Merge pull request #3846 from pkulzc/master

Internal changes for object detection

research/object_detection/models/faster_rcnn_pnas_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 models.faster_rcnn_pnas_feature_extractor."""
+
+import tensorflow as tf
+
+from object_detection.models import faster_rcnn_pnas_feature_extractor as frcnn_pnas
+
+
+class FasterRcnnPNASFeatureExtractorTest(tf.test.TestCase):
+
+  def _build_feature_extractor(self, first_stage_features_stride):
+    return frcnn_pnas.FasterRCNNPNASFeatureExtractor(
+        is_training=False,
+        first_stage_features_stride=first_stage_features_stride,
+        batch_norm_trainable=False,
+        reuse_weights=None,
+        weight_decay=0.0)
+
+  def test_extract_proposal_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 299, 299, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 19, 19, 4320])
+
+  def test_extract_proposal_features_input_size_224(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 224, 224, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 14, 14, 4320])
+
+  def test_extract_proposal_features_input_size_112(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [1, 112, 112, 3], maxval=255, dtype=tf.float32)
+    rpn_feature_map, _ = feature_extractor.extract_proposal_features(
+        preprocessed_inputs, scope='TestScope')
+    features_shape = tf.shape(rpn_feature_map)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [1, 7, 7, 4320])
+
+  def test_extract_proposal_features_dies_on_invalid_stride(self):
+    with self.assertRaises(ValueError):
+      self._build_feature_extractor(first_stage_features_stride=99)
+
+  def test_extract_proposal_features_dies_with_incorrect_rank_inputs(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    preprocessed_inputs = tf.random_uniform(
+        [224, 224, 3], maxval=255, dtype=tf.float32)
+    with self.assertRaises(ValueError):
+      feature_extractor.extract_proposal_features(
+          preprocessed_inputs, scope='TestScope')
+
+  def test_extract_box_classifier_features_returns_expected_size(self):
+    feature_extractor = self._build_feature_extractor(
+        first_stage_features_stride=16)
+    proposal_feature_maps = tf.random_uniform(
+        [2, 17, 17, 1088], maxval=255, dtype=tf.float32)
+    proposal_classifier_features = (
+        feature_extractor.extract_box_classifier_features(
+            proposal_feature_maps, scope='TestScope'))
+    features_shape = tf.shape(proposal_classifier_features)
+
+    init_op = tf.global_variables_initializer()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      features_shape_out = sess.run(features_shape)
+      self.assertAllEqual(features_shape_out, [2, 9, 9, 4320])
+
+  def test_filter_scaling_computation(self):
+    expected_filter_scaling = {
+        ((4, 8), 2): 1.0,
+        ((4, 8), 7): 2.0,
+        ((4, 8), 8): 2.0,
+        ((4, 8), 9): 4.0
+    }
+    for args, filter_scaling in expected_filter_scaling.items():
+      reduction_indices, start_cell_num = args
+      self.assertAlmostEqual(
+          frcnn_pnas._filter_scaling(reduction_indices, start_cell_num),
+          filter_scaling)
+
+
+if __name__ == '__main__':
+  tf.test.main()

research/object_detection/models/ssd_inception_v2_feature_extractor.py

@@ -37,7 +37,8 @@ class SSDInceptionV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
                batch_norm_trainable=True,
                reuse_weights=None,
                use_explicit_padding=False,
-               use_depthwise=False):
+               use_depthwise=False,
+               inplace_batchnorm_update=False):
     """InceptionV2 Feature Extractor for SSD Models.
 
     Args:
@@ -55,11 +56,16 @@ class SSDInceptionV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
       use_explicit_padding: Whether to use explicit padding when extracting
         features. Default is False.
       use_depthwise: Whether to use depthwise convolutions. Default is False.
+      inplace_batchnorm_update: Whether to update batch_norm inplace during
+        training. This is required for batch norm to work correctly on TPUs.
+        When this is false, user must add a control dependency on
+        tf.GraphKeys.UPDATE_OPS for train/loss op in order to update the batch
+        norm moving average parameters.
     """
     super(SSDInceptionV2FeatureExtractor, self).__init__(
         is_training, depth_multiplier, min_depth, pad_to_multiple,
         conv_hyperparams, batch_norm_trainable, reuse_weights,
-        use_explicit_padding, use_depthwise)
+        use_explicit_padding, use_depthwise, inplace_batchnorm_update)
 
   def preprocess(self, resized_inputs):
     """SSD preprocessing.
@@ -76,7 +82,7 @@ class SSDInceptionV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
     """
     return (2.0 / 255.0) * resized_inputs - 1.0
 
-  def extract_features(self, preprocessed_inputs):
+  def _extract_features(self, preprocessed_inputs):
     """Extract features from preprocessed inputs.
 
     Args:

research/object_detection/models/ssd_inception_v3_feature_extractor.py

@@ -37,7 +37,8 @@ class SSDInceptionV3FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
                batch_norm_trainable=True,
                reuse_weights=None,
                use_explicit_padding=False,
-               use_depthwise=False):
+               use_depthwise=False,
+               inplace_batchnorm_update=False):
     """InceptionV3 Feature Extractor for SSD Models.
 
     Args:
@@ -55,11 +56,16 @@ class SSDInceptionV3FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
       use_explicit_padding: Whether to use explicit padding when extracting
         features. Default is False.
       use_depthwise: Whether to use depthwise convolutions. Default is False.
+      inplace_batchnorm_update: Whether to update batch_norm inplace during
+        training. This is required for batch norm to work correctly on TPUs.
+        When this is false, user must add a control dependency on
+        tf.GraphKeys.UPDATE_OPS for train/loss op in order to update the batch
+        norm moving average parameters.
     """
     super(SSDInceptionV3FeatureExtractor, self).__init__(
         is_training, depth_multiplier, min_depth, pad_to_multiple,
         conv_hyperparams, batch_norm_trainable, reuse_weights,
-        use_explicit_padding, use_depthwise)
+        use_explicit_padding, use_depthwise, inplace_batchnorm_update)
 
   def preprocess(self, resized_inputs):
     """SSD preprocessing.
@@ -76,7 +82,7 @@ class SSDInceptionV3FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
     """
     return (2.0 / 255.0) * resized_inputs - 1.0
 
-  def extract_features(self, preprocessed_inputs):
+  def _extract_features(self, preprocessed_inputs):
     """Extract features from preprocessed inputs.
 
     Args:

research/object_detection/models/ssd_mobilenet_v1_feature_extractor.py

@@ -38,7 +38,8 @@ class SSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
                batch_norm_trainable=True,
                reuse_weights=None,
                use_explicit_padding=False,
-               use_depthwise=False):
+               use_depthwise=False,
+               inplace_batchnorm_update=False):
     """MobileNetV1 Feature Extractor for SSD Models.
 
     Args:
@@ -57,11 +58,16 @@ class SSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
         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.
+      inplace_batchnorm_update: Whether to update batch_norm inplace during
+        training. This is required for batch norm to work correctly on TPUs.
+        When this is false, user must add a control dependency on
+        tf.GraphKeys.UPDATE_OPS for train/loss op in order to update the batch
+        norm moving average parameters.
     """
     super(SSDMobileNetV1FeatureExtractor, self).__init__(
         is_training, depth_multiplier, min_depth, pad_to_multiple,
         conv_hyperparams, batch_norm_trainable, reuse_weights,
-        use_explicit_padding, use_depthwise)
+        use_explicit_padding, use_depthwise, inplace_batchnorm_update)
 
   def preprocess(self, resized_inputs):
     """SSD preprocessing.
@@ -78,7 +84,7 @@ class SSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
     """
     return (2.0 / 255.0) * resized_inputs - 1.0
 
-  def extract_features(self, preprocessed_inputs):
+  def _extract_features(self, preprocessed_inputs):
     """Extract features from preprocessed inputs.
 
     Args:

research/object_detection/models/ssd_mobilenet_v2_feature_extractor.py

@@ -39,7 +39,8 @@ class SSDMobileNetV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
                batch_norm_trainable=True,
                reuse_weights=None,
                use_explicit_padding=False,
-               use_depthwise=False):
+               use_depthwise=False,
+               inplace_batchnorm_update=False):
     """MobileNetV2 Feature Extractor for SSD Models.
 
     Mobilenet v2 (experimental), designed by sandler@. More details can be found
@@ -60,11 +61,16 @@ class SSDMobileNetV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
       use_explicit_padding: Whether to use explicit padding when extracting
         features. Default is False.
       use_depthwise: Whether to use depthwise convolutions. Default is False.
+      inplace_batchnorm_update: Whether to update batch_norm inplace during
+        training. This is required for batch norm to work correctly on TPUs.
+        When this is false, user must add a control dependency on
+        tf.GraphKeys.UPDATE_OPS for train/loss op in order to update the batch
+        norm moving average parameters.
     """
     super(SSDMobileNetV2FeatureExtractor, self).__init__(
         is_training, depth_multiplier, min_depth, pad_to_multiple,
         conv_hyperparams, batch_norm_trainable, reuse_weights,
-        use_explicit_padding, use_depthwise)
+        use_explicit_padding, use_depthwise, inplace_batchnorm_update)
 
   def preprocess(self, resized_inputs):
     """SSD preprocessing.
@@ -81,7 +87,7 @@ class SSDMobileNetV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
     """
     return (2.0 / 255.0) * resized_inputs - 1.0
 
-  def extract_features(self, preprocessed_inputs):
+  def _extract_features(self, preprocessed_inputs):
     """Extract features from preprocessed inputs.
 
     Args:

research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor.py

@@ -43,7 +43,8 @@ class _SSDResnetV1FpnFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
                batch_norm_trainable=True,
                reuse_weights=None,
                use_explicit_padding=False,
-               use_depthwise=False):
+               use_depthwise=False,
+               inplace_batchnorm_update=False):
     """SSD FPN feature extractor based on Resnet v1 architecture.
 
     Args:
@@ -66,6 +67,11 @@ class _SSDResnetV1FpnFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
       use_explicit_padding: Whether to use explicit padding when extracting
         features. Default is False. UNUSED currently.
       use_depthwise: Whether to use depthwise convolutions. UNUSED currently.
+      inplace_batchnorm_update: Whether to update batch_norm inplace during
+        training. This is required for batch norm to work correctly on TPUs.
+        When this is false, user must add a control dependency on
+        tf.GraphKeys.UPDATE_OPS for train/loss op in order to update the batch
+        norm moving average parameters.
 
     Raises:
       ValueError: On supplying invalid arguments for unused arguments.
@@ -73,7 +79,7 @@ class _SSDResnetV1FpnFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
     super(_SSDResnetV1FpnFeatureExtractor, self).__init__(
         is_training, depth_multiplier, min_depth, pad_to_multiple,
         conv_hyperparams, batch_norm_trainable, reuse_weights,
-        use_explicit_padding)
+        use_explicit_padding, inplace_batchnorm_update)
     if self._depth_multiplier != 1.0:
       raise ValueError('Only depth 1.0 is supported, found: {}'.
                        format(self._depth_multiplier))
@@ -110,7 +116,7 @@ class _SSDResnetV1FpnFeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):
         filtered_image_features[feature_name] = feature
     return filtered_image_features
 
-  def extract_features(self, preprocessed_inputs):
+  def _extract_features(self, preprocessed_inputs):
     """Extract features from preprocessed inputs.
 
     Args:
@@ -176,7 +182,8 @@ class SSDResnet50V1FpnFeatureExtractor(_SSDResnetV1FpnFeatureExtractor):
                batch_norm_trainable=True,
                reuse_weights=None,
                use_explicit_padding=False,
-               use_depthwise=False):
+               use_depthwise=False,
+               inplace_batchnorm_update=False):
     """Resnet50 v1 FPN Feature Extractor for SSD Models.
 
     Args:
@@ -194,11 +201,17 @@ class SSDResnet50V1FpnFeatureExtractor(_SSDResnetV1FpnFeatureExtractor):
       use_explicit_padding: Whether to use explicit padding when extracting
         features. Default is False. UNUSED currently.
       use_depthwise: Whether to use depthwise convolutions. UNUSED currently.
+      inplace_batchnorm_update: Whether to update batch_norm inplace during
+        training. This is required for batch norm to work correctly on TPUs.
+        When this is false, user must add a control dependency on
+        tf.GraphKeys.UPDATE_OPS for train/loss op in order to update the batch
+        norm moving average parameters.
     """
     super(SSDResnet50V1FpnFeatureExtractor, self).__init__(
         is_training, depth_multiplier, min_depth, pad_to_multiple,
         conv_hyperparams, resnet_v1.resnet_v1_50, 'resnet_v1_50', 'fpn',
-        batch_norm_trainable, reuse_weights, use_explicit_padding)
+        batch_norm_trainable, reuse_weights, use_explicit_padding,
+        inplace_batchnorm_update)
 
 
 class SSDResnet101V1FpnFeatureExtractor(_SSDResnetV1FpnFeatureExtractor):
@@ -212,7 +225,8 @@ class SSDResnet101V1FpnFeatureExtractor(_SSDResnetV1FpnFeatureExtractor):
                batch_norm_trainable=True,
                reuse_weights=None,
                use_explicit_padding=False,
-               use_depthwise=False):
+               use_depthwise=False,
+               inplace_batchnorm_update=False):
     """Resnet101 v1 FPN Feature Extractor for SSD Models.
 
     Args:
@@ -230,11 +244,17 @@ class SSDResnet101V1FpnFeatureExtractor(_SSDResnetV1FpnFeatureExtractor):
       use_explicit_padding: Whether to use explicit padding when extracting
         features. Default is False. UNUSED currently.
       use_depthwise: Whether to use depthwise convolutions. UNUSED currently.
+      inplace_batchnorm_update: Whether to update batch_norm inplace during
+        training. This is required for batch norm to work correctly on TPUs.
+        When this is false, user must add a control dependency on
+        tf.GraphKeys.UPDATE_OPS for train/loss op in order to update the batch
+        norm moving average parameters.
     """
     super(SSDResnet101V1FpnFeatureExtractor, self).__init__(
         is_training, depth_multiplier, min_depth, pad_to_multiple,
         conv_hyperparams, resnet_v1.resnet_v1_101, 'resnet_v1_101', 'fpn',
-        batch_norm_trainable, reuse_weights, use_explicit_padding)
+        batch_norm_trainable, reuse_weights, use_explicit_padding,
+        inplace_batchnorm_update)
 
 
 class SSDResnet152V1FpnFeatureExtractor(_SSDResnetV1FpnFeatureExtractor):
@@ -248,7 +268,8 @@ class SSDResnet152V1FpnFeatureExtractor(_SSDResnetV1FpnFeatureExtractor):
                batch_norm_trainable=True,
                reuse_weights=None,
                use_explicit_padding=False,
-               use_depthwise=False):
+               use_depthwise=False,
+               inplace_batchnorm_update=False):
     """Resnet152 v1 FPN Feature Extractor for SSD Models.
 
     Args:
@@ -266,8 +287,14 @@ class SSDResnet152V1FpnFeatureExtractor(_SSDResnetV1FpnFeatureExtractor):
       use_explicit_padding: Whether to use explicit padding when extracting
         features. Default is False. UNUSED currently.
       use_depthwise: Whether to use depthwise convolutions. UNUSED currently.
+      inplace_batchnorm_update: Whether to update batch_norm inplace during
+        training. This is required for batch norm to work correctly on TPUs.
+        When this is false, user must add a control dependency on
+        tf.GraphKeys.UPDATE_OPS for train/loss op in order to update the batch
+        norm moving average parameters.
     """
     super(SSDResnet152V1FpnFeatureExtractor, self).__init__(
         is_training, depth_multiplier, min_depth, pad_to_multiple,
         conv_hyperparams, resnet_v1.resnet_v1_152, 'resnet_v1_152', 'fpn',
-        batch_norm_trainable, reuse_weights, use_explicit_padding)
+        batch_norm_trainable, reuse_weights, use_explicit_padding,
+        inplace_batchnorm_update)

research/object_detection/protos/eval.proto

@@ -68,4 +68,8 @@ message EvalConfig {
   // Whether to keep image identifier in filename when exported to
   // visualization_export_dir.
   optional bool keep_image_id_for_visualization_export = 19 [default=false];
+
+  // Whether to retain original images (i.e. not pre-processed) in the tensor
+  // dictionary, so that they can be displayed in Tensorboard.
+  optional bool retain_original_images = 23 [default=true];
 }

research/object_detection/protos/faster_rcnn.proto

@@ -131,6 +131,12 @@ message FasterRcnn {
   // to use sigmoid loss and enable merge_multiple_label_boxes.
   // If not specified, Softmax loss is used as default.
   optional ClassificationLoss second_stage_classification_loss = 29;
+
+  // Whether to update batch_norm inplace during training. This is required
+  // for batch norm to work correctly on TPUs. When this is false, user must add
+  // a control dependency on tf.GraphKeys.UPDATE_OPS for train/loss op in order
+  // to update the batch norm moving average parameters.
+  optional bool inplace_batchnorm_update = 30 [default = false];
 }
 
 

research/object_detection/protos/input_reader.proto

@@ -51,6 +51,9 @@ message InputReader {
   // Number of reader instances to create.
   optional uint32 num_readers = 6 [default=32];
 
+  // Number of records to read from each reader at once.
+  optional uint32 read_block_length = 15 [default=32];
+
   // Number of decoded records to prefetch before batching.
   optional uint32 prefetch_size = 13 [default = 512];
 

research/object_detection/protos/ssd.proto

@@ -59,6 +59,12 @@ message Ssd {
 
   // Loss configuration for training.
   optional Loss loss = 11;
+
+  // Whether to update batch_norm inplace during training. This is required
+  // for batch norm to work correctly on TPUs. When this is false, user must add
+  // a control dependency on tf.GraphKeys.UPDATE_OPS for train/loss op in order
+  // to update the batch norm moving average parameters.
+  optional bool inplace_batchnorm_update = 15 [default = false];
 }
 
 

research/object_detection/protos/train.proto

@@ -94,4 +94,9 @@ message TrainConfig {
   // Whether to remove padding along `num_boxes` dimension of the groundtruth
   // tensors.
   optional bool unpad_groundtruth_tensors = 21 [default=true];
+
+  // Whether to retain original images (i.e. not pre-processed) in the tensor
+  // dictionary, so that they can be displayed in Tensorboard. Note that this
+  // will lead to a larger memory footprint.
+  optional bool retain_original_images = 23 [default=false];
 }

research/object_detection/trainer.py

@@ -264,29 +264,6 @@ def train(create_tensor_dict_fn, create_model_fn, train_config, master, task,
           total_num_replicas=worker_replicas)
       sync_optimizer = training_optimizer
 
-    # Create ops required to initialize the model from a given checkpoint.
-    init_fn = None
-    if train_config.fine_tune_checkpoint:
-      if not train_config.fine_tune_checkpoint_type:
-        # train_config.from_detection_checkpoint field is deprecated. For
-        # backward compatibility, fine_tune_checkpoint_type is set based on
-        # from_detection_checkpoint.
-        if train_config.from_detection_checkpoint:
-          train_config.fine_tune_checkpoint_type = 'detection'
-        else:
-          train_config.fine_tune_checkpoint_type = 'classification'
-      var_map = detection_model.restore_map(
-          fine_tune_checkpoint_type=train_config.fine_tune_checkpoint_type,
-          load_all_detection_checkpoint_vars=(
-              train_config.load_all_detection_checkpoint_vars))
-      available_var_map = (variables_helper.
-                           get_variables_available_in_checkpoint(
-                               var_map, train_config.fine_tune_checkpoint))
-      init_saver = tf.train.Saver(available_var_map)
-      def initializer_fn(sess):
-        init_saver.restore(sess, train_config.fine_tune_checkpoint)
-      init_fn = initializer_fn
-
     with tf.device(deploy_config.optimizer_device()):
       regularization_losses = (None if train_config.add_regularization_loss
                                else [])
@@ -354,6 +331,29 @@ def train(create_tensor_dict_fn, create_model_fn, train_config, master, task,
     saver = tf.train.Saver(
         keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
 
+    # Create ops required to initialize the model from a given checkpoint.
+    init_fn = None
+    if train_config.fine_tune_checkpoint:
+      if not train_config.fine_tune_checkpoint_type:
+        # train_config.from_detection_checkpoint field is deprecated. For
+        # backward compatibility, fine_tune_checkpoint_type is set based on
+        # from_detection_checkpoint.
+        if train_config.from_detection_checkpoint:
+          train_config.fine_tune_checkpoint_type = 'detection'
+        else:
+          train_config.fine_tune_checkpoint_type = 'classification'
+      var_map = detection_model.restore_map(
+          fine_tune_checkpoint_type=train_config.fine_tune_checkpoint_type,
+          load_all_detection_checkpoint_vars=(
+              train_config.load_all_detection_checkpoint_vars))
+      available_var_map = (variables_helper.
+                           get_variables_available_in_checkpoint(
+                               var_map, train_config.fine_tune_checkpoint))
+      init_saver = tf.train.Saver(available_var_map)
+      def initializer_fn(sess):
+        init_saver.restore(sess, train_config.fine_tune_checkpoint)
+      init_fn = initializer_fn
+
     slim.learning.train(
         train_tensor,
         logdir=train_dir,

research/object_detection/utils/config_util.py

@@ -14,10 +14,13 @@
 # ==============================================================================
 """Functions for reading and updating configuration files."""
 
+import os
 import tensorflow as tf
 
 from google.protobuf import text_format
 
+from tensorflow.python.lib.io import file_io
+
 from object_detection.protos import eval_pb2
 from object_detection.protos import input_reader_pb2
 from object_detection.protos import model_pb2
@@ -119,6 +122,24 @@ def create_pipeline_proto_from_configs(configs):
   return pipeline_config
 
 
+def save_pipeline_config(pipeline_config, directory):
+  """Saves a pipeline config text file to disk.
+
+  Args:
+    pipeline_config: A pipeline_pb2.TrainEvalPipelineConfig.
+    directory: The model directory into which the pipeline config file will be
+      saved.
+  """
+  if not file_io.file_exists(directory):
+    file_io.recursive_create_dir(directory)
+  pipeline_config_path = os.path.join(directory, "pipeline.config")
+  config_text = text_format.MessageToString(pipeline_config)
+  with tf.gfile.Open(pipeline_config_path, "wb") as f:
+    tf.logging.info("Writing pipeline config file to %s",
+                    pipeline_config_path)
+    f.write(config_text)
+
+
 def get_configs_from_multiple_files(model_config_path="",
                                     train_config_path="",
                                     train_input_config_path="",

research/object_detection/utils/config_util_test.py

@@ -110,6 +110,23 @@ class ConfigUtilTest(tf.test.TestCase):
         config_util.create_pipeline_proto_from_configs(configs))
     self.assertEqual(pipeline_config, pipeline_config_reconstructed)
 
+  def test_save_pipeline_config(self):
+    """Tests that the pipeline config is properly saved to disk."""
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.model.faster_rcnn.num_classes = 10
+    pipeline_config.train_config.batch_size = 32
+    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
+    pipeline_config.eval_config.num_examples = 20
+    pipeline_config.eval_input_reader.queue_capacity = 100
+
+    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
+    configs = config_util.get_configs_from_pipeline_file(
+        os.path.join(self.get_temp_dir(), "pipeline.config"))
+    pipeline_config_reconstructed = (
+        config_util.create_pipeline_proto_from_configs(configs))
+
+    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
+
   def test_get_configs_from_multiple_files(self):
     """Tests that proto configs can be read from multiple files."""
     temp_dir = self.get_temp_dir()

research/object_detection/utils/context_manager.py

+# Copyright 2018 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.
+# ==============================================================================
+"""Python context management helper."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+class IdentityContextManager(object):
+  """Returns an identity context manager that does nothing.
+
+  This is helpful in setting up conditional `with` statement as below:
+
+  with slim.arg_scope(x) if use_slim_scope else IdentityContextManager():
+    do_stuff()
+
+  """
+
+  def __enter__(self):
+    return None
+
+  def __exit__(self, exec_type, exec_value, traceback):
+    del exec_type
+    del exec_value
+    del traceback
+    return False
+

research/object_detection/utils/context_manager_test.py

+# Copyright 2018 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 tensorflow_models.object_detection.utils.context_manager."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from object_detection.utils import context_manager
+
+
+class ContextManagerTest(tf.test.TestCase):
+
+  def test_identity_context_manager(self):
+    with context_manager.IdentityContextManager() as identity_context:
+      self.assertIsNone(identity_context)
+
+
+if __name__ == '__main__':
+  tf.test.main()

research/object_detection/utils/dataset_util.py

@@ -123,11 +123,16 @@ def read_dataset(file_read_func, decode_func, input_files, config):
   if config.shuffle:
     filename_dataset = filename_dataset.shuffle(
         config.filenames_shuffle_buffer_size)
+  elif config.num_readers > 1:
+    tf.logging.warning('`shuffle` is false, but the input data stream is '
+                       'still slightly shuffled since `num_readers` > 1.')
+
   filename_dataset = filename_dataset.repeat(config.num_epochs or None)
 
   records_dataset = filename_dataset.apply(
       tf.contrib.data.parallel_interleave(
-          file_read_func, cycle_length=config.num_readers, sloppy=True))
+          file_read_func, cycle_length=config.num_readers,
+          block_length=config.read_block_length, sloppy=True))
   if config.shuffle:
     records_dataset.shuffle(config.shuffle_buffer_size)
   tensor_dataset = records_dataset.map(

research/object_detection/utils/ops.py

@@ -180,26 +180,24 @@ def pad_to_multiple(tensor, multiple):
     padded_tensor_width = int(
         math.ceil(float(tensor_width) / multiple) * multiple)
 
-  if (padded_tensor_height == tensor_height and
-      padded_tensor_width == tensor_width):
-    return tensor
-
   if tensor_depth is None:
     tensor_depth = tf.shape(tensor)[3]
 
   # Use tf.concat instead of tf.pad to preserve static shape
-  height_pad = tf.zeros([
-      batch_size, padded_tensor_height - tensor_height, tensor_width,
-      tensor_depth
-  ])
-  padded_tensor = tf.concat([tensor, height_pad], 1)
-  width_pad = tf.zeros([
-      batch_size, padded_tensor_height, padded_tensor_width - tensor_width,
-      tensor_depth
-  ])
-  padded_tensor = tf.concat([padded_tensor, width_pad], 2)
-
-  return padded_tensor
+  if padded_tensor_height != tensor_height:
+    height_pad = tf.zeros([
+        batch_size, padded_tensor_height - tensor_height, tensor_width,
+        tensor_depth
+    ])
+    tensor = tf.concat([tensor, height_pad], 1)
+  if padded_tensor_width != tensor_width:
+    width_pad = tf.zeros([
+        batch_size, padded_tensor_height, padded_tensor_width - tensor_width,
+        tensor_depth
+    ])
+    tensor = tf.concat([tensor, width_pad], 2)
+
+  return tensor
 
 
 def padded_one_hot_encoding(indices, depth, left_pad):

research/object_detection/utils/ops_test.py

@@ -136,6 +136,13 @@ class OpsTestPadToMultiple(tf.test.TestCase):
       padded_tensor_out = sess.run(padded_tensor)
     self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
 
+  def test_non_square_padding(self):
+    tensor = tf.constant([[[[0.], [0.]]]])
+    padded_tensor = ops.pad_to_multiple(tensor, 2)
+    with self.test_session() as sess:
+      padded_tensor_out = sess.run(padded_tensor)
+    self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
+
   def test_padding(self):
     tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
     padded_tensor = ops.pad_to_multiple(tensor, 4)

research/object_detection/utils/variables_helper.py

@@ -42,7 +42,7 @@ def filter_variables(variables, filter_regex_list, invert=False):
     a list of filtered variables.
   """
   kept_vars = []
-  variables_to_ignore_patterns = filter(None, filter_regex_list)
+  variables_to_ignore_patterns = list(filter(None, filter_regex_list))
   for var in variables:
     add = True
     for pattern in variables_to_ignore_patterns: