Exporting ONNX Model from Different Frameworks¶
We will start with the process of exporting an ONNX model from various deep learning frameworks such as PyTorch, TensorFlow, and Caffe2.
Exporting from PyTorch¶
PyTorch is a popular deep learning framework that provides dynamic computation graphs. To export a PyTorch model to ONNX format, you need to follow these steps:
import torch
import torchvision.models as models
# Load a pre-trained PyTorch model
model = models.resnet18(pretrained=True)
# Set the model to evaluation mode
model.cpu().eval()
# Define example input tensor
input_tensor = torch.randn(1, 3, 224, 224).cpu() # Replace with your own input shape
# Export the model to ONNX format
torch.onnx.export(model, input_tensor, "model.onnx", verbose=True, opset=13)
In the above code, we import the necessary libraries and load a pre-trained ResNet-18 model. We then set the model to evaluation mode and define an example input tensor. Finally, we use the torch.onnx.export function to export the model to the ONNX format. You can replace "model.onnx" with the desired output file path.
Exporting from TensorFlow¶
TensorFlow is another widely used deep learning framework that provides both static and dynamic computation graphs. Here's how you can export a TensorFlow model to ONNX format:
import tensorflow as tf
from tensorflow.keras.applications import ResNet50
import tf2onnx
# Load a pre-trained TensorFlow model
model = ResNet50(weights='imagenet')
# onnx_model = tf2onnx.convert.from_keras(model)
input_tensor_spec = (tf.TensorSpec((None, 224, 224, 3), tf.float32, name="input"),)
# Convert the TensorFlow model to ONNX format
# Save the ONNX model to a file
model_proto, _ = tf2onnx.convert.from_keras(model, input_signature=input_tensor_spec, opset=13, output_path="model.onnx")
In the above code, we import the necessary libraries and load a pre-trained ResNet-50 model using Keras. We then convert the TensorFlow model to the ONNX format using the tf2onnx.convert.from_keras function. Finally, we save the ONNX model to a file using the tf2onnx.save_model function.
In case you have save tensorflow model or saved checkpoint path of tensorflow model, you can load the model in tensorflow/keras or you can directly use the following commands from tf2onnx libraries.
# You have path to your saved TensorFlow model
python -m tf2onnx.convert --saved-model tensorflow-model-path --opset 11 --output model.onnx
# For checkpoint format:
python -m tf2onnx.convert --checkpoint tensorflow-model-meta-file-path --output model.onnx --inputs input0:0,input1:0 --outputs output0:0
# For graphdef format:
python -m tf2onnx.convert --graphdef tensorflow-model-graphdef-file --output model.onnx --inputs input:0,input:0 --outputs output0:0
You can refer this notebook from tf2onnx
Exporting from Caffe2¶
Caffe2 is a deep learning framework primarily developed by Facebook. It focuses on performance and is widely used for production deployments. Here's an example of exporting a Caffe2 model to ONNX format:
from caffe2.python.onnx import backend
from caffe2.python.predictor import predictor_exporter
# Load the Caffe2 model from .pb and .init files
init_net = "model.init" # Replace with the path to the .init file
predict_net = "model.pb" # Replace with the path to the .pb file
# Export the Caffe2 model to ONNX format
onnx_model = backend.prepare(model, device='CPU')
onnx_model.export_onnx("model.onnx")
In the above code, we pre-trained caffe2 model from .init and .pb file. Finally, we use the caffe2.python.onnx.backend.prepare function to export the model to the ONNX format. You can replace "model.onnx" with the desired output file path. Please refer caffe2-installation to setup the environment
Loading ONNX Model¶
After exporting the model from your preferred framework to ONNX, you can load it for inference using the onnx package. Here's an example: