[Nano] Update Trainer -> InferenceOptimizer for Related Examples

python/nano/tutorial/inference/pytorch/pytorch_inference_onnx.py

@@ -20,7 +20,6 @@
 # pip install onnx onnxruntime
 # ```
 
-import torch
 import torch
 from torchvision.models import resnet18
 
@@ -35,17 +34,18 @@
     predictions = y_hat.argmax(dim=1)
     print(predictions)
 
-    # Accelerated Inference Using ONNX Runtime
-    from bigdl.nano.pytorch import Trainer
-    ort_model = Trainer.trace(model_ft,
-                              accelerator="onnxruntime",
-                              input_sample=torch.rand(1, 3, 224, 224))
+    # Accelerated Inference Using ONNXRuntime
+    from bigdl.nano.pytorch import InferenceOptimizer
+    ort_model = InferenceOptimizer.trace(model_ft,
+                                         accelerator="onnxruntime",
+                                         input_sample=torch.rand(1, 3, 224, 224))
 
     y_hat = ort_model(x)
     predictions = y_hat.argmax(dim=1)
     print(predictions)
 
     # Save Optimized Model
+    from bigdl.nano.pytorch import Trainer
     Trainer.save(ort_model, "./optimized_model")
 
     # Load the Optimized Model

python/nano/tutorial/inference/pytorch/pytorch_inference_openvino.py

@@ -20,7 +20,6 @@
 # pip install openvino-dev
 # ```
 
-import torch
 import torch
 from torchvision.models import resnet18
 
@@ -35,15 +34,16 @@
     print(predictions)
 
     # Accelerated Inference Using OpenVINO
-    from bigdl.nano.pytorch import Trainer
-    ov_model = Trainer.trace(model_ft,
-                             accelerator="openvino",
-                             input_sample=torch.rand(1, 3, 224, 224))
+    from bigdl.nano.pytorch import InferenceOptimizer
+    ov_model = InferenceOptimizer.trace(model_ft,
+                                        accelerator="openvino",
+                                        input_sample=torch.rand(1, 3, 224, 224))
     y_hat = ov_model(x)
     predictions = y_hat.argmax(dim=1)
     print(predictions)
 
     # Save Optimized Model
+    from bigdl.nano.pytorch import Trainer
     Trainer.save(ov_model, "./optimized_model")
 
     # Load the Optimized Model

python/nano/tutorial/inference/pytorch/pytorch_quantization.py

@@ -24,7 +24,6 @@
 from torchvision import transforms
 from torchvision.datasets import OxfordIIITPet
 from torch.utils.data.dataloader import DataLoader
-import torch
 from torchvision.models import resnet18
 from bigdl.nano.pytorch import Trainer
 from torchmetrics import Accuracy
@@ -93,7 +92,9 @@ def finetune_pet_dataset(model_ft):
     print(predictions)
 
     # Static Quantization for PyTorch
-    q_model = Trainer.quantize(model, calib_dataloader=DataLoader(train_dataset, batch_size=32))
+    from bigdl.nano.pytorch import InferenceOptimizer
+    q_model = InferenceOptimizer.quantize(model, 
+                                          calib_dataloader=DataLoader(train_dataset, batch_size=32))
 
     # Inference with Quantized Model
     y_hat = q_model(x)

python/nano/tutorial/inference/pytorch/pytorch_quantization_onnx.py

@@ -25,7 +25,6 @@
 from torchvision import transforms
 from torchvision.datasets import OxfordIIITPet
 from torch.utils.data.dataloader import DataLoader
-import torch
 from torchvision.models import resnet18
 from bigdl.nano.pytorch import Trainer
 from torchmetrics import Accuracy
@@ -94,9 +93,10 @@ def finetune_pet_dataset(model_ft):
     print(predictions)
 
     # Static Quantization for ONNX
-    q_model = Trainer.quantize(model,
-                               accelerator='onnxruntime',
-                               calib_dataloader=DataLoader(train_dataset, batch_size=32))
+    from bigdl.nano.pytorch import InferenceOptimizer
+    q_model = InferenceOptimizer.quantize(model,
+                                 accelerator='onnxruntime',
+                                 calib_dataloader=DataLoader(train_dataset, batch_size=32))
 
     # Inference with Quantized Model
     y_hat = q_model(x)

python/nano/tutorial/inference/pytorch/pytorch_quantization_openvino.py

@@ -24,7 +24,6 @@
 from torchvision import transforms
 from torchvision.datasets import OxfordIIITPet
 from torch.utils.data.dataloader import DataLoader
-import torch
 from torchvision.models import resnet18
 from bigdl.nano.pytorch import Trainer
 from torchmetrics import Accuracy
@@ -93,9 +92,10 @@ def finetune_pet_dataset(model_ft):
     print(predictions)
 
     # Static Quantization for OpenVINO
-    q_model = Trainer.quantize(model,
-                               accelerator='openvino',
-                               calib_dataloader=DataLoader(train_dataset, batch_size=32))
+    from bigdl.nano.pytorch import InferenceOptimizer
+    q_model = InferenceOptimizer.quantize(model,
+                                          accelerator='openvino',
+                                          calib_dataloader=DataLoader(train_dataset, batch_size=32))
 
     # Inference with Quantized Model
     y_hat = q_model(x)