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import random
from typing import List
import sentencepiece as spm
import torch
import torchvision
from data_module import LRS3DataModule
from lightning import Batch
from lightning_av import AVBatch
class FunctionalModule(torch.nn.Module):
def __init__(self, functional):
super().__init__()
self.functional = functional
def forward(self, input):
return self.functional(input)
class AdaptiveTimeMask(torch.nn.Module):
def __init__(self, window, stride):
super().__init__()
self.window = window
self.stride = stride
def forward(self, x):
cloned = x.clone()
length = cloned.size(1)
n_mask = int((length + self.stride - 0.1) // self.stride)
ts = torch.randint(0, self.window, size=(n_mask, 2))
for t, t_end in ts:
if length - t <= 0:
continue
t_start = random.randrange(0, length - t)
if t_start == t_start + t:
continue
t_end += t_start
cloned[:, t_start:t_end] = 0
return cloned
def _extract_labels(sp_model, samples: List):
targets = [sp_model.encode(sample[-1].lower()) for sample in samples]
lengths = torch.tensor([len(elem) for elem in targets]).to(dtype=torch.int32)
targets = torch.nn.utils.rnn.pad_sequence(
[torch.tensor(elem) for elem in targets],
batch_first=True,
padding_value=1.0,
).to(dtype=torch.int32)
return targets, lengths
def _extract_features(video_pipeline, audio_pipeline, samples, args):
raw_videos = []
raw_audios = []
for sample in samples:
if args.modality == "video":
raw_videos.append(sample[0])
if args.modality == "audio":
raw_audios.append(sample[0])
if args.modality == "audiovisual":
length = min(len(sample[0]) // 640, len(sample[1]))
raw_audios.append(sample[0][: length * 640])
raw_videos.append(sample[1][:length])
if args.modality == "video" or args.modality == "audiovisual":
videos = torch.nn.utils.rnn.pad_sequence(raw_videos, batch_first=True)
videos = video_pipeline(videos)
video_lengths = torch.tensor([elem.shape[0] for elem in videos], dtype=torch.int32)
if args.modality == "audio" or args.modality == "audiovisual":
audios = torch.nn.utils.rnn.pad_sequence(raw_audios, batch_first=True)
audios = audio_pipeline(audios)
audio_lengths = torch.tensor([elem.shape[0] // 640 for elem in audios], dtype=torch.int32)
if args.modality == "video":
return videos, video_lengths
if args.modality == "audio":
return audios, audio_lengths
if args.modality == "audiovisual":
return audios, videos, audio_lengths, video_lengths
class TrainTransform:
def __init__(self, sp_model_path: str, args):
self.args = args
self.sp_model = spm.SentencePieceProcessor(model_file=sp_model_path)
self.train_video_pipeline = torch.nn.Sequential(
FunctionalModule(lambda x: x / 255.0),
torchvision.transforms.RandomCrop(88),
torchvision.transforms.RandomHorizontalFlip(0.5),
FunctionalModule(lambda x: x.transpose(0, 1)),
torchvision.transforms.Grayscale(),
FunctionalModule(lambda x: x.transpose(0, 1)),
AdaptiveTimeMask(10, 25),
torchvision.transforms.Normalize(0.421, 0.165),
)
self.train_audio_pipeline = torch.nn.Sequential(
AdaptiveTimeMask(10, 25),
)
def __call__(self, samples: List):
targets, target_lengths = _extract_labels(self.sp_model, samples)
if self.args.modality == "audio":
audios, audio_lengths = _extract_features(
self.train_video_pipeline, self.train_audio_pipeline, samples, self.args
)
return Batch(audios, audio_lengths, targets, target_lengths)
if self.args.modality == "video":
videos, video_lengths = _extract_features(
self.train_video_pipeline, self.train_audio_pipeline, samples, self.args
)
return Batch(videos, video_lengths, targets, target_lengths)
if self.args.modality == "audiovisual":
audios, videos, audio_lengths, video_lengths = _extract_features(
self.train_video_pipeline, self.train_audio_pipeline, samples, self.args
)
return AVBatch(audios, videos, audio_lengths, video_lengths, targets, target_lengths)
class ValTransform:
def __init__(self, sp_model_path: str, args):
self.args = args
self.sp_model = spm.SentencePieceProcessor(model_file=sp_model_path)
self.valid_video_pipeline = torch.nn.Sequential(
FunctionalModule(lambda x: x / 255.0),
torchvision.transforms.CenterCrop(88),
FunctionalModule(lambda x: x.transpose(0, 1)),
torchvision.transforms.Grayscale(),
FunctionalModule(lambda x: x.transpose(0, 1)),
torchvision.transforms.Normalize(0.421, 0.165),
)
self.valid_audio_pipeline = torch.nn.Sequential(
FunctionalModule(lambda x: x),
)
def __call__(self, samples: List):
targets, target_lengths = _extract_labels(self.sp_model, samples)
if self.args.modality == "audio":
audios, audio_lengths = _extract_features(
self.valid_video_pipeline, self.valid_audio_pipeline, samples, self.args
)
return Batch(audios, audio_lengths, targets, target_lengths)
if self.args.modality == "video":
videos, video_lengths = _extract_features(
self.valid_video_pipeline, self.valid_audio_pipeline, samples, self.args
)
return Batch(videos, video_lengths, targets, target_lengths)
if self.args.modality == "audiovisual":
audios, videos, audio_lengths, video_lengths = _extract_features(
self.valid_video_pipeline, self.valid_audio_pipeline, samples, self.args
)
return AVBatch(audios, videos, audio_lengths, video_lengths, targets, target_lengths)
class TestTransform:
def __init__(self, sp_model_path: str, args):
self.val_transforms = ValTransform(sp_model_path, args)
def __call__(self, sample):
return self.val_transforms([sample]), [sample]
def get_data_module(args, sp_model_path, max_frames=1800):
train_transform = TrainTransform(sp_model_path=sp_model_path, args=args)
val_transform = ValTransform(sp_model_path=sp_model_path, args=args)
test_transform = TestTransform(sp_model_path=sp_model_path, args=args)
return LRS3DataModule(
args=args,
train_transform=train_transform,
val_transform=val_transform,
test_transform=test_transform,
max_frames=max_frames,
)
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