# nnViewer
**nnViewer** is a Python library designed to provide an intuitive GUI for visualizing the structure and flow of a `torch.nn.Module`. Whether you're debugging or exploring complex neural networks, nnViewer makes it easier to understand your models.
## Installation
Install the library via pip:
```bash
pip install nnViewer
```
## Quick Start
Here's an example of how to use nnViewer with a Hugging Face model:
```python
from transformers import AutoImageProcessor, AutoModel
from PIL import Image
import requests
from nnViewer.back.graph_initializer import wrap_model
from nnViewer.front.gui import run_gui
# Load an image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
# Load the model and processor
processor = AutoImageProcessor.from_pretrained('facebook/dinov2-large')
model = AutoModel.from_pretrained('facebook/dinov2-large')
# Prepare the inputs
inputs = processor(images=image, return_tensors="pt")
# Initialize the graph
graph_init = wrap_model(model)
# Run the model to populate the graph
model(**inputs)
# Launch the GUI
run_gui(graph_init.graph)
```
## Overview
### `wrap_model(model: nn.Module) -> GraphInitializer`
Wraps a `torch.nn.Module` to initialize the computational graph for visualization.
### `run_gui(graph)`
Launches the GUI to display the computational graph.
## Contributing
Contributions are welcome! If you find any issues or have feature requests, feel free to open a GitHub issue or submit a pull request.
## License
This project is licensed under the MIT License. See the `LICENSE` file for more details.
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"description": "# nnViewer\n\n**nnViewer** is a Python library designed to provide an intuitive GUI for visualizing the structure and flow of a `torch.nn.Module`. Whether you're debugging or exploring complex neural networks, nnViewer makes it easier to understand your models.\n\n## Installation\n\nInstall the library via pip:\n\n```bash\npip install nnViewer\n```\n\n## Quick Start\n\nHere's an example of how to use nnViewer with a Hugging Face model:\n\n```python\nfrom transformers import AutoImageProcessor, AutoModel\nfrom PIL import Image\nimport requests\n\nfrom nnViewer.back.graph_initializer import wrap_model\nfrom nnViewer.front.gui import run_gui\n\n# Load an image\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\nimage = Image.open(requests.get(url, stream=True).raw)\n\n# Load the model and processor\nprocessor = AutoImageProcessor.from_pretrained('facebook/dinov2-large')\nmodel = AutoModel.from_pretrained('facebook/dinov2-large')\n\n# Prepare the inputs\ninputs = processor(images=image, return_tensors=\"pt\")\n\n# Initialize the graph\ngraph_init = wrap_model(model)\n\n# Run the model to populate the graph\nmodel(**inputs)\n\n# Launch the GUI\nrun_gui(graph_init.graph)\n```\n\n## Overview\n\n### `wrap_model(model: nn.Module) -> GraphInitializer`\nWraps a `torch.nn.Module` to initialize the computational graph for visualization.\n\n### `run_gui(graph)`\nLaunches the GUI to display the computational graph.\n\n## Contributing\n\nContributions are welcome! If you find any issues or have feature requests, feel free to open a GitHub issue or submit a pull request.\n\n## License\n\nThis project is licensed under the MIT License. See the `LICENSE` file for more details.\n",
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