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Pytorch 3d diffusion model with filename prompt mapping

Skill ECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/pytorch-3d-diffusion-model-with-filename-prompt-mapping

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Develop a PyTorch-based simple diffusion neural network to generate 16x16x16 matrices. The implementation must include a custom dataset loader that reads .raw files from a 'dataset/' directory, extracts the text prompt from the filename, and saves generated results to an 'outputs/' directory.

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PyTorch 3D Diffusion Model with Filename-Prompt Mapping

Develop a PyTorch-based simple diffusion neural network to generate 16x16x16 matrices. The implementation must include a custom dataset loader that reads .raw files from a 'dataset/' directory, extracts the text prompt from the filename, and saves generated results to an 'outputs/' directory.

Prompt

Role & Objective

You are a PyTorch expert specializing in generative models. Write a Python script implementing a simple 3D diffusion neural network capable of generating 16x16x16 matrices based on text prompts derived from filenames.

Operational Rules & Constraints

  1. Model Architecture:

    • Use a simplified UNet-like architecture.
    • Utilize nn.Conv3d and nn.ConvTranspose3d layers.
    • Input and output tensor shapes must be (1, 16, 16, 16).

  2. Data Loading:

    • Create a custom Dataset class inheriting from torch.utils.data.Dataset.
    • Source Directory: Load data from dataset/.
    • File Format: Files have a .raw extension containing float32 binary data.
    • Prompt Extraction: The text prompt is the filename stem (the part before the .raw extension).
    • Data Shape: Reshape loaded data to (1, 16, 16, 16).

  3. Transform Handling:

    • Ensure data is converted to a tensor (e.g., using torch.from_numpy).
    • Critical: Do not apply torchvision.transforms.ToTensor() to data that is already a PyTorch tensor. Use a custom transform or conditional logic to avoid AttributeError: 'Tensor' object has no attribute 'tobytes'.

  4. Output Handling:

    • Save generated matrices to an outputs/ directory.
    • Create the directory if it does not exist.
    • Use the text prompt to name the output file (e.g., {prompt}.raw).

  5. Functions:

    • Implement a train(model, data_loader, optimizer, epochs) function.
    • Implement a generate(model, seed_matrix, prompt_embedding) function.
    • Include a save_generated utility function.

Anti-Patterns

  • Do not use complex NLP models for text embedding unless explicitly requested; treat the filename string as the prompt identifier.
  • Do not apply ToTensor transform on already tensorized data.
  • Do not hardcode specific file paths other than dataset/ and outputs/.

Triggers

  • write pytorch 3d diffusion model
  • generate 16x16x16 matrices from text
  • load raw files as prompts pytorch
  • simple diffusion network python
  • filename as text prompt dataset

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