prepare data, train, and eval on py3

research/deeplab/datasets/build_cityscapes_data.py

@@ -154,10 +154,10 @@ def _convert_dataset(dataset_split):
             i + 1, num_images, shard_id))
         sys.stdout.flush()
         # Read the image.
-        image_data = tf.gfile.FastGFile(image_files[i], 'r').read()
+        image_data = tf.gfile.FastGFile(image_files[i], 'rb').read()
         height, width = image_reader.read_image_dims(image_data)
         # Read the semantic segmentation annotation.
-        seg_data = tf.gfile.FastGFile(label_files[i], 'r').read()
+        seg_data = tf.gfile.FastGFile(label_files[i], 'rb').read()
         seg_height, seg_width = label_reader.read_image_dims(seg_data)
         if height != seg_height or width != seg_width:
           raise RuntimeError('Shape mismatched between image and label.')

research/deeplab/datasets/build_data.py

@@ -125,7 +125,10 @@ def _bytes_list_feature(values):
   Returns:
     A TF-Feature.
   """
-  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))
+  def norm2bytes(value):
+      return value.encode() if isinstance(value, str) else value
+
+  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[norm2bytes(values)]))
 
 
 def image_seg_to_tfexample(image_data, filename, height, width, seg_data):

research/deeplab/datasets/build_voc2012_data.py

@@ -114,13 +114,13 @@ def _convert_dataset(dataset_split):
         # Read the image.
         image_filename = os.path.join(
             FLAGS.image_folder, filenames[i] + '.' + FLAGS.image_format)
-        image_data = tf.gfile.FastGFile(image_filename, 'r').read()
+        image_data = tf.gfile.FastGFile(image_filename, 'rb').read()
         height, width = image_reader.read_image_dims(image_data)
         # Read the semantic segmentation annotation.
         seg_filename = os.path.join(
             FLAGS.semantic_segmentation_folder,
             filenames[i] + '.' + FLAGS.label_format)
-        seg_data = tf.gfile.FastGFile(seg_filename, 'r').read()
+        seg_data = tf.gfile.FastGFile(seg_filename, 'rb').read()
         seg_height, seg_width = label_reader.read_image_dims(seg_data)
         if height != seg_height or width != seg_width:
           raise RuntimeError('Shape mismatched between image and label.')

research/deeplab/eval.py

@@ -17,6 +17,7 @@
 See model.py for more details and usage.
 """
 
+import six
 import math
 import tensorflow as tf
 from deeplab import common
@@ -144,7 +145,7 @@ def main(unused_argv):
     metrics_to_values, metrics_to_updates = (
         tf.contrib.metrics.aggregate_metric_map(metric_map))
 
-    for metric_name, metric_value in metrics_to_values.iteritems():
+    for metric_name, metric_value in six.iteritems(metrics_to_values):
       slim.summaries.add_scalar_summary(
           metric_value, metric_name, print_summary=True)
 
@@ -163,7 +164,7 @@ def main(unused_argv):
         checkpoint_dir=FLAGS.checkpoint_dir,
         logdir=FLAGS.eval_logdir,
         num_evals=num_batches,
-        eval_op=metrics_to_updates.values(),
+        eval_op=list(metrics_to_updates.values()),
         max_number_of_evaluations=num_eval_iters,
         eval_interval_secs=FLAGS.eval_interval_secs)
 

research/deeplab/train.py

@@ -17,6 +17,7 @@
 See model.py for more details and usage.
 """
 
+import six
 import tensorflow as tf
 from deeplab import common
 from deeplab import model
@@ -190,7 +191,7 @@ def _build_deeplab(inputs_queue, outputs_to_num_classes, ignore_label):
       is_training=True,
       fine_tune_batch_norm=FLAGS.fine_tune_batch_norm)
 
-  for output, num_classes in outputs_to_num_classes.iteritems():
+  for output, num_classes in six.iteritems(outputs_to_num_classes):
     train_utils.add_softmax_cross_entropy_loss_for_each_scale(
         outputs_to_scales_to_logits[output],
         samples[common.LABEL],
@@ -217,7 +218,7 @@ def main(unused_argv):
   assert FLAGS.train_batch_size % config.num_clones == 0, (
       'Training batch size not divisble by number of clones (GPUs).')
 
-  clone_batch_size = FLAGS.train_batch_size / config.num_clones
+  clone_batch_size = int(FLAGS.train_batch_size / config.num_clones)
 
   # Get dataset-dependent information.
   dataset = segmentation_dataset.get_dataset(

research/deeplab/utils/train_utils.py

@@ -14,6 +14,8 @@
 # ==============================================================================
 """Utility functions for training."""
 
+import six
+
 import tensorflow as tf
 
 slim = tf.contrib.slim
@@ -44,7 +46,7 @@ def add_softmax_cross_entropy_loss_for_each_scale(scales_to_logits,
   if labels is None:
     raise ValueError('No label for softmax cross entropy loss.')
 
-  for scale, logits in scales_to_logits.iteritems():
+  for scale, logits in six.iteritems(scales_to_logits):
     loss_scope = None
     if scope:
       loss_scope = '%s_%s' % (scope, scale)