update changes in the utils directory.

research/object_detection/utils/BUILD

@@ -14,6 +14,20 @@ py_library(
     deps = ["//tensorflow"],
 )
 
+py_library(
+    name = "config_util",
+    srcs = ["config_util.py"],
+    deps = [
+        "//pyglib/logging",
+        "//tensorflow",
+        "//tensorflow_models/object_detection/protos:eval_py_pb2",
+        "//tensorflow_models/object_detection/protos:input_reader_py_pb2",
+        "//tensorflow_models/object_detection/protos:model_py_pb2",
+        "//tensorflow_models/object_detection/protos:pipeline_py_pb2",
+        "//tensorflow_models/object_detection/protos:train_py_pb2",
+    ],
+)
+
 py_library(
     name = "dataset_util",
     srcs = ["dataset_util.py"],
@@ -35,7 +49,9 @@ py_library(
 py_library(
     name = "learning_schedules",
     srcs = ["learning_schedules.py"],
-    deps = ["//tensorflow"],
+    deps = [
+        "//tensorflow",
+    ],
 )
 
 py_library(
@@ -70,9 +86,11 @@ py_library(
     name = "object_detection_evaluation",
     srcs = ["object_detection_evaluation.py"],
     deps = [
+        ":label_map_util",
         ":metrics",
         ":per_image_evaluation",
         "//tensorflow",
+        "//tensorflow_models/object_detection/core:standard_fields",
     ],
 )
 
@@ -120,7 +138,7 @@ py_library(
         "//tensorflow_models/object_detection/core:box_list",
         "//tensorflow_models/object_detection/core:box_predictor",
         "//tensorflow_models/object_detection/core:matcher",
-        "//tensorflow_models/object_detection/utils:shape_utils"
+        "//tensorflow_models/object_detection/utils:shape_utils",
     ],
 )
 
@@ -137,6 +155,8 @@ py_library(
     srcs = ["visualization_utils.py"],
     deps = [
         "//third_party/py/PIL:pil",
+        "//third_party/py/matplotlib",
+        "//third_party/py/six",
         "//tensorflow",
     ],
 )
@@ -150,6 +170,19 @@ py_test(
     ],
 )
 
+py_test(
+    name = "config_util_test",
+    srcs = ["config_util_test.py"],
+    deps = [
+        ":config_util",
+        "//tensorflow:tensorflow_google",
+        "//tensorflow_models/object_detection/protos:input_reader_py_pb2",
+        "//tensorflow_models/object_detection/protos:model_py_pb2",
+        "//tensorflow_models/object_detection/protos:pipeline_py_pb2",
+        "//tensorflow_models/object_detection/protos:train_py_pb2",
+    ],
+)
+
 py_test(
     name = "dataset_util_test",
     srcs = ["dataset_util_test.py"],
@@ -220,6 +253,7 @@ py_test(
     deps = [
         ":object_detection_evaluation",
         "//tensorflow",
+        "//tensorflow_models/object_detection/core:standard_fields",
     ],
 )
 
@@ -281,8 +315,12 @@ py_test(
 py_test(
     name = "visualization_utils_test",
     srcs = ["visualization_utils_test.py"],
+    data = [
+        "//tensorflow_models/object_detection/test_images:image1.jpg",
+    ],
     deps = [
         ":visualization_utils",
+        "//pyglib/flags",
         "//third_party/py/PIL:pil",
     ],
 )

research/object_detection/utils/config_util.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.
+# ==============================================================================
+"""Functions for reading and updating configuration files."""
+
+import tensorflow as tf
+
+from google.protobuf import text_format
+
+from object_detection.protos import eval_pb2
+from object_detection.protos import input_reader_pb2
+from object_detection.protos import model_pb2
+from object_detection.protos import pipeline_pb2
+from object_detection.protos import train_pb2
+
+
+def get_configs_from_pipeline_file(pipeline_config_path):
+  """Reads configuration from a pipeline_pb2.TrainEvalPipelineConfig.
+
+  Args:
+    pipeline_config_path: Path to pipeline_pb2.TrainEvalPipelineConfig text
+      proto.
+
+  Returns:
+    Dictionary of configuration objects. Keys are `model`, `train_config`,
+      `train_input_config`, `eval_config`, `eval_input_config`. Value are the
+      corresponding config objects.
+  """
+  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+  with tf.gfile.GFile(pipeline_config_path, "r") as f:
+    proto_str = f.read()
+    text_format.Merge(proto_str, pipeline_config)
+
+  configs = {}
+  configs["model"] = pipeline_config.model
+  configs["train_config"] = pipeline_config.train_config
+  configs["train_input_config"] = pipeline_config.train_input_reader
+  configs["eval_config"] = pipeline_config.eval_config
+  configs["eval_input_config"] = pipeline_config.eval_input_reader
+
+  return configs
+
+
+def create_pipeline_proto_from_configs(configs):
+  """Creates a pipeline_pb2.TrainEvalPipelineConfig from configs dictionary.
+
+  This function nearly performs the inverse operation of
+  get_configs_from_pipeline_file(). Instead of returning a file path, it returns
+  a `TrainEvalPipelineConfig` object.
+
+  Args:
+    configs: Dictionary of configs. See get_configs_from_pipeline_file().
+
+  Returns:
+    A fully populated pipeline_pb2.TrainEvalPipelineConfig.
+  """
+  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+  pipeline_config.model.CopyFrom(configs["model"])
+  pipeline_config.train_config.CopyFrom(configs["train_config"])
+  pipeline_config.train_input_reader.CopyFrom(configs["train_input_config"])
+  pipeline_config.eval_config.CopyFrom(configs["eval_config"])
+  pipeline_config.eval_input_reader.CopyFrom(configs["eval_input_config"])
+  return pipeline_config
+
+
+def get_configs_from_multiple_files(model_config_path="",
+                                    train_config_path="",
+                                    train_input_config_path="",
+                                    eval_config_path="",
+                                    eval_input_config_path=""):
+  """Reads training configuration from multiple config files.
+
+  Args:
+    model_config_path: Path to model_pb2.DetectionModel.
+    train_config_path: Path to train_pb2.TrainConfig.
+    train_input_config_path: Path to input_reader_pb2.InputReader.
+    eval_config_path: Path to eval_pb2.EvalConfig.
+    eval_input_config_path: Path to input_reader_pb2.InputReader.
+
+  Returns:
+    Dictionary of configuration objects. Keys are `model`, `train_config`,
+      `train_input_config`, `eval_config`, `eval_input_config`. Key/Values are
+        returned only for valid (non-empty) strings.
+  """
+  configs = {}
+  if model_config_path:
+    model_config = model_pb2.DetectionModel()
+    with tf.gfile.GFile(model_config_path, "r") as f:
+      text_format.Merge(f.read(), model_config)
+      configs["model"] = model_config
+
+  if train_config_path:
+    train_config = train_pb2.TrainConfig()
+    with tf.gfile.GFile(train_config_path, "r") as f:
+      text_format.Merge(f.read(), train_config)
+      configs["train_config"] = train_config
+
+  if train_input_config_path:
+    train_input_config = input_reader_pb2.InputReader()
+    with tf.gfile.GFile(train_input_config_path, "r") as f:
+      text_format.Merge(f.read(), train_input_config)
+      configs["train_input_config"] = train_input_config
+
+  if eval_config_path:
+    eval_config = eval_pb2.EvalConfig()
+    with tf.gfile.GFile(eval_config_path, "r") as f:
+      text_format.Merge(f.read(), eval_config)
+      configs["eval_config"] = eval_config
+
+  if eval_input_config_path:
+    eval_input_config = input_reader_pb2.InputReader()
+    with tf.gfile.GFile(eval_input_config_path, "r") as f:
+      text_format.Merge(f.read(), eval_input_config)
+      configs["eval_input_config"] = eval_input_config
+
+  return configs
+
+
+def get_number_of_classes(model_config):
+  """Returns the number of classes for a detection model.
+
+  Args:
+    model_config: A model_pb2.DetectionModel.
+
+  Returns:
+    Number of classes.
+
+  Raises:
+    ValueError: If the model type is not recognized.
+  """
+  meta_architecture = model_config.WhichOneof("model")
+  if meta_architecture == "faster_rcnn":
+    return model_config.faster_rcnn.num_classes
+  if meta_architecture == "ssd":
+    return model_config.ssd.num_classes
+
+  raise ValueError("Expected the model to be one of 'faster_rcnn' or 'ssd'.")
+
+
+def get_optimizer_type(train_config):
+  """Returns the optimizer type for training.
+
+  Args:
+    train_config: A train_pb2.TrainConfig.
+
+  Returns:
+    The type of the optimizer
+  """
+  return train_config.optimizer.WhichOneof("optimizer")
+
+
+def get_learning_rate_type(optimizer_config):
+  """Returns the learning rate type for training.
+
+  Args:
+    optimizer_config: An optimizer_pb2.Optimizer.
+
+  Returns:
+    The type of the learning rate.
+  """
+  return optimizer_config.learning_rate.WhichOneof("learning_rate")
+
+
+def merge_external_params_with_configs(configs, hparams=None, **kwargs):
+  """Updates `configs` dictionary based on supplied parameters.
+
+  This utility is for modifying specific fields in the object detection configs.
+  Say that one would like to experiment with different learning rates, momentum
+  values, or batch sizes. Rather than creating a new config text file for each
+  experiment, one can use a single base config file, and update particular
+  values.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    hparams: A `HParams`.
+    **kwargs: Extra keyword arguments that are treated the same way as
+      attribute/value pairs in `hparams`. Note that hyperparameters with the
+      same names will override keyword arguments.
+
+  Returns:
+    `configs` dictionary.
+  """
+
+  if hparams:
+    kwargs.update(hparams.values())
+  for key, value in kwargs.iteritems():
+    if key == "learning_rate":
+      _update_initial_learning_rate(configs, value)
+      tf.logging.info("Overwriting learning rate: %f", value)
+    if key == "batch_size":
+      _update_batch_size(configs, value)
+      tf.logging.info("Overwriting batch size: %d", value)
+    if key == "momentum_optimizer_value":
+      _update_momentum_optimizer_value(configs, value)
+      tf.logging.info("Overwriting momentum optimizer value: %f", value)
+    if key == "classification_localization_weight_ratio":
+      # Localization weight is fixed to 1.0.
+      _update_classification_localization_weight_ratio(configs, value)
+    if key == "focal_loss_gamma":
+      _update_focal_loss_gamma(configs, value)
+    if key == "focal_loss_alpha":
+      _update_focal_loss_alpha(configs, value)
+    if key == "train_steps":
+      _update_train_steps(configs, value)
+      tf.logging.info("Overwriting train steps: %d", value)
+    if key == "eval_steps":
+      _update_eval_steps(configs, value)
+      tf.logging.info("Overwriting eval steps: %d", value)
+    if key == "train_input_path":
+      _update_input_path(configs["train_input_config"], value)
+      tf.logging.info("Overwriting train input path: %s", value)
+    if key == "eval_input_path":
+      _update_input_path(configs["eval_input_config"], value)
+      tf.logging.info("Overwriting eval input path: %s", value)
+    if key == "label_map_path":
+      if value:
+        _update_label_map_path(configs, value)
+        tf.logging.info("Overwriting label map path: %s", value)
+  return configs
+
+
+def _update_initial_learning_rate(configs, learning_rate):
+  """Updates `configs` to reflect the new initial learning rate.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    learning_rate: Initial learning rate for optimizer.
+
+  Raises:
+    TypeError: if optimizer type is not supported, or if learning rate type is
+      not supported.
+  """
+
+  optimizer_type = get_optimizer_type(configs["train_config"])
+  if optimizer_type == "rms_prop_optimizer":
+    optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
+  elif optimizer_type == "momentum_optimizer":
+    optimizer_config = configs["train_config"].optimizer.momentum_optimizer
+  elif optimizer_type == "adam_optimizer":
+    optimizer_config = configs["train_config"].optimizer.adam_optimizer
+  else:
+    raise TypeError("Optimizer %s is not supported." % optimizer_type)
+
+  learning_rate_type = get_learning_rate_type(optimizer_config)
+  if learning_rate_type == "constant_learning_rate":
+    constant_lr = optimizer_config.learning_rate.constant_learning_rate
+    constant_lr.learning_rate = learning_rate
+  elif learning_rate_type == "exponential_decay_learning_rate":
+    exponential_lr = (
+        optimizer_config.learning_rate.exponential_decay_learning_rate)
+    exponential_lr.initial_learning_rate = learning_rate
+  elif learning_rate_type == "manual_step_learning_rate":
+    manual_lr = optimizer_config.learning_rate.manual_step_learning_rate
+    original_learning_rate = manual_lr.initial_learning_rate
+    learning_rate_scaling = float(learning_rate) / original_learning_rate
+    manual_lr.initial_learning_rate = learning_rate
+    for schedule in manual_lr.schedule:
+      schedule.learning_rate *= learning_rate_scaling
+  else:
+    raise TypeError("Learning rate %s is not supported." % learning_rate_type)
+
+
+def _update_batch_size(configs, batch_size):
+  """Updates `configs` to reflect the new training batch size.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    batch_size: Batch size to use for training (Ideally a power of 2). Inputs
+      are rounded, and capped to be 1 or greater.
+  """
+  configs["train_config"].batch_size = max(1, int(round(batch_size)))
+
+
+def _update_momentum_optimizer_value(configs, momentum):
+  """Updates `configs` to reflect the new momentum value.
+
+  Momentum is only supported for RMSPropOptimizer and MomentumOptimizer. For any
+  other optimizer, no changes take place. The configs dictionary is updated in
+  place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    momentum: New momentum value. Values are clipped at 0.0 and 1.0.
+
+  Raises:
+    TypeError: If the optimizer type is not `rms_prop_optimizer` or
+    `momentum_optimizer`.
+  """
+  optimizer_type = get_optimizer_type(configs["train_config"])
+  if optimizer_type == "rms_prop_optimizer":
+    optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
+  elif optimizer_type == "momentum_optimizer":
+    optimizer_config = configs["train_config"].optimizer.momentum_optimizer
+  else:
+    raise TypeError("Optimizer type must be one of `rms_prop_optimizer` or "
+                    "`momentum_optimizer`.")
+
+  optimizer_config.momentum_optimizer_value = min(max(0.0, momentum), 1.0)
+
+
+def _update_classification_localization_weight_ratio(configs, ratio):
+  """Updates the classification/localization weight loss ratio.
+
+  Detection models usually define a loss weight for both classification and
+  objectness. This function updates the weights such that the ratio between
+  classification weight to localization weight is the ratio provided.
+  Arbitrarily, localization weight is set to 1.0.
+
+  Note that in the case of Faster R-CNN, this same ratio is applied to the first
+  stage objectness loss weight relative to localization loss weight.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    ratio: Desired ratio of classification (and/or objectness) loss weight to
+      localization loss weight.
+  """
+  meta_architecture = configs["model"].WhichOneof("model")
+  if meta_architecture == "faster_rcnn":
+    model = configs["model"].faster_rcnn
+    model.first_stage_localization_loss_weight = 1.0
+    model.first_stage_objectness_loss_weight = ratio
+    model.second_stage_localization_loss_weight = 1.0
+    model.second_stage_classification_loss_weight = ratio
+  if meta_architecture == "ssd":
+    model = configs["model"].ssd
+    model.loss.localization_weight = 1.0
+    model.loss.classification_weight = ratio
+
+
+def _get_classification_loss(model_config):
+  """Returns the classification loss for a model."""
+  meta_architecture = model_config.WhichOneof("model")
+  if meta_architecture == "faster_rcnn":
+    model = model_config.faster_rcnn
+    classification_loss = model.second_stage_classification_loss
+  if meta_architecture == "ssd":
+    model = model_config.ssd
+    classification_loss = model.loss.classification_loss
+  else:
+    raise TypeError("Did not recognize the model architecture.")
+  return classification_loss
+
+
+def _update_focal_loss_gamma(configs, gamma):
+  """Updates the gamma value for a sigmoid focal loss.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    gamma: Exponent term in focal loss.
+
+  Raises:
+    TypeError: If the classification loss is not `weighted_sigmoid_focal`.
+  """
+  classification_loss = _get_classification_loss(configs["model"])
+  classification_loss_type = classification_loss.WhichOneof(
+      "classification_loss")
+  if classification_loss_type != "weighted_sigmoid_focal":
+    raise TypeError("Classification loss must be `weighted_sigmoid_focal`.")
+  classification_loss.weighted_sigmoid_focal.gamma = gamma
+
+
+def _update_focal_loss_alpha(configs, alpha):
+  """Updates the alpha value for a sigmoid focal loss.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    alpha: Class weight multiplier for sigmoid loss.
+
+  Raises:
+    TypeError: If the classification loss is not `weighted_sigmoid_focal`.
+  """
+  classification_loss = _get_classification_loss(configs["model"])
+  classification_loss_type = classification_loss.WhichOneof(
+      "classification_loss")
+  if classification_loss_type != "weighted_sigmoid_focal":
+    raise TypeError("Classification loss must be `weighted_sigmoid_focal`.")
+  classification_loss.weighted_sigmoid_focal.alpha = alpha
+
+
+def _update_train_steps(configs, train_steps):
+  """Updates `configs` to reflect new number of training steps."""
+  configs["train_config"].num_steps = int(train_steps)
+
+
+def _update_eval_steps(configs, eval_steps):
+  """Updates `configs` to reflect new number of eval steps per evaluation."""
+  configs["eval_config"].num_examples = int(eval_steps)
+
+
+def _update_input_path(input_config, input_path):
+  """Updates input configuration to reflect a new input path.
+
+  The input_config object is updated in place, and hence not returned.
+
+  Args:
+    input_config: A input_reader_pb2.InputReader.
+    input_path: A path to data or list of paths.
+
+  Raises:
+    TypeError: if input reader type is not `tf_record_input_reader`.
+  """
+  input_reader_type = input_config.WhichOneof("input_reader")
+  if input_reader_type == "tf_record_input_reader":
+    input_config.tf_record_input_reader.ClearField("input_path")
+    if isinstance(input_path, list):
+      input_config.tf_record_input_reader.input_path.extend(input_path)
+    else:
+      input_config.tf_record_input_reader.input_path.append(input_path)
+  else:
+    raise TypeError("Input reader type must be `tf_record_input_reader`.")
+
+
+def _update_label_map_path(configs, label_map_path):
+  """Updates the label map path for both train and eval input readers.
+
+  The configs dictionary is updated in place, and hence not returned.
+
+  Args:
+    configs: Dictionary of configuration objects. See outputs from
+      get_configs_from_pipeline_file() or get_configs_from_multiple_files().
+    label_map_path: New path to `StringIntLabelMap` pbtxt file.
+  """
+  configs["train_input_config"].label_map_path = label_map_path
+  configs["eval_input_config"].label_map_path = label_map_path

research/object_detection/utils/config_util_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 object_detection.utils.config_util."""
+
+import os
+
+import google3
+import tensorflow.google as tf
+
+from google.protobuf import text_format
+
+from object_detection.protos import eval_pb2
+from object_detection.protos import input_reader_pb2
+from object_detection.protos import model_pb2
+from object_detection.protos import pipeline_pb2
+from object_detection.protos import train_pb2
+from object_detection.utils import config_util
+
+
+def _write_config(config, config_path):
+  """Writes a config object to disk."""
+  config_text = text_format.MessageToString(config)
+  with tf.gfile.Open(config_path, "wb") as f:
+    f.write(config_text)
+
+
+def _update_optimizer_with_constant_learning_rate(optimizer, learning_rate):
+  """Adds a new constant learning rate."""
+  constant_lr = optimizer.learning_rate.constant_learning_rate
+  constant_lr.learning_rate = learning_rate
+
+
+def _update_optimizer_with_exponential_decay_learning_rate(
+    optimizer, learning_rate):
+  """Adds a new exponential decay learning rate."""
+  exponential_lr = optimizer.learning_rate.exponential_decay_learning_rate
+  exponential_lr.initial_learning_rate = learning_rate
+
+
+def _update_optimizer_with_manual_step_learning_rate(
+    optimizer, initial_learning_rate, learning_rate_scaling):
+  """Adds a learning rate schedule."""
+  manual_lr = optimizer.learning_rate.manual_step_learning_rate
+  manual_lr.initial_learning_rate = initial_learning_rate
+  for i in range(3):
+    schedule = manual_lr.schedule.add()
+    schedule.learning_rate = initial_learning_rate * learning_rate_scaling**i
+
+
+class ConfigUtilTest(tf.test.TestCase):
+
+  def test_get_configs_from_pipeline_file(self):
+    """Test that proto configs can be read from pipeline config file."""
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    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
+
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    self.assertProtoEquals(pipeline_config.model, configs["model"])
+    self.assertProtoEquals(pipeline_config.train_config,
+                           configs["train_config"])
+    self.assertProtoEquals(pipeline_config.train_input_reader,
+                           configs["train_input_config"])
+    self.assertProtoEquals(pipeline_config.eval_config,
+                           configs["eval_config"])
+    self.assertProtoEquals(pipeline_config.eval_input_reader,
+                           configs["eval_input_config"])
+
+  def test_create_pipeline_proto_from_configs(self):
+    """Tests that proto can be reconstructed from configs dictionary."""
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    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
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    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()
+
+    # Write model config file.
+    model_config_path = os.path.join(temp_dir, "model.config")
+    model = model_pb2.DetectionModel()
+    model.faster_rcnn.num_classes = 10
+    _write_config(model, model_config_path)
+
+    # Write train config file.
+    train_config_path = os.path.join(temp_dir, "train.config")
+    train_config = train_config = train_pb2.TrainConfig()
+    train_config.batch_size = 32
+    _write_config(train_config, train_config_path)
+
+    # Write train input config file.
+    train_input_config_path = os.path.join(temp_dir, "train_input.config")
+    train_input_config = input_reader_pb2.InputReader()
+    train_input_config.label_map_path = "path/to/label_map"
+    _write_config(train_input_config, train_input_config_path)
+
+    # Write eval config file.
+    eval_config_path = os.path.join(temp_dir, "eval.config")
+    eval_config = eval_pb2.EvalConfig()
+    eval_config.num_examples = 20
+    _write_config(eval_config, eval_config_path)
+
+    # Write eval input config file.
+    eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
+    eval_input_config = input_reader_pb2.InputReader()
+    eval_input_config.label_map_path = "path/to/another/label_map"
+    _write_config(eval_input_config, eval_input_config_path)
+
+    configs = config_util.get_configs_from_multiple_files(
+        model_config_path=model_config_path,
+        train_config_path=train_config_path,
+        train_input_config_path=train_input_config_path,
+        eval_config_path=eval_config_path,
+        eval_input_config_path=eval_input_config_path)
+    self.assertProtoEquals(model, configs["model"])
+    self.assertProtoEquals(train_config, configs["train_config"])
+    self.assertProtoEquals(train_input_config,
+                           configs["train_input_config"])
+    self.assertProtoEquals(eval_config, configs["eval_config"])
+    self.assertProtoEquals(eval_input_config,
+                           configs["eval_input_config"])
+
+  def _assertOptimizerWithNewLearningRate(self, optimizer_name):
+    """Asserts successful updating of all learning rate schemes."""
+    original_learning_rate = 0.7
+    learning_rate_scaling = 0.1
+    hparams = tf.HParams(learning_rate=0.15)
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    # Constant learning rate.
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
+    _update_optimizer_with_constant_learning_rate(optimizer,
+                                                  original_learning_rate)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
+    constant_lr = optimizer.learning_rate.constant_learning_rate
+    self.assertAlmostEqual(hparams.learning_rate, constant_lr.learning_rate)
+
+    # Exponential decay learning rate.
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
+    _update_optimizer_with_exponential_decay_learning_rate(
+        optimizer, original_learning_rate)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
+    exponential_lr = optimizer.learning_rate.exponential_decay_learning_rate
+    self.assertAlmostEqual(hparams.learning_rate,
+                           exponential_lr.initial_learning_rate)
+
+    # Manual step learning rate.
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
+    _update_optimizer_with_manual_step_learning_rate(
+        optimizer, original_learning_rate, learning_rate_scaling)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
+    manual_lr = optimizer.learning_rate.manual_step_learning_rate
+    self.assertAlmostEqual(hparams.learning_rate,
+                           manual_lr.initial_learning_rate)
+    for i, schedule in enumerate(manual_lr.schedule):
+      self.assertAlmostEqual(hparams.learning_rate * learning_rate_scaling**i,
+                             schedule.learning_rate)
+
+  def testRMSPropWithNewLearingRate(self):
+    """Tests new learning rates for RMSProp Optimizer."""
+    self._assertOptimizerWithNewLearningRate("rms_prop_optimizer")
+
+  def testMomentumOptimizerWithNewLearningRate(self):
+    """Tests new learning rates for Momentum Optimizer."""
+    self._assertOptimizerWithNewLearningRate("momentum_optimizer")
+
+  def testAdamOptimizerWithNewLearningRate(self):
+    """Tests new learning rates for Adam Optimizer."""
+    self._assertOptimizerWithNewLearningRate("adam_optimizer")
+
+  def testNewBatchSize(self):
+    """Tests that batch size is updated appropriately."""
+    original_batch_size = 2
+    hparams = tf.HParams(batch_size=16)
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.train_config.batch_size = original_batch_size
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    new_batch_size = configs["train_config"].batch_size
+    self.assertEqual(16, new_batch_size)
+
+  def testNewBatchSizeWithClipping(self):
+    """Tests that batch size is clipped to 1 from below."""
+    original_batch_size = 2
+    hparams = tf.HParams(batch_size=0.5)
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.train_config.batch_size = original_batch_size
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    new_batch_size = configs["train_config"].batch_size
+    self.assertEqual(1, new_batch_size)  # Clipped to 1.0.
+
+  def testNewMomentumOptimizerValue(self):
+    """Tests that new momentum value is updated appropriately."""
+    original_momentum_value = 0.4
+    hparams = tf.HParams(momentum_optimizer_value=1.1)
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    optimizer_config = pipeline_config.train_config.optimizer.rms_prop_optimizer
+    optimizer_config.momentum_optimizer_value = original_momentum_value
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
+    new_momentum_value = optimizer_config.momentum_optimizer_value
+    self.assertAlmostEqual(1.0, new_momentum_value)  # Clipped to 1.0.
+
+  def testNewClassificationLocalizationWeightRatio(self):
+    """Tests that the loss weight ratio is updated appropriately."""
+    original_localization_weight = 0.1
+    original_classification_weight = 0.2
+    new_weight_ratio = 5.0
+    hparams = tf.HParams(
+        classification_localization_weight_ratio=new_weight_ratio)
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.model.ssd.loss.localization_weight = (
+        original_localization_weight)
+    pipeline_config.model.ssd.loss.classification_weight = (
+        original_classification_weight)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    loss = configs["model"].ssd.loss
+    self.assertAlmostEqual(1.0, loss.localization_weight)
+    self.assertAlmostEqual(new_weight_ratio, loss.classification_weight)
+
+  def testNewFocalLossParameters(self):
+    """Tests that the loss weight ratio is updated appropriately."""
+    original_alpha = 1.0
+    original_gamma = 1.0
+    new_alpha = 0.3
+    new_gamma = 2.0
+    hparams = tf.HParams(focal_loss_alpha=new_alpha, focal_loss_gamma=new_gamma)
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    classification_loss = pipeline_config.model.ssd.loss.classification_loss
+    classification_loss.weighted_sigmoid_focal.alpha = original_alpha
+    classification_loss.weighted_sigmoid_focal.gamma = original_gamma
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(configs, hparams)
+    classification_loss = configs["model"].ssd.loss.classification_loss
+    self.assertAlmostEqual(new_alpha,
+                           classification_loss.weighted_sigmoid_focal.alpha)
+    self.assertAlmostEqual(new_gamma,
+                           classification_loss.weighted_sigmoid_focal.gamma)
+
+  def testMergingKeywordArguments(self):
+    """Tests that keyword arguments get merged as do hyperparameters."""
+    original_num_train_steps = 100
+    original_num_eval_steps = 5
+    desired_num_train_steps = 10
+    desired_num_eval_steps = 1
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.train_config.num_steps = original_num_train_steps
+    pipeline_config.eval_config.num_examples = original_num_eval_steps
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(
+        configs,
+        train_steps=desired_num_train_steps,
+        eval_steps=desired_num_eval_steps)
+    train_steps = configs["train_config"].num_steps
+    eval_steps = configs["eval_config"].num_examples
+    self.assertEqual(desired_num_train_steps, train_steps)
+    self.assertEqual(desired_num_eval_steps, eval_steps)
+
+  def testGetNumberOfClasses(self):
+    """Tests that number of classes can be retrieved."""
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    pipeline_config.model.faster_rcnn.num_classes = 20
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    number_of_classes = config_util.get_number_of_classes(configs["model"])
+    self.assertEqual(20, number_of_classes)
+
+  def testNewTrainInputPath(self):
+    """Tests that train input path can be overwritten with single file."""
+    original_train_path = ["path/to/data"]
+    new_train_path = "another/path/to/data"
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
+    reader_config.input_path.extend(original_train_path)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(
+        configs, train_input_path=new_train_path)
+    reader_config = configs["train_input_config"].tf_record_input_reader
+    final_path = reader_config.input_path
+    self.assertEqual([new_train_path], final_path)
+
+  def testNewTrainInputPathList(self):
+    """Tests that train input path can be overwritten with multiple files."""
+    original_train_path = ["path/to/data"]
+    new_train_path = ["another/path/to/data", "yet/another/path/to/data"]
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
+    reader_config.input_path.extend(original_train_path)
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(
+        configs, train_input_path=new_train_path)
+    reader_config = configs["train_input_config"].tf_record_input_reader
+    final_path = reader_config.input_path
+    self.assertEqual(new_train_path, final_path)
+
+  def testNewLabelMapPath(self):
+    """Tests that label map path can be overwritten in input readers."""
+    original_label_map_path = "path/to/original/label_map"
+    new_label_map_path = "path//to/new/label_map"
+    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
+
+    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
+    train_input_reader = pipeline_config.train_input_reader
+    train_input_reader.label_map_path = original_label_map_path
+    eval_input_reader = pipeline_config.eval_input_reader
+    eval_input_reader.label_map_path = original_label_map_path
+    _write_config(pipeline_config, pipeline_config_path)
+
+    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
+    configs = config_util.merge_external_params_with_configs(
+        configs, label_map_path=new_label_map_path)
+    self.assertEqual(new_label_map_path,
+                     configs["train_input_config"].label_map_path)
+    self.assertEqual(new_label_map_path,
+                     configs["eval_input_config"].label_map_path)
+
+
+if __name__ == "__main__":
+  tf.test.main()

research/object_detection/utils/label_map_util.py

@@ -124,11 +124,12 @@ def load_labelmap(path):
   return label_map
 
 
-def get_label_map_dict(label_map_path):
+def get_label_map_dict(label_map_path, use_display_name=False):
   """Reads a label map and returns a dictionary of label names to id.
 
   Args:
     label_map_path: path to label_map.
+    use_display_name: whether to use the label map items' display names as keys.
 
   Returns:
     A dictionary mapping label names to id.
@@ -136,5 +137,30 @@ def get_label_map_dict(label_map_path):
   label_map = load_labelmap(label_map_path)
   label_map_dict = {}
   for item in label_map.item:
+    if use_display_name:
+      label_map_dict[item.display_name] = item.id
+    else:
       label_map_dict[item.name] = item.id
   return label_map_dict
+
+
+def create_category_index_from_labelmap(label_map_path):
+  """Reads a label map and returns a category index.
+
+  Args:
+    label_map_path: Path to `StringIntLabelMap` proto text file.
+
+  Returns:
+    A category index, which is a dictionary that maps integer ids to dicts
+    containing categories, e.g.
+    {1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}, ...}
+  """
+  label_map = load_labelmap(label_map_path)
+  max_num_classes = max(item.id for item in label_map.item)
+  categories = convert_label_map_to_categories(label_map, max_num_classes)
+  return create_category_index(categories)
+
+
+def create_class_agnostic_category_index():
+  """Creates a category index with a single `object` class."""
+  return {1: {'id': 1, 'name': 'object'}}

research/object_detection/utils/label_map_util_test.py

@@ -53,6 +53,26 @@ class LabelMapUtilTest(tf.test.TestCase):
     self.assertEqual(label_map_dict['dog'], 1)
     self.assertEqual(label_map_dict['cat'], 2)
 
+  def test_get_label_map_dict_display(self):
+    label_map_string = """
+      item {
+        id:2
+        display_name:'cat'
+      }
+      item {
+        id:1
+        display_name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+
+    label_map_dict = label_map_util.get_label_map_dict(
+        label_map_path, use_display_name=True)
+    self.assertEqual(label_map_dict['dog'], 1)
+    self.assertEqual(label_map_dict['cat'], 2)
+
   def test_load_bad_label_map(self):
     label_map_string = """
       item {
@@ -164,6 +184,34 @@ class LabelMapUtilTest(tf.test.TestCase):
         }
     }, category_index)
 
+  def test_create_category_index_from_labelmap(self):
+    label_map_string = """
+      item {
+        id:2
+        name:'cat'
+      }
+      item {
+        id:1
+        name:'dog'
+      }
+    """
+    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
+    with tf.gfile.Open(label_map_path, 'wb') as f:
+      f.write(label_map_string)
+
+    category_index = label_map_util.create_category_index_from_labelmap(
+        label_map_path)
+    self.assertDictEqual({
+        1: {
+            'name': u'dog',
+            'id': 1
+        },
+        2: {
+            'name': u'cat',
+            'id': 2
+        }
+    }, category_index)
+
 
 if __name__ == '__main__':
   tf.test.main()

research/object_detection/utils/learning_schedules.py

@@ -12,9 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Library of common learning rate schedules."""
 
+import numpy as np
 import tensorflow as tf
 
 
@@ -59,6 +59,54 @@ def exponential_decay_with_burnin(global_step,
       lambda: post_burnin_learning_rate)
 
 
+def cosine_decay_with_warmup(global_step,
+                             learning_rate_base,
+                             total_steps,
+                             warmup_learning_rate=0.0,
+                             warmup_steps=0):
+  """Cosine decay schedule with warm up period.
+
+  Cosine annealing learning rate as described in:
+    Loshchilov and Hutter, SGDR: Stochastic Gradient Descent with Warm Restarts.
+    ICLR 2017. https://arxiv.org/abs/1608.03983
+  In this schedule, the learning rate grows linearly from warmup_learning_rate
+  to learning_rate_base for warmup_steps, then transitions to a cosine decay
+  schedule.
+
+  Args:
+    global_step: int64 (scalar) tensor representing global step.
+    learning_rate_base: base learning rate.
+    total_steps: total number of training steps.
+    warmup_learning_rate: initial learning rate for warm up.
+    warmup_steps: number of warmup steps.
+
+  Returns:
+    a (scalar) float tensor representing learning rate.
+
+  Raises:
+    ValueError: if warmup_learning_rate is larger than learning_rate_base,
+      or if warmup_steps is larger than total_steps.
+  """
+  if learning_rate_base < warmup_learning_rate:
+    raise ValueError('learning_rate_base must be larger '
+                     'or equal to warmup_learning_rate.')
+  if total_steps < warmup_steps:
+    raise ValueError('total_steps must be larger or equal to '
+                     'warmup_steps.')
+  learning_rate = 0.5 * learning_rate_base * (
+      1 + tf.cos(np.pi * tf.cast(
+          global_step - warmup_steps, tf.float32
+      ) / float(total_steps - warmup_steps)))
+  if warmup_steps > 0:
+    slope = (learning_rate_base - warmup_learning_rate) / warmup_steps
+    pre_cosine_learning_rate = slope * tf.cast(
+        global_step, tf.float32) + warmup_learning_rate
+    learning_rate = tf.cond(
+        tf.less(global_step, warmup_steps), lambda: pre_cosine_learning_rate,
+        lambda: learning_rate)
+  return learning_rate
+
+
 def manual_stepping(global_step, boundaries, rates):
   """Manually stepped learning rate schedule.
 
@@ -96,8 +144,8 @@ def manual_stepping(global_step, boundaries, rates):
                      'number of boundary points by exactly 1.')
   step_boundaries = tf.constant(boundaries, tf.int64)
   learning_rates = tf.constant(rates, tf.float32)
-  unreached_boundaries = tf.reshape(tf.where(
-      tf.greater(step_boundaries, global_step)), [-1])
+  unreached_boundaries = tf.reshape(
+      tf.where(tf.greater(step_boundaries, global_step)), [-1])
   unreached_boundaries = tf.concat([unreached_boundaries, [len(boundaries)]], 0)
   index = tf.reshape(tf.reduce_min(unreached_boundaries), [1])
   return tf.reshape(tf.slice(learning_rates, index, [1]), [])

research/object_detection/utils/learning_schedules_test.py

@@ -40,6 +40,25 @@ class LearningSchedulesTest(tf.test.TestCase):
         output_rates.append(output_rate)
       self.assertAllClose(output_rates, exp_rates)
 
+  def testCosineDecayWithWarmup(self):
+    global_step = tf.placeholder(tf.int32, [])
+    learning_rate_base = 1.0
+    total_steps = 100
+    warmup_learning_rate = 0.1
+    warmup_steps = 9
+    input_global_steps = [0, 4, 8, 9, 100]
+    exp_rates = [0.1, 0.5, 0.9, 1.0, 0]
+    learning_rate = learning_schedules.cosine_decay_with_warmup(
+        global_step, learning_rate_base, total_steps,
+        warmup_learning_rate, warmup_steps)
+    with self.test_session() as sess:
+      output_rates = []
+      for input_global_step in input_global_steps:
+        output_rate = sess.run(learning_rate,
+                               feed_dict={global_step: input_global_step})
+        output_rates.append(output_rate)
+      self.assertAllClose(output_rates, exp_rates)
+
   def testManualStepping(self):
     global_step = tf.placeholder(tf.int64, [])
     boundaries = [2, 3, 7]

research/object_detection/utils/metrics.py

@@ -17,7 +17,6 @@
 from __future__ import division
 
 import numpy as np
-from six import moves
 
 
 def compute_precision_recall(scores, labels, num_gt):
@@ -104,7 +103,7 @@ def compute_average_precision(precision, recall):
     raise ValueError("Precision must be in the range of [0, 1].")
   if np.amin(recall) < 0 or np.amax(recall) > 1:
     raise ValueError("recall must be in the range of [0, 1].")
-  if not all(recall[i] <= recall[i + 1] for i in moves.range(len(recall) - 1)):
+  if not all(recall[i] <= recall[i + 1] for i in range(len(recall) - 1)):
     raise ValueError("recall must be a non-decreasing array")
 
   recall = np.concatenate([[0], recall, [1]])

research/object_detection/utils/np_box_list.py

@@ -16,7 +16,6 @@
 """Numpy BoxList classes and functions."""
 
 import numpy as np
-from six import moves
 
 
 class BoxList(object):
@@ -128,7 +127,7 @@ class BoxList(object):
           ymin, and all xmax of boxes are equal or greater than xmin.
     """
     if data.shape[0] > 0:
-      for i in moves.range(data.shape[0]):
+      for i in range(data.shape[0]):
         if data[i, 0] > data[i, 2] or data[i, 1] > data[i, 3]:
           return False
     return True

research/object_detection/utils/object_detection_evaluation_test.py

@@ -18,9 +18,309 @@
 import numpy as np
 import tensorflow as tf
 
+from object_detection.core import standard_fields
 from object_detection.utils import object_detection_evaluation
 
 
+class OpenImagesV2EvaluationTest(tf.test.TestCase):
+
+  def test_returns_correct_metric_values(self):
+    categories = [{
+        'id': 1,
+        'name': 'cat'
+    }, {
+        'id': 2,
+        'name': 'dog'
+    }, {
+        'id': 3,
+        'name': 'elephant'
+    }]
+
+    oiv2_evaluator = object_detection_evaluation.OpenImagesDetectionEvaluator(
+        categories)
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array(
+        [[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]], dtype=float)
+    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
+    oiv2_evaluator.add_single_ground_truth_image_info(image_key1, {
+        standard_fields.InputDataFields.groundtruth_boxes:
+            groundtruth_boxes1,
+        standard_fields.InputDataFields.groundtruth_classes:
+            groundtruth_class_labels1
+    })
+    image_key2 = 'img2'
+    groundtruth_boxes2 = np.array(
+        [[10, 10, 11, 11], [500, 500, 510, 510], [10, 10, 12, 12]], dtype=float)
+    groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
+    groundtruth_is_group_of_list2 = np.array([False, True, False], dtype=bool)
+    oiv2_evaluator.add_single_ground_truth_image_info(image_key2, {
+        standard_fields.InputDataFields.groundtruth_boxes:
+            groundtruth_boxes2,
+        standard_fields.InputDataFields.groundtruth_classes:
+            groundtruth_class_labels2,
+        standard_fields.InputDataFields.groundtruth_group_of:
+            groundtruth_is_group_of_list2
+    })
+    image_key3 = 'img3'
+    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
+    groundtruth_class_labels3 = np.array([2], dtype=int)
+    oiv2_evaluator.add_single_ground_truth_image_info(image_key3, {
+        standard_fields.InputDataFields.groundtruth_boxes:
+            groundtruth_boxes3,
+        standard_fields.InputDataFields.groundtruth_classes:
+            groundtruth_class_labels3
+    })
+    # Add detections
+    image_key = 'img2'
+    detected_boxes = np.array(
+        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
+        dtype=float)
+    detected_class_labels = np.array([1, 1, 3], dtype=int)
+    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
+    oiv2_evaluator.add_single_detected_image_info(image_key, {
+        standard_fields.DetectionResultFields.detection_boxes:
+            detected_boxes,
+        standard_fields.DetectionResultFields.detection_scores:
+            detected_scores,
+        standard_fields.DetectionResultFields.detection_classes:
+            detected_class_labels
+    })
+    metrics = oiv2_evaluator.evaluate()
+    self.assertAlmostEqual(
+        metrics['OpenImagesV2/PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
+    self.assertAlmostEqual(
+        metrics['OpenImagesV2/PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
+    self.assertAlmostEqual(
+        metrics['OpenImagesV2/PerformanceByCategory/AP@0.5IOU/cat'], 0.16666666)
+    self.assertAlmostEqual(metrics['OpenImagesV2/Precision/mAP@0.5IOU'],
+                           0.05555555)
+    oiv2_evaluator.clear()
+    self.assertFalse(oiv2_evaluator._image_ids)
+
+
+class PascalEvaluationTest(tf.test.TestCase):
+
+  def test_returns_correct_metric_values(self):
+    categories = [{'id': 1, 'name': 'cat'},
+                  {'id': 2, 'name': 'dog'},
+                  {'id': 3, 'name': 'elephant'}]
+    #  Add groundtruth
+    pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
+        categories)
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                  dtype=float)
+    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
+    pascal_evaluator.add_single_ground_truth_image_info(
+        image_key1,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes1,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels1})
+    image_key2 = 'img2'
+    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
+                                   [10, 10, 12, 12]], dtype=float)
+    groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
+    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
+    pascal_evaluator.add_single_ground_truth_image_info(
+        image_key2,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes2,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels2,
+         standard_fields.InputDataFields.groundtruth_difficult:
+         groundtruth_is_difficult_list2})
+    image_key3 = 'img3'
+    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
+    groundtruth_class_labels3 = np.array([2], dtype=int)
+    pascal_evaluator.add_single_ground_truth_image_info(
+        image_key3,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes3,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels3})
+
+    # Add detections
+    image_key = 'img2'
+    detected_boxes = np.array(
+        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
+        dtype=float)
+    detected_class_labels = np.array([1, 1, 3], dtype=int)
+    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
+    pascal_evaluator.add_single_detected_image_info(
+        image_key,
+        {standard_fields.DetectionResultFields.detection_boxes: detected_boxes,
+         standard_fields.DetectionResultFields.detection_scores:
+         detected_scores,
+         standard_fields.DetectionResultFields.detection_classes:
+         detected_class_labels})
+
+    metrics = pascal_evaluator.evaluate()
+    self.assertAlmostEqual(
+        metrics['PASCAL/PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
+    self.assertAlmostEqual(
+        metrics['PASCAL/PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
+    self.assertAlmostEqual(
+        metrics['PASCAL/PerformanceByCategory/AP@0.5IOU/cat'], 0.16666666)
+    self.assertAlmostEqual(metrics['PASCAL/Precision/mAP@0.5IOU'], 0.05555555)
+    pascal_evaluator.clear()
+    self.assertFalse(pascal_evaluator._image_ids)
+
+  def test_value_error_on_duplicate_images(self):
+    categories = [{'id': 1, 'name': 'cat'},
+                  {'id': 2, 'name': 'dog'},
+                  {'id': 3, 'name': 'elephant'}]
+    #  Add groundtruth
+    pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
+        categories)
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                  dtype=float)
+    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
+    pascal_evaluator.add_single_ground_truth_image_info(
+        image_key1,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes1,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels1})
+    with self.assertRaises(ValueError):
+      pascal_evaluator.add_single_ground_truth_image_info(
+          image_key1,
+          {standard_fields.InputDataFields.groundtruth_boxes:
+           groundtruth_boxes1,
+           standard_fields.InputDataFields.groundtruth_classes:
+           groundtruth_class_labels1})
+
+
+class WeightedPascalEvaluationTest(tf.test.TestCase):
+
+  def setUp(self):
+    self.categories = [{'id': 1, 'name': 'cat'},
+                       {'id': 2, 'name': 'dog'},
+                       {'id': 3, 'name': 'elephant'}]
+
+  def create_and_add_common_ground_truth(self):
+    #  Add groundtruth
+    self.wp_eval = (
+        object_detection_evaluation.WeightedPascalDetectionEvaluator(
+            self.categories))
+
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                  dtype=float)
+    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
+    self.wp_eval.add_single_ground_truth_image_info(
+        image_key1,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes1,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels1})
+    # add 'img2' separately
+    image_key3 = 'img3'
+    groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
+    groundtruth_class_labels3 = np.array([2], dtype=int)
+    self.wp_eval.add_single_ground_truth_image_info(
+        image_key3,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes3,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels3})
+
+  def add_common_detected(self):
+    image_key = 'img2'
+    detected_boxes = np.array(
+        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
+        dtype=float)
+    detected_class_labels = np.array([1, 1, 3], dtype=int)
+    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
+    self.wp_eval.add_single_detected_image_info(
+        image_key,
+        {standard_fields.DetectionResultFields.detection_boxes: detected_boxes,
+         standard_fields.DetectionResultFields.detection_scores:
+         detected_scores,
+         standard_fields.DetectionResultFields.detection_classes:
+         detected_class_labels})
+
+  def test_returns_correct_metric_values(self):
+    self.create_and_add_common_ground_truth()
+    image_key2 = 'img2'
+    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
+                                   [10, 10, 12, 12]], dtype=float)
+    groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
+    self.wp_eval.add_single_ground_truth_image_info(
+        image_key2,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes2,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels2
+        })
+    self.add_common_detected()
+
+    metrics = self.wp_eval.evaluate()
+    self.assertAlmostEqual(
+        metrics[self.wp_eval._metric_prefix +
+                'PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
+    self.assertAlmostEqual(
+        metrics[self.wp_eval._metric_prefix +
+                'PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
+    self.assertAlmostEqual(
+        metrics[self.wp_eval._metric_prefix +
+                'PerformanceByCategory/AP@0.5IOU/cat'], 0.5 / 4)
+    self.assertAlmostEqual(metrics[self.wp_eval._metric_prefix +
+                                   'Precision/mAP@0.5IOU'],
+                           1. / (4 + 1 + 2) / 3)
+    self.wp_eval.clear()
+    self.assertFalse(self.wp_eval._image_ids)
+
+  def test_returns_correct_metric_values_with_difficult_list(self):
+    self.create_and_add_common_ground_truth()
+    image_key2 = 'img2'
+    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
+                                   [10, 10, 12, 12]], dtype=float)
+    groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
+    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
+    self.wp_eval.add_single_ground_truth_image_info(
+        image_key2,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes2,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels2,
+         standard_fields.InputDataFields.groundtruth_difficult:
+         groundtruth_is_difficult_list2
+        })
+    self.add_common_detected()
+
+    metrics = self.wp_eval.evaluate()
+    self.assertAlmostEqual(
+        metrics[self.wp_eval._metric_prefix +
+                'PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
+    self.assertAlmostEqual(
+        metrics[self.wp_eval._metric_prefix +
+                'PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
+    self.assertAlmostEqual(
+        metrics[self.wp_eval._metric_prefix +
+                'PerformanceByCategory/AP@0.5IOU/cat'], 0.5 / 3)
+    self.assertAlmostEqual(metrics[self.wp_eval._metric_prefix +
+                                   'Precision/mAP@0.5IOU'],
+                           1. / (3 + 1 + 2) / 3)
+    self.wp_eval.clear()
+    self.assertFalse(self.wp_eval._image_ids)
+
+  def test_value_error_on_duplicate_images(self):
+    #  Add groundtruth
+    self.wp_eval = (
+        object_detection_evaluation.WeightedPascalDetectionEvaluator(
+            self.categories))
+    image_key1 = 'img1'
+    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
+                                  dtype=float)
+    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
+    self.wp_eval.add_single_ground_truth_image_info(
+        image_key1,
+        {standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes1,
+         standard_fields.InputDataFields.groundtruth_classes:
+         groundtruth_class_labels1})
+    with self.assertRaises(ValueError):
+      self.wp_eval.add_single_ground_truth_image_info(
+          image_key1,
+          {standard_fields.InputDataFields.groundtruth_boxes:
+           groundtruth_boxes1,
+           standard_fields.InputDataFields.groundtruth_classes:
+           groundtruth_class_labels1})
+
+
 class ObjectDetectionEvaluationTest(tf.test.TestCase):
 
   def setUp(self):
@@ -28,27 +328,29 @@ class ObjectDetectionEvaluationTest(tf.test.TestCase):
     self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
         num_groundtruth_classes)
 
-    image_key1 = "img1"
+    image_key1 = 'img1'
     groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                   dtype=float)
     groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
     self.od_eval.add_single_ground_truth_image_info(
         image_key1, groundtruth_boxes1, groundtruth_class_labels1)
-    image_key2 = "img2"
+    image_key2 = 'img2'
     groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                    [10, 10, 12, 12]], dtype=float)
     groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
     groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
+    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
     self.od_eval.add_single_ground_truth_image_info(
         image_key2, groundtruth_boxes2, groundtruth_class_labels2,
-        groundtruth_is_difficult_list2)
-    image_key3 = "img3"
+        groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)
+
+    image_key3 = 'img3'
     groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
     groundtruth_class_labels3 = np.array([1], dtype=int)
     self.od_eval.add_single_ground_truth_image_info(
         image_key3, groundtruth_boxes3, groundtruth_class_labels3)
 
-    image_key = "img2"
+    image_key = 'img2'
     detected_boxes = np.array(
         [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
         dtype=float)
@@ -58,7 +360,7 @@ class ObjectDetectionEvaluationTest(tf.test.TestCase):
         image_key, detected_boxes, detected_scores, detected_class_labels)
 
   def test_add_single_ground_truth_image_info(self):
-    expected_num_gt_instances_per_class = np.array([3, 1, 2], dtype=int)
+    expected_num_gt_instances_per_class = np.array([3, 1, 1], dtype=int)
     expected_num_gt_imgs_per_class = np.array([2, 1, 2], dtype=int)
     self.assertTrue(np.array_equal(expected_num_gt_instances_per_class,
                                    self.od_eval.num_gt_instances_per_class))
@@ -66,15 +368,20 @@ class ObjectDetectionEvaluationTest(tf.test.TestCase):
                                    self.od_eval.num_gt_imgs_per_class))
     groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                    [10, 10, 12, 12]], dtype=float)
-    self.assertTrue(np.allclose(self.od_eval.groundtruth_boxes["img2"],
+    self.assertTrue(np.allclose(self.od_eval.groundtruth_boxes['img2'],
                                 groundtruth_boxes2))
     groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
     self.assertTrue(np.allclose(
-        self.od_eval.groundtruth_is_difficult_list["img2"],
+        self.od_eval.groundtruth_is_difficult_list['img2'],
         groundtruth_is_difficult_list2))
+    groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
+    self.assertTrue(
+        np.allclose(self.od_eval.groundtruth_is_group_of_list['img2'],
+                    groundtruth_is_group_of_list2))
+
     groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
     self.assertTrue(np.array_equal(self.od_eval.groundtruth_class_labels[
-        "img1"], groundtruth_class_labels1))
+        'img1'], groundtruth_class_labels1))
 
   def test_add_single_detected_image_info(self):
     expected_scores_per_class = [[np.array([0.8, 0.7], dtype=float)], [],
@@ -121,5 +428,5 @@ class ObjectDetectionEvaluationTest(tf.test.TestCase):
     self.assertAlmostEqual(expected_mean_corloc, mean_corloc)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
   tf.test.main()

research/object_detection/utils/ops.py

@@ -15,6 +15,7 @@
 
 """A module for helper tensorflow ops."""
 import math
+import numpy as np
 import six
 
 import tensorflow as tf
@@ -156,6 +157,10 @@ 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]
 
@@ -285,6 +290,7 @@ def retain_groundtruth(tensor_dict, valid_indices):
   Args:
     tensor_dict: a dictionary of following groundtruth tensors -
       fields.InputDataFields.groundtruth_boxes
+      fields.InputDataFields.groundtruth_instance_masks
       fields.InputDataFields.groundtruth_classes
       fields.InputDataFields.groundtruth_is_crowd
       fields.InputDataFields.groundtruth_area
@@ -312,7 +318,8 @@ def retain_groundtruth(tensor_dict, valid_indices):
         tensor_dict[fields.InputDataFields.groundtruth_boxes])[0], 1)
     for key in tensor_dict:
       if key in [fields.InputDataFields.groundtruth_boxes,
-                 fields.InputDataFields.groundtruth_classes]:
+                 fields.InputDataFields.groundtruth_classes,
+                 fields.InputDataFields.groundtruth_instance_masks]:
         valid_dict[key] = tf.gather(tensor_dict[key], valid_indices)
       # Input decoder returns empty tensor when these fields are not provided.
       # Needs to reshape into [num_boxes, -1] for tf.gather() to work.
@@ -358,12 +365,49 @@ def retain_groundtruth_with_positive_classes(tensor_dict):
   return retain_groundtruth(tensor_dict, keep_indices)
 
 
+def replace_nan_groundtruth_label_scores_with_ones(label_scores):
+  """Replaces nan label scores with 1.0.
+
+  Args:
+    label_scores: a tensor containing object annoation label scores.
+
+  Returns:
+    a tensor where NaN label scores have been replaced by ones.
+  """
+  return tf.where(
+      tf.is_nan(label_scores), tf.ones(tf.shape(label_scores)), label_scores)
+
+
+def filter_groundtruth_with_crowd_boxes(tensor_dict):
+  """Filters out groundtruth with boxes corresponding to crowd.
+
+  Args:
+    tensor_dict: a dictionary of following groundtruth tensors -
+      fields.InputDataFields.groundtruth_boxes
+      fields.InputDataFields.groundtruth_classes
+      fields.InputDataFields.groundtruth_is_crowd
+      fields.InputDataFields.groundtruth_area
+      fields.InputDataFields.groundtruth_label_types
+
+  Returns:
+    a dictionary of tensors containing only the groundtruth that have bounding
+    boxes.
+  """
+  if fields.InputDataFields.groundtruth_is_crowd in tensor_dict:
+    is_crowd = tensor_dict[fields.InputDataFields.groundtruth_is_crowd]
+    is_not_crowd = tf.logical_not(is_crowd)
+    is_not_crowd_indices = tf.where(is_not_crowd)
+    tensor_dict = retain_groundtruth(tensor_dict, is_not_crowd_indices)
+  return tensor_dict
+
+
 def filter_groundtruth_with_nan_box_coordinates(tensor_dict):
   """Filters out groundtruth with no bounding boxes.
 
   Args:
     tensor_dict: a dictionary of following groundtruth tensors -
       fields.InputDataFields.groundtruth_boxes
+      fields.InputDataFields.groundtruth_instance_masks
       fields.InputDataFields.groundtruth_classes
       fields.InputDataFields.groundtruth_is_crowd
       fields.InputDataFields.groundtruth_area
@@ -649,3 +693,49 @@ def reframe_box_masks_to_image_masks(box_masks, boxes, image_height,
                                          crop_size=[image_height, image_width],
                                          extrapolation_value=0.0)
   return tf.squeeze(image_masks, axis=3)
+
+
+def merge_boxes_with_multiple_labels(boxes, classes, num_classes):
+  """Merges boxes with same coordinates and returns K-hot encoded classes.
+
+  Args:
+    boxes: A tf.float32 tensor with shape [N, 4] holding N boxes.
+    classes: A tf.int32 tensor with shape [N] holding class indices.
+      The class index starts at 0.
+    num_classes: total number of classes to use for K-hot encoding.
+
+  Returns:
+    merged_boxes: A tf.float32 tensor with shape [N', 4] holding boxes,
+      where N' <= N.
+    class_encodings: A tf.int32 tensor with shape [N', num_classes] holding
+      k-hot encodings for the merged boxes.
+    merged_box_indices: A tf.int32 tensor with shape [N'] holding original
+      indices of the boxes.
+  """
+  def merge_numpy_boxes(boxes, classes, num_classes):
+    """Python function to merge numpy boxes."""
+    if boxes.size < 1:
+      return (np.zeros([0, 4], dtype=np.float32),
+              np.zeros([0, num_classes], dtype=np.int32),
+              np.zeros([0], dtype=np.int32))
+    box_to_class_indices = {}
+    for box_index in range(boxes.shape[0]):
+      box = tuple(boxes[box_index, :].tolist())
+      class_index = classes[box_index]
+      if box not in box_to_class_indices:
+        box_to_class_indices[box] = [box_index, np.zeros([num_classes])]
+      box_to_class_indices[box][1][class_index] = 1
+    merged_boxes = np.vstack(box_to_class_indices.keys()).astype(np.float32)
+    class_encodings = [item[1] for item in box_to_class_indices.values()]
+    class_encodings = np.vstack(class_encodings).astype(np.int32)
+    merged_box_indices = [item[0] for item in box_to_class_indices.values()]
+    merged_box_indices = np.array(merged_box_indices).astype(np.int32)
+    return merged_boxes, class_encodings, merged_box_indices
+
+  merged_boxes, class_encodings, merged_box_indices = tf.py_func(
+      merge_numpy_boxes, [boxes, classes, num_classes],
+      [tf.float32, tf.int32, tf.int32])
+  merged_boxes = tf.reshape(merged_boxes, [-1, 4])
+  class_encodings = tf.reshape(class_encodings, [-1, num_classes])
+  merged_box_indices = tf.reshape(merged_box_indices, [-1])
+  return merged_boxes, class_encodings, merged_box_indices

research/object_detection/utils/ops_test.py

@@ -602,6 +602,64 @@ class RetainGroundTruthWithPositiveClasses(tf.test.TestCase):
         self.assertAllEqual(expected_tensors[key], output_tensors[key])
 
 
+class ReplaceNaNGroundtruthLabelScoresWithOnes(tf.test.TestCase):
+
+  def test_replace_nan_groundtruth_label_scores_with_ones(self):
+    label_scores = tf.constant([np.nan, 1.0, np.nan])
+    output_tensor = ops.replace_nan_groundtruth_label_scores_with_ones(
+        label_scores)
+    expected_tensor = [1.0, 1.0, 1.0]
+    with self.test_session():
+      output_tensor = output_tensor.eval()
+      self.assertAllClose(expected_tensor, output_tensor)
+
+  def test_input_equals_output_when_no_nans(self):
+    input_label_scores = [0.5, 1.0, 1.0]
+    label_scores_tensor = tf.constant(input_label_scores)
+    output_label_scores = ops.replace_nan_groundtruth_label_scores_with_ones(
+        label_scores_tensor)
+    with self.test_session():
+      output_label_scores = output_label_scores.eval()
+      self.assertAllClose(input_label_scores, output_label_scores)
+
+
+class GroundtruthFilterWithCrowdBoxesTest(tf.test.TestCase):
+
+  def test_filter_groundtruth_with_crowd_boxes(self):
+    input_tensors = {
+        fields.InputDataFields.groundtruth_boxes:
+        [[0.1, 0.2, 0.6, 0.8], [0.2, 0.4, 0.1, 0.8]],
+        fields.InputDataFields.groundtruth_classes:
+        [1, 2],
+        fields.InputDataFields.groundtruth_is_crowd:
+        [True, False],
+        fields.InputDataFields.groundtruth_area:
+        [100.0, 238.7]
+    }
+
+    expected_tensors = {
+        fields.InputDataFields.groundtruth_boxes:
+        [[0.2, 0.4, 0.1, 0.8]],
+        fields.InputDataFields.groundtruth_classes:
+        [2],
+        fields.InputDataFields.groundtruth_is_crowd:
+        [False],
+        fields.InputDataFields.groundtruth_area:
+        [238.7]
+    }
+
+    output_tensors = ops.filter_groundtruth_with_crowd_boxes(
+        input_tensors)
+    with self.test_session() as sess:
+      output_tensors = sess.run(output_tensors)
+      for key in [fields.InputDataFields.groundtruth_boxes,
+                  fields.InputDataFields.groundtruth_area]:
+        self.assertAllClose(expected_tensors[key], output_tensors[key])
+      for key in [fields.InputDataFields.groundtruth_classes,
+                  fields.InputDataFields.groundtruth_is_crowd]:
+        self.assertAllEqual(expected_tensors[key], output_tensors[key])
+
+
 class GroundtruthFilterWithNanBoxTest(tf.test.TestCase):
 
   def test_filter_groundtruth_with_nan_box_coordinates(self):
@@ -1029,5 +1087,46 @@ class ReframeBoxMasksToImageMasksTest(tf.test.TestCase):
       self.assertAllClose(np_image_masks, np_expected_image_masks)
 
 
+class MergeBoxesWithMultipleLabelsTest(tf.test.TestCase):
+
+  def testMergeBoxesWithMultipleLabels(self):
+    boxes = tf.constant(
+        [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75],
+         [0.25, 0.25, 0.75, 0.75]],
+        dtype=tf.float32)
+    class_indices = tf.constant([0, 4, 2], dtype=tf.int32)
+    num_classes = 5
+    merged_boxes, merged_classes, merged_box_indices = (
+        ops.merge_boxes_with_multiple_labels(boxes, class_indices, num_classes))
+    expected_merged_boxes = np.array(
+        [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=np.float32)
+    expected_merged_classes = np.array(
+        [[1, 0, 1, 0, 0], [0, 0, 0, 0, 1]], dtype=np.int32)
+    expected_merged_box_indices = np.array([0, 1], dtype=np.int32)
+    with self.test_session() as sess:
+      np_merged_boxes, np_merged_classes, np_merged_box_indices = sess.run(
+          [merged_boxes, merged_classes, merged_box_indices])
+      if np_merged_classes[0, 0] != 1:
+        expected_merged_boxes = expected_merged_boxes[::-1, :]
+        expected_merged_classes = expected_merged_classes[::-1, :]
+        expected_merged_box_indices = expected_merged_box_indices[::-1, :]
+      self.assertAllClose(np_merged_boxes, expected_merged_boxes)
+      self.assertAllClose(np_merged_classes, expected_merged_classes)
+      self.assertAllClose(np_merged_box_indices, expected_merged_box_indices)
+
+  def testMergeBoxesWithEmptyInputs(self):
+    boxes = tf.constant([[]])
+    class_indices = tf.constant([])
+    num_classes = 5
+    merged_boxes, merged_classes, merged_box_indices = (
+        ops.merge_boxes_with_multiple_labels(boxes, class_indices, num_classes))
+    with self.test_session() as sess:
+      np_merged_boxes, np_merged_classes, np_merged_box_indices = sess.run(
+          [merged_boxes, merged_classes, merged_box_indices])
+      self.assertAllEqual(np_merged_boxes.shape, [0, 4])
+      self.assertAllEqual(np_merged_classes.shape, [0, 5])
+      self.assertAllEqual(np_merged_box_indices.shape, [0])
+
+
 if __name__ == '__main__':
   tf.test.main()

research/object_detection/utils/per_image_evaluation.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Evaluate Object Detection result on a single image.
 
 Annotate each detected result as true positives or false positive according to
@@ -47,11 +46,17 @@ class PerImageEvaluation(object):
     self.nms_max_output_boxes = nms_max_output_boxes
     self.num_groundtruth_classes = num_groundtruth_classes
 
-  def compute_object_detection_metrics(self, detected_boxes, detected_scores,
-                                       detected_class_labels, groundtruth_boxes,
-                                       groundtruth_class_labels,
-                                       groundtruth_is_difficult_lists):
-    """Compute Object Detection related metrics from a single image.
+  def compute_object_detection_metrics(
+      self, detected_boxes, detected_scores, detected_class_labels,
+      groundtruth_boxes, groundtruth_class_labels,
+      groundtruth_is_difficult_lists, groundtruth_is_group_of_list):
+    """Evaluates detections as being tp, fp or ignored from a single image.
+
+    The evaluation is done in two stages:
+     1. All detections are matched to non group-of boxes; true positives are
+        determined and detections matched to difficult boxes are ignored.
+     2. Detections that are determined as false positives are matched against
+        group-of boxes and ignored if matched.
 
     Args:
       detected_boxes: A float numpy array of shape [N, 4], representing N
@@ -67,6 +72,8 @@ class PerImageEvaluation(object):
           representing M class labels of object instances in ground truth
       groundtruth_is_difficult_lists: A boolean numpy array of length M denoting
           whether a ground truth box is a difficult instance or not
+      groundtruth_is_group_of_list: A boolean numpy array of length M denoting
+          whether a ground truth box has group-of tag
 
     Returns:
       scores: A list of C float numpy arrays. Each numpy array is of
@@ -85,7 +92,8 @@ class PerImageEvaluation(object):
     scores, tp_fp_labels = self._compute_tp_fp(
         detected_boxes, detected_scores, detected_class_labels,
         groundtruth_boxes, groundtruth_class_labels,
-        groundtruth_is_difficult_lists)
+        groundtruth_is_difficult_lists, groundtruth_is_group_of_list)
+
     is_class_correctly_detected_in_image = self._compute_cor_loc(
         detected_boxes, detected_scores, detected_class_labels,
         groundtruth_boxes, groundtruth_class_labels)
@@ -116,10 +124,10 @@ class PerImageEvaluation(object):
     is_class_correctly_detected_in_image = np.zeros(
         self.num_groundtruth_classes, dtype=int)
     for i in range(self.num_groundtruth_classes):
-      gt_boxes_at_ith_class = groundtruth_boxes[
-          groundtruth_class_labels == i, :]
-      detected_boxes_at_ith_class = detected_boxes[
-          detected_class_labels == i, :]
+      gt_boxes_at_ith_class = groundtruth_boxes[groundtruth_class_labels ==
+                                                i, :]
+      detected_boxes_at_ith_class = detected_boxes[detected_class_labels ==
+                                                   i, :]
       detected_scores_at_ith_class = detected_scores[detected_class_labels == i]
       is_class_correctly_detected_in_image[i] = (
           self._compute_is_aclass_correctly_detected_in_image(
@@ -157,7 +165,8 @@ class PerImageEvaluation(object):
 
   def _compute_tp_fp(self, detected_boxes, detected_scores,
                      detected_class_labels, groundtruth_boxes,
-                     groundtruth_class_labels, groundtruth_is_difficult_lists):
+                     groundtruth_class_labels, groundtruth_is_difficult_lists,
+                     groundtruth_is_group_of_list):
     """Labels true/false positives of detections of an image across all classes.
 
     Args:
@@ -174,6 +183,8 @@ class PerImageEvaluation(object):
           representing M class labels of object instances in ground truth
       groundtruth_is_difficult_lists: A boolean numpy array of length M denoting
           whether a ground truth box is a difficult instance or not
+      groundtruth_is_group_of_list: A boolean numpy array of length M denoting
+          whether a ground truth box has group-of tag
 
     Returns:
       result_scores: A list of float numpy arrays. Each numpy array is of
@@ -190,12 +201,15 @@ class PerImageEvaluation(object):
                                                 ), :]
       groundtruth_is_difficult_list_at_ith_class = (
           groundtruth_is_difficult_lists[groundtruth_class_labels == i])
+      groundtruth_is_group_of_list_at_ith_class = (
+          groundtruth_is_group_of_list[groundtruth_class_labels == i])
       detected_boxes_at_ith_class = detected_boxes[(detected_class_labels == i
                                                    ), :]
       detected_scores_at_ith_class = detected_scores[detected_class_labels == i]
       scores, tp_fp_labels = self._compute_tp_fp_for_single_class(
           detected_boxes_at_ith_class, detected_scores_at_ith_class,
-          gt_boxes_at_ith_class, groundtruth_is_difficult_list_at_ith_class)
+          gt_boxes_at_ith_class, groundtruth_is_difficult_list_at_ith_class,
+          groundtruth_is_group_of_list_at_ith_class)
       result_scores.append(scores)
       result_tp_fp_labels.append(tp_fp_labels)
     return result_scores, result_tp_fp_labels
@@ -207,9 +221,9 @@ class PerImageEvaluation(object):
     return (detected_boxes[valid_indices, :], detected_scores[valid_indices],
             detected_class_labels[valid_indices])
 
-  def _compute_tp_fp_for_single_class(self, detected_boxes, detected_scores,
-                                      groundtruth_boxes,
-                                      groundtruth_is_difficult_list):
+  def _compute_tp_fp_for_single_class(
+      self, detected_boxes, detected_scores, groundtruth_boxes,
+      groundtruth_is_difficult_list, groundtruth_is_group_of_list):
     """Labels boxes detected with the same class from the same image as tp/fp.
 
     Args:
@@ -220,10 +234,19 @@ class PerImageEvaluation(object):
       groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
           box coordinates
       groundtruth_is_difficult_list: A boolean numpy array of length M denoting
-          whether a ground truth box is a difficult instance or not
+          whether a ground truth box is a difficult instance or not. If a
+          groundtruth box is difficult, every detection matching this box
+          is ignored.
+      groundtruth_is_group_of_list: A boolean numpy array of length M denoting
+          whether a ground truth box has group-of tag. If a groundtruth box
+          is group-of box, every detection matching this box is ignored.
 
     Returns:
-      scores: A numpy array representing the detection scores
+      Two arrays of the same size, containing all boxes that were evaluated as
+      being true positives or false positives; if a box matched to a difficult
+      box or to a group-of box, it is ignored.
+
+      scores: A numpy array representing the detection scores.
       tp_fp_labels: a boolean numpy array indicating whether a detection is a
           true positive.
 
@@ -239,22 +262,51 @@ class PerImageEvaluation(object):
 
     if groundtruth_boxes.size == 0:
       return scores, np.zeros(detected_boxlist.num_boxes(), dtype=bool)
-    gt_boxlist = np_box_list.BoxList(groundtruth_boxes)
 
-    iou = np_box_list_ops.iou(detected_boxlist, gt_boxlist)
-    max_overlap_gt_ids = np.argmax(iou, axis=1)
-    is_gt_box_detected = np.zeros(gt_boxlist.num_boxes(), dtype=bool)
     tp_fp_labels = np.zeros(detected_boxlist.num_boxes(), dtype=bool)
     is_matched_to_difficult_box = np.zeros(
         detected_boxlist.num_boxes(), dtype=bool)
+    is_matched_to_group_of_box = np.zeros(
+        detected_boxlist.num_boxes(), dtype=bool)
+
+    # The evaluation is done in two stages:
+    # 1. All detections are matched to non group-of boxes; true positives are
+    #    determined and detections matched to difficult boxes are ignored.
+    # 2. Detections that are determined as false positives are matched against
+    #    group-of boxes and ignored if matched.
+
+    # Tp-fp evaluation for non-group of boxes (if any).
+    gt_non_group_of_boxlist = np_box_list.BoxList(
+        groundtruth_boxes[~groundtruth_is_group_of_list, :])
+    if gt_non_group_of_boxlist.num_boxes() > 0:
+      groundtruth_nongroup_of_is_difficult_list = groundtruth_is_difficult_list[
+          ~groundtruth_is_group_of_list]
+      iou = np_box_list_ops.iou(detected_boxlist, gt_non_group_of_boxlist)
+      max_overlap_gt_ids = np.argmax(iou, axis=1)
+      is_gt_box_detected = np.zeros(
+          gt_non_group_of_boxlist.num_boxes(), dtype=bool)
       for i in range(detected_boxlist.num_boxes()):
         gt_id = max_overlap_gt_ids[i]
         if iou[i, gt_id] >= self.matching_iou_threshold:
-        if not groundtruth_is_difficult_list[gt_id]:
+          if not groundtruth_nongroup_of_is_difficult_list[gt_id]:
             if not is_gt_box_detected[gt_id]:
               tp_fp_labels[i] = True
               is_gt_box_detected[gt_id] = True
           else:
             is_matched_to_difficult_box[i] = True
-    return scores[~is_matched_to_difficult_box], tp_fp_labels[
-        ~is_matched_to_difficult_box]
+
+    # Tp-fp evaluation for group of boxes.
+    gt_group_of_boxlist = np_box_list.BoxList(
+        groundtruth_boxes[groundtruth_is_group_of_list, :])
+    if gt_group_of_boxlist.num_boxes() > 0:
+      ioa = np_box_list_ops.ioa(gt_group_of_boxlist, detected_boxlist)
+      max_overlap_group_of_gt = np.max(ioa, axis=0)
+      for i in range(detected_boxlist.num_boxes()):
+        if (not tp_fp_labels[i] and not is_matched_to_difficult_box[i] and
+            max_overlap_group_of_gt[i] >= self.matching_iou_threshold):
+          is_matched_to_group_of_box[i] = True
+
+    return scores[~is_matched_to_difficult_box
+                  & ~is_matched_to_group_of_box], tp_fp_labels[
+                      ~is_matched_to_difficult_box
+                      & ~is_matched_to_group_of_box]

research/object_detection/utils/per_image_evaluation_test.py

@@ -41,9 +41,12 @@ class SingleClassTpFpWithDifficultBoxesTest(tf.test.TestCase):
   def test_match_to_not_difficult_box(self):
     groundtruth_groundtruth_is_difficult_list = np.array([False, True],
                                                          dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, False], dtype=bool)
     scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
         self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
-        groundtruth_groundtruth_is_difficult_list)
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
     expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
     expected_tp_fp_labels = np.array([False, True, False], dtype=bool)
     self.assertTrue(np.allclose(expected_scores, scores))
@@ -52,15 +55,64 @@ class SingleClassTpFpWithDifficultBoxesTest(tf.test.TestCase):
   def test_match_to_difficult_box(self):
     groundtruth_groundtruth_is_difficult_list = np.array([True, False],
                                                          dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, False], dtype=bool)
     scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
         self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
-        groundtruth_groundtruth_is_difficult_list)
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
     expected_scores = np.array([0.8, 0.5], dtype=float)
     expected_tp_fp_labels = np.array([False, False], dtype=bool)
     self.assertTrue(np.allclose(expected_scores, scores))
     self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
 
 
+class SingleClassTpFpWithGroupOfBoxesTest(tf.test.TestCase):
+
+  def setUp(self):
+    num_groundtruth_classes = 1
+    matching_iou_threshold = 0.5
+    nms_iou_threshold = 1.0
+    nms_max_output_boxes = 10000
+    self.eval = per_image_evaluation.PerImageEvaluation(
+        num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold,
+        nms_max_output_boxes)
+
+    self.detected_boxes = np.array(
+        [[0, 0, 1, 1], [0, 0, 2, 1], [0, 0, 3, 1]], dtype=float)
+    self.detected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
+    self.groundtruth_boxes = np.array(
+        [[0, 0, 1, 1], [0, 0, 5, 5], [10, 10, 20, 20]], dtype=float)
+
+  def test_match_to_non_group_of_and_group_of_box(self):
+    groundtruth_groundtruth_is_difficult_list = np.array(
+        [False, False, False], dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, True, True], dtype=bool)
+    expected_scores = np.array([0.8], dtype=float)
+    expected_tp_fp_labels = np.array([True], dtype=bool)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+  def test_match_two_to_group_of_box(self):
+    groundtruth_groundtruth_is_difficult_list = np.array(
+        [False, False, False], dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [True, False, True], dtype=bool)
+    expected_scores = np.array([0.5], dtype=float)
+    expected_tp_fp_labels = np.array([False], dtype=bool)
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+
 class SingleClassTpFpNoDifficultBoxesTest(tf.test.TestCase):
 
   def setUp(self):
@@ -84,9 +136,11 @@ class SingleClassTpFpNoDifficultBoxesTest(tf.test.TestCase):
   def test_no_true_positives(self):
     groundtruth_boxes = np.array([[100, 100, 105, 105]], dtype=float)
     groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
     scores, tp_fp_labels = self.eval1._compute_tp_fp_for_single_class(
         self.detected_boxes, self.detected_scores, groundtruth_boxes,
-        groundtruth_groundtruth_is_difficult_list)
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
     expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
     expected_tp_fp_labels = np.array([False, False, False], dtype=bool)
     self.assertTrue(np.allclose(expected_scores, scores))
@@ -95,9 +149,11 @@ class SingleClassTpFpNoDifficultBoxesTest(tf.test.TestCase):
   def test_one_true_positives_with_large_iou_threshold(self):
     groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
     groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
     scores, tp_fp_labels = self.eval1._compute_tp_fp_for_single_class(
         self.detected_boxes, self.detected_scores, groundtruth_boxes,
-        groundtruth_groundtruth_is_difficult_list)
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
     expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
     expected_tp_fp_labels = np.array([False, True, False], dtype=bool)
     self.assertTrue(np.allclose(expected_scores, scores))
@@ -106,9 +162,11 @@ class SingleClassTpFpNoDifficultBoxesTest(tf.test.TestCase):
   def test_one_true_positives_with_very_small_iou_threshold(self):
     groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
     groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
     scores, tp_fp_labels = self.eval2._compute_tp_fp_for_single_class(
         self.detected_boxes, self.detected_scores, groundtruth_boxes,
-        groundtruth_groundtruth_is_difficult_list)
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
     expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
     expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
     self.assertTrue(np.allclose(expected_scores, scores))
@@ -117,9 +175,12 @@ class SingleClassTpFpNoDifficultBoxesTest(tf.test.TestCase):
   def test_two_true_positives_with_large_iou_threshold(self):
     groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3.5, 3.5]], dtype=float)
     groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, False], dtype=bool)
     scores, tp_fp_labels = self.eval1._compute_tp_fp_for_single_class(
         self.detected_boxes, self.detected_scores, groundtruth_boxes,
-        groundtruth_groundtruth_is_difficult_list)
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
     expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
     expected_tp_fp_labels = np.array([False, True, True], dtype=bool)
     self.assertTrue(np.allclose(expected_scores, scores))
@@ -145,10 +206,13 @@ class MultiClassesTpFpTest(tf.test.TestCase):
     groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3.5, 3.5]], dtype=float)
     groundtruth_class_labels = np.array([0, 2], dtype=int)
     groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=float)
+    groundtruth_groundtruth_is_group_of_list = np.array(
+        [False, False], dtype=bool)
     scores, tp_fp_labels, _ = eval1.compute_object_detection_metrics(
         detected_boxes, detected_scores, detected_class_labels,
         groundtruth_boxes, groundtruth_class_labels,
-        groundtruth_groundtruth_is_difficult_list)
+        groundtruth_groundtruth_is_difficult_list,
+        groundtruth_groundtruth_is_group_of_list)
     expected_scores = [np.array([0.8], dtype=float)] * 3
     expected_tp_fp_labels = [np.array([True]), np.array([False]), np.array([True
                                                                            ])]

research/object_detection/utils/visualization_utils.py

@@ -20,6 +20,8 @@ The functions do not return a value, instead they modify the image itself.
 
 """
 import collections
+import functools
+import matplotlib.pyplot as plt
 import numpy as np
 import PIL.Image as Image
 import PIL.ImageColor as ImageColor
@@ -132,6 +134,8 @@ def draw_bounding_box_on_image(image,
 
   Each string in display_str_list is displayed on a separate line above the
   bounding box in black text on a rectangle filled with the input 'color'.
+  If the top of the bounding box extends to the edge of the image, the strings
+  are displayed below the bounding box.
 
   Args:
     image: a PIL.Image object.
@@ -161,7 +165,17 @@ def draw_bounding_box_on_image(image,
   except IOError:
     font = ImageFont.load_default()
 
+  # If the total height of the display strings added to the top of the bounding
+  # box exceeds the top of the image, stack the strings below the bounding box
+  # instead of above.
+  display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
+  # Each display_str has a top and bottom margin of 0.05x.
+  total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)
+
+  if top > total_display_str_height:
     text_bottom = top
+  else:
+    text_bottom = bottom + total_display_str_height
   # Reverse list and print from bottom to top.
   for display_str in display_str_list[::-1]:
     text_width, text_height = font.getsize(display_str)
@@ -241,6 +255,53 @@ def draw_bounding_boxes_on_image(image,
                                boxes[i, 3], color, thickness, display_str_list)
 
 
+def draw_bounding_boxes_on_image_tensors(images,
+                                         boxes,
+                                         classes,
+                                         scores,
+                                         category_index,
+                                         max_boxes_to_draw=20,
+                                         min_score_thresh=0.2):
+  """Draws bounding boxes on batch of image tensors.
+
+  Args:
+    images: A 4D uint8 image tensor of shape [N, H, W, C].
+    boxes: [N, max_detections, 4] float32 tensor of detection boxes.
+    classes: [N, max_detections] int tensor of detection classes. Note that
+      classes are 1-indexed.
+    scores: [N, max_detections] float32 tensor of detection scores.
+    category_index: a dict that maps integer ids to category dicts. e.g.
+      {1: {1: 'dog'}, 2: {2: 'cat'}, ...}
+    max_boxes_to_draw: Maximum number of boxes to draw on an image. Default 20.
+    min_score_thresh: Minimum score threshold for visualization. Default 0.2.
+
+  Returns:
+    4D image tensor of type uint8, with boxes drawn on top.
+  """
+  visualize_boxes_fn = functools.partial(
+      visualize_boxes_and_labels_on_image_array,
+      category_index=category_index,
+      instance_masks=None,
+      keypoints=None,
+      use_normalized_coordinates=True,
+      max_boxes_to_draw=max_boxes_to_draw,
+      min_score_thresh=min_score_thresh,
+      agnostic_mode=False,
+      line_thickness=4)
+
+  def draw_boxes((image, boxes, classes, scores)):
+    """Draws boxes on image."""
+    image_with_boxes = tf.py_func(visualize_boxes_fn,
+                                  [image, boxes, classes, scores], tf.uint8)
+    return image_with_boxes
+
+  images = tf.map_fn(
+      draw_boxes, (images, boxes, classes, scores),
+      dtype=tf.uint8,
+      back_prop=False)
+  return images
+
+
 def draw_keypoints_on_image_array(image,
                                   keypoints,
                                   color='red',
@@ -295,8 +356,8 @@ def draw_mask_on_image_array(image, mask, color='red', alpha=0.7):
 
   Args:
     image: uint8 numpy array with shape (img_height, img_height, 3)
-    mask: a float numpy array of shape (img_height, img_height) with
-      values between 0 and 1
+    mask: a uint8 numpy array of shape (img_height, img_height) with
+      values between either 0 or 1.
     color: color to draw the keypoints with. Default is red.
     alpha: transparency value between 0 and 1. (default: 0.7)
 
@@ -305,9 +366,9 @@ def draw_mask_on_image_array(image, mask, color='red', alpha=0.7):
   """
   if image.dtype != np.uint8:
     raise ValueError('`image` not of type np.uint8')
-  if mask.dtype != np.float32:
-    raise ValueError('`mask` not of type np.float32')
-  if np.any(np.logical_or(mask > 1.0, mask < 0.0)):
+  if mask.dtype != np.uint8:
+    raise ValueError('`mask` not of type np.uint8')
+  if np.any(np.logical_and(mask != 1, mask != 0)):
     raise ValueError('`mask` elements should be in [0, 1]')
   rgb = ImageColor.getrgb(color)
   pil_image = Image.fromarray(image)
@@ -336,13 +397,14 @@ def visualize_boxes_and_labels_on_image_array(image,
 
   This function groups boxes that correspond to the same location
   and creates a display string for each detection and overlays these
-  on the image.  Note that this function modifies the image array in-place
-  and does not return anything.
+  on the image. Note that this function modifies the image in place, and returns
+  that same image.
 
   Args:
     image: uint8 numpy array with shape (img_height, img_width, 3)
     boxes: a numpy array of shape [N, 4]
-    classes: a numpy array of shape [N]
+    classes: a numpy array of shape [N]. Note that class indices are 1-based,
+      and match the keys in the label map.
     scores: a numpy array of shape [N] or None.  If scores=None, then
       this function assumes that the boxes to be plotted are groundtruth
       boxes and plot all boxes as black with no classes or scores.
@@ -361,6 +423,9 @@ def visualize_boxes_and_labels_on_image_array(image,
       class-agnostic mode or not.  This mode will display scores but ignore
       classes.
     line_thickness: integer (default: 4) controlling line width of the boxes.
+
+  Returns:
+    uint8 numpy array with shape (img_height, img_width, 3) with overlaid boxes.
   """
   # Create a display string (and color) for every box location, group any boxes
   # that correspond to the same location.
@@ -423,3 +488,36 @@ def visualize_boxes_and_labels_on_image_array(image,
           color=color,
           radius=line_thickness / 2,
           use_normalized_coordinates=use_normalized_coordinates)
+
+  return image
+
+
+def add_cdf_image_summary(values, name):
+  """Adds a tf.summary.image for a CDF plot of the values.
+
+  Normalizes `values` such that they sum to 1, plots the cumulative distribution
+  function and creates a tf image summary.
+
+  Args:
+    values: a 1-D float32 tensor containing the values.
+    name: name for the image summary.
+  """
+  def cdf_plot(values):
+    """Numpy function to plot CDF."""
+    normalized_values = values / np.sum(values)
+    sorted_values = np.sort(normalized_values)
+    cumulative_values = np.cumsum(sorted_values)
+    fraction_of_examples = (np.arange(cumulative_values.size, dtype=np.float32)
+                            / cumulative_values.size)
+    fig = plt.figure(frameon=False)
+    ax = fig.add_subplot('111')
+    ax.plot(fraction_of_examples, cumulative_values)
+    ax.set_ylabel('cumulative normalized values')
+    ax.set_xlabel('fraction of examples')
+    fig.canvas.draw()
+    width, height = fig.get_size_inches() * fig.get_dpi()
+    image = np.fromstring(fig.canvas.tostring_rgb(), dtype='uint8').reshape(
+        1, height, width, 3)
+    return image
+  cdf_plot = tf.py_func(cdf_plot, [values], tf.uint8)
+  tf.summary.image(name, cdf_plot)

research/object_detection/utils/visualization_utils_test.py

@@ -20,6 +20,7 @@ https://drive.google.com/a/google.com/file/d/0B5HnKS_hMsNARERpU3MtU3I5RFE/view?u
 
 """
 
+import os
 
 import numpy as np
 import PIL.Image as Image
@@ -27,6 +28,8 @@ import tensorflow as tf
 
 from object_detection.utils import visualization_utils
 
+_TESTDATA_PATH = 'object_detection/test_images'
+
 
 class VisualizationUtilsTest(tf.test.TestCase):
 
@@ -110,6 +113,42 @@ class VisualizationUtilsTest(tf.test.TestCase):
     self.assertEqual(width_original, width_final)
     self.assertEqual(height_original, height_final)
 
+  def test_draw_bounding_boxes_on_image_tensors(self):
+    """Tests that bounding box utility produces reasonable results."""
+    category_index = {1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}}
+
+    fname = os.path.join(_TESTDATA_PATH, 'image1.jpg')
+    image_np = np.array(Image.open(fname))
+    images_np = np.stack((image_np, image_np), axis=0)
+
+    with tf.Graph().as_default():
+      images_tensor = tf.constant(value=images_np, dtype=tf.uint8)
+      boxes = tf.constant([[[0.4, 0.25, 0.75, 0.75], [0.5, 0.3, 0.6, 0.9]],
+                           [[0.25, 0.25, 0.75, 0.75], [0.1, 0.3, 0.6, 1.0]]])
+      classes = tf.constant([[1, 1], [1, 2]], dtype=tf.int64)
+      scores = tf.constant([[0.8, 0.1], [0.6, 0.5]])
+      images_with_boxes = (
+          visualization_utils.draw_bounding_boxes_on_image_tensors(
+              images_tensor,
+              boxes,
+              classes,
+              scores,
+              category_index,
+              min_score_thresh=0.2))
+
+      with self.test_session() as sess:
+        sess.run(tf.global_variables_initializer())
+
+        # Write output images for visualization.
+        images_with_boxes_np = sess.run(images_with_boxes)
+        self.assertEqual(images_np.shape, images_with_boxes_np.shape)
+        for i in range(images_with_boxes_np.shape[0]):
+          img_name = 'image_' + str(i) + '.png'
+          output_file = os.path.join(self.get_temp_dir(), img_name)
+          print 'Writing output image %d to %s' % (i, output_file)
+          image_pil = Image.fromarray(images_with_boxes_np[i, ...])
+          image_pil.save(output_file)
+
   def test_draw_keypoints_on_image(self):
     test_image = self.create_colorful_test_image()
     test_image = Image.fromarray(test_image)
@@ -138,14 +177,21 @@ class VisualizationUtilsTest(tf.test.TestCase):
   def test_draw_mask_on_image_array(self):
     test_image = np.asarray([[[0, 0, 0], [0, 0, 0]],
                              [[0, 0, 0], [0, 0, 0]]], dtype=np.uint8)
-    mask = np.asarray([[0.0, 1.0],
-                       [1.0, 1.0]], dtype=np.float32)
+    mask = np.asarray([[0, 1],
+                       [1, 1]], dtype=np.uint8)
     expected_result = np.asarray([[[0, 0, 0], [0, 0, 127]],
                                   [[0, 0, 127], [0, 0, 127]]], dtype=np.uint8)
     visualization_utils.draw_mask_on_image_array(test_image, mask,
                                                  color='Blue', alpha=.5)
     self.assertAllEqual(test_image, expected_result)
 
+  def test_add_cdf_image_summary(self):
+    values = [0.1, 0.2, 0.3, 0.4, 0.42, 0.44, 0.46, 0.48, 0.50]
+    visualization_utils.add_cdf_image_summary(values, 'PositiveAnchorLoss')
+    cdf_image_summary = tf.get_collection(key=tf.GraphKeys.SUMMARIES)[0]
+    with self.test_session():
+      cdf_image_summary.eval()
+
 
 if __name__ == '__main__':
   tf.test.main()