This is an implemention of torch's conv2d using scipy correlate2d.

conv2d_simplified.py

import numpy as np
from scipy import signal

def conv2d_simplified(input, weight, bias=None, padding=0):
    # This is an implemention of torch's conv2d using scipy correlate2d. Only
    # limited options are supported for simplicity.
    # Inspired by https://github.com/99991/NumPyConv2D/
    c_out, c_in_by_groups, kh, kw = weight.shape
    if not isinstance(padding, int):
        raise NotImplementedError()
    
    if padding:
        input = np.pad(input, ((0, 0), (0, 0), (padding, padding), (padding, padding)), "constant")

    outArr = np.empty((input.shape[0], c_out, input.shape[2]+1-kh, input.shape[3]+1-kw))
    al = np.empty((outArr.shape[2], outArr.shape[3]))

    for k in range(input.shape[0]):
        for i in range(weight.shape[0]):
            al[:, :] = 0.0

            for j in range(weight.shape[1]):
                al += signal.correlate2d(input[k, j, :, :], weight[i, j, :, :], 'valid')

            outArr[k, i, :, :] = al

    if bias is not None:
        outArr = outArr + bias.reshape(1, c_out, 1, 1)

    return outArr