From 905ffc94bf0b10d5d1a92f361869a142c69aaa22 Mon Sep 17 00:00:00 2001
From: Amir MOHAMMADI <amir.mohammadi@idiap.ch>
Date: Wed, 17 Apr 2019 16:16:49 +0200
Subject: [PATCH] small changes: nit, bug fix, small features
---
bob/learn/tensorflow/layers/Maxout.py | 57 ++++++++++---------
bob/learn/tensorflow/layers/__init__.py | 7 ++-
bob/learn/tensorflow/loss/ContrastiveLoss.py | 13 ++---
bob/learn/tensorflow/network/utils.py | 7 ++-
.../tensorflow/script/compute_statistics.py | 18 +++---
bob/learn/tensorflow/script/eval.py | 3 +-
6 files changed, 53 insertions(+), 52 deletions(-)
diff --git a/bob/learn/tensorflow/layers/Maxout.py b/bob/learn/tensorflow/layers/Maxout.py
index d1abd0e5..69c05520 100644
--- a/bob/learn/tensorflow/layers/Maxout.py
+++ b/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
diff --git a/bob/learn/tensorflow/layers/__init__.py b/bob/learn/tensorflow/layers/__init__.py
index 8a810de3..a392020c 100644
--- a/bob/learn/tensorflow/layers/__init__.py
+++ b/bob/learn/tensorflow/layers/__init__.py
@@ -1,4 +1,4 @@
-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('_')]
diff --git a/bob/learn/tensorflow/loss/ContrastiveLoss.py b/bob/learn/tensorflow/loss/ContrastiveLoss.py
index a949aba5..81e3ac7b 100644
--- a/bob/learn/tensorflow/loss/ContrastiveLoss.py
+++ b/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)
diff --git a/bob/learn/tensorflow/network/utils.py b/bob/learn/tensorflow/network/utils.py
index a98b3c79..780a1682 100644
--- a/bob/learn/tensorflow/network/utils.py
+++ b/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,
)
diff --git a/bob/learn/tensorflow/script/compute_statistics.py b/bob/learn/tensorflow/script/compute_statistics.py
index a8945a4c..b876c5e4 100644
--- a/bob/learn/tensorflow/script/compute_statistics.py
+++ b/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))
diff --git a/bob/learn/tensorflow/script/eval.py b/bob/learn/tensorflow/script/eval.py
index 95cbb12a..99bd4e54 100644
--- a/bob/learn/tensorflow/script/eval.py
+++ b/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
--
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