diff --git a/bob/__init__.py b/bob/__init__.py
index 2ab1e28b150f0549def9963e9e87de3fdd6b2579..edbb4090fca046b19d22d3982711084621bff3be 100644
--- a/bob/__init__.py
+++ b/bob/__init__.py
@@ -1,3 +1,4 @@
# see https://docs.python.org/3/library/pkgutil.html
from pkgutil import extend_path
+
__path__ = extend_path(__path__, __name__)
diff --git a/bob/ip/__init__.py b/bob/ip/__init__.py
index 2ab1e28b150f0549def9963e9e87de3fdd6b2579..edbb4090fca046b19d22d3982711084621bff3be 100644
--- a/bob/ip/__init__.py
+++ b/bob/ip/__init__.py
@@ -1,3 +1,4 @@
# see https://docs.python.org/3/library/pkgutil.html
from pkgutil import extend_path
+
__path__ = extend_path(__path__, __name__)
diff --git a/bob/ip/tensorflow_extractor/DrGanMSU.py b/bob/ip/tensorflow_extractor/DrGanMSU.py
deleted file mode 100644
index 65caaa1f422f538995484aa307b1f95773e9721b..0000000000000000000000000000000000000000
--- a/bob/ip/tensorflow_extractor/DrGanMSU.py
+++ /dev/null
@@ -1,453 +0,0 @@
-#!/usr/bin/env python
-# encoding: utf-8
-
-
-import numpy
-import tensorflow as tf
-import os
-from bob.extension import rc
-import logging
-import bob.extension.download
-import bob.io.base
-logger = logging.getLogger(__name__)
-
-
-class batch_norm(object):
- """Code modification of http://stackoverflow.com/a/33950177"""
-
- def __init__(self, epsilon=1e-5, momentum=0.9, name="batch_norm"):
- with tf.variable_scope(name):
- self.epsilon = epsilon
- self.momentum = momentum
- self.name = name
-
- def __call__(self, x, train=True, reuse=False):
- return tf.contrib.layers.batch_norm(x,
- decay=self.momentum,
- updates_collections=None,
- epsilon=self.epsilon,
- scale=True,
- reuse=reuse,
- is_training=train,
- scope=self.name)
-
-
-def conv2d(input_, output_dim,
- k_h=3, k_w=3, d_h=2, d_w=2, stddev=0.02,
- name="conv2d", reuse=False):
- with tf.variable_scope(name, reuse=reuse):
- w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],
- initializer=tf.truncated_normal_initializer(stddev=stddev))
- conv = tf.nn.conv2d(
- input_, w, strides=[
- 1, d_h, d_w, 1], padding='SAME')
-
- biases = tf.get_variable(
- 'biases',
- [output_dim],
- initializer=tf.constant_initializer(0.0))
- conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
-
- return conv
-
-
-def elu(x, name='elu'):
- return tf.nn.elu(x, name)
-
-
-class DRGAN(object):
- """
- class implementing the DR GAN, as described in
-
-
- Note: this code has orginally been done by L.Tran @ MSU, and is heavily influenced
- by the DCGAN code here:
-
- I simplified it to the maximum to increase readability, and to limit its usage
- to face image encoding.
-
- **Parameters**
-
- image_size: int
- The size of the (squared) image.
-
- gf_dim: int
- The number of feature maps in the first convolutional layer (encoder and discriminator).
-
- gfc_dim: int
- The dimension of the encoded id (output of the encoder).
- """
-
- def __init__(self, image_size=96, gf_dim=32, gfc_dim=320):
-
- self.image_size = image_size
- self.gf_dim = gf_dim
- self.gfc_dim = gfc_dim
-
- # batch normalization
- self.g_bn0_0 = batch_norm(name='g_k_bn0_0')
- self.g_bn0_1 = batch_norm(name='g_k_bn0_1')
- self.g_bn0_2 = batch_norm(name='g_k_bn0_2')
- self.g_bn0_3 = batch_norm(name='g_k_bn0_3')
- self.g_bn1_0 = batch_norm(name='g_k_bn1_0')
- self.g_bn1_1 = batch_norm(name='g_k_bn1_1')
- self.g_bn1_2 = batch_norm(name='g_k_bn1_2')
- self.g_bn1_3 = batch_norm(name='g_k_bn1_3')
- self.g_bn2_0 = batch_norm(name='g_k_bn2_0')
- self.g_bn2_1 = batch_norm(name='g_k_bn2_1')
- self.g_bn2_2 = batch_norm(name='g_k_bn2_2')
- self.g_bn2_3 = batch_norm(name='g_k_bn2_3')
- self.g_bn3_0 = batch_norm(name='g_k_bn3_0')
- self.g_bn3_1 = batch_norm(name='g_k_bn3_1')
- self.g_bn3_2 = batch_norm(name='g_k_bn3_2')
- self.g_bn3_3 = batch_norm(name='g_k_bn3_3')
- self.g_bn4_0 = batch_norm(name='g_k_bn4_0')
- self.g_bn4_1 = batch_norm(name='g_k_bn4_1')
- self.g_bn4_2 = batch_norm(name='g_k_bn4_2')
- self.g_bn4_c = batch_norm(name='g_h_bn4_c')
- self.g_bn5 = batch_norm(name='g_k_bn5')
-
- def generator_encoder(self, image, is_reuse=False, is_training=True):
- """ Function that define the graph doing the encoding of a face image.
-
- **Parameters**
-
- image: numpy array
- The input image
-
- is_reuse: bool
- Reuse variables.
-
- is_training: bool
- Flag for training mode
-
- **Returns**
- The encoded id
- """
- s16 = int(self.image_size / 16)
- k0_0 = image
-
- k0_1 = elu(
- self.g_bn0_1(
- conv2d(
- k0_0,
- self.gf_dim * 1,
- d_h=1,
- d_w=1,
- name='g_k01_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k01_prelu')
- k0_2 = elu(
- self.g_bn0_2(
- conv2d(
- k0_1,
- self.gf_dim * 2,
- d_h=1,
- d_w=1,
- name='g_k02_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k02_prelu')
-
- k1_0 = elu(
- self.g_bn1_0(
- conv2d(
- k0_2,
- self.gf_dim * 2,
- d_h=2,
- d_w=2,
- name='g_k10_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k10_prelu')
- k1_1 = elu(
- self.g_bn1_1(
- conv2d(
- k1_0,
- self.gf_dim * 2,
- d_h=1,
- d_w=1,
- name='g_k11_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k11_prelu')
- k1_2 = elu(
- self.g_bn1_2(
- conv2d(
- k1_1,
- self.gf_dim * 4,
- d_h=1,
- d_w=1,
- name='g_k12_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k12_prelu')
-
- k2_0 = elu(
- self.g_bn2_0(
- conv2d(
- k1_2,
- self.gf_dim * 4,
- d_h=2,
- d_w=2,
- name='g_k20_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k20_prelu')
- k2_1 = elu(
- self.g_bn2_1(
- conv2d(
- k2_0,
- self.gf_dim * 3,
- d_h=1,
- d_w=1,
- name='g_k21_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k21_prelu')
- k2_2 = elu(
- self.g_bn2_2(
- conv2d(
- k2_1,
- self.gf_dim * 6,
- d_h=1,
- d_w=1,
- name='g_k22_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k22_prelu')
-
- k3_0 = elu(
- self.g_bn3_0(
- conv2d(
- k2_2,
- self.gf_dim * 6,
- d_h=2,
- d_w=2,
- name='g_k30_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k30_prelu')
- k3_1 = elu(
- self.g_bn3_1(
- conv2d(
- k3_0,
- self.gf_dim * 4,
- d_h=1,
- d_w=1,
- name='g_k31_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k31_prelu')
- k3_2 = elu(
- self.g_bn3_2(
- conv2d(
- k3_1,
- self.gf_dim * 8,
- d_h=1,
- d_w=1,
- name='g_k32_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k32_prelu')
-
- k4_0 = elu(
- self.g_bn4_0(
- conv2d(
- k3_2,
- self.gf_dim * 8,
- d_h=2,
- d_w=2,
- name='g_k40_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k40_prelu')
- k4_1 = elu(
- self.g_bn4_1(
- conv2d(
- k4_0,
- self.gf_dim * 5,
- d_h=1,
- d_w=1,
- name='g_k41_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse),
- name='g_k41_prelu')
- k4_2 = self.g_bn4_2(
- conv2d(
- k4_1,
- self.gfc_dim,
- d_h=1,
- d_w=1,
- name='g_k42_conv',
- reuse=is_reuse),
- train=is_training,
- reuse=is_reuse)
-
- k5 = tf.nn.avg_pool(
- k4_2, ksize=[
- 1, s16, s16, 1], strides=[
- 1, 1, 1, 1], padding='VALID')
- k5 = tf.reshape(k5, [-1, self.gfc_dim])
-
- # dropout if training
- if (is_training):
- k5 = tf.nn.dropout(k5, keep_prob=0.6)
-
- return k5
-
-
-class DrGanMSUExtractor(object):
- """Wrapper for the free DRGan by L.Tran @ MSU:
-
- To use this class as a bob.bio.base extractor::
-
- from bob.bio.base.extractor import Extractor
- class DrGanMSUExtractorBioBase(DrGanMSUExtractor, Extractor):
- pass
- extractor = DrGanMSUExtractorBioBase()
-
-
- **Parameters:**
-
- model_file:
- Path to the model
-
- image_size: list
- The input image size (WxHxC)
-
- """
-
- def __init__(self, model_path=rc["bob.ip.tensorflow_extractor.drgan_modelpath"], image_size=[96, 96, 3]):
-
- self.image_size = image_size
- self.session = tf.Session()
-
- # placeholder for the input image
- data_shape = [1] + self.image_size
- self.X = tf.placeholder(tf.float32, shape=data_shape)
-
- # the encoder
- self.drgan = DRGAN()
- self.encode = self.drgan.generator_encoder(
- self.X, is_reuse=False, is_training=False)
-
- # If the path is not, set the default path
- if model_path is None:
- model_path = self.get_modelpath()
-
- # If does not exist, download
- if not os.path.exists(model_path):
- bob.io.base.create_directories_safe(DrGanMSUExtractor.get_modelpath())
- zip_file = os.path.join(DrGanMSUExtractor.get_modelpath(),
- "DR_GAN_model.zip")
- urls = [
- # This is a private link at Idiap to save bandwidth.
- "http://beatubulatest.lab.idiap.ch/private/wheels/gitlab/"
- "DR_GAN_model.zip",
- ]
-
- bob.extension.download.download_and_unzip(urls, zip_file)
-
- self.saver = tf.train.Saver()
- # Reestore either from the last checkpoint or from a particular checkpoint
- if os.path.isdir(model_path):
- self.saver.restore(self.session,
- tf.train.latest_checkpoint(model_path))
- else:
- self.saver.restore(self.session, model_path)
-
- @staticmethod
- def get_modelpath():
-
- # Priority to the RC path
- model_path = rc[DrGanMSUExtractor.get_rcvariable()]
-
- if model_path is None:
- import pkg_resources
- model_path = pkg_resources.resource_filename(__name__,
- 'data/DR_GAN_model')
-
- return model_path
-
-
- @staticmethod
- def get_rcvariable():
- return "bob.ip.tensorflow_extractor.drgan_modelpath"
-
-
- def __call__(self, image):
- """__call__(image) -> feature
-
- Extract features
-
- **Parameters:**
-
- image : 3D :py:class:`numpy.ndarray` (floats)
- The image to extract the features from.
-
- **Returns:**
-
- feature : 2D :py:class:`numpy.ndarray` (floats)
- The extracted features
- """
- def bob2skimage(bob_image):
- """
- Convert bob color image to the skcit image
- """
-
- if len(bob_image.shape) == 3:
- skimage = numpy.zeros(
- shape=(
- bob_image.shape[1],
- bob_image.shape[2],
- 3),
- dtype=numpy.uint8)
- skimage[:, :, 0] = bob_image[0, :, :]
- skimage[:, :, 1] = bob_image[1, :, :]
- skimage[:, :, 2] = bob_image[2, :, :]
- else:
- skimage = numpy.zeros(
- shape=(
- bob_image.shape[0],
- bob_image.shape[1],
- 1))
- skimage[:, :, 0] = bob_image[:, :]
- return skimage
-
- def rescaleToUint8(image):
- result = numpy.zeros_like(image)
- for channel in range(image.shape[2]):
- min_image = numpy.min(image[:, :, channel])
- max_image = numpy.max(image[:, :, channel])
- if (max_image - min_image) != 0:
- result[:, :, channel] = 255.0 * \
- ((image[:, :, channel] - min_image) / (max_image - min_image))
- else:
- result[:, :, channel] = 0
- result = result.astype('uint8')
- return result
-
- # encode the provided image
- image = rescaleToUint8(image)
- image = bob2skimage(image)
- image = numpy.array(image / 127.5 - 1).astype(numpy.float32)
- shape = [1] + list(image.shape)
- img = numpy.reshape(image, tuple(shape))
- encoded_id = self.session.run(self.encode, feed_dict={self.X: img})
- return encoded_id
-
diff --git a/bob/ip/tensorflow_extractor/Extractor.py b/bob/ip/tensorflow_extractor/Extractor.py
old mode 100755
new mode 100644
index a3c3f6d120f4f5baa1218fcc83faee7cd12d6f94..96e02d82acb69442698a27a65e7d44a9d53d38cc
--- a/bob/ip/tensorflow_extractor/Extractor.py
+++ b/bob/ip/tensorflow_extractor/Extractor.py
@@ -35,14 +35,17 @@ class Extractor(object):
self.graph = graph
# Initializing the variables of the current graph
- self.session = tf.Session()
- self.session.run(tf.global_variables_initializer())
+ self.session = tf.compat.v1.Session()
+ self.session.run(tf.compat.v1.global_variables_initializer())
# Loading the last checkpoint and overwriting the current variables
- saver = tf.train.Saver()
+ saver = tf.compat.v1.train.Saver()
if os.path.splitext(checkpoint_filename)[1] == ".meta":
- saver.restore(self.session, tf.train.latest_checkpoint(os.path.dirname(checkpoint_filename)))
+ saver.restore(
+ self.session,
+ tf.train.latest_checkpoint(os.path.dirname(checkpoint_filename)),
+ )
elif os.path.isdir(checkpoint_filename):
saver.restore(self.session, tf.train.latest_checkpoint(checkpoint_filename))
else:
@@ -52,10 +55,8 @@ class Extractor(object):
if debug:
self.session = tf_debug.LocalCLIDebugWrapperSession(self.session)
-
def __del__(self):
- tf.reset_default_graph()
-
+ tf.compat.v1.reset_default_graph()
def __call__(self, data):
"""
@@ -73,4 +74,3 @@ class Extractor(object):
"""
return self.session.run(self.graph, feed_dict={self.input_tensor: data})
-
diff --git a/bob/ip/tensorflow_extractor/FaceNet.py b/bob/ip/tensorflow_extractor/FaceNet.py
index 2c2838f460697a7aa28760e97b4bd40ae5bf726c..d7a58e591fbe788e41df3ca9d0cdee1a90ad8ec6 100644
--- a/bob/ip/tensorflow_extractor/FaceNet.py
+++ b/bob/ip/tensorflow_extractor/FaceNet.py
@@ -12,6 +12,8 @@ import bob.io.base
logger = logging.getLogger(__name__)
+FACENET_MODELPATH_KEY = "bob.ip.tensorflow_extractor.facenet_modelpath"
+
def prewhiten(img):
mean = numpy.mean(img)
@@ -24,18 +26,18 @@ def prewhiten(img):
def get_model_filenames(model_dir):
# code from https://github.com/davidsandberg/facenet
files = os.listdir(model_dir)
- meta_files = [s for s in files if s.endswith('.meta')]
+ meta_files = [s for s in files if s.endswith(".meta")]
if len(meta_files) == 0:
- raise ValueError(
- 'No meta file found in the model directory (%s)' % model_dir)
+ raise ValueError("No meta file found in the model directory (%s)" % model_dir)
elif len(meta_files) > 1:
raise ValueError(
- 'There should not be more than one meta file in the model '
- 'directory (%s)' % model_dir)
+ "There should not be more than one meta file in the model "
+ "directory (%s)" % model_dir
+ )
meta_file = meta_files[0]
max_step = -1
for f in files:
- step_str = re.match(r'(^model-[\w\- ]+.ckpt-(\d+))', f)
+ step_str = re.match(r"(^model-[\w\- ]+.ckpt-(\d+))", f)
if step_str is not None and len(step_str.groups()) >= 2:
step = int(step_str.groups()[1])
if step > max_step:
@@ -74,11 +76,12 @@ class FaceNet(object):
"""
def __init__(
- self,
- model_path=rc["bob.ip.tensorflow_extractor.facenet_modelpath"],
- image_size=160,
- layer_name='embeddings:0',
- **kwargs):
+ self,
+ model_path=rc[FACENET_MODELPATH_KEY],
+ image_size=160,
+ layer_name="embeddings:0",
+ **kwargs
+ ):
super(FaceNet, self).__init__()
self.model_path = model_path
self.image_size = image_size
@@ -101,14 +104,12 @@ class FaceNet(object):
self.model_path = self.get_modelpath()
if not os.path.exists(self.model_path):
bob.io.base.create_directories_safe(FaceNet.get_modelpath())
- zip_file = os.path.join(FaceNet.get_modelpath(),
- "20170512-110547.zip")
+ zip_file = os.path.join(FaceNet.get_modelpath(), "20170512-110547.zip")
urls = [
- # This is a private link at Idiap to save bandwidth.
- "http://beatubulatest.lab.idiap.ch/private/wheels/gitlab/"
+ # This link only works in Idiap CI to save bandwidth.
+ "http://www.idiap.ch/private/wheels/gitlab/"
"facenet_model2_20170512-110547.zip",
# this link to dropbox would work for everybody
- # previous link to gogle drive would require cookies
"https://www.dropbox.com/s/"
"k7bhxe58q7d48g7/facenet_model2_20170512-110547.zip?dl=1",
]
@@ -117,51 +118,43 @@ class FaceNet(object):
# code from https://github.com/davidsandberg/facenet
model_exp = os.path.expanduser(self.model_path)
with self.graph.as_default():
- if (os.path.isfile(model_exp)):
- logger.info('Model filename: %s' % model_exp)
- with tf.gfile.FastGFile(model_exp, 'rb') as f:
- graph_def = tf.GraphDef()
+ if os.path.isfile(model_exp):
+ logger.info("Model filename: %s" % model_exp)
+ with tf.compat.v1.gfile.FastGFile(model_exp, "rb") as f:
+ graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
- tf.import_graph_def(graph_def, name='')
+ tf.import_graph_def(graph_def, name="")
else:
- logger.info('Model directory: %s' % model_exp)
+ logger.info("Model directory: %s" % model_exp)
meta_file, ckpt_file = get_model_filenames(model_exp)
- logger.info('Metagraph file: %s' % meta_file)
- logger.info('Checkpoint file: %s' % ckpt_file)
+ logger.info("Metagraph file: %s" % meta_file)
+ logger.info("Checkpoint file: %s" % ckpt_file)
- saver = tf.train.import_meta_graph(
- os.path.join(model_exp, meta_file))
- saver.restore(self.session,
- os.path.join(model_exp, ckpt_file))
+ saver = tf.compat.v1.train.import_meta_graph(
+ os.path.join(model_exp, meta_file)
+ )
+ saver.restore(self.session, os.path.join(model_exp, ckpt_file))
# Get input and output tensors
self.images_placeholder = self.graph.get_tensor_by_name("input:0")
self.embeddings = self.graph.get_tensor_by_name(self.layer_name)
- self.phase_train_placeholder = self.graph.get_tensor_by_name(
- "phase_train:0")
+ self.phase_train_placeholder = self.graph.get_tensor_by_name("phase_train:0")
logger.info("Successfully loaded the model.")
def __call__(self, img):
images = self._check_feature(img)
if self.session is None:
self.graph = tf.Graph()
- self.session = tf.Session(graph=self.graph)
+ self.session = tf.compat.v1.Session(graph=self.graph)
if self.embeddings is None:
self.load_model()
- feed_dict = {self.images_placeholder: images,
- self.phase_train_placeholder: False}
- features = self.session.run(
- self.embeddings, feed_dict=feed_dict)
+ feed_dict = {
+ self.images_placeholder: images,
+ self.phase_train_placeholder: False,
+ }
+ features = self.session.run(self.embeddings, feed_dict=feed_dict)
return features.flatten()
- @staticmethod
- def get_rcvariable():
- """
- Variable name used in the Bob Global Configuration System
- https://www.idiap.ch/software/bob/docs/bob/bob.extension/stable/rc.html
- """
- return "bob.ip.tensorflow_extractor.facenet_modelpath"
-
@staticmethod
def get_modelpath():
"""
@@ -173,11 +166,13 @@ class FaceNet(object):
"""
# Priority to the RC path
- model_path = rc[FaceNet.get_rcvariable()]
+ model_path = rc["bob.ip.tensorflow_extractor.facenet_modelpath"]
if model_path is None:
import pkg_resources
+
model_path = pkg_resources.resource_filename(
- __name__, 'data/FaceNet/20170512-110547')
+ __name__, "data/FaceNet/20170512-110547"
+ )
return model_path
diff --git a/bob/ip/tensorflow_extractor/MTCNN.py b/bob/ip/tensorflow_extractor/MTCNN.py
index 1e21778714e17bf259c8b9180b3d5f24eec339fe..03e9830640e31faa035a2c15edde9a1e36b19684 100644
--- a/bob/ip/tensorflow_extractor/MTCNN.py
+++ b/bob/ip/tensorflow_extractor/MTCNN.py
@@ -39,14 +39,14 @@ class MTCNN:
graph = tf.Graph()
with graph.as_default():
with open(model_path, "rb") as f:
- graph_def = tf.GraphDef.FromString(f.read())
+ graph_def = tf.compat.v1.GraphDef.FromString(f.read())
tf.import_graph_def(graph_def, name="")
self.graph = graph
- config = tf.ConfigProto(
+ config = tf.compat.v1.ConfigProto(
intra_op_parallelism_threads=multiprocessing.cpu_count(),
inter_op_parallelism_threads=multiprocessing.cpu_count(),
)
- self.sess = tf.Session(graph=graph, config=config)
+ self.sess = tf.compat.v1.Session(graph=graph, config=config)
def detect(self, img):
"""Detects all faces in the image.
diff --git a/bob/ip/tensorflow_extractor/Vgg16.py b/bob/ip/tensorflow_extractor/Vgg16.py
deleted file mode 100644
index bc0dd8bad105edad8362028a23cd4584d26a6fa3..0000000000000000000000000000000000000000
--- a/bob/ip/tensorflow_extractor/Vgg16.py
+++ /dev/null
@@ -1,150 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-import tensorflow as tf
-from tensorflow.contrib.slim.python.slim.nets import vgg
-import tensorflow.contrib.slim as slim
-from tensorflow.contrib.layers.python.layers import layers as layers_lib
-from tensorflow.contrib import layers
-from tensorflow.contrib.framework.python.ops import arg_scope
-from .Extractor import Extractor
-import numpy
-import bob.extension.download
-import os
-
-def vgg_16(inputs,
- reuse=None,
- dropout_keep_prob=0.5,
- weight_decay=0.0005,
- mode=tf.estimator.ModeKeys.TRAIN, **kwargs):
- """
- Oxford Net VGG 16-Layers version E Example from tf-slim
-
- Adapted from here.
- https://raw.githubusercontent.com/tensorflow/models/master/research/slim/nets/vgg.py
-
- **Parameters**:
-
- inputs: a 4-D tensor of size [batch_size, height, width, 3].
-
- reuse: whether or not the network and its variables should be reused. To be
- able to reuse 'scope' must be given.
-
- mode:
- Estimator mode keys
- """
- end_points = dict()
- with tf.variable_scope('vgg_16', reuse=reuse):
-
- with arg_scope([layers.conv2d, layers_lib.fully_connected],
- activation_fn=tf.nn.relu,
- weights_regularizer=None,
- biases_initializer=tf.zeros_initializer(),
- trainable=mode==tf.estimator.ModeKeys.PREDICT):
-
- # Collect outputs for conv2d, fully_connected and max_pool2d.
-
- net = layers_lib.repeat(inputs, 2, layers.conv2d, 64, [3, 3], scope='conv1')
- net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
- end_points['conv1'] = net
-
- net = layers_lib.repeat(net, 2, layers.conv2d, 128, [3, 3], scope='conv2')
- net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
- end_points['conv2'] = net
-
- net = layers_lib.repeat(net, 3, layers.conv2d, 256, [3, 3], scope='conv3')
- net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
- end_points['conv3'] = net
-
- net = layers_lib.repeat(net, 3, layers.conv2d, 512, [3, 3], scope='conv4')
- net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
- end_points['conv4'] = net
-
- net = layers_lib.repeat(net, 3, layers.conv2d, 512, [3, 3], scope='conv5')
- net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
- end_points['conv5'] = net
-
- net = slim.flatten(net)
-
- net = layers.fully_connected(net, 4096, scope='fc6')
- end_points['fc6'] = net
-
- net = layers.fully_connected(net, 4096, scope='fc7', activation_fn=None)
-
- end_points['fc7'] = net
-
- return net, end_points
-
-
-class VGGFace(Extractor):
- """
- Extract features using the VGG model
- http://www.robots.ox.ac.uk/~vgg/software/vgg_face/
-
- This was converted with the script https://github.com/tiagofrepereira2012
-
- """
-
- def __init__(self, checkpoint_filename=None, debug=False):
- # Average image provided in
- # http://www.robots.ox.ac.uk/~vgg/software/vgg_face/
- self.average_img = [129.1863, 104.7624, 93.5940]
-
- if checkpoint_filename is None:
- checkpoint_filename = os.path.join(VGGFace.get_vggpath(),"vgg_face_tf")
-
- # Downloading the model if necessary
- if not os.path.exists(checkpoint_filename):
- zip_file = os.path.join(VGGFace.get_vggpath(), "vgg_face_tf.tar.gz")
-
- urls = [
- # This is a private link at Idiap to save bandwidth.
- "https://www.idiap.ch/software/bob/data/bob/bob.ip.tensorflow_extractor/master/"
- "vgg_face_tf.tar.gz",
- "http://www.idiap.ch/software/bob/data/bob/bob.ip.tensorflow_extractor/master/"
- "vgg_face_tf.tar.gz",
- ]
-
- bob.extension.download.download_and_unzip(urls, zip_file)
-
- input_tensor = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
- graph = vgg_16(input_tensor)[0]
-
- super(VGGFace, self).__init__(checkpoint_filename=os.path.join(checkpoint_filename),
- input_tensor=input_tensor,
- graph=graph,
- debug=debug)
-
-
- def __call__(self, image):
-
- if len(image.shape) == 3:
-
- # Converting from RGB to BGR
- R = image[0, :, :] - self.average_img[0]
- G = image[1, :, :] - self.average_img[1]
- B = image[2, :, :] - self.average_img[2]
-
- # Converting to
- bgr_image = numpy.zeros(shape=image.shape)
- bgr_image[0, :, :] = B
- bgr_image[1, :, :] = G
- bgr_image[2, :, :] = R
-
- # SWAPING TO CxHxW to HxWxC
- bgr_image = numpy.moveaxis(bgr_image,0,-1)
- bgr_image = numpy.expand_dims(bgr_image,0)
-
- if self.session is None:
- self.session = tf.InteractiveSession()
- return self.session.run(self.graph, feed_dict={self.input_tensor: bgr_image})[0]
- else:
- raise ValueError("Image should have 3 channels")
-
-
- @staticmethod
- def get_vggpath():
- import pkg_resources
- return pkg_resources.resource_filename(__name__, 'data')
-
diff --git a/bob/ip/tensorflow_extractor/__init__.py b/bob/ip/tensorflow_extractor/__init__.py
old mode 100755
new mode 100644
index 3899dd2eb915bedb2ac434089f95d739ea96f276..43d1428f7440c81e1c03bf8b4de1825717220067
--- a/bob/ip/tensorflow_extractor/__init__.py
+++ b/bob/ip/tensorflow_extractor/__init__.py
@@ -1,43 +1,17 @@
#!/usr/bin/env python
-def scratch_network(inputs, end_point="fc1", reuse=False):
-
- import tensorflow as tf
- slim = tf.contrib.slim
-
- # Creating a random network
- initializer = tf.contrib.layers.xavier_initializer(seed=10)
- end_points = dict()
-
- graph = slim.conv2d(inputs, 10, [3, 3], activation_fn=tf.nn.relu, stride=1,
- scope='conv1', weights_initializer=initializer,
- reuse=reuse)
- end_points["conv1"] = graph
-
- graph = slim.max_pool2d(graph, [4, 4], scope='pool1')
- end_points["pool1"] = graph
-
- graph = slim.flatten(graph, scope='flatten1')
- end_points["flatten1"] = graph
-
- graph = slim.fully_connected(graph, 10, activation_fn=None, scope='fc1',
- weights_initializer=initializer, reuse=reuse)
- end_points["fc1"] = graph
-
- return end_points[end_point]
def get_config():
"""Returns a string containing the configuration information.
"""
import bob.extension
+
return bob.extension.get_config(__name__)
-from .Extractor import Extractor
from .FaceNet import FaceNet
-from .DrGanMSU import DrGanMSUExtractor
-from .Vgg16 import VGGFace, vgg_16
from .MTCNN import MTCNN
+from .Extractor import Extractor
# gets sphinx autodoc done right - don't remove it
@@ -56,13 +30,7 @@ def __appropriate__(*args):
obj.__module__ = __name__
-__appropriate__(
- Extractor,
- FaceNet,
- DrGanMSUExtractor,
- VGGFace,
- MTCNN,
-)
+__appropriate__(FaceNet, MTCNN, Extractor)
# gets sphinx autodoc done right - don't remove it
-__all__ = [_ for _ in dir() if not _.startswith('_')]
+__all__ = [_ for _ in dir() if not _.startswith("_")]
diff --git a/bob/ip/tensorflow_extractor/data/checkpoint b/bob/ip/tensorflow_extractor/data/checkpoint
deleted file mode 100644
index b12413de4ce3b1d66ef87edc2c45f1025ade150a..0000000000000000000000000000000000000000
--- a/bob/ip/tensorflow_extractor/data/checkpoint
+++ /dev/null
@@ -1,2 +0,0 @@
-model_checkpoint_path: "model.ckp"
-all_model_checkpoint_paths: "model.ckp"
diff --git a/bob/ip/tensorflow_extractor/data/model.ckp.data-00000-of-00001 b/bob/ip/tensorflow_extractor/data/model.ckp.data-00000-of-00001
deleted file mode 100644
index 2a854a2e6819b8fa65b82031237bef1d2e891e02..0000000000000000000000000000000000000000
Binary files a/bob/ip/tensorflow_extractor/data/model.ckp.data-00000-of-00001 and /dev/null differ
diff --git a/bob/ip/tensorflow_extractor/data/model.ckp.index b/bob/ip/tensorflow_extractor/data/model.ckp.index
deleted file mode 100644
index ad2bc305a685a829d683d664e9ab0622bcd7c1e0..0000000000000000000000000000000000000000
Binary files a/bob/ip/tensorflow_extractor/data/model.ckp.index and /dev/null differ
diff --git a/bob/ip/tensorflow_extractor/data/model.ckp.meta b/bob/ip/tensorflow_extractor/data/model.ckp.meta
deleted file mode 100644
index da24801f6e8a6ed46e0c433684867d712e215d68..0000000000000000000000000000000000000000
Binary files a/bob/ip/tensorflow_extractor/data/model.ckp.meta and /dev/null differ
diff --git a/bob/ip/tensorflow_extractor/test.py b/bob/ip/tensorflow_extractor/test.py
index dff72a5ba1faa6372228f12bdcc6454a5a395aa0..7ef9d2becca0ccc3f906e6be020008829319aab8 100644
--- a/bob/ip/tensorflow_extractor/test.py
+++ b/bob/ip/tensorflow_extractor/test.py
@@ -1,47 +1,11 @@
import bob.io.base
import bob.io.image
from bob.io.base.test_utils import datafile
-import bob.ip.tensorflow_extractor
-import tensorflow as tf
-
-import pkg_resources
import numpy
import json
-import os
numpy.random.seed(10)
-slim = tf.contrib.slim
-from . import scratch_network
-
-
-def test_output():
-
- # Loading MNIST model
- filename = os.path.join(
- pkg_resources.resource_filename(__name__, "data"), "model.ckp"
- )
- inputs = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
-
- # Testing the last output
- graph = scratch_network(inputs)
- extractor = bob.ip.tensorflow_extractor.Extractor(filename, inputs, graph)
-
- data = numpy.random.rand(2, 28, 28, 1).astype("float32")
- output = extractor(data)
- assert extractor(data).shape == (2, 10)
- del extractor
-
- # Testing flatten
- inputs = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
- graph = scratch_network(inputs, end_point="flatten1")
- extractor = bob.ip.tensorflow_extractor.Extractor(filename, inputs, graph)
-
- data = numpy.random.rand(2, 28, 28, 1).astype("float32")
- output = extractor(data)
- assert output.shape == (2, 1690)
- del extractor
-
def test_facenet():
from bob.ip.tensorflow_extractor import FaceNet
@@ -52,24 +16,6 @@ def test_facenet():
assert output.size == 128, output.shape
-def test_drgan():
- from bob.ip.tensorflow_extractor import DrGanMSUExtractor
-
- extractor = DrGanMSUExtractor()
- data = numpy.random.rand(3, 96, 96).astype("uint8")
- output = extractor(data)
- assert output.size == 320, output.shape
-
-
-def test_vgg16():
- pass
- # from bob.ip.tensorflow_extractor import VGGFace
- # extractor = VGGFace()
- # data = numpy.random.rand(3, 224, 224).astype("uint8")
- # output = extractor(data)
- # assert output.size == 4096, output.shape
-
-
def test_mtcnn():
test_image = datafile("mtcnn/test_image.png", __name__)
ref_numbers = datafile("mtcnn/mtcnn.hdf5", __name__)
diff --git a/doc/conf.py b/doc/conf.py
index bb602aa67dd0932d115cd02503ac188f78ff4b2d..298524a74f42007cf18f38cd39bf240fe91b8cd4 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -10,30 +10,31 @@ import pkg_resources
# -- General configuration -----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
-needs_sphinx = '1.3'
+needs_sphinx = "1.3"
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = [
- 'sphinx.ext.todo',
- 'sphinx.ext.coverage',
- 'sphinx.ext.ifconfig',
- 'sphinx.ext.autodoc',
- 'sphinx.ext.autosummary',
- 'sphinx.ext.doctest',
- 'sphinx.ext.graphviz',
- 'sphinx.ext.intersphinx',
- 'sphinx.ext.napoleon',
- 'sphinx.ext.viewcode',
- 'matplotlib.sphinxext.plot_directive'
- ]
+ "sphinx.ext.todo",
+ "sphinx.ext.coverage",
+ "sphinx.ext.ifconfig",
+ "sphinx.ext.autodoc",
+ "sphinx.ext.autosummary",
+ "sphinx.ext.doctest",
+ "sphinx.ext.graphviz",
+ "sphinx.ext.intersphinx",
+ "sphinx.ext.napoleon",
+ "sphinx.ext.viewcode",
+ "matplotlib.sphinxext.plot_directive",
+]
import sphinx
+
if sphinx.__version__ >= "1.4.1":
- extensions.append('sphinx.ext.imgmath')
- imgmath_image_format = 'svg'
+ extensions.append("sphinx.ext.imgmath")
+ imgmath_image_format = "svg"
else:
- extensions.append('sphinx.ext.pngmath')
+ extensions.append("sphinx.ext.pngmath")
# Be picky about warnings
nitpicky = True
@@ -42,13 +43,13 @@ nitpicky = True
nitpick_ignore = []
# Allows the user to override warnings from a separate file
-if os.path.exists('nitpick-exceptions.txt'):
- for line in open('nitpick-exceptions.txt'):
+if os.path.exists("nitpick-exceptions.txt"):
+ for line in open("nitpick-exceptions.txt"):
if line.strip() == "" or line.startswith("#"):
continue
dtype, target = line.split(None, 1)
target = target.strip()
- try: # python 2.x
+ try: # python 2.x
target = unicode(target)
except NameError:
pass
@@ -64,25 +65,27 @@ autosummary_generate = True
numfig = True
# If we are on OSX, the 'dvipng' path maybe different
-dvipng_osx = '/opt/local/libexec/texlive/binaries/dvipng'
-if os.path.exists(dvipng_osx): pngmath_dvipng = dvipng_osx
+dvipng_osx = "/opt/local/libexec/texlive/binaries/dvipng"
+if os.path.exists(dvipng_osx):
+ pngmath_dvipng = dvipng_osx
# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
# The suffix of source filenames.
-source_suffix = '.rst'
+source_suffix = ".rst"
# The encoding of source files.
-#source_encoding = 'utf-8-sig'
+# source_encoding = 'utf-8-sig'
# The master toctree document.
-master_doc = 'index'
+master_doc = "index"
# General information about the project.
-project = u'bob.ip.tensorflow_extractor'
+project = u"bob.ip.tensorflow_extractor"
import time
-copyright = u'%s, Idiap Research Institute' % time.strftime('%Y')
+
+copyright = u"%s, Idiap Research Institute" % time.strftime("%Y")
# Grab the setup entry
distribution = pkg_resources.require(project)[0]
@@ -98,42 +101,42 @@ release = distribution.version
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
-#language = None
+# language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
-#today = ''
+# today = ''
# Else, today_fmt is used as the format for a strftime call.
-#today_fmt = '%B %d, %Y'
+# today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
-exclude_patterns = ['links.rst']
+exclude_patterns = ["links.rst"]
# The reST default role (used for this markup: `text`) to use for all documents.
-#default_role = None
+# default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
-#add_function_parentheses = True
+# add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
-#add_module_names = True
+# add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
-#show_authors = False
+# show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
+pygments_style = "sphinx"
# A list of ignored prefixes for module index sorting.
-#modindex_common_prefix = []
+# modindex_common_prefix = []
# Some variables which are useful for generated material
-project_variable = project.replace('.', '_')
-short_description = u'Tensorflow bindings'
-owner = [u'Idiap Research Institute']
+project_variable = project.replace(".", "_")
+short_description = u"Tensorflow bindings"
+owner = [u"Idiap Research Institute"]
# -- Options for HTML output ---------------------------------------------------
@@ -141,80 +144,81 @@ owner = [u'Idiap Research Institute']
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
import sphinx_rtd_theme
-html_theme = 'sphinx_rtd_theme'
+
+html_theme = "sphinx_rtd_theme"
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
-#html_theme_options = {}
+# html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
-#html_title = None
+# html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
-#html_short_title = project_variable
+# html_short_title = project_variable
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
-html_logo = 'img/logo.png'
+html_logo = "img/logo.png"
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
-html_favicon = 'img/favicon.ico'
+html_favicon = "img/favicon.ico"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
-#html_static_path = ['_static']
+# html_static_path = ['_static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
-#html_last_updated_fmt = '%b %d, %Y'
+# html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
-#html_use_smartypants = True
+# html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
-#html_sidebars = {}
+# html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
-#html_additional_pages = {}
+# html_additional_pages = {}
# If false, no module index is generated.
-#html_domain_indices = True
+# html_domain_indices = True
# If false, no index is generated.
-#html_use_index = True
+# html_use_index = True
# If true, the index is split into individual pages for each letter.
-#html_split_index = False
+# html_split_index = False
# If true, links to the reST sources are added to the pages.
-#html_show_sourcelink = True
+# html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
-#html_show_sphinx = True
+# html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
-#html_show_copyright = True
+# html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
-#html_use_opensearch = ''
+# html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
-#html_file_suffix = None
+# html_file_suffix = None
# Output file base name for HTML help builder.
-htmlhelp_basename = project_variable + u'_doc'
+htmlhelp_basename = project_variable + u"_doc"
# -- Post configuration --------------------------------------------------------
@@ -224,44 +228,48 @@ rst_epilog = """
.. |project| replace:: Bob
.. |version| replace:: %s
.. |current-year| date:: %%Y
-""" % (version,)
+""" % (
+ version,
+)
# Default processing flags for sphinx
-autoclass_content = 'class'
-autodoc_member_order = 'bysource'
+autoclass_content = "class"
+autodoc_member_order = "bysource"
autodoc_default_flags = [
- 'members',
- 'show-inheritance',
- ]
+ "members",
+ "show-inheritance",
+]
# For inter-documentation mapping:
from bob.extension.utils import link_documentation, load_requirements
+
sphinx_requirements = "extra-intersphinx.txt"
if os.path.exists(sphinx_requirements):
- intersphinx_mapping = link_documentation(
- additional_packages=['python','numpy'] + \
- load_requirements(sphinx_requirements)
- )
+ intersphinx_mapping = link_documentation(
+ additional_packages=["python", "numpy"] + load_requirements(sphinx_requirements)
+ )
else:
- intersphinx_mapping = link_documentation()
+ intersphinx_mapping = link_documentation()
# We want to remove all private (i.e. _. or __.__) members
# that are not in the list of accepted functions
-accepted_private_functions = ['__array__']
+accepted_private_functions = ["__array__"]
+
def member_function_test(app, what, name, obj, skip, options):
- # test if we have a private function
- if len(name) > 1 and name[0] == '_':
- # test if this private function should be allowed
- if name not in accepted_private_functions:
- # omit privat functions that are not in the list of accepted private functions
- return skip
- else:
- # test if the method is documented
- if not hasattr(obj, '__doc__') or not obj.__doc__:
- return skip
- return False
+ # test if we have a private function
+ if len(name) > 1 and name[0] == "_":
+ # test if this private function should be allowed
+ if name not in accepted_private_functions:
+ # omit privat functions that are not in the list of accepted private functions
+ return skip
+ else:
+ # test if the method is documented
+ if not hasattr(obj, "__doc__") or not obj.__doc__:
+ return skip
+ return False
+
def setup(app):
- app.connect('autodoc-skip-member', member_function_test)
+ app.connect("autodoc-skip-member", member_function_test)
diff --git a/doc/guide.rst b/doc/guide.rst
index 529a2d8dc4e9059c2fc2e2927dfd7402dbb19192..874d5741f3b4f127d8b918ea8791dc2c7b06c8de 100644
--- a/doc/guide.rst
+++ b/doc/guide.rst
@@ -2,52 +2,8 @@
User guide
===========
-Using as a feature extractor
-----------------------------
-In this example we'll take pretrained network using MNIST and
-In this example we take the output of the layer `fc7` of the VGG face model as
-features.
-
-.. doctest:: tensorflowtest
-
- >>> import numpy
- >>> import bob.ip.tensorflow_extractor
- >>> import bob.db.mnist
- >>> from bob.ip.tensorflow_extractor import scratch_network
- >>> import os
- >>> import pkg_resources
- >>> import tensorflow as tf
-
- >>> # Loading some samples from mnist
- >>> db = bob.db.mnist.Database()
- >>> images = db.data(groups='train', labels=[0,1,2,3,4,5,6,7,8,9])[0][0:3]
- >>> images = numpy.reshape(images, (3, 28, 28, 1)) * 0.00390625 # Normalizing the data
-
- >>> # preparing my inputs
- >>> inputs = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
- >>> graph = scratch_network(inputs)
-
- >>> # loading my model and projecting
- >>> filename = os.path.join(pkg_resources.resource_filename("bob.ip.tensorflow_extractor", 'data'), 'model.ckp')
- >>> extractor = bob.ip.tensorflow_extractor.Extractor(filename, inputs, graph)
- >>> extractor(images).shape
- (3, 10)
-
-
-.. note::
-
- The models will automatically download to the data folder of this package as
- soon as you start using them.
-
-Using as a convolutional filter
--------------------------------
-
-In this example we plot some outputs of the convolutional layer `conv1`.
-
-
-
Facenet Model
-------------
@@ -57,27 +13,11 @@ Check `here for more info <py_api.html#bob.ip.tensorflow_extractor.FaceNet>`_
.. note::
- The models will automatically download to the data folder of this package and save it in
+ The models will automatically download to the data folder of this package and save it in
``[env-path]./bob/ip/tensorflow_extractor/data/FaceNet``.
If you want want set another path for this model do::
-
- $ bob config set bob.ip.tensorflow_extractor.facenet_modelpath /path/to/mydatabase
-
-
-
-DRGan from L.Tran @ MSU:
-------------------------
-:ref:`bob.bio.base <bob.bio.base>` wrapper to the DRGan model trained by L.Tran @ MSU.
-Check `here <py_api.html#bob.ip.tensorflow_extractor.DrGanMSUExtractor>`_ for more info
-
-.. note::
-
- The models will automatically download to the data folder of this package and save it in
- ``[env-path]./bob/ip/tensorflow_extractor/data/DR_GAN_model``.
- If you want want set another path for this model do::
-
- $ bob config set bob.ip.tensorflow_extractor.drgan_modelpath /path/to/mydatabase
+ $ bob config set bob.ip.tensorflow_extractor.facenet_modelpath /path/to/mydatabase
diff --git a/doc/index.rst b/doc/index.rst
index f1a28b13ad355f0cf063064e89aa25e4176db25f..c32a01ceb55e85957c718e0efb7a428e9b3eb978 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -5,7 +5,7 @@
Bob interface for feature extraction using Tensorflow
=======================================================
-This package contains functionality to extract features from CNNs trained with
+This package contains functionality to extract features from Neural Networks trained with
Tensorflow http://tensorflow.org/
Index
diff --git a/setup.py b/setup.py
index 44b73fce9d43db1bbe5460d3491e3e59929b7d29..8f682f4cd25f99b2e3bb853f6414662cfd85eb74 100644
--- a/setup.py
+++ b/setup.py
@@ -34,42 +34,38 @@
# administrative interventions.
from setuptools import setup, dist
-dist.Distribution(dict(setup_requires=['bob.extension']))
+
+dist.Distribution(dict(setup_requires=["bob.extension"]))
from bob.extension.utils import load_requirements, find_packages
+
install_requires = load_requirements()
# The only thing we do in this file is to call the setup() function with all
# parameters that define our package.
setup(
-
# This is the basic information about your project. Modify all this
# information before releasing code publicly.
- name = 'bob.ip.tensorflow_extractor',
- version = open("version.txt").read().rstrip(),
- description = 'Feature extractor using tensorflow CNNs',
-
- url = 'https://gitlab.idiap.ch/tiago.pereira/bob.ip.caffe_extractor',
- license = 'BSD',
- author = 'Tiago de Freitas Pereira',
- author_email = 'tiago.pereira@idiap.ch',
- keywords = 'bob, biometric recognition, evaluation',
-
+ name="bob.ip.tensorflow_extractor",
+ version=open("version.txt").read().rstrip(),
+ description="Feature extractor using tensorflow CNNs",
+ url="https://gitlab.idiap.ch/tiago.pereira/bob.ip.caffe_extractor",
+ license="BSD",
+ author="Tiago de Freitas Pereira",
+ author_email="tiago.pereira@idiap.ch",
+ keywords="bob, biometric recognition, evaluation",
# If you have a better, long description of your package, place it on the
# 'doc' directory and then hook it here
- long_description = open('README.rst').read(),
-
+ long_description=open("README.rst").read(),
# This line is required for any distutils based packaging.
- packages = find_packages(),
- include_package_data = True,
-
+ packages=find_packages(),
+ include_package_data=True,
# This line defines which packages should be installed when you "install"
# this package. All packages that are mentioned here, but are not installed
# on the current system will be installed locally and only visible to the
# scripts of this package. Don't worry - You won't need administrative
# privileges when using buildout.
- install_requires = install_requires,
-
+ install_requires=install_requires,
# Your project should be called something like 'bob.<foo>' or
# 'bob.<foo>.<bar>'. To implement this correctly and still get all your
# packages to be imported w/o problems, you need to implement namespaces
@@ -80,8 +76,6 @@ setup(
# Our database packages are good examples of namespace implementations
# using several layers. You can check them out here:
# https://github.com/idiap/bob/wiki/Satellite-Packages
-
-
# This entry defines which scripts you will have inside the 'bin' directory
# once you install the package (or run 'bin/buildout'). The order of each
# entry under 'console_scripts' is like this:
@@ -93,17 +87,16 @@ setup(
# installed under 'example/foo.py' that contains a function which
# implements the 'main()' function of particular script you want to have
# should be referred as 'example.foo:main'.
-
# Classifiers are important if you plan to distribute this package through
# PyPI. You can find the complete list of classifiers that are valid and
# useful here (http://pypi.python.org/pypi?%3Aaction=list_classifiers).
- classifiers = [
- 'Framework :: Bob',
- 'Development Status :: 4 - Beta',
- 'Intended Audience :: Developers',
- 'License :: OSI Approved :: BSD License',
- 'Natural Language :: English',
- 'Programming Language :: Python',
- 'Topic :: Scientific/Engineering :: Artificial Intelligence',
+ classifiers=[
+ "Framework :: Bob",
+ "Development Status :: 4 - Beta",
+ "Intended Audience :: Developers",
+ "License :: OSI Approved :: BSD License",
+ "Natural Language :: English",
+ "Programming Language :: Python",
+ "Topic :: Scientific/Engineering :: Artificial Intelligence",
],
)