def prepare_ir(argv: argparse.Namespace):
is_tf, is_caffe, is_mxnet, is_kaldi, is_onnx = deduce_framework_by_namespace(
argv)
if not any([is_tf, is_caffe, is_mxnet, is_kaldi, is_onnx]):
raise Error(
'Framework {} is not a valid target. Please use --framework with one from the list: caffe, tf, '
'mxnet, kaldi, onnx. ' + refer_to_faq_msg(15), argv.framework)
if is_tf and not argv.input_model and not argv.saved_model_dir and not argv.input_meta_graph:
raise Error(
'Path to input model or saved model dir is required: use --input_model, --saved_model_dir or '
'--input_meta_graph')
elif is_mxnet and not argv.input_model and not argv.input_symbol and not argv.pretrained_model_name:
raise Error(
'Path to input model or input symbol or pretrained_model_name is required: use --input_model or '
'--input_symbol or --pretrained_model_name')
elif is_caffe and not argv.input_model and not argv.input_proto:
raise Error(
'Path to input model or input proto is required: use --input_model or --input_proto'
)
elif (is_kaldi or is_onnx) and not argv.input_model:
raise Error('Path to input model is required: use --input_model.')
if is_kaldi:
argv.generate_experimental_IR_V10 = False
log.debug(str(argv))
log.debug("Model Optimizer started")
model_name = "<UNKNOWN_NAME>"
if argv.model_name:
model_name = argv.model_name
elif argv.input_model:
model_name = get_model_name(argv.input_model)
elif is_tf and argv.saved_model_dir:
model_name = "saved_model"
elif is_tf and argv.input_meta_graph:
model_name = get_model_name(argv.input_meta_graph)
elif is_mxnet and argv.input_symbol:
model_name = get_model_name(argv.input_symbol)
argv.model_name = model_name
log.debug('Output model name would be {}{{.xml, .bin}}'.format(
argv.model_name))
# if --input_proto is not provided, try to retrieve another one
# by suffix substitution from model file name
if is_caffe and not argv.input_proto:
argv.input_proto = replace_ext(argv.input_model, '.caffemodel',
'.prototxt')
if not argv.input_proto:
raise Error(
"Cannot find prototxt file: for Caffe please specify --input_proto - a "
+
"protobuf file that stores topology and --input_model that stores "
+ "pretrained weights. " + refer_to_faq_msg(20))
log.info('Deduced name for prototxt: {}'.format(argv.input_proto))
if not argv.silent:
print_argv(argv, is_caffe, is_tf, is_mxnet, is_kaldi, is_onnx,
argv.model_name)
ret_code = check_requirements(framework=argv.framework)
if ret_code:
raise Error(
'check_requirements exit with return code {}'.format(ret_code))
if is_tf and argv.tensorflow_use_custom_operations_config is not None:
argv.transformations_config = argv.tensorflow_use_custom_operations_config
mean_file_offsets = None
if is_caffe and argv.mean_file and argv.mean_values:
raise Error(
'Both --mean_file and mean_values are specified. Specify either mean file or mean values. '
+ refer_to_faq_msg(17))
elif is_caffe and argv.mean_file and argv.mean_file_offsets:
values = get_tuple_values(argv.mean_file_offsets,
t=int,
num_exp_values=2)
mean_file_offsets = np.array([int(x) for x in values[0].split(',')])
if not all([offset >= 0 for offset in mean_file_offsets]):
raise Error(
"Negative value specified for --mean_file_offsets option. "
"Please specify positive integer values in format '(x,y)'. " +
refer_to_faq_msg(18))
argv.mean_file_offsets = mean_file_offsets
if argv.scale and argv.scale_values:
raise Error(
'Both --scale and --scale_values are defined. Specify either scale factor or scale values per input '
+ 'channels. ' + refer_to_faq_msg(19))
if argv.scale and argv.scale < 1.0:
log.error(
"The scale value is less than 1.0. This is most probably an issue because the scale value specifies "
"floating point value which all input values will be *divided*.",
extra={'is_warning': True})
if argv.input_model and (is_tf and argv.saved_model_dir):
raise Error('Both --input_model and --saved_model_dir are defined. '
'Specify either input model or saved model directory.')
if is_tf:
if argv.saved_model_tags is not None:
if ' ' in argv.saved_model_tags:
raise Error(
'Incorrect saved model tag was provided. Specify --saved_model_tags with no spaces in it'
)
argv.saved_model_tags = argv.saved_model_tags.split(',')
argv.output = argv.output.split(',') if argv.output else None
argv.placeholder_shapes, argv.placeholder_data_types = get_placeholder_shapes(
argv.input, argv.input_shape, argv.batch)
mean_values = parse_tuple_pairs(argv.mean_values)
scale_values = parse_tuple_pairs(argv.scale_values)
mean_scale = get_mean_scale_dictionary(mean_values, scale_values,
argv.input)
argv.mean_scale_values = mean_scale
if not os.path.exists(argv.output_dir):
try:
os.makedirs(argv.output_dir)
except PermissionError as e:
raise Error(
"Failed to create directory {}. Permission denied! " +
refer_to_faq_msg(22), argv.output_dir) from e
else:
if not os.access(argv.output_dir, os.W_OK):
raise Error(
"Output directory {} is not writable for current user. " +
refer_to_faq_msg(22), argv.output_dir)
log.debug("Placeholder shapes : {}".format(argv.placeholder_shapes))
ret_res = 1
if hasattr(argv,
'extensions') and argv.extensions and argv.extensions != '':
extensions = argv.extensions.split(',')
else:
extensions = None
argv.freeze_placeholder_with_value, argv.input = get_freeze_placeholder_values(
argv.input, argv.freeze_placeholder_with_value)
graph = None
if is_tf:
import mo.pipeline.tf as mo_tf
from mo.front.tf.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
graph = mo_tf.driver(argv)
elif is_caffe:
import mo.pipeline.caffe as mo_caffe
from mo.front.caffe.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
graph = mo_caffe.driver(argv)
elif is_mxnet:
import mo.pipeline.mx as mo_mxnet
from mo.front.mxnet.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
graph = mo_mxnet.driver(argv)
elif is_kaldi:
import mo.pipeline.kaldi as mo_kaldi
from mo.front.kaldi.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
graph = mo_kaldi.driver(argv)
elif is_onnx:
import mo.pipeline.onnx as mo_onnx
from mo.front.onnx.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
graph = mo_onnx.driver(argv)
return graph
def driver(argv: argparse.Namespace):
if argv.version:
print('Version of Model Optimizer is: {}'.format(get_version()))
return 0
init_logger(argv.log_level.upper(), argv.silent)
start_time = datetime.datetime.now()
if not argv.framework:
if 'saved_model_dir' in argv and argv.saved_model_dir or \
'input_meta_graph' in argv and argv.input_meta_graph:
argv.framework = 'tf'
elif 'input_symbol ' in argv and argv.input_symbol or \
'pretrained_model_name' in argv and argv.pretrained_model_name:
argv.framework = 'mxnet'
elif 'input_proto' in argv and argv.input_proto:
argv.framework = 'caffe'
elif argv.input_model is None:
raise Error('Path to input model is required: use --input_model.')
else:
argv.framework = guess_framework_by_ext(argv.input_model)
if not argv.framework:
raise Error(
'Framework name can not be deduced from the given options: {}={}. '
+
'Use --framework to choose one of caffe, tf, mxnet, kaldi, onnx',
'--input_model',
argv.input_model,
refer_to_faq_msg(15),
)
is_tf, is_caffe, is_mxnet, is_kaldi, is_onnx = (
argv.framework == x for x in ['tf', 'caffe', 'mxnet', 'kaldi', 'onnx'])
if is_tf and not argv.input_model and not argv.saved_model_dir and not argv.input_meta_graph:
raise Error(
'Path to input model or saved model dir is required: use --input_model, --saved_model_dir or '
'--input_meta_graph')
elif is_mxnet and not argv.input_model and not argv.input_symbol and not argv.pretrained_model_name:
raise Error(
'Path to input model or input symbol or pretrained_model_name is required: use --input_model or '
'--input_symbol or --pretrained_model_name')
elif is_caffe and not argv.input_model and not argv.input_proto:
raise Error(
'Path to input model or input proto is required: use --input_model or --input_proto'
)
elif (is_kaldi or is_onnx) and not argv.input_model:
raise Error('Path to input model is required: use --input_model.')
log.debug(str(argv))
log.debug("Model Optimizer started")
model_name = "<UNKNOWN_NAME>"
if argv.model_name:
model_name = argv.model_name
elif argv.input_model:
model_name = get_model_name(argv.input_model)
elif is_tf and argv.saved_model_dir:
model_name = "saved_model"
elif is_tf and argv.input_meta_graph:
model_name = get_model_name(argv.input_meta_graph)
elif is_mxnet and argv.input_symbol:
model_name = get_model_name(argv.input_symbol)
log.debug('Output model name would be {}{{.xml, .bin}}'.format(model_name))
# if --input_proto is not provided, try to retrieve another one
# by suffix substitution from model file name
if is_caffe and not argv.input_proto:
argv.input_proto = replace_ext(argv.input_model, '.caffemodel',
'.prototxt')
if not argv.input_proto:
raise Error(
"Cannot find prototxt file: for Caffe please specify --input_proto - a "
+
"protobuf file that stores topology and --input_model that stores "
+ "pretrained weights. " + refer_to_faq_msg(20))
log.info('Deduced name for prototxt: {}'.format(argv.input_proto))
if not argv.silent:
print_argv(argv, is_caffe, is_tf, is_mxnet, is_kaldi, is_onnx,
model_name)
if not any([is_tf, is_caffe, is_mxnet, is_kaldi, is_onnx]):
raise Error(
'Framework {} is not a valid target. ' +
'Please use --framework with one from the list: caffe, tf, mxnet, kaldi, onnx. '
+ refer_to_faq_msg(15), argv.framework)
ret_code = check_requirements(framework=argv.framework)
if ret_code:
return ret_code
if is_mxnet and not argv.input_shape:
raise Error(
'Input shape is required to convert MXNet model. Please provide it with --input_shape. '
+ refer_to_faq_msg(16))
mean_file_offsets = None
if is_caffe and argv.mean_file and argv.mean_values:
raise Error(
'Both --mean_file and mean_values are specified. Specify either mean file or mean values. '
+ refer_to_faq_msg(17))
elif is_caffe and argv.mean_file and argv.mean_file_offsets:
values = get_tuple_values(argv.mean_file_offsets,
t=int,
num_exp_values=2)
mean_file_offsets = np.array([int(x) for x in values[0].split(',')])
if not all([offset >= 0 for offset in mean_file_offsets]):
raise Error(
"Negative value specified for --mean_file_offsets option. "
"Please specify positive integer values in format '(x,y)'. " +
refer_to_faq_msg(18))
custom_layers_mapping_path = argv.k if is_caffe and argv.k else None
if argv.scale and argv.scale_values:
raise Error(
'Both --scale and --scale_values are defined. Specify either scale factor or scale values per input '
+ 'channels. ' + refer_to_faq_msg(19))
if argv.scale and argv.scale < 1.0:
log.error(
"The scale value is less than 1.0. This is most probably an issue because the scale value specifies "
"floating point value which all input values will be *divided*.",
extra={'is_warning': True})
if argv.input_model and (is_tf and argv.saved_model_dir):
raise Error('Both --input_model and --saved_model_dir are defined. '
'Specify either input model or saved model directory.')
if is_tf:
if argv.saved_model_tags is not None:
if ' ' in argv.saved_model_tags:
raise Error(
'Incorrect saved model tag was provided. Specify --saved_model_tags with no spaces in it'
)
argv.saved_model_tags = argv.saved_model_tags.split(',')
argv.output = argv.output.split(',') if argv.output else None
argv.placeholder_shapes = get_placeholder_shapes(argv.input,
argv.input_shape,
argv.batch)
mean_values = parse_tuple_pairs(argv.mean_values)
scale_values = parse_tuple_pairs(argv.scale_values)
mean_scale = get_mean_scale_dictionary(mean_values, scale_values,
argv.input)
argv.mean_scale_values = mean_scale
if not os.path.exists(argv.output_dir):
try:
os.makedirs(argv.output_dir)
except PermissionError as e:
raise Error(
"Failed to create directory {}. Permission denied! " +
refer_to_faq_msg(22), argv.output_dir) from e
else:
if not os.access(argv.output_dir, os.W_OK):
raise Error(
"Output directory {} is not writable for current user. " +
refer_to_faq_msg(22), argv.output_dir)
log.debug("Placeholder shapes : {}".format(argv.placeholder_shapes))
ret_res = 1
if hasattr(argv,
'extensions') and argv.extensions and argv.extensions != '':
extensions = argv.extensions.split(',')
else:
extensions = None
argv.freeze_placeholder_with_value, argv.input = get_freeze_placeholder_values(
argv.input, argv.freeze_placeholder_with_value)
if is_tf:
import mo.pipeline.tf as mo_tf
from mo.front.tf.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
ret_res = mo_tf.tf2nx(argv,
argv.input_model,
model_name,
argv.output_dir,
is_binary=not argv.input_model_is_text)
elif is_caffe:
import mo.pipeline.caffe as mo_caffe
from mo.front.caffe.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
ret_res = mo_caffe.driver(
argv,
argv.input_proto,
argv.input_model,
model_name,
argv.output_dir,
argv.caffe_parser_path,
mean_file=argv.mean_file,
mean_file_offsets=mean_file_offsets,
custom_layers_mapping_path=custom_layers_mapping_path)
elif is_mxnet:
import mo.pipeline.mx as mo_mxnet
from mo.front.mxnet.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
ret_res = mo_mxnet.driver(argv, argv.input_model, model_name,
argv.output_dir)
elif is_kaldi:
import mo.pipeline.kaldi as mo_kaldi
from mo.front.kaldi.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
ret_res = mo_kaldi.driver(argv, argv.input_model, model_name,
argv.output_dir)
elif is_onnx:
import mo.pipeline.onnx as mo_onnx
from mo.front.onnx.register_custom_ops import get_front_classes
import_extensions.load_dirs(argv.framework, extensions,
get_front_classes)
ret_res = mo_onnx.driver(argv, argv.input_model, model_name,
argv.output_dir)
if ret_res != 0:
return ret_res
if not (is_tf and argv.tensorflow_custom_operations_config_update):
output_dir = argv.output_dir if argv.output_dir != '.' else os.getcwd()
print('\n[ SUCCESS ] Generated IR model.')
print('[ SUCCESS ] XML file: {}.xml'.format(
os.path.join(output_dir, model_name)))
print('[ SUCCESS ] BIN file: {}.bin'.format(
os.path.join(output_dir, model_name)))
elapsed_time = datetime.datetime.now() - start_time
print('[ SUCCESS ] Total execution time: {:.2f} seconds. '.format(
elapsed_time.total_seconds()))
return ret_res