refactor config parsing in train.py binaries and use functions in utils/config_utils.py instead.

research/object_detection/BUILD

@@ -7,7 +7,6 @@ package(
 licenses(["notice"])
 
 # Apache 2.0
-
 py_binary(
     name = "train",
     srcs = [
@@ -18,10 +17,7 @@ py_binary(
         "//tensorflow",
         "//tensorflow_models/object_detection/builders:input_reader_builder",
         "//tensorflow_models/object_detection/builders:model_builder",
-        "//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",
+        "//tensorflow_models/object_detection/utils:config_util",
     ],
 )
 
@@ -33,6 +29,7 @@ py_library(
         "//tensorflow_models/object_detection/builders:optimizer_builder",
         "//tensorflow_models/object_detection/builders:preprocessor_builder",
         "//tensorflow_models/object_detection/core:batcher",
+        "//tensorflow_models/object_detection/core:preprocessor",
         "//tensorflow_models/object_detection/core:standard_fields",
         "//tensorflow_models/object_detection/utils:ops",
         "//tensorflow_models/object_detection/utils:variables_helper",

research/object_detection/train.py

@@ -46,15 +46,10 @@ import json
 import os
 import tensorflow as tf
 
-from google.protobuf import text_format
-
 from object_detection import trainer
 from object_detection.builders import input_reader_builder
 from object_detection.builders import model_builder
-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
 
 tf.logging.set_verbosity(tf.logging.INFO)
 
@@ -88,61 +83,31 @@ flags.DEFINE_string('model_config_path', '',
 FLAGS = flags.FLAGS
 
 
-def get_configs_from_pipeline_file():
-  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.
-
-  Reads training config from file specified by pipeline_config_path flag.
-
-  Returns:
-    model_config: model_pb2.DetectionModel
-    train_config: train_pb2.TrainConfig
-    input_config: input_reader_pb2.InputReader
-  """
-  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
-  with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
-    text_format.Merge(f.read(), pipeline_config)
-
-  model_config = pipeline_config.model
-  train_config = pipeline_config.train_config
-  input_config = pipeline_config.train_input_reader
-
-  return model_config, train_config, input_config
-
-
-def get_configs_from_multiple_files():
-  """Reads training configuration from multiple config files.
-
-  Reads the training config from the following files:
-    model_config: Read from --model_config_path
-    train_config: Read from --train_config_path
-    input_config: Read from --input_config_path
-
-  Returns:
-    model_config: model_pb2.DetectionModel
-    train_config: train_pb2.TrainConfig
-    input_config: input_reader_pb2.InputReader
-  """
-  train_config = train_pb2.TrainConfig()
-  with tf.gfile.GFile(FLAGS.train_config_path, 'r') as f:
-    text_format.Merge(f.read(), train_config)
-
-  model_config = model_pb2.DetectionModel()
-  with tf.gfile.GFile(FLAGS.model_config_path, 'r') as f:
-    text_format.Merge(f.read(), model_config)
-
-  input_config = input_reader_pb2.InputReader()
-  with tf.gfile.GFile(FLAGS.input_config_path, 'r') as f:
-    text_format.Merge(f.read(), input_config)
-
-  return model_config, train_config, input_config
-
-
 def main(_):
   assert FLAGS.train_dir, '`train_dir` is missing.'
+  if FLAGS.task == 0: tf.gfile.MakeDirs(FLAGS.train_dir)
   if FLAGS.pipeline_config_path:
-    model_config, train_config, input_config = get_configs_from_pipeline_file()
+    configs = config_util.get_configs_from_pipeline_file(
+        FLAGS.pipeline_config_path)
+    if FLAGS.task == 0:
+      tf.gfile.Copy(FLAGS.pipeline_config_path,
+                    os.path.join(FLAGS.train_dir, 'pipeline.config'),
+                    overwrite=True)
   else:
-    model_config, train_config, input_config = get_configs_from_multiple_files()
+    configs = config_util.get_configs_from_multiple_files(
+        model_config_path=FLAGS.model_config_path,
+        train_config_path=FLAGS.train_config_path,
+        train_input_config_path=FLAGS.input_config_path)
+    if FLAGS.task == 0:
+      for name, config in [('model.config', FLAGS.model_config_path),
+                           ('train.config', FLAGS.train_config_path),
+                           ('input.config', FLAGS.input_config_path)]:
+        tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name),
+                      overwrite=True)
+
+  model_config = configs['model']
+  train_config = configs['train_config']
+  input_config = configs['train_input_config']
 
   model_fn = functools.partial(
       model_builder.build,

research/object_detection/trainer.py

@@ -35,9 +35,9 @@ from deployment import model_deploy
 slim = tf.contrib.slim
 
 
-def _create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
-                        batch_queue_capacity, num_batch_queue_threads,
-                        prefetch_queue_capacity, data_augmentation_options):
+def create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
+                       batch_queue_capacity, num_batch_queue_threads,
+                       prefetch_queue_capacity, data_augmentation_options):
   """Sets up reader, prefetcher and returns input queue.
 
   Args:
@@ -65,9 +65,16 @@ def _create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
   float_images = tf.to_float(images)
   tensor_dict[fields.InputDataFields.image] = float_images
 
+  include_instance_masks = (fields.InputDataFields.groundtruth_instance_masks
+                            in tensor_dict)
+  include_keypoints = (fields.InputDataFields.groundtruth_keypoints
+                       in tensor_dict)
   if data_augmentation_options:
-    tensor_dict = preprocessor.preprocess(tensor_dict,
-                                          data_augmentation_options)
+    tensor_dict = preprocessor.preprocess(
+        tensor_dict, data_augmentation_options,
+        func_arg_map=preprocessor.get_default_func_arg_map(
+            include_instance_masks=include_instance_masks,
+            include_keypoints=include_keypoints))
 
   input_queue = batcher.BatchQueue(
       tensor_dict,
@@ -78,56 +85,83 @@ def _create_input_queue(batch_size_per_clone, create_tensor_dict_fn,
   return input_queue
 
 
-def _get_inputs(input_queue, num_classes):
-  """Dequeue batch and construct inputs to object detection model.
+def get_inputs(input_queue, num_classes, merge_multiple_label_boxes=False):
+  """Dequeues batch and constructs inputs to object detection model.
 
   Args:
     input_queue: BatchQueue object holding enqueued tensor_dicts.
     num_classes: Number of classes.
+    merge_multiple_label_boxes: Whether to merge boxes with multiple labels
+      or not. Defaults to false. Merged boxes are represented with a single
+      box and a k-hot encoding of the multiple labels associated with the
+      boxes.
 
   Returns:
     images: a list of 3-D float tensor of images.
+    image_keys: a list of string keys for the images.
     locations_list: a list of tensors of shape [num_boxes, 4]
       containing the corners of the groundtruth boxes.
     classes_list: a list of padded one-hot tensors containing target classes.
     masks_list: a list of 3-D float tensors of shape [num_boxes, image_height,
       image_width] containing instance masks for objects if present in the
       input_queue. Else returns None.
+    keypoints_list: a list of 3-D float tensors of shape [num_boxes,
+      num_keypoints, 2] containing keypoints for objects if present in the
+      input queue. Else returns None.
   """
   read_data_list = input_queue.dequeue()
   label_id_offset = 1
   def extract_images_and_targets(read_data):
+    """Extract images and targets from the input dict."""
     image = read_data[fields.InputDataFields.image]
+    key = ''
+    if fields.InputDataFields.source_id in read_data:
+      key = read_data[fields.InputDataFields.source_id]
     location_gt = read_data[fields.InputDataFields.groundtruth_boxes]
     classes_gt = tf.cast(read_data[fields.InputDataFields.groundtruth_classes],
                          tf.int32)
     classes_gt -= label_id_offset
-    classes_gt = util_ops.padded_one_hot_encoding(indices=classes_gt,
-                                                  depth=num_classes, left_pad=0)
+    if merge_multiple_label_boxes:
+      location_gt, classes_gt, _ = util_ops.merge_boxes_with_multiple_labels(
+          location_gt, classes_gt, num_classes)
+    else:
+      classes_gt = util_ops.padded_one_hot_encoding(
+          indices=classes_gt, depth=num_classes, left_pad=0)
     masks_gt = read_data.get(fields.InputDataFields.groundtruth_instance_masks)
-    return image, location_gt, classes_gt, masks_gt
+    keypoints_gt = read_data.get(fields.InputDataFields.groundtruth_keypoints)
+    if (merge_multiple_label_boxes and (
+        masks_gt is not None or keypoints_gt is not None)):
+      raise NotImplementedError('Multi-label support is only for boxes.')
+    return image, key, location_gt, classes_gt, masks_gt, keypoints_gt
+
   return zip(*map(extract_images_and_targets, read_data_list))
 
 
-def _create_losses(input_queue, create_model_fn):
+def _create_losses(input_queue, create_model_fn, train_config):
   """Creates loss function for a DetectionModel.
 
   Args:
     input_queue: BatchQueue object holding enqueued tensor_dicts.
     create_model_fn: A function to create the DetectionModel.
+    train_config: a train_pb2.TrainConfig protobuf.
   """
   detection_model = create_model_fn()
-  (images, groundtruth_boxes_list, groundtruth_classes_list,
-   groundtruth_masks_list
-  ) = _get_inputs(input_queue, detection_model.num_classes)
+  (images, _, groundtruth_boxes_list, groundtruth_classes_list,
+   groundtruth_masks_list, groundtruth_keypoints_list) = get_inputs(
+       input_queue,
+       detection_model.num_classes,
+       train_config.merge_multiple_label_boxes)
   images = [detection_model.preprocess(image) for image in images]
   images = tf.concat(images, 0)
   if any(mask is None for mask in groundtruth_masks_list):
     groundtruth_masks_list = None
+  if any(keypoints is None for keypoints in groundtruth_keypoints_list):
+    groundtruth_keypoints_list = None
 
   detection_model.provide_groundtruth(groundtruth_boxes_list,
                                       groundtruth_classes_list,
-                                      groundtruth_masks_list)
+                                      groundtruth_masks_list,
+                                      groundtruth_keypoints_list)
   prediction_dict = detection_model.predict(images)
 
   losses_dict = detection_model.loss(prediction_dict)
@@ -176,19 +210,21 @@ def train(create_tensor_dict_fn, create_model_fn, train_config, master, task,
       global_step = slim.create_global_step()
 
     with tf.device(deploy_config.inputs_device()):
-      input_queue = _create_input_queue(train_config.batch_size // num_clones,
-                                        create_tensor_dict_fn,
-                                        train_config.batch_queue_capacity,
-                                        train_config.num_batch_queue_threads,
-                                        train_config.prefetch_queue_capacity,
-                                        data_augmentation_options)
+      input_queue = create_input_queue(
+          train_config.batch_size // num_clones, create_tensor_dict_fn,
+          train_config.batch_queue_capacity,
+          train_config.num_batch_queue_threads,
+          train_config.prefetch_queue_capacity, data_augmentation_options)
 
     # Gather initial summaries.
+    # TODO(rathodv): See if summaries can be added/extracted from global tf
+    # collections so that they don't have to be passed around.
     summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
     global_summaries = set([])
 
     model_fn = functools.partial(_create_losses,
-                                 create_model_fn=create_model_fn)
+                                 create_model_fn=create_model_fn,
+                                 train_config=train_config)
     clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue])
     first_clone_scope = clones[0].scope
 

research/object_detection/trainer_test.py

@@ -32,6 +32,7 @@ NUMBER_OF_CLASSES = 2
 def get_input_function():
   """A function to get test inputs. Returns an image with one box."""
   image = tf.random_uniform([32, 32, 3], dtype=tf.float32)
+  key = tf.constant('image_000000')
   class_label = tf.random_uniform(
       [1], minval=0, maxval=NUMBER_OF_CLASSES, dtype=tf.int32)
   box_label = tf.random_uniform(
@@ -39,6 +40,7 @@ def get_input_function():
 
   return {
       fields.InputDataFields.image: image,
+      fields.InputDataFields.key: key,
       fields.InputDataFields.groundtruth_classes: class_label,
       fields.InputDataFields.groundtruth_boxes: box_label
   }