearity=None)
net['fc8'] = DenseLayer(
net['fc7_dropout'], num_units=1000, nonlinearity=None)
net['prob'] = NonlinearityLayer(net['fc8'], softmax)

return net
58 changes: 39 additions & 19 deletions modelzoo/vgg19.py
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Expand Up @@ -6,41 +6,61 @@
# Download pretrained weights from:
# https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg19.pkl

from lasagne.layers import InputLayer, DenseLayer, NonlinearityLayer
from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
from lasagne.layers import InputLayer
from lasagne.layers import DenseLayer
from lasagne.layers import NonlinearityLayer
from lasagne.layers import DropoutLayer
from lasagne.layers import Pool2DLayer as PoolLayer
from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
from lasagne.nonlinearities import softmax


def build_model():
net = {}
net['input'] = InputLayer((None, 3, 224, 224))
net['conv1_1'] = ConvLayer(net['input'], 64, 3, pad=1)
net['conv1_2'] = ConvLayer(net['conv1_1'], 64, 3, pad=1)
net['conv1_1'] = ConvLayer(
net['input'], 64, 3, pad=1, flip_filters=False)
net['conv1_2'] = ConvLayer(
net['conv1_1'], 64, 3, pad=1, flip_filters=False)
net['pool1'] = PoolLayer(net['conv1_2'], 2)
net['conv2_1'] = ConvLayer(net['pool1'], 128, 3, pad=1)
net['conv2_2'] = ConvLayer(net['conv2_1'], 128, 3, pad=1)
net['conv2_1'] = ConvLayer(
net['pool1'], 128, 3, pad=1, flip_filters=False)
net['conv2_2'] = ConvLayer(
net['conv2_1'], 128, 3, pad=1, flip_filters=False)
net['pool2'] = PoolLayer(net['conv2_2'], 2)
net['conv3_1'] = ConvLayer(net['pool2'], 256, 3, pad=1)
net['conv3_2'] = ConvLayer(net['conv3_1'], 256, 3, pad=1)
net['conv3_3'] = ConvLayer(net['conv3_2'], 256, 3, pad=1)
net['conv3_4'] = ConvLayer(net['conv3_3'], 256, 3, pad=1)
net['conv3_1'] = ConvLayer(
net['pool2'], 256, 3, pad=1, flip_filters=False)
net['conv3_2'] = ConvLayer(
net['conv3_1'], 256, 3, pad=1, flip_filters=False)
net['conv3_3'] = ConvLayer(
net['conv3_2'], 256, 3, pad=1, flip_filters=False)
net['conv3_4'] = ConvLayer(
net['conv3_3'], 256, 3, pad=1, flip_filters=False)
net['pool3'] = PoolLayer(net['conv3_4'], 2)
net['conv4_1'] = ConvLayer(net['pool3'], 512, 3, pad=1)
net['conv4_2'] = ConvLayer(net['conv4_1'], 512, 3, pad=1)
net['conv4_3'] = ConvLayer(net['conv4_2'], 512, 3, pad=1)
net['conv4_4'] = ConvLayer(net['conv4_3'], 512, 3, pad=1)
net['conv4_1'] = ConvLayer(
net['pool3'], 512, 3, pad=1, flip_filters=False)
net['conv4_2'] = ConvLayer(
net['conv4_1'], 512, 3, pad=1, flip_filters=False)
net['conv4_3'] = ConvLayer(
net['conv4_2'], 512, 3, pad=1, flip_filters=False)
net['conv4_4'] = ConvLayer(
net['conv4_3'], 512, 3, pad=1, flip_filters=False)
net['pool4'] = PoolLayer(net['conv4_4'], 2)
net['conv5_1'] = ConvLayer(net['pool4'], 512, 3, pad=1)
net['conv5_2'] = ConvLayer(net['conv5_1'], 512, 3, pad=1)
net['conv5_3'] = ConvLayer(net['conv5_2'], 512, 3, pad=1)
net['conv5_4'] = ConvLayer(net['conv5_3'], 512, 3, pad=1)
net['conv5_1'] = ConvLayer(
net['pool4'], 512, 3, pad=1, flip_filters=False)
net['conv5_2'] = ConvLayer(
net['conv5_1'], 512, 3, pad=1, flip_filters=False)
net['conv5_3'] = ConvLayer(
net['conv5_2'], 512, 3, pad=1, flip_filters=False)
net['conv5_4'] = ConvLayer(
net['conv5_3'], 512, 3, pad=1, flip_filters=False)
net['pool5'] = PoolLayer(net['conv5_4'], 2)
net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
net['fc6_dropout'] = DropoutLayer(net['fc6'], p=0.5)
net['fc7'] = DenseLayer(net['fc6_dropout'], num_units=4096)
net['fc7_dropout'] = DropoutLayer(net['fc7'], p=0.5)
net['fc8'] = DenseLayer(net['fc7_dropout'], num_units=1000, nonlinearity=None)
net['fc8'] = DenseLayer(
net['fc7_dropout'], num_units=1000, nonlinearity=None)
net['prob'] = NonlinearityLayer(net['fc8'], softmax)

return net
23 changes: 16 additions & 7 deletions modelzoo/vgg_cnn_s.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,11 +7,13 @@
# Download pretrained weights from:
# https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg_cnn_s.pkl

from lasagne.layers import InputLayer, DenseLayer, DropoutLayer
from lasagne.layers import DenseLayer
from lasagne.layers import DropoutLayer
from lasagne.layers import InputLayer
from lasagne.layers import LocalResponseNormalization2DLayer as NormLayer
from lasagne.layers import NonlinearityLayer
from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
from lasagne.layers import MaxPool2DLayer as PoolLayer
from lasagne.layers import LocalResponseNormalization2DLayer as NormLayer
from lasagne.nonlinearities import softmax


Expand All @@ -22,30 +24,37 @@ def build_model():
net['conv1'] = ConvLayer(net['input'],
num_filters=96,
filter_size=7,
stride=2)
stride=2,
flip_filters=False)
# caffe has alpha = alpha * pool_size
net['norm1'] = NormLayer(net['conv1'], alpha=0.0001)
net['pool1'] = PoolLayer(net['norm1'],
pool_size=3,
stride=3,
ignore_border=False)
net['conv2'] = ConvLayer(net['pool1'], num_filters=256, filter_size=5)
net['conv2'] = ConvLayer(net['pool1'],
num_filters=256,
filter_size=5,
flip_filters=False)
net['pool2'] = PoolLayer(net['conv2'],
pool_size=2,
stride=2,
ignore_border=False)
net['conv3'] = ConvLayer(net['pool2'],
num_filters=512,
filter_size=3,
pad=1)
pad=1,
flip_filters=False)
net['conv4'] = ConvLayer(net['conv3'],
num_filters=512,
filter_size=3,
pad=1)
pad=1,
flip_filters=False)
net['conv5'] = ConvLayer(net['conv4'],
num_filters=512,
filter_size=3,
pad=1)
pad=1,
flip_filters=False)
net['pool5'] = PoolLayer(net['conv5'],
pool_size=3,
stride=3,
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