View the runnable example on GitHub
Save and Load ONNXRuntime Model#
This example illustrates how to save and load a model accelerated by onnxruntime. In this example, we use a ResNet18 model pretrained. Then, by calling trace(model, accelerator="onnxruntime"...)
, we can obtain a model accelarated by onnxruntime method provided by BigDL-Nano for inference. By calling save(model_name, path)
, we could save the model to a folder. By calling load(path)
, we could load the model from a folder.
To inference using Bigdl-nano InferenceOptimizer, the following packages need to be installed first. We recommend you to use Miniconda to prepare the environment and install the following packages in a conda environment.
You can create a conda environment by executing:
# "nano" is conda environment name, you can use any name you like.
conda create -n nano python=3.7 setuptools=58.0.4
conda activate nano
📝 Note:
During your installation, there may be some warnings or errors about version, just ignore them.
[ ]:
# Necessary packages for inference accelaration
!pip install --pre --upgrade bigdl-nano[pytorch,inference]
First, prepare model. We use a pretrained ResNet18 model(model_ft
in following code) in this example.
[ ]:
import torch
from torchvision.models import resnet18
model_ft = resnet18(pretrained=True)
model_ft.eval()
Accelerate Inference Using ONNX Runtime
[ ]:
from bigdl.nano.pytorch import InferenceOptimizer
ort_model = InferenceOptimizer.trace(model_ft,
accelerator="onnxruntime",
input_sample=torch.rand(1, 3, 224, 224))
with InferenceOptimizer.get_context(ort_model):
x = torch.rand(2, 3, 224, 224)
y_hat = ort_model(x)
predictions = y_hat.argmax(dim=1)
print(predictions)
Save Optimized Model The saved model files will be saved at “./optimized_model_ort” directory There are 2 files in optimized_model_ort, users only need to take “.onnx” file for further usage:
nano_model_meta.yml: meta information of the saved model checkpoint
onnx_saved_model.onnx: model checkpoint for general use, describes model structure
[ ]:
InferenceOptimizer.save(ort_model, "./optimized_model_ort")
Load the Optimized Model
[ ]:
loaded_model = InferenceOptimizer.load("./optimized_model_ort")
Inference with the Loaded Model
[ ]:
with InferenceOptimizer.get_context(loaded_model):
y_hat = loaded_model(x)
predictions = y_hat.argmax(dim=1)
print(predictions)
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