[Nano] add __call__ method for OpenVINOModel

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

@@ -30,6 +30,9 @@ def __init__(self, ie_network: str, device='CPU'):
     def forward_step(self, *inputs):
         return self._infer_request.infer(list(inputs))
 
+    def __call__(self, *inputs):
+        return self.forward_step(*inputs)
+
     @property
     def forward_args(self):
         return self._forward_args
@@ -50,18 +53,17 @@ def ie_network(self, model):
         input_names = [t.any_name for t in self._ie_network.inputs]
         self._forward_args = input_names
 
-    def _save_model(self, path):
+    def _save(self, path):
         """
-        Save PytorchOpenVINOModel to local as xml and bin file
+        Save OpenVINOModel to local as xml and bin file
 
         :param path: Directory to save the model.
         """
-        path = Path(path)
-        path.mkdir(exist_ok=True)
-        invalidInputError(self.ie_network,
-                          "self.ie_network shouldn't be None.")
-        xml_path = path / self.status['xml_path']
-        save(self.ie_network, xml_path)
+        if self._model_exists_or_err("self.ie_network shouldn't be None."):
+            path = Path(path)
+            path.mkdir(exist_ok=True)
+            xml_path = path / 'ov_saved_model.xml'
+            save(self.ie_network, xml_path)
 
     def pot(self,
             dataloader,
@@ -148,3 +150,8 @@ def pot(self,
             model = Core().read_model(model_path)
             model.reshape(orig_shape)
         return model
+
+    def _model_exists_or_err(self, err_msg):
+        if self.ie_network is None:
+            invalidInputError(False, err_msg)
+        return True

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

@@ -22,9 +22,10 @@
 from .utils import export
 import torch
 from bigdl.nano.utils.log4Error import invalidInputError
+from ..core.utils import save
 
 
-class PytorchOpenVINOModel(OpenVINOModel, AcceleratedLightningModule):
+class PytorchOpenVINOModel(AcceleratedLightningModule):
     def __init__(self, model, input_sample=None, logging=True, **export_kwargs):
         """
         Create a OpenVINO model from pytorch.
@@ -43,16 +44,14 @@ 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'
-            OpenVINOModel.__init__(self, ov_model_path)
-            AcceleratedLightningModule.__init__(self, None)
+            self.ov_model = OpenVINOModel(ov_model_path)
+            super().__init__(self.ov_model)
 
     def on_forward_start(self, inputs):
-        if self.ie_network is None:
-            invalidInputError(False,
-                              "Please create an instance by PytorchOpenVINOModel()"
-                              " or PytorchOpenVINOModel.load()")
-        inputs = self.tensors_to_numpy(inputs)
-        return inputs
+        if self.ov_model._model_exists_or_err("Please create an instance by PytorchOpenVINOModel()"
+                                              " or PytorchOpenVINOModel.load()"):
+            inputs = self.tensors_to_numpy(inputs)
+            return inputs
 
     def on_forward_end(self, outputs):
         outputs = self.numpy_to_tensors(outputs.values())
@@ -64,6 +63,10 @@ def status(self):
         status.update({"xml_path": 'ov_saved_model.xml', "weight_path": 'ov_saved_model.bin'})
         return status
 
+    @property
+    def forward_args(self):
+        return self.ov_model.forward_args
+
     @staticmethod
     def _load(path):
         """
@@ -95,7 +98,19 @@ def pot(self,
         if metric:
             metric = PytorchOpenVINOMetric(metric=metric, higher_better=higher_better)
         dataloader = PytorchOpenVINODataLoader(dataloader, collate_fn=self.tensors_to_numpy)
-        model = super().pot(dataloader, metric=metric, drop_type=drop_type,
-                            maximal_drop=maximal_drop, max_iter_num=max_iter_num,
-                            n_requests=n_requests, sample_size=sample_size)
+        model = self.ov_model.pot(dataloader, metric=metric, drop_type=drop_type,
+                                  maximal_drop=maximal_drop, max_iter_num=max_iter_num,
+                                  n_requests=n_requests, sample_size=sample_size)
         return PytorchOpenVINOModel(model)
+
+    def _save_model(self, path):
+        """
+        Save PytorchOpenVINOModel to local as xml and bin file
+
+        :param path: Directory to save the model.
+        """
+        if self.ov_model._model_exists_or_err(err_msg="model shouldn't be None"):
+            path = Path(path)
+            path.mkdir(exist_ok=True)
+            xml_path = path / self.status['xml_path']
+            save(self.ov_model.ie_network, xml_path)

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

@@ -20,9 +20,10 @@
 from .utils import export
 import tensorflow as tf
 from bigdl.nano.utils.log4Error import invalidInputError
+from ..core.utils import save
 
 
-class KerasOpenVINOModel(OpenVINOModel, AcceleratedKerasModel):
+class KerasOpenVINOModel(AcceleratedKerasModel):
     def __init__(self, model):
         """
         Create a OpenVINO model from Keras.
@@ -39,16 +40,17 @@ def __init__(self, model):
             if isinstance(model, tf.keras.Model):
                 export(model, str(dir / 'tmp.xml'))
                 ov_model_path = dir / 'tmp.xml'
-            OpenVINOModel.__init__(self, ov_model_path)
-            AcceleratedKerasModel.__init__(self, None)
+            self.ov_model = OpenVINOModel(ov_model_path)
+            super().__init__(self.ov_model)
+
+    def forward_step(self, *inputs):
+        return self.ov_model.forward_step(*inputs)
 
     def on_forward_start(self, inputs):
-        if self.ie_network is None:
-            invalidInputError(False,
-                              "Please create an instance by KerasOpenVINOModel()"
-                              " or KerasOpenVINOModel.load()")
-        inputs = self.tensors_to_numpy(inputs)
-        return inputs
+        if self.ov_model._model_exists_or_err("Please create an instance by KerasOpenVINOModel()"
+                                              " or KerasOpenVINOModel.load()"):
+            inputs = self.tensors_to_numpy(inputs)
+            return inputs
 
     def on_forward_end(self, outputs):
         outputs = tuple(outputs.values())
@@ -79,3 +81,15 @@ def _load(path):
             invalidInputError(False, "nano_model_meta.yml must specify 'xml_path' for loading.")
         xml_path = Path(path) / status['xml_path']
         return KerasOpenVINOModel(xml_path)
+
+    def _save_model(self, path):
+        """
+        Save KerasOpenVINOModel to local as xml and bin file
+
+        :param path: Directory to save the model.
+        """
+        if self.ov_model._model_exists_or_err("model shouldn't be None"):
+            path = Path(path)
+            path.mkdir(exist_ok=True)
+            xml_path = path / self.status['xml_path']
+            save(self.ov_model.ie_network, xml_path)

python/nano/test/openvino/basic/test_openvino.py

@@ -24,5 +24,5 @@ class TestOpenVINO(TestCase):
     def test_openvino_model(self):
         openvino_model = OpenVINOModel("./intel/resnet18-xnor-binary-onnx-0001/FP16-INT1/resnet18-xnor-binary-onnx-0001.xml")
         x = np.random.randn(1, 3, 224, 224)
-        y_hat = openvino_model.forward_step(x)
+        y_hat = openvino_model(x)
         assert tuple(next(iter(y_hat)).shape) == (1, 1000)