Nano : Enhancement for output format of InferenceOptimizer (intel-ana…

…lytics#5705)

* first commit

* improve output of optimize

* fix method_type for method lack of dependency

* update openvino model for thread num

* add thread num for trace and quantize

* update based on comment: modify output, add progress bar and remove method type

* modify latency to latency(ms)

* add thread_num for Trainer.trace and Trainer.quantize

* fix bug of openvino cpu_num

python/nano/example/pytorch/inference_pipeline/resnet/inference_pipeline.py

@@ -45,7 +45,7 @@ def accuracy(pred, target):
                        validation_data=datamodule.val_dataloader(limit_num_samples=160),
                        metric=accuracy,
                        direction="max",
-                       cpu_num=1,
+                       thread_num=1,
                        latency_sample_num=30)
 
     # 4. Get the best model under specific restrictions or without restrictions

python/nano/src/bigdl/nano/deps/openvino/core/model.py

@@ -22,9 +22,10 @@
 
 
 class OpenVINOModel:
-    def __init__(self, ie_network: str, device='CPU'):
+    def __init__(self, ie_network: str, device='CPU', thread_num=None):
         self._ie = Core()
         self._device = device
+        self.thread_num = thread_num
         self.ie_network = ie_network
 
     def forward_step(self, *inputs):
@@ -47,8 +48,13 @@ def ie_network(self, model):
             self._ie_network = self._ie.read_model(model=str(model))
         else:
             self._ie_network = model
+        if self.thread_num is not None:
+            config = {"CPU_THREADS_NUM": str(self.thread_num)}
+        else:
+            config = {}
         self._compiled_model = self._ie.compile_model(model=self.ie_network,
-                                                      device_name=self._device)
+                                                      device_name=self._device,
+                                                      config=config)
         self._infer_request = self._compiled_model.create_infer_request()
         input_names = [t.any_name for t in self._ie_network.inputs]
         self._forward_args = input_names

python/nano/src/bigdl/nano/deps/openvino/openvino_api.py

@@ -16,20 +16,23 @@
 from functools import partial
 
 
-def PytorchOpenVINOModel(model, input_sample=None, logging=True, **export_kwargs):
+def PytorchOpenVINOModel(model, input_sample=None, thread_num=None,
+                         logging=True, **export_kwargs):
     """
     Create a OpenVINO model from pytorch.
 
     :param model: Pytorch model to be converted to OpenVINO for inference or
                   path to Openvino saved model.
     :param input_sample: A set of inputs for trace, defaults to None if you have trace before or
-                         model is a LightningModule with any dataloader attached, defaults to None
+                         model is a LightningModule with any dataloader attached, defaults to None.
+    :param thread_num: a int represents how many threads(cores) is needed for
+                       inference. default: None.
     :param logging: whether to log detailed information of model conversion. default: True.
     :param **export_kwargs: will be passed to torch.onnx.export function.
     :return: PytorchOpenVINOModel model for OpenVINO inference.
     """
     from .pytorch.model import PytorchOpenVINOModel
-    return PytorchOpenVINOModel(model, input_sample, logging, **export_kwargs)
+    return PytorchOpenVINOModel(model, input_sample, thread_num, logging, **export_kwargs)
 
 
 def load_openvino_model(path):

python/nano/src/bigdl/nano/deps/openvino/pytorch/model.py

@@ -26,7 +26,8 @@
 
 
 class PytorchOpenVINOModel(AcceleratedLightningModule):
-    def __init__(self, model, input_sample=None, logging=True, **export_kwargs):
+    def __init__(self, model, input_sample=None, thread_num=None,
+                 logging=True, **export_kwargs):
         """
         Create a OpenVINO model from pytorch.
 
@@ -35,6 +36,8 @@ def __init__(self, model, input_sample=None, logging=True, **export_kwargs):
         :param input_sample: A set of inputs for trace, defaults to None if you have trace before or
                              model is a LightningModule with any dataloader attached,
                              defaults to None.
+        :param thread_num: a int represents how many threads(cores) is needed for
+                           inference. default: None.
         :param logging: whether to log detailed information of model conversion. default: True.
         :param **export_kwargs: will be passed to torch.onnx.export function.
         """
@@ -44,7 +47,7 @@ def __init__(self, model, input_sample=None, logging=True, **export_kwargs):
             if isinstance(model, torch.nn.Module):
                 export(model, input_sample, str(dir / 'tmp.xml'), logging, **export_kwargs)
                 ov_model_path = dir / 'tmp.xml'
-            self.ov_model = OpenVINOModel(ov_model_path)
+            self.ov_model = OpenVINOModel(ov_model_path, thread_num=thread_num)
             super().__init__(self.ov_model)
 
     def on_forward_start(self, inputs):