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integrate_export_v2

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pull/7385/head
bai-yangfan 4 years ago
parent f68d7b01d3
commit 0288c91c5d
1 changed files with 69 additions and 71 deletions
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140
mindspore/train/serialization.py
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@ -464,7 +464,7 @@ def _fill_param_into_net(net, parameter_list):
load_param_into_net(net, parameter_dict)
def export(net, *inputs, file_name, file_format='AIR', quant_export=None, **kwargs):
def export(net, *inputs, file_name, file_format='AIR', **kwargs):
"""
Export the MindSpore prediction model to a file in the specified format.
@ -480,80 +480,78 @@ def export(net, *inputs, file_name, file_format='AIR', quant_export=None, **kwar
- MINDIR: MindSpore Native Intermidiate Representation for Anf. An intermidiate representation format
for MindSpore models.
Recommended suffix for output file is '.mindir'.
quant_export (str): Quantitative export choise. Default: None.
kwargs (dict): Configuration options dictionary.
- quant_mode: The mode of quant.
- mean: Input data mean. Default: 127.5.
- std_dev: Input data variance. Default: 127.5.
"""
if quant_export == 'MANUAL':
mean = kwargs.get('mean', None)
std_dev = kwargs.get('std_dev', None)
QuantExport(net, *inputs, file_name, mean, std_dev, file_format='AIR', quant_manual_export=True)
elif quant_export == 'AUTO':
mean = kwargs.get('mean', None)
std_dev = kwargs.get('std_dev', None)
QuantExport(net, *inputs, file_name, mean, std_dev, file_format='AIR')
else:
logger.info("exporting model file:%s format:%s.", file_name, file_format)
check_input_data(*inputs, data_class=Tensor)
if file_format == 'GEIR':
logger.warning(f"Format 'GEIR' is deprecated, it would be removed in future release, use 'AIR' instead.")
file_format = 'AIR'
supported_formats = ['AIR', 'ONNX', 'MINDIR']
if file_format not in supported_formats:
raise ValueError(f'Illegal file format {file_format}, it must be one of {supported_formats}')
# When dumping ONNX file, switch network mode to infer when it is training(NOTE: ONNX only designed for prediction)
is_dump_onnx_in_training = net.training and file_format == 'ONNX'
if is_dump_onnx_in_training:
net.set_train(mode=False)
# export model
net.init_parameters_data()
if file_format == 'AIR':
phase_name = 'export.air'
graph_id, _ = _executor.compile(net, *inputs, phase=phase_name)
_executor.export(file_name, graph_id)
elif file_format == 'ONNX': # file_format is 'ONNX'
phase_name = 'export.onnx'
graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
onnx_stream = _executor._get_func_graph_proto(graph_id)
with open(file_name, 'wb') as f:
os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
f.write(onnx_stream)
elif file_format == 'MINDIR': # file_format is 'MINDIR'
phase_name = 'export.mindir'
graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
onnx_stream = _executor._get_func_graph_proto(graph_id, 'mind_ir')
with open(file_name, 'wb') as f:
os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
f.write(onnx_stream)
# restore network training mode
if is_dump_onnx_in_training:
net.set_train(mode=True)
def QuantExport(network, file_name, mean, std_dev, *inputs, file_format='AIR', quant_manual_export=False):
logger.info("exporting model file:%s format:%s.", file_name, file_format)
check_input_data(*inputs, data_class=Tensor)
net = _quant_export(net, *inputs, file_format='AIR', **kwargs)
_export(net, file_name, file_format, *inputs)
def _export(net, file_name, file_format, *inputs):
"""
Exports MindSpore quantization predict model to deploy with AIR and MINDIR.
It is an internal conversion function. Export the MindSpore prediction model to a file in the specified format.
"""
logger.info("exporting model file:%s format:%s.", file_name, file_format)
check_input_data(*inputs, data_class=Tensor)
Args:
network (Cell): MindSpore network produced by `convert_quant_network`.
file_name (str): File name of model to export.
mean (int, float): Input data mean. Default: 127.5.
std_dev (int, float): Input data variance. Default: 127.5.
inputs (Tensor): Inputs of the `quantization aware training network`.
file_format (str): MindSpore currently supports 'AIR' and 'MINDIR' format for exported
quantization aware model. Default: 'AIR'.
- AIR: Graph Engine Intermidiate Representation. An intermidiate representation format of
Ascend model.
- MINDIR: MindSpore Native Intermidiate Representation for Anf. An intermidiate representation format
for MindSpore models.
Recommended suffix for output file is '.mindir'.
quant_manual_export (bool): Is it manual quantitative export. Default: False.
if file_format == 'GEIR':
logger.warning(f"Format 'GEIR' is deprecated, it would be removed in future release, use 'AIR' instead.")
file_format = 'AIR'
supported_formats = ['AIR', 'ONNX', 'MINDIR']
if file_format not in supported_formats:
raise ValueError(f'Illegal file format {file_format}, it must be one of {supported_formats}')
# When dumping ONNX file, switch network mode to infer when it is training(NOTE: ONNX only designed for prediction)
is_dump_onnx_in_training = net.training and file_format == 'ONNX'
if is_dump_onnx_in_training:
net.set_train(mode=False)
# export model
net.init_parameters_data()
if file_format == 'AIR':
phase_name = 'export.air'
graph_id, _ = _executor.compile(net, *inputs, phase=phase_name)
_executor.export(file_name, graph_id)
elif file_format == 'ONNX': # file_format is 'ONNX'
phase_name = 'export.onnx'
graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
onnx_stream = _executor._get_func_graph_proto(graph_id)
with open(file_name, 'wb') as f:
os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
f.write(onnx_stream)
elif file_format == 'MINDIR': # file_format is 'MINDIR'
phase_name = 'export.mindir'
graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False)
onnx_stream = _executor._get_func_graph_proto(graph_id, 'mind_ir')
with open(file_name, 'wb') as f:
os.chmod(file_name, stat.S_IWUSR | stat.S_IRUSR)
f.write(onnx_stream)
# restore network training mode
if is_dump_onnx_in_training:
net.set_train(mode=True)
def _quant_export(network, *inputs, file_format='AIR', **kwargs):
"""
Exports MindSpore quantization predict model to deploy with AIR and MINDIR.
"""
if not kwargs.get('quant_mode', None):
return network
supported_device = ["Ascend", "GPU"]
supported_formats = ['AIR', 'MINDIR']
quant_mode_formats = ['AUTO', 'MANUAL']
mean = kwargs['mean'] if kwargs.get('mean', None) else 127.5
std_dev = kwargs['std_dev'] if kwargs.get('std_dev', None) else 127.5
mean = mean if mean else 127.5
std_dev = std_dev if std_dev else 127.5
quant_mode = kwargs['quant_mode']
if quant_mode not in quant_mode_formats:
raise KeyError(f'Quant_mode input is wrong, Please choose the right mode of the quant_mode.')
mean = Validator.check_type("mean", mean, (int, float))
std_dev = Validator.check_type("std_dev", std_dev, (int, float))
@ -566,17 +564,17 @@ def QuantExport(network, file_name, mean, std_dev, *inputs, file_format='AIR', q
network.set_train(False)
if file_format == "MINDIR":
if quant_manual_export:
if quant_mode == 'MANUAL':
exporter = quant.ExportManualQuantNetwork(network, mean, std_dev, *inputs, is_mindir=True)
else:
exporter = quant.ExportToQuantInferNetwork(network, mean, std_dev, *inputs, is_mindir=True)
else:
if quant_manual_export:
if quant_mode == 'MANUAL':
exporter = quant.ExportManualQuantNetwork(network, mean, std_dev, *inputs)
else:
exporter = quant.ExportToQuantInferNetwork(network, mean, std_dev, *inputs)
deploy_net = exporter.run()
export(deploy_net, *inputs, file_name=file_name, file_format=file_format)
return deploy_net
def parse_print(print_file_name):

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