NumPyConv2D

Sometimes, PyTorch can be a bit much when all you want is a simple 2d convolution and don't particularly care about speed. This repository implements a fully vectorized conv2d function similar to PyTorch's torch.nn.functional.conv2d with support for stride, padding, dilation, groups and all kinds of padding. The only dependency is NumPy.

Example

from conv2d import conv2d
import numpy as np

# Number of input images, channels, height, width, output channels, kernel size
n, c, h, w, c_out, kernel_size = 5, 3, 32, 32, 4, 3

# Random input image batch and kernel weights
input = np.random.rand(n, c, h, w)
weight = np.random.rand(c_out, c, kernel_size, kernel_size)

# Simple example usage of conv2d convolution
output = conv2d(input, weight)

# Advanced usage with all parameters
output = conv2d(input, weight, bias=np.random.rand(c_out), stride=(2, 2),
    padding=(0, 0), dilation=(2, 2), groups=1, padding_mode="replicate")

Notes

• The tests compare this implementation with the output of PyTorch's Conv2d layer and might trigger the following warning, which is caused by PyTorch and can be safely ignored: /home/user/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:459: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at ../aten/src/ATen/native/Convolution.cpp:1003.) return F.conv2d(input, weight, bias, self.stride,
• This function actually computes a correlation instead of a convolution since the kernel is not flipped (same as PyTorch and other popular deep learning frameworks). If you want a "real" convolution, you can flip the kernel weights: weight[:, :, ::-1, ::-1]
• If you want a fast conv2d implementation, use PyTorch instead. The main purpose of this implementation is that it is lightweight.