commit b5de1f4036c3d555acd49906e08d1048c0593e41
parent c543b567cfb680d73033f958f5984e31bca97e66
Author: Steven Atkinson <steven@atkinson.mn>
Date: Tue, 10 Dec 2024 20:55:47 -0600
Test for longer-than-65,536 inputs for all model types (#513)
Diffstat:
6 files changed, 87 insertions(+), 77 deletions(-)
diff --git a/tests/test_nam/test_models/_convolutional.py b/tests/test_nam/test_models/_convolutional.py
@@ -1,32 +0,0 @@
-# File: _conv_mixin.py
-# Created Date: Saturday November 23rd 2024
-# Author: Steven Atkinson (steven@atkinson.mn)
-
-"""
-Mix-in tests for models with a convolution layer
-"""
-
-import pytest as _pytest
-import torch as _torch
-
-from .base import Base as _Base
-
-
-class Convolutional(_Base):
- @_pytest.mark.skipif(
- not _torch.backends.mps.is_available(), reason="MPS-specific test"
- )
- def test_process_input_longer_than_65536(self):
- """
- Processing inputs longer than 65,536 samples using the MPS backend can
- cause problems.
-
- See: https://github.com/sdatkinson/neural-amp-modeler/issues/505
-
- Assert that precautions are taken.
- """
-
- x = _torch.zeros((65_536 + 1,)).to("mps")
-
- model = self._construct().to("mps")
- model(x)
diff --git a/tests/test_nam/test_models/base.py b/tests/test_nam/test_models/base.py
@@ -2,12 +2,15 @@
# Created Date: Saturday June 4th 2022
# Author: Steven Atkinson (steven@atkinson.mn)
-import abc
-from pathlib import Path
-from tempfile import TemporaryDirectory
+import abc as _abc
+from pathlib import Path as _Path
+from tempfile import TemporaryDirectory as _TemporaryDirectory
+import pytest as _pytest
+import torch as _torch
-class Base(abc.ABC):
+
+class Base(_abc.ABC):
@classmethod
def setup_class(cls, C, args=None, kwargs=None):
cls._C = C
@@ -20,8 +23,42 @@ class Base(abc.ABC):
def test_export(self, args=None, kwargs=None):
model = self._construct(args=args, kwargs=kwargs)
- with TemporaryDirectory() as tmpdir:
- model.export(Path(tmpdir))
+ with _TemporaryDirectory() as tmpdir:
+ model.export(_Path(tmpdir))
+
+ @_pytest.mark.parametrize(
+ "device",
+ (
+ _pytest.param(
+ "cuda",
+ marks=_pytest.mark.skipif(
+ not _torch.cuda.is_available(), reason="CUDA-specific test"
+ ),
+ ),
+ _pytest.param(
+ "mps",
+ marks=_pytest.mark.skipif(
+ not _torch.backends.mps.is_available(), reason="MPS-specific test"
+ ),
+ ),
+ ),
+ )
+ def test_process_input_longer_than_65536_on(self, device: str):
+ """
+ Processing inputs longer than 65,536 samples using various accelerator
+ backends can cause problems.
+
+ See:
+ * https://github.com/sdatkinson/neural-amp-modeler/issues/505
+ * https://github.com/sdatkinson/neural-amp-modeler/issues/512
+
+ (Funny that both have the same length limit--65,536...)
+
+ Assert that precautions are taken.
+ """
+ x = _torch.zeros((65_536 + 1,)).to(device)
+ model = self._construct().to(device)
+ model(x)
def _construct(self, C=None, args=None, kwargs=None):
C = self._C if C is None else C
diff --git a/tests/test_nam/test_models/test_conv_net.py b/tests/test_nam/test_models/test_conv_net.py
@@ -6,10 +6,10 @@ import pytest as _pytest
from nam.models import conv_net
-from ._convolutional import Convolutional as _Convolutional
+from .base import Base as _Base
-class TestConvNet(_Convolutional):
+class TestConvNet(_Base):
@classmethod
def setup_class(cls):
channels = 3
diff --git a/tests/test_nam/test_models/test_linear.py b/tests/test_nam/test_models/test_linear.py
@@ -6,10 +6,10 @@ import pytest as _pytest
from nam.models import linear as _linear
-from ._convolutional import Convolutional as _Convolutional
+from .base import Base as _Base
-class TestLinear(_Convolutional):
+class TestLinear(_Base):
@classmethod
def setup_class(cls):
C = _linear.Linear
diff --git a/tests/test_nam/test_models/test_recurrent.py b/tests/test_nam/test_models/test_recurrent.py
@@ -2,29 +2,26 @@
# Created Date: Sunday July 17th 2022
# Author: Steven Atkinson (steven@atkinson.mn)
-from pathlib import Path
-from tempfile import TemporaryDirectory
+import pytest as _pytest
+import torch as _torch
-import pytest
-import torch
+from nam.models import recurrent as _recurrent
-from nam.models import recurrent
+from .base import Base as _Base
-from .base import Base
+_metadata_loudness_x_mocked = 0.1 * _torch.randn((11,)) # Shorter for speed
-_metadata_loudness_x_mocked = 0.1 * torch.randn((11,)) # Shorter for speed
-
-class TestLSTM(Base):
+class TestLSTM(_Base):
@classmethod
def setup_class(cls):
- class LSTMWithMocks(recurrent.LSTM):
+ class LSTMWithMocks(_recurrent.LSTM):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._get_initial_state_burn_in = 7
@classmethod
- def _metadata_loudness_x(cls) -> torch.Tensor:
+ def _metadata_loudness_x(cls) -> _torch.Tensor:
return _metadata_loudness_x_mocked
num_layers = 2
@@ -37,15 +34,20 @@ class TestLSTM(Base):
cls._num_layers = num_layers
cls._hidden_size = hidden_size
- def test_get_initial_state_cpu(self):
- return self._t_initial_state("cpu")
-
- @pytest.mark.skipif(not torch.cuda.is_available(), reason="GPU test")
- def test_get_initial_state_gpu(self):
- self._t_initial_state("cuda")
-
- def _t_initial_state(self, device):
+ @_pytest.mark.parametrize(
+ "device",
+ (
+ "cpu",
+ _pytest.param(
+ "cuda",
+ marks=_pytest.mark.skipif(
+ not _torch.cuda.is_available(), reason="GPU test"
+ ),
+ ),
+ ),
+ )
+ def test_get_initial_state_on(self, device: str):
model = self._construct().to(device)
h, c = model._get_initial_state()
- assert isinstance(h, torch.Tensor)
- assert isinstance(c, torch.Tensor)
+ assert isinstance(h, _torch.Tensor)
+ assert isinstance(c, _torch.Tensor)
diff --git a/tests/test_nam/test_models/test_wavenet.py b/tests/test_nam/test_models/test_wavenet.py
@@ -2,19 +2,22 @@
# Created Date: Friday May 5th 2023
# Author: Steven Atkinson (steven@atkinson.mn)
-import pytest
-import torch
+import pytest as _pytest
+import torch as _torch
-from nam.models.wavenet import WaveNet
-from nam.train.core import Architecture, get_wavenet_config
+from nam.models.wavenet import WaveNet as _WaveNet
+from nam.train.core import (
+ Architecture as _Architecture,
+ get_wavenet_config as _get_wavenet_config,
+)
-from ._convolutional import Convolutional as _Convolutional
+from .base import Base as _Base
-class TestWaveNet(_Convolutional):
+class TestWaveNet(_Base):
@classmethod
def setup_class(cls):
- C = WaveNet
+ C = _WaveNet
args = ()
kwargs = {
"layers_configs": [
@@ -31,12 +34,12 @@ class TestWaveNet(_Convolutional):
super().setup_class(C, args, kwargs)
def test_import_weights(self):
- config = get_wavenet_config(Architecture.FEATHER)
- model_1 = WaveNet.init_from_config(config)
- model_2 = WaveNet.init_from_config(config)
+ config = _get_wavenet_config(_Architecture.FEATHER)
+ model_1 = _WaveNet.init_from_config(config)
+ model_2 = _WaveNet.init_from_config(config)
batch_size = 2
- x = torch.randn(batch_size, model_1.receptive_field + 23)
+ x = _torch.randn(batch_size, model_1.receptive_field + 23)
y1 = model_1(x)
y2_before = model_2(x)
@@ -44,9 +47,9 @@ class TestWaveNet(_Convolutional):
model_2.import_weights(model_1._export_weights())
y2_after = model_2(x)
- assert not torch.allclose(y2_before, y1)
- assert torch.allclose(y2_after, y1)
+ assert not _torch.allclose(y2_before, y1)
+ assert _torch.allclose(y2_after, y1)
if __name__ == "__main__":
- pytest.main()
+ _pytest.main()
