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Zero
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# from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
# # from moviepy.audio.AudioClip import AudioArrayClip
# from moviepy.audio.io.AudioFileClip import AudioFileClip
from torch.utils.data import DataLoader
from dataset import AudioVideoDataset, LatentDataset
import torch as th
import numpy as np
import einops
from moviepy.audio.io.AudioFileClip import AudioFileClip
from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
from diffusers.models import AutoencoderKL
from converter import denormalize, denormalize_spectrogram
import soundfile as sf
import os
import json
import torch
from tqdm import tqdm
#################################################################################
# Video Utils #
#################################################################################
def preprocess_video(video):
# video = 255*(video+1)/2.0 # [-1,1] -> [0,1] -> [0,255]
# video = th.clamp(video, 0, 255).to(dtype=th.uint8, device="cuda")
video = out2img(video)
video = einops.rearrange(video, 't c h w -> t h w c').cpu().numpy()
return video
def preprocess_video_batch(videos):
B = videos.shape[0]
videos_prep = np.empty(B, dtype=np.ndarray)
for b in range(B):
videos_prep[b] = preprocess_video(videos[b])
videos_prep = np.stack(videos_prep, axis=0)
return videos_prep
def save_latents(video, audio, y, output_path, name_prefix, ext=".pt"):
os.makedirs(output_path, exist_ok=True)
th.save(
{
"video":video,
"audio":audio,
"y":y
}, os.path.join(output_path, name_prefix + ext))
def save_multimodal(video, audio, output_path, name_prefix, video_fps=10, audio_fps=16000, audio_dir=None):
if not audio_dir:
audio_dir = output_path
#prepare folders
audio_dir = os.path.join(audio_dir, "audio")
os.makedirs(audio_dir, exist_ok=True)
audio_path = os.path.join(audio_dir, name_prefix + "_audio.wav")
video_dir = os.path.join(output_path, "video")
os.makedirs(video_dir, exist_ok=True)
video_path = os.path.join(video_dir, name_prefix + "_video.mp4")
#save audio
sf.write(audio_path, audio, samplerate=audio_fps)
#save video
video = preprocess_video(video)
imgs = [img for img in video]
video_clip = ImageSequenceClip(imgs, fps=video_fps)
audio_clip = AudioFileClip(audio_path)
video_clip = video_clip.with_audio(audio_clip)
video_clip.write_videofile(video_path, video_fps, audio=True, audio_fps=audio_fps)
def get_dataloader(args, logger, sequence_length, train, latents=False):
if latents:
train_set = LatentDataset(args.data_path, train=train)
else:
train_set = AudioVideoDataset(
args.data_path,
train=train,
sample_every_n_frames=1,
resolution=args.image_size,
sequence_length = sequence_length,
audio_channels = 1,
sample_rate=16000,
min_length=1,
ignore_cache=args.ignore_cache,
labeled=args.num_classes > 0,
target_video_fps=args.target_video_fps,
)
loader = DataLoader(
train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True
)
if logger is not None:
logger.info(f'{"Train" if train else "Test"} Dataset contains {len(train_set)}, images ({args.data_path})')
else:
print(f'{"Train" if train else "Test"} Dataset contains {len(train_set)}, images ({args.data_path})')
return loader
@torch.no_grad()
def encode_video(video, vae, use_sd_vae = False):
b, t, c, h, w = video.shape
video = einops.rearrange(video, "b t c h w-> (b t) c h w")
if use_sd_vae:
video = vae.encode(video).latent_dist.sample().mul_(0.18215)
else:
video = vae.encode(video)*vae.cfg.scaling_factor
video = einops.rearrange(video, "(b t) c h w -> b t c h w", t=t)
return video
@torch.no_grad()
def decode_video(video, vae):
b = video.shape[0]
video_decoded = []
video = einops.rearrange(video, "b t c h w -> (b t) c h w")
#use minibatch to avoid memory error
for i in range(0, video.shape[0], b):
if isinstance(vae, AutoencoderKL):
video_decoded.append(vae.decode(video[i:i+b] / 0.18215).sample.detach().cpu())
else:
video_decoded.append(vae.decode(video[i:i+b] / vae.cfg.scaling_factor).detach().cpu())
video = torch.cat(video_decoded, dim=0)
video = einops.rearrange(video, "(b t) c h w ->b t c h w",b=b)
return video
def generate_sample(vae,
rectified_flow,
forward_fn,
video_length,
video_latent_size,
audio_latent_size,
y,
cfg_scale,
device):
with torch.no_grad():
v_z = torch.randn(video_latent_size, device=device)*rectified_flow.noise_scale
a_z = torch.randn(audio_latent_size, device=device)*rectified_flow.noise_scale
model_kwargs = dict(y=y, cfg_scale=cfg_scale) if cfg_scale else dict(y=y)
sample_fn = rectified_flow.sample(
forward_fn, v_z, a_z, model_kwargs=model_kwargs, progress=True)()
video = []
audio = []
for _ in tqdm(range(video_length), desc="Generating frames"):
video_samples, audio_samples = next(sample_fn)
video.append(video_samples)
audio.append(audio_samples)
video = torch.stack(video, dim=1)
audio = torch.stack(audio, dim=1)
video = decode_video(video, vae)
audio = einops.rearrange(audio, "B T C N F -> B C N (T F)")
return video, audio
def generate_sample_a2v(vae,
rectified_flow,
forward_fn,
video_length,
video_latent_size,
audio,
y,
device,
cfg_scale=1,
scale=1):
v_z = torch.randn(video_latent_size, device=device)*rectified_flow.noise_scale
model_kwargs = dict(y=y, cfg_scale=cfg_scale) if cfg_scale else dict(y=y)
sample_fn = rectified_flow.sample_a2v(
forward_fn, v_z, audio, model_kwargs=model_kwargs, scale=scale, progress=True)()
video = []
for i in tqdm(range(video_length), desc="Generating frames"):
video_samples = next(sample_fn)
video.append(video_samples)
video = torch.stack(video, dim=1)
video = decode_video(video, vae)
audio = einops.rearrange(audio, "B T C N F -> B C N (T F)")
return video, audio
def generate_sample_v2a(vae,
rectified_flow,
forward_fn,
video_length,
video,
audio_latent_size,
y,
device,
cfg_scale=1,
scale=1):
a_z = torch.randn(audio_latent_size, device=device)*rectified_flow.noise_scale
model_kwargs = dict(y=y, cfg_scale=cfg_scale) if cfg_scale else dict(y=y)
sample_fn = rectified_flow.sample_v2a(
forward_fn, video, a_z, model_kwargs=model_kwargs, scale=scale, progress=True)()
audio = []
for i in tqdm(range(video_length), desc="Generating frames"):
audio_samples = next(sample_fn)
audio.append(audio_samples)
audio = torch.stack(audio, dim=1)
video = decode_video(video, vae)
audio = einops.rearrange(audio, "B T C N F -> B C N (T F)")
return video, audio
def dict_to_json(path, args):
with open(path, 'w') as f:
json.dump(args.__dict__, f, indent=2)
def json_to_dict(path, args):
with open(path, 'r') as f:
args.__dict__ = json.load(f)
return args
def log_args(args, logger):
text = ""
for k, v in vars(args).items():
text += f'{k}={v}\n'
logger.info(f"##### ARGS #####\n{text}")
def out2img(samples):
return th.clamp(127.5 * samples + 128.0, 0, 255).to(
dtype=th.uint8
).cuda()
def get_gpu_usage():
device = th.device('cuda:0')
free, total = th.cuda.mem_get_info(device)
mem_used_MB = (total - free) / 1024 ** 2
return mem_used_MB
def get_wavs(norm_spec, vocoder, audio_scale, device):
norm_spec = norm_spec.squeeze(1)
norm_spec = norm_spec / audio_scale
post_norm_spec = denormalize(norm_spec).to(device)
raw_chunk_spec = denormalize_spectrogram(post_norm_spec)
wavs = vocoder.inference(raw_chunk_spec)
return wavs |