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vgg16.py
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vgg16.py
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# VGG-16, 16-layer model from the paper:
# "Very Deep Convolutional Networks for Large-Scale Image Recognition"
# Original source: https://gist.github.com/ksimonyan/211839e770f7b538e2d8
# License: non-commercial use only
# Download pretrained weights from:
# https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg16.pkl
from collections import OrderedDict
import numpy
try:
import cPickle as pickle
except:
import pickle
import lasagne
import lasagne.layers
from lasagne.layers import (InputLayer, DenseLayer,
NonlinearityLayer, ConcatLayer)
from lasagne.nonlinearities import softmax
from padded import PaddedConv2DLayer
from padded import PaddedPool2DLayer
import theano
class Vgg16Layer(lasagne.layers.Layer):
def __init__(self,
l_in=InputLayer((None, 3, 224, 224)),
get_layer='prob',
padded=True,
trainable=False,
regularizable=False,
name='vgg'):
super(Vgg16Layer, self).__init__(l_in, name)
self.l_in = l_in
self.get_layer = get_layer
self.padded = padded
self.trainable = trainable
self.regularizable = regularizable
if padded:
ConvLayer = PaddedConv2DLayer
PoolLayer = PaddedPool2DLayer
else:
try:
ConvLayer = lasagne.layers.dnn.Conv2DDNNLayer
except AttributeError:
ConvLayer = lasagne.layers.Conv2DLayer
PoolLayer = lasagne.layers.Pool2DLayer
net = OrderedDict()
net['input'] = l_in
net['bgr'] = RGBtoBGRLayer(net['input'])
net['conv1_1'] = ConvLayer(
net['bgr'], 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, 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, 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, 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, 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)
if 'fc' in get_layer or get_layer == 'prob':
net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
net['fc7'] = DenseLayer(net['fc6'], num_units=4096)
net['fc8'] = DenseLayer(net['fc7'],
num_units=1000,
nonlinearity=None)
net['prob'] = NonlinearityLayer(net['fc8'], softmax)
self.concat_sublayers = []
if 'concat' in get_layer:
n_pool = get_layer[6:]
get_layer = 'pool' + str(n_pool)
l_concat = net['conv1_1']
for i in range(int(n_pool)):
l_conv = net['conv' + str(i+1) + '_1']
l_pool = net['pool' + str(i+1)]
l_new = ConvLayer(
l_concat, l_conv.num_filters, 2, pad=0, stride=2,
flip_filters=True,
name='vgg16_skipconnection_conv_' + str(i+1))
self.concat_sublayers.append(l_new)
l_concat = ConcatLayer(
(l_pool, l_new), axis=1,
name='vgg16_skipconnection_concat_' + str(i))
self.concat_sublayers.append(l_concat)
out_layer = l_concat
else:
out_layer = net[get_layer]
reached = False
# Collect garbage
for el in net.iteritems():
if reached:
del(net[el[0]])
if el[0] == get_layer:
reached = True
self.sublayers = net
# Set names to layers
for name in net.keys():
if not net[name].name:
net[name].name = 'vgg16_' + name
# Reload weights
nparams = len(lasagne.layers.get_all_params(net.values()))
with open('w_vgg16.pkl', 'rb') as f:
# Note: in python3 use the pickle.load parameter
# `encoding='latin-1'`
vgg16_w = pickle.load(f)['param values']
lasagne.layers.set_all_param_values(net.values(), vgg16_w[:nparams])
# Do not train or regularize vgg
if not trainable or not regularizable:
all_layers = net.values()
for vgg_layer in all_layers:
if 'concat' not in vgg_layer.name:
layer_params = vgg_layer.get_params()
for p in layer_params:
if not regularizable:
try:
vgg_layer.params[p].remove('regularizable')
except KeyError:
pass
if not trainable:
try:
vgg_layer.params[p].remove('trainable')
except KeyError:
pass
# save the vgg sublayers
self.out_layer = out_layer
# HACK LASAGNE
# This will set `self.input_layer`, which is needed by Lasagne to find
# the layers with the get_all_layers() helper function in the
# case of a layer with sublayers
if isinstance(self.out_layer, tuple):
self.input_layer = None
else:
self.input_layer = self.out_layer
def get_output_for(self, input_var, **kwargs):
# HACK LASAGNE
# This is needed, jointly with the previous hack, to ensure that
# this layer behaves as its last sublayer (namely,
# self.input_layer)
return input_var
def get_output_shape_for(self, input_shape):
c_input_shape = input_shape
# iterate through vgg
for name, layer in self.sublayers.items()[1:]:
output_shape = layer.get_output_shape_for(input_shape)
input_shape = output_shape
# iterate through the parallel network if any
for layer in self.concat_sublayers:
if isinstance(layer, ConcatLayer):
c_input_shape = (c_input_shape, c_input_shape)
output_shape = layer.get_output_shape_for(c_input_shape)
c_input_shape = output_shape
return output_shape
class RGBtoBGRLayer(lasagne.layers.Layer):
def __init__(self, l_in, bgr_mean=numpy.array([103.939, 116.779, 123.68]),
data_format='bc01', **kwargs):
"""A Layer to normalize and convert images from RGB to BGR
This layer converts images from RGB to BGR to adapt to Caffe
that uses OpenCV, which uses BGR. It also subtracts the
per-pixel mean.
Parameters
----------
l_in : :class:``lasagne.layers.Layer``
The incoming layer, typically an
:class:``lasagne.layers.InputLayer``
bgr_mean : iterable of 3 ints
The mean of each channel. By default, the ImageNet
mean values are used.
data_format : str
The format of l_in, either `b01c` (batch, rows, cols,
channels) or `bc01` (batch, channels, rows, cols)
"""
super(RGBtoBGRLayer, self).__init__(l_in, **kwargs)
assert data_format in ['bc01', 'b01c']
self.l_in = l_in
floatX = theano.config.floatX
self.bgr_mean = bgr_mean.astype(floatX)
self.data_format = data_format
def get_output_for(self, input_im, **kwargs):
if self.data_format == 'bc01':
input_im = input_im[:, ::-1, :, :]
input_im -= self.bgr_mean[:, numpy.newaxis, numpy.newaxis]
else:
input_im = input_im[:, :, :, ::-1]
input_im -= self.bgr_mean
return input_im