Add several extractors which are usefull at inference time

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', **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.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)
+
+        return self.session.run(self.output, feed_dict={self.input: data})
+
+    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
+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('_')]

[Tiago de Freitas Pereira]

With this commit we should remove some code from bob.ip.tensorflow_extractor to avoid code repetition.

[Amir MOHAMMADI]

so you are going to make bob.ip... depend on bob.learn...?

[Tiago de Freitas Pereira]

It makes more sense bob.learn.tensorflow is a very stable package.

Is this make sense for you?