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86 lines
2.8 KiB
86 lines
2.8 KiB
# MIT License, Copyright (c) 2020 Alexandre Défossez.
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Modified from julius(https://github.com/adefossez/julius/blob/main/tests/test_fftconv.py)
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import random
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import sys
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import unittest
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import paddle
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import paddle.nn.functional as F
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from audio.audiotools.core import fft_conv1d
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from audio.audiotools.core import FFTConv1D
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TOLERANCE = 1e-4 # as relative delta in percentage
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class _BaseTest(unittest.TestCase):
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def setUp(self):
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paddle.seed(1234)
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random.seed(1234)
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def assertSimilar(self, a, b, msg=None, tol=TOLERANCE):
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delta = 100 * paddle.norm(a - b, p=2) / paddle.norm(b, p=2)
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self.assertLessEqual(delta.numpy(), tol, msg)
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def compare_paddle(self, *args, msg=None, tol=TOLERANCE, **kwargs):
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y_ref = F.conv1d(*args, **kwargs)
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y = fft_conv1d(*args, **kwargs)
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self.assertEqual(list(y.shape), list(y_ref.shape), msg)
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self.assertSimilar(y, y_ref, msg, tol)
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class TestFFTConv1d(_BaseTest):
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def test_same_as_paddle(self):
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for _ in range(5):
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kernel_size = random.randrange(4, 128)
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batch_size = random.randrange(1, 6)
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length = random.randrange(kernel_size, 1024)
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chin = random.randrange(1, 12)
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chout = random.randrange(1, 12)
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bias = random.random() < 0.5
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if random.random() < 0.5:
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padding = 0
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else:
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padding = random.randrange(kernel_size // 2, 2 * kernel_size)
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x = paddle.randn([batch_size, chin, length])
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w = paddle.randn([chout, chin, kernel_size])
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keys = ["length", "kernel_size", "chin", "chout", "bias"]
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loc = locals()
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state = {key: loc[key] for key in keys}
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if bias:
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bias = paddle.randn([chout])
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else:
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bias = None
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for stride in [1, 2, 5]:
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state["stride"] = stride
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self.compare_paddle(
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x, w, bias, stride, padding, msg=repr(state))
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def test_small_input(self):
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x = paddle.randn([1, 5, 19])
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w = paddle.randn([10, 5, 32])
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with self.assertRaises(RuntimeError):
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fft_conv1d(x, w)
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x = paddle.randn([1, 5, 19])
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w = paddle.randn([10, 5, 19])
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self.assertEqual(list(fft_conv1d(x, w).shape), [1, 10, 1])
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def test_module(self):
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x = paddle.randn([16, 4, 1024])
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mod = FFTConv1D(4, 5, 8, bias_attr=True)
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mod(x)
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mod = FFTConv1D(4, 5, 8, bias_attr=False)
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mod(x)
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def test_dynamic_graph(self):
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x = paddle.randn([16, 4, 1024])
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mod = FFTConv1D(4, 5, 8, bias_attr=True)
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self.assertEqual(list(mod(x).shape), [16, 5, 1024 - 8 + 1])
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if __name__ == "__main__":
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unittest.main()
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