167 lines
7.4 KiB
Python
167 lines
7.4 KiB
Python
import multiprocessing as mp
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from collections import defaultdict
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from pathlib import Path
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from torch.utils.data import DataLoader, ConcatDataset, WeightedRandomSampler
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from torchvision.transforms import Compose, RandomApply
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from tqdm import tqdm
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from ml_lib.audio_toolset.audio_io import NormalizeLocal
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from ml_lib.audio_toolset.audio_to_mel_dataset import LibrosaAudioToMelDataset
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from ml_lib.audio_toolset.mel_augmentation import NoiseInjection, LoudnessManipulator, ShiftTime, MaskAug
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from ml_lib.utils._basedatamodule import _BaseDataModule, DATA_OPTION_test, DATA_OPTION_train, DATA_OPTION_devel
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from ml_lib.utils.equal_sampler import EqualSampler
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from ml_lib.utils.transforms import ToTensor
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data_options = [DATA_OPTION_test, DATA_OPTION_train, DATA_OPTION_devel]
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class PrimatesLibrosaDatamodule(_BaseDataModule):
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class_names = {key: val for val, key in enumerate(['background', 'chimpanze', 'geunon', 'mandrille', 'redcap'])}
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@property
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def shape(self):
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return self.datasets[DATA_OPTION_train].datasets[0][0][1].shape
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@property
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def mel_folder(self):
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return self.root / 'mel_folder'
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@property
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def wav_folder(self):
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return self.root / 'wav'
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def __init__(self, data_root, batch_size, num_worker, sr, n_mels, n_fft, hop_length, sampler=None,
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sample_segment_len=40, sample_hop_len=15, random_apply_chance=0.5,
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loudness_ratio=0.3, shift_ratio=0.3, noise_ratio=0.3, mask_ratio=0.3):
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super(PrimatesLibrosaDatamodule, self).__init__()
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self.sampler = sampler
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self.samplers = None
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self.sample_hop_len = sample_hop_len
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self.sample_segment_len = sample_segment_len
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self.num_worker = num_worker or 1
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self.batch_size = batch_size
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self.root = Path(data_root) / 'primates'
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self.mel_length_in_seconds = 0.7
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# Mel Transforms - will be pushed with all other paramters by self.__dict__ to subdataset-class
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self.mel_kwargs = dict(sr=sr, n_mels=n_mels, n_fft=n_fft, hop_length=hop_length)
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# Utility
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self.utility_transforms = Compose([NormalizeLocal(), ToTensor()])
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# Data Augmentations
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self.random_apply_chance = random_apply_chance
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self.mel_augmentations = Compose([
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RandomApply([NoiseInjection(noise_ratio)], p=random_apply_chance),
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RandomApply([LoudnessManipulator(loudness_ratio)], p=random_apply_chance),
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RandomApply([ShiftTime(shift_ratio)], p=random_apply_chance),
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RandomApply([MaskAug(mask_ratio)], p=random_apply_chance),
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self.utility_transforms])
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def train_dataloader(self):
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return DataLoader(dataset=self.datasets[DATA_OPTION_train], num_workers=self.num_worker, pin_memory=True,
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sampler=self.samplers[DATA_OPTION_train], batch_size=self.batch_size)
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# Validation Dataloader
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def val_dataloader(self):
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return DataLoader(dataset=self.datasets[DATA_OPTION_devel], num_workers=self.num_worker, pin_memory=True,
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sampler=self.samplers[DATA_OPTION_devel], batch_size=self.batch_size)
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# Test Dataloader
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def test_dataloader(self):
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return DataLoader(dataset=self.datasets[DATA_OPTION_test], shuffle=False,
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batch_size=self.batch_size, pin_memory=True,
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num_workers=self.num_worker)
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def _build_subdataset(self, row, build=False):
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slice_file_name, class_name = row.strip().split(',')
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class_id = self.class_names.get(class_name, -1)
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audio_file_path = self.wav_folder / slice_file_name
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# DATA OPTION DIFFERENTIATION !!!!!!!!!!! - Begin
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kwargs = self.__dict__
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if any([x in slice_file_name for x in [DATA_OPTION_devel, DATA_OPTION_test]]):
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kwargs.update(mel_augmentations=self.utility_transforms)
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# DATA OPTION DIFFERENTIATION !!!!!!!!!!! - End
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target_frames = self.mel_length_in_seconds * self.mel_kwargs['sr']
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sample_segment_length = target_frames // self.mel_kwargs['hop_length'] + 1
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kwargs.update(sample_segment_len=sample_segment_length, sample_hop_len=sample_segment_length//2)
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mel_dataset = LibrosaAudioToMelDataset(audio_file_path, class_id, **kwargs)
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if build:
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assert mel_dataset.build_mel()
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return mel_dataset, class_id, slice_file_name
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def prepare_data(self, *args, **kwargs):
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datasets = dict()
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for data_option in data_options:
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with open(Path(self.root) / 'lab' / f'{data_option}.csv', mode='r') as f:
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# Exclude the header
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_ = next(f)
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all_rows = list(f)
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chunksize = len(all_rows) // max(self.num_worker, 1)
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dataset = list()
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with mp.Pool(processes=self.num_worker) as pool:
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from itertools import repeat
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results = pool.starmap_async(self._build_subdataset, zip(all_rows, repeat(True, len(all_rows))),
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chunksize=chunksize)
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for sub_dataset in results.get():
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dataset.append(sub_dataset[0])
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datasets[data_option] = ConcatDataset(dataset)
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self.datasets = datasets
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return datasets
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def setup(self, stag=None):
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datasets = dict()
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samplers = dict()
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weights = dict()
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for data_option in data_options:
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with open(Path(self.root) / 'lab' / f'{data_option}.csv', mode='r') as f:
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# Exclude the header
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_ = next(f)
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all_rows = list(f)
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dataset = list()
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for row in all_rows:
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mel_dataset, class_id, _ = self._build_subdataset(row)
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dataset.append(mel_dataset)
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datasets[data_option] = ConcatDataset(dataset)
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# Build Weighted Sampler for train and val
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if data_option in [DATA_OPTION_train, DATA_OPTION_devel]:
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if self.sampler == EqualSampler.__name__:
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class_idxs = [[idx for idx, (_, __, label) in enumerate(datasets[data_option]) if label == class_idx]
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for class_idx in range(len(self.class_names))
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]
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samplers[data_option] = EqualSampler(class_idxs)
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elif self.sampler == WeightedRandomSampler.__name__:
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class_counts = defaultdict(lambda: 0)
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for _, __, label in datasets[data_option]:
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class_counts[label] += 1
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len_largest_class = max(class_counts.values())
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weights[data_option] = [1 / class_counts[x] for x in range(len(class_counts))]
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weights[data_option] = [weights[data_option][datasets[data_option][i][-1]]
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for i in range(len(datasets[data_option]))]
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samplers[data_option] = WeightedRandomSampler(weights[data_option],
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len_largest_class * len(self.class_names))
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else:
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samplers[data_option] = None
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self.datasets = datasets
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self.samplers = samplers
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return datasets
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def purge(self):
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import shutil
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shutil.rmtree(self.mel_folder, ignore_errors=True)
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print('Mel Folder has been recursively deleted')
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print(f'Folder still exists: {self.mel_folder.exists()}')
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return not self.mel_folder.exists()
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