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.

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187085190  by Zhichao Lu:

    More informative error message.

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186935376  by Zhichao Lu:

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

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186808249  by Zhichao Lu:

    Fix documentation re: number of stages.

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186775014  by Zhichao Lu:

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

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186729028  by Zhichao Lu:

    Minor fixes to object detection.

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186723716  by Zhichao Lu:

    Fix tf_example_decoder.py initailization issue.

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186668505  by Zhichao Lu:

    Remove unused import.

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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.

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186410844  by Zhichao Lu:

    Fix PythonPath Dependencies.

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186365384  by Zhichao Lu:

    Made some of the functions in exporter public so they can be reused.

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186341438  by Zhichao Lu:

    Re-introducing check that label-map-path must be a valid (non-empty) string prior to overwriting pipeline config.

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186036984  by Zhichao Lu:

    Adding default hyperparameters and allowing for overriding them via flags.

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186026006  by Zhichao Lu:

    Strip `eval_` prefix from name argument give to TPUEstimator.evaluate since it adds the same prefix internally.

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186016042  by Zhichao Lu:

    Add an option to evaluate models on training data.

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185944986  by Zhichao Lu:

    let _update_label_map_path go through even if the path is empty

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185860781  by Zhichao Lu:

    Add random normal initializer option to hyperparams builder.

    Scale the regression losses outside of the box encoder by adjusting huber loss delta and regression loss weight.

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185846325  by Zhichao Lu:

    Add an option to normalize localization loss by the code size(number of box coordinates) in SSD Meta architecture.

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185761217  by Zhichao Lu:

    Change multiscale_grid_anchor_generator to return anchors in normalized coordinates by default and add option to configure it.

    In SSD meta architecture, TargetAssigner operates in normalized coordinate space (i.e, groundtruth boxes are in normalized coordinates) hence we need the option to generate anchors in normalized coordinates.

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185747733  by Zhichao Lu:

    Change the smooth L1 localization implementationt to use tf.losses.huber_loss and expose the delta parameter in the proto.

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185715309  by Zhichao Lu:

    Obviates the need for prepadding on mobilenet v1 and v2 for fully convolutional models.

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185685695  by Zhichao Lu:

    Fix manual stepping schedule to return first rate when there are no boundaries

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185621650  by Zhichao Lu:

    Added target assigner proto for configuring negative class weights.

--

PiperOrigin-RevId: 187187978

research/object_detection/utils/json_utils.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.
+# ==============================================================================
 """Utilities for dealing with writing json strings.
 
 json_utils wraps json.dump and json.dumps so that they can be used to safely

research/object_detection/utils/json_utils_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 google3.image.understanding.object_detection.utils.json_utils."""
 import os
 

research/object_detection/utils/learning_schedules.py

@@ -142,6 +142,7 @@ def manual_stepping(global_step, boundaries, rates):
   if len(rates) != len(boundaries) + 1:
     raise ValueError('Number of provided learning rates must exceed '
                      'number of boundary points by exactly 1.')
+  if not boundaries: return tf.constant(rates[0])
   step_boundaries = tf.constant(boundaries, tf.int32)
   num_boundaries = len(boundaries)
   learning_rates = tf.constant(rates, tf.float32)

research/object_detection/utils/learning_schedules_test.py

@@ -75,5 +75,21 @@ class LearningSchedulesTest(test_case.TestCase):
     exp_rates = [1.0, 1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]
     self.assertAllClose(output_rates, exp_rates)
 
+  def testManualSteppingWithZeroBoundaries(self):
+    def graph_fn(global_step):
+      boundaries = []
+      rates = [0.01]
+      learning_rate = learning_schedules.manual_stepping(
+          global_step, boundaries, rates)
+      return (learning_rate,)
+
+    output_rates = [
+        self.execute(graph_fn, [np.array(i).astype(np.int64)])
+        for i in range(4)
+    ]
+    exp_rates = [0.01] * 4
+    self.assertAllClose(output_rates, exp_rates)
+
+
 if __name__ == '__main__':
   tf.test.main()

research/object_detection/utils/np_box_list_ops.py

@@ -19,7 +19,6 @@ Example box operations that are supported:
   * Areas: compute bounding box areas
   * IOU: pairwise intersection-over-union scores
 """
-
 import numpy as np
 
 from object_detection.utils import np_box_list

research/object_detection/utils/np_box_mask_list_ops.py

@@ -19,7 +19,6 @@ Example box operations that are supported:
   * Areas: compute bounding box areas
   * IOU: pairwise intersection-over-union scores
 """
-
 import numpy as np
 
 from object_detection.utils import np_box_list_ops

research/object_detection/utils/ops.py

@@ -224,7 +224,7 @@ def padded_one_hot_encoding(indices, depth, left_pad):
     ValueError: if `indices` does not have rank 1 or if `left_pad` or `depth are
       either negative or non-integers.
 
-  TODO: add runtime checks for depth and indices.
+  TODO(rathodv): add runtime checks for depth and indices.
   """
   if depth < 0 or not isinstance(depth, six.integer_types):
     raise ValueError('`depth` must be a non-negative integer.')
@@ -474,7 +474,7 @@ def normalize_to_target(inputs,
   Note that the rank of `inputs` must be known and the dimension to which
   normalization is to be applied should be statically defined.
 
-  TODO: Add option to scale by L2 norm of the entire input.
+  TODO(jonathanhuang): Add option to scale by L2 norm of the entire input.
 
   Args:
     inputs: A `Tensor` of arbitrary size.
@@ -704,7 +704,7 @@ def reframe_box_masks_to_image_masks(box_masks, boxes, image_height,
   Returns:
     A tf.float32 tensor of size [num_masks, image_height, image_width].
   """
-  # TODO: Make this a public function.
+  # TODO(rathodv): Make this a public function.
   def transform_boxes_relative_to_boxes(boxes, reference_boxes):
     boxes = tf.reshape(boxes, [-1, 2, 2])
     min_corner = tf.expand_dims(reference_boxes[:, 0:2], 1)

research/object_detection/utils/shape_utils.py

@@ -152,7 +152,7 @@ def static_or_dynamic_map_fn(fn, elems, dtype=None,
   Tensors or lists of Tensors).  Likewise, the output of `fn` can only be a
   Tensor or list of Tensors.
 
-  TODO: make this function fully interchangeable with tf.map_fn.
+  TODO(jonathanhuang): make this function fully interchangeable with tf.map_fn.
 
   Args:
     fn: The callable to be performed. It accepts one argument, which will have

research/object_detection/utils/test_case.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.
+# ==============================================================================
 """A convenience wrapper around tf.test.TestCase to enable TPU tests."""
 
 import tensorflow as tf

research/object_detection/utils/variables_helper.py

@@ -23,7 +23,7 @@ import tensorflow as tf
 slim = tf.contrib.slim
 
 
-# TODO: Consider replacing with tf.contrib.filter_variables in
+# TODO(derekjchow): Consider replacing with tf.contrib.filter_variables in
 # tensorflow/contrib/framework/python/ops/variables.py
 def filter_variables(variables, filter_regex_list, invert=False):
   """Filters out the variables matching the filter_regex.
@@ -104,7 +104,7 @@ def get_variables_available_in_checkpoint(variables,
   Inspects given checkpoint and returns the subset of variables that are
   available in it.
 
-  TODO: force input and output to be a dictionary.
+  TODO(rathodv): force input and output to be a dictionary.
 
   Args:
     variables: a list or dictionary of variables to find in checkpoint.

research/object_detection/utils/visualization_utils_test.py

@@ -13,12 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Tests for image.understanding.object_detection.core.visualization_utils.
-
-Testing with visualization in the following colab:
-https://drive.google.com/a/google.com/file/d/0B5HnKS_hMsNARERpU3MtU3I5RFE/view?usp=sharing
-
-"""
+"""Tests for image.understanding.object_detection.core.visualization_utils."""
 import logging
 import os