Serron

Mathematical Morphology module for PyTorch (CUDA), providing differentiable operators and learnable network layers.

Install

Prebuilt wheels are published for CPython 3.12–3.14 on Linux (x86_64, aarch64) and Windows (x86_64). A working CUDA-enabled PyTorch (torch >= 2.12) must already be installed in the environment.

pip install serron

Usage

Alpha: the CUDA kernels are not wired up yet, so the operators below currently raise NotImplementedError.

Serron works on standard PyTorch tensors laid out as (N, C, H, W) and living on a CUDA device.

Functional operators

Stateless operators live in serron.functional and are re-exported at the top level. Each takes an input tensor, a structuring element, and an optional border mode:

import torch
import serron
from serron import BorderMode
from serron import structuring_element as se

x = torch.rand(1, 1, 256, 256, device="cuda")
kernel = se.disk(3, device="cuda")

eroded = serron.erosion(x, kernel)
dilated = serron.dilatation(x, kernel)
opened = serron.opening(x, kernel)
closed = serron.closing(x, kernel)

# Derived operators
grad = serron.gradient(x, kernel)  # dilate(x) - erode(x)
white = serron.top_hat(x, kernel)  # x - open(x)
black = serron.black_hat(x, kernel)  # close(x) - x

# Control border handling
eroded_reflect = serron.erosion(x, kernel, border=BorderMode.REFLECT)

Available operators: erosion, dilatation, opening, closing, gradient, top_hat, black_hat.

Structuring elements

serron.structuring_element builds common SE shapes on the requested device:

from serron import structuring_element as se

se.square(5, device="cuda")  # (5, 5) full square
se.cross(5, device="cuda")  # (5, 5) plus shape
se.disk(3, device="cuda")  # (7, 7) disk, radius 3
se.diamond(3, device="cuda")  # (7, 7) diamond, radius 3
se.from_tensor(my_weights)  # wrap an arbitrary 2-D tensor as a grayscale SE

Border modes

BorderMode controls how out-of-bounds neighbors are handled:

Mode	Behavior
`BorderMode.REPLICATE`	Repeat the edge value (default)
`BorderMode.REFLECT`	Mirror across the edge
`BorderMode.CONSTANT`	Pad with a constant

Learnable layers

serron also exposes torch.nn.Module layers with a learnable structuring element, so morphology can be trained end-to-end inside a network. Each layer takes the number of channels and a kernel_size:

import torch
from serron import Erosion2d, Dilation2d, Opening2d, Closing2d
from serron import BorderMode

layer = Erosion2d(channels=3, kernel_size=5, border=BorderMode.REPLICATE).cuda()

x = torch.rand(8, 3, 64, 64, device="cuda")
y = layer(x)  # forward pass; layer.weight is a trainable (C, k, k) SE
y.sum().backward()  # gradients flow into layer.weight

Available layers: Erosion2d, Dilation2d, Opening2d, Closing2d.

Building from source

Clone repo

git clone git@github.com:vhrabar/serron.git
cd serron

Choosing a torch build

torch is pulled from a specific wheel index via mutually-exclusive extras. Pick the one matching your machine — plain uv sync (no extra) falls back to the default CUDA-enabled wheel from PyPI:

uv sync --extra cu132   # CUDA build
uv sync --extra cpu     # CPU-only build

Building the wheel

Building the CUDA extension from source needs the CUDA 13.X toolkit (nvcc):

uv sync --package serron --no-dev --group build --extra cu132
uv build --package serron --wheel --no-build-isolation

On a GPU-less machine, sync the CPU torch build instead, the extension then builds C++ only (no nvcc required):

uv sync --package serron --no-dev --group build --extra cpu
uv build --package serron --wheel --no-build-isolation

License

MIT

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0.3.1

Aug 31, 2026 10 files

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0.2.0 This release

Aug 26, 2026 10 files

0.1.1

Jul 25, 2026 10 files

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Jul 18, 2026 10 files

0.0.1

Jul 10, 2026 10 files

serron 0.2.0

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