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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,92 @@ | ||
| import math | ||
| from typing import Literal | ||
|
|
||
| import torch | ||
| import torch.nn.functional | ||
| from torch import Tensor | ||
|
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|
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| def convolve( | ||
| input: Tensor, | ||
| other: Tensor, | ||
| mode: Literal["full", "same", "valid"] = "full", | ||
| ) -> Tensor: | ||
| if input.ndim != other.ndim: | ||
| raise ValueError | ||
|
|
||
| for i in range(input.ndim - 1): | ||
| a = input.size(i) | ||
| b = other.size(i) | ||
|
|
||
| if a == b or a == 1 or b == 1: | ||
| continue | ||
|
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| raise ValueError | ||
|
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| if mode not in {"full", "same", "valid"}: | ||
| raise ValueError | ||
|
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| x_size, y_size = input.shape[-1], other.shape[-1] | ||
|
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| if input.shape[-1] < other.shape[-1]: | ||
| input, other = other, input | ||
|
|
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| if input.shape[:-1] != other.shape[:-1]: | ||
| input_shape = [] | ||
|
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| for i, j in zip(input.shape[:-1], other.shape[:-1], strict=False): | ||
| input_shape = [*input_shape, max(i, j)] | ||
|
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| input = torch.broadcast_to( | ||
| input, | ||
| [*input_shape, input.shape[-1]], | ||
| ) | ||
|
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| other = torch.broadcast_to( | ||
| other, | ||
| [*input_shape, other.shape[-1]], | ||
| ) | ||
|
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| input = torch.reshape( | ||
| input, | ||
| [math.prod(input.shape[:-1]), input.shape[-1]], | ||
| ) | ||
|
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| other = torch.unsqueeze( | ||
| torch.flip( | ||
| torch.reshape( | ||
| other, | ||
| [math.prod(input.shape[:-1]), other.shape[-1]], | ||
| ), | ||
| dims=[-1], | ||
| ), | ||
| dim=1, | ||
| ) | ||
|
|
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| output = torch.reshape( | ||
| torch.nn.functional.conv1d( | ||
| input, | ||
| other, | ||
| groups=input.shape[0], | ||
| padding=other.shape[-1] - 1, | ||
| ), | ||
| [*input.shape[:-1], -1], | ||
| ) | ||
|
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| match mode: | ||
| case "same": | ||
| size = x_size | ||
|
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| m = (output.shape[-1] - size) // 2 | ||
| n = m + size | ||
|
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| output = output[..., m:n] | ||
| case "valid": | ||
| size = max(x_size, y_size) - min(x_size, y_size) + 1 | ||
|
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| m = (output.shape[-1] - size) // 2 | ||
| n = m + size | ||
|
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| output = output[..., m:n] | ||
|
|
||
| return output |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,39 @@ | ||
| import beignet | ||
| import hypothesis.extra.numpy | ||
| import hypothesis.strategies | ||
| import numpy | ||
| import torch.testing | ||
|
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||
|
|
||
| @hypothesis.strategies.composite | ||
| def _strategy(function): | ||
| size = function( | ||
| hypothesis.strategies.integers( | ||
| min_value=128, | ||
| max_value=512, | ||
| ), | ||
| ) | ||
|
|
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| input = torch.rand([size]) | ||
| other = torch.rand([size]) | ||
|
|
||
| return ( | ||
| { | ||
| "input": input, | ||
| "other": other, | ||
| }, | ||
| torch.reshape( | ||
| torch.from_numpy(numpy.convolve(input, other)), | ||
| [1, -1], | ||
| ), | ||
| ) | ||
|
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|
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| @hypothesis.given(_strategy()) | ||
| def test_convolve(data): | ||
| parameters, expected = data | ||
|
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| torch.testing.assert_allclose( | ||
| beignet.convolve(**parameters), | ||
| expected, | ||
| ) |