Merge remote-tracking branch 'upstream/master'

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()