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https://github.com/macaodha/batdetect2.git
synced 2025-06-29 14:41:58 +02:00
Temporary remove compat params module
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mbsantiago
parent
285c6a3347
commit
9fc713d390
@ -1,152 +1,152 @@
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from batdetect2.preprocess import (
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AmplitudeScaleConfig,
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AudioConfig,
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FrequencyConfig,
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LogScaleConfig,
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PcenConfig,
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PreprocessingConfig,
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ResampleConfig,
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Scales,
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SpecSizeConfig,
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SpectrogramConfig,
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STFTConfig,
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)
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from batdetect2.preprocess.spectrogram import get_spectrogram_resolution
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from batdetect2.targets import (
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LabelConfig,
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TagInfo,
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TargetConfig,
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)
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from batdetect2.train.preprocess import (
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TrainPreprocessingConfig,
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)
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def get_spectrogram_scale(scale: str) -> Scales:
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if scale == "pcen":
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return PcenConfig()
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if scale == "log":
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return LogScaleConfig()
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return AmplitudeScaleConfig()
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def get_preprocessing_config(params: dict) -> PreprocessingConfig:
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return PreprocessingConfig(
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audio=AudioConfig(
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resample=ResampleConfig(
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samplerate=params["target_samp_rate"],
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method="poly",
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),
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scale=params["scale_raw_audio"],
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center=params["scale_raw_audio"],
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duration=None,
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),
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spectrogram=SpectrogramConfig(
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stft=STFTConfig(
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window_duration=params["fft_win_length"],
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window_overlap=params["fft_overlap"],
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window_fn="hann",
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),
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frequencies=FrequencyConfig(
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min_freq=params["min_freq"],
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max_freq=params["max_freq"],
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),
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scale=get_spectrogram_scale(params["spec_scale"]),
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spectral_mean_substraction=params["denoise_spec_avg"],
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size=SpecSizeConfig(
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height=params["spec_height"],
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resize_factor=params["resize_factor"],
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),
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peak_normalize=params["max_scale_spec"],
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),
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)
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def get_training_preprocessing_config(
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params: dict,
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) -> TrainPreprocessingConfig:
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generic = params["generic_class"][0]
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preprocessing = get_preprocessing_config(params)
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freq_bin_width, time_bin_width = get_spectrogram_resolution(
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preprocessing.spectrogram
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)
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return TrainPreprocessingConfig(
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preprocessing=preprocessing,
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target=TargetConfig(
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classes=[
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TagInfo(key="class", value=class_name)
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for class_name in params["class_names"]
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],
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generic_class=TagInfo(
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key="class",
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value=generic,
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),
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include=[
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TagInfo(key="event", value=event)
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for event in params["events_of_interest"]
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],
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exclude=[
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TagInfo(key="class", value=value)
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for value in params["classes_to_ignore"]
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],
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),
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labels=LabelConfig(
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position="bottom-left",
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time_scale=1 / time_bin_width,
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frequency_scale=1 / freq_bin_width,
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sigma=params["target_sigma"],
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),
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)
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# 'standardize_classs_names_ip',
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# 'convert_to_genus',
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# 'genus_mapping',
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# 'standardize_classs_names',
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# 'genus_names',
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# ['data_dir',
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# 'ann_dir',
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# 'train_split',
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# 'model_name',
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# 'num_filters',
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# 'experiment',
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# 'model_file_name',
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# 'op_im_dir',
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# 'op_im_dir_test',
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# 'notes',
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# 'spec_divide_factor',
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# 'detection_overlap',
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# 'ignore_start_end',
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# 'detection_threshold',
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# 'nms_kernel_size',
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# 'nms_top_k_per_sec',
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# 'aug_prob',
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# 'augment_at_train',
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# 'augment_at_train_combine',
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# 'echo_max_delay',
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# 'stretch_squeeze_delta',
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# 'mask_max_time_perc',
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# 'mask_max_freq_perc',
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# 'spec_amp_scaling',
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# 'aug_sampling_rates',
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# 'train_loss',
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# 'det_loss_weight',
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# 'size_loss_weight',
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# 'class_loss_weight',
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# 'individual_loss_weight',
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# 'emb_dim',
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# 'lr',
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# 'batch_size',
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# 'num_workers',
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# 'num_epochs',
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# 'num_eval_epochs',
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# 'device',
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# 'save_test_image_during_train',
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# 'save_test_image_after_train',
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# 'train_sets',
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# 'test_sets',
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# 'class_inv_freq',
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# 'ip_height']
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# from batdetect2.preprocess import (
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# AmplitudeScaleConfig,
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# AudioConfig,
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# FrequencyConfig,
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# LogScaleConfig,
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# PcenConfig,
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# PreprocessingConfig,
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# ResampleConfig,
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# Scales,
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# SpecSizeConfig,
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# SpectrogramConfig,
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# STFTConfig,
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# )
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# from batdetect2.preprocess.spectrogram import get_spectrogram_resolution
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# from batdetect2.targets import (
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# LabelConfig,
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# TagInfo,
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# TargetConfig,
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# )
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# from batdetect2.train.preprocess import (
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# TrainPreprocessingConfig,
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# )
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#
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#
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# def get_spectrogram_scale(scale: str) -> Scales:
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# if scale == "pcen":
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# return PcenConfig()
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# if scale == "log":
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# return LogScaleConfig()
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# return AmplitudeScaleConfig()
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#
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#
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# def get_preprocessing_config(params: dict) -> PreprocessingConfig:
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# return PreprocessingConfig(
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# audio=AudioConfig(
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# resample=ResampleConfig(
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# samplerate=params["target_samp_rate"],
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# method="poly",
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# ),
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# scale=params["scale_raw_audio"],
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# center=params["scale_raw_audio"],
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# duration=None,
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# ),
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# spectrogram=SpectrogramConfig(
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# stft=STFTConfig(
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# window_duration=params["fft_win_length"],
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# window_overlap=params["fft_overlap"],
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# window_fn="hann",
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# ),
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# frequencies=FrequencyConfig(
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# min_freq=params["min_freq"],
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# max_freq=params["max_freq"],
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# ),
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# scale=get_spectrogram_scale(params["spec_scale"]),
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# spectral_mean_substraction=params["denoise_spec_avg"],
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# size=SpecSizeConfig(
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# height=params["spec_height"],
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# resize_factor=params["resize_factor"],
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# ),
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# peak_normalize=params["max_scale_spec"],
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# ),
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# )
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#
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#
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# def get_training_preprocessing_config(
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# params: dict,
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# ) -> TrainPreprocessingConfig:
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# generic = params["generic_class"][0]
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# preprocessing = get_preprocessing_config(params)
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#
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# freq_bin_width, time_bin_width = get_spectrogram_resolution(
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# preprocessing.spectrogram
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# )
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#
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# return TrainPreprocessingConfig(
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# preprocessing=preprocessing,
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# target=TargetConfig(
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# classes=[
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# TagInfo(key="class", value=class_name)
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# for class_name in params["class_names"]
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# ],
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# generic_class=TagInfo(
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# key="class",
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# value=generic,
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# ),
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# include=[
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# TagInfo(key="event", value=event)
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# for event in params["events_of_interest"]
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# ],
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# exclude=[
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# TagInfo(key="class", value=value)
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# for value in params["classes_to_ignore"]
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# ],
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# ),
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# labels=LabelConfig(
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# position="bottom-left",
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# time_scale=1 / time_bin_width,
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# frequency_scale=1 / freq_bin_width,
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# sigma=params["target_sigma"],
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# ),
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# )
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#
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#
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# # 'standardize_classs_names_ip',
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# # 'convert_to_genus',
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# # 'genus_mapping',
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# # 'standardize_classs_names',
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# # 'genus_names',
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#
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# # ['data_dir',
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# # 'ann_dir',
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# # 'train_split',
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# # 'model_name',
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# # 'num_filters',
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# # 'experiment',
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# # 'model_file_name',
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# # 'op_im_dir',
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# # 'op_im_dir_test',
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# # 'notes',
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# # 'spec_divide_factor',
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# # 'detection_overlap',
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# # 'ignore_start_end',
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# # 'detection_threshold',
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# # 'nms_kernel_size',
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# # 'nms_top_k_per_sec',
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# # 'aug_prob',
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# # 'augment_at_train',
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# # 'augment_at_train_combine',
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# # 'echo_max_delay',
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# # 'stretch_squeeze_delta',
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# # 'mask_max_time_perc',
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# # 'mask_max_freq_perc',
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# # 'spec_amp_scaling',
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# # 'aug_sampling_rates',
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# # 'train_loss',
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# # 'det_loss_weight',
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# # 'size_loss_weight',
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# # 'class_loss_weight',
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# # 'individual_loss_weight',
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# # 'emb_dim',
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# # 'lr',
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# # 'batch_size',
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# # 'num_workers',
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# # 'num_epochs',
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# # 'num_eval_epochs',
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# # 'device',
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# # 'save_test_image_during_train',
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# # 'save_test_image_after_train',
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# # 'train_sets',
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# # 'test_sets',
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# # 'class_inv_freq',
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# # 'ip_height']
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