# Copyright 2016 Google Inc. 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.
# ==============================================================================
"""A library to evaluate Inception on a single GPU.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import math
import os.path
import time
import numpy as np
import tensorflow as tf
from inception import image_processing
from inception import inception_model as inception
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('eval_dir', '/tmp/imagenet_eval',
"""Directory where to write event logs.""")
tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/imagenet_train',
"""Directory where to read model checkpoints.""")
# Flags governing the frequency of the eval.
tf.app.flags.DEFINE_integer('eval_interval_secs', 60 * 5,
"""How often to run the eval.""")
tf.app.flags.DEFINE_boolean('run_once', False,
"""Whether to run eval only once.""")
# Flags governing the data used for the eval.
tf.app.flags.DEFINE_integer('num_examples', 50000,
"""Number of examples to run. Note that the eval """
"""ImageNet dataset contains 50000 examples.""")
tf.app.flags.DEFINE_string('subset', 'validation',
"""Either 'validation' or 'train'.""")
def _eval_once(saver, summary_writer, top_1_op, top_5_op, summary_op):
"""Runs Eval once.
Args:
saver: Saver.
summary_writer: Summary writer.
top_1_op: Top 1 op.
top_5_op: Top 5 op.
summary_op: Summary op.
"""
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
if os.path.isabs(ckpt.model_checkpoint_path):
# Restores from checkpoint with absolute path.
saver.restore(sess, ckpt.model_checkpoint_path)
else:
# Restores from checkpoint with relative path.
saver.restore(sess, os.path.join(FLAGS.checkpoint_dir,
ckpt.model_checkpoint_path))
# Assuming model_checkpoint_path looks something like:
# /my-favorite-path/imagenet_train/model.ckpt-0,
# extract global_step from it.
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
print('Succesfully loaded model from %s at step=%s.' %
(ckpt.model_checkpoint_path, global_step))
else:
print('No checkpoint file found')
return
# Start the queue runners.
coord = tf.train.Coordinator()
try:
threads = []
for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
start=True))
num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
# Counts the number of correct predictions.
count_top_1 = 0.0
count_top_5 = 0.0
total_sample_count = num_iter * FLAGS.batch_size
step = 0
print('%s: starting evaluation on (%s).' % (datetime.now(), FLAGS.subset))
start_time = time.time()
while step < num_iter and not coord.should_stop():
top_1, top_5 = sess.run([top_1_op, top_5_op])
#print('TOP 1: %s \nTOP 5: %s' % (top_1,top_5))
count_top_1 += np.sum(top_1)
count_top_5 += np.sum(top_5)
step += 1
if step % 20 == 0:
duration = time.time() - start_time
sec_per_batch = duration / 20.0
examples_per_sec = FLAGS.batch_size / sec_per_batch
print('%s: [%d batches out of %d] (%.1f examples/sec; %.3f'
'sec/batch)' % (datetime.now(), step, num_iter,
examples_per_sec, sec_per_batch))
start_time = time.time()
# Compute precision @ 1.
precision_at_1 = count_top_1 / total_sample_count
recall_at_5 = count_top_5 / total_sample_count
print('%s: precision @ 1 = %.4f recall @ 5 = %.4f [%d examples]' %
(datetime.now(), precision_at_1, recall_at_5, total_sample_count))
summary = tf.Summary()
summary.ParseFromString(sess.run(summary_op))
summary.value.add(tag='Precision @ 1', simple_value=precision_at_1)
summary.value.add(tag='Recall @ 5', simple_value=recall_at_5)
summary_writer.add_summary(summary, global_step)
except Exception as e: # pylint: disable=broad-except
coord.request_stop(e)
coord.request_stop()
coord.join(threads, stop_grace_period_secs=10)
def evaluate(dataset):
"""Evaluate model on Dataset for a number of steps."""
with tf.Graph().as_default():
# Get images and labels from the dataset.
images, labels, _ = image_processing.inputs(dataset)
# Number of classes in the Dataset label set plus 1.
# Label 0 is reserved for an (unused) background class.
num_classes = dataset.num_classes() + 1
# Build a Graph that computes the logits predictions from the
# inference model.
logits, _ = inception.inference(images, num_classes)
# Calculate predictions.
top_1_op = tf.nn.in_top_k(logits, labels, 1)
top_5_op = tf.nn.in_top_k(logits, labels, 5)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
inception.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
graph_def = tf.get_default_graph().as_graph_def()
summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir,
graph_def=graph_def)
while True:
_eval_once(saver, summary_writer, top_1_op, top_5_op, summary_op)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
