Export PyTorch neural networks to Scratch sprites.

cattorch transpiles a torch.nn.Module into a .sprite3 file that can be imported directly into any Scratch project. The generated sprite uses only standard Scratch blocks, so no extensions or modifications are required.

cattorch does not export training scripts, you will need to train your model with torch before exporting to a Scratch sprite.

pip install cattorch

Requires Python 3.10+ and PyTorch 2.0+.

import torch
import torch.nn as nn
from cattorch import transpile

class TwoLayerNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(4, 8)
        self.fc2 = nn.Linear(8, 3)

    def forward(self, x):
        return self.fc2(torch.relu(self.fc1(x)))

model = TwoLayerNet()
# train your model first! then:
# transpile(model, example input, sprite name)
transpile(model, torch.randn(1, 4), "two_layer_net")
# => two_layer_net.sprite3

In Scratch, the sprite reads its input from a list called input and writes results to a list called output. It is up to you to add logic to fill the input tensor and run the generated code blocks.

If the model takes multiple input tensors, the additional inputs are named input_1, input_2, etc.

These are sufficient for architectures like MLPs and single-head transformers (including full pre-norm transformer blocks with residual connections).

More operations are planned for the future, such as those required to support full LLMs, CNNs, and more.

• Project size: Scratch limits projects to 5 MB. cattorch warns at 4 MB and errors at 5 MB.
• List length: Scratch lists can hold at most 200,000 items. cattorch raises an error if any weight tensor or intermediate list exceeds this.

MIT