Merged commit includes the following changes:

196161788  by Zhichao Lu:

    Add eval_on_train_steps parameter.

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

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196151742  by Zhichao Lu:

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

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196148940  by Zhichao Lu:

    Release ssdlite mobilenet v2 coco trained model.

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196058528  by Zhichao Lu:

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

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195818367  by Zhichao Lu:

    Add support for exporting detection keypoints.

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195745420  by Zhichao Lu:

    Introduce include_metrics_per_category option to Object Detection eval_config.

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195734733  by Zhichao Lu:

    Rename SSDLite config to be more explicit.

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195717383  by Zhichao Lu:

    Add quantized training to object_detection.

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195683542  by Zhichao Lu:

    Fix documentation for the interaction of fine_tune_checkpoint_type and load_all_detection_checkpoint_vars interaction.

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195668233  by Zhichao Lu:

    Using batch size from params dictionary if present.

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195570173  by Zhichao Lu:

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

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195316756  by Zhichao Lu:

    Internal change

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PiperOrigin-RevId: 196161788

research/object_detection/utils/config_util.py

@@ -22,6 +22,7 @@ from google.protobuf import text_format
 from tensorflow.python.lib.io import file_io
 
 from object_detection.protos import eval_pb2
+from object_detection.protos import graph_rewriter_pb2
 from object_detection.protos import input_reader_pb2
 from object_detection.protos import model_pb2
 from object_detection.protos import pipeline_pb2
@@ -111,9 +112,27 @@ def create_configs_from_pipeline_proto(pipeline_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
+  if pipeline_config.HasField("graph_rewriter"):
+    configs["graph_rewriter_config"] = pipeline_config.graph_rewriter
+
   return configs
 
 
+def get_graph_rewriter_config_from_file(graph_rewriter_config_file):
+  """Parses config for graph rewriter.
+
+  Args:
+    graph_rewriter_config_file: file path to the graph rewriter config.
+
+  Returns:
+    graph_rewriter_pb2.GraphRewriter proto
+  """
+  graph_rewriter_config = graph_rewriter_pb2.GraphRewriter()
+  with tf.gfile.GFile(graph_rewriter_config_file, "r") as f:
+    text_format.Merge(f.read(), graph_rewriter_config)
+  return graph_rewriter_config
+
+
 def create_pipeline_proto_from_configs(configs):
   """Creates a pipeline_pb2.TrainEvalPipelineConfig from configs dictionary.
 
@@ -132,6 +151,8 @@ def create_pipeline_proto_from_configs(configs):
   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"])
+  if "graph_rewriter_config" in configs:
+    pipeline_config.graph_rewriter.CopyFrom(configs["graph_rewriter_config"])
   return pipeline_config
 
 
@@ -157,7 +178,8 @@ def get_configs_from_multiple_files(model_config_path="",
                                     train_config_path="",
                                     train_input_config_path="",
                                     eval_config_path="",
-                                    eval_input_config_path=""):
+                                    eval_input_config_path="",
+                                    graph_rewriter_config_path=""):
   """Reads training configuration from multiple config files.
 
   Args:
@@ -166,6 +188,7 @@ def get_configs_from_multiple_files(model_config_path="",
     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.
+    graph_rewriter_config_path: Path to graph_rewriter_pb2.GraphRewriter.
 
   Returns:
     Dictionary of configuration objects. Keys are `model`, `train_config`,
@@ -203,6 +226,10 @@ def get_configs_from_multiple_files(model_config_path="",
       text_format.Merge(f.read(), eval_input_config)
       configs["eval_input_config"] = eval_input_config
 
+  if graph_rewriter_config_path:
+    configs["graph_rewriter_config"] = get_graph_rewriter_config_from_file(
+        graph_rewriter_config_path)
+
   return configs
 
 

research/object_detection/utils/dataset_util.py

@@ -132,7 +132,7 @@ def read_dataset(file_read_func, decode_func, input_files, config):
   records_dataset = filename_dataset.apply(
       tf.contrib.data.parallel_interleave(
           file_read_func, cycle_length=config.num_readers,
-          block_length=config.read_block_length, sloppy=True))
+          block_length=config.read_block_length, sloppy=config.shuffle))
   if config.shuffle:
     records_dataset.shuffle(config.shuffle_buffer_size)
   tensor_dataset = records_dataset.map(