Properly expose batch_size from OpenVINO similarly to TensorRT

[democat3457]

This is the fixed version of #8437.

This PR exposes OpenVINO's network batch_size similarly to TensorRT's batch_size in DetectMultiBackend. This allows for a more consistent and cross-backend indication of whether a fixed non-one batch size exists and what the value of the batch size is.

Use case:
Current way to get a model's fixed batch size

model = DetectMultiBackend(path, device=torch.device(device))
batch_size = model.batch_size if hasattr(model, "batch_size") else None
if model.xml: # explicit handling of openvino
  ... # a lot of stuff

New way to get a model's fixed batch size

model = DetectMultiBackend(path, device=torch.device(device))
batch_size = model.batch_size if hasattr(model, "batch_size") else None
# no need for explicit handling of openvino, code is much cleaner

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Enhancements for OpenVINO model loading in YOLOv5.

📊 Key Changes

• Added OpenVINO Layout and get_batch to model loading.
• Implemented layout setting for parameter tensors to "NCHW" format if they have an empty layout.
• Added code to handle static batch dimension for model input, setting batch size accordingly.

🎯 Purpose & Impact

• 🛠 Ensures compatibility of YOLOv5 with the latest OpenVINO API changes.
• 🚀 Facilitates a smooth YOLOv5 model deployment using OpenVINO backend, potentially improving inference performance on Intel hardware.
• 👥 Users deploying YOLOv5 models on OpenVINO will experience improved ease of use and robustness.

[glenn-jocher]

@democat3457 PR is merged. Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐