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A_lmdb_VAT_function2.py
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A_lmdb_VAT_function2.py
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import argparse
import datetime
import json
import numpy as np
import os
import time
import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch.distributed as dist
import timm
import random
# wogaide
# assert timm.__version__ == "0.3.2" # version check
import timm.optim.optim_factory as optim_factory
import utils.misc as misc
from utils.misc import NativeScalerWithGradNormCount as NativeScaler
import sys
import numpy as np
import torch
# import torchaudio
from decord import VideoReader, cpu
from torch import optim
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoFeatureExtractor, VideoMAEFeatureExtractor, Trainer, HfArgumentParser, \
set_seed, is_torch_tpu_available, AutoConfig, AutoTokenizer, VideoMAEConfig, \
VideoMAEForPreTraining, ViTMAEForPreTraining, ViTMAEConfig, ViTModel
from datasets import load_dataset
import torchvision.transforms as V_T
# import torchaudio.transforms as A_T
from os.path import join as opj
from transformers.trainer_utils import get_last_checkpoint
import os
from utils.datacollator import DataCollator
from models.crossformer import cross_former
from arguments.data import DataTrainingArguments
from arguments.model import ModelArguments
import transformers
from utils.fea_extractor import AudioFeatureExtractor
from utils.general import sample_frame_indices
from engine_pretrain_VAT import train_one_epoch
import csv
import librosa
from utils.lmdb_class import lmdb_handle
import lmdb
from multiprocessing import Process
def csv_read(csv_path='train.csv'):
vat_list = []
with open(csv_path, encoding="utf8") as f:
csv_reader = csv.DictReader(f)
for line in csv_reader:
vat_list.append([line['video'],line['audio'],line['text']])
return vat_list
def get_args_parser():
parser = argparse.ArgumentParser('MAE pre-training', add_help=False)
# parser.add_argument('--batch_size', default=24, type=int,
# help='Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus')
parser.add_argument('--epochs', default=800, type=int)
parser.add_argument('--accum_iter', default=1, type=int,
help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)')
# Model parameters
parser.add_argument('--model', default='mae_vit_base_pixel2', type=str, metavar='MODEL',
help='Name of model to train')
parser.add_argument('--input_size', default=224, type=int,
help='images input size')
parser.add_argument('--mask_ratio', default=0.9, type=float,
help='Masking ratio (percentage of removed patches).')
parser.add_argument('--norm_pix_loss', action='store_true',default=True,#--norm_pix_loss
help='Use (per-patch) normalized pixels as targets for computing loss')
parser.set_defaults(norm_pix_loss=False)
# Optimizer parameters
parser.add_argument('--weight_decay', type=float, default=0.05,
help='weight decay (default: 0.05)')
parser.add_argument('--lr', type=float, default=None, metavar='LR',
help='learning rate (absolute lr)')
parser.add_argument('--blr', type=float, default=1e-3, metavar='LR',
help='base learning rate: absolute_lr = base_lr * total_batch_size / 256')
parser.add_argument('--min_lr', type=float, default=0., metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0')
parser.add_argument('--warmup_epochs', type=int, default=40, metavar='N',
help='epochs to warmup LR')
# Dataset parameters
parser.add_argument('--data_path', default='/remote-home/share/ImageNet-m/ImageNet2012/', type=str,
help='dataset path')
parser.add_argument('--output_dir', default='./output_dir',
help='path where to save, empty for no saving')
parser.add_argument('--log_dir', default='./output_dir',
help='path where to tensorboard log')
parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--resume', default='',
help='resume from checkpoint')
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='start epoch')
parser.add_argument('--num_workers', default=10, type=int)
parser.add_argument('--pin_mem', action='store_true',
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
parser.set_defaults(pin_mem=True)
parser.add_argument('--use_amp', default=True,
help='')
parser.add_argument('--world_size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--clip_grad', default=0.5, type=float)
parser.add_argument('--local_rank', default=-1, type=int)
parser.add_argument('--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')
#------------------------model start !--------------------------------------------
parser.add_argument('--fusion_model_name_or_path', default='google/vit-base-patch16-224-in21k',
help='')
parser.add_argument('--fusion_config_name', default='configs/crossformer/vit_config_tiny.json',
help='')
parser.add_argument('--text_model_name_or_path', default='microsoft/deberta-v2-xlarge',
help='')
parser.add_argument('--text_config_name', default='configs/text/bert_config_tiny.json',
help='')
parser.add_argument('--tokenizer_name', default='microsoft/deberta-v2-xlarge',
help='')
parser.add_argument('--audio_model_name_or_path', default='facebook/vit-mae-base',
help='')
parser.add_argument('--audio_config_name', default='configs/audio/mae_config_tiny.json',
help='')
parser.add_argument('--mae_feature_extractor_name', default='facebook/vit-mae-base',
help='')
parser.add_argument('--video_model_name_or_path', default='MCG-NJU/videomae-base',
help='')
parser.add_argument('--video_config_name', default='configs/video/videomae_config_tiny.json',
help='')
parser.add_argument('--videomae_feature_extractor_name', default='MCG-NJU/videomae-base',
help='')
parser.add_argument('--cache_dir', default='cache_dir',
help='')
#------------------------model over-----------------------------------------------------
#------------------------ data start! --------------------------------------------------
parser.add_argument('--audio_root', default='you_0_5',#you_0_5
help='')
parser.add_argument('--text_root', default='you_0_5',#you_0_5
help='')
parser.add_argument('--video_root', default='you_0_5',#you_0_5
help='')
parser.add_argument('--train_file', default='test_11111.csv', help='')
parser.add_argument('--validation_file', default='validation.csv',
help='')
parser.add_argument('--per_device_train_batch_size', default=1,type=int,
help='')
parser.add_argument('--contrastive_dim', default=768,type=int,
help='')
parser.add_argument('--contrastive_loss_before_fusion', default=False, type=bool,
help='')
parser.add_argument('--max_seq_length', default=None,
help='')
parser.add_argument('--pad_to_max_length', default=False,
help='')
parser.add_argument('--max_train_samples', default=None,
help='')
parser.add_argument('--do_train', default=True,
help='')
parser.add_argument('--mlm_probability', default=0.3,type=float,
help='')
parser.add_argument('--video_mask_ratio', default=0.8,type=float,
help='')
parser.add_argument('--audio_mask_ratio', default=0.3,type=float,
help='')
parser.add_argument('--vt_match_ratio', default=0.5,type=float,
help='')
#------------------------ data over! ----------------------------------------------------
#------------------------ lmdb start! ----------------------------------------------------
parser.add_argument('--lmdb_vat_folder', default='lmdb_you_0_5',type=str,
help='')
parser.add_argument('--part', default='0_5',type=str,
help='')
#------------------------ lmdb over! ----------------------------------------------------
return parser
def main(args):
#------------------------------ DDP init --------------------------------------------
if args.dist_url != "env://":
dist.init_process_group(
backend='nccl',
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank
)#初始化
assert dist.is_initialized()
if args.rank==0:
print('进程组初始化完成')
set_seed(args.world_size+args.seed)
torch.cuda.set_device(args.local_rank)
device = torch.device('cuda:{}'.format(torch.cuda.current_device()))
elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
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)
torch.distributed.init_process_group(backend='nccl', init_method="env://",
world_size=args.world_size, rank=args.rank)
# torch.distributed.barrier()
device = torch.device('cuda:{}'.format(torch.cuda.current_device()))
# import torch.nn as nn
# model = nn.Linear(1, 1, bias=False)
# model = torch.nn.parallel.DistributedDataParallel(model,device_ids=[args.local_rank],output_device=args.local_rank,find_unused_parameters=False)#
#------------------------------- training_logs start! -------------------------------------
model_args=args
data_args=args
training_args=args
audio_config = AutoConfig.from_pretrained(model_args.audio_config_name)
audio_feature_extractor = AudioFeatureExtractor()
text_config = AutoConfig.from_pretrained(model_args.text_config_name)
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)
## -----------video-----------
video_config = AutoConfig.from_pretrained(model_args.video_config_name)
video_feature_extractor = VideoMAEFeatureExtractor.from_pretrained(model_args.videomae_feature_extractor_name, cache_dir=model_args.cache_dir)
fusion_config = AutoConfig.from_pretrained(model_args.fusion_config_name)
#------------------------------- training_logs over! -------------------------------------
#---------------------------- video audio text function start!-------------------------------
def audio_function(data_args,rela_path, waveform_len=audio_config.image_size*audio_config.image_size):
def get_audio(path):
waveform, sample_rate = librosa.load(path)
# waveform = waveform.mean(0).numpy()
length = len(waveform)
if length >= waveform_len:
indices = sample_frame_indices(clip_len=waveform_len, frame_sample_rate=1, seg_len=length)
waveform = waveform[indices]
else:
waveform = np.hstack([waveform for _ in range(waveform_len // length)] + [waveform[:waveform_len % length]])
return waveform.reshape(1, audio_config.image_size, audio_config.image_size)
waveform = get_audio(opj(data_args.audio_root,rela_path))
pixel_values = audio_feature_extractor(waveforms=waveform, return_tensors="pt")['pixel_values']
id = rela_path.split('.mp3')[0][-11:]
return 'audio@{}'.format(id), pixel_values, id
padding = "max_length" if data_args.pad_to_max_length else True
if data_args.max_seq_length is None:
max_seq_length = tokenizer.model_max_length
if max_seq_length > 512:
max_seq_length = 512
else:
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
def tokenize_function(data_args, rela_path):
text = open(opj(data_args.text_root, rela_path), 'r').read()
try:
title = text.split('#')[0]
if title[-1]==' ':
title = title[:-1]
except:
print('This txt does not has a title!')
# 时间长度,第一行(title+cls), title_tokenization
# return {'text_{}'.format(path): pixel_values}
pixel_values = tokenizer(title, padding=padding, truncation=True, max_length=max_seq_length, return_attention_mask=True, return_tensors="pt")
id = rela_path.split('.txt')[0][-11:]
return 'text@{}'.format(id), pixel_values['input_ids'],id
def video_function(data_args,rela_path):
def read_video(file_path):
videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0))
videoreader.seek(0)
indices = sample_frame_indices(clip_len=video_config.num_frames, frame_sample_rate=4, seg_len=len(videoreader))
video = videoreader.get_batch(list(indices)).asnumpy()
return video
video = list(read_video(opj(data_args.video_root,rela_path)))
pixel_values = video_feature_extractor(video, return_tensors="pt")['pixel_values']
id = rela_path.split('.mp4')[0][-11:]
return 'video@{}'.format(id), pixel_values, id
#---------------------------- video audio text function over!-------------------------------
vat_rela_path_list = csv_read(args.train_file)
"""because the lmdb.open(lock=false) for multiprocessing, so we must
ensure the csv don't has the same video_id !!!!!!!
"""
# vat_rela_path_list = list(set(vat_rela_path_list))
per_rank_num = len(vat_rela_path_list)//args.world_size + 1
vat_rela_path_list.sort()# ensure the order of each rank
def per_rank_proc_op(rank, per_rank_num, pro_, pros):
assert args.part is not None, 'please add the youtube part number as args.part'
lmdb_vat_path= opj(args.lmdb_vat_folder,'youtube_part_{}_rank_{}_pro_{}'.format(args.part,args.rank,pro_))
os.makedirs(args.lmdb_vat_folder,exist_ok=True)
lmdb_vat=lmdb_handle(lmdb_vat_path)
print('lmdb {} has been created!'.format(lmdb_vat_path))
cnt = 0
vat_rela_path_list_rank = vat_rela_path_list[per_rank_num * (rank):per_rank_num * (rank + 1)]
vat_rela_path_list_rank.sort()
rank_len = len(vat_rela_path_list_rank) # 每个GPU分的class数目
per_pro = rank_len // pros + 1 # gpu上每个进程分的数目
vat_rela_path_list_rank_pro = vat_rela_path_list_rank[per_pro * (pro_):per_pro * (pro_ + 1)] # 指定GPU上每个pro分的class
print(f'rank:{rank},pro_:{pro_},list_len:{len(vat_rela_path_list_rank_pro)}')
cache = {}
#------------------------------------------ video-audio-text write into lmdb start!-------------------------------------
fail_cnt = 0
success_cnt = 0
for idx, vat_rela_path in enumerate(vat_rela_path_list_rank_pro):
# [1.mp4,1.mp3,1.txt]
# vat_rela_path = ['youtube_5/https___www_youtube_com_shorts_6YtjOlMfaqI.mp4', 'youtube_5/https___www_youtube_com_shorts_6YtjOlMfaqI.mp3', 'youtube_5/https___www_youtube_com_shorts_6YtjOlMfaqI.txt']
id_audio = 'audio@'+vat_rela_path[1].split('.mp3')[0][-11:]
if lmdb_vat.get(id_audio.encode()) is None:
try:
#[video_rela_path, audio_rela_path , text_rela_path ]
name_text, text_tokens, id_text = tokenize_function(args, vat_rela_path[2])
name_video, video_tokens, id_video = video_function(args, vat_rela_path[0])
name_audio, audio_tokens, id_audio = audio_function(args, vat_rela_path[1])
# assert id_text == id_audio == id_video, 'video-audio-text is not matched!!!!'
cache[name_audio]=audio_tokens
cache[name_text]=text_tokens
cache[name_video]=video_tokens
success_cnt += 1
print('rank {},pro {}, success:{}'.format(rank,pro_,success_cnt))
# print(name_video,'!!!!!')
if len(cache)%60==0:
lmdb_vat.add_tensors(cache)
print('rank {},pro {}, the {}th batches'.format(rank,pro_,idx//60))
cache = {}
except Exception as e:
print(vat_rela_path,e)
# MP4 error etc.
print('Rank:{},pro_:{}, lmdb_vat_write fail in csv line {}'.format(rank,pro_,idx))
fail_cnt+=1
if len(cache)!=0:
lmdb_vat.add_tensors(cache)
print('Rank:{},pro_:{} has been finished!,lmdb stat is {}'.format(args.rank,pro_, lmdb_vat.stat()))
#------------------------------------------ video-audio-text write into lmdb over!-------------------------------------
def per_rank_read(rank, per_rank_num, pros=None):
# 上面是rank-gpu 下面是每个rank进行多进程
process_list = []
for pro_ in range(pros): # 在最上面
p = Process(target=per_rank_proc_op, args=([rank, per_rank_num, pro_, pros]))
p.start()
process_list.append(p)
for j in process_list:
p.join()
per_rank_read(args.rank, per_rank_num, pros=32)
# t_gather = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat) + 2 * args.rank * (1+1j)
# return t_gather
# y = model(x)
# dist.barrier()
# per_rank_proc_op(0, per_rank_num, pro_=0, pros=1)
def test_lmdb(lmdb_vat_path):
env=lmdb.open(lmdb_vat_path)
txn = env.begin()
print(txn.get('video@-oIXEtE11-0'.encode()))
# {text:torch.LongTensor->np.int64, audio:torch.FloatTensor->np.float32, video: torch.FloatTensor->np.float32}
for key,value in txn.cursor():
key = key.decode()
if key.startswith('text'):
pixel_values = np.frombuffer(value,dtype=np.int64)#np.int32
elif key.startswith('video') :
pixel_values = np.frombuffer(value,dtype=np.float32)#np.int32
# pixel_values = pixel_values.reshape(-1,16,3,224,224)
elif key.startswith('audio'):
pixel_values = np.frombuffer(value,dtype=np.float32)#np.int32
# pixel_values = pixel_values.reshape(-1,1,224,224)
pixel_values = torch.from_numpy(pixel_values)
print(key,pixel_values)
# break
print(txn.stat())
if __name__ =='__main__':
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import warnings
warnings.filterwarnings("ignore")
# import pdb
# pdb.set_trace()
args = get_args_parser()
args = args.parse_args()
main(args)
import time
time.sleep(1000)
# t_gather = torch.tensor([1, 2], dtype=torch.cfloat).cuda(args.local_rank)
# tensor_list = [torch.zeros(2, dtype=torch.cfloat).cuda(args.local_rank) for _ in range(args.world_size)] #.cuda(args.local_rank)
# dist.all_gather(tensor_list, t_gather)
# print(tensor_list)
# print('11111111111')
# dist.destroy_process_group()#销毁进程组
# print('*'*50)
# test_lmdb(args.lmdb_vat_folder)