File size: 12,247 Bytes
5a510e7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 |
# pylint: disable=R0801
"""
talking_video_dataset.py
This module defines the TalkingVideoDataset class, a custom PyTorch dataset
for handling talking video data. The dataset uses video files, masks, and
embeddings to prepare data for tasks such as video generation and
speech-driven video animation.
Classes:
TalkingVideoDataset
Dependencies:
json
random
torch
decord.VideoReader, decord.cpu
PIL.Image
torch.utils.data.Dataset
torchvision.transforms
Example:
from talking_video_dataset import TalkingVideoDataset
from torch.utils.data import DataLoader
# Example configuration for the Wav2Vec model
class Wav2VecConfig:
def __init__(self, audio_type, model_scale, features):
self.audio_type = audio_type
self.model_scale = model_scale
self.features = features
wav2vec_cfg = Wav2VecConfig(audio_type="wav2vec2", model_scale="base", features="feature")
# Initialize dataset
dataset = TalkingVideoDataset(
img_size=(512, 512),
sample_rate=16000,
audio_margin=2,
n_motion_frames=0,
n_sample_frames=16,
data_meta_paths=["path/to/meta1.json", "path/to/meta2.json"],
wav2vec_cfg=wav2vec_cfg,
)
# Initialize dataloader
dataloader = DataLoader(dataset, batch_size=4, shuffle=True)
# Fetch one batch of data
batch = next(iter(dataloader))
print(batch["pixel_values_vid"].shape) # Example output: (4, 16, 3, 512, 512)
The TalkingVideoDataset class provides methods for loading video frames, masks,
audio embeddings, and other relevant data, applying transformations, and preparing
the data for training and evaluation in a deep learning pipeline.
Attributes:
img_size (tuple): The dimensions to resize the video frames to.
sample_rate (int): The audio sample rate.
audio_margin (int): The margin for audio sampling.
n_motion_frames (int): The number of motion frames.
n_sample_frames (int): The number of sample frames.
data_meta_paths (list): List of paths to the JSON metadata files.
wav2vec_cfg (object): Configuration for the Wav2Vec model.
Methods:
augmentation(images, transform, state=None): Apply transformation to input images.
__getitem__(index): Get a sample from the dataset at the specified index.
__len__(): Return the length of the dataset.
"""
import json
import random
from typing import List
import torch
from decord import VideoReader, cpu
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
class TalkingVideoDataset(Dataset):
"""
A dataset class for processing talking video data.
Args:
img_size (tuple, optional): The size of the output images. Defaults to (512, 512).
sample_rate (int, optional): The sample rate of the audio data. Defaults to 16000.
audio_margin (int, optional): The margin for the audio data. Defaults to 2.
n_motion_frames (int, optional): The number of motion frames. Defaults to 0.
n_sample_frames (int, optional): The number of sample frames. Defaults to 16.
data_meta_paths (list, optional): The paths to the data metadata. Defaults to None.
wav2vec_cfg (dict, optional): The configuration for the wav2vec model. Defaults to None.
Attributes:
img_size (tuple): The size of the output images.
sample_rate (int): The sample rate of the audio data.
audio_margin (int): The margin for the audio data.
n_motion_frames (int): The number of motion frames.
n_sample_frames (int): The number of sample frames.
data_meta_paths (list): The paths to the data metadata.
wav2vec_cfg (dict): The configuration for the wav2vec model.
"""
def __init__(
self,
img_size=(512, 512),
sample_rate=16000,
audio_margin=2,
n_motion_frames=0,
n_sample_frames=16,
data_meta_paths=None,
wav2vec_cfg=None,
):
super().__init__()
self.sample_rate = sample_rate
self.img_size = img_size
self.audio_margin = audio_margin
self.n_motion_frames = n_motion_frames
self.n_sample_frames = n_sample_frames
self.audio_type = wav2vec_cfg.audio_type
self.audio_model = wav2vec_cfg.model_scale
self.audio_features = wav2vec_cfg.features
vid_meta = []
for data_meta_path in data_meta_paths:
with open(data_meta_path, "r", encoding="utf-8") as f:
vid_meta.extend(json.load(f))
self.vid_meta = vid_meta
self.length = len(self.vid_meta)
self.pixel_transform = transforms.Compose(
[
transforms.Resize(self.img_size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
self.cond_transform = transforms.Compose(
[
transforms.Resize(self.img_size),
transforms.ToTensor(),
]
)
self.attn_transform_64 = transforms.Compose(
[
transforms.Resize((64,64)),
transforms.ToTensor(),
]
)
self.attn_transform_32 = transforms.Compose(
[
transforms.Resize((32, 32)),
transforms.ToTensor(),
]
)
self.attn_transform_16 = transforms.Compose(
[
transforms.Resize((16, 16)),
transforms.ToTensor(),
]
)
self.attn_transform_8 = transforms.Compose(
[
transforms.Resize((8, 8)),
transforms.ToTensor(),
]
)
def augmentation(self, images, transform, state=None):
"""
Apply the given transformation to the input images.
Args:
images (List[PIL.Image] or PIL.Image): The input images to be transformed.
transform (torchvision.transforms.Compose): The transformation to be applied to the images.
state (torch.ByteTensor, optional): The state of the random number generator.
If provided, it will set the RNG state to this value before applying the transformation. Defaults to None.
Returns:
torch.Tensor: The transformed images as a tensor.
If the input was a list of images, the tensor will have shape (f, c, h, w),
where f is the number of images, c is the number of channels, h is the height, and w is the width.
If the input was a single image, the tensor will have shape (c, h, w),
where c is the number of channels, h is the height, and w is the width.
"""
if state is not None:
torch.set_rng_state(state)
if isinstance(images, List):
transformed_images = [transform(img) for img in images]
ret_tensor = torch.stack(transformed_images, dim=0) # (f, c, h, w)
else:
ret_tensor = transform(images) # (c, h, w)
return ret_tensor
def __getitem__(self, index):
video_meta = self.vid_meta[index]
video_path = video_meta["video_path"]
mask_path = video_meta["mask_path"]
lip_mask_union_path = video_meta.get("sep_mask_lip", None)
face_mask_union_path = video_meta.get("sep_mask_face", None)
full_mask_union_path = video_meta.get("sep_mask_border", None)
face_emb_path = video_meta["face_emb_path"]
audio_emb_path = video_meta[
f"{self.audio_type}_emb_{self.audio_model}_{self.audio_features}"
]
tgt_mask_pil = Image.open(mask_path)
video_frames = VideoReader(video_path, ctx=cpu(0))
assert tgt_mask_pil is not None, "Fail to load target mask."
assert (video_frames is not None and len(video_frames) > 0), "Fail to load video frames."
video_length = len(video_frames)
assert (
video_length
> self.n_sample_frames + self.n_motion_frames + 2 * self.audio_margin
)
start_idx = random.randint(
self.n_motion_frames,
video_length - self.n_sample_frames - self.audio_margin - 1,
)
videos = video_frames[start_idx : start_idx + self.n_sample_frames]
frame_list = [
Image.fromarray(video).convert("RGB") for video in videos.asnumpy()
]
face_masks_list = [Image.open(face_mask_union_path)] * self.n_sample_frames
lip_masks_list = [Image.open(lip_mask_union_path)] * self.n_sample_frames
full_masks_list = [Image.open(full_mask_union_path)] * self.n_sample_frames
assert face_masks_list[0] is not None, "Fail to load face mask."
assert lip_masks_list[0] is not None, "Fail to load lip mask."
assert full_masks_list[0] is not None, "Fail to load full mask."
face_emb = torch.load(face_emb_path)
audio_emb = torch.load(audio_emb_path)
indices = (
torch.arange(2 * self.audio_margin + 1) - self.audio_margin
) # Generates [-2, -1, 0, 1, 2]
center_indices = torch.arange(
start_idx,
start_idx + self.n_sample_frames,
).unsqueeze(1) + indices.unsqueeze(0)
audio_tensor = audio_emb[center_indices]
ref_img_idx = random.randint(
self.n_motion_frames,
video_length - self.n_sample_frames - self.audio_margin - 1,
)
ref_img = video_frames[ref_img_idx].asnumpy()
ref_img = Image.fromarray(ref_img)
if self.n_motion_frames > 0:
motions = video_frames[start_idx - self.n_motion_frames : start_idx]
motion_list = [
Image.fromarray(motion).convert("RGB") for motion in motions.asnumpy()
]
# transform
state = torch.get_rng_state()
pixel_values_vid = self.augmentation(frame_list, self.pixel_transform, state)
pixel_values_mask = self.augmentation(tgt_mask_pil, self.cond_transform, state)
pixel_values_mask = pixel_values_mask.repeat(3, 1, 1)
pixel_values_face_mask = [
self.augmentation(face_masks_list, self.attn_transform_64, state),
self.augmentation(face_masks_list, self.attn_transform_32, state),
self.augmentation(face_masks_list, self.attn_transform_16, state),
self.augmentation(face_masks_list, self.attn_transform_8, state),
]
pixel_values_lip_mask = [
self.augmentation(lip_masks_list, self.attn_transform_64, state),
self.augmentation(lip_masks_list, self.attn_transform_32, state),
self.augmentation(lip_masks_list, self.attn_transform_16, state),
self.augmentation(lip_masks_list, self.attn_transform_8, state),
]
pixel_values_full_mask = [
self.augmentation(full_masks_list, self.attn_transform_64, state),
self.augmentation(full_masks_list, self.attn_transform_32, state),
self.augmentation(full_masks_list, self.attn_transform_16, state),
self.augmentation(full_masks_list, self.attn_transform_8, state),
]
pixel_values_ref_img = self.augmentation(ref_img, self.pixel_transform, state)
pixel_values_ref_img = pixel_values_ref_img.unsqueeze(0)
if self.n_motion_frames > 0:
pixel_values_motion = self.augmentation(
motion_list, self.pixel_transform, state
)
pixel_values_ref_img = torch.cat(
[pixel_values_ref_img, pixel_values_motion], dim=0
)
sample = {
"video_dir": video_path,
"pixel_values_vid": pixel_values_vid,
"pixel_values_mask": pixel_values_mask,
"pixel_values_face_mask": pixel_values_face_mask,
"pixel_values_lip_mask": pixel_values_lip_mask,
"pixel_values_full_mask": pixel_values_full_mask,
"audio_tensor": audio_tensor,
"pixel_values_ref_img": pixel_values_ref_img,
"face_emb": face_emb,
}
return sample
def __len__(self):
return len(self.vid_meta)
|