View the runnable example on GitHub

Save and Load Optimized IPEX Model#

This example illustrates how to save and load a model accelerated by IPEX. In this example, we use a ResNet18 model pretrained. Then, by calling InferenceOptimizer.trace(..., use_ipex=True), we can obtain a model accelerated by IPEX method. By calling InferenceOptimizer.save(model_name, path) , we could save the model to a folder. By calling InferenceOptimizer.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]


First, prepare model. We need load the pretrained ResNet18 model.

[ ]:

import torch
from torchvision.models import resnet18

model_ft = resnet18(pretrained=True)
model_ft.eval()


Accelerate Inference Using IPEX

[ ]:

from bigdl.nano.pytorch import InferenceOptimizer
ipex_model = InferenceOptimizer.trace(model_ft,
use_ipex=True)


Save Optimized IPEX Model The saved model files will be saved at “./optimized_model_ipex” directory There are 2 files in optimized_model_ipex, users only need to take “ckpt.pth” file for further usage:

• nano_model_meta.yml: meta information of the saved model checkpoint

• ckpt.pth: pytorch state dict checkpoint for general use, describes model structure

[ ]:

InferenceOptimizer.save(ipex_model, "./optimized_model_ipex")


Load the Optimized Model

📝 Note

• For a model accelerated by JIT, OpenVINO or ONNXRuntime, we saved the structure of its network, so we don’t need its unaccelerated model when we load the optimized model.

• For a model accelerated by IPEX, we only store the state_dict which is simply a python dictionary object that maps each layer to its parameter tensor when saving the model, so when we load the optimized model, we need to pass in the orginal model.

[ ]:

loaded_model = InferenceOptimizer.load("./optimized_model_ipex", model=model_ft)


Inference with the Loaded Model

[ ]:

with InferenceOptimizer.get_context(loaded_model):
x = torch.rand(2, 3, 224, 224)
y_hat = loaded_model(x)
predictions = y_hat.argmax(dim=1)
print(predictions)


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