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