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ddb382a | 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 | import logging
log = logging.getLogger()
import os
from pathlib import Path
from typing import Optional, Union
from PIL import Image
from transformers import AutoProcessor
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
import mediapy
import torch.nn.functional as F
import numpy as np
import subprocess
from torchvision.utils import save_image
try:
from moviepy import VideoFileClip
except ImportError:
from moviepy.editor import VideoFileClip
_CLIP_FPS = 4
_CLIP_SIZE = 288
_SYNC_FPS = 25
_SYNC_SIZE = 224
def pad_to_square(video_tensor):
if len(video_tensor.shape) != 4:
raise ValueError("Input tensor must have shape (l, c, h, w)")
l, c, h, w = video_tensor.shape
max_side = max(h, w)
pad_h = max_side - h
pad_w = max_side - w
padding = (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2)
video_padded = F.pad(video_tensor, pad=padding, mode='constant', value=0)
video_tensor = F.interpolate(video_padded, size=(_CLIP_SIZE, _CLIP_SIZE), mode='bilinear', align_corners=False)
return video_tensor
def get_video_duration(video_path):
video = VideoFileClip(str(video_path))
return video.duration
class VGGSound(Dataset):
def __init__(
self,
root: Path,
*,
tsv_path: Path,
sample_rate: int = 44100,
normalize_audio: bool = False,
start_row: int = None,
end_row: int = None,
save_dir: str = '',
use_variable_length: bool = False,
video_encoder: str = 'videoprism',
video_resolution: int = _CLIP_SIZE,
inference_mode: bool = False,
video_fps: int = _CLIP_FPS
):
self.inference_mode = inference_mode
self.sample_rate=sample_rate
self.root = Path(root)
self.normalize_audio = normalize_audio
self.use_variable_length = use_variable_length
self.video_encoder = video_encoder
self.video_resolution = video_resolution
self.video_fps = video_fps
self.videos = []
self.caption_cot = []
df_list = pd.read_csv(tsv_path, sep=',', dtype={'id': str}).to_dict('records')
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}.npz'): continue
caption_cot = record['caption_cot']
if not os.path.exists(os.path.join(self.root, id)+".mp4"):
continue
self.videos.append(id)
self.caption_cot.append(caption_cot)
log.info(f'processing {len(self.videos)} videos')
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]
caption_cot = self.caption_cot[idx]
duration_sec= get_video_duration(self.root / (video_id + '.mp4'))
reader = StreamingMediaDecoder(self.root / (video_id + '.mp4'))
reader.add_basic_video_stream(
frames_per_chunk=int(_CLIP_FPS * duration_sec),
frame_rate=_CLIP_FPS,
format='rgb24',
)
reader.add_basic_video_stream(
frames_per_chunk=int(_SYNC_FPS * duration_sec),
frame_rate=_SYNC_FPS,
format='rgb24',
)
if not self.inference_mode:
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]
if not self.inference_mode:
audio_chunk = data_chunk[2]
audio_chunk = audio_chunk.transpose(0, 1)
else:
num_samples = int(self.sample_rate * duration_sec)
audio_chunk = torch.randn((2, num_samples))
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 sync_chunk is None:
raise RuntimeError(f'Sync video returned None {video_id}')
if not self.inference_mode:
sample_rate = int(reader.get_out_stream_info(2).sample_rate)
else:
sample_rate = self.sample_rate
abs_max = audio_chunk[0].abs().max()
if self.normalize_audio:
abs_max = audio_chunk.abs().max()
audio_chunk = audio_chunk / abs_max * 0.95
clip_expected_length = int(_CLIP_FPS * duration_sec)
sync_expected_length = int(_SYNC_FPS * duration_sec)
clip_chunk = clip_chunk[:clip_expected_length]
if clip_chunk.shape[0] != clip_expected_length:
current_length = clip_chunk.shape[0]
padding_needed = clip_expected_length - current_length
# If assertion passes, proceed with padding
if padding_needed > 0:
last_frame = clip_chunk[-1]
padding = last_frame.repeat(padding_needed, 1, 1, 1)
clip_chunk = torch.cat((clip_chunk, padding), dim=0)
clip_chunk = pad_to_square(clip_chunk)
clip_chunk = clip_chunk.permute(0, 2, 3, 1)
clip_chunk = mediapy.to_float01(clip_chunk)
sync_chunk = sync_chunk[:sync_expected_length]
if sync_chunk.shape[0] != sync_expected_length:
# padding using the last frame, but no more than 2
current_length = sync_chunk.shape[0]
last_frame = sync_chunk[-1]
padding = last_frame.repeat(sync_expected_length - current_length, 1, 1, 1)
sync_chunk = torch.cat((sync_chunk, padding), dim=0)
sync_chunk = self.sync_transform(sync_chunk)
data = {
'id': video_id,
'caption_cot': caption_cot,
'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:
logging.error(f'Error loading {self.videos[idx]}: {e}')
return None
def __len__(self) -> int:
return len(self.videos)
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