remove the extractors folder

bob/learn/tensorflow/extractors/Base.py

-import tensorflow as tf
-import os
-import numpy as np
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-def normalize_checkpoint_path(path):
-    if os.path.splitext(path)[1] == ".meta":
-        filename = os.path.splitext(path)[0]
-    elif os.path.isdir(path):
-        filename = tf.train.latest_checkpoint(path)
-    else:
-        filename = path
-
-    return filename
-
-
-class Base:
-    def __init__(self, output_name, input_shape, checkpoint, scopes,
-                 input_transform=None, output_transform=None,
-                 input_dtype='float32', extra_feed=None, **kwargs):
-
-        self.output_name = output_name
-        self.input_shape = input_shape
-        self.checkpoint = normalize_checkpoint_path(checkpoint)
-        self.scopes = scopes
-        self.input_transform = input_transform
-        self.output_transform = output_transform
-        self.input_dtype = input_dtype
-        self.extra_feed = extra_feed
-        self.session = None
-        super().__init__(**kwargs)
-
-    def load(self):
-        self.session = tf.Session(graph=tf.Graph())
-
-        with self.session.as_default(), self.session.graph.as_default():
-
-            self.input = data = tf.placeholder(self.input_dtype, self.input_shape)
-
-            if self.input_transform is not None:
-                data = self.input_transform(data)
-
-            self.output = self.get_output(data, tf.estimator.ModeKeys.PREDICT)
-
-            if self.output_transform is not None:
-                self.output = self.output_transform(self.output)
-
-            tf.train.init_from_checkpoint(
-                ckpt_dir_or_file=self.checkpoint,
-                assignment_map=self.scopes,
-            )
-            # global_variables_initializer must run after init_from_checkpoint
-            self.session.run(tf.global_variables_initializer())
-            logger.info('Restored the model from %s', self.checkpoint)
-
-    def __call__(self, data):
-        if self.session is None:
-            self.load()
-
-        data = np.ascontiguousarray(data, dtype=self.input_dtype)
-        feed_dict = {self.input: data}
-        if self.extra_feed is not None:
-            feed_dict.update(self.extra_feed)
-
-        return self.session.run(self.output, feed_dict=feed_dict)
-
-    def get_output(self, data, mode):
-        raise NotImplementedError()

bob/learn/tensorflow/extractors/Estimator.py

-import tensorflow as tf
-from .Base import Base
-
-
-class Estimator(Base):
-    def __init__(self, estimator, **kwargs):
-        self.estimator = estimator
-        kwargs['checkpoint'] = kwargs.get('checkpoint', estimator.model_dir)
-        super().__init__(**kwargs)
-
-    def get_output(self, data, mode):
-        features = {'data': data, 'key': tf.constant(['key'])}
-        self.estimator_spec = self.estimator._call_model_fn(
-            features, None, mode, None)
-        self.end_points = self.estimator.end_points
-        return self.end_points[self.output_name]

bob/learn/tensorflow/extractors/Generic.py

-from .Base import Base
-
-
-class Generic(Base):
-    def __init__(self, architecture, **kwargs):
-
-        self.architecture = architecture
-        super().__init__(**kwargs)
-
-    def get_output(self, data, mode):
-        self.end_points = self.architecture(data, mode=mode)[1]
-        return self.end_points[self.output_name]

bob/learn/tensorflow/extractors/__init__.py

-from .Base import Base, normalize_checkpoint_path
-from .Generic import Generic
-from .Estimator import Estimator
-
-
-# gets sphinx autodoc done right - don't remove it
-def __appropriate__(*args):
-    """Says object was actually declared here, an not on the import module.
-
-    Parameters:
-
-        *args: An iterable of objects to modify
-
-    Resolves `Sphinx referencing issues
-    <https://github.com/sphinx-doc/sphinx/issues/3048>`
-    """
-
-    for obj in args:
-        obj.__module__ = __name__
-
-
-__appropriate__(
-    Base,
-    Generic,
-    Estimator,
-)
-__all__ = [_ for _ in dir() if not _.startswith('_')]