from PIL import Image from torchvision import transforms from transformers import OFATokenizer, OFAModel from transformers.models.ofa.generate import sequence_generator # from generate import sequence_generator import os.path from argparse import ArgumentParser from torch.utils import data import json import torch import torch.distributed as dist import os import os.path as osp from os.path import join as opj import pandas as pd from random import randint import cv2 import torch from torch.utils.data import Dataset, DataLoader import decord import glob import subprocess import time class my_dataset(Dataset): def __init__(self, args): super().__init__() self.args = args self.shuffle = True self.resolution = args.resolution if args.train_file.endswith('.csv'): self.train_file = pd.read_csv(args.train_file) elif args.train_file.endswith('.json'): # coco_vat_vat0_11_all_id_rootfolder_clsidx_spacy.json # 格式： id : { 'idx_list' : [0], 'root_folder' : 'coco_vat_9' } if hasattr(args, 'part_nums') and args.part_nums >1: self.part_nums = args.part_nums else: self.part_nums = 100000 self.part_index = args.part_index t1 = time.time() with open(args.train_file, 'r', encoding='utf-8') as f: self.train_file = json.load(f) if type(self.train_file) is str: self.train_file = json.loads(self.train_file) self.id_list = list(self.train_file.keys()) #============================= # obtain subset of self.id_list, so that deduplication time is less than 30min self.id_list = self.id_list[self.part_nums*(self.part_index-1):self.part_nums*self.part_index] print(f'Nums of train_file is {len(self.id_list)},part_index:{self.part_index}, first:{self.id_list[0]}') self.no_caption_id_list = [] self.exist_caption_path_list = {} for idx, id in enumerate(self.id_list): caption_json = osp.join('/apdcephfs_cq3/share_1311970/A_Youtube',self.train_file[id]['root_folder'],f'{id}_caption.json') mp4_path = osp.join('/apdcephfs_cq3/share_1311970/A_Youtube',self.train_file[id]['root_folder'],f'{id}.mp4') "existcap的数目包括video不存在的,所以有点虚大" if not os.path.exists(caption_json) and os.path.exists(mp4_path): self.no_caption_id_list.append(mp4_path) else: self.exist_caption_path_list[caption_json]=True if idx%10000==0: print(f'Time_cost:{time.time()-t1}s, idx:{idx}, caption_json:{caption_json}') print(f'Nums of no_caption_id_list is {len(self.no_caption_id_list)}, first:{self.no_caption_id_list[0]}') print(f'Nums of exist_caption_path_list is {len(self.exist_caption_path_list)}') if args.rank==0: success_file=f"part_{args.part_index}_success_nocap_{len(self.no_caption_id_list)}_existcap{len(self.exist_caption_path_list)}" os.system(f"touch {success_file}") t2 = time.time() print(f'Time cost:{t2-t1}s') # print('======',self.exist_file_list,'====',self.no_caption_id_list) mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] self.patch_resize_transform = transforms.Compose([ lambda image: image.convert("RGB"), transforms.Resize((self.resolution, self.resolution), interpolation=Image.BICUBIC), transforms.ToTensor(), transforms.Normalize(mean=mean, std=std) ]) print('Dataset nums is {}'.format(self.__len__())) time.sleep(10) def __len__(self): return len(self.no_caption_id_list) def random_sample(self): return self.__getitem__(randint(0, self.__len__() - 1)) def sequential_sample(self, idx): if idx >= self.__len__() - 1: return self.__getitem__(0) return self.__getitem__(idx + 1) def skip_sample(self, idx): if self.shuffle: return self.random_sample() return self.sequential_sample(idx=idx) def get_frames_from_video_opencv(self, batchsize=1, video_path=None, caption_nums_per_video=8): # 加载视频 video_path = video_path cap = cv2.VideoCapture(video_path) # 确定要提取的帧数 num_frames = caption_nums_per_video # 计算每隔多少帧提取一次 total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) step = total_frames // num_frames # 用于存储提取的图像的tensor frames = torch.empty(num_frames, 3, self.resolution, self.resolution) # 直接读取指定帧 for i in range(num_frames): # 计算要提取的帧的索引 idx = i * step # 设置当前帧为所需的帧 cap.set(cv2.CAP_PROP_POS_FRAMES, idx) # 读取该帧 ret, frame = cap.read() if not ret: break # 转换为PIL Image并进行缩放 Image_frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) frame = self.patch_resize_transform(Image_frame) # 将numpy数组转换为tensor并存储在frames中 frames[i] = frame # 打印输出frames的形状 # print(frames.shape) cap.release() return frames def get_frames_from_video(self, batchsize=1, video_path=None, caption_nums_per_video = 8, ): # 加载视频 video_path = video_path vr = decord.VideoReader(video_path) # 确定要提取的帧数 num_frames = caption_nums_per_video # 计算每隔多少帧提取一次 step = len(vr) // num_frames # 用于存储提取的图像的tensor frames = torch.empty(num_frames, 3, self.resolution, self.resolution) # 从视频中提取图像 for i in range(num_frames): # 计算要提取的帧的索引 idx = i * step # 从视频中读取帧 decord_frame = vr[idx].asnumpy() Image_frame = Image.fromarray(decord_frame) frame = self.patch_resize_transform(Image_frame)#.unsqueeze(0) # 将numpy数组转换为tensor并存储在frames中 frames[i] = frame # 打印输出frames的形状 # print(frames.shape) vr.close() return frames def __getitem__(self, idx): try: # video_id = self.filter_train_file[idx] # video_path = opj(self.vat_root, video_id) video_path = self.no_caption_id_list[idx] video_id = video_path.split('/')[-1].split('.')[0] # 假如多个程序一起跑，其他已经生成了，就跳过 caption_video_json = video_path.replace('.mp4', '_caption.json') if caption_video_json in self.exist_caption_path_list: print('parallel task has process it :{}, this is duplication!!!!!!!!!!!!!!!!!!!!!!!!!!'.format(caption_video_json)) # return '===========', None, torch.random(8,3,self.resolution, self.resolution) # return self.skip_sample(idx) # if os.path.exists(caption_video_json): # print('parallel task has process it :{}'.format(caption_video_json)) # # return '===========', None, torch.random(8,3,self.resolution, self.resolution) # return self.skip_sample(idx) if not osp.exists(video_path): print('video {} is not exists and skip this idx! '.format(video_path)) return self.skip_sample(idx) video_frames = self.get_frames_from_video_opencv( video_path = video_path, caption_nums_per_video = args.caption_nums_per_video) return video_id, video_path, video_frames except Exception as e: print('Read video error in {},{} and we have skip this !, this will not cause error!'.format(idx,e)) return self.skip_sample(idx) def synchronize(): if not dist.is_available(): return if not dist.is_initialized(): return world_size = dist.get_world_size() if world_size == 1: return dist.barrier() def ids_captions_save(args, video_ids, video_paths, caption_list): for i, video_path in enumerate(video_paths): caption_video_json = video_path.replace('.mp4', '_caption.json') video_16captions = caption_list[i * args.caption_nums_per_video : (i+1) * args.caption_nums_per_video] video_caption_dict = { video_ids[i] : video_16captions } if osp.exists(caption_video_json): print('{} is exist, please check your train file'.format(caption_video_json)) continue with open(caption_video_json, 'w', encoding = 'utf-8') as f: json.dump(video_caption_dict, f) print('Success :{}'.format(caption_video_json)) def ofa(args): """https://huggingface.co/OFA-Sys/ofa-large""" ######################################## model start ############################# ckpt_dir = 'OFA-Sys/ofa-large-caption' # ckpt_dir = 'ofa-large-caption' tokenizer = OFATokenizer.from_pretrained(ckpt_dir) # tokenizer = OFATokenizer.from_pretrained(ckpt_dir, use_fast=False) model = OFAModel.from_pretrained(ckpt_dir, use_cache=True).cuda(args.local_rank) if args.rank==0: print('模型初始化完成') model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank) model.eval() if args.rank==0: print('DDP model') ######################################## model over ############################# ######################################## dataset start ############################# if args.rank == 0: print('dataset 初始化') # 好像报错我记得 # if not osp.exists(args.exist_caption_id_list_json): # command = "find {} -name '*_caption.json'".format(args.vat_root) # output = subprocess.check_output(command, shell=True).decode().strip() # # 将输出结果按行拆分并保存到一个列表中 # file_list = output.split('\n') # # 将列表转换为JSON字符串 # json_list = json.dumps(file_list) # # 将JSON字符串写入文件 # json_path = args.exist_caption_id_list_json # with open(json_path, 'w', encoding='utf-8') as f: # f.write(json_list) # print('{} is saved, nums of caption file is {}'.format(json_path,len(file_list))) train_dataset = my_dataset(args) if args.rank == 0: print('dataset_len: ',train_dataset.__len__()) print('loading dataset is complete!') train_sampler = torch.utils.data.distributed.DistributedSampler( train_dataset, num_replicas=args.world_size, rank=args.rank ) if args.rank == 0: print('正在同步') synchronize() if args.rank == 0: print('dataloader 初始化') dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True, sampler=train_sampler, drop_last=True, ) ######################################## dataset over ############################# ######################################## ofa caption start ############################# txt = " what does the image describe?" inputs_ids = tokenizer([txt for i in range(args.batch_size * args.caption_nums_per_video)], return_tensors="pt").input_ids for index, (video_ids, video_paths, videos_frames) in enumerate(dataloader): bs, cap_nums, c, h, w = videos_frames.shape videos_frames = videos_frames.view(-1, c, h, w ) # import ipdb # ipdb.set_trace() gen = model.module.generate(inputs_ids.cuda(args.local_rank), patch_images=videos_frames.cuda(args.local_rank), num_beams=5, no_repeat_ngram_size=3) caption_list = tokenizer.batch_decode(gen, skip_special_tokens=True) ids_captions_save(args, video_ids, video_paths, caption_list) ######################################## ofa caption over ############################# def init_distributed_mode(args): args.rank = int(os.environ["RANK"]) args.world_size = int(os.environ['WORLD_SIZE']) args.local_rank = int(os.environ['LOCAL_RANK']) torch.cuda.set_device(args.local_rank) args.dist_backend = 'nccl' args.dist_url = 'env://' torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank, timeout=datetime.timedelta(seconds=5400)) torch.distributed.barrier() import utils.misc as misc def glob1(path = '/apdcephfs_cq3/share_1311970/A_Youtube/coco_vat', json_path = None): import time t1 = time.time() import glob file_list = glob.glob('{}/*_caption.json'.format(path), recursive=True) json_list = json.dumps(file_list) print(f'caption.json sum is {len(json_list)}') # 将JSON字符串写入文件 # json_path = 'coco_vat_exist_caption_id_list_03141026.json' with open(json_path, 'w', encoding='utf-8') as f: f.write(json_list) print('{} is saved, nums of caption file is {}'.format(json_path,len(file_list))) t2 = time.time() print('!!!!!!!!{}s'.format(t2-t1)) return file_list def main(args): # args.rank = int(os.environ['RANK']) # 获取当前进程号 # args.world_size = int(os.environ['WORLD_SIZE']) # args.local_rank = int(os.environ['LOCAL_RANK']) # torch.cuda.set_device(args.local_rank) # dist.init_process_group( # backend='nccl',init_method='env://',world_size=args.world_size,rank=args.rank # ) # assert torch.distributed.is_initialized() # dist.barrier() misc.init_distributed_mode(args) if args.rank == 0: print('进程组初始化完成') print("started") print("started caption_json count!") # glob1(json_path=args.exist_caption_id_list_json) # 'coco_vat_exist_caption_id_list_03141026.json' ###########################################################3 import time t1=time.time() ofa(args) t2 = time.time() if args.rank == 0: print('Time : ',t2-t1,' s') dist.destroy_process_group() # 销毁进程组 def test_dataset(args): # command = "find {} -name '*_caption.json'".format(args.vat_root) # output = subprocess.check_output(command, shell=True).decode().strip() # # 将输出结果按行拆分并保存到一个列表中 # file_list = output.split('\n') # # 将列表转换为JSON字符串 # json_list = json.dumps(file_list) # # 将JSON字符串写入文件 # json_path = args.exist_caption_id_list_json # with open(json_path, 'w', encoding='utf-8') as f: # f.write(json_list) # print('{} is saved, nums of caption file is {}'.format(json_path,len(file_list))) train_dataset = my_dataset(args) loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.num_workers) from time import time for i, sample in enumerate(loader): video_ids, video_paths, videos_frames = sample # import ipdb # ipdb.set_trace() print(i, video_ids, video_paths, videos_frames.shape) if __name__ == "__main__": import time # time.sleep(10000) import datetime # 获取当前时间 now = datetime.datetime.now() # 获取当前月份 month = now.month # 获取当前日期 day = now.day # 获取当前小时 hour = now.hour parser = ArgumentParser() parser.add_argument('--caption_nums_per_video', type=int, default=8, help='process rank') parser.add_argument('--batch_size', type=int, default=2) parser.add_argument('--train_file', type=str, required=True) parser.add_argument('--vat_root', type=str,default=None) parser.add_argument('--num_workers', type=int, default=1) parser.add_argument('--resolution', type=int, default=480) # parser.add_argument('--exist_caption_id_list_json', type=str, default=f'coco_vat_exist_caption_id_list_{month}{day}{hour}.json',help='') parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') parser.add_argument('--local_rank', default=-1, type=int) parser.add_argument('--dist_on_itp', action='store_true') # --dist_on_itp ddp parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') parser.add_argument('--distributed', default=True, help='url used to set up distributed training') parser.add_argument('--gpus', default=[0, 1, 2, 3], help='DP CUDA devices') parser.add_argument('--part_index', default=1, type=int, help='used to split train_file_id into different parts, and generate caption from part_index 1 to ....') parser.add_argument('--part_nums', default=1000, type=int, help='used to split train_file_id into different parts, and generate caption from part_index 1 to ....') args = parser.parse_args() main(args) success_file=f"part_{args.part_index}_success" os.system(f"touch {success_file}") """ python3 -m torch.distributed.launch --nproc_per_node 1 --master_port 29504 ofa_ddp.py \ --train_file "/apdcephfs_cq3/share_1311970/A_Youtube/coco_vat_890w_id_title_folderidx_merge.json" \ --num_workers 8 --batch_size 1 \ --part_index 11 \ --part_nums 10000 """