Created
September 10, 2024 03:43
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This is an implemention of torch's conv2d using scipy correlate2d.
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| 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 |
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