causalvideovae/dataset/t2v_datasets.py

import json
import os, io, csv, math, random
import numpy as np
import torchvision
from einops import rearrange
from decord import VideoReader
from os.path import join as opj
import gc
import torch
import torchvision.transforms as transforms
from torch.utils.data.dataset import Dataset
from tqdm import tqdm
from PIL import Image

from ..utils.dataset_utils import DecordInit
from ..utils.utils import text_preprocessing


def random_video_noise(t, c, h, w):
    vid = torch.rand(t, c, h, w) * 255.0
    vid = vid.to(torch.uint8)
    return vid

class T2V_dataset(Dataset):
    def __init__(self, args, transform, temporal_sample, tokenizer):
        self.image_data = args.image_data
        self.video_data = args.video_data
        self.num_frames = args.num_frames
        self.transform = transform
        self.temporal_sample = temporal_sample
        self.tokenizer = tokenizer
        self.model_max_length = args.model_max_length
        self.v_decoder = DecordInit()

        self.vid_cap_list = self.get_vid_cap_list()
        
        self.use_image_num = args.use_image_num
        self.use_img_from_vid = args.use_img_from_vid
        if self.use_image_num != 0 and not self.use_img_from_vid:
            self.img_cap_list = self.get_img_cap_list()


    def __len__(self):
        return len(self.vid_cap_list)

    def __getitem__(self, idx):
        try:
        # import ipdb;ipdb.set_trace()
            video_data = self.get_video(idx)
            image_data = {}
            if self.use_image_num != 0 and self.use_img_from_vid:
                image_data = self.get_image_from_video(video_data)
            elif self.use_image_num != 0 and not self.use_img_from_vid:
                image_data = self.get_image(idx)
            else:
                raise NotImplementedError
            gc.collect()
            return dict(video_data=video_data, image_data=image_data)
        except Exception as e:
            # print(f'Error with {e}, {self.vid_cap_list[idx]}')
            if os.path.exists(self.vid_cap_list[idx]['path']) and '_resize_1080p' in self.vid_cap_list[idx]['path']:
                os.remove(self.vid_cap_list[idx]['path'])
                print('remove:', self.vid_cap_list[idx]['path'])
            return self.__getitem__(random.randint(0, self.__len__() - 1))

    def get_video(self, idx):
        # video = random.choice([random_video_noise(65, 3, 720, 360) * 255, random_video_noise(65, 3, 1024, 1024), random_video_noise(65, 3, 360, 720)])
        # # print('random shape', video.shape)
        # input_ids = torch.ones(1, 120).to(torch.long).squeeze(0)
        # cond_mask = torch.cat([torch.ones(1, 60).to(torch.long), torch.ones(1, 60).to(torch.long)], dim=1).squeeze(0)
        
        video_path = self.vid_cap_list[idx]['path']
        frame_idx = self.vid_cap_list[idx]['frame_idx']
        #print('before decord')
        video = self.decord_read(video_path, frame_idx)
        # video = self.tv_read(video_path, frame_idx)
        #print('after decord')
        video = self.transform(video)  # T C H W -> T C H W
        # del raw_video
        # gc.collect()
        # video = torch.rand(65, 3, 512, 512)
        #print('after transform')
        video = video.transpose(0, 1)  # T C H W -> C T H W
        text = self.vid_cap_list[idx]['cap']

        text = text_preprocessing(text)
        text_tokens_and_mask = self.tokenizer(
            text,
            max_length=self.model_max_length,
            padding='max_length',
            truncation=True,
            return_attention_mask=True,
            add_special_tokens=True,
            return_tensors='pt'
        )
        input_ids = text_tokens_and_mask['input_ids']
        cond_mask = text_tokens_and_mask['attention_mask']
        return dict(video=video, input_ids=input_ids, cond_mask=cond_mask)

    def get_image_from_video(self, video_data):
        select_image_idx = np.linspace(0, self.num_frames-1, self.use_image_num, dtype=int)
        assert self.num_frames >= self.use_image_num
        image = [video_data['video'][:, i:i+1] for i in select_image_idx]  # num_img [c, 1, h, w]
        input_ids = video_data['input_ids'].repeat(self.use_image_num, 1)  # self.use_image_num, l
        cond_mask = video_data['cond_mask'].repeat(self.use_image_num, 1)  # self.use_image_num, l
        return dict(image=image, input_ids=input_ids, cond_mask=cond_mask)

    def get_image(self, idx):
        idx = idx % len(self.img_cap_list)  # out of range
        image_data = self.img_cap_list[idx]  # [{'path': path, 'cap': cap}, ...]
        
        image = [Image.open(i['path']).convert('RGB') for i in image_data] # num_img [h, w, c]
        image = [torch.from_numpy(np.array(i)) for i in image] # num_img [h, w, c]
        image = [rearrange(i, 'h w c -> c h w').unsqueeze(0) for i in image] # num_img [1 c h w]
        image = [self.transform(i) for i in image]  # num_img [1 C H W] -> num_img [1 C H W]
        image = [i.transpose(0, 1) for i in image]  # num_img [1 C H W] -> num_img [C 1 H W]

        caps = [i['cap'] for i in image_data]
        text = [text_preprocessing(cap) for cap in caps]
        input_ids, cond_mask = [], []
        for t in text:
            text_tokens_and_mask = self.tokenizer(
                t,
                max_length=self.model_max_length,
                padding='max_length',
                truncation=True,
                return_attention_mask=True,
                add_special_tokens=True,
                return_tensors='pt'
            )
            input_ids.append(text_tokens_and_mask['input_ids'])
            cond_mask.append(text_tokens_and_mask['attention_mask'])
        input_ids = torch.cat(input_ids)  # self.use_image_num, l
        cond_mask = torch.cat(cond_mask)  # self.use_image_num, l
        return dict(image=image, input_ids=input_ids, cond_mask=cond_mask)

    def tv_read(self, path, frame_idx=None):
        vframes, aframes, info = torchvision.io.read_video(filename=path, pts_unit='sec', output_format='TCHW')
        total_frames = len(vframes)
        if frame_idx is None:
            start_frame_ind, end_frame_ind = self.temporal_sample(total_frames)
        else:
            start_frame_ind, end_frame_ind = frame_idx.split(':')
            start_frame_ind, end_frame_ind = int(start_frame_ind), int(end_frame_ind)
        # assert end_frame_ind - start_frame_ind >= self.num_frames
        frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, self.num_frames, dtype=int)
        # frame_indice = np.linspace(0, 63, self.num_frames, dtype=int)

        video = vframes[frame_indice]  # (T, C, H, W)

        return video

    def decord_read(self, path, frame_idx=None):
        decord_vr = self.v_decoder(path)
        total_frames = len(decord_vr)
        # Sampling video frames
        if frame_idx is None:
            start_frame_ind, end_frame_ind = self.temporal_sample(total_frames)
        else:
            start_frame_ind, end_frame_ind = frame_idx.split(':')
            start_frame_ind, end_frame_ind = int(start_frame_ind), int(end_frame_ind)
            start_frame_ind, end_frame_ind = int(start_frame_ind), int(start_frame_ind) + self.num_frames
        # assert end_frame_ind - start_frame_ind >= self.num_frames
        frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, self.num_frames, dtype=int)
        # frame_indice = np.linspace(0, 63, self.num_frames, dtype=int)

        video_data = decord_vr.get_batch(frame_indice).asnumpy()
        video_data = torch.from_numpy(video_data)
        video_data = video_data.permute(0, 3, 1, 2)  # (T, H, W, C) -> (T C H W)
        return video_data


    def get_vid_cap_list(self):
        vid_cap_lists = []
        with open(self.video_data, 'r') as f:
            folder_anno = [i.strip().split(',') for i in f.readlines() if len(i.strip()) > 0]
            # print(folder_anno)
        for folder, anno in folder_anno:
            with open(anno, 'r') as f:
                vid_cap_list = json.load(f)
            print(f'Building {anno}...')
            for i in tqdm(range(len(vid_cap_list))):
                path = opj(folder, vid_cap_list[i]['path'])
                if os.path.exists(path.replace('.mp4', '_resize_1080p.mp4')):
                    path = path.replace('.mp4', '_resize_1080p.mp4')
                vid_cap_list[i]['path'] = path

            vid_cap_lists += vid_cap_list
        return vid_cap_lists

    def get_img_cap_list(self):
        img_cap_lists = []
        with open(self.image_data, 'r') as f:
            folder_anno = [i.strip().split(',') for i in f.readlines() if len(i.strip()) > 0]
        for folder, anno in folder_anno:
            with open(anno, 'r') as f:
                img_cap_list = json.load(f)
            print(f'Building {anno}...')
            for i in tqdm(range(len(img_cap_list))):
                img_cap_list[i]['path'] = opj(folder, img_cap_list[i]['path'])
            img_cap_lists += img_cap_list
        img_cap_lists = [img_cap_lists[i: i+self.use_image_num] for i in range(0, len(img_cap_lists), self.use_image_num)]
        return img_cap_lists[:-1]  # drop last to avoid error length