CREStereo-Pytorch is a non-official implementation of the original CREStereo model designed for stereo depth estimation, originally built on the MegEngine framework. This blog article guides you through the setup and usage of the CREStereo-Pytorch model, along with troubleshooting tips to tackle common issues. Let’s get started!

Setting Up CREStereo-Pytorch

Before diving into using the model, you need to set up your environment. Ensure you have PyTorch installed along with all necessary dependencies.

Training the Model

You have two options for training your model: using Distributed Data Parallel (DDP) or Data Parallel (DP). Here’s a breakdown of both methods:

• Training with DDP:

  To use distributed training, modify the cfgstrain.yaml file by setting dist: True. Execute the command:

  python -m torch.distributed.launch --nproc_per_node=8 train.py

• Training with DP:

  For standard training, change the cfgstrain.yaml file to set dist: False and run:

  python train.py

Downloading Pretrained Model

If you prefer to use a pretrained model, follow these steps:

1. Download the pretrained model from here.
2. Save the model file in the models directory:

[models](https://github.com/ibaiGorordo/CREStereo-Pytorch/tree/main/models)

3. For those who want to convert the original MegEngine weights, use the convert_weights.py script after placing the MegEngine weights file (crestereo_eth3d.mge) in the same models folder.

Converting to ONNX

Running your model in ONNX format requires a couple of steps. You will convert your model’s weights following these instructions:

1. Make sure you have the modelscrestereo_eth3d.pth file, either from downloading or training.
2. Use the convert_to_onnx.py script to convert the model:
• First part: Convert the model with an initial flow estimate (quick process).
• Second part: Convert the model without an initial flow estimate (may takes longer, requires PyTorch = 1.12).
3. To verify your ONNX models, run the test_onnx_model.py script.

Understanding the Code: The Analogy

Imagine you’re building a complex machine (CREStereo). Each part of this machine has a specific role, just like each line of code in our implementation. Here’s how some key sections of the code function:

• The train.py script is like the assembly instructions for your machine; it tells you how to connect all the parts (model components) to work together.
• When you run convert_weights.py, it’s akin to transforming raw materials (MegEngine weights) into functional components (Pytorch-ready formats), ensuring each piece fits perfectly in the construction.
• Finally, the convert_to_onnx.py script allows your machine to communicate with others, ensuring it can output and interact in different formats (ONNX) for diverse applications.

Troubleshooting Tips

If you encounter issues while using CREStereo-Pytorch, consider the following troubleshooting ideas:

• Ensure you’ve followed the appropriate training configuration (DDP or DP) based on your needs.
• Check if all requisite libraries are installed and up-to-date, especially for ONNX compatibility.
• If experiencing unexpected results, validate that the input images are of the correct size during ONNX conversion.
• Revisit your configuration settings in cfgstrain.yaml to ensure they align with the intended setup.
• If problems persist, refer to the issues section of the CREStereo repository for community insights.

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Licenses

For your reference, below are the licenses associated with the resources used:

• CREStereo (Apache License 2.0)
• RAFT (BSD 3-Clause)
• LoFTR (Apache License 2.0)
• Grid sample replacement
• torch2mge

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.