Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Adopt PyTorch's test util to torchscript test #640

Merged
merged 2 commits into from
May 14, 2020
Merged

Adopt PyTorch's test util to torchscript test #640

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
5958f67
Adopt PyTorch's test util to torchscript test
mthrok May 14, 2020
cc81618
Use assertEqual
mthrok May 14, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
11 changes: 7 additions & 4 deletions test/common_utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,12 +1,13 @@
import os
import tempfile
import unittest
from typing import Type, Iterable
from contextlib import contextmanager
from shutil import copytree

import torch
from torch.testing._internal.common_utils import TestCase
import torchaudio
import pytest

_TEST_DIR_PATH = os.path.dirname(os.path.realpath(__file__))
BACKENDS = torchaudio._backend._audio_backends
Expand Down Expand Up @@ -87,6 +88,9 @@ class TestBaseMixin:
device = None


_SKIP_IF_NO_CUDA = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available')


def define_test_suite(testbase: Type[TestBaseMixin], dtype: str, device: str):
if dtype not in ['float32', 'float64']:
raise NotImplementedError(f'Unexpected dtype: {dtype}')
Expand All @@ -95,11 +99,10 @@ def define_test_suite(testbase: Type[TestBaseMixin], dtype: str, device: str):

name = f'Test{testbase.__name__}_{device.upper()}_{dtype.capitalize()}'
attrs = {'dtype': getattr(torch, dtype), 'device': torch.device(device)}
testsuite = type(name, (testbase,), attrs)
testsuite = type(name, (testbase, TestCase), attrs)

if device == 'cuda':
testsuite = pytest.mark.skipif(
not torch.cuda.is_available(), reason='CUDA not available')(testsuite)
testsuite = _SKIP_IF_NO_CUDA(testsuite)
return testsuite


Expand Down
2 changes: 1 addition & 1 deletion test/test_functional.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ def test_simple(self):
a_coeffs = torch.tensor([1, 0, 0, 0], dtype=self.dtype, device=self.device)
output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs)

torch.testing.assert_allclose(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5)
self.assertEqual(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5)

def test_clamp(self):
input_signal = torch.ones(1, 44100 * 1, dtype=self.dtype, device=self.device)
Expand Down
64 changes: 28 additions & 36 deletions test/test_torchscript_consistency.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,6 +1,5 @@
"""Test suites for jit-ability and its numerical compatibility"""
import unittest
import pytest

import torch
import torchaudio
Expand All @@ -10,29 +9,18 @@
import common_utils


def _assert_functional_consistency(func, tensor, shape_only=False):
ts_func = torch.jit.script(func)
output = func(tensor)
ts_output = ts_func(tensor)

if shape_only:
assert ts_output.shape == output.shape, (ts_output.shape, output.shape)
else:
torch.testing.assert_allclose(ts_output, output)


def _assert_transforms_consistency(transform, tensor):
ts_transform = torch.jit.script(transform)
output = transform(tensor)
ts_output = ts_transform(tensor)
torch.testing.assert_allclose(ts_output, output)


class Functional(common_utils.TestBaseMixin):
"""Implements test for `functinoal` modul that are performed for different devices"""
def _assert_consistency(self, func, tensor, shape_only=False):
tensor = tensor.to(device=self.device, dtype=self.dtype)
return _assert_functional_consistency(func, tensor, shape_only=shape_only)

ts_func = torch.jit.script(func)
output = func(tensor)
ts_output = ts_func(tensor)
if shape_only:
ts_output = ts_output.shape
output = output.shape
self.assertEqual(ts_output, output)

def test_spectrogram(self):
def func(tensor):
Expand Down Expand Up @@ -209,7 +197,7 @@ def func(tensor):

def test_lfilter(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I'd make this a decorator then and have separate tests for each dtype

Copy link
Collaborator Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I think that's outside of the scope of this PR.


filepath = common_utils.get_asset_path('whitenoise.wav')
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand Down Expand Up @@ -253,7 +241,7 @@ def func(tensor):

def test_lowpass(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path('whitenoise.wav')
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand All @@ -267,7 +255,7 @@ def func(tensor):

def test_highpass(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path('whitenoise.wav')
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand All @@ -281,7 +269,7 @@ def func(tensor):

def test_allpass(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path('whitenoise.wav')
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand All @@ -296,7 +284,7 @@ def func(tensor):

def test_bandpass_with_csg(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand All @@ -312,7 +300,7 @@ def func(tensor):

def test_bandpass_without_csg(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand All @@ -328,7 +316,7 @@ def func(tensor):

def test_bandreject(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand All @@ -343,7 +331,7 @@ def func(tensor):

def test_band_with_noise(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand All @@ -359,7 +347,7 @@ def func(tensor):

def test_band_without_noise(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand All @@ -375,7 +363,7 @@ def func(tensor):

def test_treble(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand All @@ -391,7 +379,7 @@ def func(tensor):

def test_deemph(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand All @@ -404,7 +392,7 @@ def func(tensor):

def test_riaa(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand All @@ -417,7 +405,7 @@ def func(tensor):

def test_equalizer(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand All @@ -433,7 +421,7 @@ def func(tensor):

def test_perf_biquad_filtering(self):
if self.dtype == torch.float64:
pytest.xfail("This test is known to fail for float64")
raise unittest.SkipTest("This test is known to fail for float64")

filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
Expand Down Expand Up @@ -514,7 +502,7 @@ def func(tensor):

def test_phaser(self):
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, sample_rate = torchaudio.load(filepath, normalization=True)
waveform, _ = torchaudio.load(filepath, normalization=True)

def func(tensor):
gain_in = 0.5
Expand All @@ -533,7 +521,11 @@ class Transforms(common_utils.TestBaseMixin):
def _assert_consistency(self, transform, tensor):
tensor = tensor.to(device=self.device, dtype=self.dtype)
transform = transform.to(device=self.device, dtype=self.dtype)
_assert_transforms_consistency(transform, tensor)

ts_transform = torch.jit.script(transform)
output = transform(tensor)
ts_output = ts_transform(tensor)
self.assertEqual(ts_output, output)

def test_Spectrogram(self):
tensor = torch.rand((1, 1000))
Expand Down