jax.scipy.signal.convolve2d

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jax.scipy.signal.convolve2d#

jax.scipy.signal.convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0, precision=None)[source]#

Convolution of two 2-dimensional arrays.

JAX implementation of scipy.signal.convolve2d().

Parameters:

  • in1 (Array) – left-hand input to the convolution. Must have in1.ndim == 2.

  • in2 (Array) – right-hand input to the convolution. Must have in2.ndim == 2.

  • mode (str) –

    controls the size of the output. Available operations are:

    • "full": (default) output the full convolution of the inputs.

    • "same": return a centered portion of the "full" output which is the same size as in1.

    • "valid": return the portion of the "full" output which do not depend on padding at the array edges.

  • boundary (str) – only "fill" is supported.

  • fillvalue (float) – only 0 is supported.

  • method –

    controls the computation method. Options are

    • "auto": (default) always uses the "direct" method.

    • "direct": lower to jax.lax.conv_general_dilated().

    • "fft": compute the result via a fast Fourier transform.

  • precision (PrecisionLike | None) – Specify the precision of the computation. Refer to jax.lax.Precision for a description of available values.

Returns:

Array containing the convolved result.

Return type:

Array

See also

Examples

A few 2D convolution examples:

>>> x = jnp.array([[1, 2],
...                [3, 4]])
>>> y = jnp.array([[2, 1, 1],
...                [4, 3, 4],
...                [1, 3, 2]])

Full 2D convolution uses implicit zero-padding at the edges:

>>> jax.scipy.signal.convolve2d(x, y, mode='full')
Array([[ 2.,  5.,  3.,  2.],
       [10., 22., 17., 12.],
       [13., 30., 32., 20.],
       [ 3., 13., 18.,  8.]], dtype=float32)

Specifying mode = 'same' returns a centered 2D convolution of the same size as the first input:

>>> jax.scipy.signal.convolve2d(x, y, mode='same')
Array([[22., 17.],
       [30., 32.]], dtype=float32)

Specifying mode = 'valid' returns only the portion of 2D convolution where the two arrays fully overlap:

>>> jax.scipy.signal.convolve2d(x, y, mode='valid')
Array([[22., 17.],
       [30., 32.]], dtype=float32)
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