123 lines
5.7 KiB
Python
123 lines
5.7 KiB
Python
"""create dataset and dataloader"""
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import torch
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import torch.utils.data
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from munch import munchify
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from utils.util import opt_get
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def create_dataloader(dataset, dataset_opt, opt=None, sampler=None, collate_fn=None, shuffle=True):
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phase = dataset_opt['phase']
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pin_memory = opt_get(dataset_opt, ['pin_memory'], True)
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if phase == 'train':
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if opt_get(opt, ['dist'], False):
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world_size = torch.distributed.get_world_size()
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num_workers = dataset_opt['n_workers']
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assert dataset_opt['batch_size'] % world_size == 0
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batch_size = dataset_opt['batch_size'] // world_size
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else:
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num_workers = dataset_opt['n_workers']
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batch_size = dataset_opt['batch_size']
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return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle,
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num_workers=num_workers, sampler=sampler, drop_last=True,
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pin_memory=pin_memory, collate_fn=collate_fn, persistent_workers=True)
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else:
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batch_size = dataset_opt['batch_size'] or 1
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return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=0,
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pin_memory=pin_memory, collate_fn=collate_fn)
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def create_dataset(dataset_opt, return_collate=False):
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mode = dataset_opt['mode']
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collate = None
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# datasets for image restoration
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if mode == 'fullimage':
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from data.images.full_image_dataset import FullImageDataset as D
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elif mode == 'single_image_extensible':
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from data.images.single_image_dataset import SingleImageDataset as D
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elif mode == 'multi_frame_extensible':
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from data.images.multi_frame_dataset import MultiFrameDataset as D
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elif mode == 'combined':
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from data.combined_dataset import CombinedDataset as D
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elif mode == 'multiscale':
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from data.images.multiscale_dataset import MultiScaleDataset as D
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elif mode == 'paired_frame':
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from data.images.paired_frame_dataset import PairedFrameDataset as D
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elif mode == 'stylegan2':
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from data.images.stylegan2_dataset import Stylegan2Dataset as D
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elif mode == 'imagefolder':
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from data.images.image_folder_dataset import ImageFolderDataset as D
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elif mode == 'torch_dataset':
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from data.torch_dataset import TorchDataset as D
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elif mode == 'byol_dataset':
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from data.images.byol_attachment import ByolDatasetWrapper as D
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elif mode == 'byol_structured_dataset':
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from data.images.byol_attachment import StructuredCropDatasetWrapper as D
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elif mode == 'random_aug_wrapper':
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from data.images.byol_attachment import DatasetRandomAugWrapper as D
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elif mode == 'random_dataset':
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from data.images.random_dataset import RandomDataset as D
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elif mode == 'zipfile':
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from data.images.zip_file_dataset import ZipFileDataset as D
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elif mode == 'nv_tacotron':
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from data.audio.nv_tacotron_dataset import TextWavLoader as D
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from data.audio.nv_tacotron_dataset import TextMelCollate as C
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from models.audio.tts.tacotron2 import create_hparams
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default_params = create_hparams()
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default_params.update(dataset_opt)
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dataset_opt = munchify(default_params)
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if opt_get(dataset_opt, ['needs_collate'], True):
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collate = C()
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elif mode == 'paired_voice_audio':
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from data.audio.paired_voice_audio_dataset import TextWavLoader as D
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from models.audio.tts.tacotron2 import create_hparams
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default_params = create_hparams()
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default_params.update(dataset_opt)
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dataset_opt = munchify(default_params)
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elif mode == 'fast_paired_voice_audio':
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from data.audio.fast_paired_dataset import FastPairedVoiceDataset as D
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from models.audio.tts.tacotron2 import create_hparams
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default_params = create_hparams()
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default_params.update(dataset_opt)
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dataset_opt = munchify(default_params)
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elif mode == 'fast_paired_voice_audio_with_phonemes':
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from data.audio.fast_paired_dataset_with_phonemes import FastPairedVoiceDataset as D
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from models.audio.tts.tacotron2 import create_hparams
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default_params = create_hparams()
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default_params.update(dataset_opt)
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dataset_opt = munchify(default_params)
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elif mode == 'gpt_tts':
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from data.audio.gpt_tts_dataset import GptTtsDataset as D
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from data.audio.gpt_tts_dataset import GptTtsCollater as C
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collate = C(dataset_opt)
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elif mode == 'unsupervised_audio':
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from data.audio.unsupervised_audio_dataset import UnsupervisedAudioDataset as D
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elif mode == 'unsupervised_audio_with_noise':
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from data.audio.audio_with_noise_dataset import AudioWithNoiseDataset as D
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elif mode == 'preprocessed_mel':
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from data.audio.preprocessed_mel_dataset import PreprocessedMelDataset as D
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elif mode == 'grand_conjoined_voice':
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from data.audio.grand_conjoined_dataset import GrandConjoinedDataset as D
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from data.zero_pad_dict_collate import ZeroPadDictCollate as C
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if opt_get(dataset_opt, ['needs_collate'], False):
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collate = C()
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else:
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raise NotImplementedError('Dataset [{:s}] is not recognized.'.format(mode))
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dataset = D(dataset_opt)
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if return_collate:
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return dataset, collate
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else:
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return dataset
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def get_dataset_debugger(dataset_opt):
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mode = dataset_opt['mode']
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if mode == 'paired_voice_audio':
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from data.audio.paired_voice_audio_dataset import PairedVoiceDebugger
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return PairedVoiceDebugger()
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elif mode == 'fast_paired_voice_audio':
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from data.audio.fast_paired_dataset import FastPairedVoiceDebugger
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return FastPairedVoiceDebugger()
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return None |