hallo/datasets/talk_video.py

# 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)