Update the usage of the 'absl-py' python library from TFLite Micro utils

PiperOrigin-RevId: 344322150
Change-Id: Ida045ffe25f630663ab91170c72d370e3f7f7634

tensorflow/lite/python/BUILD

@@ -433,9 +433,9 @@ py_binary(
     srcs_version = "PY2AND3",
     visibility = ["//visibility:public"],
     deps = [
-        ":lite",
         ":util",
-        "@six_archive//:six",
+        "@absl_py//absl:app",
+        "@absl_py//absl/flags",
     ],
 )
 

tensorflow/lite/python/convert_file_to_c_source.py

@@ -1,4 +1,3 @@
-# Lint as: python2, python3
 # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,94 +12,61 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Python command line interface for converting TF Lite files into C source."""
+"""Converts a TFLite model to a TFLite Micro model (C++ Source)."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import argparse
+from absl import app
-import sys
+from absl import flags
 
 from tensorflow.lite.python import util
-from tensorflow.python.platform import app
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string("input_tflite_file", None,
+                    "Full path name to the input TFLite model file.")
+flags.DEFINE_string(
+    "output_source_file", None,
+    "Full path name to the output TFLite Micro model (C++ Source) file).")
+flags.DEFINE_string("output_header_file", None,
+                    "Full filepath of the output C header file.")
+flags.DEFINE_string("array_variable_name", None,
+                    "Name to use for the C data array variable.")
+flags.DEFINE_integer("line_width", 80, "Width to use for formatting.")
+flags.DEFINE_string("include_guard", None,
+                    "Name to use for the C header include guard.")
+flags.DEFINE_string("include_path", None,
+                    "Optional path to include in generated source file.")
+flags.DEFINE_boolean(
+    "use_tensorflow_license", False,
+    "Whether to prefix the generated files with the TF Apache2 license.")
+
+flags.mark_flag_as_required("input_tflite_file")
+flags.mark_flag_as_required("output_source_file")
+flags.mark_flag_as_required("output_header_file")
+flags.mark_flag_as_required("array_variable_name")
 
 
-def run_main(_):
+def main(_):
-  """Main in convert_file_to_c_source.py."""
+  with open(FLAGS.input_tflite_file, "rb") as input_handle:
-
-  parser = argparse.ArgumentParser(
-      description=("Command line tool to run TensorFlow Lite Converter."))
-
-  parser.add_argument(
-      "--input_tflite_file",
-      type=str,
-      help="Full filepath of the input TensorFlow Lite file.",
-      required=True)
-
-  parser.add_argument(
-      "--output_source_file",
-      type=str,
-      help="Full filepath of the output C source file.",
-      required=True)
-
-  parser.add_argument(
-      "--output_header_file",
-      type=str,
-      help="Full filepath of the output C header file.",
-      required=True)
-
-  parser.add_argument(
-      "--array_variable_name",
-      type=str,
-      help="Name to use for the C data array variable.",
-      required=True)
-
-  parser.add_argument(
-      "--line_width", type=int, help="Width to use for formatting.", default=80)
-
-  parser.add_argument(
-      "--include_guard",
-      type=str,
-      help="Name to use for the C header include guard.",
-      default=None)
-
-  parser.add_argument(
-      "--include_path",
-      type=str,
-      help="Optional path to include in generated source file.",
-      default=None)
-
-  parser.add_argument(
-      "--use_tensorflow_license",
-      dest="use_tensorflow_license",
-      help="Whether to prefix the generated files with the TF Apache2 license.",
-      action="store_true")
-  parser.set_defaults(use_tensorflow_license=False)
-
-  flags, _ = parser.parse_known_args(args=sys.argv[1:])
-
-  with open(flags.input_tflite_file, "rb") as input_handle:
     input_data = input_handle.read()
 
   source, header = util.convert_bytes_to_c_source(
       data=input_data,
-      array_name=flags.array_variable_name,
+      array_name=FLAGS.array_variable_name,
-      max_line_width=flags.line_width,
+      max_line_width=FLAGS.line_width,
-      include_guard=flags.include_guard,
+      include_guard=FLAGS.include_guard,
-      include_path=flags.include_path,
+      include_path=FLAGS.include_path,
-      use_tensorflow_license=flags.use_tensorflow_license)
+      use_tensorflow_license=FLAGS.use_tensorflow_license)
 
-  with open(flags.output_source_file, "w") as source_handle:
+  with open(FLAGS.output_source_file, "w") as source_handle:
     source_handle.write(source)
 
-  with open(flags.output_header_file, "w") as header_handle:
+  with open(FLAGS.output_header_file, "w") as header_handle:
     header_handle.write(header)
 
 
-def main():
-  app.run(main=run_main, argv=sys.argv[:1])
-
-
 if __name__ == "__main__":
-  main()
+  app.run(main)

tensorflow/lite/python/convert_file_to_c_source_test.sh

@@ -34,7 +34,7 @@ ${TEST_SRCDIR}${SCRIPT_BASE_DIR}/tensorflow/lite/python/convert_file_to_c_source
   --line_width=80 \
   --include_guard="SOME_GUARD_H_" \
   --include_path="some/guard.h" \
-  --use_tensorflow_license
+  --use_tensorflow_license=True
 
 if ! grep -q 'const unsigned char g_some_array' ${OUTPUT_SOURCE_FILE}; then
   echo "ERROR: No array found in output '${OUTPUT_SOURCE_FILE}'"

tensorflow/lite/tools/BUILD

@@ -52,8 +52,9 @@ py_binary(
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:image_ops",
         "//tensorflow/python:io_ops",
-        "//tensorflow/python:platform",
         "//tensorflow/python:session",
+        "@absl_py//absl:app",
+        "@absl_py//absl/flags",
     ],
 )
 

tensorflow/lite/tools/convert_image_to_csv.py

@@ -1,4 +1,3 @@
-# Lint as: python2, python3
 # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,27 +14,35 @@
 # ==============================================================================
 r"""This tool converts an image file into a CSV data array.
 
-Designed to help create test inputs that can be shared between Python and
+Loads JPEG or PNG input files, resizes them, optionally converts to grayscale,
-on-device test cases to investigate accuracy issues.
+and writes out as comma-separated variables, one image per row. Designed to
+help create test inputs that can be shared between Python and on-device test
+cases to investigate accuracy issues.
 
-Example usage:
-
-python convert_image_to_csv.py some_image.jpg --width=16 --height=20 \
-  --want_grayscale
 """
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import argparse
 import sys
 
+from absl import app
+from absl import flags
+
 from tensorflow.python.client import session
 from tensorflow.python.framework import ops
 from tensorflow.python.framework.errors_impl import NotFoundError
 from tensorflow.python.ops import image_ops
 from tensorflow.python.ops import io_ops
-from tensorflow.python.platform import app
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_multi_string("image_file_names", None,
+                          "List of paths to the input images.")
+flags.DEFINE_integer("width", 96, "Width to scale images to.")
+flags.DEFINE_integer("height", 96, "Height to scale images to.")
+flags.DEFINE_boolean("want_grayscale", False,
+                     "Whether to convert the image to monochrome.")
 
 
 def get_image(width, height, want_grayscale, filepath):
@@ -55,10 +62,9 @@ def get_image(width, height, want_grayscale, filepath):
     with session.Session():
       file_data = io_ops.read_file(filepath)
       channels = 1 if want_grayscale else 3
-      image_tensor = image_ops.decode_image(file_data,
+      image_tensor = image_ops.decode_image(file_data, channels=channels).eval()
-                                            channels=channels).eval()
+      resized_tensor = image_ops.resize_images_v2(image_tensor,
-      resized_tensor = image_ops.resize_images_v2(
+                                                  (height, width)).eval()
-          image_tensor, (height, width)).eval()
   return resized_tensor
 
 
@@ -73,43 +79,19 @@ def array_to_int_csv(array_data):
   """
   flattened_array = array_data.flatten()
   array_as_strings = [item.astype(int).astype(str) for item in flattened_array]
-  return ','.join(array_as_strings)
+  return ",".join(array_as_strings)
 
 
-def run_main(_):
+def main(_):
-  """Application run loop."""
+  for image_file_name in FLAGS.image_file_names:
-  parser = argparse.ArgumentParser(
-      description='Loads JPEG or PNG input files, resizes them, optionally'
-      ' converts to grayscale, and writes out as comma-separated variables,'
-      ' one image per row.')
-  parser.add_argument(
-      'image_file_names',
-      type=str,
-      nargs='+',
-      help='List of paths to the input images.')
-  parser.add_argument(
-      '--width', type=int, default=96, help='Width to scale images to.')
-  parser.add_argument(
-      '--height', type=int, default=96, help='Height to scale images to.')
-  parser.add_argument(
-      '--want_grayscale',
-      action='store_true',
-      help='Whether to convert the image to monochrome.')
-  args = parser.parse_args()
-
-  for image_file_name in args.image_file_names:
     try:
-      image_data = get_image(args.width, args.height, args.want_grayscale,
+      image_data = get_image(FLAGS.width, FLAGS.height, FLAGS.want_grayscale,
                              image_file_name)
       print(array_to_int_csv(image_data))
     except NotFoundError:
-      sys.stderr.write('Image file not found at {0}\n'.format(image_file_name))
+      sys.stderr.write("Image file not found at {0}\n".format(image_file_name))
       sys.exit(1)
 
 
-def main():
+if __name__ == "__main__":
-  app.run(main=run_main, argv=sys.argv[:1])
+  app.run(main)
-
-
-if __name__ == '__main__':
-  main()