update offline metrics for Open Images dataset.

research/object_detection/metrics/BUILD

@@ -0,0 +1,55 @@
+# Tensorflow Object Detection API: main runnables.
+
+package(
+    default_visibility = ["//visibility:public"],
+)
+
+licenses(["notice"])
+
+# Apache 2.0
+
+py_binary(
+    name = "offline_eval_map_corloc",
+    srcs = [
+        "offline_eval_map_corloc.py",
+    ],
+    deps = [
+        ":tf_example_parser",
+        "//tensorflow_models/object_detection:evaluator",
+        "//tensorflow_models/object_detection/builders:input_reader_builder",
+        "//tensorflow_models/object_detection/core:standard_fields",
+        "//tensorflow_models/object_detection/utils:config_util",
+        "//tensorflow_models/object_detection/utils:label_map_util",
+    ],
+)
+
+py_test(
+    name = "offline_eval_map_corloc_test",
+    srcs = [
+        "offline_eval_map_corloc_test.py",
+    ],
+    deps = [
+        ":offline_eval_map_corloc",
+        "//tensorflow",
+    ],
+)
+
+py_library(
+    name = "tf_example_parser",
+    srcs = ["tf_example_parser.py"],
+    deps = [
+        "//tensorflow",
+        "//tensorflow_models/object_detection/core:data_parser",
+        "//tensorflow_models/object_detection/core:standard_fields",
+    ],
+)
+
+py_test(
+    name = "tf_example_parser_test",
+    srcs = ["tf_example_parser_test.py"],
+    deps = [
+        ":tf_example_parser",
+        "//tensorflow",
+        "//tensorflow_models/object_detection/core:standard_fields",
+    ],
+)

research/object_detection/metrics/offline_eval_map_corloc.py

@@ -0,0 +1,173 @@
+# 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.
+# ==============================================================================
+r"""Evaluation executable for detection data.
+
+This executable evaluates precomputed detections produced by a detection
+model and writes the evaluation results into csv file metrics.csv, stored
+in the directory, specified by --eval_dir.
+
+The evaluation metrics set is supplied in object_detection.protos.EvalConfig
+in metrics_set field.
+Currently two set of metrics are supported:
+- pascal_voc_metrics: standard PASCAL VOC 2007 metric
+- open_images_metrics: Open Image V2 metric
+All other field of object_detection.protos.EvalConfig are ignored.
+
+Example usage:
+    ./compute_metrics \
+        --eval_dir=path/to/eval_dir \
+        --eval_config_path=path/to/evaluation/configuration/file \
+        --input_config_path=path/to/input/configuration/file
+"""
+import csv
+import os
+import re
+import tensorflow as tf
+
+from object_detection import evaluator
+from object_detection.core import standard_fields
+from object_detection.metrics import tf_example_parser
+from object_detection.utils import config_util
+from object_detection.utils import label_map_util
+
+flags = tf.app.flags
+tf.logging.set_verbosity(tf.logging.INFO)
+
+flags.DEFINE_string('eval_dir', None, 'Directory to write eval summaries to.')
+flags.DEFINE_string('eval_config_path', None,
+                    'Path to an eval_pb2.EvalConfig config file.')
+flags.DEFINE_string('input_config_path', None,
+                    'Path to an eval_pb2.InputConfig config file.')
+
+FLAGS = flags.FLAGS
+
+
+def _generate_sharded_filenames(filename):
+  m = re.search(r'@(\d{1,})', filename)
+  if m:
+    num_shards = int(m.group(1))
+    return [
+        re.sub(r'@(\d{1,})', '-%.5d-of-%.5d' % (i, num_shards), filename)
+        for i in range(num_shards)
+    ]
+  else:
+    return [filename]
+
+
+def _generate_filenames(filenames):
+  result = []
+  for filename in filenames:
+    result += _generate_sharded_filenames(filename)
+  return result
+
+
+def read_data_and_evaluate(input_config, eval_config):
+  """Reads pre-computed object detections and groundtruth from tf_record.
+
+  Args:
+    input_config: input config proto of type
+      object_detection.protos.InputReader.
+    eval_config: evaluation config proto of type
+      object_detection.protos.EvalConfig.
+
+  Returns:
+    Evaluated detections metrics.
+
+  Raises:
+    ValueError: if input_reader type is not supported or metric type is unknown.
+  """
+  if input_config.WhichOneof('input_reader') == 'tf_record_input_reader':
+    input_paths = input_config.tf_record_input_reader.input_path
+
+    label_map = label_map_util.load_labelmap(input_config.label_map_path)
+    max_num_classes = max([item.id for item in label_map.item])
+    categories = label_map_util.convert_label_map_to_categories(
+        label_map, max_num_classes)
+
+    object_detection_evaluators = evaluator.get_evaluators(
+        eval_config, categories)
+    # Support a single evaluator
+    object_detection_evaluator = object_detection_evaluators[0]
+
+    skipped_images = 0
+    processed_images = 0
+    for input_path in _generate_filenames(input_paths):
+      tf.logging.info('Processing file: {0}'.format(input_path))
+
+      record_iterator = tf.python_io.tf_record_iterator(path=input_path)
+      data_parser = tf_example_parser.TfExampleDetectionAndGTParser()
+
+      for string_record in record_iterator:
+        tf.logging.log_every_n(tf.logging.INFO, 'Processed %d images...', 1000,
+                               processed_images)
+        processed_images += 1
+
+        example = tf.train.Example()
+        example.ParseFromString(string_record)
+        decoded_dict = data_parser.parse(example)
+
+        if decoded_dict:
+          object_detection_evaluator.add_single_ground_truth_image_info(
+              decoded_dict[standard_fields.DetectionResultFields.key],
+              decoded_dict)
+          object_detection_evaluator.add_single_detected_image_info(
+              decoded_dict[standard_fields.DetectionResultFields.key],
+              decoded_dict)
+        else:
+          skipped_images += 1
+          tf.logging.info('Skipped images: {0}'.format(skipped_images))
+
+    return object_detection_evaluator.evaluate()
+
+  raise ValueError('Unsupported input_reader_config.')
+
+
+def write_metrics(metrics, output_dir):
+  """Write metrics to the output directory.
+
+  Args:
+    metrics: A dictionary containing metric names and values.
+    output_dir: Directory to write metrics to.
+  """
+  tf.logging.info('Writing metrics.')
+
+  with open(os.path.join(output_dir, 'metrics.csv'), 'w') as csvfile:
+    metrics_writer = csv.writer(csvfile, delimiter=',')
+    for metric_name, metric_value in metrics.items():
+      metrics_writer.writerow([metric_name, str(metric_value)])
+
+
+def main(argv):
+  del argv
+  required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
+  for flag_name in required_flags:
+    if not getattr(FLAGS, flag_name):
+      raise ValueError('Flag --{} is required'.format(flag_name))
+
+  configs = config_util.get_configs_from_multiple_files(
+      eval_input_config_path=FLAGS.input_config_path,
+      eval_config_path=FLAGS.eval_config_path)
+
+  eval_config = configs['eval_config']
+  input_config = configs['eval_input_config']
+
+  metrics = read_data_and_evaluate(input_config, eval_config)
+
+  # Save metrics
+  write_metrics(metrics, FLAGS.eval_dir)
+
+
+if __name__ == '__main__':
+  tf.app.run(main)

research/object_detection/metrics/offline_eval_map_corloc_test.py

@@ -0,0 +1,58 @@
+# 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 utilities in offline_eval_map_corloc binary."""
+
+import tensorflow as tf
+
+from object_detection.metrics import offline_eval_map_corloc as offline_eval
+
+
+class OfflineEvalMapCorlocTest(tf.test.TestCase):
+
+  def test_generateShardedFilenames(self):
+    test_filename = '/path/to/file'
+    result = offline_eval._generate_sharded_filenames(test_filename)
+    self.assertEqual(result, [test_filename])
+
+    test_filename = '/path/to/file-00000-of-00050'
+    result = offline_eval._generate_sharded_filenames(test_filename)
+    self.assertEqual(result, [test_filename])
+
+    result = offline_eval._generate_sharded_filenames('/path/to/@3.sst')
+    self.assertEqual(result, [
+        '/path/to/-00000-of-00003.sst', '/path/to/-00001-of-00003.sst',
+        '/path/to/-00002-of-00003.sst'
+    ])
+
+    result = offline_eval._generate_sharded_filenames('/path/to/abc@3')
+    self.assertEqual(result, [
+        '/path/to/abc-00000-of-00003', '/path/to/abc-00001-of-00003',
+        '/path/to/abc-00002-of-00003'
+    ])
+
+    result = offline_eval._generate_sharded_filenames('/path/to/@1')
+    self.assertEqual(result, ['/path/to/-00000-of-00001'])
+
+  def test_generateFilenames(self):
+    test_filenames = ['/path/to/file', '/path/to/@3.sst']
+    result = offline_eval._generate_filenames(test_filenames)
+    self.assertEqual(result, [
+        '/path/to/file', '/path/to/-00000-of-00003.sst',
+        '/path/to/-00001-of-00003.sst', '/path/to/-00002-of-00003.sst'
+    ])
+
+
+if __name__ == '__main__':
+  tf.test.main()