Spaces:
Running
on
Zero
Running
on
Zero
File size: 10,415 Bytes
052cf68 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 |
import logging
import os
from pathlib import Path
from typing import Optional, Union
import pandas as pd
import torch
import torchaudio
from torch.utils.data.dataset import Dataset
from torchvision.transforms import v2
from torio.io import StreamingMediaDecoder
from torchvision.utils import save_image
log = logging.getLogger()
_CLIP_SIZE = 384
_CLIP_FPS = 8.0
_SYNC_SIZE = 224
_SYNC_FPS = 25.0
class VGGSound(Dataset):
def __init__(
self,
root: Union[str, Path],
*,
tsv_path: Union[str, Path] = 'dataset/vggsound/split_txt/train_caption.csv',
sample_rate: int = 44_100,
duration_sec: float = 9.0,
audio_samples: Optional[int] = 397312,
normalize_audio: bool = False,
start_row: Optional[int] = None,
end_row: Optional[int] = None,
save_dir: str = 'data/vggsound/video_latents_text/train'
):
self.root = Path(root)
self.normalize_audio = normalize_audio
if audio_samples is None:
self.audio_samples = int(sample_rate * duration_sec)
else:
self.audio_samples = audio_samples
effective_duration = audio_samples / sample_rate
# make sure the duration is close enough, within 15ms
assert abs(effective_duration - duration_sec) < 0.015, \
f'audio_samples {audio_samples} does not match duration_sec {duration_sec}'
videos = sorted(os.listdir(self.root))
videos = set([Path(v).stem for v in videos]) # remove extensions
# videos = []
self.labels = []
self.videos = []
missing_videos = []
# read the tsv for subset information
df_list = pd.read_csv(tsv_path, sep=',', dtype={'id': str}).to_dict('records')
# 控制处理的行范围
if start_row is not None and end_row is not None:
df_list = df_list[start_row:end_row]
for record in df_list:
id = record['id']
if os.path.exists(f'{save_dir}/{id}.pth'): continue
label = record['caption']
if id in videos:
# self.labels.append(label)
self.labels[id] = label
self.videos.append(id)
else:
missing_videos.append(id)
log.info(f'{len(videos)} videos found in {root}')
log.info(f'{len(self.videos)} videos found in {tsv_path}')
log.info(f'{len(missing_videos)} videos missing in {root}')
self.sample_rate = sample_rate
self.duration_sec = duration_sec
self.expected_audio_length = self.audio_samples
self.clip_expected_length = int(_CLIP_FPS * self.duration_sec)
self.sync_expected_length = int(_SYNC_FPS * self.duration_sec)
self.clip_transform = v2.Compose([
v2.Resize((_CLIP_SIZE, _CLIP_SIZE), interpolation=v2.InterpolationMode.BICUBIC),
v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
])
self.sync_transform = v2.Compose([
v2.Resize(_SYNC_SIZE, interpolation=v2.InterpolationMode.BICUBIC),
v2.CenterCrop(_SYNC_SIZE),
v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
self.resampler = {}
def sample(self, idx: int) -> dict[str, torch.Tensor]:
video_id = self.videos[idx]
label = self.labels[idx]
reader = StreamingMediaDecoder(self.root / (video_id + '.mp4'))
reader.add_basic_video_stream(
frames_per_chunk=int(_CLIP_FPS * self.duration_sec),
frame_rate=_CLIP_FPS,
format='rgb24',
)
reader.add_basic_video_stream(
frames_per_chunk=int(_SYNC_FPS * self.duration_sec),
frame_rate=_SYNC_FPS,
format='rgb24',
)
reader.add_basic_audio_stream(frames_per_chunk=2**30,)
reader.fill_buffer()
data_chunk = reader.pop_chunks()
clip_chunk = data_chunk[0]
sync_chunk = data_chunk[1]
audio_chunk = data_chunk[2]
if len(audio_chunk.shape) != 2:
raise RuntimeError(f'error audio shape {video_id}')
if clip_chunk is None:
raise RuntimeError(f'CLIP video returned None {video_id}')
# if clip_chunk.shape[0] < self.clip_expected_length:
# raise RuntimeError(
# f'CLIP video too short {video_id}, expected {self.clip_expected_length}, got {clip_chunk.shape[0]}'
# )
if sync_chunk is None:
raise RuntimeError(f'Sync video returned None {video_id}')
# if sync_chunk.shape[0] < self.sync_expected_length:
# raise RuntimeError(
# f'Sync video too short {video_id}, expected {self.sync_expected_length}, got {sync_chunk.shape[0]}'
# )
# import ipdb
# ipdb.set_trace()
# process audio
sample_rate = int(reader.get_out_stream_info(2).sample_rate)
audio_chunk = audio_chunk.transpose(0, 1)
abs_max = audio_chunk[0].abs().max()
# audio_chunk = audio_chunk.mean(dim=0) # mono
# if self.normalize_audio:
# abs_max = audio_chunk.abs().max()
# audio_chunk = audio_chunk / abs_max * 0.95
if abs_max <= 1e-6:
if audio_chunk.shape[0] > 1 and audio_chunk[1].abs().max() > 1e-6:
audio_chunk = audio_chunk[1:2]
else:
raise RuntimeError(f'Audio is silent {video_id}')
# if abs_max <= 1e-6:
# raise RuntimeError(f'Audio is silent {video_id}')
# ensure the stereo audio
if audio_chunk.shape[0] < 2:
audio_chunk = audio_chunk.repeat(2, 1)
# resample
if sample_rate == self.sample_rate:
audio_chunk = audio_chunk
else:
if sample_rate not in self.resampler:
# https://pytorch.org/audio/stable/tutorials/audio_resampling_tutorial.html#kaiser-best
self.resampler[sample_rate] = torchaudio.transforms.Resample(
sample_rate,
self.sample_rate,
lowpass_filter_width=64,
rolloff=0.9475937167399596,
resampling_method='sinc_interp_kaiser',
beta=14.769656459379492,
)
audio_chunk = self.resampler[sample_rate](audio_chunk)
if audio_chunk.shape[1] < self.expected_audio_length:
# zero-padding audio
padding_length = self.expected_audio_length - audio_chunk.shape[1]
# 创建 padding 张量，大小为 [batch_size, padding_length]，值为0
padding = torch.zeros(audio_chunk.shape[0], padding_length)
# 将原始音频和 padding 沿第 1 维度拼接在一起
audio_chunk = torch.cat((audio_chunk, padding), dim=1)
# raise RuntimeError(f'Audio too short {video_id}')
audio_chunk = audio_chunk[:,:self.expected_audio_length]
# truncate the video
clip_chunk = clip_chunk[:self.clip_expected_length]
# import ipdb
# ipdb.set_trace()
if clip_chunk.shape[0] != self.clip_expected_length:
current_length = clip_chunk.shape[0]
padding_needed = self.clip_expected_length - current_length
# Check that padding needed is no more than 2
assert padding_needed < 4, f'Padding no more than 2 frames allowed, but {padding_needed} needed'
# If assertion passes, proceed with padding
if padding_needed > 0:
last_frame = clip_chunk[-1]
log.info(last_frame.shape)
# Repeat the last frame to reach the expected length
padding = last_frame.repeat(padding_needed, 1, 1, 1)
clip_chunk = torch.cat((clip_chunk, padding), dim=0)
# raise RuntimeError(f'CLIP video wrong length {video_id}, '
# f'expected {self.clip_expected_length}, '
# f'got {clip_chunk.shape[0]}')
# save_image(clip_chunk[0] / 255.0,'ori.png')
clip_chunk = self.clip_transform(clip_chunk)
# temp_img = clip_chunk[0].permute(1, 2, 0) * 255
# save_image(clip_chunk[0],'scale.png')
sync_chunk = sync_chunk[:self.sync_expected_length]
if sync_chunk.shape[0] != self.sync_expected_length:
# padding using the last frame, but no more than 2
current_length = sync_chunk.shape[0]
last_frame = sync_chunk[-1]
# 重复最后一帧以进行填充
padding = last_frame.repeat(self.sync_expected_length - current_length, 1, 1, 1)
assert self.sync_expected_length - current_length < 12, f'sync can pad no more than 2 while {self.sync_expected_length - current_length}'
sync_chunk = torch.cat((sync_chunk, padding), dim=0)
# raise RuntimeError(f'Sync video wrong length {video_id}, '
# f'expected {self.sync_expected_length}, '
# f'got {sync_chunk.shape[0]}')
sync_chunk = self.sync_transform(sync_chunk)
assert audio_chunk.shape[1] == self.expected_audio_length and clip_chunk.shape[0] == self.clip_expected_length \
and sync_chunk.shape[0] == self.sync_expected_length, 'error processed data shape'
data = {
'id': video_id,
'caption': label,
'audio': audio_chunk,
'clip_video': clip_chunk,
'sync_video': sync_chunk,
}
return data
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
try:
return self.sample(idx)
except Exception as e:
log.error(f'Error loading video {self.videos[idx]}: {e}')
return None
def __len__(self):
return len(self.labels)
# dataset = VGGSound(
# root="data/vggsound/video/test",
# tsv_path="data/vggsound/split_txt/temp.csv",
# sample_rate=44100,
# duration_sec=9.0,
# audio_samples=397312,
# start_row=0,
# end_row=None,
# save_dir="data/vggsound/video_latents_text/test"
# )
# dataset[0] |