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# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import sys
from dataclasses import dataclass
from typing import Optional, List
from omegaconf import II
from fairseq.data.iterators import GroupedEpochBatchIterator
from fairseq.dataclass import FairseqDataclass
from fairseq.tasks import FairseqTask, register_task
from fairseq.tasks.audio_pretraining import AudioPretrainingConfig, AudioPretrainingTask
from fairseq.tasks.masked_lm import MaskedLMConfig, MaskedLMTask
from .mae_image_pretraining import MaeImagePretrainingConfig, MaeImagePretrainingTask
from examples.data2vec.data.modality import Modality
from fairseq.data.audio.multi_modality_dataset import (
MultiModalityDataset,
ModalityDatasetItem,
)
@dataclass
class MultimodalPretrainingConfig(FairseqDataclass):
audio: Optional[AudioPretrainingConfig] = None
image: Optional[MaeImagePretrainingConfig] = None
text: Optional[MaskedLMConfig] = None
audio_ratio: float = 1
image_ratio: float = 1
text_ratio: float = 1
max_tokens: Optional[int] = II("dataset.max_tokens")
batch_size: Optional[int] = II("dataset.batch_size")
update_freq: List[int] = II("optimization.update_freq")
rebuild_batches: bool = True
@register_task("multimodal_pretraining", dataclass=MultimodalPretrainingConfig)
class MultimodalPretrainingTask(FairseqTask):
""" """
cfg: MultimodalPretrainingConfig
def __init__(self, cfg: MultimodalPretrainingConfig):
super().__init__(cfg)
self.audio_task = (
AudioPretrainingTask(cfg.audio) if cfg.audio is not None else None
)
self.image_task = (
MaeImagePretrainingTask(cfg.image) if cfg.image is not None else None
)
self.text_task = MaskedLMTask(cfg.text) if cfg.text is not None else None
self.mult_ratios = []
@classmethod
def setup_task(cls, cfg: MultimodalPretrainingConfig, **kwargs):
"""Setup the task (e.g., load dictionaries).
Args:
cfg (AudioPretrainingConfig): configuration of this task
"""
return cls(cfg)
def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs):
datasets = []
self.mult_ratios = []
def load_ds(task, name, ratio):
if task is not None:
task.load_dataset(split)
ds = ModalityDatasetItem(
datasetname=name,
dataset=task.dataset(split),
max_positions=task.max_positions(),
max_tokens=self.cfg.max_tokens,
max_sentences=self.cfg.batch_size,
)
datasets.append(ds)
self.mult_ratios.append(ratio)
load_ds(self.audio_task, Modality.AUDIO, self.cfg.audio_ratio)
load_ds(self.image_task, Modality.IMAGE, self.cfg.image_ratio)
load_ds(self.text_task, Modality.TEXT, self.cfg.text_ratio)
assert len(datasets) > 0
self.datasets[split] = MultiModalityDataset(datasets)
@property
def supported_modalities(self):
modalities = []
if self.cfg.text is not None:
modalities.append(Modality.TEXT)
if self.cfg.audio is not None:
modalities.append(Modality.AUDIO)
if self.cfg.image is not None:
modalities.append(Modality.IMAGE)
return modalities
def get_batch_iterator(
self,
dataset,
max_tokens=None,
max_sentences=None,
max_positions=None,
ignore_invalid_inputs=False,
required_batch_size_multiple=1,
seed=1,
num_shards=1,
shard_id=0,
num_workers=0,
epoch=0,
data_buffer_size=0,
disable_iterator_cache=False,
skip_remainder_batch=False,
grouped_shuffling=False,
update_epoch_batch_itr=False,
):
# initialize the dataset with the correct starting epoch
dataset.set_epoch(epoch)
batch_samplers = dataset.get_batch_samplers(
self.mult_ratios, required_batch_size_multiple, seed
)
# return a reusable, sharded iterator
epoch_iter = GroupedEpochBatchIterator(
dataset=dataset,
collate_fn=dataset.collater,
batch_samplers=batch_samplers,
seed=seed,
num_shards=num_shards,
shard_id=shard_id,
num_workers=num_workers,
epoch=epoch,
mult_rate=max(self.cfg.update_freq),
buffer_size=data_buffer_size,
skip_remainder_batch=skip_remainder_batch,
)
self.dataset_to_epoch_iter[dataset] = {} # refresh it every epoch
return epoch_iter
@property
def source_dictionary(self):
return None
@property
def target_dictionary(self):
return None
def max_positions(self):
"""Maximum input length supported by the encoder."""
return sys.maxsize, sys.maxsize
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