| import logging |
| import random |
|
|
| import numpy as np |
| import torch |
| from omegaconf import DictConfig |
| from torch.utils.data import DataLoader, Dataset |
| from torch.utils.data.dataloader import default_collate |
| from torch.utils.data.distributed import DistributedSampler |
|
|
| from .eval.audiocaps import AudioCapsData |
| from .eval.video_dataset import MovieGen, VGGSound |
| from .extracted_audio import ExtractedAudio |
| from .extracted_vgg import ExtractedVGG |
| from .mm_dataset import MultiModalDataset |
| from ..utils.dist_utils import local_rank |
|
|
| log = logging.getLogger() |
|
|
|
|
| |
| def worker_init_fn(worker_id: int): |
| worker_seed = torch.initial_seed() % (2**31) + worker_id + local_rank * 1000 |
| np.random.seed(worker_seed) |
| random.seed(worker_seed) |
| log.debug(f'Worker {worker_id} re-seeded with seed {worker_seed} in rank {local_rank}') |
|
|
|
|
| def load_vgg_data(cfg: DictConfig, data_cfg: DictConfig) -> Dataset: |
| dataset = ExtractedVGG(tsv_path=data_cfg.tsv, |
| data_dim=cfg.data_dim, |
| premade_mmap_dir=data_cfg.memmap_dir) |
|
|
| return dataset |
|
|
|
|
| def load_audio_data(cfg: DictConfig, data_cfg: DictConfig) -> Dataset: |
| dataset = ExtractedAudio(tsv_path=data_cfg.tsv, |
| data_dim=cfg.data_dim, |
| premade_mmap_dir=data_cfg.memmap_dir) |
|
|
| return dataset |
|
|
|
|
| def setup_training_datasets(cfg: DictConfig) -> tuple[Dataset, DistributedSampler, DataLoader]: |
| if cfg.mini_train: |
| vgg = load_vgg_data(cfg, cfg.data.ExtractedVGG_val) |
| audiocaps = load_audio_data(cfg, cfg.data.AudioCaps) |
| dataset = MultiModalDataset([vgg], [audiocaps]) |
| if cfg.example_train: |
| video = load_vgg_data(cfg, cfg.data.Example_video) |
| audio = load_audio_data(cfg, cfg.data.Example_audio) |
| dataset = MultiModalDataset([video], [audio]) |
| else: |
| |
| freesound = load_audio_data(cfg, cfg.data.FreeSound) |
| vgg = load_vgg_data(cfg, cfg.data.ExtractedVGG) |
| audiocaps = load_audio_data(cfg, cfg.data.AudioCaps) |
| audioset_sl = load_audio_data(cfg, cfg.data.AudioSetSL) |
| bbcsound = load_audio_data(cfg, cfg.data.BBCSound) |
| clotho = load_audio_data(cfg, cfg.data.Clotho) |
| dataset = MultiModalDataset([vgg] * cfg.vgg_oversample_rate, |
| [audiocaps, audioset_sl, bbcsound, freesound, clotho]) |
|
|
| batch_size = cfg.batch_size |
| num_workers = cfg.num_workers |
| pin_memory = cfg.pin_memory |
| sampler, loader = construct_loader(dataset, |
| batch_size, |
| num_workers, |
| shuffle=True, |
| drop_last=True, |
| pin_memory=pin_memory) |
|
|
| return dataset, sampler, loader |
|
|
|
|
| def setup_test_datasets(cfg): |
| dataset = load_vgg_data(cfg, cfg.data.ExtractedVGG_test) |
|
|
| batch_size = cfg.batch_size |
| num_workers = cfg.num_workers |
| pin_memory = cfg.pin_memory |
| sampler, loader = construct_loader(dataset, |
| batch_size, |
| num_workers, |
| shuffle=False, |
| drop_last=False, |
| pin_memory=pin_memory) |
|
|
| return dataset, sampler, loader |
|
|
|
|
| def setup_val_datasets(cfg: DictConfig) -> tuple[Dataset, DataLoader, DataLoader]: |
| if cfg.example_train: |
| dataset = load_vgg_data(cfg, cfg.data.Example_video) |
| else: |
| dataset = load_vgg_data(cfg, cfg.data.ExtractedVGG_val) |
|
|
| val_batch_size = cfg.batch_size |
| val_eval_batch_size = cfg.eval_batch_size |
| num_workers = cfg.num_workers |
| pin_memory = cfg.pin_memory |
| _, val_loader = construct_loader(dataset, |
| val_batch_size, |
| num_workers, |
| shuffle=False, |
| drop_last=False, |
| pin_memory=pin_memory) |
| _, eval_loader = construct_loader(dataset, |
| val_eval_batch_size, |
| num_workers, |
| shuffle=False, |
| drop_last=False, |
| pin_memory=pin_memory) |
|
|
| return dataset, val_loader, eval_loader |
|
|
|
|
| def setup_eval_dataset(dataset_name: str, cfg: DictConfig) -> tuple[Dataset, DataLoader]: |
| if dataset_name.startswith('audiocaps_full'): |
| dataset = AudioCapsData(cfg.eval_data.AudioCaps_full.audio_path, |
| cfg.eval_data.AudioCaps_full.csv_path) |
| elif dataset_name.startswith('audiocaps'): |
| dataset = AudioCapsData(cfg.eval_data.AudioCaps.audio_path, |
| cfg.eval_data.AudioCaps.csv_path) |
| elif dataset_name.startswith('moviegen'): |
| dataset = MovieGen(cfg.eval_data.MovieGen.video_path, |
| cfg.eval_data.MovieGen.jsonl_path, |
| duration_sec=cfg.duration_s) |
| elif dataset_name.startswith('vggsound'): |
| dataset = VGGSound(cfg.eval_data.VGGSound.video_path, |
| cfg.eval_data.VGGSound.csv_path, |
| duration_sec=cfg.duration_s) |
| else: |
| raise ValueError(f'Invalid dataset name: {dataset_name}') |
|
|
| batch_size = cfg.batch_size |
| num_workers = cfg.num_workers |
| pin_memory = cfg.pin_memory |
| _, loader = construct_loader(dataset, |
| batch_size, |
| num_workers, |
| shuffle=False, |
| drop_last=False, |
| pin_memory=pin_memory, |
| error_avoidance=True) |
| return dataset, loader |
|
|
|
|
| def error_avoidance_collate(batch): |
| batch = list(filter(lambda x: x is not None, batch)) |
| return default_collate(batch) |
|
|
|
|
| def construct_loader(dataset: Dataset, |
| batch_size: int, |
| num_workers: int, |
| *, |
| shuffle: bool = True, |
| drop_last: bool = True, |
| pin_memory: bool = False, |
| error_avoidance: bool = False) -> tuple[DistributedSampler, DataLoader]: |
| train_sampler = DistributedSampler(dataset, rank=local_rank, shuffle=shuffle) |
| train_loader = DataLoader(dataset, |
| batch_size, |
| sampler=train_sampler, |
| num_workers=num_workers, |
| worker_init_fn=worker_init_fn, |
| drop_last=drop_last, |
| persistent_workers=num_workers > 0, |
| pin_memory=pin_memory, |
| collate_fn=error_avoidance_collate if error_avoidance else None) |
| return train_sampler, train_loader |
|
|
