Nano: upgrade openvino to 2022.3.0 and torchmetics to 0.11.0 (#7091)

docs/readthedocs/source/doc/Nano/Overview/pytorch_inference.md

@@ -160,12 +160,12 @@ There are a few arguments required only by INC, and you should not specify or mo
 
 Here is an example to use INC with accuracy control as below. It will search for a model within 1% accuracy drop with 10 trials.
 ```python
-from torchmetrics.classification import Accuracy
+from torchmetrics.classification import MulticlassAccuracy
 InferenceOptimizer.quantize(model,
                             precision='int8',
                             accelerator=None,
                             calib_data=dataloader,
-                            metric=Accuracy()
+                            metric=MulticlassAccuracy(num_classes=10)
                             accuracy_criterion={'relative': 0.01, 'higher_is_better': True},
                             approach='static',
                             method='fx',
@@ -182,7 +182,7 @@ InferenceOptimizer.quantize(model,
                             precision='int8',
                             accelerator='openvino',
                             calib_data=dataloader,
-                            metric=Accuracy()
+                            metric=MulticlassAccuracy(num_classes=10)
                             accuracy_criterion={'relative': 0.01, 'higher_is_better': True},
                             approach='static',
                             max_trials=10,

docs/readthedocs/source/doc/Nano/Overview/tensorflow_inference.md

@@ -76,12 +76,12 @@ There are a few arguments required only by INC.
 - `outputs`:     A list of output names. Default: None, automatically get names from the graph.
 Here is an example to use INC with accuracy control as below. It will search for a model within 1% accuracy drop with 10 trials.
 ```python
-from torchmetrics.classification import Accuracy
+from torchmetrics.classification import MulticlassAccuracy
 
 q_model = model.quantize(precision='int8',
                          accelerator=None,
                          calib_dataset= train_dataset,
-                         metric=Accuracy(),
+                         metric=MulticlassAccuracy(num_classes=10),
                          accuracy_criterion={'relative': 0.01, 'higher_is_better': True},
                          approach='static',
                          tuning_strategy='bayesian',

docs/readthedocs/source/doc/Nano/QuickStart/pytorch_quantization_inc.md

@@ -52,7 +52,7 @@ train_dataloader = DataLoader(train_dataset, batch_size=32)
 import torch
 from torchvision.models import resnet18
 from bigdl.nano.pytorch import Trainer
-from torchmetrics import Accuracy
+from torchmetrics.classification import MulticlassAccuracy
 model_ft = resnet18(pretrained=True)
 num_ftrs = model_ft.fc.in_features
 
@@ -62,7 +62,7 @@ loss_ft = torch.nn.CrossEntropyLoss()
 optimizer_ft = torch.optim.SGD(model_ft.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
 
 # Compile our model with loss function, optimizer.
-model = Trainer.compile(model_ft, loss_ft, optimizer_ft, metrics=[Accuracy])
+model = Trainer.compile(model_ft, loss_ft, optimizer_ft, metrics=[MulticlassAccuracy(num_classes=37)])
 trainer = Trainer(max_epochs=5)
 trainer.fit(model, train_dataloader=train_dataloader)
 
@@ -79,8 +79,8 @@ Quantization is widely used to compress models to a lower precision, which not o
 Without extra accelerator, `InferenceOptimizer.quantize()` returns a pytorch module with desired precision and accuracy. You can add quantization as below:
 ```python
 from bigdl.nano.pytorch import InferenceOptimizer
-from torchmetrics.functional import accuracy
+from torchmetrics.classification import MulticlassAccuracy
-q_model = InferenceOptimizer.quantize(model, calib_data=train_dataloader, metric=accuracy)
+q_model = InferenceOptimizer.quantize(model, calib_data=train_dataloader, metric=MulticlassAccuracy(num_classes=37))
 
 # run simple prediction
 y_hat = q_model(x)

docs/readthedocs/source/doc/Nano/QuickStart/pytorch_quantization_inc_onnx.md

@@ -51,7 +51,7 @@ train_dataloader = DataLoader(train_dataset, batch_size=32)
 import torch
 from torchvision.models import resnet18
 from bigdl.nano.pytorch import Trainer
-from torchmetrics import Accuracy
+from torchmetrics.classification import MulticlassAccuracy
 model_ft = resnet18(pretrained=True)
 num_ftrs = model_ft.fc.in_features
 
@@ -61,7 +61,7 @@ loss_ft = torch.nn.CrossEntropyLoss()
 optimizer_ft = torch.optim.SGD(model_ft.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
 
 # Compile our model with loss function, optimizer.
-model = Trainer.compile(model_ft, loss_ft, optimizer_ft, metrics=[Accuracy])
+model = Trainer.compile(model_ft, loss_ft, optimizer_ft, metrics=[MulticlassAccuracy(num_classes=37)])
 trainer = Trainer(max_epochs=5)
 trainer.fit(model, train_dataloader=train_dataloader)
 
@@ -78,8 +78,8 @@ With the ONNXRuntime accelerator, `InferenceOptimizer.quantize()` will return a
 you can add quantization as below:
 ```python
 from bigdl.nano.pytorch import InferenceOptimizer
-from torchmetrics.functional import accuracy
+from torchmetrics.classification import MulticlassAccuracy
-ort_q_model = InferenceOptimizer.quantize(model, accelerator='onnxruntime', calib_data=train_dataloader, metric=accuracy)
+ort_q_model = InferenceOptimizer.quantize(model, accelerator='onnxruntime', calib_data=train_dataloader, metric=MulticlassAccuracy(num_classes=37))
 
 # run simple prediction
 y_hat = ort_q_model(x)

docs/readthedocs/source/doc/Nano/QuickStart/pytorch_quantization_openvino.md

@@ -77,7 +77,6 @@ y_hat.argmax(dim=1)
 Accelerator='openvino' means using OpenVINO POT to do quantization. The quantization can be added as below:
 ```python
 from bigdl.nano.pytorch import InferenceOptimizer
-from torchmetrics import Accuracy
 ov_q_model = InferenceOptimizer.quantize(model, accelerator="openvino", calib_data=data_loader)
 
 # run simple prediction