from __future__ import print_function
import collections
import os
import numpy
try:
from PIL import Image
available = True
except ImportError as e:
available = False
_import_error = e
from chainer.dataset.convert import concat_examples
from chainer.dataset import download
from chainer import flag
from chainer.functions.activation.relu import relu
from chainer.functions.activation.softmax import softmax
from chainer.functions.array.reshape import reshape
from chainer.functions.math.average import average
from chainer.functions.noise.dropout import dropout
from chainer.functions.normalization.local_response_normalization import (
local_response_normalization)
from chainer.functions.pooling.average_pooling_2d import average_pooling_2d
from chainer.functions.pooling.max_pooling_2d import max_pooling_2d
from chainer.initializers import constant
from chainer.initializers import uniform
from chainer import link
from chainer.links.connection.convolution_2d import Convolution2D
from chainer.links.connection.inception import Inception
from chainer.links.connection.linear import Linear
from chainer.serializers import npz
from chainer.utils import imgproc
from chainer.variable import Variable
[docs]class GoogLeNet(link.Chain):
"""A pre-trained GoogLeNet model provided by BVLC [1].
When you specify the path of the pre-trained chainer model serialized as
a ``.npz`` file in the constructor, this chain model automatically
initializes all the parameters with it.
This model would be useful when you want to extract a semantic feature
vector per image, or fine-tune the model on a different dataset.
If you want to manually convert the pre-trained caffemodel to a chainer
model that can be specified in the constructor,
please use ``convert_caffemodel_to_npz`` classmethod instead.
GoogLeNet, which is also called Inception-v1, is an architecture of
convolutional neural network proposed in 2014. This model is relatively
lightweight and requires small memory footprint during training compared
with modern architectures such as ResNet. Therefore, if you fine-tune your
network based on a model pre-trained by Imagenet and need to train it with
large batch size, GoogLeNet may be useful. On the other hand, if you just
want an off-the-shelf classifier, we recommend you to use ResNet50 or other
models since they are more accurate than GoogLeNet.
.. [1] `<https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet>`_
Args:
pretrained_model (str): the destination of the pre-trained
chainer model serialized as a ``.npz`` file.
If this argument is specified as ``auto``,
it automatically downloads the caffemodel from the internet.
Note that in this case the converted chainer model is stored
on ``$CHAINER_DATASET_ROOT/pfnet/chainer/models`` directory,
where ``$CHAINER_DATASET_ROOT`` is set as
``$HOME/.chainer/dataset`` unless you specify another value
as a environment variable. The converted chainer model is
automatically used from the second time.
If the argument is specified as ``None``, all the parameters
are not initialized by the pre-trained model, but the default
initializer used in BVLC, i.e.,
``chainer.initializers.LeCunUniform(scale=1.0)``.
Note that, in Caffe, when weight_filler is specified as
"xavier" type without variance_norm parameter, the weights are
initialized by Uniform(-s, s), where
:math:`s = \\sqrt{\\frac{3}{fan_{in}}}` and :math:`fan_{in}` is the
number of input units. This corresponds to LeCunUniform in Chainer
but not GlorotUniform.
Attributes:
available_layers (list of str): The list of available layer names
used by ``__call__`` and ``extract`` methods.
"""
def __init__(self, pretrained_model='auto'):
if pretrained_model:
# As a sampling process is time-consuming,
# we employ a zero initializer for faster computation.
kwargs = {'initialW': constant.Zero()}
else:
# employ default initializers used in BVLC. For more detail, see
# https://github.com/pfnet/chainer/pull/2424#discussion_r109642209
kwargs = {'initialW': uniform.LeCunUniform(scale=1.0)}
super(GoogLeNet, self).__init__(
conv1=Convolution2D(3, 64, 7, stride=2, pad=3, **kwargs),
conv2_reduce=Convolution2D(64, 64, 1, **kwargs),
conv2=Convolution2D(64, 192, 3, stride=1, pad=1, **kwargs),
inc3a=Inception(192, 64, 96, 128, 16, 32, 32),
inc3b=Inception(256, 128, 128, 192, 32, 96, 64),
inc4a=Inception(480, 192, 96, 208, 16, 48, 64),
inc4b=Inception(512, 160, 112, 224, 24, 64, 64),
inc4c=Inception(512, 128, 128, 256, 24, 64, 64),
inc4d=Inception(512, 112, 144, 288, 32, 64, 64),
inc4e=Inception(528, 256, 160, 320, 32, 128, 128),
inc5a=Inception(832, 256, 160, 320, 32, 128, 128),
inc5b=Inception(832, 384, 192, 384, 48, 128, 128),
loss3_fc=Linear(1024, 1000, **kwargs),
loss1_conv=Convolution2D(512, 128, 1, **kwargs),
loss1_fc1=Linear(2048, 1024, **kwargs),
loss1_fc2=Linear(1024, 1000, **kwargs),
loss2_conv=Convolution2D(528, 128, 1, **kwargs),
loss2_fc1=Linear(2048, 1024, **kwargs),
loss2_fc2=Linear(1024, 1000, **kwargs)
)
if pretrained_model == 'auto':
_retrieve(
'bvlc_googlenet.npz',
'http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel',
self)
elif pretrained_model:
npz.load_npz(pretrained_model, self)
self.functions = collections.OrderedDict([
('conv1', [self.conv1, relu]),
('pool1', [_max_pooling_2d, _local_response_normalization]),
('conv2_reduce', [self.conv2_reduce, relu]),
('conv2', [self.conv2, relu, _local_response_normalization]),
('pool2', [_max_pooling_2d]),
('inception_3a', [self.inc3a]),
('inception_3b', [self.inc3b]),
('pool3', [_max_pooling_2d]),
('inception_4a', [self.inc4a]),
('inception_4b', [self.inc4b]),
('inception_4c', [self.inc4c]),
('inception_4d', [self.inc4d]),
('inception_4e', [self.inc4e]),
('pool4', [_max_pooling_2d]),
('inception_5a', [self.inc5a]),
('inception_5b', [self.inc5b]),
('pool5', [_average_pooling_2d_k7]),
('loss3_fc', [_dropout, self.loss3_fc]),
('prob', [softmax]),
# Since usually the following outputs are not used, they are put
# after 'prob' to be skipped for efficiency.
('loss1_fc2', [_average_pooling_2d_k5, self.loss1_conv, relu,
self.loss1_fc1, relu, self.loss1_fc2]),
('loss2_fc2', [_average_pooling_2d_k5, self.loss2_conv, relu,
self.loss2_fc1, relu, self.loss2_fc2])
])
@property
def available_layers(self):
return list(self.functions.keys())
@classmethod
[docs] def convert_caffemodel_to_npz(cls, path_caffemodel, path_npz):
"""Converts a pre-trained caffemodel to a chainer model.
Args:
path_caffemodel (str): Path of the pre-trained caffemodel.
path_npz (str): Path of the converted chainer model.
"""
# As CaffeFunction uses shortcut symbols,
# we import CaffeFunction here.
from chainer.links.caffe.caffe_function import CaffeFunction
caffemodel = CaffeFunction(path_caffemodel)
chainermodel = cls(pretrained_model=None)
_transfer_googlenet(caffemodel, chainermodel)
npz.save_npz(path_npz, chainermodel, compression=False)
[docs] def __call__(self, x, layers=['prob'], train=False):
"""Computes all the feature maps specified by ``layers``.
Args:
x (~chainer.Variable): Input variable. It should be prepared by
``prepare`` function.
layers (list of str): The list of layer names you want to extract.
train (bool): If ``True``, Dropout runs in training mode.
Returns:
Dictionary of ~chainer.Variable: A directory in which
the key contains the layer name and the value contains
the corresponding feature map variable.
"""
h = x
activations = {}
inception_4a_cache = None
inception_4d_cache = None
target_layers = set(layers)
for key, funcs in self.functions.items():
if len(target_layers) == 0:
break
if key == 'loss1_fc2':
h = inception_4a_cache
elif key == 'loss2_fc2':
h = inception_4d_cache
for func in funcs:
if func is _dropout:
h = func(h, train=train)
else:
h = func(h)
if key in target_layers:
activations[key] = h
target_layers.remove(key)
if key == 'inception_4a':
inception_4a_cache = h
elif key == 'inception_4d':
inception_4d_cache = h
return activations
[docs] def predict(self, images, oversample=True):
"""Computes all the probabilities of given images.
Args:
images (iterable of PIL.Image or numpy.ndarray): Input images.
oversample (bool): If ``True``, it averages results across
center, corners, and mirrors. Otherwise, it uses only the
center.
Returns:
~chainer.Variable: Output that contains the class probabilities
of given images.
"""
x = concat_examples([prepare(img, size=(256, 256)) for img in images])
if oversample:
x = imgproc.oversample(x, crop_dims=(224, 224))
else:
x = x[:, :, 16:240, 16:240]
# Set volatile option to ON to reduce memory consumption
x = Variable(self.xp.asarray(x), volatile=flag.ON)
y = self(x, layers=['prob'])['prob']
if oversample:
n = y.data.shape[0] // 10
y_shape = y.data.shape[1:]
y = reshape(y, (n, 10) + y_shape)
y = average(y, axis=1)
return y
[docs]def prepare(image, size=(224, 224)):
"""Converts the given image to the numpy array for GoogLeNet.
Note that you have to call this method before ``__call__``
because the pre-trained GoogLeNet model requires to resize the given
image, covert the RGB to the BGR, subtract the mean,
and permute the dimensions before calling.
Args:
image (PIL.Image or numpy.ndarray): Input image.
If an input is ``numpy.ndarray``, its shape must be
``(height, width)``, ``(height, width, channels)``,
or ``(channels, height, width)``, and
the order of the channels must be RGB.
size (pair of ints): Size of converted images.
If ``None``, the given image is not resized.
Returns:
numpy.ndarray: The converted output array.
"""
if not available:
raise ImportError('PIL cannot be loaded. Install Pillow!\n'
'The actual import error is as follows:\n' +
str(_import_error))
if isinstance(image, numpy.ndarray):
if image.ndim == 3:
if image.shape[0] == 1:
image = image[0, :, :]
elif image.shape[0] == 3:
image = image.transpose((1, 2, 0))
image = Image.fromarray(image.astype(numpy.uint8))
image = image.convert('RGB')
if size:
image = image.resize(size)
image = numpy.asarray(image, dtype=numpy.float32)
image = image[:, :, ::-1]
image -= numpy.array([104.0, 117.0, 123.0], dtype=numpy.float32) # BGR
image = image.transpose((2, 0, 1))
return image
def _transfer_inception(src, dst, names):
for name in names:
chain = getattr(dst, 'inc{}'.format(name))
src_prefix = 'inception_{}/'.format(name)
chain.conv1.W.data[:] = src[src_prefix + '1x1'].W.data
chain.conv1.b.data[:] = src[src_prefix + '1x1'].b.data
chain.proj3.W.data[:] = src[src_prefix + '3x3_reduce'].W.data
chain.proj3.b.data[:] = src[src_prefix + '3x3_reduce'].b.data
chain.conv3.W.data[:] = src[src_prefix + '3x3'].W.data
chain.conv3.b.data[:] = src[src_prefix + '3x3'].b.data
chain.proj5.W.data[:] = src[src_prefix + '5x5_reduce'].W.data
chain.proj5.b.data[:] = src[src_prefix + '5x5_reduce'].b.data
chain.conv5.W.data[:] = src[src_prefix + '5x5'].W.data
chain.conv5.b.data[:] = src[src_prefix + '5x5'].b.data
chain.projp.W.data[:] = src[src_prefix + 'pool_proj'].W.data
chain.projp.b.data[:] = src[src_prefix + 'pool_proj'].b.data
def _transfer_googlenet(src, dst):
# 1 #################################################################
dst.conv1.W.data[:] = src['conv1/7x7_s2'].W.data
dst.conv1.b.data[:] = src['conv1/7x7_s2'].b.data
# 2 #################################################################
dst.conv2_reduce.W.data[:] = src['conv2/3x3_reduce'].W.data
dst.conv2_reduce.b.data[:] = src['conv2/3x3_reduce'].b.data
dst.conv2.W.data[:] = src['conv2/3x3'].W.data
dst.conv2.b.data[:] = src['conv2/3x3'].b.data
# 3, 4, 5 ###########################################################
_transfer_inception(src, dst, ['3a', '3b',
'4a', '4b', '4c', '4d', '4e',
'5a', '5b'])
# outputs ############################################################
dst.loss1_conv.W.data[:] = src['loss1/conv'].W.data
dst.loss1_conv.b.data[:] = src['loss1/conv'].b.data
dst.loss1_fc1.W.data[:] = src['loss1/fc'].W.data
dst.loss1_fc1.b.data[:] = src['loss1/fc'].b.data
dst.loss1_fc2.W.data[:] = src['loss1/classifier'].W.data
dst.loss1_fc2.b.data[:] = src['loss1/classifier'].b.data
dst.loss2_conv.W.data[:] = src['loss2/conv'].W.data
dst.loss2_conv.b.data[:] = src['loss2/conv'].b.data
dst.loss2_fc1.W.data[:] = src['loss2/fc'].W.data
dst.loss2_fc1.b.data[:] = src['loss2/fc'].b.data
dst.loss2_fc2.W.data[:] = src['loss2/classifier'].W.data
dst.loss2_fc2.b.data[:] = src['loss2/classifier'].b.data
dst.loss3_fc.W.data[:] = src['loss3/classifier'].W.data
dst.loss3_fc.b.data[:] = src['loss3/classifier'].b.data
def _max_pooling_2d(x):
return max_pooling_2d(x, ksize=3, stride=2)
def _local_response_normalization(x):
return local_response_normalization(x, n=5, k=1, alpha=1e-4 / 5)
def _average_pooling_2d_k5(x):
return average_pooling_2d(x, ksize=5, stride=3)
def _average_pooling_2d_k7(x):
return average_pooling_2d(x, ksize=7, stride=1)
def _dropout(x, train):
return dropout(x, ratio=0.4, train=train)
def _make_npz(path_npz, url, model):
path_caffemodel = download.cached_download(url)
print('Now loading caffemodel (usually it may take few minutes)')
GoogLeNet.convert_caffemodel_to_npz(path_caffemodel, path_npz)
npz.load_npz(path_npz, model)
return model
def _retrieve(name_npz, url, model):
root = download.get_dataset_directory('pfnet/chainer/models/')
path = os.path.join(root, name_npz)
return download.cache_or_load_file(
path, lambda path: _make_npz(path, url, model),
lambda path: npz.load_npz(path, model))