Update for py3 and some internal changes (#7786)

research/deeplab/evaluation/streaming_metrics_test.py

+# Lint as: python2, python3
 # Copyright 2019 The TensorFlow Authors All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

research/deeplab/evaluation/test_utils.py

+# Lint as: python2, python3
 # Copyright 2019 The TensorFlow Authors All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -26,6 +27,7 @@ from absl import flags
 import numpy as np
 import scipy.misc
 import six
+from six.moves import map
 
 FLAGS = flags.FLAGS
 

research/deeplab/evaluation/test_utils_test.py

+# Lint as: python2, python3
 # Copyright 2019 The TensorFlow Authors All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

research/deeplab/export_model.py

+# Lint as: python2, python3
 # Copyright 2018 The TensorFlow Authors All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -16,13 +17,15 @@
 
 import os
 import tensorflow as tf
+from google3.learning.brain.contrib import quantize as contrib_quantize
+from google3.learning.brain.contrib import slim as contrib_slim
 
 from tensorflow.python.tools import freeze_graph
 from deeplab import common
 from deeplab import input_preprocess
 from deeplab import model
 
-slim = tf.contrib.slim
+slim = contrib_slim
 flags = tf.app.flags
 
 FLAGS = flags.FLAGS
@@ -63,10 +66,14 @@ flags.DEFINE_bool('save_inference_graph', False,
 # Input name of the exported model.
 _INPUT_NAME = 'ImageTensor'
 
-# Output name of the exported model.
+# Output name of the exported predictions.
 _OUTPUT_NAME = 'SemanticPredictions'
 _RAW_OUTPUT_NAME = 'RawSemanticPredictions'
 
+# Output name of the exported probabilities.
+_OUTPUT_PROB_NAME = 'SemanticProbabilities'
+_RAW_OUTPUT_PROB_NAME = 'RawSemanticProbabilities'
+
 
 def _create_input_tensors():
   """Creates and prepares input tensors for DeepLab model.
@@ -139,11 +146,16 @@ def main(unused_argv):
     raw_predictions = tf.identity(
         tf.cast(predictions[common.OUTPUT_TYPE], tf.float32),
         _RAW_OUTPUT_NAME)
+    raw_probabilities = tf.identity(
+        predictions[common.OUTPUT_TYPE + model.PROB_SUFFIX],
+        _RAW_OUTPUT_PROB_NAME)
+
     # Crop the valid regions from the predictions.
-    semantic_predictions = tf.slice(
-        raw_predictions,
-        [0, 0, 0],
-        [1, resized_image_size[0], resized_image_size[1]])
+    semantic_predictions = raw_predictions[
+        :, :resized_image_size[0], :resized_image_size[1]]
+    semantic_probabilities = raw_probabilities[
+        :, :resized_image_size[0], :resized_image_size[1]]
+
     # Resize back the prediction to the original image size.
     def _resize_label(label, label_size):
       # Expand dimension of label to [1, height, width, 1] for resize operation.
@@ -157,8 +169,12 @@ def main(unused_argv):
     semantic_predictions = _resize_label(semantic_predictions, image_size)
     semantic_predictions = tf.identity(semantic_predictions, name=_OUTPUT_NAME)
 
+    semantic_probabilities = tf.image.resize_bilinear(
+        semantic_probabilities, image_size, align_corners=True,
+        name=_OUTPUT_PROB_NAME)
+
     if FLAGS.quantize_delay_step >= 0:
-      tf.contrib.quantize.create_eval_graph()
+      contrib_quantize.create_eval_graph()
 
     saver = tf.train.Saver(tf.all_variables())
 
@@ -169,7 +185,7 @@ def main(unused_argv):
         graph_def,
         saver.as_saver_def(),
         FLAGS.checkpoint_path,
-        _OUTPUT_NAME,
+        _OUTPUT_NAME + ',' + _OUTPUT_PROB_NAME,
         restore_op_name=None,
         filename_tensor_name=None,
         output_graph=FLAGS.export_path,

research/deeplab/g3doc/quantize.md

@@ -98,12 +98,12 @@ For quantized (8bit) model, un-tar'ed directory includes:
 
 *   a converted TFlite FlatBuffer file (frozen_inference_graph.tflite)
 
-Checkpoint name                                                                                                                              | Eval OS | Eval scales | Left-right Flip | Multiply-Adds | Quantize | PASCAL mIOU  | File Size
--------------------------------------------------------------------------------------------------------------------------------------------- | :-----: | :---------: | :-------------: | :-----------: | :------: | :----------: | :-------:
-[mobilenetv2_dm05_coco_voc_trainaug](http://download.tensorflow.org/models/deeplabv3_mnv2_dm05_pascal_trainaug_2018_10_01.tar.gz)            | 16      | [1.0]       | No              | 0.88B         | No       | 70.19% (val) | 7.6MB
-[mobilenetv2_dm05_coco_voc_trainaug_8bit](http://download.tensorflow.org/models/deeplabv3_mnv2_dm05_pascal_train_aug_8bit_2019_04_26.tar.gz) | 16      | [1.0]       | No              | 0.88B         | Yes      | 69.65% (val) | 8.2MB
-[mobilenetv2_coco_voc_trainaug](http://download.tensorflow.org/models/deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz)                     | 16      | [1.0]       | No              | 2.75B         | No       | 75.32% (val) | 23MB
-[mobilenetv2_coco_voc_trainaug_8bit](http://download.tensorflow.org/models/deeplabv3_mnv2_pascal_train_aug_8bit_2019_04_26.tar.gz)           | 16      | [1.0]       | No              | 2.75B         | Yes      | 74.26% (val) | 24MB
+Checkpoint name                                                                                                                              | Eval OS | Eval scales | Left-right Flip | Multiply-Adds | Quantize | PASCAL mIOU  | Folder Size | TFLite File Size
+-------------------------------------------------------------------------------------------------------------------------------------------- | :-----: | :---------: | :-------------: | :-----------: | :------: | :----------: | :-------: | :-------:
+[mobilenetv2_dm05_coco_voc_trainaug](http://download.tensorflow.org/models/deeplabv3_mnv2_dm05_pascal_trainaug_2018_10_01.tar.gz)            | 16      | [1.0]       | No              | 0.88B         | No       | 70.19% (val) | 7.6MB | N/A
+[mobilenetv2_dm05_coco_voc_trainaug_8bit](http://download.tensorflow.org/models/deeplabv3_mnv2_dm05_pascal_train_aug_8bit_2019_04_26.tar.gz) | 16      | [1.0]       | No              | 0.88B         | Yes      | 69.65% (val) | 8.2MB | 751.1KB
+[mobilenetv2_coco_voc_trainaug](http://download.tensorflow.org/models/deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz)                     | 16      | [1.0]       | No              | 2.75B         | No       | 75.32% (val) | 23MB  | N/A
+[mobilenetv2_coco_voc_trainaug_8bit](http://download.tensorflow.org/models/deeplabv3_mnv2_pascal_train_aug_8bit_2019_04_26.tar.gz)           | 16      | [1.0]       | No              | 2.75B         | Yes      | 74.26% (val) | 24MB  | 2.2MB
 
 Note that you might need the nightly build of TensorFlow (see
 [here](https://www.tensorflow.org/install) for install instructions) to convert

research/deeplab/input_preprocess.py

+# Lint as: python2, python3
 # Copyright 2018 The TensorFlow Authors All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

research/deeplab/model_test.py

+# Lint as: python2, python3
 # Copyright 2018 The TensorFlow Authors All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

research/deeplab/utils/get_dataset_colormap.py

+# Lint as: python2, python3
 # Copyright 2018 The TensorFlow Authors All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -26,7 +27,11 @@ defined by the different datasets. Supported colormaps are:
 * PASCAL VOC 2012 (http://host.robots.ox.ac.uk/pascal/VOC/).
 """
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import numpy as np
+from six.moves import range
 
 # Dataset names.
 _ADE20K = 'ade20k'
@@ -39,7 +44,7 @@ _DATASET_MAX_ENTRIES = {
     _ADE20K: 151,
     _CITYSCAPES: 256,
     _MAPILLARY_VISTAS: 66,
-    _PASCAL: 256,
+    _PASCAL: 512,
 }
 
 
@@ -318,7 +323,7 @@ def create_pascal_label_colormap():
   colormap = np.zeros((_DATASET_MAX_ENTRIES[_PASCAL], 3), dtype=int)
   ind = np.arange(_DATASET_MAX_ENTRIES[_PASCAL], dtype=int)
 
-  for shift in reversed(range(8)):
+  for shift in reversed(list(range(8))):
     for channel in range(3):
       colormap[:, channel] |= bit_get(ind, channel) << shift
     ind >>= 3

research/deeplab/utils/get_dataset_colormap_test.py

+# Lint as: python2, python3
 # Copyright 2018 The TensorFlow Authors All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -54,7 +55,7 @@ class VisualizationUtilTest(tf.test.TestCase):
 
   def testUnExpectedLabelValueForLabelToPASCALColorImage(self):
     """Raise ValueError when input value exceeds range."""
-    label = np.array([[120], [300]])
+    label = np.array([[120], [600]])
     with self.assertRaises(ValueError):
       get_dataset_colormap.label_to_color_image(
           label, get_dataset_colormap.get_pascal_name())

research/deeplab/utils/save_annotation.py

+# Lint as: python2, python3
 # Copyright 2018 The TensorFlow Authors All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

research/deeplab/vis.py

+# Lint as: python2, python3
 # Copyright 2018 The TensorFlow Authors All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -17,10 +18,16 @@
 
 See model.py for more details and usage.
 """
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os.path
 import time
 import numpy as np
+from six.moves import range
 import tensorflow as tf
+from tensorflow.contrib import quantize as contrib_quantize
+from tensorflow.contrib import training as contrib_training
 from deeplab import common
 from deeplab import model
 from deeplab.datasets import data_generator
@@ -80,7 +87,7 @@ flags.DEFINE_string('vis_split', 'val',
 
 flags.DEFINE_string('dataset_dir', None, 'Where the dataset reside.')
 
-flags.DEFINE_enum('colormap_type', 'pascal', ['pascal', 'cityscapes'],
+flags.DEFINE_enum('colormap_type', 'pascal', ['pascal', 'cityscapes', 'ade20k'],
                   'Visualization colormap type.')
 
 flags.DEFINE_boolean('also_save_raw_predictions', False,
@@ -268,12 +275,12 @@ def main(unused_argv):
 
     tf.train.get_or_create_global_step()
     if FLAGS.quantize_delay_step >= 0:
-      tf.contrib.quantize.create_eval_graph()
+      contrib_quantize.create_eval_graph()
 
     num_iteration = 0
     max_num_iteration = FLAGS.max_number_of_iterations
 
-    checkpoints_iterator = tf.contrib.training.checkpoints_iterator(
+    checkpoints_iterator = contrib_training.checkpoints_iterator(
         FLAGS.checkpoint_dir, min_interval_secs=FLAGS.eval_interval_secs)
     for checkpoint_path in checkpoints_iterator:
       num_iteration += 1