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# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=line-too-long
"""Evaluation for Domain Separation Networks (DSNs)."""
# pylint: enable=line-too-long
import math
import numpy as np
from six.moves import xrange
import tensorflow as tf
from domain_adaptation.datasets import dataset_factory
from domain_adaptation.domain_separation import losses
from domain_adaptation.domain_separation import models
slim = tf.contrib.slim
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer('batch_size', 32,
'The number of images in each batch.')
tf.app.flags.DEFINE_string('master', '',
'BNS name of the TensorFlow master to use.')
tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/da/',
'Directory where the model was written to.')
tf.app.flags.DEFINE_string(
'eval_dir', '/tmp/da/',
'Directory where we should write the tf summaries to.')
tf.app.flags.DEFINE_string('dataset_dir', None,
'The directory where the dataset files are stored.')
tf.app.flags.DEFINE_string('dataset', 'mnist_m',
'Which dataset to test on: "mnist", "mnist_m".')
tf.app.flags.DEFINE_string('split', 'valid',
'Which portion to test on: "valid", "test".')
tf.app.flags.DEFINE_integer('num_examples', 1000, 'Number of test examples.')
tf.app.flags.DEFINE_string('basic_tower', 'dann_mnist',
'The basic tower building block.')
tf.app.flags.DEFINE_bool('enable_precision_recall', False,
'If True, precision and recall for each class will '
'be added to the metrics.')
tf.app.flags.DEFINE_bool('use_logging', False, 'Debugging messages.')
def quaternion_metric(predictions, labels):
params = {'batch_size': FLAGS.batch_size, 'use_logging': False}
logcost = losses.log_quaternion_loss_batch(predictions, labels, params)
return slim.metrics.streaming_mean(logcost)
def angle_diff(true_q, pred_q):
angles = 2 * (
180.0 /
np.pi) * np.arccos(np.abs(np.sum(np.multiply(pred_q, true_q), axis=1)))
return angles
def provide_batch_fn():
""" The provide_batch function to use. """
return dataset_factory.provide_batch
def main(_):
g = tf.Graph()
with g.as_default():
# Load the data.
images, labels = provide_batch_fn()(
FLAGS.dataset, FLAGS.split, FLAGS.dataset_dir, 4, FLAGS.batch_size, 4)
num_classes = labels['classes'].get_shape().as_list()[1]
tf.summary.image('eval_images', images, max_outputs=3)
# Define the model:
with tf.variable_scope('towers'):
basic_tower = getattr(models, FLAGS.basic_tower)
predictions, endpoints = basic_tower(
images,
num_classes=num_classes,
is_training=False,
batch_norm_params=None)
metric_names_to_values = {}
# Define the metrics:
if 'quaternions' in labels: # Also have to evaluate pose estimation!
quaternion_loss = quaternion_metric(labels['quaternions'],
endpoints['quaternion_pred'])
angle_errors, = tf.py_func(
angle_diff, [labels['quaternions'], endpoints['quaternion_pred']],
[tf.float32])
metric_names_to_values[
'Angular mean error'] = slim.metrics.streaming_mean(angle_errors)
metric_names_to_values['Quaternion Loss'] = quaternion_loss
accuracy = tf.contrib.metrics.streaming_accuracy(
tf.argmax(predictions, 1), tf.argmax(labels['classes'], 1))
predictions = tf.argmax(predictions, 1)
labels = tf.argmax(labels['classes'], 1)
metric_names_to_values['Accuracy'] = accuracy
if FLAGS.enable_precision_recall:
for i in xrange(num_classes):
index_map = tf.one_hot(i, depth=num_classes)
name = 'PR/Precision_{}'.format(i)
metric_names_to_values[name] = slim.metrics.streaming_precision(
tf.gather(index_map, predictions), tf.gather(index_map, labels))
name = 'PR/Recall_{}'.format(i)
metric_names_to_values[name] = slim.metrics.streaming_recall(
tf.gather(index_map, predictions), tf.gather(index_map, labels))
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map(
metric_names_to_values)
# Create the summary ops such that they also print out to std output:
summary_ops = []
for metric_name, metric_value in names_to_values.iteritems():
op = tf.summary.scalar(metric_name, metric_value)
op = tf.Print(op, [metric_value], metric_name)
summary_ops.append(op)
# This ensures that we make a single pass over all of the data.
num_batches = math.ceil(FLAGS.num_examples / float(FLAGS.batch_size))
# Setup the global step.
slim.get_or_create_global_step()
slim.evaluation.evaluation_loop(
FLAGS.master,
checkpoint_dir=FLAGS.checkpoint_dir,
logdir=FLAGS.eval_dir,
num_evals=num_batches,
eval_op=names_to_updates.values(),
summary_op=tf.summary.merge(summary_ops))
if __name__ == '__main__':
tf.app.run()
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