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https://github.com/macaodha/batdetect2.git
synced 2026-04-04 15:20:19 +02:00
Update tests
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mbsantiago
parent
46c02962f3
commit
0d590a26cc
@ -83,17 +83,17 @@ class Preprocessor(torch.nn.Module, PreprocessorProtocol):
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super().__init__()
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self.audio_transforms = torch.nn.Sequential(
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*[
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*(
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build_audio_transform(step, samplerate=input_samplerate)
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for step in config.audio_transforms
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]
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)
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)
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self.spectrogram_transforms = torch.nn.Sequential(
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*[
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*(
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build_spectrogram_transform(step, samplerate=input_samplerate)
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for step in config.spectrogram_transforms
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]
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)
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)
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self.spectrogram_builder = build_spectrogram_builder(
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@ -79,11 +79,6 @@ def default_audio_config() -> AudioConfig:
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return AudioConfig()
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# ---------------------------------------------------------------------------
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# center_tensor
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# ---------------------------------------------------------------------------
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def test_center_tensor_zero_mean():
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"""Output tensor should have a mean very close to zero."""
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wav = torch.tensor([1.0, 2.0, 3.0, 4.0])
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@ -97,11 +92,6 @@ def test_center_tensor_preserves_shape():
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assert result.shape == wav.shape
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# ---------------------------------------------------------------------------
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# peak_normalize
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# ---------------------------------------------------------------------------
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def test_peak_normalize_max_is_one():
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"""After peak normalisation, the maximum absolute value should be 1."""
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wav = torch.tensor([0.1, -0.4, 0.2, 0.8, -0.3])
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@ -116,25 +106,12 @@ def test_peak_normalize_zero_tensor_unchanged():
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assert (result == 0).all()
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def test_peak_normalize_preserves_sign():
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"""Signs of all elements should be preserved after normalisation."""
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wav = torch.tensor([-2.0, 1.0, -0.5])
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result = peak_normalize(wav)
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assert (result < 0).sum() == 2
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assert result[0].item() < 0
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def test_peak_normalize_preserves_shape():
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wav = torch.randn(2, 512)
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result = peak_normalize(wav)
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assert result.shape == wav.shape
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# ---------------------------------------------------------------------------
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# CenterAudio
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# ---------------------------------------------------------------------------
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def test_center_audio_forward_zero_mean():
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module = CenterAudio()
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wav = torch.tensor([1.0, 3.0, 5.0])
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@ -148,15 +125,10 @@ def test_center_audio_from_config():
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assert isinstance(module, CenterAudio)
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# ---------------------------------------------------------------------------
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# ScaleAudio
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# ---------------------------------------------------------------------------
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def test_scale_audio_peak_normalises_to_one():
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"""ScaleAudio.forward should scale the peak absolute value to 1."""
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module = ScaleAudio()
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wav = torch.tensor([0.0, 0.25, -0.5, 0.1])
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wav = torch.tensor([0.0, 0.25, 0.1])
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result = module(wav)
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assert abs(result.abs().max().item() - 1.0) < 1e-6
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@ -175,11 +147,6 @@ def test_scale_audio_from_config():
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assert isinstance(module, ScaleAudio)
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# ---------------------------------------------------------------------------
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# FixDuration
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# ---------------------------------------------------------------------------
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def test_fix_duration_truncates_long_input():
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"""Waveform longer than target should be truncated to the target length."""
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target_samples = int(SAMPLERATE * 0.5)
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@ -218,11 +185,6 @@ def test_fix_duration_from_config():
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assert module.length == int(SAMPLERATE * 0.256)
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# ---------------------------------------------------------------------------
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# build_audio_transform dispatch
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# ---------------------------------------------------------------------------
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def test_build_audio_transform_center_audio():
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config = CenterAudioConfig()
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module = build_audio_transform(config, samplerate=SAMPLERATE)
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@ -26,7 +26,6 @@ from batdetect2.preprocess.spectrogram import (
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)
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SAMPLERATE = 256_000
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# 0.256 s at 256 kHz = 65536 samples — a convenient power-of-two-sized clip
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CLIP_SAMPLES = int(SAMPLERATE * 0.256)
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@ -40,11 +39,6 @@ def make_sine_wav(
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return torch.sin(2 * torch.pi * freq * t)
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# ---------------------------------------------------------------------------
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# build_preprocessor — construction
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# ---------------------------------------------------------------------------
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def test_build_preprocessor_returns_protocol():
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"""build_preprocessor should return a Preprocessor instance."""
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preprocessor = build_preprocessor()
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@ -68,11 +62,6 @@ def test_build_preprocessor_with_explicit_config():
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assert isinstance(preprocessor, Preprocessor)
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# ---------------------------------------------------------------------------
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# Output shape and dtype
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# ---------------------------------------------------------------------------
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def test_preprocessor_output_is_2d():
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"""The preprocessor output should be a 2-D tensor (freq_bins × time_frames)."""
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preprocessor = build_preprocessor(input_samplerate=SAMPLERATE)
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@ -108,11 +97,6 @@ def test_preprocessor_output_is_finite():
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assert torch.isfinite(result).all()
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# ---------------------------------------------------------------------------
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# process_numpy
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# ---------------------------------------------------------------------------
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def test_preprocessor_process_numpy_accepts_ndarray():
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"""process_numpy should accept a NumPy array and return a NumPy array."""
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preprocessor = build_preprocessor(input_samplerate=SAMPLERATE)
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@ -130,11 +114,6 @@ def test_preprocessor_process_numpy_matches_forward():
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np.testing.assert_array_almost_equal(result_pt, result_np)
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# ---------------------------------------------------------------------------
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# Attributes
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# ---------------------------------------------------------------------------
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def test_preprocessor_min_max_freq_attributes():
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"""min_freq and max_freq should match the FrequencyConfig values."""
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config = PreprocessingConfig(
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@ -150,11 +129,6 @@ def test_preprocessor_input_samplerate_attribute():
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assert preprocessor.input_samplerate == SAMPLERATE
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# ---------------------------------------------------------------------------
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# compute_output_samplerate
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# ---------------------------------------------------------------------------
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def test_compute_output_samplerate_defaults():
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"""At default settings, output_samplerate should equal 1000 fps."""
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config = PreprocessingConfig()
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@ -169,11 +143,6 @@ def test_preprocessor_output_samplerate_attribute_matches_compute():
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assert abs(preprocessor.output_samplerate - expected) < 1e-6
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# ---------------------------------------------------------------------------
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# generate_spectrogram (raw STFT)
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# ---------------------------------------------------------------------------
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def test_generate_spectrogram_shape():
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"""generate_spectrogram should return the full STFT without crop or resize."""
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config = PreprocessingConfig()
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@ -193,29 +162,18 @@ def test_generate_spectrogram_larger_than_forward():
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assert raw.shape[0] > processed.shape[0]
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# ---------------------------------------------------------------------------
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# Audio transforms pipeline (FixDuration)
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# ---------------------------------------------------------------------------
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def test_preprocessor_with_fix_duration_audio_transform():
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"""A FixDuration audio transform should produce consistent output shapes."""
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config = PreprocessingConfig(
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audio_transforms=[FixDurationConfig(duration=0.256)],
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)
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preprocessor = build_preprocessor(config, input_samplerate=SAMPLERATE)
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# Feed different lengths; output shape should be the same after fix
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for n_samples in [CLIP_SAMPLES - 1000, CLIP_SAMPLES, CLIP_SAMPLES + 1000]:
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wav = torch.randn(n_samples)
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result = preprocessor(wav)
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assert result.ndim == 2
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# ---------------------------------------------------------------------------
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# YAML round-trip
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# ---------------------------------------------------------------------------
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def test_preprocessor_yaml_roundtrip(tmp_path: pathlib.Path):
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"""PreprocessingConfig serialised to YAML and reloaded should produce
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a functionally identical preprocessor."""
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@ -31,11 +31,6 @@ from batdetect2.preprocess.spectrogram import (
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SAMPLERATE = 256_000
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# ---------------------------------------------------------------------------
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# STFTConfig / _spec_params_from_config
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# ---------------------------------------------------------------------------
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def test_stft_config_defaults_give_correct_params():
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"""Default STFTConfig at 256 kHz should give n_fft=512, hop_length=128."""
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config = STFTConfig()
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@ -52,11 +47,6 @@ def test_stft_config_custom_params():
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assert hop_length == 512
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# ---------------------------------------------------------------------------
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# build_spectrogram_builder
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# ---------------------------------------------------------------------------
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def test_spectrogram_builder_output_shape():
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"""Builder should produce a spectrogram with the expected number of bins."""
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config = STFTConfig()
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@ -81,11 +71,6 @@ def test_spectrogram_builder_output_is_nonnegative():
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assert (spec >= 0).all()
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# ---------------------------------------------------------------------------
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# FrequencyCrop
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# ---------------------------------------------------------------------------
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def test_frequency_crop_output_shape():
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"""FrequencyCrop should reduce the number of frequency bins."""
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config = STFTConfig()
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@ -128,11 +113,6 @@ def test_frequency_crop_no_crop_when_bounds_are_none():
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assert cropped.shape == spec.shape
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# ---------------------------------------------------------------------------
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# PCEN
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# ---------------------------------------------------------------------------
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def test_pcen_output_shape_preserved():
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"""PCEN should not change the shape of the spectrogram."""
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config = PcenConfig()
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@ -160,11 +140,6 @@ def test_pcen_output_dtype_matches_input():
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assert result.dtype == spec.dtype
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# ---------------------------------------------------------------------------
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# SpectralMeanSubtraction
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# ---------------------------------------------------------------------------
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def test_spectral_mean_subtraction_output_nonnegative():
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"""SpectralMeanSubtraction clamps output to >= 0."""
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module = SpectralMeanSubtraction()
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@ -195,11 +170,6 @@ def test_spectral_mean_subtraction_from_config():
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assert isinstance(module, SpectralMeanSubtraction)
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# ---------------------------------------------------------------------------
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# PeakNormalize (spectrogram-level)
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# ---------------------------------------------------------------------------
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def test_peak_normalize_spec_max_is_one():
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"""PeakNormalize should scale the spectrogram peak to 1."""
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module = PeakNormalize()
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@ -222,11 +192,6 @@ def test_peak_normalize_from_config():
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assert isinstance(module, PeakNormalize)
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# ---------------------------------------------------------------------------
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# ScaleAmplitude
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# ---------------------------------------------------------------------------
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def test_scale_amplitude_db_output_is_finite():
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"""AmplitudeToDB scaling should produce finite values for positive input."""
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module = ScaleAmplitude(scale="db")
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@ -251,11 +216,6 @@ def test_scale_amplitude_from_config():
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assert module.scale == "db"
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# ---------------------------------------------------------------------------
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# ResizeSpec
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# ---------------------------------------------------------------------------
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def test_resize_spec_output_shape():
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"""ResizeSpec should produce the target height and scaled width."""
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module = ResizeSpec(height=64, time_factor=0.5)
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@ -287,11 +247,6 @@ def test_resize_spec_from_config():
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assert module.time_factor == 0.25
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# ---------------------------------------------------------------------------
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# build_spectrogram_transform dispatch
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# ---------------------------------------------------------------------------
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def test_build_spectrogram_transform_pcen():
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config = PcenConfig()
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module = build_spectrogram_transform(config, samplerate=SAMPLERATE)
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