ux code update to use lpot.experimental API

Author: ftian1

lpot/ux/components/benchmark/benchmark_model.py

@@ -65,7 +65,7 @@ def benchmark_model(
     datatype: str = "",
 ) -> List[Dict[str, Any]]:
     """Execute benchmark."""
-    from lpot import Benchmark, common
+    from lpot.experimental import Benchmark, common
 
     benchmark_results = []
 

lpot/ux/components/configuration_wizard/params_feeder.py

@@ -213,7 +213,7 @@ def get_metrics(self) -> List[Dict[str, Any]]:
             check_module("ignite")
         else:
             check_module(framework)
-        from lpot.metric.metric import framework_metrics
+        from lpot.experimental.metric.metric import framework_metrics
 
         help_dict = load_help_lpot_params("metrics")
         if framework == "onnxrt":

lpot/ux/components/tune/tune_model.py

@@ -62,7 +62,7 @@ def tune_model(
     framework: str,
 ) -> None:
     """Execute tuning."""
-    from lpot import Quantization, common
+    from lpot.experimental import Quantization, common
 
     if framework == "onnxrt":
         import onnx

lpot/ux/utils/templates/dataloader_and_metric_template.txt

@@ -16,8 +16,8 @@ class Dataset(object):
         pass
 
 # Define a customized Metric function
-from lpot import Quantization, common
-from lpot.metric import BaseMetric
+from lpot.experimental import Quantization, common
+from lpot.experimental.metric import BaseMetric
 
 
 class MyMetric(BaseMetric):
@@ -41,4 +41,4 @@ dataset = Dataset()
 quantizer.calib_dataloader = common.DataLoader(dataset, batch_size=1)
 quantizer.metric = common.Metric(metric_cls=MyMetric, name='my_metric')
 quantized_model = quantizer()
-quantized_model.save('{{model_output_path}}')
+quantized_model.save('{{model_output_path}}')

lpot/ux/utils/templates/dataloader_template.txt

@@ -1,4 +1,4 @@
-from lpot import Quantization, common
+from lpot.experimental import Quantization, common
 
 class Dataset(object):
     def __init__(self, *args):
@@ -22,4 +22,4 @@ quantizer.model = common.Model('{{model_path}}')
 dataset = Dataset()
 quantizer.calib_dataloader = common.DataLoader(dataset, batch_size=1)
 quantized_model = quantizer()
-quantized_model.save('{{model_output_path}}')
+quantized_model.save('{{model_output_path}}')

lpot/ux/utils/templates/metric_template.txt

@@ -1,6 +1,6 @@
 # Define a customized Metric function
-from lpot import Quantization, common
-from lpot.metric import BaseMetric
+from lpot.experimental import Quantization, common
+from lpot.experimental.metric import BaseMetric
 
 
 class MyMetric(BaseMetric):
@@ -21,4 +21,4 @@ quantizer = Quantization('{{config_path}}')
 quantizer.model = common.Model('{{model_path}}')
 quantizer.metric = common.Metric(metric_cls=MyMetric, name='my_metric')
 quantized_model = quantizer()
-quantized_model.save('{{model_output_path}}')
+quantized_model.save('{{model_output_path}}')