Merged commit includes the following changes:

196161788  by Zhichao Lu:

    Add eval_on_train_steps parameter.

    Since the number of samples in train dataset is usually different to the number of samples in the eval dataset.

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196151742  by Zhichao Lu:

    Add an optional random sampling process for SSD meta arch and update mean stddev coder to use default std dev when corresponding tensor is not added to boxlist field.

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196148940  by Zhichao Lu:

    Release ssdlite mobilenet v2 coco trained model.

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196058528  by Zhichao Lu:

    Apply FPN feature map generation before we add additional layers on top of resnet feature extractor.

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195818367  by Zhichao Lu:

    Add support for exporting detection keypoints.

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195745420  by Zhichao Lu:

    Introduce include_metrics_per_category option to Object Detection eval_config.

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195734733  by Zhichao Lu:

    Rename SSDLite config to be more explicit.

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195717383  by Zhichao Lu:

    Add quantized training to object_detection.

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195683542  by Zhichao Lu:

    Fix documentation for the interaction of fine_tune_checkpoint_type and load_all_detection_checkpoint_vars interaction.

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195668233  by Zhichao Lu:

    Using batch size from params dictionary if present.

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195570173  by Zhichao Lu:

    A few fixes to get new estimator API eval to match legacy detection eval binary by (1) plumbing `is_crowd` annotations through to COCO evaluator, (2) setting the `sloppy` flag in tf.contrib.data.parallel_interleave based on whether shuffling is enabled, and (3) saving the original image instead of the resized original image, which allows for small/medium/large mAP metrics to be properly computed.

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195316756  by Zhichao Lu:

    Internal change

--

PiperOrigin-RevId: 196161788

research/object_detection/box_coders/mean_stddev_box_coder.py

@@ -25,6 +25,14 @@
 class MeanStddevBoxCoder(box_coder.BoxCoder):
   """Mean stddev box coder."""
 
+  def __init__(self, stddev=0.01):
+    """Constructor for MeanStddevBoxCoder.
+
+    Args:
+      stddev: The standard deviation used to encode and decode boxes.
+    """
+    self._stddev = stddev
+
   @property
   def code_size(self):
     return 4
@@ -34,37 +42,38 @@ def _encode(self, boxes, anchors):
 
     Args:
       boxes: BoxList holding N boxes to be encoded.
-      anchors: BoxList of N anchors.  We assume that anchors has an associated
-        stddev field.
+      anchors: BoxList of N anchors.
 
     Returns:
       a tensor representing N anchor-encoded boxes
+
     Raises:
-      ValueError: if the anchors BoxList does not have a stddev field
+      ValueError: if the anchors still have deprecated stddev field.
     """
-    if not anchors.has_field('stddev'):
-      raise ValueError('anchors must have a stddev field')
     box_corners = boxes.get()
+    if anchors.has_field('stddev'):
+      raise ValueError("'stddev' is a parameter of MeanStddevBoxCoder and "
+                       "should not be specified in the box list.")
     means = anchors.get()
-    stddev = anchors.get_field('stddev')
-    return (box_corners - means) / stddev
+    return (box_corners - means) / self._stddev
 
   def _decode(self, rel_codes, anchors):
     """Decode.
 
     Args:
       rel_codes: a tensor representing N anchor-encoded boxes.
-      anchors: BoxList of anchors.  We assume that anchors has an associated
-        stddev field.
+      anchors: BoxList of anchors.
 
     Returns:
       boxes: BoxList holding N bounding boxes
+
     Raises:
-      ValueError: if the anchors BoxList does not have a stddev field
+      ValueError: if the anchors still have deprecated stddev field and expects
+        the decode method to use stddev value from that field.
     """
-    if not anchors.has_field('stddev'):
-      raise ValueError('anchors must have a stddev field')
     means = anchors.get()
-    stddevs = anchors.get_field('stddev')
-    box_corners = rel_codes * stddevs + means
+    if anchors.has_field('stddev'):
+      raise ValueError("'stddev' is a parameter of MeanStddevBoxCoder and "
+                       "should not be specified in the box list.")
+    box_corners = rel_codes * self._stddev + means
     return box_list.BoxList(box_corners)

research/object_detection/box_coders/mean_stddev_box_coder_test.py

@@ -28,11 +28,9 @@ def testGetCorrectRelativeCodesAfterEncoding(self):
     boxes = box_list.BoxList(tf.constant(box_corners))
     expected_rel_codes = [[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]]
     prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
-    prior_stddevs = tf.constant(2 * [4 * [.1]])
     priors = box_list.BoxList(prior_means)
-    priors.add_field('stddev', prior_stddevs)
 
-    coder = mean_stddev_box_coder.MeanStddevBoxCoder()
+    coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
     rel_codes = coder.encode(boxes, priors)
     with self.test_session() as sess:
       rel_codes_out = sess.run(rel_codes)
@@ -42,11 +40,9 @@ def testGetCorrectBoxesAfterDecoding(self):
     rel_codes = tf.constant([[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]])
     expected_box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
     prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
-    prior_stddevs = tf.constant(2 * [4 * [.1]])
     priors = box_list.BoxList(prior_means)
-    priors.add_field('stddev', prior_stddevs)
 
-    coder = mean_stddev_box_coder.MeanStddevBoxCoder()
+    coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
     decoded_boxes = coder.decode(rel_codes, priors)
     decoded_box_corners = decoded_boxes.get()
     with self.test_session() as sess:

research/object_detection/builders/box_coder_builder.py

@@ -55,7 +55,8 @@ def build(box_coder_config):
         ])
   if (box_coder_config.WhichOneof('box_coder_oneof') ==
       'mean_stddev_box_coder'):
-    return mean_stddev_box_coder.MeanStddevBoxCoder()
+    return mean_stddev_box_coder.MeanStddevBoxCoder(
+        stddev=box_coder_config.mean_stddev_box_coder.stddev)
   if box_coder_config.WhichOneof('box_coder_oneof') == 'square_box_coder':
     return square_box_coder.SquareBoxCoder(scale_factors=[
         box_coder_config.square_box_coder.y_scale,

research/object_detection/builders/graph_rewriter_builder.py

@@ -0,0 +1,42 @@
+# 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.
+# ==============================================================================
+"""Functions for quantized training and evaluation."""
+
+import tensorflow as tf
+
+
+def build(graph_rewriter_config, is_training):
+  """Returns a function that modifies default graph based on options.
+
+  Args:
+    graph_rewriter_config: graph_rewriter_pb2.GraphRewriter proto.
+    is_training: whether in training of eval mode.
+  """
+  def graph_rewrite_fn():
+    """Function to quantize weights and activation of the default graph."""
+    if (graph_rewriter_config.quantization.weight_bits != 8 or
+        graph_rewriter_config.quantization.activation_bits != 8):
+      raise ValueError('Only 8bit quantization is supported')
+
+    # Quantize the graph by inserting quantize ops for weights and activations
+    if is_training:
+      tf.contrib.quantize.create_training_graph(
+          input_graph=tf.get_default_graph(),
+          quant_delay=graph_rewriter_config.quantization.delay)
+    else:
+      tf.contrib.quantize.create_eval_graph(input_graph=tf.get_default_graph())
+
+    tf.contrib.layers.summarize_collection('quant_vars')
+  return graph_rewrite_fn

research/object_detection/builders/graph_rewriter_builder_test.py

@@ -0,0 +1,57 @@
+# 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 graph_rewriter_builder."""
+import mock
+import tensorflow as tf
+from object_detection.builders import graph_rewriter_builder
+from object_detection.protos import graph_rewriter_pb2
+
+
+class QuantizationBuilderTest(tf.test.TestCase):
+
+  def testQuantizationBuilderSetsUpCorrectTrainArguments(self):
+    with mock.patch.object(
+        tf.contrib.quantize, 'create_training_graph') as mock_quant_fn:
+      with mock.patch.object(tf.contrib.layers,
+                             'summarize_collection') as mock_summarize_col:
+        graph_rewriter_proto = graph_rewriter_pb2.GraphRewriter()
+        graph_rewriter_proto.quantization.delay = 10
+        graph_rewriter_proto.quantization.weight_bits = 8
+        graph_rewriter_proto.quantization.activation_bits = 8
+        graph_rewrite_fn = graph_rewriter_builder.build(
+            graph_rewriter_proto, is_training=True)
+        graph_rewrite_fn()
+        _, kwargs = mock_quant_fn.call_args
+        self.assertEqual(kwargs['input_graph'], tf.get_default_graph())
+        self.assertEqual(kwargs['quant_delay'], 10)
+        mock_summarize_col.assert_called_with('quant_vars')
+
+  def testQuantizationBuilderSetsUpCorrectEvalArguments(self):
+    with mock.patch.object(tf.contrib.quantize,
+                           'create_eval_graph') as mock_quant_fn:
+      with mock.patch.object(tf.contrib.layers,
+                             'summarize_collection') as mock_summarize_col:
+        graph_rewriter_proto = graph_rewriter_pb2.GraphRewriter()
+        graph_rewriter_proto.quantization.delay = 10
+        graph_rewrite_fn = graph_rewriter_builder.build(
+            graph_rewriter_proto, is_training=False)
+        graph_rewrite_fn()
+        _, kwargs = mock_quant_fn.call_args
+        self.assertEqual(kwargs['input_graph'], tf.get_default_graph())
+        mock_summarize_col.assert_called_with('quant_vars')
+
+
+if __name__ == '__main__':
+  tf.test.main()

research/object_detection/builders/losses_builder.py

@@ -15,6 +15,7 @@
 
 """A function to build localization and classification losses from config."""
 
+from object_detection.core import balanced_positive_negative_sampler as sampler
 from object_detection.core import losses
 from object_detection.protos import losses_pb2
 
@@ -34,9 +35,12 @@ def build(loss_config):
     classification_weight: Classification loss weight.
     localization_weight: Localization loss weight.
     hard_example_miner: Hard example miner object.
+    random_example_sampler: BalancedPositiveNegativeSampler object.
 
   Raises:
     ValueError: If hard_example_miner is used with sigmoid_focal_loss.
+    ValueError: If random_example_sampler is getting non-positive value as
+      desired positive example fraction.
   """
   classification_loss = _build_classification_loss(
       loss_config.classification_loss)
@@ -54,9 +58,16 @@ def build(loss_config):
         loss_config.hard_example_miner,
         classification_weight,
         localization_weight)
-  return (classification_loss, localization_loss,
-          classification_weight,
-          localization_weight, hard_example_miner)
+  random_example_sampler = None
+  if loss_config.HasField('random_example_sampler'):
+    if loss_config.random_example_sampler.positive_sample_fraction <= 0:
+      raise ValueError('RandomExampleSampler should not use non-positive'
+                       'value as positive sample fraction.')
+    random_example_sampler = sampler.BalancedPositiveNegativeSampler(
+        positive_fraction=loss_config.random_example_sampler.
+        positive_sample_fraction)
+  return (classification_loss, localization_loss, classification_weight,
+          localization_weight, hard_example_miner, random_example_sampler)
 
 
 def build_hard_example_miner(config,