openvino/model-optimizer/mo/main.py

# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import argparse
import datetime
import logging as log
import os
import platform
import subprocess
import sys
import traceback
from collections import OrderedDict
from copy import deepcopy

import numpy as np

try:
    import openvino_telemetry as tm
except ImportError:
    import mo.utils.telemetry_stub as tm

from extensions.back.SpecialNodesFinalization import RemoveConstOps, CreateConstNodesReplacement, NormalizeTI
from mo.back.ie_ir_ver_2.emitter import append_ir_info
from mo.graph.graph import Graph
from mo.middle.pattern_match import for_graph_and_each_sub_graph_recursively
from mo.pipeline.common import prepare_emit_ir, get_ir_version
from mo.pipeline.unified import unified_pipeline
from mo.utils import import_extensions
from mo.utils.cli_parser import get_placeholder_shapes, get_tuple_values, get_model_name, \
    get_common_cli_options, get_caffe_cli_options, get_tf_cli_options, get_mxnet_cli_options, get_kaldi_cli_options, \
    get_onnx_cli_options, get_mean_scale_dictionary, parse_tuple_pairs, get_freeze_placeholder_values, get_meta_info, \
    parse_transform, check_available_transforms
from mo.utils.error import Error, FrameworkError
from mo.utils.find_ie_version import find_ie_version
from mo.utils.get_ov_update_message import get_ov_update_message
from mo.utils.guess_framework import deduce_framework_by_namespace
from mo.utils.logger import init_logger
from mo.utils.model_analysis import AnalysisResults
from mo.utils.utils import refer_to_faq_msg
from mo.utils.telemetry_utils import send_params_info, send_framework_info
from mo.utils.version import get_version, get_simplified_mo_version, get_simplified_ie_version
from mo.utils.versions_checker import check_requirements  # pylint: disable=no-name-in-module


def replace_ext(name: str, old: str, new: str):
    base, ext = os.path.splitext(name)
    log.debug("base: {}, ext: {}".format(base, ext))
    if ext == old:
        return base + new


def print_argv(argv: argparse.Namespace, is_caffe: bool, is_tf: bool, is_mxnet: bool, is_kaldi: bool, is_onnx: bool,
               model_name: str):
    print('Model Optimizer arguments:')
    props = OrderedDict()
    props['common_args'] = get_common_cli_options(model_name)
    if is_caffe:
        props['caffe_args'] = get_caffe_cli_options()
    if is_tf:
        props['tf_args'] = get_tf_cli_options()
    if is_mxnet:
        props['mxnet_args'] = get_mxnet_cli_options()
    if is_kaldi:
        props['kaldi_args'] = get_kaldi_cli_options()
    if is_onnx:
        props['onnx_args'] = get_onnx_cli_options()

    framework_specifics_map = {
        'common_args': 'Common parameters:',
        'caffe_args': 'Caffe specific parameters:',
        'tf_args': 'TensorFlow specific parameters:',
        'mxnet_args': 'MXNet specific parameters:',
        'kaldi_args': 'Kaldi specific parameters:',
        'onnx_args': 'ONNX specific parameters:',
    }

    lines = []
    for key in props:
        lines.append(framework_specifics_map[key])
        for (op, desc) in props[key].items():
            if isinstance(desc, list):
                lines.append('\t{}: \t{}'.format(desc[0], desc[1](getattr(argv, op, 'NONE'))))
            else:
                if op == 'k':
                    default_path = os.path.join(os.path.dirname(sys.argv[0]),
                                                'extensions/front/caffe/CustomLayersMapping.xml')
                    if getattr(argv, op, 'NONE') == default_path:
                        lines.append('\t{}: \t{}'.format(desc, 'Default'))
                        continue
                lines.append('\t{}: \t{}'.format(desc, getattr(argv, op, 'NONE')))
    print('\n'.join(lines), flush=True)


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.')

    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)

    # This try-except is additional reinsurance that the IE
    # dependency search does not break the MO pipeline
    try:
        argv.ie_is_available = find_ie_version(silent=argv.silent)

        if not argv.ie_is_available and not argv.silent:
            print("[ WARNING ] Could not find the Inference Engine Python API. At this moment, the Inference Engine dependency is not required, but will be required in future releases.")
            print("[ WARNING ] Consider building the Inference Engine Python API from sources or try to install OpenVINO (TM) Toolkit using \"install_prerequisites.{}\"".format(
                    "bat" if sys.platform == "windows" else "sh"))
            # If the IE was not found, it will not print the MO version, so we have to print it manually
            print("{}: \t{}".format("Model Optimizer version", get_version()))
    except Exception as e:
        argv.ie_is_available = False

    # This is just to check that transform key is valid and transformations are available
    check_available_transforms(parse_transform(argv.transform), argv.ie_is_available)

    if argv.legacy_ir_generation and len(argv.transform) != 0:
        raise Error("--legacy_ir_generation and --transform keys can not be used at the same time.")

    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

    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))

    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:
        from mo.front.tf.register_custom_ops import get_front_classes
        import_extensions.load_dirs(argv.framework, extensions, get_front_classes)
    elif is_caffe:
        send_framework_info('caffe')
        from mo.front.caffe.register_custom_ops import get_front_classes
        import_extensions.load_dirs(argv.framework, extensions, get_front_classes)
    elif is_mxnet:
        send_framework_info('mxnet')
        from mo.front.mxnet.register_custom_ops import get_front_classes
        import_extensions.load_dirs(argv.framework, extensions, get_front_classes)
    elif is_kaldi:
        send_framework_info('kaldi')
        from mo.front.kaldi.register_custom_ops import get_front_classes
        import_extensions.load_dirs(argv.framework, extensions, get_front_classes)
    elif is_onnx:
        send_framework_info('onnx')
        from mo.front.onnx.register_custom_ops import get_front_classes
        import_extensions.load_dirs(argv.framework, extensions, get_front_classes)
    graph = unified_pipeline(argv)
    return graph


def emit_ir(graph: Graph, argv: argparse.Namespace):
    NormalizeTI().find_and_replace_pattern(graph)
    for_graph_and_each_sub_graph_recursively(graph, RemoveConstOps().find_and_replace_pattern)
    for_graph_and_each_sub_graph_recursively(graph, CreateConstNodesReplacement().find_and_replace_pattern)

    mean_data = deepcopy(graph.graph['mf']) if 'mf' in graph.graph else None
    input_names = deepcopy(graph.graph['input_names']) if 'input_names' in graph.graph else []

    # Remove temporary ie_is_available key from argv no to have it in IR
    ie_is_available = argv.ie_is_available
    del argv.ie_is_available

    prepare_emit_ir(graph=graph,
                    data_type=graph.graph['cmd_params'].data_type,
                    output_dir=argv.output_dir,
                    output_model_name=argv.model_name,
                    mean_data=mean_data,
                    input_names=input_names,
                    meta_info=get_meta_info(argv),
                    use_temporary_path=True)

    # This graph cleanup is required to avoid double memory consumption
    graph.clear()

    if not (argv.framework == 'tf' and argv.tensorflow_custom_operations_config_update):
        output_dir = argv.output_dir if argv.output_dir != '.' else os.getcwd()
        orig_model_name = os.path.normpath(os.path.join(output_dir, argv.model_name))

        return_code = "not executed"
        # This try-except is additional reinsurance that the IE
        # dependency search does not break the MO pipeline
        try:
            if not argv.legacy_ir_generation and ie_is_available:
                path_to_offline_transformations = os.path.join(os.path.realpath(os.path.dirname(__file__)), 'back',
                                                               'offline_transformations.py')
                status = subprocess.run([sys.executable, path_to_offline_transformations,
                                         "--input_model", orig_model_name,
                                         "--framework", argv.framework,
                                         "--transform", argv.transform], env=os.environ)
                return_code = status.returncode
        except Exception as e:
            return_code = "failed"
            log.error(e, extra={'is_warning': True})

        message = str(dict({
            "platform": platform.system(),
            "mo_version": get_simplified_mo_version(),
            "ie_version": get_simplified_ie_version(env=os.environ),
            "python_version": sys.version,
            "return_code": return_code
        }))
        t = tm.Telemetry()
        t.send_event('mo', 'offline_transformations_status', message)

        # if IR wasn't produced by offline_transformations step we need to fallback to IR
        # produced by prepare_ir. This IR needs to be renamed from XXX_tmp.xml to XXX.xml
        suffixes = [".xml", ".bin", ".mapping"]
        if return_code != 0:
            if len(argv.transform) != 0:
                # Remove temporary IR before throwing exception
                for suf in suffixes:
                    path_to_file = orig_model_name + "_tmp" + suf
                    if os.path.exists(path_to_file):
                        os.remove(path_to_file)
                raise Error("Failed to apply transformations: {}".format(argv.transform))

            log.error("Using fallback to produce IR.", extra={'is_warning': True})
            for suf in suffixes:
                # remove existing files
                path_to_file = orig_model_name + suf
                if os.path.exists(path_to_file):
                    os.remove(path_to_file)

                # rename tmp IR to original name
                os.rename(orig_model_name + "_tmp" + suf, orig_model_name + suf)
        else:
            for suf in suffixes:
                # remove existing files
                path_to_file = orig_model_name + "_tmp" + suf
                if os.path.exists(path_to_file):
                    os.remove(path_to_file)

            # add meta information to IR
            append_ir_info(file=orig_model_name,
                           meta_info=get_meta_info(argv),
                           mean_data=mean_data,
                           input_names=input_names)

        print('[ SUCCESS ] Generated IR version {} model.'.format(get_ir_version(argv)))
        print('[ SUCCESS ] XML file: {}.xml'.format(orig_model_name))
        print('[ SUCCESS ] BIN file: {}.bin'.format(orig_model_name))

    return 0


def driver(argv: argparse.Namespace):
    init_logger(argv.log_level.upper(), argv.silent)

    start_time = datetime.datetime.now()

    ret_res = emit_ir(prepare_ir(argv), argv)

    if ret_res != 0:
        return ret_res

    elapsed_time = datetime.datetime.now() - start_time
    print('[ SUCCESS ] Total execution time: {:.2f} seconds. '.format(elapsed_time.total_seconds()))

    try:
        import resource
        mem_usage = round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024)
        if sys.platform == 'darwin':
            mem_usage = round(mem_usage / 1024)
        print('[ SUCCESS ] Memory consumed: {} MB. '.format(mem_usage))
    except ImportError:
        pass

    return ret_res


def main(cli_parser: argparse.ArgumentParser, framework: str):
    telemetry = tm.Telemetry(app_name='Model Optimizer', app_version=get_simplified_mo_version())
    telemetry.start_session('mo')
    telemetry.send_event('mo', 'version', get_simplified_mo_version())
    try:
        # Initialize logger with 'ERROR' as default level to be able to form nice messages
        # before arg parser deliver log_level requested by user
        init_logger('ERROR', False)

        argv = cli_parser.parse_args()
        send_params_info(argv, cli_parser)

        if framework:
            argv.framework = framework

        ov_update_message = None
        if not hasattr(argv, 'silent') or not argv.silent:
            ov_update_message = get_ov_update_message()
        ret_code = driver(argv)
        if ov_update_message:
            print(ov_update_message)
        telemetry.send_event('mo', 'conversion_result', 'success')
        telemetry.end_session('mo')
        telemetry.force_shutdown(1.0)
        return ret_code
    except (FileNotFoundError, NotADirectoryError) as e:
        log.error('File {} was not found'.format(str(e).split('No such file or directory:')[1]))
        log.debug(traceback.format_exc())
    except Error as err:
        analysis_results = AnalysisResults()
        if analysis_results.get_messages() is not None:
            for el in analysis_results.get_messages():
                log.error(el, extra={'analysis_info': True})
        log.error(err)
        log.debug(traceback.format_exc())
    except FrameworkError as err:
        log.error(err, extra={'framework_error': True})
        log.debug(traceback.format_exc())
    except Exception as err:
        log.error("-------------------------------------------------")
        log.error("----------------- INTERNAL ERROR ----------------")
        log.error("Unexpected exception happened.")
        log.error("Please contact Model Optimizer developers and forward the following information:")
        log.error(str(err))
        log.error(traceback.format_exc())
        log.error("---------------- END OF BUG REPORT --------------")
        log.error("-------------------------------------------------")

    telemetry.send_event('mo', 'conversion_result', 'fail')
    telemetry.end_session('mo')
    telemetry.force_shutdown(1.0)
    return 1


if __name__ == "__main__":
    from mo.utils.cli_parser import get_all_cli_parser
    sys.exit(main(get_all_cli_parser(), None))