aigc-apps-EasyAnimate/easyanimate/data/dataset_video.py

import csv
import gc
import io
import json
import math
import os
import random
from contextlib import contextmanager
from threading import Thread

import albumentations
import cv2
import numpy as np
import torch
import torchvision.transforms as transforms
from decord import VideoReader
from einops import rearrange
from func_timeout import FunctionTimedOut, func_timeout
from PIL import Image
from torch.utils.data import BatchSampler, Sampler
from torch.utils.data.dataset import Dataset

VIDEO_READER_TIMEOUT = 20

def get_random_mask(shape):
    f, c, h, w = shape
    
    mask_index = np.random.randint(0, 4)
    mask = torch.zeros((f, 1, h, w), dtype=torch.uint8)
    if mask_index == 0:
        mask[1:, :, :, :] = 1
    elif mask_index == 1:
        mask_frame_index = 1
        mask[mask_frame_index:-mask_frame_index, :, :, :] = 1
    elif mask_index == 2:
        center_x = torch.randint(0, w, (1,)).item()
        center_y = torch.randint(0, h, (1,)).item()
        block_size_x = torch.randint(w // 4, w // 4 * 3, (1,)).item()  # 方块的宽度范围
        block_size_y = torch.randint(h // 4, h // 4 * 3, (1,)).item()  # 方块的高度范围

        start_x = max(center_x - block_size_x // 2, 0)
        end_x = min(center_x + block_size_x // 2, w)
        start_y = max(center_y - block_size_y // 2, 0)
        end_y = min(center_y + block_size_y // 2, h)
        mask[:, :, start_y:end_y, start_x:end_x] = 1
    elif mask_index == 3:
        center_x = torch.randint(0, w, (1,)).item()
        center_y = torch.randint(0, h, (1,)).item()
        block_size_x = torch.randint(w // 4, w // 4 * 3, (1,)).item()  # 方块的宽度范围
        block_size_y = torch.randint(h // 4, h // 4 * 3, (1,)).item()  # 方块的高度范围

        start_x = max(center_x - block_size_x // 2, 0)
        end_x = min(center_x + block_size_x // 2, w)
        start_y = max(center_y - block_size_y // 2, 0)
        end_y = min(center_y + block_size_y // 2, h)

        mask_frame_before = np.random.randint(0, f // 2)
        mask_frame_after = np.random.randint(f // 2, f)
        mask[mask_frame_before:mask_frame_after, :, start_y:end_y, start_x:end_x] = 1
    else:
        raise ValueError(f"The mask_index {mask_index} is not define")
    return mask


@contextmanager
def VideoReader_contextmanager(*args, **kwargs):
    vr = VideoReader(*args, **kwargs)
    try:
        yield vr
    finally:
        del vr
        gc.collect()


def get_video_reader_batch(video_reader, batch_index):
    frames = video_reader.get_batch(batch_index).asnumpy()
    return frames


class WebVid10M(Dataset):
    def __init__(
            self,
            csv_path, video_folder,
            sample_size=256, sample_stride=4, sample_n_frames=16,
            enable_bucket=False, enable_inpaint=False, is_image=False,
        ):
        print(f"loading annotations from {csv_path} ...")
        with open(csv_path, 'r') as csvfile:
            self.dataset = list(csv.DictReader(csvfile))
        self.length = len(self.dataset)
        print(f"data scale: {self.length}")

        self.video_folder    = video_folder
        self.sample_stride   = sample_stride
        self.sample_n_frames = sample_n_frames
        self.enable_bucket   = enable_bucket
        self.enable_inpaint  = enable_inpaint
        self.is_image        = is_image
        
        sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size)
        self.pixel_transforms = transforms.Compose([
            transforms.Resize(sample_size[0]),
            transforms.CenterCrop(sample_size),
            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
        ])
    
    def get_batch(self, idx):
        video_dict = self.dataset[idx]
        videoid, name, page_dir = video_dict['videoid'], video_dict['name'], video_dict['page_dir']
        
        video_dir    = os.path.join(self.video_folder, f"{videoid}.mp4")
        video_reader = VideoReader(video_dir)
        video_length = len(video_reader)
        
        if not self.is_image:
            clip_length = min(video_length, (self.sample_n_frames - 1) * self.sample_stride + 1)
            start_idx   = random.randint(0, video_length - clip_length)
            batch_index = np.linspace(start_idx, start_idx + clip_length - 1, self.sample_n_frames, dtype=int)
        else:
            batch_index = [random.randint(0, video_length - 1)]

        if not self.enable_bucket:
            pixel_values = torch.from_numpy(video_reader.get_batch(batch_index).asnumpy()).permute(0, 3, 1, 2).contiguous()
            pixel_values = pixel_values / 255.
            del video_reader
        else:
            pixel_values = video_reader.get_batch(batch_index).asnumpy()

        if self.is_image:
            pixel_values = pixel_values[0]
        return pixel_values, name

    def __len__(self):
        return self.length

    def __getitem__(self, idx):
        while True:
            try:
                pixel_values, name = self.get_batch(idx)
                break

            except Exception as e:
                print("Error info:", e)
                idx = random.randint(0, self.length-1)

        if not self.enable_bucket:
            pixel_values = self.pixel_transforms(pixel_values)
        if self.enable_inpaint:
            mask = get_random_mask(pixel_values.size())
            mask_pixel_values = pixel_values * (1 - mask) + torch.ones_like(pixel_values) * -1 * mask
            sample = dict(pixel_values=pixel_values, mask_pixel_values=mask_pixel_values, mask=mask, text=name)
        else:
            sample = dict(pixel_values=pixel_values, text=name)
        return sample


class VideoDataset(Dataset):
    def __init__(
        self,
        json_path, video_folder=None,
        sample_size=256, sample_stride=4, sample_n_frames=16,
        enable_bucket=False, enable_inpaint=False
    ):
        print(f"loading annotations from {json_path} ...")
        self.dataset = json.load(open(json_path, 'r'))
        self.length = len(self.dataset)
        print(f"data scale: {self.length}")

        self.video_folder    = video_folder
        self.sample_stride   = sample_stride
        self.sample_n_frames = sample_n_frames
        self.enable_bucket   = enable_bucket
        self.enable_inpaint  = enable_inpaint
        
        sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size)
        self.pixel_transforms = transforms.Compose(
            [
                transforms.Resize(sample_size[0]),
                transforms.CenterCrop(sample_size),
                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
            ]
        )
    
    def get_batch(self, idx):
        video_dict = self.dataset[idx]
        video_id, name = video_dict['file_path'], video_dict['text']

        if self.video_folder is None:
            video_dir = video_id
        else:
            video_dir = os.path.join(self.video_folder, video_id)

        with VideoReader_contextmanager(video_dir, num_threads=2) as video_reader:
            video_length = len(video_reader)
        
            clip_length = min(video_length, (self.sample_n_frames - 1) * self.sample_stride + 1)
            start_idx   = random.randint(0, video_length - clip_length)
            batch_index = np.linspace(start_idx, start_idx + clip_length - 1, self.sample_n_frames, dtype=int)

            try:
                sample_args = (video_reader, batch_index)
                pixel_values = func_timeout(
                    VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
                )
            except FunctionTimedOut:
                raise ValueError(f"Read {idx} timeout.")
            except Exception as e:
                raise ValueError(f"Failed to extract frames from video. Error is {e}.")

            if not self.enable_bucket:
                pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous()
                pixel_values = pixel_values / 255.
                del video_reader
            else:
                pixel_values = pixel_values

            return pixel_values, name

    def __len__(self):
        return self.length

    def __getitem__(self, idx):
        while True:
            try:
                pixel_values, name = self.get_batch(idx)
                break

            except Exception as e:
                print("Error info:", e)
                idx = random.randint(0, self.length-1)

        if not self.enable_bucket:
            pixel_values = self.pixel_transforms(pixel_values)
        if self.enable_inpaint:
            mask = get_random_mask(pixel_values.size())
            mask_pixel_values = pixel_values * (1 - mask) + torch.ones_like(pixel_values) * -1 * mask
            sample = dict(pixel_values=pixel_values, mask_pixel_values=mask_pixel_values, mask=mask, text=name)
        else:
            sample = dict(pixel_values=pixel_values, text=name)
        return sample


if __name__ == "__main__":
    if 1:
        dataset = VideoDataset(
            json_path="/home/zhoumo.xjq/disk3/datasets/webvidval/results_2M_val.json",
            sample_size=256,
            sample_stride=4, sample_n_frames=16,
        )

    if 0:
        dataset = WebVid10M(
            csv_path="/mnt/petrelfs/guoyuwei/projects/datasets/webvid/results_2M_val.csv",
            video_folder="/mnt/petrelfs/guoyuwei/projects/datasets/webvid/2M_val",
            sample_size=256,
            sample_stride=4, sample_n_frames=16,
            is_image=False,
        )

    dataloader = torch.utils.data.DataLoader(dataset, batch_size=4, num_workers=0,)
    for idx, batch in enumerate(dataloader):
        print(batch["pixel_values"].shape, len(batch["text"]))