def get_reader(input_format,
output_format,
input_shape=None,
permissive=False,
with_weights=True,
custom_converters=None):
if input_format == 'nnef':
from nnef_tools.io.nnef.nnef_io import Reader
configs = [NNEFParserConfig.STANDARD_CONFIG]
if output_format in [
'tensorflow-pb', 'tensorflow-py', 'tensorflow-lite'
]:
from nnef_tools.conversion.tensorflow import nnef_to_tf
configs.append(nnef_to_tf.ParserConfig)
elif output_format in ['onnx']:
from nnef_tools.conversion.onnx import nnef_to_onnx
configs.append(nnef_to_onnx.ParserConfig)
elif output_format in ['caffe']:
from nnef_tools.conversion.caffe import nnef_to_caffe
configs.append(nnef_to_caffe.ParserConfig)
else:
assert False
configs += NNEFParserConfig.load_configs(custom_converters,
load_standard=False)
return Reader(parser_configs=configs,
unify=(output_format in ['caffe']))
elif input_format == 'tensorflow-pb':
# TODO custom converter
from nnef_tools.io.tensorflow.tf_pb_io import Reader
return Reader(convert_to_tf_py=True, input_shape=input_shape)
elif input_format == 'tensorflow-py':
from nnef_tools.io.tensorflow.tf_py_io import Reader
if custom_converters:
custom_traceable_functions = get_tf_py_custom_traceable_functions(
custom_converters)
else:
custom_traceable_functions = None
return Reader(expand_gradients=True,
custom_traceable_functions=custom_traceable_functions)
elif input_format == 'tensorflow-lite':
# TODO custom converter
from nnef_tools.io.tensorflow.tflite_io import Reader
return Reader(convert_to_tf_py=True)
elif input_format == 'onnx':
# TODO custom converter
from nnef_tools.io.onnx.onnx_io import Reader
return Reader(propagate_shapes=True, input_shape=input_shape)
elif input_format == 'caffe':
# TODO custom converter
from nnef_tools.io.caffe.caffe_io import Reader
return Reader()
else:
assert False
def main():
try:
args = get_args(sys.argv)
if not args.output:
if sys.stdout.isatty():
raise utils.NNEFToolsException("No output provided.")
utils.set_stdout_to_binary()
args.params = InputSources(args.params)
if args.seed != -1:
np.random.seed(args.seed)
parser_configs = NNEFParserConfig.load_configs(args.custom_operations,
load_standard=True)
reader = nnef_io.Reader(parser_configs=parser_configs,
input_shape=args.shape)
# read without weights
graph = reader(
os.path.join(args.network, 'graph.nnef') if os.path.
isdir(args.network) else args.network)
inputs = tuple(
args.params.create_input(name=input.name,
np_dtype=input.get_numpy_dtype(),
shape=input.shape,
allow_bigger_batch=True)
for input in graph.inputs)
if args.output:
for tensor, array in zip(graph.inputs, inputs):
nnef_io.write_nnef_tensor(
os.path.join(args.output, tensor.name + '.dat'), array)
else:
for array in inputs:
nnef.write_tensor(sys.stdout, array)
except Exception as e:
print('Error: {}'.format(e), file=sys.stderr)
exit(1)
def run_using_argv(argv):
try:
args = get_args(argv)
write_outputs = args.output_names is None or args.output_names
if args.input is None:
if sys.stdin.isatty():
raise utils.NNEFToolsException("No input provided!")
utils.set_stdin_to_binary()
if write_outputs:
if args.output is None:
if sys.stdout.isatty():
raise utils.NNEFToolsException("No output provided!")
utils.set_stdout_to_binary()
parent_dir_of_input_model = os.path.dirname(
utils.path_without_trailing_separator(args.network))
tmp_dir = None
if args.network.endswith('.tgz'):
nnef_path = tmp_dir = tempfile.mkdtemp(
prefix="nnef_", dir=parent_dir_of_input_model)
utils.tgz_extract(args.network, nnef_path)
else:
nnef_path = args.network
try:
parser_configs = NNEFParserConfig.load_configs(
args.custom_operations, load_standard=True)
# read without weights
reader = nnef_io.Reader(parser_configs=parser_configs,
infer_shapes=False)
graph = reader(
os.path.join(nnef_path, 'graph.nnef') if os.path.
isdir(nnef_path) else nnef_path)
if args.input is None:
inputs = tuple(
nnef.read_tensor(sys.stdin)
for _ in range(len(graph.inputs)))
elif len(args.input) == 1 and os.path.isdir(args.input[0]):
inputs = tuple(
nnef_io.read_nnef_tensor(
os.path.join(args.input[0], tensor.name + '.dat'))
for tensor in graph.inputs)
else:
inputs = tuple(
nnef_io.read_nnef_tensor(path) for path in args.input)
reader = nnef_io.Reader(parser_configs=parser_configs,
input_shape=tuple(
list(input.shape) for input in inputs))
graph = reader(nnef_path)
tensor_hooks = []
stats_hook = None
if args.stats:
stats_hook = backend.StatisticsHook()
tensor_hooks.append(stats_hook)
if write_outputs and args.output_names is not None:
if '*' in args.output_names:
tensor_hooks.append(
backend.ActivationExportHook(
tensor_names=[
t.name for t in graph.tensors
if not t.is_constant and not t.is_variable
],
output_directory=args.output))
else:
tensor_hooks.append(
backend.ActivationExportHook(
tensor_names=args.output_names,
output_directory=args.output))
if args.permissive:
backend.try_to_fix_unsupported_attributes(graph)
outputs = backend.run(nnef_graph=graph,
inputs=inputs,
device=args.device,
custom_operations=get_custom_runners(
args.custom_operations),
tensor_hooks=tensor_hooks)
if write_outputs and args.output_names is None:
if args.output is None:
for array in outputs:
nnef.write_tensor(sys.stdout, array)
else:
for tensor, array in zip(graph.outputs, outputs):
nnef_io.write_nnef_tensor(
os.path.join(args.output, tensor.name + '.dat'),
array)
if stats_hook:
if args.stats.endswith('/') or args.stats.endswith('\\'):
stats_path = os.path.join(nnef_path, args.stats,
'graph.stats')
else:
stats_path = os.path.join(nnef_path, args.stats)
stats_hook.save_statistics(stats_path)
if tmp_dir and (args.stats and _is_inside(nnef_path, args.stats)):
if args.network.endswith('.tgz'):
print("Info: Changing input archive", file=sys.stderr)
shutil.move(args.network,
args.network + '.nnef-tools-backup')
utils.tgz_compress(dir_path=nnef_path,
file_path=args.network)
os.remove(args.network + '.nnef-tools-backup')
else:
output_path = args.network.rsplit('.', 1)[0] + '.nnef.tgz'
backup_path = output_path + '.nnef-tools-backup'
if os.path.exists(output_path):
shutil.move(output_path, backup_path)
utils.tgz_compress(dir_path=nnef_path,
file_path=output_path)
if os.path.exists(backup_path):
os.remove(backup_path)
finally:
if tmp_dir:
shutil.rmtree(tmp_dir)
except utils.NNEFToolsException as e:
print("Error: " + str(e), file=sys.stderr)
exit(1)
except nnef.Error as e:
print("Error: " + str(e), file=sys.stderr)
exit(1)
