slowfast 跑多卡的时候遇到问题_slow fast 无法使用多卡

问题描述：

在57 服务器上跑：

python tools/run_net.py --cfg configs/Kinetics/X3D_XS.yaml NUM_GPUS 4 TRAIN.BATCH_SIZE 8 SOLVER.BASE_LR 0.0125 DATA.PATH_TO_DATA_DIR /mnt/data/geguojing/Heart_data/annotations20201112

 

目前这个问题没有解决，57 的服务器一直在被占用。调试不了 

185 上的出现的错误是：

对比了自己的代码和测试代码：

import torch
import utils.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data.sampler import RandomSampler
from torch.utils.data._utils.collate import default_collate
import pdb
 
def manu_collate(batch):
    inputs, bbox = zip(*batch)
    inputs = default_collate(inputs)
    collate_bbox = [i[0] for i in bbox]
    return inputs, collate_bbox
class AVA_DATA(torch.utils.data.DataLoader):
    def __init__(self):
        print('heihei')
        pass
    def __getitem__(self, index):
        return torch.ones(3,224,224)*index, [index]
        # return torch.ones(3,224,224)*indx
    def __len__(self):
        return 16
 
 
class AVA_MODEL(torch.nn.Module):
    def __init__(self):
        super(AVA_MODEL, self).__init__()
        self.conv1 = torch.nn.Conv2d(3,64,3,1)
        self.tmp = None
        # for i in range(20):
        #     self.tmp[i]=0
    def forward(self,x,bbox,tmp):
 
        # print(x.device.index)
        self.tmp.append(1)
        if x.device.index==0:
            print(self.tmp)
            # tmp.append(1)
            # print(tmp)
        # # if 1:
        #     print('***start***')
        #     print(self.tmp)
        #     for j in bbox:
        #         self.tmp[j]=j
        #     print('***end***')
        #     print(bbox, self.tmp)
        # self.tmp.append(1)
        # print(self.tmp)
        # tmp.append(1)
        # print(tmp)
        # print(len(bbox))
        res = self.conv1(x)
        return res
    
 
def run(
    local_rank, num_proc, func, init_method, shard_id, num_shards, backend,
):
 
    # Initialize the process group.
    world_size = num_proc * num_shards
    rank = shard_id * num_proc + local_rank
 
    try:
        torch.distributed.init_process_group(
            backend=backend,
            init_method=init_method,
            world_size=world_size,
            rank=rank,
        )
    except Exception as e:
        raise e
 
    torch.cuda.set_device(local_rank)
    func()
 
 
def launch_job(func, init_method='tcp://localhost:9999', daemon=False, dis=True):
    """
    Run 'func' on one or more GPUs, specified in cfg
    Args:
        cfg (CfgNode): configs. Details can be found in
            slowfast/config/defaults.py
        init_method (str): initialization method to launch the job with multiple
            devices.
        func (function): job to run on GPU(s)
        daemon (bool): The spawned processes’ daemon flag. If set to True,
            daemonic processes will be created
    """
    if dis:
        torch.multiprocessing.spawn(
            run,
            nprocs=4,
            args=(
                4,
                func,
                init_method,
                0,
                1,
                'nccl',
                
               
            ),
            daemon=daemon,
        )
    else:
        func()
 
def train():
    model = AVA_MODEL()
    cur_device = torch.cuda.current_device()
    model = model.cuda(device=cur_device)
    # model = torch.nn.DataParallel(model).cuda()
    model = torch.nn.parallel.DistributedDataParallel(
            module=model, device_ids=[cur_device], output_device=cur_device)
    dataset = AVA_DATA()
    dataloader = torch.utils.data.DataLoader(
            dataset,
            batch_size=1,
            shuffle=False,
            sampler=DistributedSampler(dataset),
            # sampler=None,
            num_workers=8,
            pin_memory=True,
            drop_last=True,
            collate_fn=manu_collate,
            # worker_init_fn=None,
        )
    tmp = []
    model.train()
    for i in range(200000000):
        shuffle_dataset(dataloader,i)
        for iter, (inputs,bbox) in enumerate(dataloader):
            # print(i,iter)
            # inputs = inputs.cuda(non_blocking=True)
            # res = model(inputs,bbox,tmp)
            for j in bbox:
                tmp.append(j)
        dist.synchronize()
        tmp = dist.all_gather_unaligned(tmp)
        if dist.get_rank()==0:
            print(iter,tmp)
        break
    model.module.tmp = tmp
    for i in range(2):
        for iter, (inputs,bbox) in enumerate(dataloader):
            # print(i,iter)
            inputs = inputs.cuda(non_blocking=True)
            res = model(inputs,bbox,tmp)
            # tmp.append(1)
def shuffle_dataset(loader, cur_epoch):
    """"
    Shuffles the data.
    Args:
        loader (loader): data loader to perform shuffle.
        cur_epoch (int): number of the current epoch.
    """
    # sampler = (
    #     loader.batch_sampler.sampler
    #     if isinstance(loader.batch_sampler, ShortCycleBatchSampler)
    #     else loader.sampler
    # )
    # assert isinstance(
    #     sampler, (RandomSampler, DistributedSampler)
    # ), "Sampler type '{}' not supported".format(type(sampler))
    # # RandomSampler handles shuffling automatically
    sampler = loader.sampler
    if isinstance(sampler, DistributedSampler):
        # DistributedSampler shuffles data based on epoch
        sampler.set_epoch(cur_epoch)
if __name__ == "__main__":
    tmp = dict()
    pdb.set_trace()
    for i in range(20):
        tmp[i]=0
    pdb.set_trace()
    launch_job(train)    
    # train(tmp)
        

发现init_method 程序里面没有定义 

给init_method 一个定义：tcp://localhost：9999

 

查看每一个函数定义与测试代码的异同点

cfg.SHRAD_ID 0

cfg.NUM_SHARDS 1

cfg.DIST_BACKEND 为nccl

57上的nccl 的问题 不知道修改这个是不是就可以了

进到run 里面print 打印 

总结下来就是：不知道改了哪里就好了

pdb去掉就好了

最好是加打印输出