yangninghua

``` def training(self, epoch): train_loss = 0.0 self.model.train() for m in self.model.modules(): if isinstance(m, SynchronizedBatchNorm2d): m.eval() elif isinstance(m, nn.BatchNorm2d): m.eval() tbar = tqdm(self.train_loader) or def training(self, epoch): train_loss = 0.0...

``` def make_data_loader(args, **kwargs): if args.dataset == 'pascal': train_set = pascal.VOCSegmentation(args, split='train') val_set = pascal.VOCSegmentation(args, split='val') if args.use_sbd: sbd_train = sbd.SBDSegmentation(args, split=['train', 'val']) train_set = combine_dbs.CombineDBs([train_set, sbd_train], excluded=[val_set]) num_class =...

1

pytorch1.1.0 Ubuntu16.04 me too

``` import argparse import time import datetime import os import shutil import sys cur_path = os.path.abspath(os.path.dirname(__file__)) root_path = os.path.split(cur_path)[0] sys.path.append(root_path) import torch import torch.nn as nn import torch.utils.data as data...

``` nohup /root/train/results/ynh_copy/anaconda3_py3.7/bin/python train_new.py \ --model deeplabv3 \ --backbone resnet50 \ --dataset pascal_voc \ --lr 0.01 \ --epochs 80 \ --gpu-ids 0,1,2,3 \ --batch_size 16 #>out.log 2>&1 & ```

I modified the code to make it possible to multi-GPU parallel, but using: Self.model = nn.DataParallel(self.model, device_ids=args.gpu_ids)


 I don't know if this is correct

> 只能识别单个字吗？ 请问编译完怎么测试