TF cifar10 cnn tutorial: use tensorflow-datasets to load the data. (t…

…ensorflow#5906)

* TF cifar10 cnn tutorial: use tensorflow-datasets to load the data.

* Load cifar10 in memory.

* Fix imports

* More import fixes

tutorials/image/cifar10/cifar10.py

@@ -35,12 +35,8 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import re
-import sys
-import tarfile
 
-from six.moves import urllib
 import tensorflow as tf
 
 import cifar10_input
@@ -50,8 +46,6 @@
 # Basic model parameters.
 tf.app.flags.DEFINE_integer('batch_size', 128,
                             """Number of images to process in a batch.""")
-tf.app.flags.DEFINE_string('data_dir', '/tmp/cifar10_data',
-                           """Path to the CIFAR-10 data directory.""")
 tf.app.flags.DEFINE_boolean('use_fp16', False,
                             """Train the model using fp16.""")
 
@@ -73,8 +67,6 @@
 # names of the summaries when visualizing a model.
 TOWER_NAME = 'tower'
 
-DATA_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'
-
 
 def _activation_summary(x):
   """Helper to create summaries for activations.
@@ -91,8 +83,7 @@ def _activation_summary(x):
   # session. This helps the clarity of presentation on tensorboard.
   tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)
   tf.summary.histogram(tensor_name + '/activations', x)
-  tf.summary.scalar(tensor_name + '/sparsity',
-                                       tf.nn.zero_fraction(x))
+  tf.summary.scalar(tensor_name + '/sparsity', tf.nn.zero_fraction(x))
 
 
 def _variable_on_cpu(name, shape, initializer):
@@ -145,15 +136,8 @@ def distorted_inputs():
   Returns:
     images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
     labels: Labels. 1D tensor of [batch_size] size.
-
-  Raises:
-    ValueError: If no data_dir
   """
-  if not FLAGS.data_dir:
-    raise ValueError('Please supply a data_dir')
-  data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
-  images, labels = cifar10_input.distorted_inputs(data_dir=data_dir,
-                                                  batch_size=FLAGS.batch_size)
+  images, labels = cifar10_input.distorted_inputs(batch_size=FLAGS.batch_size)
   if FLAGS.use_fp16:
     images = tf.cast(images, tf.float16)
     labels = tf.cast(labels, tf.float16)
@@ -169,15 +153,8 @@ def inputs(eval_data):
   Returns:
     images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
     labels: Labels. 1D tensor of [batch_size] size.
-
-  Raises:
-    ValueError: If no data_dir
   """
-  if not FLAGS.data_dir:
-    raise ValueError('Please supply a data_dir')
-  data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
   images, labels = cifar10_input.inputs(eval_data=eval_data,
-                                        data_dir=data_dir,
                                         batch_size=FLAGS.batch_size)
   if FLAGS.use_fp16:
     images = tf.cast(images, tf.float16)
@@ -240,7 +217,7 @@ def inference(images):
   # local3
   with tf.variable_scope('local3') as scope:
     # Move everything into depth so we can perform a single matrix multiply.
-    reshape = tf.reshape(pool2, [images.get_shape().as_list()[0], -1])
+    reshape = tf.keras.layers.Flatten()(pool2)
     dim = reshape.get_shape()[1].value
     weights = _variable_with_weight_decay('weights', shape=[dim, 384],
                                           stddev=0.04, wd=0.004)
@@ -374,24 +351,3 @@ def train(total_loss, global_step):
     variables_averages_op = variable_averages.apply(tf.trainable_variables())
 
   return variables_averages_op
-
-
-def maybe_download_and_extract():
-  """Download and extract the tarball from Alex's website."""
-  dest_directory = FLAGS.data_dir
-  if not os.path.exists(dest_directory):
-    os.makedirs(dest_directory)
-  filename = DATA_URL.split('/')[-1]
-  filepath = os.path.join(dest_directory, filename)
-  if not os.path.exists(filepath):
-    def _progress(count, block_size, total_size):
-      sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename,
-          float(count * block_size) / float(total_size) * 100.0))
-      sys.stdout.flush()
-    filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
-    print()
-    statinfo = os.stat(filepath)
-    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
-  extracted_dir_path = os.path.join(dest_directory, 'cifar-10-batches-bin')
-  if not os.path.exists(extracted_dir_path):
-    tarfile.open(filepath, 'r:gz').extractall(dest_directory)

tutorials/image/cifar10/cifar10_eval.py

@@ -51,12 +51,12 @@
                            """Either 'test' or 'train_eval'.""")
 tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/cifar10_train',
                            """Directory where to read model checkpoints.""")
-tf.app.flags.DEFINE_integer('eval_interval_secs', 60 * 5,
+tf.app.flags.DEFINE_integer('eval_interval_secs', 5,
                             """How often to run the eval.""")
-tf.app.flags.DEFINE_integer('num_examples', 10000,
+tf.app.flags.DEFINE_integer('num_examples', 1000,
                             """Number of examples to run.""")
 tf.app.flags.DEFINE_boolean('run_once', False,
-                         """Whether to run eval only once.""")
+                            """Whether to run eval only once.""")
 
 
 def eval_once(saver, summary_writer, top_k_op, summary_op):
@@ -89,7 +89,7 @@ def eval_once(saver, summary_writer, top_k_op, summary_op):
         threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
                                          start=True))
 
-      num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
+      num_iter = int(math.ceil(float(FLAGS.num_examples) / FLAGS.batch_size))
       true_count = 0  # Counts the number of correct predictions.
       total_sample_count = num_iter * FLAGS.batch_size
       step = 0
@@ -146,7 +146,6 @@ def evaluate():
 
 
 def main(argv=None):  # pylint: disable=unused-argument
-  cifar10.maybe_download_and_extract()
   if tf.gfile.Exists(FLAGS.eval_dir):
     tf.gfile.DeleteRecursively(FLAGS.eval_dir)
   tf.gfile.MakeDirs(FLAGS.eval_dir)