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I implemented a simplified version using scipy for learning purposes:

```python 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 ```

```python 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 ```

I implemented a simplified version using scipy for learning purposes:

```python 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 ``` If you want to do a convolution, you have to flip the kernel because the conv2d function actually uses the cross-correlation operator.

```python 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 ``` If you want to do a convolution, you have to flip the kernel because the conv2d function actually uses the cross-correlation operator.

Present in both answers;
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Present only in the old answer;