Merged commit includes the following changes:

187187978  by Zhichao Lu:

    Only updating hyperparameters if they have non-null values.

--
187097690  by Zhichao Lu:

    Rewrite some conditions a bit more clearly.

--
187085190  by Zhichao Lu:

    More informative error message.

--
186935376  by Zhichao Lu:

    Added option to evaluator.evaluate to use custom evaluator objects.

--
186808249  by Zhichao Lu:

    Fix documentation re: number of stages.

--
186775014  by Zhichao Lu:

    Change anchor generator interface to return a list of BoxLists containing anchors for different feature map layers.

--
186729028  by Zhichao Lu:

    Minor fixes to object detection.

--
186723716  by Zhichao Lu:

    Fix tf_example_decoder.py initailization issue.

--
186668505  by Zhichao Lu:

    Remove unused import.

--
186475361  by Zhichao Lu:

    Update the box predictor interface to return list of predictions - one from each feature map - instead of stacking them into one large tensor.

--
186410844  by Zhich...

research/object_detection/models/feature_map_generators_test.py

@@ -40,7 +40,7 @@ EMBEDDED_SSD_MOBILENET_V1_LAYOUT = {
 }
 
 
-# TODO: add tests with different anchor strides.
+# TODO(rathodv): add tests with different anchor strides.
 class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
 
   def test_get_expected_feature_map_shapes_with_inception_v2(self):

research/object_detection/models/ssd_feature_extractor_test.py

@@ -27,13 +27,17 @@ from object_detection.utils import test_case
 class SsdFeatureExtractorTestBase(test_case.TestCase):
 
   @abstractmethod
-  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple):
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                use_explicit_padding=False):
     """Constructs a new feature extractor.
 
     Args:
       depth_multiplier: float depth multiplier for feature extractor
       pad_to_multiple: the nearest multiple to zero pad the input height and
         width dimensions to.
+      use_explicit_padding: use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
     Returns:
       an ssd_meta_arch.SSDFeatureExtractor object.
     """
@@ -41,10 +45,11 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
 
   def check_extract_features_returns_correct_shape(
       self, batch_size, image_height, image_width, depth_multiplier,
-      pad_to_multiple, expected_feature_map_shapes):
+      pad_to_multiple, expected_feature_map_shapes, use_explicit_padding=False):
     def graph_fn(image_tensor):
       feature_extractor = self._create_feature_extractor(depth_multiplier,
-                                                         pad_to_multiple)
+                                                         pad_to_multiple,
+                                                         use_explicit_padding)
       feature_maps = feature_extractor.extract_features(image_tensor)
       return feature_maps
 
@@ -57,10 +62,11 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
 
   def check_extract_features_returns_correct_shapes_with_dynamic_inputs(
       self, batch_size, image_height, image_width, depth_multiplier,
-      pad_to_multiple, expected_feature_map_shapes):
+      pad_to_multiple, expected_feature_map_shapes, use_explicit_padding=False):
     def graph_fn(image_height, image_width):
       feature_extractor = self._create_feature_extractor(depth_multiplier,
-                                                         pad_to_multiple)
+                                                         pad_to_multiple,
+                                                         use_explicit_padding)
       image_tensor = tf.random_uniform([batch_size, image_height, image_width,
                                         3], dtype=tf.float32)
       feature_maps = feature_extractor.extract_features(image_tensor)

research/object_detection/models/ssd_mobilenet_v1_feature_extractor.py

@@ -53,8 +53,9 @@ class SSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
         (e.g. 1), it is desirable to disable batch norm update and use
         pretrained batch norm params.
       reuse_weights: Whether to reuse variables. Default is None.
-      use_explicit_padding: Whether to use explicit padding when extracting
-        features. Default is False.
+      use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
       use_depthwise: Whether to use depthwise convolutions. Default is False.
     """
     super(SSDMobileNetV1FeatureExtractor, self).__init__(
@@ -100,7 +101,7 @@ class SSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
     }
 
     with slim.arg_scope(self._conv_hyperparams):
-      # TODO: Enable fused batch norm once quantization supports it.
+      # TODO(skligys): Enable fused batch norm once quantization supports it.
       with slim.arg_scope([slim.batch_norm], fused=False):
         with tf.variable_scope('MobilenetV1',
                                reuse=self._reuse_weights) as scope:
@@ -109,6 +110,7 @@ class SSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
               final_endpoint='Conv2d_13_pointwise',
               min_depth=self._min_depth,
               depth_multiplier=self._depth_multiplier,
+              use_explicit_padding=self._use_explicit_padding,
               scope=scope)
           feature_maps = feature_map_generators.multi_resolution_feature_maps(
               feature_map_layout=feature_map_layout,

research/object_detection/models/ssd_mobilenet_v1_feature_extractor_test.py

@@ -27,7 +27,8 @@ class SsdMobilenetV1FeatureExtractorTest(
     ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
 
   def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
-                                is_training=True, batch_norm_trainable=True):
+                                is_training=True, batch_norm_trainable=True,
+                                use_explicit_padding=False):
     """Constructs a new feature extractor.
 
     Args:
@@ -37,6 +38,9 @@ class SsdMobilenetV1FeatureExtractorTest(
       is_training: whether the network is in training mode.
       batch_norm_trainable: Whether to update batch norm parameters during
         training or not.
+      use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
     Returns:
       an ssd_meta_arch.SSDFeatureExtractor object.
     """
@@ -45,7 +49,8 @@ class SsdMobilenetV1FeatureExtractorTest(
       conv_hyperparams = sc
     return ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor(
         is_training, depth_multiplier, min_depth, pad_to_multiple,
-        conv_hyperparams, batch_norm_trainable)
+        conv_hyperparams, batch_norm_trainable=batch_norm_trainable,
+        use_explicit_padding=use_explicit_padding)
 
   def test_extract_features_returns_correct_shapes_128(self):
     image_height = 128
@@ -57,7 +62,10 @@ class SsdMobilenetV1FeatureExtractorTest(
                                   (2, 1, 1, 256), (2, 1, 1, 128)]
     self.check_extract_features_returns_correct_shape(
         2, image_height, image_width, depth_multiplier, pad_to_multiple,
-        expected_feature_map_shape)
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
 
   def test_extract_features_returns_correct_shapes_299(self):
     image_height = 299
@@ -69,7 +77,10 @@ class SsdMobilenetV1FeatureExtractorTest(
                                   (2, 2, 2, 256), (2, 1, 1, 128)]
     self.check_extract_features_returns_correct_shape(
         2, image_height, image_width, depth_multiplier, pad_to_multiple,
-        expected_feature_map_shape)
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
 
   def test_extract_features_with_dynamic_image_shape(self):
     image_height = 128
@@ -81,7 +92,10 @@ class SsdMobilenetV1FeatureExtractorTest(
                                   (2, 1, 1, 256), (2, 1, 1, 128)]
     self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
         2, image_height, image_width, depth_multiplier, pad_to_multiple,
-        expected_feature_map_shape)
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
 
   def test_extract_features_returns_correct_shapes_enforcing_min_depth(self):
     image_height = 299
@@ -93,7 +107,10 @@ class SsdMobilenetV1FeatureExtractorTest(
                                   (2, 2, 2, 32), (2, 1, 1, 32)]
     self.check_extract_features_returns_correct_shape(
         2, image_height, image_width, depth_multiplier, pad_to_multiple,
-        expected_feature_map_shape)
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
 
   def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
     image_height = 299
@@ -105,7 +122,10 @@ class SsdMobilenetV1FeatureExtractorTest(
                                   (2, 2, 2, 256), (2, 1, 1, 128)]
     self.check_extract_features_returns_correct_shape(
         2, image_height, image_width, depth_multiplier, pad_to_multiple,
-        expected_feature_map_shape)
+        expected_feature_map_shape, use_explicit_padding=False)
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape, use_explicit_padding=True)
 
   def test_extract_features_raises_error_with_invalid_image_size(self):
     image_height = 32

research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor.py

+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
 """SSD Feature Pyramid Network (FPN) feature extractors based on Resnet v1.
 
 See https://arxiv.org/abs/1708.02002 for details.
@@ -87,7 +101,7 @@ class _SSDResnetV1FpnFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
     return resized_inputs - [[channel_means]]
 
   def _filter_features(self, image_features):
-    # TODO: Change resnet endpoint to strip scope prefixes instead
+    # TODO(rathodv): Change resnet endpoint to strip scope prefixes instead
     # of munging the scope here.
     filtered_image_features = dict({})
     for key, feature in image_features.items():

research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_test.py

+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
 """Tests for ssd resnet v1 FPN feature extractors."""
 import tensorflow as tf
 
@@ -10,14 +24,16 @@ class SSDResnet50V1FeatureExtractorTest(
     SSDResnetFPNFeatureExtractorTestBase):
   """SSDResnet50v1Fpn feature extractor test."""
 
-  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple):
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                use_explicit_padding=False):
     min_depth = 32
     conv_hyperparams = {}
     batch_norm_trainable = True
     is_training = True
     return ssd_resnet_v1_fpn_feature_extractor.SSDResnet50V1FpnFeatureExtractor(
         is_training, depth_multiplier, min_depth, pad_to_multiple,
-        conv_hyperparams, batch_norm_trainable)
+        conv_hyperparams, batch_norm_trainable,
+        use_explicit_padding=use_explicit_padding)
 
   def _resnet_scope_name(self):
     return 'resnet_v1_50'
@@ -28,7 +44,8 @@ class SSDResnet101V1FeatureExtractorTest(
     SSDResnetFPNFeatureExtractorTestBase):
   """SSDResnet101v1Fpn feature extractor test."""
 
-  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple):
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                use_explicit_padding=False):
     min_depth = 32
     conv_hyperparams = {}
     batch_norm_trainable = True
@@ -36,7 +53,8 @@ class SSDResnet101V1FeatureExtractorTest(
     return (
         ssd_resnet_v1_fpn_feature_extractor.SSDResnet101V1FpnFeatureExtractor(
             is_training, depth_multiplier, min_depth, pad_to_multiple,
-            conv_hyperparams, batch_norm_trainable))
+            conv_hyperparams, batch_norm_trainable,
+            use_explicit_padding=use_explicit_padding))
 
   def _resnet_scope_name(self):
     return 'resnet_v1_101'
@@ -47,7 +65,8 @@ class SSDResnet152V1FeatureExtractorTest(
     SSDResnetFPNFeatureExtractorTestBase):
   """SSDResnet152v1Fpn feature extractor test."""
 
-  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple):
+  def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
+                                use_explicit_padding=False):
     min_depth = 32
     conv_hyperparams = {}
     batch_norm_trainable = True
@@ -55,7 +74,8 @@ class SSDResnet152V1FeatureExtractorTest(
     return (
         ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor(
             is_training, depth_multiplier, min_depth, pad_to_multiple,
-            conv_hyperparams, batch_norm_trainable))
+            conv_hyperparams, batch_norm_trainable,
+            use_explicit_padding=use_explicit_padding))
 
   def _resnet_scope_name(self):
     return 'resnet_v1_152'

research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_testbase.py

+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
 """Tests for ssd resnet v1 FPN feature extractors."""
 import abc
 import numpy as np

research/object_detection/object_detection_tutorial.ipynb

@@ -58,7 +58,7 @@
    "outputs": [],
    "source": [
     "# This is needed to display the images.\n",
-    "%matplotlib inline"
+    "%matplotlib inline",
    ]
   },
   {

research/object_detection/protos/BUILD

-# Tensorflow Object Detection API: Configuration protos.
-
-package(
-    default_visibility = ["//visibility:public"],
-)
-
-licenses(["notice"])
-
-proto_library(
-    name = "argmax_matcher_proto",
-    srcs = ["argmax_matcher.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "argmax_matcher_py_pb2",
-    api_version = 2,
-    deps = [":argmax_matcher_proto"],
-)
-
-proto_library(
-    name = "bipartite_matcher_proto",
-    srcs = ["bipartite_matcher.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "bipartite_matcher_py_pb2",
-    api_version = 2,
-    deps = [":bipartite_matcher_proto"],
-)
-
-proto_library(
-    name = "matcher_proto",
-    srcs = ["matcher.proto"],
-    cc_api_version = 2,
-    deps = [
-        ":argmax_matcher_proto",
-        ":bipartite_matcher_proto",
-    ],
-)
-
-py_proto_library(
-    name = "matcher_py_pb2",
-    api_version = 2,
-    deps = [":matcher_proto"],
-)
-
-proto_library(
-    name = "faster_rcnn_box_coder_proto",
-    srcs = ["faster_rcnn_box_coder.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "faster_rcnn_box_coder_py_pb2",
-    api_version = 2,
-    deps = [":faster_rcnn_box_coder_proto"],
-)
-
-proto_library(
-    name = "keypoint_box_coder_proto",
-    srcs = ["keypoint_box_coder.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "keypoint_box_coder_py_pb2",
-    api_version = 2,
-    deps = [":keypoint_box_coder_proto"],
-)
-
-proto_library(
-    name = "mean_stddev_box_coder_proto",
-    srcs = ["mean_stddev_box_coder.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "mean_stddev_box_coder_py_pb2",
-    api_version = 2,
-    deps = [":mean_stddev_box_coder_proto"],
-)
-
-proto_library(
-    name = "square_box_coder_proto",
-    srcs = ["square_box_coder.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "square_box_coder_py_pb2",
-    api_version = 2,
-    deps = [":square_box_coder_proto"],
-)
-
-proto_library(
-    name = "box_coder_proto",
-    srcs = ["box_coder.proto"],
-    cc_api_version = 2,
-    deps = [
-        ":faster_rcnn_box_coder_proto",
-        ":keypoint_box_coder_proto",
-        ":mean_stddev_box_coder_proto",
-        ":square_box_coder_proto",
-    ],
-)
-
-py_proto_library(
-    name = "box_coder_py_pb2",
-    api_version = 2,
-    deps = [":box_coder_proto"],
-)
-
-proto_library(
-    name = "grid_anchor_generator_proto",
-    srcs = ["grid_anchor_generator.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "grid_anchor_generator_py_pb2",
-    api_version = 2,
-    deps = [":grid_anchor_generator_proto"],
-)
-
-proto_library(
-    name = "ssd_anchor_generator_proto",
-    srcs = ["ssd_anchor_generator.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "ssd_anchor_generator_py_pb2",
-    api_version = 2,
-    deps = [":ssd_anchor_generator_proto"],
-)
-
-proto_library(
-    name = "multiscale_anchor_generator_proto",
-    srcs = ["multiscale_anchor_generator.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "multiscale_anchor_generator_py_pb2",
-    api_version = 2,
-    deps = [":multiscale_anchor_generator_proto"],
-)
-
-proto_library(
-    name = "anchor_generator_proto",
-    srcs = ["anchor_generator.proto"],
-    cc_api_version = 2,
-    deps = [
-        ":grid_anchor_generator_proto",
-        ":multiscale_anchor_generator_proto",
-        ":ssd_anchor_generator_proto",
-    ],
-)
-
-py_proto_library(
-    name = "anchor_generator_py_pb2",
-    api_version = 2,
-    deps = [":anchor_generator_proto"],
-)
-
-proto_library(
-    name = "input_reader_proto",
-    srcs = ["input_reader.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "input_reader_py_pb2",
-    api_version = 2,
-    deps = [":input_reader_proto"],
-)
-
-proto_library(
-    name = "losses_proto",
-    srcs = ["losses.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "losses_py_pb2",
-    api_version = 2,
-    deps = [":losses_proto"],
-)
-
-proto_library(
-    name = "optimizer_proto",
-    srcs = ["optimizer.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "optimizer_py_pb2",
-    api_version = 2,
-    deps = [":optimizer_proto"],
-)
-
-proto_library(
-    name = "post_processing_proto",
-    srcs = ["post_processing.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "post_processing_py_pb2",
-    api_version = 2,
-    deps = [":post_processing_proto"],
-)
-
-proto_library(
-    name = "hyperparams_proto",
-    srcs = ["hyperparams.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "hyperparams_py_pb2",
-    api_version = 2,
-    deps = [":hyperparams_proto"],
-)
-
-proto_library(
-    name = "box_predictor_proto",
-    srcs = ["box_predictor.proto"],
-    cc_api_version = 2,
-    deps = [":hyperparams_proto"],
-)
-
-py_proto_library(
-    name = "box_predictor_py_pb2",
-    api_version = 2,
-    deps = [":box_predictor_proto"],
-)
-
-proto_library(
-    name = "region_similarity_calculator_proto",
-    srcs = ["region_similarity_calculator.proto"],
-    cc_api_version = 2,
-    deps = [],
-)
-
-py_proto_library(
-    name = "region_similarity_calculator_py_pb2",
-    api_version = 2,
-    deps = [":region_similarity_calculator_proto"],
-)
-
-proto_library(
-    name = "preprocessor_proto",
-    srcs = ["preprocessor.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "preprocessor_py_pb2",
-    api_version = 2,
-    deps = [":preprocessor_proto"],
-)
-
-proto_library(
-    name = "train_proto",
-    srcs = ["train.proto"],
-    cc_api_version = 2,
-    deps = [
-        ":optimizer_proto",
-        ":preprocessor_proto",
-    ],
-)
-
-py_proto_library(
-    name = "train_py_pb2",
-    api_version = 2,
-    deps = [":train_proto"],
-)
-
-proto_library(
-    name = "eval_proto",
-    srcs = ["eval.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "eval_py_pb2",
-    api_version = 2,
-    deps = [":eval_proto"],
-)
-
-proto_library(
-    name = "image_resizer_proto",
-    srcs = ["image_resizer.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "image_resizer_py_pb2",
-    api_version = 2,
-    deps = [":image_resizer_proto"],
-)
-
-proto_library(
-    name = "faster_rcnn_proto",
-    srcs = ["faster_rcnn.proto"],
-    cc_api_version = 2,
-    deps = [
-        ":box_predictor_proto",
-        "//tensorflow/models/research/object_detection/protos:anchor_generator_proto",
-        "//tensorflow/models/research/object_detection/protos:hyperparams_proto",
-        "//tensorflow/models/research/object_detection/protos:image_resizer_proto",
-        "//tensorflow/models/research/object_detection/protos:losses_proto",
-        "//tensorflow/models/research/object_detection/protos:post_processing_proto",
-    ],
-)
-
-proto_library(
-    name = "ssd_proto",
-    srcs = ["ssd.proto"],
-    cc_api_version = 2,
-    deps = [
-        ":anchor_generator_proto",
-        ":box_coder_proto",
-        ":box_predictor_proto",
-        ":hyperparams_proto",
-        ":image_resizer_proto",
-        ":losses_proto",
-        ":matcher_proto",
-        ":post_processing_proto",
-        ":region_similarity_calculator_proto",
-    ],
-)
-
-proto_library(
-    name = "model_proto",
-    srcs = ["model.proto"],
-    cc_api_version = 2,
-    deps = [
-        ":faster_rcnn_proto",
-        ":ssd_proto",
-    ],
-)
-
-py_proto_library(
-    name = "model_py_pb2",
-    api_version = 2,
-    deps = [":model_proto"],
-)
-
-proto_library(
-    name = "pipeline_proto",
-    srcs = ["pipeline.proto"],
-    cc_api_version = 2,
-    deps = [
-        ":eval_proto",
-        ":input_reader_proto",
-        ":model_proto",
-        ":train_proto",
-    ],
-)
-
-py_proto_library(
-    name = "pipeline_py_pb2",
-    api_version = 2,
-    deps = [":pipeline_proto"],
-)
-
-proto_library(
-    name = "string_int_label_map_proto",
-    srcs = ["string_int_label_map.proto"],
-    cc_api_version = 2,
-)
-
-py_proto_library(
-    name = "string_int_label_map_py_pb2",
-    api_version = 2,
-    deps = [":string_int_label_map_proto"],
-)

research/object_detection/protos/hyperparams.proto

@@ -65,6 +65,7 @@ message Initializer {
   oneof initializer_oneof {
     TruncatedNormalInitializer truncated_normal_initializer = 1;
     VarianceScalingInitializer variance_scaling_initializer = 2;
+    RandomNormalInitializer random_normal_initializer = 3;
   }
 }
 
@@ -89,6 +90,13 @@ message VarianceScalingInitializer {
   optional Mode mode = 3 [default = FAN_IN];
 }
 
+// Configuration proto for random normal initializer. See
+// https://www.tensorflow.org/api_docs/python/tf/random_normal_initializer
+message RandomNormalInitializer {
+  optional float mean = 1 [default = 0.0];
+  optional float stddev = 2 [default = 1.0];
+}
+
 // Configuration proto for batch norm to apply after convolution op. See
 // https://www.tensorflow.org/api_docs/python/tf/contrib/layers/batch_norm
 message BatchNorm {

research/object_detection/protos/losses.proto

@@ -38,11 +38,17 @@ message WeightedL2LocalizationLoss {
   optional bool anchorwise_output = 1 [default=false];
 }
 
-// SmoothL1 (Huber) location loss: .5 * x ^ 2 if |x| < 1 else |x| - .5
+// SmoothL1 (Huber) location loss.
+// The smooth L1_loss is defined elementwise as .5 x^2 if |x| <= delta and
+// 0.5 x^2 + delta * (|x|-delta) otherwise, where x is the difference between
+// predictions and target.
 message WeightedSmoothL1LocalizationLoss {
   // DEPRECATED, do not use.
   // Output loss per anchor.
   optional bool anchorwise_output = 1 [default=false];
+
+  // Delta value for huber loss.
+  optional float delta = 2 [default=1.0];
 }
 
 // Intersection over union location loss: 1 - IOU

research/object_detection/protos/multiscale_anchor_generator.proto

@@ -20,4 +20,7 @@ message MultiscaleAnchorGenerator {
 
   // Number of intermediate scale each scale octave
   optional int32 scales_per_octave = 5 [default = 2];
+
+  // Whether to produce anchors in normalized coordinates.
+  optional bool normalize_coordinates = 6 [default = true];
 }

research/object_detection/protos/ssd.proto

@@ -36,6 +36,10 @@ message Ssd {
   // zeros vector or a one-hot vector (where background is the 0th class).
   optional bool encode_background_as_zeros = 12 [default=false];
 
+  // classification weight to be associated to negative
+  // anchors (default: 1.0). The weight must be in [0., 1.].
+  optional float negative_class_weight = 13 [default = 1.0];
+
   // Box predictor to attach to the features.
   optional BoxPredictor box_predictor = 7;
 
@@ -49,6 +53,10 @@ message Ssd {
   // the anchors.
   optional bool normalize_loss_by_num_matches = 10 [default=true];
 
+  // Whether to normalize the localization loss by the code size of the box
+  // encodings. This is applied along with other normalization factors.
+  optional bool normalize_loc_loss_by_codesize = 14 [default=false];
+
   // Loss configuration for training.
   optional Loss loss = 11;
 }

research/object_detection/samples/configs/BUILD

-package(
-    default_visibility = ["//visibility:public"],
-)
-
-licenses(["notice"])
-
-exports_files([
-    "faster_rcnn_resnet50_pets.config",
-    "ssd_inception_v2_pets.config",
-    "ssd_mobilenet_v1_focal_loss_pets.config",
-])

research/object_detection/test_data/BUILD

-package(
-    default_visibility = ["//visibility:public"],
-)
-
-licenses(["notice"])
-
-exports_files([
-    "pets_examples.record",
-])

research/object_detection/test_images/BUILD

-package(
-    default_visibility = ["//visibility:public"],
-)
-
-licenses(["notice"])
-
-exports_files([
-    "image1.jpg",
-    "image2.jpg",
-])

research/object_detection/trainer.py

@@ -235,7 +235,7 @@ def train(create_tensor_dict_fn, create_model_fn, train_config, master, task,
           train_config.prefetch_queue_capacity, data_augmentation_options)
 
     # Gather initial summaries.
-    # TODO: See if summaries can be added/extracted from global tf
+    # TODO(rathodv): See if summaries can be added/extracted from global tf
     # collections so that they don't have to be passed around.
     summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
     global_summaries = set([])

research/object_detection/utils/BUILD

-# Tensorflow Object Detection API: Utility functions.
-
-package(
-    default_visibility = ["//visibility:public"],
-)
-
-licenses(["notice"])
-
-# Apache 2.0
-
-py_library(
-    name = "test_case",
-    srcs = ["test_case.py"],
-    deps = ["//tensorflow"],
-)
-
-py_library(
-    name = "category_util",
-    srcs = ["category_util.py"],
-    deps = ["//tensorflow"],
-)
-
-py_library(
-    name = "config_util",
-    srcs = ["config_util.py"],
-    deps = [
-        "//pyglib/logging",
-        "//tensorflow",
-        "//tensorflow/models/research/object_detection/protos:eval_py_pb2",
-        "//tensorflow/models/research/object_detection/protos:image_resizer_py_pb2",
-        "//tensorflow/models/research/object_detection/protos:input_reader_py_pb2",
-        "//tensorflow/models/research/object_detection/protos:model_py_pb2",
-        "//tensorflow/models/research/object_detection/protos:pipeline_py_pb2",
-        "//tensorflow/models/research/object_detection/protos:train_py_pb2",
-    ],
-)
-
-py_library(
-    name = "dataset_util",
-    srcs = ["dataset_util.py"],
-    deps = [
-        "//tensorflow",
-    ],
-)
-
-py_library(
-    name = "json_utils",
-    srcs = ["json_utils.py"],
-    deps = [],
-)
-
-py_test(
-    name = "json_utils_test",
-    srcs = ["json_utils_test.py"],
-    deps = [
-        ":json_utils",
-        "//tensorflow",
-    ],
-)
-
-py_library(
-    name = "label_map_util",
-    srcs = ["label_map_util.py"],
-    deps = [
-        "//google/protobuf",
-        "//tensorflow",
-        "//tensorflow/models/research/object_detection/protos:string_int_label_map_py_pb2",
-    ],
-)
-
-py_library(
-    name = "learning_schedules",
-    srcs = ["learning_schedules.py"],
-    deps = [
-        "//tensorflow",
-    ],
-)
-
-py_library(
-    name = "metrics",
-    srcs = ["metrics.py"],
-    deps = ["//numpy"],
-)
-
-py_library(
-    name = "np_box_list",
-    srcs = ["np_box_list.py"],
-    deps = ["//numpy"],
-)
-
-py_library(
-    name = "np_box_mask_list",
-    srcs = ["np_box_mask_list.py"],
-    deps = [
-        ":np_box_list",
-        "//numpy",
-    ],
-)
-
-py_library(
-    name = "np_box_list_ops",
-    srcs = ["np_box_list_ops.py"],
-    deps = [
-        ":np_box_list",
-        ":np_box_ops",
-        "//numpy",
-    ],
-)
-
-py_library(
-    name = "np_box_mask_list_ops",
-    srcs = ["np_box_mask_list_ops.py"],
-    deps = [
-        ":np_box_list_ops",
-        ":np_box_mask_list",
-        ":np_mask_ops",
-        "//numpy",
-    ],
-)
-
-py_library(
-    name = "np_box_ops",
-    srcs = ["np_box_ops.py"],
-    deps = ["//tensorflow"],
-)
-
-py_library(
-    name = "np_mask_ops",
-    srcs = ["np_mask_ops.py"],
-    deps = ["//numpy"],
-)
-
-py_library(
-    name = "object_detection_evaluation",
-    srcs = ["object_detection_evaluation.py"],
-    deps = [
-        ":label_map_util",
-        ":metrics",
-        ":per_image_evaluation",
-        "//tensorflow",
-        "//tensorflow/models/research/object_detection/core:standard_fields",
-    ],
-)
-
-py_library(
-    name = "ops",
-    srcs = ["ops.py"],
-    deps = [
-        ":shape_utils",
-        ":static_shape",
-        "//tensorflow",
-        "//tensorflow/models/research/object_detection/core:box_list",
-        "//tensorflow/models/research/object_detection/core:box_list_ops",
-        "//tensorflow/models/research/object_detection/core:standard_fields",
-    ],
-)
-
-py_library(
-    name = "per_image_evaluation",
-    srcs = ["per_image_evaluation.py"],
-    deps = [
-        ":np_box_list",
-        ":np_box_list_ops",
-        ":np_box_mask_list",
-        ":np_box_mask_list_ops",
-        "//tensorflow",
-    ],
-)
-
-py_library(
-    name = "shape_utils",
-    srcs = ["shape_utils.py"],
-    deps = [
-        ":static_shape",
-        "//tensorflow",
-    ],
-)
-
-py_library(
-    name = "static_shape",
-    srcs = ["static_shape.py"],
-    deps = [],
-)
-
-py_library(
-    name = "test_utils",
-    srcs = ["test_utils.py"],
-    deps = [
-        "//tensorflow",
-        "//tensorflow/models/research/object_detection/core:anchor_generator",
-        "//tensorflow/models/research/object_detection/core:box_coder",
-        "//tensorflow/models/research/object_detection/core:box_list",
-        "//tensorflow/models/research/object_detection/core:box_predictor",
-        "//tensorflow/models/research/object_detection/core:matcher",
-        "//tensorflow/models/research/object_detection/utils:shape_utils",
-    ],
-)
-
-py_library(
-    name = "variables_helper",
-    srcs = ["variables_helper.py"],
-    deps = [
-        "//tensorflow",
-    ],
-)
-
-py_library(
-    name = "visualization_utils",
-    srcs = ["visualization_utils.py"],
-    deps = [
-        "//PIL:pil",
-        "//Tkinter",  # buildcleaner: keep
-        "//matplotlib",
-        "//six",
-        "//tensorflow",
-        "//tensorflow/models/research/object_detection/core:standard_fields",
-    ],
-)
-
-py_test(
-    name = "category_util_test",
-    srcs = ["category_util_test.py"],
-    deps = [
-        ":category_util",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "config_util_test",
-    srcs = ["config_util_test.py"],
-    deps = [
-        ":config_util",
-        "//tensorflow",
-        "//tensorflow/models/research/object_detection/protos:image_resizer_py_pb2",
-        "//tensorflow/models/research/object_detection/protos:input_reader_py_pb2",
-        "//tensorflow/models/research/object_detection/protos:model_py_pb2",
-        "//tensorflow/models/research/object_detection/protos:pipeline_py_pb2",
-        "//tensorflow/models/research/object_detection/protos:train_py_pb2",
-    ],
-)
-
-py_test(
-    name = "dataset_util_test",
-    srcs = ["dataset_util_test.py"],
-    deps = [
-        ":dataset_util",
-        "//tensorflow",
-        "//tensorflow/models/research/object_detection/protos:input_reader_py_pb2",
-    ],
-)
-
-py_test(
-    name = "label_map_util_test",
-    srcs = ["label_map_util_test.py"],
-    deps = [
-        ":label_map_util",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "learning_schedules_test",
-    srcs = ["learning_schedules_test.py"],
-    deps = [
-        ":learning_schedules",
-        ":test_case",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "metrics_test",
-    srcs = ["metrics_test.py"],
-    deps = [
-        ":metrics",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "np_box_list_test",
-    srcs = ["np_box_list_test.py"],
-    deps = [
-        ":np_box_list",
-        "//numpy",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "np_box_mask_list_test",
-    srcs = ["np_box_mask_list_test.py"],
-    deps = [
-        ":np_box_mask_list",
-        "//numpy",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "np_box_list_ops_test",
-    srcs = ["np_box_list_ops_test.py"],
-    deps = [
-        ":np_box_list",
-        ":np_box_list_ops",
-        "//numpy",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "np_box_mask_list_ops_test",
-    srcs = ["np_box_mask_list_ops_test.py"],
-    deps = [
-        ":np_box_mask_list",
-        ":np_box_mask_list_ops",
-        "//numpy",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "np_box_ops_test",
-    srcs = ["np_box_ops_test.py"],
-    deps = [
-        ":np_box_ops",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "np_mask_ops_test",
-    srcs = ["np_mask_ops_test.py"],
-    deps = [
-        ":np_mask_ops",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "object_detection_evaluation_test",
-    srcs = ["object_detection_evaluation_test.py"],
-    deps = [
-        ":object_detection_evaluation",
-        "//tensorflow",
-        "//tensorflow/models/research/object_detection/core:standard_fields",
-    ],
-)
-
-py_test(
-    name = "ops_test",
-    srcs = ["ops_test.py"],
-    deps = [
-        ":ops",
-        ":test_case",
-        "//tensorflow",
-        "//tensorflow/models/research/object_detection/core:standard_fields",
-    ],
-)
-
-py_test(
-    name = "per_image_evaluation_test",
-    srcs = ["per_image_evaluation_test.py"],
-    deps = [
-        ":per_image_evaluation",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "shape_utils_test",
-    srcs = ["shape_utils_test.py"],
-    deps = [
-        ":shape_utils",
-        "//numpy",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "static_shape_test",
-    srcs = ["static_shape_test.py"],
-    deps = [
-        ":static_shape",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "test_utils_test",
-    srcs = ["test_utils_test.py"],
-    deps = [
-        ":test_utils",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "variables_helper_test",
-    srcs = ["variables_helper_test.py"],
-    deps = [
-        ":variables_helper",
-        "//tensorflow",
-    ],
-)
-
-py_test(
-    name = "visualization_utils_test",
-    srcs = ["visualization_utils_test.py"],
-    data = [
-        "//tensorflow/models/research/object_detection/test_images:image1.jpg",
-    ],
-    deps = [
-        ":visualization_utils",
-        "//pyglib/flags",
-        "//PIL:pil",
-    ],
-)

research/object_detection/utils/config_util.py

@@ -241,6 +241,10 @@ def merge_external_params_with_configs(configs, hparams=None, **kwargs):
   if hparams:
     kwargs.update(hparams.values())
   for key, value in kwargs.items():
+    # pylint: disable=g-explicit-bool-comparison
+    if value == "" or value is None:
+      continue
+    # pylint: enable=g-explicit-bool-comparison
     if key == "learning_rate":
       _update_initial_learning_rate(configs, value)
       tf.logging.info("Overwriting learning rate: %f", value)
@@ -270,9 +274,8 @@ def merge_external_params_with_configs(configs, hparams=None, **kwargs):
       _update_input_path(configs["eval_input_config"], value)
       tf.logging.info("Overwriting eval input path: %s", value)
     if key == "label_map_path":
-      if value:
-        _update_label_map_path(configs, value)
-        tf.logging.info("Overwriting label map path: %s", value)
+      _update_label_map_path(configs, value)
+      tf.logging.info("Overwriting label map path: %s", value)
     if key == "mask_type":
       _update_mask_type(configs, value)
       tf.logging.info("Overwritten mask type: %s", value)

research/object_detection/utils/config_util_test.py

@@ -397,6 +397,27 @@ class ConfigUtilTest(tf.test.TestCase):
     self.assertEqual(new_label_map_path,
                      configs["eval_input_config"].label_map_path)
 
+  def testDontOverwriteEmptyLabelMapPath(self):
+    """Tests that label map path will not by overwritten with empty string."""
+    original_label_map_path = "path/to/original/label_map"
+    new_label_map_path = ""
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    train_input_reader = pipeline_config.train_input_reader
+    train_input_reader.label_map_path = original_label_map_path
+    eval_input_reader = pipeline_config.eval_input_reader
+    eval_input_reader.label_map_path = original_label_map_path
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(
+        configs, label_map_path=new_label_map_path)
+    self.assertEqual(original_label_map_path,
+                     configs["train_input_config"].label_map_path)
+    self.assertEqual(original_label_map_path,
+                     configs["eval_input_config"].label_map_path)
+
   def testNewMaskType(self):
     """Tests that mask type can be overwritten in input readers."""
     original_mask_type = input_reader_pb2.NUMERICAL_MASKS