small changes: nit, bug fix, small features

bob/learn/tensorflow/layers/Maxout.py

@@ -3,22 +3,19 @@
 # @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
 # @date:  Fri 04 Aug 2017 14:14:22 CEST
 
-## MAXOUT IMPLEMENTED FOR TENSORFLOW
-
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import gen_array_ops
+# MAXOUT IMPLEMENTED FOR TENSORFLOW
 
 from tensorflow.python.layers import base
+import tensorflow as tf
 
 
 def maxout(inputs, num_units, axis=-1, name=None):
-    return MaxOut(num_units=num_units, axis=axis, name=name)(inputs)
+    return Maxout(num_units=num_units, axis=axis, name=name)(inputs)
 
 
-class MaxOut(base.Layer):
+class Maxout(base.Layer):
     """
-     Adds a maxout op from 
+     Adds a maxout op from
 
     "Maxout Networks"
 
@@ -41,33 +38,37 @@ class MaxOut(base.Layer):
     """
 
     def __init__(self, num_units, axis=-1, name=None, **kwargs):
-        super(MaxOut, self).__init__(name=name, trainable=False, **kwargs)
+        super(Maxout, self).__init__(name=name, trainable=False, **kwargs)
         self.axis = axis
         self.num_units = num_units
 
     def call(self, inputs, training=False):
-        inputs = ops.convert_to_tensor(inputs)
+        inputs = tf.convert_to_tensor(inputs)
         shape = inputs.get_shape().as_list()
-        if self.axis is None:
-            # Assume that channel is the last dimension
-            self.axis = -1
-        num_channels = shape[self.axis]
-        if num_channels % self.num_units:
-            raise ValueError('number of features({}) is not '
-                             'a multiple of num_units({})'.format(
-                                 num_channels, self.num_units))
-        shape[self.axis] = -1
-        shape += [num_channels // self.num_units]
-
         # Dealing with batches with arbitrary sizes
         for i in range(len(shape)):
             if shape[i] is None:
-                shape[i] = gen_array_ops.shape(inputs)[i]
-
-        outputs = math_ops.reduce_max(
-            gen_array_ops.reshape(inputs, shape), -1, keep_dims=False)
-        shape = outputs.get_shape().as_list()
-        shape[self.axis] = self.num_units
-        outputs.set_shape(shape)
+                shape[i] = tf.shape(inputs)[i]
 
+        num_channels = shape[self.axis]
+        if not isinstance(num_channels, tf.Tensor) and num_channels % self.num_units:
+            raise ValueError(
+                "number of features({}) is not "
+                "a multiple of num_units({})".format(num_channels, self.num_units)
+            )
+
+        if self.axis < 0:
+            axis = self.axis + len(shape)
+        else:
+            axis = self.axis
+        assert axis >= 0, "Find invalid axis: {}".format(self.axis)
+
+        expand_shape = shape[:]
+        expand_shape[axis] = self.num_units
+        k = num_channels // self.num_units
+        expand_shape.insert(axis, k)
+
+        outputs = tf.math.reduce_max(
+            tf.reshape(inputs, expand_shape), axis, keepdims=False
+        )
         return outputs

bob/learn/tensorflow/layers/__init__.py

-from .Maxout import maxout
+from .Maxout import Maxout, maxout
 
 
 # gets sphinx autodoc done right - don't remove it
@@ -17,5 +17,8 @@ def __appropriate__(*args):
         obj.__module__ = __name__
 
 
-__appropriate__(maxout)
+__appropriate__(
+    Maxout,
+    maxout,
+)
 __all__ = [_ for _ in dir() if not _.startswith('_')]

bob/learn/tensorflow/loss/ContrastiveLoss.py

@@ -19,9 +19,9 @@ def contrastive_loss(left_embedding,
     http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
 
     :math:`L = 0.5 * (1-Y) * D^2 + 0.5 * (Y) * {max(0, margin - D)}^2`
-    
+
     where, `0` are assign for pairs from the same class and `1` from pairs from different classes.
-    
+
 
     **Parameters**
 
@@ -65,15 +65,10 @@ def contrastive_loss(left_embedding,
 
         with tf.name_scope("total_loss"):
             loss = 0.5 * (within_class + between_class)
-            loss = tf.reduce_mean(loss, name="total_loss_raw")
-            tf.summary.scalar('loss_raw', loss)
+            loss = tf.reduce_mean(loss, name="contrastive_loss")
             tf.add_to_collection(tf.GraphKeys.LOSSES, loss)
 
-            ## Appending the regularization loss
-            #regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
-            #loss = tf.add_n([loss] + regularization_losses, name="total_loss")
-
-        tf.summary.scalar('loss', loss)
+        tf.summary.scalar('contrastive_loss', loss)
         tf.summary.scalar('between_class', between_class_loss)
         tf.summary.scalar('within_class', within_class_loss)
 

bob/learn/tensorflow/network/utils.py

@@ -10,8 +10,9 @@ def append_logits(graph,
                   n_classes,
                   reuse=False,
                   l2_regularizer=5e-05,
-                  weights_std=0.1, trainable_variables=None):
-    trainable = is_trainable('Logits', trainable_variables)
+                  weights_std=0.1, trainable_variables=None,
+                  name='Logits'):
+    trainable = is_trainable(name, trainable_variables)
     return slim.fully_connected(
         graph,
         n_classes,
@@ -19,7 +20,7 @@ def append_logits(graph,
         weights_initializer=tf.truncated_normal_initializer(
             stddev=weights_std),
         weights_regularizer=slim.l2_regularizer(l2_regularizer),
-        scope='Logits',
+        scope=name,
         reuse=reuse,
         trainable=trainable,
     )

bob/learn/tensorflow/script/compute_statistics.py

@@ -15,7 +15,15 @@ logger = logging.getLogger(__name__)
 
 
 @click.command(
-    entry_point_group='bob.learn.tensorflow.config', cls=ConfigCommand)
+    entry_point_group='bob.learn.tensorflow.config', cls=ConfigCommand,
+    epilog="""\b
+An example configuration could be::
+    # define the database:
+    from bob.bio.base.test.dummy.database import database
+    groups = ['dev']
+    biofiles = database.all_files(groups)
+"""
+)
 @click.option(
     '--database',
     '-d',
@@ -50,14 +58,6 @@ def compute_statistics(database, biofiles, load_data, multiple_samples,
 
     This script works with bob.bio.base databases. It will load all the samples
     and print their mean.
-
-    An example configuration could be::
-
-        # define the database:
-        from bob.bio.base.test.dummy.database import database
-
-        groups = ['dev']
-        biofiles = database.all_files(groups)
     """
     log_parameters(logger, ignore=('biofiles', ))
     logger.debug("len(biofiles): %d", len(biofiles))

bob/learn/tensorflow/script/eval.py

@@ -264,7 +264,8 @@ def eval(estimator, eval_input_fn, hooks, run_once, eval_interval_secs, name,
                 continue
 
             # evaluate based on the just copied checkpoint_path
-            checkpoint_path = checkpoint_path.replace(estimator.model_dir, eval_dir)
+            checkpoint_path = checkpoint_path.replace(estimator.model_dir, eval_dir + os.sep)
+            checkpoint_path = os.path.abspath(checkpoint_path)
             logger.debug("Evaluating the model from %s", checkpoint_path)
 
             # Evaluate

[Tiago de Freitas Pereira]

I'm good with this one, thanks