mirror of
https://github.com/macaodha/batdetect2.git
synced 2025-06-29 22:51:58 +02:00
Temporary remove compat params module
This commit is contained in:
mbsantiago
parent
285c6a3347
commit
9fc713d390
@ -1,152 +1,152 @@
|
||||
from batdetect2.preprocess import (
|
||||
AmplitudeScaleConfig,
|
||||
AudioConfig,
|
||||
FrequencyConfig,
|
||||
LogScaleConfig,
|
||||
PcenConfig,
|
||||
PreprocessingConfig,
|
||||
ResampleConfig,
|
||||
Scales,
|
||||
SpecSizeConfig,
|
||||
SpectrogramConfig,
|
||||
STFTConfig,
|
||||
)
|
||||
from batdetect2.preprocess.spectrogram import get_spectrogram_resolution
|
||||
from batdetect2.targets import (
|
||||
LabelConfig,
|
||||
TagInfo,
|
||||
TargetConfig,
|
||||
)
|
||||
from batdetect2.train.preprocess import (
|
||||
TrainPreprocessingConfig,
|
||||
)
|
||||
|
||||
|
||||
def get_spectrogram_scale(scale: str) -> Scales:
|
||||
if scale == "pcen":
|
||||
return PcenConfig()
|
||||
if scale == "log":
|
||||
return LogScaleConfig()
|
||||
return AmplitudeScaleConfig()
|
||||
|
||||
|
||||
def get_preprocessing_config(params: dict) -> PreprocessingConfig:
|
||||
return PreprocessingConfig(
|
||||
audio=AudioConfig(
|
||||
resample=ResampleConfig(
|
||||
samplerate=params["target_samp_rate"],
|
||||
method="poly",
|
||||
),
|
||||
scale=params["scale_raw_audio"],
|
||||
center=params["scale_raw_audio"],
|
||||
duration=None,
|
||||
),
|
||||
spectrogram=SpectrogramConfig(
|
||||
stft=STFTConfig(
|
||||
window_duration=params["fft_win_length"],
|
||||
window_overlap=params["fft_overlap"],
|
||||
window_fn="hann",
|
||||
),
|
||||
frequencies=FrequencyConfig(
|
||||
min_freq=params["min_freq"],
|
||||
max_freq=params["max_freq"],
|
||||
),
|
||||
scale=get_spectrogram_scale(params["spec_scale"]),
|
||||
spectral_mean_substraction=params["denoise_spec_avg"],
|
||||
size=SpecSizeConfig(
|
||||
height=params["spec_height"],
|
||||
resize_factor=params["resize_factor"],
|
||||
),
|
||||
peak_normalize=params["max_scale_spec"],
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def get_training_preprocessing_config(
|
||||
params: dict,
|
||||
) -> TrainPreprocessingConfig:
|
||||
generic = params["generic_class"][0]
|
||||
preprocessing = get_preprocessing_config(params)
|
||||
|
||||
freq_bin_width, time_bin_width = get_spectrogram_resolution(
|
||||
preprocessing.spectrogram
|
||||
)
|
||||
|
||||
return TrainPreprocessingConfig(
|
||||
preprocessing=preprocessing,
|
||||
target=TargetConfig(
|
||||
classes=[
|
||||
TagInfo(key="class", value=class_name)
|
||||
for class_name in params["class_names"]
|
||||
],
|
||||
generic_class=TagInfo(
|
||||
key="class",
|
||||
value=generic,
|
||||
),
|
||||
include=[
|
||||
TagInfo(key="event", value=event)
|
||||
for event in params["events_of_interest"]
|
||||
],
|
||||
exclude=[
|
||||
TagInfo(key="class", value=value)
|
||||
for value in params["classes_to_ignore"]
|
||||
],
|
||||
),
|
||||
labels=LabelConfig(
|
||||
position="bottom-left",
|
||||
time_scale=1 / time_bin_width,
|
||||
frequency_scale=1 / freq_bin_width,
|
||||
sigma=params["target_sigma"],
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
# 'standardize_classs_names_ip',
|
||||
# 'convert_to_genus',
|
||||
# 'genus_mapping',
|
||||
# 'standardize_classs_names',
|
||||
# 'genus_names',
|
||||
|
||||
# ['data_dir',
|
||||
# 'ann_dir',
|
||||
# 'train_split',
|
||||
# 'model_name',
|
||||
# 'num_filters',
|
||||
# 'experiment',
|
||||
# 'model_file_name',
|
||||
# 'op_im_dir',
|
||||
# 'op_im_dir_test',
|
||||
# 'notes',
|
||||
# 'spec_divide_factor',
|
||||
# 'detection_overlap',
|
||||
# 'ignore_start_end',
|
||||
# 'detection_threshold',
|
||||
# 'nms_kernel_size',
|
||||
# 'nms_top_k_per_sec',
|
||||
# 'aug_prob',
|
||||
# 'augment_at_train',
|
||||
# 'augment_at_train_combine',
|
||||
# 'echo_max_delay',
|
||||
# 'stretch_squeeze_delta',
|
||||
# 'mask_max_time_perc',
|
||||
# 'mask_max_freq_perc',
|
||||
# 'spec_amp_scaling',
|
||||
# 'aug_sampling_rates',
|
||||
# 'train_loss',
|
||||
# 'det_loss_weight',
|
||||
# 'size_loss_weight',
|
||||
# 'class_loss_weight',
|
||||
# 'individual_loss_weight',
|
||||
# 'emb_dim',
|
||||
# 'lr',
|
||||
# 'batch_size',
|
||||
# 'num_workers',
|
||||
# 'num_epochs',
|
||||
# 'num_eval_epochs',
|
||||
# 'device',
|
||||
# 'save_test_image_during_train',
|
||||
# 'save_test_image_after_train',
|
||||
# 'train_sets',
|
||||
# 'test_sets',
|
||||
# 'class_inv_freq',
|
||||
# 'ip_height']
|
||||
# from batdetect2.preprocess import (
|
||||
# AmplitudeScaleConfig,
|
||||
# AudioConfig,
|
||||
# FrequencyConfig,
|
||||
# LogScaleConfig,
|
||||
# PcenConfig,
|
||||
# PreprocessingConfig,
|
||||
# ResampleConfig,
|
||||
# Scales,
|
||||
# SpecSizeConfig,
|
||||
# SpectrogramConfig,
|
||||
# STFTConfig,
|
||||
# )
|
||||
# from batdetect2.preprocess.spectrogram import get_spectrogram_resolution
|
||||
# from batdetect2.targets import (
|
||||
# LabelConfig,
|
||||
# TagInfo,
|
||||
# TargetConfig,
|
||||
# )
|
||||
# from batdetect2.train.preprocess import (
|
||||
# TrainPreprocessingConfig,
|
||||
# )
|
||||
#
|
||||
#
|
||||
# def get_spectrogram_scale(scale: str) -> Scales:
|
||||
# if scale == "pcen":
|
||||
# return PcenConfig()
|
||||
# if scale == "log":
|
||||
# return LogScaleConfig()
|
||||
# return AmplitudeScaleConfig()
|
||||
#
|
||||
#
|
||||
# def get_preprocessing_config(params: dict) -> PreprocessingConfig:
|
||||
# return PreprocessingConfig(
|
||||
# audio=AudioConfig(
|
||||
# resample=ResampleConfig(
|
||||
# samplerate=params["target_samp_rate"],
|
||||
# method="poly",
|
||||
# ),
|
||||
# scale=params["scale_raw_audio"],
|
||||
# center=params["scale_raw_audio"],
|
||||
# duration=None,
|
||||
# ),
|
||||
# spectrogram=SpectrogramConfig(
|
||||
# stft=STFTConfig(
|
||||
# window_duration=params["fft_win_length"],
|
||||
# window_overlap=params["fft_overlap"],
|
||||
# window_fn="hann",
|
||||
# ),
|
||||
# frequencies=FrequencyConfig(
|
||||
# min_freq=params["min_freq"],
|
||||
# max_freq=params["max_freq"],
|
||||
# ),
|
||||
# scale=get_spectrogram_scale(params["spec_scale"]),
|
||||
# spectral_mean_substraction=params["denoise_spec_avg"],
|
||||
# size=SpecSizeConfig(
|
||||
# height=params["spec_height"],
|
||||
# resize_factor=params["resize_factor"],
|
||||
# ),
|
||||
# peak_normalize=params["max_scale_spec"],
|
||||
# ),
|
||||
# )
|
||||
#
|
||||
#
|
||||
# def get_training_preprocessing_config(
|
||||
# params: dict,
|
||||
# ) -> TrainPreprocessingConfig:
|
||||
# generic = params["generic_class"][0]
|
||||
# preprocessing = get_preprocessing_config(params)
|
||||
#
|
||||
# freq_bin_width, time_bin_width = get_spectrogram_resolution(
|
||||
# preprocessing.spectrogram
|
||||
# )
|
||||
#
|
||||
# return TrainPreprocessingConfig(
|
||||
# preprocessing=preprocessing,
|
||||
# target=TargetConfig(
|
||||
# classes=[
|
||||
# TagInfo(key="class", value=class_name)
|
||||
# for class_name in params["class_names"]
|
||||
# ],
|
||||
# generic_class=TagInfo(
|
||||
# key="class",
|
||||
# value=generic,
|
||||
# ),
|
||||
# include=[
|
||||
# TagInfo(key="event", value=event)
|
||||
# for event in params["events_of_interest"]
|
||||
# ],
|
||||
# exclude=[
|
||||
# TagInfo(key="class", value=value)
|
||||
# for value in params["classes_to_ignore"]
|
||||
# ],
|
||||
# ),
|
||||
# labels=LabelConfig(
|
||||
# position="bottom-left",
|
||||
# time_scale=1 / time_bin_width,
|
||||
# frequency_scale=1 / freq_bin_width,
|
||||
# sigma=params["target_sigma"],
|
||||
# ),
|
||||
# )
|
||||
#
|
||||
#
|
||||
# # 'standardize_classs_names_ip',
|
||||
# # 'convert_to_genus',
|
||||
# # 'genus_mapping',
|
||||
# # 'standardize_classs_names',
|
||||
# # 'genus_names',
|
||||
#
|
||||
# # ['data_dir',
|
||||
# # 'ann_dir',
|
||||
# # 'train_split',
|
||||
# # 'model_name',
|
||||
# # 'num_filters',
|
||||
# # 'experiment',
|
||||
# # 'model_file_name',
|
||||
# # 'op_im_dir',
|
||||
# # 'op_im_dir_test',
|
||||
# # 'notes',
|
||||
# # 'spec_divide_factor',
|
||||
# # 'detection_overlap',
|
||||
# # 'ignore_start_end',
|
||||
# # 'detection_threshold',
|
||||
# # 'nms_kernel_size',
|
||||
# # 'nms_top_k_per_sec',
|
||||
# # 'aug_prob',
|
||||
# # 'augment_at_train',
|
||||
# # 'augment_at_train_combine',
|
||||
# # 'echo_max_delay',
|
||||
# # 'stretch_squeeze_delta',
|
||||
# # 'mask_max_time_perc',
|
||||
# # 'mask_max_freq_perc',
|
||||
# # 'spec_amp_scaling',
|
||||
# # 'aug_sampling_rates',
|
||||
# # 'train_loss',
|
||||
# # 'det_loss_weight',
|
||||
# # 'size_loss_weight',
|
||||
# # 'class_loss_weight',
|
||||
# # 'individual_loss_weight',
|
||||
# # 'emb_dim',
|
||||
# # 'lr',
|
||||
# # 'batch_size',
|
||||
# # 'num_workers',
|
||||
# # 'num_epochs',
|
||||
# # 'num_eval_epochs',
|
||||
# # 'device',
|
||||
# # 'save_test_image_during_train',
|
||||
# # 'save_test_image_after_train',
|
||||
# # 'train_sets',
|
||||
# # 'test_sets',
|
||||
# # 'class_inv_freq',
|
||||
# # 'ip_height']
|
||||
|
||||
Loading…
Reference in New Issue
Block a user