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Remove possible manual seeds from test files. #2436

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Original file line number Diff line number Diff line change
@@ -55,7 +55,6 @@ class TestLibriSpeechRNNTModule(TorchaudioTestCase):
@classmethod
def setUpClass(cls) -> None:
super().setUpClass()
torch.random.manual_seed(31)

@parameterized.expand(
[
Original file line number Diff line number Diff line change
@@ -49,7 +49,6 @@ class TestMuSTCRNNTModule(TorchaudioTestCase):
@classmethod
def setUpClass(cls) -> None:
super().setUpClass()
torch.random.manual_seed(31)

@parameterized.expand(
[
Original file line number Diff line number Diff line change
@@ -53,7 +53,6 @@ class TestTEDLIUM3RNNTModule(TorchaudioTestCase):
@classmethod
def setUpClass(cls) -> None:
super().setUpClass()
torch.random.manual_seed(31)

@parameterized.expand(
[
Original file line number Diff line number Diff line change
@@ -9,7 +9,6 @@ class TestCropAudioLabel(TorchaudioTestCase):
@classmethod
def setUpClass(cls) -> None:
super().setUpClass()
torch.random.manual_seed(31)

@parameterized.expand(
[
5 changes: 0 additions & 5 deletions test/torchaudio_unittest/functional/autograd_impl.py
Original file line number Diff line number Diff line change
@@ -232,28 +232,23 @@ def test_bandreject_biquad(self, central_freq, Q):
self.assert_grad(F.bandreject_biquad, (x, sr, central_freq, Q))

def test_deemph_biquad(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=1)
self.assert_grad(F.deemph_biquad, (x, 44100))

def test_flanger(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=8000, duration=0.01, n_channels=1)
self.assert_grad(F.flanger, (x, 44100))

def test_gain(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=8000, duration=0.01, n_channels=1)
self.assert_grad(F.gain, (x, 1.1))

def test_overdrive(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=8000, duration=0.01, n_channels=1)
self.assert_grad(F.gain, (x,))

@parameterized.expand([(True,), (False,)])
def test_phaser(self, sinusoidal):
torch.random.manual_seed(2434)
sr = 8000
x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1)
self.assert_grad(F.phaser, (x, sr, sinusoidal))
20 changes: 0 additions & 20 deletions test/torchaudio_unittest/functional/batch_consistency_test.py
Original file line number Diff line number Diff line change
@@ -52,7 +52,6 @@ def test_griffinlim(self):
momentum = 0.99
n_iter = 32
length = 1000
torch.random.manual_seed(0)
batch = torch.rand(self.batch_size, 1, 201, 6)
kwargs = {
"window": window,
@@ -80,7 +79,6 @@ def test_griffinlim(self):
def test_detect_pitch_frequency(self, sample_rate, n_channels):
# Use different frequencies to ensure each item in the batch returns a
# different answer.
torch.manual_seed(0)
frequencies = torch.randint(100, 1000, [self.batch_size])
waveforms = torch.stack(
[
@@ -103,7 +101,6 @@ def test_detect_pitch_frequency(self, sample_rate, n_channels):
]
)
def test_amplitude_to_DB(self, top_db):
torch.manual_seed(0)
spec = torch.rand(self.batch_size, 2, 100, 100) * 200

amplitude_mult = 20.0
@@ -137,7 +134,6 @@ def test_amplitude_to_DB_itemwise_clamps(self):
top_db = 20.0

# Make a batch of noise
torch.manual_seed(0)
spec = torch.rand([2, 2, 100, 100]) * 200
# Make one item blow out the other
spec[0] += 50
@@ -158,7 +154,6 @@ def test_amplitude_to_DB_not_channelwise_clamps(self):
db_mult = math.log10(max(amin, ref))
top_db = 40.0

torch.manual_seed(0)
spec = torch.rand([1, 2, 100, 100]) * 200
# Make one channel blow out the other
spec[:, 0] += 50
@@ -173,7 +168,6 @@ def test_amplitude_to_DB_not_channelwise_clamps(self):
assert (difference >= 1e-5).any()

def test_contrast(self):
torch.random.manual_seed(0)
waveforms = torch.rand(self.batch_size, 2, 100) - 0.5
kwargs = {
"enhancement_amount": 80.0,
@@ -182,7 +176,6 @@ def test_contrast(self):
self.assert_batch_consistency(func, inputs=(waveforms,))

def test_dcshift(self):
torch.random.manual_seed(0)
waveforms = torch.rand(self.batch_size, 2, 100) - 0.5
kwargs = {
"shift": 0.5,
@@ -192,7 +185,6 @@ def test_dcshift(self):
self.assert_batch_consistency(func, inputs=(waveforms,))

def test_overdrive(self):
torch.random.manual_seed(0)
waveforms = torch.rand(self.batch_size, 2, 100) - 0.5
kwargs = {
"gain": 45,
@@ -215,7 +207,6 @@ def test_phaser(self):
self.assert_batch_consistency(func, inputs=(batch,))

def test_flanger(self):
torch.random.manual_seed(0)
waveforms = torch.rand(self.batch_size, 2, 100) - 0.5
sample_rate = 44100
kwargs = {
@@ -234,7 +225,6 @@ def test_flanger(self):
name_func=_name_from_args,
)
def test_sliding_window_cmn(self, center, norm_vars):
torch.manual_seed(0)
spectrogram = torch.rand(self.batch_size, 2, 1024, 1024) * 200
kwargs = {
"center": center,
@@ -281,15 +271,13 @@ def test_compute_kaldi_pitch(self):

def test_lfilter(self):
signal_length = 2048
torch.manual_seed(2434)
x = torch.randn(self.batch_size, signal_length)
a = torch.rand(self.batch_size, 3)
b = torch.rand(self.batch_size, 3)
self.assert_batch_consistency(F.lfilter, inputs=(x, a, b))

def test_filtfilt(self):
signal_length = 2048
torch.manual_seed(2434)
x = torch.randn(self.batch_size, signal_length)
a = torch.rand(self.batch_size, 3)
b = torch.rand(self.batch_size, 3)
@@ -319,7 +307,6 @@ def test_psd_with_mask(self):
self.assert_batch_consistency(F.psd, (specgram, mask))

def test_mvdr_weights_souden(self):
torch.random.manual_seed(2434)
batch_size = 2
channel = 4
n_fft_bin = 10
@@ -332,7 +319,6 @@ def test_mvdr_weights_souden(self):
self.assert_batch_consistency(func, (psd_noise, psd_speech))

def test_mvdr_weights_souden_with_tensor(self):
torch.random.manual_seed(2434)
batch_size = 2
channel = 4
n_fft_bin = 10
@@ -343,7 +329,6 @@ def test_mvdr_weights_souden_with_tensor(self):
self.assert_batch_consistency(F.mvdr_weights_souden, (psd_noise, psd_speech, reference_channel))

def test_mvdr_weights_rtf(self):
torch.random.manual_seed(2434)
batch_size = 2
channel = 4
n_fft_bin = 129
@@ -356,7 +341,6 @@ def test_mvdr_weights_rtf(self):
self.assert_batch_consistency(func, (rtf, psd_noise))

def test_mvdr_weights_rtf_with_tensor(self):
torch.random.manual_seed(2434)
batch_size = 2
channel = 4
n_fft_bin = 129
@@ -367,7 +351,6 @@ def test_mvdr_weights_rtf_with_tensor(self):
self.assert_batch_consistency(F.mvdr_weights_rtf, (rtf, psd_noise, reference_channel))

def test_rtf_evd(self):
torch.random.manual_seed(2434)
batch_size = 2
channel = 4
n_fft_bin = 5
@@ -382,7 +365,6 @@ def test_rtf_evd(self):
]
)
def test_rtf_power(self, n_iter):
torch.random.manual_seed(2434)
channel = 4
batch_size = 2
n_fft_bin = 10
@@ -402,7 +384,6 @@ def test_rtf_power(self, n_iter):
]
)
def test_rtf_power_with_tensor(self, n_iter):
torch.random.manual_seed(2434)
channel = 4
batch_size = 2
n_fft_bin = 10
@@ -417,7 +398,6 @@ def test_rtf_power_with_tensor(self, n_iter):
self.assert_batch_consistency(func, (psd_speech, psd_noise, reference_channel))

def test_apply_beamforming(self):
torch.random.manual_seed(2434)
sr = 8000
n_fft = 400
batch_size, num_channels = 2, 3
Original file line number Diff line number Diff line change
@@ -35,7 +35,6 @@ def _smoke_test(self, format, compression, check_num_frames):
The purpose of this test suite is to verify that apply_codec functionalities do not exhibit
abnormal behaviors.
"""
torch.random.manual_seed(42)
sample_rate = 8000
num_frames = 3 * sample_rate
num_channels = 2
Original file line number Diff line number Diff line change
@@ -131,7 +131,6 @@ def test_phase_vocoder(self, rate):
hop_length = 256
num_freq = 1025
num_frames = 400
torch.random.manual_seed(42)

# Due to cummulative sum, numerical error in using torch.float32 will
# result in bottom right values of the stretched sectrogram to not
Original file line number Diff line number Diff line change
@@ -269,7 +269,6 @@ def test_lfilter(self):
self._assert_consistency(F.lfilter, (waveform, a_coeffs, b_coeffs, True, True))

def test_filtfilt(self):
torch.manual_seed(296)
waveform = common_utils.get_whitenoise(sample_rate=8000)
b_coeffs = torch.rand(4, device=waveform.device, dtype=waveform.dtype)
a_coeffs = torch.rand(4, device=waveform.device, dtype=waveform.dtype)
@@ -531,7 +530,6 @@ def func(tensor):
self._assert_consistency(func, (waveform,))

def test_flanger(self):
torch.random.manual_seed(40)
waveform = torch.rand(2, 100) - 0.5

def func(tensor):
Original file line number Diff line number Diff line change
@@ -26,7 +26,6 @@ def _gen_inputs(self, input_dim, batch_size, num_frames):

def setUp(self):
super().setUp()
torch.random.manual_seed(31)

def test_torchscript_consistency_forward(self):
r"""Verify that scripting Conformer does not change the behavior of method `forward`."""
2 changes: 0 additions & 2 deletions test/torchaudio_unittest/models/ctc_decoder_test.py
Original file line number Diff line number Diff line change
@@ -9,7 +9,6 @@
TorchaudioTestCase,
)


NUM_TOKENS = 8


@@ -38,7 +37,6 @@ def _get_decoder(self, tokens=None, use_lm=True, use_lexicon=True, **kwargs):
def _get_emissions(self):
B, T, N = 4, 15, NUM_TOKENS

torch.manual_seed(0)
emissions = torch.rand(B, T, N)

return emissions
4 changes: 0 additions & 4 deletions test/torchaudio_unittest/models/rnnt/rnnt_test_impl.py
Original file line number Diff line number Diff line change
@@ -54,7 +54,6 @@ def _get_transcriber_input(self):
input_dim = input_config["input_dim"]
right_context_length = input_config["right_context_length"]

torch.random.manual_seed(31)
input = torch.rand(batch_size, max_input_length + right_context_length, input_dim).to(
device=self.device, dtype=self.dtype
)
@@ -68,7 +67,6 @@ def _get_transcriber_streaming_input(self):
input_dim = input_config["input_dim"]
right_context_length = input_config["right_context_length"]

torch.random.manual_seed(31)
input = torch.rand(batch_size, segment_length + right_context_length, input_dim).to(
device=self.device, dtype=self.dtype
)
@@ -83,7 +81,6 @@ def _get_predictor_input(self):
num_symbols = input_config["num_symbols"]
max_target_length = input_config["max_target_length"]

torch.random.manual_seed(31)
input = torch.randint(0, num_symbols, (batch_size, max_target_length)).to(device=self.device, dtype=torch.int32)
lengths = torch.randint(1, max_target_length + 1, (batch_size,)).to(device=self.device, dtype=torch.int32)
return input, lengths
@@ -95,7 +92,6 @@ def _get_joiner_input(self):
max_target_length = input_config["max_target_length"]
input_dim = input_config["encoding_dim"]

torch.random.manual_seed(31)
utterance_encodings = torch.rand(batch_size, joiner_max_input_length, input_dim).to(
device=self.device, dtype=self.dtype
)
Original file line number Diff line number Diff line change
@@ -46,8 +46,6 @@ def _get_model(self):
def test_torchscript_consistency_forward(self):
r"""Verify that scripting RNNTBeamSearch does not change the behavior of method `forward`."""

torch.random.manual_seed(31)

input_config = self._get_input_config()
batch_size = input_config["batch_size"]
max_input_length = input_config["max_input_length"]
@@ -74,8 +72,6 @@ def test_torchscript_consistency_forward(self):
def test_torchscript_consistency_infer(self):
r"""Verify that scripting RNNTBeamSearch does not change the behavior of method `infer`."""

torch.random.manual_seed(31)

input_config = self._get_input_config()
segment_length = input_config["segment_length"]
right_context_length = input_config["right_context_length"]
Original file line number Diff line number Diff line change
@@ -134,7 +134,6 @@ def test_import_wave2vec2_pretraining_model(self, config, _):
"""Wav2vec2 pretraining models from fairseq can be imported and yields the same results"""
batch_size, num_frames = 3, 1024

torch.manual_seed(0)
original = self._get_model(config).eval()
imported = import_fairseq_model(original).eval()

@@ -149,7 +148,6 @@ def test_import_hubert_pretraining_model(self, config, factory_func):
"""HuBERT pretraining models from fairseq can be imported and yields the same results"""
batch_size, num_frames = 3, 1024

torch.manual_seed(0)
original = self._get_model(config).eval()
imported = import_fairseq_model(original).eval()

@@ -241,7 +239,6 @@ def test_recreate_finetuning_model(self, config, factory_func):
reloaded.eval()

# Without mask
torch.manual_seed(0)
x = torch.randn(batch_size, num_frames)
ref, _ = imported(x)
hyp, _ = reloaded(x)
Original file line number Diff line number Diff line change
@@ -89,7 +89,6 @@ def _get_model(self, config):
raise ValueError(f'Unexpected arch: {config["architectures"]}')

def _test_import_pretrain(self, original, imported, config):
torch.manual_seed(0)
# FeatureExtractor
x = torch.randn(3, 1024)
ref = original.feature_extractor(x).transpose(1, 2)
@@ -173,7 +172,6 @@ def test_import_finetune(self, config, _):
self._test_import_finetune(original, imported, config)

def _test_recreate(self, imported, reloaded, config):
torch.manual_seed(0)
# FeatureExtractor
x = torch.randn(3, 1024)
ref, _ = imported.feature_extractor(x, None)
7 changes: 0 additions & 7 deletions test/torchaudio_unittest/models/wav2vec2/model_test.py
Original file line number Diff line number Diff line change
@@ -48,7 +48,6 @@ def _smoke_test(self, model, device, dtype):
model = model.to(device=device, dtype=dtype)
model = model.eval()

torch.manual_seed(0)
batch_size, num_frames = 3, 1024

waveforms = torch.randn(batch_size, num_frames, device=device, dtype=dtype)
@@ -84,7 +83,6 @@ def _feature_extractor_test(self, model):
model.eval()
num_layers = len(model.encoder.transformer.layers)

torch.manual_seed(0)
waveforms = torch.randn(batch_size, num_frames)
lengths = torch.randint(
low=0,
@@ -119,7 +117,6 @@ def test_extract_feature(self, factory_func):
def _test_batch_consistency(self, model):
model.eval()
batch_size, max_frames = 5, 5 * 1024
torch.manual_seed(0)
waveforms = torch.randn(batch_size, max_frames)
input_lengths = torch.tensor([i * 3200 for i in range(1, 6)])

@@ -148,7 +145,6 @@ def test_finetune_batch_consistency(self, factory_func):

def _test_zero_length(self, model):
model.eval()
torch.manual_seed(0)
batch_size = 3
waveforms = torch.randn(batch_size, 1024)
input_lengths = torch.zeros(batch_size)
@@ -172,7 +168,6 @@ def _test_torchscript(self, model):

batch_size, num_frames = 3, 1024

torch.manual_seed(0)
waveforms = torch.randn(batch_size, num_frames)
lengths = torch.randint(
low=0,
@@ -220,7 +215,6 @@ def _test_quantize_smoke_test(self, model):
# A lazy way to check that Modules are different
assert str(quantized) != str(model), "Dynamic quantization did not modify the module."

torch.manual_seed(0)
waveforms = torch.randn(batch_size, num_frames)
lengths = torch.randint(
low=0,
@@ -250,7 +244,6 @@ def _test_quantize_torchscript(self, model):
# A lazy way to check that Modules are different
assert str(quantized) != str(model), "Dynamic quantization did not modify the module."

torch.manual_seed(0)
waveforms = torch.randn(batch_size, num_frames)
lengths = torch.randint(
low=0,
Original file line number Diff line number Diff line change
@@ -44,7 +44,6 @@ def test_InverseMelScale(self):

# Run transform
transform = T.InverseMelScale(n_stft, n_mels=n_mels, sample_rate=sample_rate).to(self.device, self.dtype)
torch.random.manual_seed(0)
result = transform(input)

# Compare