integrate_export_v2

mindspore/train/serialization.py

@@ -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.
 
@@ -480,17 +480,22 @@ 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.
+    """
+    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):
+    """
+    It is an internal conversion function. Export the MindSpore prediction model to a file in the specified format.
     """
-    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)
 
@@ -529,31 +534,24 @@ def export(net, *inputs, file_name, file_format='AIR', quant_export=None, **kwar
     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):
+
+def _quant_export(network, *inputs, file_format='AIR', **kwargs):
     """
     Exports MindSpore quantization predict model to deploy with AIR and MINDIR.
-
-    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 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):