189 lines
7.8 KiB
Python
189 lines
7.8 KiB
Python
from collections import defaultdict
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from abc import ABC
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from argparse import Namespace
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import sklearn
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import torch
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import numpy as np
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from torch import nn
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from torch.optim import Adam
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from torch.utils.data import DataLoader, RandomSampler
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from torchcontrib.optim import SWA
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from torchvision.transforms import Compose, RandomApply
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from ml_lib.audio_toolset.audio_augmentation import Speed
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from ml_lib.audio_toolset.mel_augmentation import NoiseInjection, LoudnessManipulator, ShiftTime, MaskAug
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from ml_lib.audio_toolset.audio_io import AudioToMel, MelToImage, NormalizeLocal
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from ml_lib.modules.utils import LightningBaseModule
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from ml_lib.utils.transforms import ToTensor
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import variables as V
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class BaseOptimizerMixin:
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def configure_optimizers(self):
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assert isinstance(self, LightningBaseModule)
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opt = Adam(params=self.parameters(), lr=self.params.lr, weight_decay=1e-7)
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if self.params.sto_weight_avg:
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opt = SWA(opt, swa_start=10, swa_freq=5, swa_lr=0.05)
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return opt
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def on_train_end(self):
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assert isinstance(self, LightningBaseModule)
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for opt in self.trainer.optimizers:
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if isinstance(opt, SWA):
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opt.swap_swa_sgd()
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def on_epoch_end(self):
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assert isinstance(self, LightningBaseModule)
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if self.params.opt_reset_interval:
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if self.current_epoch % self.params.opt_reset_interval == 0:
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for opt in self.trainer.optimizers:
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opt.state = defaultdict(dict)
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class BaseTrainMixin:
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absolute_loss = nn.L1Loss()
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nll_loss = nn.NLLLoss()
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bce_loss = nn.BCELoss()
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def training_step(self, batch_xy, batch_nb, *args, **kwargs):
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assert isinstance(self, LightningBaseModule)
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batch_x, batch_y = batch_xy
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y = self(batch_x).main_out
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bce_loss = self.bce_loss(y, batch_y)
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return dict(loss=bce_loss)
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def training_epoch_end(self, outputs):
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assert isinstance(self, LightningBaseModule)
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keys = list(outputs[0].keys())
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summary_dict = dict(log={f'mean_{key}': torch.mean(torch.stack([output[key]
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for output in outputs]))
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for key in keys if 'loss' in key})
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return summary_dict
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class BaseValMixin:
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absolute_loss = nn.L1Loss()
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nll_loss = nn.NLLLoss()
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bce_loss = nn.BCELoss()
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def validation_step(self, batch_xy, batch_idx, dataloader_idx, *args, **kwargs):
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assert isinstance(self, LightningBaseModule)
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batch_x, batch_y = batch_xy
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y = self(batch_x).main_out
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val_bce_loss = self.bce_loss(y, batch_y)
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return dict(val_bce_loss=val_bce_loss,
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batch_idx=batch_idx, y=y, batch_y=batch_y)
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def validation_epoch_end(self, outputs, *args, **kwargs):
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assert isinstance(self, LightningBaseModule)
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summary_dict = dict(log=dict())
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for output_idx, output in enumerate(outputs):
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keys = list(output[0].keys())
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ident = '' if output_idx == 0 else '_train'
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summary_dict['log'].update({f'mean{ident}_{key}': torch.mean(torch.stack([output[key]
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for output in output]))
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for key in keys if 'loss' in key}
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)
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# UnweightedAverageRecall
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y_true = torch.cat([output['batch_y'] for output in output]) .cpu().numpy()
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y_pred = torch.cat([output['y'] for output in output]).squeeze().cpu().numpy()
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y_pred = (y_pred >= 0.5).astype(np.float32)
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uar_score = sklearn.metrics.recall_score(y_true, y_pred, labels=[0, 1], average='macro',
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sample_weight=None, zero_division='warn')
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summary_dict['log'].update({f'uar{ident}_score': uar_score})
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return summary_dict
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class BinaryMaskDatasetMixin:
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def build_dataset(self):
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assert isinstance(self, LightningBaseModule)
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# Dataset
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# =============================================================================
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# Mel Transforms
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mel_transforms_train = Compose([
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# Audio to Mel Transformations
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Speed(speed_factor=self.params.speed_factor, max_ratio=self.params.speed_ratio),
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AudioToMel(sr=self.params.sr, n_mels=self.params.n_mels, n_fft=self.params.n_fft,
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hop_length=self.params.hop_length),
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MelToImage()])
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mel_transforms = Compose([
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# Audio to Mel Transformations
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AudioToMel(sr=self.params.sr, n_mels=self.params.n_mels, n_fft=self.params.n_fft,
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hop_length=self.params.hop_length), MelToImage()])
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# Data Augmentations
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aug_transforms = Compose([
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RandomApply([
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NoiseInjection(self.params.noise_ratio),
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LoudnessManipulator(self.params.loudness_ratio),
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ShiftTime(self.params.shift_ratio),
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MaskAug(self.params.mask_ratio),
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], p=0.6),
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# Utility
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NormalizeLocal(), ToTensor()
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])
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val_transforms = Compose([NormalizeLocal(), ToTensor()])
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# Datasets
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from datasets.binar_masks import BinaryMasksDataset
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dataset = Namespace(
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**dict(
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# TRAIN DATASET
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train_dataset=BinaryMasksDataset(self.params.root, setting=V.DATA_OPTIONS.train,
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mixup=self.params.mixup,
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mel_transforms=mel_transforms_train, transforms=aug_transforms),
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# VALIDATION DATASET
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val_train_dataset=BinaryMasksDataset(self.params.root, setting=V.DATA_OPTIONS.train,
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mel_transforms=mel_transforms, transforms=val_transforms),
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val_dataset=BinaryMasksDataset(self.params.root, setting=V.DATA_OPTIONS.devel,
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mel_transforms=mel_transforms, transforms=val_transforms),
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# TEST DATASET
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test_dataset=BinaryMasksDataset(self.params.root, setting=V.DATA_OPTIONS.test,
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mel_transforms=mel_transforms, transforms=val_transforms),
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)
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)
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return dataset
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class BaseDataloadersMixin(ABC):
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# Dataloaders
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# ================================================================================
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# Train Dataloader
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def train_dataloader(self):
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assert isinstance(self, LightningBaseModule)
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# sampler = RandomSampler(self.dataset.train_dataset, True, len(self.dataset.train_dataset))
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sampler = None
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return DataLoader(dataset=self.dataset.train_dataset, shuffle=True if not sampler else None, sampler=sampler,
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batch_size=self.params.batch_size,
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num_workers=self.params.worker)
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# Test Dataloader
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def test_dataloader(self):
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assert isinstance(self, LightningBaseModule)
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return DataLoader(dataset=self.dataset.test_dataset, shuffle=False,
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batch_size=self.params.batch_size,
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num_workers=self.params.worker)
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# Validation Dataloader
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def val_dataloader(self):
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assert isinstance(self, LightningBaseModule)
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val_dataloader = DataLoader(dataset=self.dataset.val_dataset, shuffle=True,
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batch_size=self.params.batch_size, num_workers=self.params.worker)
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train_dataloader = DataLoader(self.dataset.val_train_dataset, num_workers=self.params.worker,
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batch_size=self.params.batch_size, shuffle=False)
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return [val_dataloader, train_dataloader]
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