forked from mindspore-Ecosystem/mindspore
integrate_export_v2
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@ -464,7 +464,7 @@ def _fill_param_into_net(net, parameter_list):
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load_param_into_net(net, parameter_dict)
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def export(net, *inputs, file_name, file_format='AIR', quant_export=None, **kwargs):
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def export(net, *inputs, file_name, file_format='AIR', **kwargs):
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"""
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Export the MindSpore prediction model to a file in the specified format.
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@ -480,80 +480,78 @@ def export(net, *inputs, file_name, file_format='AIR', quant_export=None, **kwar
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- MINDIR: MindSpore Native Intermidiate Representation for Anf. An intermidiate representation format
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for MindSpore models.
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Recommended suffix for output file is '.mindir'.
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quant_export (str): Quantitative export choise. Default: None.
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kwargs (dict): Configuration options dictionary.
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- quant_mode: The mode of quant.
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- mean: Input data mean. Default: 127.5.
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- std_dev: Input data variance. Default: 127.5.
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"""
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if quant_export == 'MANUAL':
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mean = kwargs.get('mean', None)
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std_dev = kwargs.get('std_dev', None)
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QuantExport(net, *inputs, file_name, mean, std_dev, file_format='AIR', quant_manual_export=True)
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elif quant_export == 'AUTO':
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mean = kwargs.get('mean', None)
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std_dev = kwargs.get('std_dev', None)
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QuantExport(net, *inputs, file_name, mean, std_dev, file_format='AIR')
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else:
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logger.info("exporting model file:%s format:%s.", file_name, file_format)
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check_input_data(*inputs, data_class=Tensor)
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logger.info("exporting model file:%s format:%s.", file_name, file_format)
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check_input_data(*inputs, data_class=Tensor)
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if file_format == 'GEIR':
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logger.warning(f"Format 'GEIR' is deprecated, it would be removed in future release, use 'AIR' instead.")
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file_format = 'AIR'
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net = _quant_export(net, *inputs, file_format='AIR', **kwargs)
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_export(net, file_name, file_format, *inputs)
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supported_formats = ['AIR', 'ONNX', 'MINDIR']
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if file_format not in supported_formats:
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raise ValueError(f'Illegal file format {file_format}, it must be one of {supported_formats}')
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# When dumping ONNX file, switch network mode to infer when it is training(NOTE: ONNX only designed for prediction)
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is_dump_onnx_in_training = net.training and file_format == 'ONNX'
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if is_dump_onnx_in_training:
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net.set_train(mode=False)
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# export model
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net.init_parameters_data()
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if file_format == 'AIR':
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phase_name = 'export.air'
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graph_id, _ = _executor.compile(net, *inputs, phase=phase_name)
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_executor.export(file_name, graph_id)
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elif file_format == 'ONNX': # file_format is 'ONNX'
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phase_name = 'export.onnx'
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graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
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onnx_stream = _executor._get_func_graph_proto(graph_id)
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with open(file_name, 'wb') as f:
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os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
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f.write(onnx_stream)
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elif file_format == 'MINDIR': # file_format is 'MINDIR'
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phase_name = 'export.mindir'
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graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
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onnx_stream = _executor._get_func_graph_proto(graph_id, 'mind_ir')
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with open(file_name, 'wb') as f:
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os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
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f.write(onnx_stream)
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# restore network training mode
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if is_dump_onnx_in_training:
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net.set_train(mode=True)
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def QuantExport(network, file_name, mean, std_dev, *inputs, file_format='AIR', quant_manual_export=False):
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def _export(net, file_name, file_format, *inputs):
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"""
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It is an internal conversion function. Export the MindSpore prediction model to a file in the specified format.
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"""
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logger.info("exporting model file:%s format:%s.", file_name, file_format)
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check_input_data(*inputs, data_class=Tensor)
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if file_format == 'GEIR':
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logger.warning(f"Format 'GEIR' is deprecated, it would be removed in future release, use 'AIR' instead.")
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file_format = 'AIR'
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supported_formats = ['AIR', 'ONNX', 'MINDIR']
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if file_format not in supported_formats:
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raise ValueError(f'Illegal file format {file_format}, it must be one of {supported_formats}')
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# When dumping ONNX file, switch network mode to infer when it is training(NOTE: ONNX only designed for prediction)
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is_dump_onnx_in_training = net.training and file_format == 'ONNX'
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if is_dump_onnx_in_training:
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net.set_train(mode=False)
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# export model
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net.init_parameters_data()
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if file_format == 'AIR':
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phase_name = 'export.air'
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graph_id, _ = _executor.compile(net, *inputs, phase=phase_name)
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_executor.export(file_name, graph_id)
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elif file_format == 'ONNX': # file_format is 'ONNX'
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phase_name = 'export.onnx'
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graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
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onnx_stream = _executor._get_func_graph_proto(graph_id)
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with open(file_name, 'wb') as f:
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os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
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f.write(onnx_stream)
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elif file_format == 'MINDIR': # file_format is 'MINDIR'
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phase_name = 'export.mindir'
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graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
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onnx_stream = _executor._get_func_graph_proto(graph_id, 'mind_ir')
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with open(file_name, 'wb') as f:
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os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
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f.write(onnx_stream)
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# restore network training mode
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if is_dump_onnx_in_training:
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net.set_train(mode=True)
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def _quant_export(network, *inputs, file_format='AIR', **kwargs):
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"""
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Exports MindSpore quantization predict model to deploy with AIR and MINDIR.
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Args:
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network (Cell): MindSpore network produced by `convert_quant_network`.
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file_name (str): File name of model to export.
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mean (int, float): Input data mean. Default: 127.5.
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std_dev (int, float): Input data variance. Default: 127.5.
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inputs (Tensor): Inputs of the `quantization aware training network`.
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file_format (str): MindSpore currently supports 'AIR' and 'MINDIR' format for exported
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quantization aware model. Default: 'AIR'.
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- AIR: Graph Engine Intermidiate Representation. An intermidiate representation format of
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Ascend model.
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- MINDIR: MindSpore Native Intermidiate Representation for Anf. An intermidiate representation format
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for MindSpore models.
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Recommended suffix for output file is '.mindir'.
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quant_manual_export (bool): Is it manual quantitative export. Default: False.
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"""
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if not kwargs.get('quant_mode', None):
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return network
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supported_device = ["Ascend", "GPU"]
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supported_formats = ['AIR', 'MINDIR']
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quant_mode_formats = ['AUTO', 'MANUAL']
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mean = mean if mean else 127.5
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std_dev = std_dev if std_dev else 127.5
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mean = kwargs['mean'] if kwargs.get('mean', None) else 127.5
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std_dev = kwargs['std_dev'] if kwargs.get('std_dev', None) else 127.5
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quant_mode = kwargs['quant_mode']
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if quant_mode not in quant_mode_formats:
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raise KeyError(f'Quant_mode input is wrong, Please choose the right mode of the quant_mode.')
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mean = Validator.check_type("mean", mean, (int, float))
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std_dev = Validator.check_type("std_dev", std_dev, (int, float))
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@ -566,17 +564,17 @@ def QuantExport(network, file_name, mean, std_dev, *inputs, file_format='AIR', q
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network.set_train(False)
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if file_format == "MINDIR":
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if quant_manual_export:
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if quant_mode == 'MANUAL':
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exporter = quant.ExportManualQuantNetwork(network, mean, std_dev, *inputs, is_mindir=True)
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else:
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exporter = quant.ExportToQuantInferNetwork(network, mean, std_dev, *inputs, is_mindir=True)
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else:
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if quant_manual_export:
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if quant_mode == 'MANUAL':
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exporter = quant.ExportManualQuantNetwork(network, mean, std_dev, *inputs)
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else:
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exporter = quant.ExportToQuantInferNetwork(network, mean, std_dev, *inputs)
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deploy_net = exporter.run()
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export(deploy_net, *inputs, file_name=file_name, file_format=file_format)
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return deploy_net
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def parse_print(print_file_name):
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