Merged commit includes the following changes:

199348852  by Zhichao Lu:

    Small typos fixes in VRD evaluation.

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199315191  by Zhichao Lu:

    Change padding shapes when additional channels are available.

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199309180  by Zhichao Lu:

    Adds minor fixes to the Object Detection API implementation.

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199298605  by Zhichao Lu:

    Force num_readers to be 1 when only input file is not sharded.

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199292952  by Zhichao Lu:

    Adds image-level labels parsing into TfExampleDetectionAndGTParser.

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199259866  by Zhichao Lu:

    Visual Relationships Evaluation executable.

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199208330  by Zhichao Lu:

    Infer train_config.batch_size as the effective batch size. Therefore we need to divide the effective batch size in trainer by train_config.replica_to_aggregate to get per worker batch size.

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199207842  by Zhichao Lu:

    Internal change.

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199204222  by Zhichao Lu:

    In case the image has more than three channels, we only take the first three channels for visualization.

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199194388  by Zhichao Lu:

    Correcting protocols description: VOC 2007 -> VOC 2012.

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199188290  by Zhichao Lu:

    Adds per-relationship APs and mAP computation to VRD evaluation.

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199158801  by Zhichao Lu:

    If available, additional channels are merged with input image.

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199099637  by Zhichao Lu:

    OpenImages Challenge metric support:
    -adding verified labels standard field for TFExample;
    -adding tfrecord creation functionality.

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198957391  by Zhichao Lu:

    Allow tf record sharding when creating pets dataset.

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198925184  by Zhichao Lu:

    Introduce moving average support for evaluation. Also adding the ability to override this configuration via config_util.

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198918186  by Zhichao Lu:

    Handles the case where there are 0 box masks.

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198809009  by Zhichao Lu:

    Plumb groundtruth weights into target assigner for Faster RCNN.

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198759987  by Zhichao Lu:

    Fix object detection test broken by shape inference.

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198668602  by Zhichao Lu:

    Adding a new input field in data_decoders/tf_example_decoder.py for storing additional channels.

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198530013  by Zhichao Lu:

    An util for hierarchical expandion of boxes and labels of OID dataset.

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198503124  by Zhichao Lu:

    Fix dimension mismatch error introduced by
    https://github.com/tensorflow/tensorflow/pull/18251, or cl/194031845.
    After above change, conv2d strictly checks for conv_dims + 2 == input_rank.

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198445807  by Zhichao Lu:

    Enabling Object Detection Challenge 2018 metric in evaluator.py framework for
    running eval job.
    Renaming old OpenImages V2 metric.

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198413950  by Zhichao Lu:

    Support generic configuration override using namespaced keys

    Useful for adding custom hyper-parameter tuning fields without having to add custom override methods to config_utils.py.

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198106437  by Zhichao Lu:

    Enable fused batchnorm now that quantization is supported.

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198048364  by Zhichao Lu:

    Add support for keypoints in tf sequence examples and some util ops.

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198004736  by Zhichao Lu:

    Relax postprocessing unit tests that are based on assumption that tf.image.non_max_suppression are stable with respect to input.

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197997513  by Zhichao Lu:

    More lenient validation for normalized box boundaries.

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197940068  by Zhichao Lu:

    A couple of minor updates/fixes:
    - Updating input reader proto with option to use display_name when decoding data.
    - Updating visualization tool to specify whether using absolute or normalized box coordinates. Appropriate boxes will now appear in TB when using model_main.py

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197920152  by Zhichao Lu:

    Add quantized training support in the new OD binaries and a config for SSD Mobilenet v1 quantized training that is TPU compatible.

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197213563  by Zhichao Lu:

    Do not share batch_norm for classification and regression tower in weight shared box predictor.

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197196757  by Zhichao Lu:

    Relax the box_predictor api to return box_prediction of shape [batch_size, num_anchors, code_size] in addition to [batch_size, num_anchors, (1|q), code_size].

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196898361  by Zhichao Lu:

    Allow per-channel scalar value to pad input image with when using keep aspect ratio resizer (when pad_to_max_dimension=True).

    In Object Detection Pipeline, we pad image before normalization and this skews batch_norm statistics during training. The option to set per channel pad value lets us truly pad with zeros.

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196592101  by Zhichao Lu:

    Fix bug regarding tfrecord shuffling in object_detection

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196320138  by Zhichao Lu:

    Fix typo in exporting_models.md

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PiperOrigin-RevId: 199348852

research/object_detection/dataset_tools/tf_record_creation_util.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.
+# ==============================================================================
+r"""Utilities for creating TFRecords of TF examples for the Open Images dataset.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+
+def open_sharded_output_tfrecords(exit_stack, base_path, num_shards):
+  """Opens all TFRecord shards for writing and adds them to an exit stack.
+
+  Args:
+    exit_stack: A context2.ExitStack used to automatically closed the TFRecords
+      opened in this function.
+    base_path: The base path for all shards
+    num_shards: The number of shards
+
+  Returns:
+    The list of opened TFRecords. Position k in the list corresponds to shard k.
+  """
+  tf_record_output_filenames = [
+      '{}-{:05d}-of-{:05d}'.format(base_path, idx, num_shards)
+      for idx in range(num_shards)
+  ]
+
+  tfrecords = [
+      exit_stack.enter_context(tf.python_io.TFRecordWriter(file_name))
+      for file_name in tf_record_output_filenames
+  ]
+
+  return tfrecords

research/object_detection/evaluator.py

@@ -39,12 +39,14 @@ EVAL_METRICS_CLASS_DICT = {
         object_detection_evaluation.PascalInstanceSegmentationEvaluator,
     'weighted_pascal_voc_instance_segmentation_metrics':
         object_detection_evaluation.WeightedPascalInstanceSegmentationEvaluator,
-    'open_images_detection_metrics':
+    'open_images_V2_detection_metrics':
         object_detection_evaluation.OpenImagesDetectionEvaluator,
     'coco_detection_metrics':
         coco_evaluation.CocoDetectionEvaluator,
     'coco_mask_metrics':
         coco_evaluation.CocoMaskEvaluator,
+    'oid_challenge_object_detection_metrics':
+        object_detection_evaluation.OpenImagesDetectionChallengeEvaluator,
 }
 
 EVAL_DEFAULT_METRIC = 'pascal_voc_detection_metrics'

research/object_detection/meta_architectures/ssd_meta_arch.py

@@ -402,8 +402,9 @@ class SSDMetaArch(model.DetectionModel):
               im_width=image_shape[2]))
       prediction_dict = self._box_predictor.predict(
           feature_maps, self._anchor_generator.num_anchors_per_location())
-      box_encodings = tf.squeeze(
-          tf.concat(prediction_dict['box_encodings'], axis=1), axis=2)
+      box_encodings = tf.concat(prediction_dict['box_encodings'], axis=1)
+      if box_encodings.shape.ndims == 4 and box_encodings.shape[2] == 1:
+        box_encodings = tf.squeeze(box_encodings, axis=2)
       class_predictions_with_background = tf.concat(
           prediction_dict['class_predictions_with_background'], axis=1)
       predictions_dict = {