Fix compatibility of object_detection for Python3 (#1610)

* make batcher compatible for py3

* make prefetcher and operations in ops compatible for py3

* use six.iteritem

* make all tests in  compatible with py3

* simplify usage of six and modify import order

* add back the space line

object_detection/core/batcher.py

@@ -78,11 +78,11 @@ class BatchQueue(object):
     """
     # Remember static shapes to set shapes of batched tensors.
     static_shapes = collections.OrderedDict(
-        {key: tensor.get_shape() for key, tensor in tensor_dict.iteritems()})
+        {key: tensor.get_shape() for key, tensor in tensor_dict.items()})
     # Remember runtime shapes to unpad tensors after batching.
     runtime_shapes = collections.OrderedDict(
         {(key, 'runtime_shapes'): tf.shape(tensor)
-         for key, tensor in tensor_dict.iteritems()})
+         for key, tensor in tensor_dict.items()})
     all_tensors = tensor_dict
     all_tensors.update(runtime_shapes)
     batched_tensors = tf.train.batch(
@@ -109,7 +109,7 @@ class BatchQueue(object):
     # Separate input tensors from tensors containing their runtime shapes.
     tensors = {}
     shapes = {}
-    for key, batched_tensor in batched_tensors.iteritems():
+    for key, batched_tensor in batched_tensors.items():
       unbatched_tensor_list = tf.unstack(batched_tensor)
       for i, unbatched_tensor in enumerate(unbatched_tensor_list):
         if isinstance(key, tuple) and key[1] == 'runtime_shapes':

object_detection/core/post_processing.py

@@ -131,7 +131,7 @@ def multiclass_non_max_suppression(boxes,
         boxlist_and_class_scores.add_field(fields.BoxListFields.masks,
                                            per_class_masks)
       if additional_fields is not None:
-        for key, tensor in additional_fields.iteritems():
+        for key, tensor in additional_fields.items():
           boxlist_and_class_scores.add_field(key, tensor)
       boxlist_filtered = box_list_ops.filter_greater_than(
           boxlist_and_class_scores, score_thresh)

object_detection/core/prefetcher.py

@@ -45,7 +45,7 @@ def prefetch(tensor_dict, capacity):
   Returns:
     a FIFO prefetcher queue
   """
-  names = tensor_dict.keys()
+  names = list(tensor_dict.keys())
   dtypes = [t.dtype for t in tensor_dict.values()]
   shapes = [t.get_shape() for t in tensor_dict.values()]
   prefetch_queue = tf.PaddingFIFOQueue(capacity, dtypes=dtypes,

object_detection/core/preprocessor_test.py

@@ -15,14 +15,19 @@
 
 """Tests for object_detection.core.preprocessor."""
 
-import mock
 import numpy as np
+import six
 
 import tensorflow as tf
 
 from object_detection.core import preprocessor
 from object_detection.core import standard_fields as fields
 
+if six.PY2:
+  import mock # pylint: disable=g-import-not-at-top
+else:
+  from unittest import mock # pylint: disable=g-import-not-at-top
+
 
 class PreprocessorTest(tf.test.TestCase):
 

object_detection/utils/ops.py

@@ -15,6 +15,7 @@
 
 """A module for helper tensorflow ops."""
 import math
+import six
 
 import tensorflow as tf
 
@@ -197,9 +198,9 @@ def padded_one_hot_encoding(indices, depth, left_pad):
 
   TODO: add runtime checks for depth and indices.
   """
-  if depth < 0 or not isinstance(depth, (int, long)):
+  if depth < 0 or not isinstance(depth, (int, long) if six.PY2 else int):
     raise ValueError('`depth` must be a non-negative integer.')
-  if left_pad < 0 or not isinstance(left_pad, (int, long)):
+  if left_pad < 0 or not isinstance(left_pad, (int, long) if six.PY2 else int):
     raise ValueError('`left_pad` must be a non-negative integer.')
   if depth == 0:
     return None
@@ -548,7 +549,7 @@ def position_sensitive_crop_regions(image,
       raise ValueError('crop_size should be divisible by num_spatial_bins')
 
     total_bins *= num_bins
-    bin_crop_size.append(crop_dim / num_bins)
+    bin_crop_size.append(crop_dim // num_bins)
 
   if not global_pool and bin_crop_size[0] != bin_crop_size[1]:
     raise ValueError('Only support square bin crop size for now.')