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test_yingram.py
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test_yingram.py
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# ------------------------------------------------------------------------ #
# Copyright 2022 SPTK Working Group #
# #
# Licensed under the Apache License, Version 2.0 (the "License"); #
# you may not use this file except in compliance with the License. #
# You may obtain a copy of the License at #
# #
# http://www.apache.org/licenses/LICENSE-2.0 #
# #
# Unless required by applicable law or agreed to in writing, software #
# distributed under the License is distributed on an "AS IS" BASIS, #
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #
# See the License for the specific language governing permissions and #
# limitations under the License. #
# ------------------------------------------------------------------------ #
import pytest
import torch
import diffsptk
import tests.utils as U
@pytest.mark.parametrize("device", ["cpu", "cuda"])
@pytest.mark.parametrize("module", [False, True])
def test_compatibility(
device, module, fl=2048, fp=80, sr=22050, lag_min=22, n_bin=20, B=2
):
if device == "cuda" and not torch.cuda.is_available():
return
if torch.get_default_dtype() != torch.double: # pragma: no cover
return # This is due to the difference in the calculation of autocorrelation.
frame = diffsptk.Frame(fl, fp, center=False).to(device)
yingram = U.choice(
module,
diffsptk.Yingram,
diffsptk.functional.yingram,
{"frame_length": fl},
{"sample_rate": sr, "lag_min": lag_min, "n_bin": n_bin},
)
if module:
yingram = yingram.to(device)
url = "https://raw.githubusercontent.com/revsic/torch-nansy/main/nansy/yingram.py"
U.call(f"curl -s {url} > tmp.py", get=False)
from tmp import Yingram as Target
target = Target(fp, fl, lag_min, fl - 1, n_bin, sr).to(device)
U.call("rm -f tmp.py", get=False)
x = diffsptk.nrand(B, sr).to(device)
y = target(x).cpu()
y_hat = yingram(frame(x)).cpu()
assert U.allclose(y, y_hat)
U.check_differentiability(device, yingram, [B, fl])