mirror of
https://github.com/macaodha/batdetect2.git
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254 lines
8.2 KiB
Python
254 lines
8.2 KiB
Python
"""Test bat detect module API."""
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import os
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from glob import glob
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import numpy as np
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import torch
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from torch import nn
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from batdetect2 import api
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PKG_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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TEST_DATA_DIR = os.path.join(PKG_DIR, "example_data", "audio")
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TEST_DATA = glob(os.path.join(TEST_DATA_DIR, "*.wav"))
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def test_load_model_with_default_params():
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"""Test loading model with default parameters."""
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model, params = api.load_model()
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assert model is not None
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assert isinstance(model, nn.Module)
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assert params is not None
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assert isinstance(params, dict)
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assert "model_name" in params
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assert "num_filters" in params
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assert "emb_dim" in params
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assert "ip_height" in params
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assert params["model_name"] == "Net2DFast"
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assert params["num_filters"] == 128
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assert params["emb_dim"] == 0
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assert params["ip_height"] == 128
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assert params["resize_factor"] == 0.5
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assert len(params["class_names"]) == 17
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def test_list_audio_files():
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"""Test listing audio files."""
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audio_files = api.list_audio_files(TEST_DATA_DIR)
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assert len(audio_files) == 3
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assert all(path.endswith((".wav", ".WAV")) for path in audio_files)
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def test_load_audio():
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"""Test loading audio."""
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audio = api.load_audio(TEST_DATA[0])
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assert audio is not None
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assert isinstance(audio, np.ndarray)
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assert audio.shape == (128000,)
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def test_generate_spectrogram():
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"""Test generating spectrogram."""
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audio = api.load_audio(TEST_DATA[0])
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spectrogram = api.generate_spectrogram(audio)
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assert spectrogram is not None
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assert isinstance(spectrogram, torch.Tensor)
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assert spectrogram.shape == (1, 1, 128, 512)
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def test_get_default_config():
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"""Test getting default configuration."""
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config = api.get_config()
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assert config is not None
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assert isinstance(config, dict)
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assert config["target_samp_rate"] == 256000
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assert config["fft_win_length"] == 0.002
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assert config["fft_overlap"] == 0.75
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assert config["resize_factor"] == 0.5
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assert config["spec_divide_factor"] == 32
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assert config["spec_height"] == 256
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assert config["spec_scale"] == "pcen"
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assert config["denoise_spec_avg"] is True
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assert config["max_scale_spec"] is False
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assert config["scale_raw_audio"] is False
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assert len(config["class_names"]) == 17
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assert config["detection_threshold"] == 0.01
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assert config["time_expansion"] == 1
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assert config["top_n"] == 3
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assert config["return_raw_preds"] is False
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assert config["max_duration"] is None
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assert config["nms_kernel_size"] == 9
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assert config["max_freq"] == 120000
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assert config["min_freq"] == 10000
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assert config["nms_top_k_per_sec"] == 200
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assert config["quiet"] is True
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assert config["chunk_size"] == 3
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assert config["cnn_features"] is False
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assert config["spec_features"] is False
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assert config["spec_slices"] is False
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def test_api_exposes_default_model():
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"""Test that API exposes default model."""
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assert hasattr(api, "model")
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assert isinstance(api.model, nn.Module)
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assert type(api.model).__name__ == "Net2DFast"
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# Check that model has expected attributes
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assert api.model.num_classes == 17
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assert api.model.num_filts == 128
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assert api.model.emb_dim == 0
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assert api.model.ip_height_rs == 128
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assert api.model.resize_factor == 0.5
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def test_api_exposes_default_config():
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"""Test that API exposes default configuration."""
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assert hasattr(api, "config")
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assert isinstance(api.config, dict)
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assert api.config["target_samp_rate"] == 256000
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assert api.config["fft_win_length"] == 0.002
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assert api.config["fft_overlap"] == 0.75
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assert api.config["resize_factor"] == 0.5
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assert api.config["spec_divide_factor"] == 32
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assert api.config["spec_height"] == 256
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assert api.config["spec_scale"] == "pcen"
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assert api.config["denoise_spec_avg"] is True
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assert api.config["max_scale_spec"] is False
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assert api.config["scale_raw_audio"] is False
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assert len(api.config["class_names"]) == 17
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assert api.config["detection_threshold"] == 0.01
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assert api.config["time_expansion"] == 1
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assert api.config["top_n"] == 3
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assert api.config["return_raw_preds"] is False
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assert api.config["max_duration"] is None
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assert api.config["nms_kernel_size"] == 9
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assert api.config["max_freq"] == 120000
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assert api.config["min_freq"] == 10000
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assert api.config["nms_top_k_per_sec"] == 200
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assert api.config["quiet"] is True
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assert api.config["chunk_size"] == 3
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assert api.config["cnn_features"] is False
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assert api.config["spec_features"] is False
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assert api.config["spec_slices"] is False
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def test_process_file_with_default_model():
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"""Test processing file with model."""
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predictions = api.process_file(TEST_DATA[0])
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assert predictions is not None
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assert isinstance(predictions, dict)
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assert "pred_dict" in predictions
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# By default will not return other features
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assert "spec_feats" not in predictions
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assert "spec_feat_names" not in predictions
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assert "cnn_feats" not in predictions
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assert "cnn_feat_names" not in predictions
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assert "spec_slices" not in predictions
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# Check that predictions are returned
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assert isinstance(predictions["pred_dict"], dict)
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pred_dict = predictions["pred_dict"]
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assert pred_dict["id"] == os.path.basename(TEST_DATA[0])
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assert pred_dict["annotated"] is False
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assert pred_dict["issues"] is False
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assert pred_dict["notes"] == "Automatically generated."
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assert pred_dict["time_exp"] == 1
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assert pred_dict["duration"] == 0.5
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assert pred_dict["class_name"] is not None
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assert len(pred_dict["annotation"]) > 0
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def test_process_spectrogram_with_default_model():
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"""Test processing spectrogram with model."""
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audio = api.load_audio(TEST_DATA[0])
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spectrogram = api.generate_spectrogram(audio)
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predictions, features = api.process_spectrogram(spectrogram)
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assert predictions is not None
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assert isinstance(predictions, list)
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assert len(predictions) > 0
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sample_pred = predictions[0]
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assert isinstance(sample_pred, dict)
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assert "class" in sample_pred
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assert "class_prob" in sample_pred
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assert "det_prob" in sample_pred
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assert "start_time" in sample_pred
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assert "end_time" in sample_pred
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assert "low_freq" in sample_pred
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assert "high_freq" in sample_pred
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assert features is not None
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assert isinstance(features, np.ndarray)
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assert len(features) == len(predictions)
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def test_process_audio_with_default_model():
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"""Test processing audio with model."""
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audio = api.load_audio(TEST_DATA[0])
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predictions, features, spec = api.process_audio(audio)
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assert predictions is not None
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assert isinstance(predictions, list)
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assert len(predictions) > 0
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sample_pred = predictions[0]
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assert isinstance(sample_pred, dict)
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assert "class" in sample_pred
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assert "class_prob" in sample_pred
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assert "det_prob" in sample_pred
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assert "start_time" in sample_pred
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assert "end_time" in sample_pred
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assert "low_freq" in sample_pred
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assert "high_freq" in sample_pred
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assert features is not None
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assert isinstance(features, np.ndarray)
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assert len(features) == len(predictions)
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assert spec is not None
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assert isinstance(spec, torch.Tensor)
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assert spec.shape == (1, 1, 128, 512)
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def test_postprocess_model_outputs():
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"""Test postprocessing model outputs."""
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# Load model outputs
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audio = api.load_audio(TEST_DATA[1])
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spec = api.generate_spectrogram(audio)
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model_outputs = api.model(spec)
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# Postprocess outputs
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predictions, features = api.postprocess(model_outputs)
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assert predictions is not None
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assert isinstance(predictions, list)
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assert len(predictions) > 0
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sample_pred = predictions[0]
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assert isinstance(sample_pred, dict)
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assert "class" in sample_pred
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assert "class_prob" in sample_pred
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assert "det_prob" in sample_pred
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assert "start_time" in sample_pred
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assert "end_time" in sample_pred
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assert "low_freq" in sample_pred
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assert "high_freq" in sample_pred
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assert features is not None
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assert isinstance(features, np.ndarray)
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assert features.shape[0] == len(predictions)
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assert features.shape[1] == 32
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