mirror of
https://github.com/macaodha/batdetect2.git
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230 lines
7.7 KiB
Python
230 lines
7.7 KiB
Python
import pytest
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from soundevent import data
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from soundevent.terms import get_term
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from batdetect2.postprocess import build_postprocessor, load_postprocess_config
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from batdetect2.preprocess import build_preprocessor, load_preprocessing_config
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from batdetect2.targets import build_targets, load_target_config
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from batdetect2.train.labels import build_clip_labeler, load_label_config
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from batdetect2.train.preprocess import generate_train_example
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from batdetect2.typing import ModelOutput
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from batdetect2.typing.preprocess import AudioLoader
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@pytest.fixture
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def build_from_config(
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create_temp_yaml,
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):
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def build(yaml_content):
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config_path = create_temp_yaml(yaml_content)
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targets_config = load_target_config(config_path, field="targets")
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preprocessing_config = load_preprocessing_config(
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config_path,
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field="preprocessing",
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)
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labels_config = load_label_config(config_path, field="labels")
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postprocessing_config = load_postprocess_config(
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config_path,
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field="postprocessing",
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)
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targets = build_targets(targets_config)
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preprocessor = build_preprocessor(preprocessing_config)
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labeller = build_clip_labeler(
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targets=targets,
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config=labels_config,
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min_freq=preprocessor.min_freq,
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max_freq=preprocessor.max_freq,
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)
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postprocessor = build_postprocessor(
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targets,
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config=postprocessing_config,
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min_freq=preprocessor.min_freq,
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max_freq=preprocessor.max_freq,
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)
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return targets, preprocessor, labeller, postprocessor
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return build
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def test_encoding_decoding_roundtrip_recovers_object(
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sample_audio_loader: AudioLoader,
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build_from_config,
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recording,
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):
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yaml_content = """
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labels:
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targets:
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roi:
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name: anchor_bbox
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anchor: bottom-left
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classes:
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classes:
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- name: pippip
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tags:
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- key: species
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value: Pipistrellus pipistrellus
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generic_class:
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- key: order
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value: Chiroptera
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preprocessing:
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"""
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_, preprocessor, labeller, postprocessor = build_from_config(yaml_content)
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geometry = data.BoundingBox(coordinates=[0.1, 40_000, 0.2, 80_000])
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se1 = data.SoundEventAnnotation(
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sound_event=data.SoundEvent(recording=recording, geometry=geometry),
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tags=[
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data.Tag(key="species", value="Pipistrellus pipistrellus"), # type: ignore
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],
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)
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clip = data.Clip(start_time=0, end_time=0.5, recording=recording)
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clip_annotation = data.ClipAnnotation(clip=clip, sound_events=[se1])
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encoded = generate_train_example(
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clip_annotation, sample_audio_loader, preprocessor, labeller
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)
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predictions = postprocessor.get_predictions(
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ModelOutput(
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detection_probs=encoded["detection_heatmap"]
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.unsqueeze(0)
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.unsqueeze(0),
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size_preds=encoded["size_heatmap"].unsqueeze(0),
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class_probs=encoded["class_heatmap"].unsqueeze(0),
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features=encoded["spectrogram"].unsqueeze(0).unsqueeze(0),
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),
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[clip],
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)[0]
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assert isinstance(predictions, data.ClipPrediction)
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assert len(predictions.sound_events) == 1
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recovered = predictions.sound_events[0]
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assert recovered.sound_event.geometry is not None
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assert isinstance(recovered.sound_event.geometry, data.BoundingBox)
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start_time_rec, low_freq_rec, end_time_rec, high_freq_rec = (
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recovered.sound_event.geometry.coordinates
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)
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start_time_or, low_freq_or, end_time_or, high_freq_or = (
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geometry.coordinates
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)
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assert start_time_rec == pytest.approx(start_time_or, abs=0.01)
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assert low_freq_rec == pytest.approx(low_freq_or, abs=1_000)
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assert end_time_rec == pytest.approx(end_time_or, abs=0.01)
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assert high_freq_rec == pytest.approx(high_freq_or, abs=1_000)
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assert len(recovered.tags) == 2
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predicted_species_tag = next(
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iter(t for t in recovered.tags if t.tag.term == get_term("species")),
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None,
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)
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assert predicted_species_tag is not None
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assert predicted_species_tag.score == 1
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assert predicted_species_tag.tag.value == "Pipistrellus pipistrellus"
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predicted_order_tag = next(
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iter(t for t in recovered.tags if t.tag.term == get_term("order")),
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None,
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)
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assert predicted_order_tag is not None
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assert predicted_order_tag.score == 1
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assert predicted_order_tag.tag.value == "Chiroptera"
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def test_encoding_decoding_roundtrip_recovers_object_with_roi_override(
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sample_audio_loader: AudioLoader,
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build_from_config,
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recording,
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):
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yaml_content = """
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labels:
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targets:
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roi:
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name: anchor_bbox
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anchor: bottom-left
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classes:
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classes:
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- name: pippip
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tags:
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- key: species
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value: Pipistrellus pipistrellus
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- name: myomyo
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tags:
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- key: species
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value: Myotis myotis
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roi:
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name: anchor_bbox
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anchor: top-left
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generic_class:
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- key: order
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value: Chiroptera
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preprocessing:
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"""
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_, preprocessor, labeller, postprocessor = build_from_config(yaml_content)
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geometry = data.BoundingBox(coordinates=[0.1, 40_000, 0.2, 80_000])
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se1 = data.SoundEventAnnotation(
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sound_event=data.SoundEvent(recording=recording, geometry=geometry),
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tags=[data.Tag(key="species", value="Myotis myotis")], # type: ignore
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)
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clip = data.Clip(start_time=0, end_time=0.5, recording=recording)
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clip_annotation = data.ClipAnnotation(clip=clip, sound_events=[se1])
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encoded = generate_train_example(
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clip_annotation,
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sample_audio_loader,
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preprocessor,
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labeller,
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)
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predictions = postprocessor.get_predictions(
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ModelOutput(
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detection_probs=encoded["detection_heatmap"]
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.unsqueeze(0)
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.unsqueeze(0),
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size_preds=encoded["size_heatmap"].unsqueeze(0),
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class_probs=encoded["class_heatmap"].unsqueeze(0),
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features=encoded["spectrogram"].unsqueeze(0).unsqueeze(0),
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),
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[clip],
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)[0]
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assert isinstance(predictions, data.ClipPrediction)
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assert len(predictions.sound_events) == 1
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recovered = predictions.sound_events[0]
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assert recovered.sound_event.geometry is not None
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assert isinstance(recovered.sound_event.geometry, data.BoundingBox)
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start_time_rec, low_freq_rec, end_time_rec, high_freq_rec = (
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recovered.sound_event.geometry.coordinates
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)
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start_time_or, low_freq_or, end_time_or, high_freq_or = (
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geometry.coordinates
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)
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assert start_time_rec == pytest.approx(start_time_or, abs=0.01)
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assert low_freq_rec == pytest.approx(low_freq_or, abs=1_000)
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assert end_time_rec == pytest.approx(end_time_or, abs=0.01)
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assert high_freq_rec == pytest.approx(high_freq_or, abs=1_000)
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assert len(recovered.tags) == 2
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predicted_species_tag = next(
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iter(t for t in recovered.tags if t.tag.term == get_term("species")),
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None,
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)
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assert predicted_species_tag is not None
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assert predicted_species_tag.score == 1
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assert predicted_species_tag.tag.value == "Myotis myotis"
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predicted_order_tag = next(
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iter(t for t in recovered.tags if t.tag.term == get_term("order")),
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None,
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)
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assert predicted_order_tag is not None
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assert predicted_order_tag.score == 1
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assert predicted_order_tag.tag.value == "Chiroptera"
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