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How to add the Vision HOPE operator to your own vision network with Vision HOPE

Use the standalone SRNL, VisionHOPEOperator, or VisionHOPEBlock modules inside a different PyTorch architecture.

  • Research
  • Advanced
  • 1 hour 15 minutes
  • From the README
  • Any agent

Who it is forA deep learning researcher building a custom image model who wants to test the Vision HOPE operator as a component in place of a standard mixing layer.

The problemReplacing an existing block in an architecture is hard to test unless the new module has a clear input and output shape contract.

The outcomeYour model runs with the Vision HOPE operator in one or more layers, and gradients flow through it during training.

What you need first

  • Linux with an NVIDIA GPU, the CUDA toolkit (including nvcc), and a C++ compiler
  • Python 3.10 (recommended) and CUDA 12.1
  • Pretrained checkpoint from Hugging Face, GitHub Releases, or Baidu Netdisk
  • For dataset tasks: ImageNet-1K, COCO 2017, or ADE20K downloaded from their official websites

Steps

  1. Install the classification requirements with python -m pip install -r requirements/classification.txt and python -m pip install --no-deps -e ..
  2. Import the modules with from visionhope.models import VisionHOPEOperator, VisionHOPEBlock or from visionhope.models import SRNL.
  3. Pass image features shaped [batch, channels, height, width] into VisionHOPEOperator(dim=64, head_dim=16) or VisionHOPEBlock(dim=64, mixer_dim=32, head_dim=16); the output keeps the same shape.
  4. For sequence inputs, pass tensors shaped [batch, tokens, dim] into SRNL(dim=64, head_dim=16, chunk_size=64), where dim must be divisible by head_dim.
  5. Run a small loss with .backward() on the output to confirm gradients reach your module's parameters.

Prompt for your agent

Using the VisionHOPE repo (PSRben/VisionHOPE), insert VisionHOPEBlock into <target model file> in place of its existing mixing layer, keeping input and output shapes as [batch, channels, height, width]. Run one training step on a random CUDA tensor and report the output shape and loss. Verify that the backward pass completes without error.

Caveat. All three components require CUDA, so CPU-only machines cannot run them even for a shape check.

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