Get empowered by the Slavic god Svetovid, whose four heads represent omniscience - the ability to see through space and time in all directions at once. With his power, you can maintain a unified perspective across your tensors, models, and massive datasets. Peer into the architecture of your neural networks and navigate high-dimensional data with absolute clarity.

array_inspector.py

NumPy Inspector

Visualize any NumPy array with instant dimension awareness. Support for high-dimensional, large arrays.

data = np.random.rand(2, 4, 32, 32).astype(np.float32)

SvetoViz.array_web(data, gap_size=10)
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pytorch_debugger.py

PyTorch Debugger

Directly load your PyTorch module, interact, and debug activations in real-time.

img = Image.open("sample_image.jpg").convert("RGB")
img_np = np.array(img).astype(np.float32) / 255.0

# Rearrange from (H, W, C) to (C, H, W) and add batch dim
input_tensor = torch.from_numpy(img_np).permute(2, 0, 1).unsqueeze(0)

# 2. Define the 2D Convolutional module
conv2d = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)

def terminal_callback(buffer, message, images, files):
    # 3. Process the image through the module
    output = conv2d(input_tensor)

    # Log the feature map shape to the SvetoViz terminal
    buffer.send_system_message(f"Processed image. Shape: {list(output.shape)}")

# 4. Start the interactive session
SvetoViz.pytorch(module=conv2d, terminal_callback=terminal_callback)
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model_viewer.py

Model Analysis

Deep-dive into model architecture. Explore layers down to a single parameter.

image_processor = DetrImageProcessor.from_pretrained(
    "facebook/detr-resnet-50",
    revision="no_timm",
    device_map="cpu")
model = DetrForObjectDetection.from_pretrained(
    "facebook/detr-resnet-50",
    revision="no_timm",
    device_map="cpu")

SvetoViz.model_web(
    model=model,
    image_processor=image_processor
)
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model_debug.py

Model Debugger

Interact with your model and investigate activations in real-time.

tokenizer = GPT2Tokenizer.from_pretrained('gpt2', device_map="cpu")
model = GPT2LMHeadModel.from_pretrained('gpt2', device_map="cpu")

def terminal_callback(buffer, message, images, files):
    text = message  # "Replace me by any text you'd like."
    buffer.send_user_message(text)
    inputs = tokenizer(text, return_tensors="pt")

    # Generate continuation
    outputs = model.generate(**inputs, max_new_tokens=50)

    # Decode to string
    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
    buffer.send_system_message(decoded)

SvetoViz.model_web(
    model=model,
    tokenizer=tokenizer,
    terminal_callback=terminal_callback
)
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dataset_handler.py

Dataset Explorer

Efficiently stream and visualize large-scale HuggingFace datasets from local storage or remote server.

hf_name = "eltorio/ROCOv2-radiology"
dataset = load_dataset(hf_name, split="train")
layout = DatasetLayout(
    name=hf_name,
    dataset=dataset,
    image_columns=["image"],
    cell_size=(400, 400),
)
SvetoViz.save_to_disc(view=layout,
                       threaded=True,
                       directory=f"/Volumes/Untitled/{hf_name}",
                       compression=CompressionMode.PNG_0,
                       tree_db_only=False
                       )

SvetoViz.load_from_disc_web(directory=f"/Volumes/Untitled/{hf_name}")
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image_browser.py

Image Browser

Browse through massive collections of images regardless of resolution.

layout = DirectoryPackedSpiralLayout(
    directory="/Users/piotrgryko/Desktop/nasaimages"
)
SvetoViz.save_to_disc(view=layout,
                       threaded=True,
                       directory="/Volumes/Untitled/universe",
                       compression=CompressionMode.JPEG_0)

SvetoViz.load_from_disc_web(directory="/Volumes/Untitled/universe")
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