Interactive Modules

The pytorch interface wraps your nn.Module and provides a terminal for live interaction. This allows you to process data and inspect the resulting tensors without leaving the visualization environment.

import svetoviz_webgpu as sv
import torch
import torch.nn as nn
from PIL import Image

# Prepare data
url = "https://cdn.freecodecamp.org/curriculum/cat-photo-app/relaxing-cat.jpg"
img = Image.open(requests.get(url, stream=True).raw).resize((64, 64))
img_np = np.array(img).transpose(2, 0, 1)
image_data = torch.from_numpy(img_np).float().unsqueeze(0)

# Your pytorch module
conv = nn.Conv2d(3, 4, 5, padding=10)

def terminal_callback(buffer, message, images, files):
    # Interact with the module as you normally do
    conv(image_data)
    buffer.send_system_message("processed")

# Start the session
sv.pytorch(module=conv, terminal_callback=terminal_callback)

PyTorch Interaction Terminal

Interaction activations are collected and presented on the screen.

Layer Relations

You can hover or click on output values to see the mappings and relations between layers. Svetoviz provides a visual explanation of the provenance for each value in the tensor.

For example, you can find the exact kernel position for convolutions, or visualize the column-to-patch mapping for operations like fold and unfold.

Kernel and Patch Mapping

Clicking an output value reveals the input-to-output spatial relationship.