Caffe2 supports exporting models directly to ONNX. Here are the steps:

1. Install Caffe2 by following the instructions provided in the official Caffe2 documentation.

2. Import the necessary libraries in your Python script:

   1 2	from caffe2.python.onnx import backend as caffe2_backend import torch.onnx

3. Load your Caffe2 model into PyTorch:

   1 2	# Assuming you already have a Caffe2 model loaded torch_model = torch.onnx.load("path/to/caffe2_model.pb")

4. Export the PyTorch model to ONNX:

   1	torch.onnx.export(torch_model, torch.randn(1, 3, 224, 224), "path/to/exported_model.onnx", verbose=True)

   Adjust the input shape and replace “path/to/exported_model.onnx” with the desired path to save the ONNX file.

PyTorch provides a straightforward way to export models to the ONNX format. Follow these steps:

1. Ensure that you have PyTorch installed. You can install it using pip:

   1	pip install torch

2. Import the necessary libraries in your Python script:

   1 2	import torch import torchvision

3. Load your PyTorch model:

   1	model = torchvision.models.resnet18(pretrained=True)

4. Export the model to ONNX:

   1	torch.onnx.export(model, torch.randn(1, 3, 224, 224), "path/to/exported_model.onnx", export_params=True)

   Make sure to replace “path/to/exported_model.onnx” with the desired path to save the ONNX file.

To export a TensorFlow model to the ONNX format, you can use the TensorFlow-ONNX converter. Here’s how:

1. Make sure you have TensorFlow and TensorFlow-ONNX installed. Install them using pip:

   1	pip install tensorflow tensorflow-onnx

2. Import the required libraries:

   1 2	import tensorflow as tf import tf2onnx

3. Load your TensorFlow model:

   1	model = tf.keras.applications.ResNet50(weights="imagenet")

4. Convert the TensorFlow model to ONNX:

   1 2	onnx_model, _ = tf2onnx.convert.from_keras(model) tf2onnx.save_model(onnx_model, "path/to/exported_model.onnx")

   Replace “path/to/exported_model.onnx” with the desired path to save the ONNX file.

After exporting the model from your preferred framework to ONNX, you can load it for inference using the onnx package. Here’s an example:

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import onnx

# Load the ONNX model
model = onnx.load("path/to/exported_model.onnx")

# Create an ONNX runtime session
import onnxruntime as ort
session = ort.InferenceSession("path/to/exported_model.onnx")

# Perform inference using the loaded model
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
input_data = np.random.rand(1, 3, 224, 224).astype(np.float32)  # Replace with your actual input data
output = session.run([output_name], {input_name: input_data})

print(output)

Remember to replace “path/to/exported_model.onnx” with the path to your exported ONNX model, and provide appropriate input data for inference.

That’s it! Now you can export models from different frameworks to ONNX and use the ONNX runtime to load and perform inference with those models.