Lstm object detection improvements (#7379)

* Replace google3.pyglib modules with tf and absl

This now matches train.py and provides more publicly available libraries.

* Add example pipeline config for SSD Interleaved V2 Model.

Compiled from model_builder_test.py and lstm_ssd_mobilenet_v1_imagenet.config,
Removed data augmentation and tranfer learning (i.e. training from checkpoint) due to errors I was seeing when trying to run with it.

* Remove unused tfrecord creation.

This was also incorrectly specified, as the keys differed from the TFSequenceExample parser.

* correct key specified in docstring

* add tflite frozen graph exporter (cli and lib).

* add tflite model exporter

* add script to test the tflite model

* add mode export documentation

* correct docstring

* rename export files to be unique across detection research work

* correct number of channels for grayscale

* add and correct copyright

research/lstm_object_detection/README.md

@@ -32,3 +32,8 @@ https://scholar.googleusercontent.com/scholar.bib?q=info:rLqvkztmWYgJ:scholar.go
 * yinxiao@google.com
 * menglong@google.com
 * yongzhe@google.com
+
+
+## Table of Contents
+
+  * <a href='g3doc/exporting_models.md'>Exporting a trained model</a>

research/lstm_object_detection/configs/lstm_ssd_interleaved_mobilenet_v2_imagenet.config

+# Copyright 2019 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.
+# ==============================================================================
+
+# For training on Imagenet Video with LSTM Interleaved Mobilenet V2
+
+[lstm_object_detection.protos.lstm_model] {
+  train_unroll_length: 4
+  eval_unroll_length: 4
+  lstm_state_depth: 320
+  depth_multipliers: 1.4
+  depth_multipliers: 0.35
+  pre_bottleneck: true
+  low_res: true
+  train_interleave_method: 'RANDOM_SKIP_SMALL'
+  eval_interleave_method: 'SKIP3'
+}
+model {
+  ssd {
+    num_classes: 30  # Num of class for imagenet vid dataset.
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    anchor_generator {
+      ssd_anchor_generator {
+        num_layers: 5
+        min_scale: 0.2
+        max_scale: 0.95
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        aspect_ratios: 3.0
+        aspect_ratios: 0.3333
+      }
+    }
+    image_resizer {
+      fixed_shape_resizer {
+        height: 320
+        width: 320
+      }
+    }
+    box_predictor {
+      convolutional_box_predictor {
+        min_depth: 0
+        max_depth: 0
+        num_layers_before_predictor: 3
+        use_dropout: false
+        dropout_keep_probability: 0.8
+        kernel_size: 3
+        box_code_size: 4
+        apply_sigmoid_to_scores: false
+        use_depthwise: true
+        conv_hyperparams {
+          activation: RELU_6,
+          regularizer {
+            l2_regularizer {
+              weight: 0.00004
+            }
+          }
+          initializer {
+            truncated_normal_initializer {
+              stddev: 0.03
+              mean: 0.0
+            }
+          }
+          batch_norm {
+            train: true,
+            scale: true,
+            center: true,
+            decay: 0.9997,
+            epsilon: 0.001,
+          }
+        }
+      }
+    }
+    feature_extractor {
+      type: 'lstm_ssd_interleaved_mobilenet_v2'
+      conv_hyperparams {
+        activation: RELU_6,
+        regularizer {
+          l2_regularizer {
+            weight: 0.00004
+          }
+        }
+        initializer {
+          truncated_normal_initializer {
+            stddev: 0.03
+            mean: 0.0
+          }
+        }
+        batch_norm {
+          train: true,
+          scale: true,
+          center: true,
+          decay: 0.9997,
+          epsilon: 0.001,
+        }
+      }
+    }
+    loss {
+      classification_loss {
+        weighted_sigmoid {
+        }
+      }
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      hard_example_miner {
+        num_hard_examples: 3000
+        iou_threshold: 0.99
+        loss_type: CLASSIFICATION
+        max_negatives_per_positive: 3
+        min_negatives_per_image: 0
+      }
+      classification_weight: 1.0
+      localization_weight: 4.0
+    }
+    normalize_loss_by_num_matches: true
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: -20.0
+        iou_threshold: 0.5
+        max_detections_per_class: 100
+        max_total_detections: 100
+      }
+      score_converter: SIGMOID
+    }
+  }
+}
+
+train_config: {
+  batch_size: 8
+  optimizer {
+    use_moving_average: false
+    rms_prop_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.002
+          decay_steps: 200000
+          decay_factor: 0.95
+        }
+      }
+      momentum_optimizer_value: 0.9
+      decay: 0.9
+      epsilon: 1.0
+    }
+  }
+  gradient_clipping_by_norm: 10.0
+  batch_queue_capacity: 12
+  prefetch_queue_capacity: 4
+}
+
+train_input_reader: {
+  shuffle_buffer_size: 32
+  queue_capacity: 12
+  prefetch_size: 12
+  min_after_dequeue: 4
+  label_map_path: "path/to/label_map"
+  external_input_reader {
+    [lstm_object_detection.protos.GoogleInputReader.google_input_reader] {
+      tf_record_video_input_reader: {
+        input_path: '/data/lstm_detection/tfrecords/test.tfrecord'
+        data_type: TF_SEQUENCE_EXAMPLE
+        video_length: 4
+      }
+    }
+  }
+}
+
+eval_config: {
+  metrics_set: "coco_evaluation_all_frames"
+  use_moving_averages: true
+  min_score_threshold: 0.5
+  max_num_boxes_to_visualize: 300
+  visualize_groundtruth_boxes: true
+  groundtruth_box_visualization_color: "red"
+}
+
+eval_input_reader {
+  label_map_path: "path/to/label_map"
+  shuffle: true
+  num_epochs: 1
+  num_parallel_batches: 1
+  num_readers: 1
+  external_input_reader {
+    [lstm_object_detection.protos.GoogleInputReader.google_input_reader] {
+      tf_record_video_input_reader: {
+        input_path: "path/to/sequence_example/data"
+        data_type: TF_SEQUENCE_EXAMPLE
+        video_length: 10
+      }
+    }
+  }
+}
+
+eval_input_reader: {
+  label_map_path: "path/to/label_map"
+  external_input_reader {
+    [lstm_object_detection.protos.GoogleInputReader.google_input_reader] {
+      tf_record_video_input_reader: {
+        input_path: "path/to/sequence_example/data"
+        data_type: TF_SEQUENCE_EXAMPLE
+        video_length: 4
+      }
+    }
+  }
+  shuffle: true
+  num_readers: 1
+}

research/lstm_object_detection/eval.py

@@ -27,8 +27,6 @@ import functools
 import os
 import tensorflow as tf
 from google.protobuf import text_format
-from google3.pyglib import app
-from google3.pyglib import flags
 from lstm_object_detection import evaluator
 from lstm_object_detection import model_builder
 from lstm_object_detection.inputs import seq_dataset_builder
@@ -107,4 +105,4 @@ def main(unused_argv):
                      FLAGS.checkpoint_dir, FLAGS.eval_dir)
 
 if __name__ == '__main__':
-  app.run()
+  tf.app.run()

research/lstm_object_detection/export_tflite_lstd_graph.py

+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""Exports an LSTM detection model to use with tf-lite.
+
+Outputs file:
+* A tflite compatible frozen graph - $output_directory/tflite_graph.pb
+
+The exported graph has the following input and output nodes.
+
+Inputs:
+'input_video_tensor': a float32 tensor of shape
+[unroll_length, height, width, 3] containing the normalized input image.
+Note that the height and width must be compatible with the height and
+width configured in the fixed_shape_image resizer options in the pipeline
+config proto.
+
+Outputs:
+If add_postprocessing_op is true: frozen graph adds a
+  TFLite_Detection_PostProcess custom op node has four outputs:
+  detection_boxes: a float32 tensor of shape [1, num_boxes, 4] with box
+  locations
+  detection_classes: a float32 tensor of shape [1, num_boxes]
+  with class indices
+  detection_scores: a float32 tensor of shape [1, num_boxes]
+  with class scores
+  num_boxes: a float32 tensor of size 1 containing the number of detected boxes
+else:
+  the graph has three outputs:
+   'raw_outputs/box_encodings': a float32 tensor of shape [1, num_anchors, 4]
+    containing the encoded box predictions.
+   'raw_outputs/class_predictions': a float32 tensor of shape
+    [1, num_anchors, num_classes] containing the class scores for each anchor
+    after applying score conversion.
+   'anchors': a float32 constant tensor of shape [num_anchors, 4]
+    containing the anchor boxes.
+
+Example Usage:
+--------------
+python lstm_object_detection/export_tflite_lstd_graph.py \
+    --pipeline_config_path path/to/lstm_pipeline.config \
+    --trained_checkpoint_prefix path/to/model.ckpt \
+    --output_directory path/to/exported_model_directory
+
+The expected output would be in the directory
+path/to/exported_model_directory (which is created if it does not exist)
+with contents:
+ - tflite_graph.pbtxt
+ - tflite_graph.pb
+Config overrides (see the `config_override` flag) are text protobufs
+(also of type pipeline_pb2.TrainEvalPipelineConfig) which are used to override
+certain fields in the provided pipeline_config_path.  These are useful for
+making small changes to the inference graph that differ from the training or
+eval config.
+
+Example Usage (in which we change the NMS iou_threshold to be 0.5 and
+NMS score_threshold to be 0.0):
+python lstm_object_detection/export_tflite_lstd_graph.py \
+    --pipeline_config_path path/to/lstm_pipeline.config \
+    --trained_checkpoint_prefix path/to/model.ckpt \
+    --output_directory path/to/exported_model_directory
+    --config_override " \
+            model{ \
+            ssd{ \
+              post_processing { \
+                batch_non_max_suppression { \
+                        score_threshold: 0.0 \
+                        iou_threshold: 0.5 \
+                } \
+             } \
+          } \
+       } \
+       "
+"""
+
+import tensorflow as tf
+from lstm_object_detection.utils import config_util
+from lstm_object_detection import export_tflite_lstd_graph_lib
+
+flags = tf.app.flags
+flags.DEFINE_string('output_directory', None, 'Path to write outputs.')
+flags.DEFINE_string(
+    'pipeline_config_path', None,
+    'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
+    'file.')
+flags.DEFINE_string('trained_checkpoint_prefix', None, 'Checkpoint prefix.')
+flags.DEFINE_integer('max_detections', 10,
+                     'Maximum number of detections (boxes) to show.')
+flags.DEFINE_integer('max_classes_per_detection', 1,
+                     'Maximum number of classes to output per detection box.')
+flags.DEFINE_integer(
+    'detections_per_class', 100,
+    'Number of anchors used per class in Regular Non-Max-Suppression.')
+flags.DEFINE_bool('add_postprocessing_op', True,
+                  'Add TFLite custom op for postprocessing to the graph.')
+flags.DEFINE_bool(
+    'use_regular_nms', False,
+    'Flag to set postprocessing op to use Regular NMS instead of Fast NMS.')
+flags.DEFINE_string(
+    'config_override', '', 'pipeline_pb2.TrainEvalPipelineConfig '
+    'text proto to override pipeline_config_path.')
+
+FLAGS = flags.FLAGS
+
+
+def main(argv):
+  del argv  # Unused.
+  flags.mark_flag_as_required('output_directory')
+  flags.mark_flag_as_required('pipeline_config_path')
+  flags.mark_flag_as_required('trained_checkpoint_prefix')
+
+  pipeline_config = config_util.get_configs_from_pipeline_file(
+    FLAGS.pipeline_config_path)
+
+  export_tflite_lstd_graph_lib.export_tflite_graph(
+      pipeline_config, FLAGS.trained_checkpoint_prefix, FLAGS.output_directory,
+      FLAGS.add_postprocessing_op, FLAGS.max_detections,
+      FLAGS.max_classes_per_detection, use_regular_nms=FLAGS.use_regular_nms)
+
+
+if __name__ == '__main__':
+  tf.app.run(main)

research/lstm_object_detection/export_tflite_lstd_graph_lib.py

+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""Exports detection models to use with tf-lite.
+
+See export_tflite_lstd_graph.py for usage.
+"""
+import os
+import tempfile
+import numpy as np
+import tensorflow as tf
+from tensorflow.core.framework import attr_value_pb2
+from tensorflow.core.framework import types_pb2
+from tensorflow.core.protobuf import saver_pb2
+from tensorflow.tools.graph_transforms import TransformGraph
+from object_detection import exporter
+from object_detection.builders import graph_rewriter_builder
+from object_detection.builders import post_processing_builder
+from object_detection.core import box_list
+
+from lstm_object_detection import model_builder
+
+_DEFAULT_NUM_CHANNELS = 3
+_DEFAULT_NUM_COORD_BOX = 4
+
+
+def get_const_center_size_encoded_anchors(anchors):
+  """Exports center-size encoded anchors as a constant tensor.
+
+  Args:
+    anchors: a float32 tensor of shape [num_anchors, 4] containing the anchor
+      boxes
+
+  Returns:
+    encoded_anchors: a float32 constant tensor of shape [num_anchors, 4]
+    containing the anchor boxes.
+  """
+  anchor_boxlist = box_list.BoxList(anchors)
+  y, x, h, w = anchor_boxlist.get_center_coordinates_and_sizes()
+  num_anchors = y.get_shape().as_list()
+
+  with tf.Session() as sess:
+    y_out, x_out, h_out, w_out = sess.run([y, x, h, w])
+  encoded_anchors = tf.constant(
+      np.transpose(np.stack((y_out, x_out, h_out, w_out))),
+      dtype=tf.float32,
+      shape=[num_anchors[0], _DEFAULT_NUM_COORD_BOX],
+      name='anchors')
+  return encoded_anchors
+
+
+def append_postprocessing_op(frozen_graph_def,
+                             max_detections,
+                             max_classes_per_detection,
+                             nms_score_threshold,
+                             nms_iou_threshold,
+                             num_classes,
+                             scale_values,
+                             detections_per_class=100,
+                             use_regular_nms=False):
+  """Appends postprocessing custom op.
+
+  Args:
+    frozen_graph_def: Frozen GraphDef for SSD model after freezing the
+      checkpoint
+    max_detections: Maximum number of detections (boxes) to show
+    max_classes_per_detection: Number of classes to display per detection
+    nms_score_threshold: Score threshold used in Non-maximal suppression in
+      post-processing
+    nms_iou_threshold: Intersection-over-union threshold used in Non-maximal
+      suppression in post-processing
+    num_classes: number of classes in SSD detector
+    scale_values: scale values is a dict with following key-value pairs
+      {y_scale: 10, x_scale: 10, h_scale: 5, w_scale: 5} that are used in decode
+      centersize boxes
+    detections_per_class: In regular NonMaxSuppression, number of anchors used
+    for NonMaxSuppression per class
+    use_regular_nms: Flag to set postprocessing op to use Regular NMS instead
+      of Fast NMS.
+
+  Returns:
+    transformed_graph_def: Frozen GraphDef with postprocessing custom op
+    appended
+    TFLite_Detection_PostProcess custom op node has four outputs:
+    detection_boxes: a float32 tensor of shape [1, num_boxes, 4] with box
+    locations
+    detection_classes: a float32 tensor of shape [1, num_boxes]
+    with class indices
+    detection_scores: a float32 tensor of shape [1, num_boxes]
+    with class scores
+    num_boxes: a float32 tensor of size 1 containing the number of detected
+    boxes
+  """
+  new_output = frozen_graph_def.node.add()
+  new_output.op = 'TFLite_Detection_PostProcess'
+  new_output.name = 'TFLite_Detection_PostProcess'
+  new_output.attr['_output_quantized'].CopyFrom(
+      attr_value_pb2.AttrValue(b=True))
+  new_output.attr['_output_types'].list.type.extend([
+      types_pb2.DT_FLOAT, types_pb2.DT_FLOAT, types_pb2.DT_FLOAT,
+      types_pb2.DT_FLOAT
+  ])
+  new_output.attr['_support_output_type_float_in_quantized_op'].CopyFrom(
+      attr_value_pb2.AttrValue(b=True))
+  new_output.attr['max_detections'].CopyFrom(
+      attr_value_pb2.AttrValue(i=max_detections))
+  new_output.attr['max_classes_per_detection'].CopyFrom(
+      attr_value_pb2.AttrValue(i=max_classes_per_detection))
+  new_output.attr['nms_score_threshold'].CopyFrom(
+      attr_value_pb2.AttrValue(f=nms_score_threshold.pop()))
+  new_output.attr['nms_iou_threshold'].CopyFrom(
+      attr_value_pb2.AttrValue(f=nms_iou_threshold.pop()))
+  new_output.attr['num_classes'].CopyFrom(
+      attr_value_pb2.AttrValue(i=num_classes))
+
+  new_output.attr['y_scale'].CopyFrom(
+      attr_value_pb2.AttrValue(f=scale_values['y_scale'].pop()))
+  new_output.attr['x_scale'].CopyFrom(
+      attr_value_pb2.AttrValue(f=scale_values['x_scale'].pop()))
+  new_output.attr['h_scale'].CopyFrom(
+      attr_value_pb2.AttrValue(f=scale_values['h_scale'].pop()))
+  new_output.attr['w_scale'].CopyFrom(
+      attr_value_pb2.AttrValue(f=scale_values['w_scale'].pop()))
+  new_output.attr['detections_per_class'].CopyFrom(
+      attr_value_pb2.AttrValue(i=detections_per_class))
+  new_output.attr['use_regular_nms'].CopyFrom(
+      attr_value_pb2.AttrValue(b=use_regular_nms))
+
+  new_output.input.extend(
+      ['raw_outputs/box_encodings', 'raw_outputs/class_predictions', 'anchors'])
+  # Transform the graph to append new postprocessing op
+  input_names = []
+  output_names = ['TFLite_Detection_PostProcess']
+  transforms = ['strip_unused_nodes']
+  transformed_graph_def = TransformGraph(frozen_graph_def, input_names,
+                                         output_names, transforms)
+  return transformed_graph_def
+
+
+def export_tflite_graph(pipeline_config,
+                        trained_checkpoint_prefix,
+                        output_dir,
+                        add_postprocessing_op,
+                        max_detections,
+                        max_classes_per_detection,
+                        detections_per_class=100,
+                        use_regular_nms=False,
+                        binary_graph_name='tflite_graph.pb',
+                        txt_graph_name='tflite_graph.pbtxt'):
+  """Exports a tflite compatible graph and anchors for ssd detection model.
+
+  Anchors are written to a tensor and tflite compatible graph
+  is written to output_dir/tflite_graph.pb.
+
+  Args:
+    pipeline_config: Dictionary of configuration objects. Keys are `model`, `train_config`,
+      `train_input_config`, `eval_config`, `eval_input_config`, `lstm_model`.
+      Value are the corresponding config objects.
+    trained_checkpoint_prefix: a file prefix for the checkpoint containing the
+      trained parameters of the SSD model.
+    output_dir: A directory to write the tflite graph and anchor file to.
+    add_postprocessing_op: If add_postprocessing_op is true: frozen graph adds a
+      TFLite_Detection_PostProcess custom op
+    max_detections: Maximum number of detections (boxes) to show
+    max_classes_per_detection: Number of classes to display per detection
+    detections_per_class: In regular NonMaxSuppression, number of anchors used
+    for NonMaxSuppression per class
+    use_regular_nms: Flag to set postprocessing op to use Regular NMS instead
+      of Fast NMS.
+    binary_graph_name: Name of the exported graph file in binary format.
+    txt_graph_name: Name of the exported graph file in text format.
+
+  Raises:
+    ValueError: if the pipeline config contains models other than ssd or uses an
+      fixed_shape_resizer and provides a shape as well.
+  """
+  model_config = pipeline_config['model']
+  lstm_config = pipeline_config['lstm_model']
+  eval_config = pipeline_config['eval_config']
+  tf.gfile.MakeDirs(output_dir)
+  if model_config.WhichOneof('model') != 'ssd':
+    raise ValueError('Only ssd models are supported in tflite. '
+                     'Found {} in config'.format(
+                         model_config.WhichOneof('model')))
+
+  num_classes = model_config.ssd.num_classes
+  nms_score_threshold = {
+      model_config.ssd.post_processing.batch_non_max_suppression.
+      score_threshold
+  }
+  nms_iou_threshold = {
+      model_config.ssd.post_processing.batch_non_max_suppression.
+      iou_threshold
+  }
+  scale_values = {}
+  scale_values['y_scale'] = {
+      model_config.ssd.box_coder.faster_rcnn_box_coder.y_scale
+  }
+  scale_values['x_scale'] = {
+      model_config.ssd.box_coder.faster_rcnn_box_coder.x_scale
+  }
+  scale_values['h_scale'] = {
+      model_config.ssd.box_coder.faster_rcnn_box_coder.height_scale
+  }
+  scale_values['w_scale'] = {
+      model_config.ssd.box_coder.faster_rcnn_box_coder.width_scale
+  }
+
+  image_resizer_config = model_config.ssd.image_resizer
+  image_resizer = image_resizer_config.WhichOneof('image_resizer_oneof')
+  num_channels = _DEFAULT_NUM_CHANNELS
+  if image_resizer == 'fixed_shape_resizer':
+    height = image_resizer_config.fixed_shape_resizer.height
+    width = image_resizer_config.fixed_shape_resizer.width
+    if image_resizer_config.fixed_shape_resizer.convert_to_grayscale:
+      num_channels = 1
+    #TODO(richardbrks) figure out how to make with a None defined batch size
+    shape = [lstm_config.eval_unroll_length, height, width, num_channels]
+  else:
+    raise ValueError(
+        'Only fixed_shape_resizer'
+        'is supported with tflite. Found {}'.format(
+            image_resizer_config.WhichOneof('image_resizer_oneof')))
+
+  video_tensor = tf.placeholder(
+    tf.float32, shape=shape, name='input_video_tensor')
+
+  detection_model = model_builder.build(model_config, lstm_config,
+    is_training=False)
+  preprocessed_video, true_image_shapes = detection_model.preprocess(
+    tf.to_float(video_tensor))
+  predicted_tensors = detection_model.predict(preprocessed_video,
+    true_image_shapes)
+  # predicted_tensors = detection_model.postprocess(predicted_tensors,
+  #   true_image_shapes)
+  # The score conversion occurs before the post-processing custom op
+  _, score_conversion_fn = post_processing_builder.build(
+      model_config.ssd.post_processing)
+  class_predictions = score_conversion_fn(
+      predicted_tensors['class_predictions_with_background'])
+
+  with tf.name_scope('raw_outputs'):
+    # 'raw_outputs/box_encodings': a float32 tensor of shape [1, num_anchors, 4]
+    #  containing the encoded box predictions. Note that these are raw
+    #  predictions and no Non-Max suppression is applied on them and
+    #  no decode center size boxes is applied to them.
+    tf.identity(predicted_tensors['box_encodings'], name='box_encodings')
+    # 'raw_outputs/class_predictions': a float32 tensor of shape
+    #  [1, num_anchors, num_classes] containing the class scores for each anchor
+    #  after applying score conversion.
+    tf.identity(class_predictions, name='class_predictions')
+  # 'anchors': a float32 tensor of shape
+  #   [4, num_anchors] containing the anchors as a constant node.
+  tf.identity(
+      get_const_center_size_encoded_anchors(predicted_tensors['anchors']),
+      name='anchors')
+
+  # Add global step to the graph, so we know the training step number when we
+  # evaluate the model.
+  tf.train.get_or_create_global_step()
+
+  # graph rewriter
+  is_quantized = ('graph_rewriter' in pipeline_config)
+  if is_quantized:
+    graph_rewriter_config = pipeline_config['graph_rewriter']
+    graph_rewriter_fn = graph_rewriter_builder.build(
+        graph_rewriter_config, is_training=False, is_export=True)
+    graph_rewriter_fn()
+
+  if model_config.ssd.feature_extractor.HasField('fpn'):
+    exporter.rewrite_nn_resize_op(is_quantized)
+
+  # freeze the graph
+  saver_kwargs = {}
+  if eval_config.use_moving_averages:
+    saver_kwargs['write_version'] = saver_pb2.SaverDef.V1
+    moving_average_checkpoint = tempfile.NamedTemporaryFile()
+    exporter.replace_variable_values_with_moving_averages(
+        tf.get_default_graph(), trained_checkpoint_prefix,
+        moving_average_checkpoint.name)
+    checkpoint_to_use = moving_average_checkpoint.name
+  else:
+    checkpoint_to_use = trained_checkpoint_prefix
+
+  saver = tf.train.Saver(**saver_kwargs)
+  input_saver_def = saver.as_saver_def()
+  frozen_graph_def = exporter.freeze_graph_with_def_protos(
+      input_graph_def=tf.get_default_graph().as_graph_def(),
+      input_saver_def=input_saver_def,
+      input_checkpoint=checkpoint_to_use,
+      output_node_names=','.join([
+          'raw_outputs/box_encodings', 'raw_outputs/class_predictions',
+          'anchors'
+      ]),
+      restore_op_name='save/restore_all',
+      filename_tensor_name='save/Const:0',
+      clear_devices=True,
+      output_graph='',
+      initializer_nodes='')
+
+  # Add new operation to do post processing in a custom op (TF Lite only)
+  #(richardbrks) Do use this or detection_model.postprocess?
+  if add_postprocessing_op:
+    transformed_graph_def = append_postprocessing_op(
+        frozen_graph_def, max_detections, max_classes_per_detection,
+        nms_score_threshold, nms_iou_threshold, num_classes, scale_values,
+        detections_per_class, use_regular_nms)
+  else:
+    # Return frozen without adding post-processing custom op
+    transformed_graph_def = frozen_graph_def
+
+  binary_graph = os.path.join(output_dir, binary_graph_name)
+  with tf.gfile.GFile(binary_graph, 'wb') as f:
+    f.write(transformed_graph_def.SerializeToString())
+  txt_graph = os.path.join(output_dir, txt_graph_name)
+  with tf.gfile.GFile(txt_graph, 'w') as f:
+    f.write(str(transformed_graph_def))

research/lstm_object_detection/export_tflite_lstd_model.py

+# Copyright 2019 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.
+# ==============================================================================
+
+import os
+from absl import flags
+import tensorflow as tf
+
+from lstm_object_detection.utils import config_util
+
+flags.DEFINE_string('export_path', None, 'Path to export model.')
+flags.DEFINE_string('frozen_graph_path', None, 'Path to frozen graph.')
+flags.DEFINE_string(
+    'pipeline_config_path', '',
+    'Path to a pipeline_pb2.TrainEvalPipelineConfig config file.')
+
+FLAGS = flags.FLAGS
+
+
+def main(_):
+    flags.mark_flag_as_required('export_path')
+    flags.mark_flag_as_required('frozen_graph_path')
+    flags.mark_flag_as_required('pipeline_config_path')
+
+    configs = config_util.get_configs_from_pipeline_file(
+        FLAGS.pipeline_config_path)
+    lstm_config = configs['lstm_model']
+
+    input_arrays = ['input_video_tensor']
+    output_arrays = [
+        'TFLite_Detection_PostProcess',
+        'TFLite_Detection_PostProcess:1',
+        'TFLite_Detection_PostProcess:2',
+        'TFLite_Detection_PostProcess:3',
+    ]
+    input_shapes = {
+        'input_video_tensor': [lstm_config.eval_unroll_length, 320, 320, 3],
+    }
+
+    converter = tf.lite.TFLiteConverter.from_frozen_graph(
+        FLAGS.frozen_graph_path, input_arrays, output_arrays,
+        input_shapes=input_shapes
+    )
+    converter.allow_custom_ops = True
+    tflite_model = converter.convert()
+    ofilename = os.path.join(FLAGS.export_path)
+    open(ofilename, "wb").write(tflite_model)
+
+
+if __name__ == '__main__':
+    tf.app.run()

research/lstm_object_detection/g3doc/exporting_models.md

+# Exporting a tflite model from a checkpoint
+
+Starting from a trained model checkpoint, creating a tflite model requires 2 steps:
+
+* exporting a tflite frozen graph from a checkpoint
+* exporting a tflite model from a frozen graph
+
+
+## Exporting a tflite frozen graph from a checkpoint
+
+With a candidate checkpoint to export, run the following command from
+tensorflow/models/research:
+
+```bash
+# from tensorflow/models/research
+PIPELINE_CONFIG_PATH={path to pipeline config}
+TRAINED_CKPT_PREFIX=/{path to model.ckpt}
+EXPORT_DIR={path to folder that will be used for export}
+python lstm_object_detection/export_tflite_lstd_graph.py \
+    --pipeline_config_path ${PIPELINE_CONFIG_PATH} \
+    --trained_checkpoint_prefix ${TRAINED_CKPT_PREFIX} \
+    --output_directory ${EXPORT_DIR} \
+	--add_preprocessing_op
+```
+
+After export, you should see the directory ${EXPORT_DIR} containing the following files:
+
+* `tflite_graph.pb`
+* `tflite_graph.pbtxt` 
+
+
+## Exporting a tflite model from a frozen graph
+
+We then take the exported tflite-compatable tflite model, and convert it to a
+TFLite FlatBuffer file by running the following:
+
+```bash
+# from tensorflow/models/research
+FROZEN_GRAPH_PATH={path to exported tflite_graph.pb}
+EXPORT_PATH={path to filename that will be used for export}
+PIPELINE_CONFIG_PATH={path to pipeline config}
+python lstm_object_detection/export_tflite_lstd_model.py \
+	   --export_path ${EXPORT_PATH} \
+	   --frozen_graph_path ${FROZEN_GRAPH_PATH} \
+	   --pipeline_config_path ${PIPELINE_CONFIG_PATH}
+```
+
+After export, you should see the file ${EXPORT_PATH} containing the FlatBuffer
+model to be used by an application.
\ No newline at end of file

research/lstm_object_detection/inputs/seq_dataset_builder_test.py

@@ -33,68 +33,6 @@ from object_detection.protos import preprocessor_pb2
 
 class DatasetBuilderTest(tf.test.TestCase):
 
-  def _create_tf_record(self):
-    path = os.path.join(self.get_temp_dir(), 'tfrecord')
-    writer = tf.python_io.TFRecordWriter(path)
-
-    image_tensor = np.random.randint(255, size=(16, 16, 3)).astype(np.uint8)
-    with self.test_session():
-      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
-
-    sequence_example = example_pb2.SequenceExample(
-        context=feature_pb2.Features(
-            feature={
-                'image/format':
-                    feature_pb2.Feature(
-                        bytes_list=feature_pb2.BytesList(
-                            value=['jpeg'.encode('utf-8')])),
-                'image/height':
-                    feature_pb2.Feature(
-                        int64_list=feature_pb2.Int64List(value=[16])),
-                'image/width':
-                    feature_pb2.Feature(
-                        int64_list=feature_pb2.Int64List(value=[16])),
-            }),
-        feature_lists=feature_pb2.FeatureLists(
-            feature_list={
-                'image/encoded':
-                    feature_pb2.FeatureList(feature=[
-                        feature_pb2.Feature(
-                            bytes_list=feature_pb2.BytesList(
-                                value=[encoded_jpeg])),
-                    ]),
-                'image/object/bbox/xmin':
-                    feature_pb2.FeatureList(feature=[
-                        feature_pb2.Feature(
-                            float_list=feature_pb2.FloatList(value=[0.0])),
-                    ]),
-                'image/object/bbox/xmax':
-                    feature_pb2.FeatureList(feature=[
-                        feature_pb2.Feature(
-                            float_list=feature_pb2.FloatList(value=[1.0]))
-                    ]),
-                'image/object/bbox/ymin':
-                    feature_pb2.FeatureList(feature=[
-                        feature_pb2.Feature(
-                            float_list=feature_pb2.FloatList(value=[0.0])),
-                    ]),
-                'image/object/bbox/ymax':
-                    feature_pb2.FeatureList(feature=[
-                        feature_pb2.Feature(
-                            float_list=feature_pb2.FloatList(value=[1.0]))
-                    ]),
-                'image/object/class/label':
-                    feature_pb2.FeatureList(feature=[
-                        feature_pb2.Feature(
-                            int64_list=feature_pb2.Int64List(value=[2]))
-                    ]),
-            }))
-
-    writer.write(sequence_example.SerializeToString())
-    writer.close()
-
-    return path
-
   def _get_model_configs_from_proto(self):
     """Creates a model text proto for testing.
 

research/lstm_object_detection/test_tflite_model.py

+# Copyright 2019 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.
+# ==============================================================================
+
+from absl import flags
+import numpy as np
+import tensorflow as tf
+
+flags.DEFINE_string('model_path', None, 'Path to model.')
+FLAGS = flags.FLAGS
+
+
+def main(_):
+
+    flags.mark_flag_as_required('model_path')
+
+    # Load TFLite model and allocate tensors.
+    interpreter = tf.lite.Interpreter(model_path=FLAGS.model_path)
+    interpreter.allocate_tensors()
+
+    # Get input and output tensors.
+    input_details = interpreter.get_input_details()
+    print 'input_details:', input_details
+    output_details = interpreter.get_output_details()
+    print 'output_details:', output_details
+
+    # Test model on random input data.
+    input_shape = input_details[0]['shape']
+    # change the following line to feed into your own data.
+    input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
+    interpreter.set_tensor(input_details[0]['index'], input_data)
+
+    interpreter.invoke()
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    print output_data
+
+
+if __name__ == '__main__':
+    tf.app.run()

research/lstm_object_detection/utils/config_util.py

@@ -37,7 +37,7 @@ def get_configs_from_pipeline_file(pipeline_config_path):
 
   Returns:
     Dictionary of configuration objects. Keys are `model`, `train_config`,
-      `train_input_config`, `eval_config`, `eval_input_config`, `lstm_confg`.
+      `train_input_config`, `eval_config`, `eval_input_config`, `lstm_model`.
       Value are the corresponding config objects.
   """
   pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()