def test_gen_conversion_log_html(self):
# Copies all required data files into a temporary folder for testing.
export_path = self.get_temp_dir()
toco_log_before_path = resource_loader.get_path_to_datafile(
"testdata/toco_log_before.pb")
toco_log_after_path = resource_loader.get_path_to_datafile(
"testdata/toco_log_after.pb")
dot_before = resource_loader.get_path_to_datafile(
"testdata/toco_tf_graph.dot")
dot_after = resource_loader.get_path_to_datafile(
"testdata/toco_tflite_graph.dot")
shutil.copy(toco_log_before_path, export_path)
shutil.copy(toco_log_after_path, export_path)
shutil.copy(dot_before, export_path)
shutil.copy(dot_after, export_path)
# Generate HTML content based on files in the test folder.
gen_html.gen_conversion_log_html(export_path, True)
result_html = os.path.join(export_path, "toco_conversion_summary.html")
with _file_io.FileIO(result_html, "r") as f_export, _file_io.FileIO(
resource_loader.get_path_to_datafile(
"testdata/generated.html"), "r") as f_expect:
expected = f_expect.read()
exported = f_export.read()
self.assertEqual(exported, expected)
def run_main(_):
"""Main in tflite_convert.py."""
use_v2_converter = tf2.enabled()
parser = _get_parser(use_v2_converter)
tflite_flags, unparsed = parser.parse_known_args(args=sys.argv[1:])
# If the user is running TensorFlow 2.X but has passed in enable_v1_converter
# then parse the flags again with the 1.X converter flags.
if tf2.enabled() and tflite_flags.enable_v1_converter:
use_v2_converter = False
parser = _get_parser(use_v2_converter)
tflite_flags, unparsed = parser.parse_known_args(args=sys.argv[1:])
# Checks if the flags are valid.
try:
if use_v2_converter:
_check_tf2_flags(tflite_flags)
else:
_check_tf1_flags(tflite_flags, unparsed)
except ValueError as e:
parser.print_usage()
file_name = os.path.basename(sys.argv[0])
sys.stderr.write("{0}: error: {1}\n".format(file_name, str(e)))
sys.exit(1)
# Convert the model according to the user provided flag.
if use_v2_converter:
_convert_tf2_model(tflite_flags)
else:
try:
_convert_tf1_model(tflite_flags)
finally:
if tflite_flags.conversion_summary_dir:
if tflite_flags.experimental_new_converter:
gen_html.gen_conversion_log_html(
tflite_flags.conversion_summary_dir,
tflite_flags.post_training_quantize,
tflite_flags.output_file)
else:
warnings.warn(
"Conversion summary will only be generated when enabling"
" the new converter via --experimental_new_converter. "
)
