Merge branch 'tensorflow:master' into panoptic-deeplab

research/object_detection/exporter.py

@@ -101,15 +101,21 @@ def rewrite_nn_resize_op(is_quantized=False):
         'FakeQuantWithMinMaxVars' if is_quantized else '*')
     stack_1_pattern = graph_matcher.OpTypePattern(
         'Pack', inputs=[input_pattern, input_pattern], ordered_inputs=False)
+    reshape_1_pattern = graph_matcher.OpTypePattern(
+        'Reshape', inputs=[stack_1_pattern, 'Const'], ordered_inputs=False)
     stack_2_pattern = graph_matcher.OpTypePattern(
-        'Pack', inputs=[stack_1_pattern, stack_1_pattern], ordered_inputs=False)
-    reshape_pattern = graph_matcher.OpTypePattern(
+        'Pack',
+        inputs=[reshape_1_pattern, reshape_1_pattern],
+        ordered_inputs=False)
+    reshape_2_pattern = graph_matcher.OpTypePattern(
         'Reshape', inputs=[stack_2_pattern, 'Const'], ordered_inputs=False)
     consumer_pattern1 = graph_matcher.OpTypePattern(
-        'Add|AddV2|Max|Mul', inputs=[reshape_pattern, '*'],
+        'Add|AddV2|Max|Mul',
+        inputs=[reshape_2_pattern, '*'],
         ordered_inputs=False)
     consumer_pattern2 = graph_matcher.OpTypePattern(
-        'StridedSlice', inputs=[reshape_pattern, '*', '*', '*'],
+        'StridedSlice',
+        inputs=[reshape_2_pattern, '*', '*', '*'],
         ordered_inputs=False)
 
     def replace_matches(consumer_pattern):
@@ -119,16 +125,17 @@ def rewrite_nn_resize_op(is_quantized=False):
       for match in matcher.match_graph(tf.get_default_graph()):
         match_counter += 1
         projection_op = match.get_op(input_pattern)
-        reshape_op = match.get_op(reshape_pattern)
+        reshape_2_op = match.get_op(reshape_2_pattern)
         consumer_op = match.get_op(consumer_pattern)
         nn_resize = tf.image.resize_nearest_neighbor(
             projection_op.outputs[0],
-            reshape_op.outputs[0].shape.dims[1:3],
+            reshape_2_op.outputs[0].shape.dims[1:3],
             align_corners=False,
-            name=os.path.split(reshape_op.name)[0] + '/resize_nearest_neighbor')
+            name=os.path.split(reshape_2_op.name)[0] +
+            '/resize_nearest_neighbor')
 
         for index, op_input in enumerate(consumer_op.inputs):
-          if op_input == reshape_op.outputs[0]:
+          if op_input == reshape_2_op.outputs[0]:
             consumer_op._update_input(index, nn_resize)  # pylint: disable=protected-access
             break
 

research/object_detection/exporter_tf1_test.py

@@ -1168,16 +1168,20 @@ class ExportInferenceGraphTest(tf.test.TestCase):
     g = tf.Graph()
     with g.as_default():
       with tf.name_scope('nearest_upsampling'):
-        x = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
-        x_stack = tf.stack([tf.stack([x] * 2, axis=3)] * 2, axis=2)
-        x_reshape = tf.reshape(x_stack, [8, 20, 20, 8])
+        x_1 = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
+        x_1_stack_1 = tf.stack([x_1] * 2, axis=3)
+        x_1_reshape_1 = tf.reshape(x_1_stack_1, [8, 10, 20, 8])
+        x_1_stack_2 = tf.stack([x_1_reshape_1] * 2, axis=2)
+        x_1_reshape_2 = tf.reshape(x_1_stack_2, [8, 20, 20, 8])
 
       with tf.name_scope('nearest_upsampling'):
         x_2 = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
-        x_stack_2 = tf.stack([tf.stack([x_2] * 2, axis=3)] * 2, axis=2)
-        x_reshape_2 = tf.reshape(x_stack_2, [8, 20, 20, 8])
+        x_2_stack_1 = tf.stack([x_2] * 2, axis=3)
+        x_2_reshape_1 = tf.reshape(x_2_stack_1, [8, 10, 20, 8])
+        x_2_stack_2 = tf.stack([x_2_reshape_1] * 2, axis=2)
+        x_2_reshape_2 = tf.reshape(x_2_stack_2, [8, 20, 20, 8])
 
-      t = x_reshape + x_reshape_2
+      t = x_1_reshape_2 + x_2_reshape_2
 
       exporter.rewrite_nn_resize_op()
 

research/object_detection/inputs.py

@@ -54,18 +54,20 @@ INPUT_BUILDER_UTIL_MAP = {
 }
 
 
-def _multiclass_scores_or_one_hot_labels(multiclass_scores,
-                                         groundtruth_boxes,
+def _multiclass_scores_or_one_hot_labels(multiclass_scores, groundtruth_boxes,
                                          groundtruth_classes, num_classes):
   """Returns one-hot encoding of classes when multiclass_scores is empty."""
+
   # Replace groundtruth_classes tensor with multiclass_scores tensor when its
   # non-empty. If multiclass_scores is empty fall back on groundtruth_classes
   # tensor.
   def true_fn():
     return tf.reshape(multiclass_scores,
                       [tf.shape(groundtruth_boxes)[0], num_classes])
+
   def false_fn():
     return tf.one_hot(groundtruth_classes, num_classes)
+
   return tf.cond(tf.size(multiclass_scores) > 0, true_fn, false_fn)
 
 
@@ -132,8 +134,7 @@ def assert_or_prune_invalid_boxes(boxes):
       This is not supported on TPUs.
   """
 
-  ymin, xmin, ymax, xmax = tf.split(
-      boxes, num_or_size_splits=4, axis=1)
+  ymin, xmin, ymax, xmax = tf.split(boxes, num_or_size_splits=4, axis=1)
 
   height_check = tf.Assert(tf.reduce_all(ymax >= ymin), [ymin, ymax])
   width_check = tf.Assert(tf.reduce_all(xmax >= xmin), [xmin, xmax])
@@ -157,7 +158,8 @@ def transform_input_data(tensor_dict,
                          use_multiclass_scores=False,
                          use_bfloat16=False,
                          retain_original_image_additional_channels=False,
-                         keypoint_type_weight=None):
+                         keypoint_type_weight=None,
+                         image_classes_field_map_empty_to_ones=True):
   """A single function that is responsible for all input data transformations.
 
   Data transformation functions are applied in the following order.
@@ -206,6 +208,9 @@ def transform_input_data(tensor_dict,
     keypoint_type_weight: A list (of length num_keypoints) containing
       groundtruth loss weights to use for each keypoint. If None, will use a
       weight of 1.
+    image_classes_field_map_empty_to_ones: A boolean flag indicating if empty
+      image classes field indicates that all classes have been labeled on this
+      image [true] or none [false].
 
   Returns:
     A dictionary keyed by fields.InputDataFields containing the tensors obtained
@@ -229,9 +234,9 @@ def transform_input_data(tensor_dict,
     raise KeyError('groundtruth_labeled_classes and groundtruth_image_classes'
                    'are provided by the decoder, but only one should be set.')
 
-  for field, map_empty_to_ones in [
-      (labeled_classes_field, True),
-      (image_classes_field, True),
+  for field, map_empty_to_ones in [(labeled_classes_field, True),
+                                   (image_classes_field,
+                                    image_classes_field_map_empty_to_ones),
                                    (verified_neg_classes_field, False),
                                    (not_exhaustive_field, False)]:
     if field in out_tensor_dict:
@@ -1044,7 +1049,9 @@ def eval_input(eval_config, eval_input_config, model_config,
         retain_original_image=eval_config.retain_original_images,
         retain_original_image_additional_channels=
         eval_config.retain_original_image_additional_channels,
-        keypoint_type_weight=keypoint_type_weight)
+        keypoint_type_weight=keypoint_type_weight,
+        image_classes_field_map_empty_to_ones=eval_config
+        .image_classes_field_map_empty_to_ones)
     tensor_dict = pad_input_data_to_static_shapes(
         tensor_dict=transform_data_fn(tensor_dict),
         max_num_boxes=eval_input_config.max_number_of_boxes,