Merged commit includes the following changes:

184048729  by Zhichao Lu:

    Modify target_assigner so that it creates regression targets taking keypoints into account.

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184027183  by Zhichao Lu:

    Resnet V1 FPN based feature extractors for SSD meta architecture in Object Detection V2 API.

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184004730  by Zhichao Lu:

    Expose a lever to override the configured mask_type.

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183933113  by Zhichao Lu:

    Weight shared convolutional box predictor as described in https://arxiv.org/abs/1708.02002

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183929669  by Zhichao Lu:

    Expanding box list operations for future data augmentations.

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183916792  by Zhichao Lu:

    Fix unrecognized assertion function in tests.

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183906851  by Zhichao Lu:

    - Change ssd meta architecture to use regression weights to compute loss normalizer.

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183871003  by Zhichao Lu:

    Fix config_util_test wrong dependency.

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183782120  by Zhichao Lu:

    Add __init__ file to third_party directories.

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183779109  by Zhichao Lu:

    Setup regular version sync.

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183768772  by Zhichao Lu:

    Make test compatible with numpy 1.12 and higher

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183767893  by Zhichao Lu:

    Make test compatible with numpy 1.12 and higher

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183719318  by Zhichao Lu:

    Use the new test interface in ssd feature extractor.

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183714671  by Zhichao Lu:

    Use the new test_case interface for all anchor generators.

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183708155  by Zhichao Lu:

    Change variable scopes in ConvolutionalBoxPredictor such that previously trained checkpoints are still compatible after the change in BoxPredictor interface

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183705798  by Zhichao Lu:

    Internal change.

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183636023  by Zhichao Lu:

    Fixing argument name for np_box_list_ops.concatenate() function.

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183490404  by Zhichao Lu:

    Make sure code that relies in SSD older code still works.

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175743440  by Zhichao Lu:

    Use Toggle instead of bool to make the layout optimizer name and usage consistent with other optimizers.

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174249903  by Zhichao Lu:

    Fix nasnet image classification and object detection by moving the option to turn ON or OFF batch norm training into it's own arg_scope used only by detection

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174048035  by Zhichao Lu:

    Fix the pointer for downloading the NAS Faster-RCNN model.

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172815204  by Zhichao Lu:

    Allow for label maps in tf.Example decoding.

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172509113  by Zhichao Lu:

    Allow for label maps in tf.Example decoding.

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172151758  by Zhichao Lu:

    Minor updates to some README files.

    As a result of these friendly issues:
    https://github.com/tensorflow/models/issues/2530
    https://github.com/tensorflow/models/issues/2534

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172147420  by Zhichao Lu:

    Fix illegal summary name and move from slim's get_or_create_global_step deprecated use of tf.contrib.framework* to tf.train*.

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169763373  by Zhichao Lu:

    Fix broken GitHub links in tensorflow and tensorflow_models resulting from The Great Models Move (a.k.a. the research subfolder)

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168071571  by Zhichao Lu:

    Move display strings to below the bounding box if they would otherwise be outside the image.

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PiperOrigin-RevId: 184048729

research/object_detection/data/BUILD

+package(
+    default_visibility = ["//visibility:public"],
+)
+
+licenses(["notice"])
+
+exports_files([
+    "pet_label_map.pbtxt",
+])

research/object_detection/data_decoders/BUILD

@@ -12,9 +12,10 @@ py_library(
     srcs = ["tf_example_decoder.py"],
     deps = [
         "//tensorflow",
-        "//tensorflow_models/object_detection/core:data_decoder",
-        "//tensorflow_models/object_detection/core:standard_fields",
-        "//tensorflow_models/object_detection/utils:label_map_util",
+        "//tensorflow/models/research/object_detection/core:data_decoder",
+        "//tensorflow/models/research/object_detection/core:standard_fields",
+        "//tensorflow/models/research/object_detection/protos:input_reader_py_pb2",
+        "//tensorflow/models/research/object_detection/utils:label_map_util",
     ],
 )
 
@@ -24,6 +25,7 @@ py_test(
     deps = [
         ":tf_example_decoder",
         "//tensorflow",
-        "//tensorflow_models/object_detection/core:standard_fields",
+        "//tensorflow/models/research/object_detection/core:standard_fields",
+        "//tensorflow/models/research/object_detection/protos:input_reader_py_pb2",
     ],
 )

research/object_detection/data_decoders/tf_example_decoder_test.py

@@ -21,6 +21,7 @@ import tensorflow as tf
 
 from object_detection.core import standard_fields as fields
 from object_detection.data_decoders import tf_example_decoder
+from object_detection.protos import input_reader_pb2
 
 
 class TfExampleDecoderTest(tf.test.TestCase):
@@ -116,6 +117,36 @@ class TfExampleDecoderTest(tf.test.TestCase):
     self.assertAllEqual(decoded_png, tensor_dict[fields.InputDataFields.image])
     self.assertEqual('image_id', tensor_dict[fields.InputDataFields.source_id])
 
+  def testDecodePngInstanceMasks(self):
+    image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    mask_1 = np.random.randint(0, 2, size=(10, 10, 1)).astype(np.uint8)
+    mask_2 = np.random.randint(0, 2, size=(10, 10, 1)).astype(np.uint8)
+    encoded_png_1 = self._EncodeImage(mask_1, encoding_type='png')
+    decoded_png_1 = np.squeeze(mask_1.astype(np.float32))
+    encoded_png_2 = self._EncodeImage(mask_2, encoding_type='png')
+    decoded_png_2 = np.squeeze(mask_2.astype(np.float32))
+    encoded_masks = [encoded_png_1, encoded_png_2]
+    decoded_masks = np.stack([decoded_png_1, decoded_png_2])
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded': self._BytesFeature(encoded_jpeg),
+                'image/format': self._BytesFeature('jpeg'),
+                'image/object/mask': self._BytesFeature(encoded_masks)
+            })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder(
+        load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS)
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(
+        decoded_masks,
+        tensor_dict[fields.InputDataFields.groundtruth_instance_masks])
+
   def testDecodeBoundingBox(self):
     image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
     encoded_jpeg = self._EncodeImage(image_tensor)
@@ -168,6 +199,48 @@ class TfExampleDecoderTest(tf.test.TestCase):
     self.assertAllEqual(bbox_classes,
                         tensor_dict[fields.InputDataFields.groundtruth_classes])
 
+  def testDecodeObjectLabelWithMapping(self):
+    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    bbox_classes_text = ['cat', 'dog']
+    example = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                'image/encoded':
+                    self._BytesFeature(encoded_jpeg),
+                'image/format':
+                    self._BytesFeature('jpeg'),
+                'image/object/class/text':
+                    self._BytesFeature(bbox_classes_text),
+            })).SerializeToString()
+
+    label_map_string = """
+      item {
+        id:3
+        name:'cat'
+      }
+      item {
+        id:1
+        name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+    example_decoder = tf_example_decoder.TfExampleDecoder(
+        label_map_proto_file=label_map_path)
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
+                         .get_shape().as_list()), [None])
+
+    with self.test_session() as sess:
+      sess.run(tf.tables_initializer())
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual([3, 1],
+                        tensor_dict[fields.InputDataFields.groundtruth_classes])
+
   def testDecodeObjectArea(self):
     image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
     encoded_jpeg = self._EncodeImage(image_tensor)
@@ -259,6 +332,30 @@ class TfExampleDecoderTest(tf.test.TestCase):
         [bool(item) for item in object_group_of],
         tensor_dict[fields.InputDataFields.groundtruth_group_of])
 
+  def testDecodeObjectWeight(self):
+    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
+    encoded_jpeg = self._EncodeImage(image_tensor)
+    object_weights = [0.75, 1.0]
+    example = tf.train.Example(features=tf.train.Features(
+        feature={
+            'image/encoded': self._BytesFeature(encoded_jpeg),
+            'image/format': self._BytesFeature('jpeg'),
+            'image/object/weight': self._FloatFeature(object_weights),
+        })).SerializeToString()
+
+    example_decoder = tf_example_decoder.TfExampleDecoder()
+    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
+
+    self.assertAllEqual((tensor_dict[
+        fields.InputDataFields.groundtruth_weights].get_shape().as_list()),
+                        [None])
+    with self.test_session() as sess:
+      tensor_dict = sess.run(tensor_dict)
+
+    self.assertAllEqual(
+        object_weights,
+        tensor_dict[fields.InputDataFields.groundtruth_weights])
+
   def testDecodeInstanceSegmentation(self):
     num_instances = 4
     image_height = 5

research/object_detection/dataset_tools/__init__.py

-

research/object_detection/dataset_tools/create_kitti_tf_record.py

@@ -120,7 +120,7 @@ def convert_kitti_to_tfrecords(data_dir, output_path, classes_to_use,
 
     # Filter all bounding boxes of this frame that are of a legal class, and
     # don't overlap with a dontcare region.
-    # TODO(talremez) filter out targets that are truncated or heavily occluded.
+    # TODO filter out targets that are truncated or heavily occluded.
     annotation_for_image = filter_annotations(img_anno, classes_to_use)
 
     example = prepare_example(image_path, annotation_for_image, label_map_dict)

research/object_detection/dataset_tools/create_kitti_tf_record_test.py

@@ -24,7 +24,7 @@ import tensorflow as tf
 from object_detection.dataset_tools import create_kitti_tf_record
 
 
-class DictToTFExampleTest(tf.test.TestCase):
+class CreateKittiTFRecordTest(tf.test.TestCase):
 
   def _assertProtoEqual(self, proto_field, expectation):
     """Helper function to assert if a proto field equals some value.