segmentation fault (core dumped)

[birdflyto]

i have tested on several cuda,cudnn and pytorch version ,the latest vesion is pytorch1.0.1 cuda9.0 cudnn7.1.2,but all the version met the same error(segmentation fault(core dumped)). i have no idea to solve the problem. Many thanks!!!

python train.py --name flame --dataroot ./datasets/ --input_nc 3 --ngf 128 --max_frames_per_gpu 4 --n_frames_total 15 --niter 20 --niter_decay 20 --save_epoch_freq 10 ------------ Options ------------- TTUR: False add_face_disc: False basic_point_only: False batchSize: 1 beta1: 0.5 checkpoints_dir: ./checkpoints continue_train: False dataroot: ./datasets/ dataset_mode: temporal debug: False densepose_only: False display_freq: 100 display_id: 0 display_winsize: 512 feat_num: 3 fg: False fg_labels: [26] fineSize: 512 fp16: False gan_mode: ls gpu_ids: [0] input_nc: 3 isTrain: True label_feat: False label_nc: 0 lambda_F: 10.0 lambda_T: 10.0 lambda_feat: 10.0 loadSize: 512 load_features: False load_pretrain: local_rank: 0 lr: 0.0002 max_dataset_size: inf max_frames_backpropagate: 1 max_frames_per_gpu: 4 max_t_step: 1 model: vid2vid nThreads: 2 n_blocks: 9 n_blocks_local: 3 n_downsample_E: 3 n_downsample_G: 3 n_frames_D: 3 n_frames_G: 3 n_frames_total: 15 n_gpus_gen: 1 n_layers_D: 3 n_local_enhancers: 1 n_scales_spatial: 1 n_scales_temporal: 2 name: flame ndf: 64 nef: 32 netE: simple netG: composite ngf: 128 niter: 20 niter_decay: 20 niter_fix_global: 0 niter_step: 5 no_canny_edge: False no_dist_map: False no_first_img: False no_flip: False no_flow: False no_ganFeat: False no_html: False no_vgg: False norm: batch num_D: 2 openpose_only: False output_nc: 3 phase: train pool_size: 1 print_freq: 100 random_drop_prob: 0.05 random_scale_points: False remove_face_labels: False resize_or_crop: scaleWidth save_epoch_freq: 10 save_latest_freq: 1000 serial_batches: False sparse_D: False tf_log: False use_instance: False use_single_G: False which_epoch: latest -------------- End ---------------- CustomDatasetDataLoader dataset [TemporalDataset] was created

training videos = 1

vid2vid ---------- Networks initialized -------------

---------- Networks initialized -------------

create web directory ./checkpoints/flame/web... Segmentation fault (core dumped)

[Ha0Tang]

+1

[birdflyto]

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是环境配置的问题，可以参考我的环境配置

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On 3/18/2020 21:31，Hao Tang<notifications@github.com> wrote： 

+1

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[birdflyto]

i've solved it,it's the misbatch of the enviornment.you can refer to my enviornment.yaml`` name: pytorch1.0 channels:

https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch anaconda https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/ https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/ https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ defaults dependencies: _libgcc_mutex=0.1=main blas=1.0=mkl ca-certificates=2019.11.27=0 certifi=2019.11.28=py36_0 cffi=1.13.2=py36h2e261b9_0 cuda100=1.0=0 cudatoolkit=9.0=h13b8566_0 freetype=2.5.5=2 intel-openmp=2019.4=243 jbig=2.1=hdba287a_0 jpeg=9b=h024ee3a_2 libedit=3.1.20181209=hc058e9b_0 libffi=3.2.1=hd88cf55_4 libgcc-ng=9.1.0=hdf63c60_0 libgfortran-ng=7.3.0=hdf63c60_0 libpng=1.6.37=hbc83047_0 libstdcxx-ng=9.1.0=hdf63c60_0 libtiff=4.0.9=he85c1e1_2 mkl=2018.0.3=1 mkl-service=1.1.2=py36h90e4bf4_5 mkl_fft=1.0.6=py36h7dd41cf_0 mkl_random=1.0.1=py36h4414c95_1 ncurses=6.1=he6710b0_1 ninja=1.9.0=py36hfd86e86_0 numpy=1.15.4=py36h1d66e8a_0 numpy-base=1.15.4=py36h81de0dd_0 olefile=0.46=py_0 openssl=1.0.2u=h7b6447c_0 pip=19.3.1=py36_0 pycparser=2.19=py_0 python=3.6.3=h6c0c0dc_5 pytorch=1.0.0=py3.6_cuda10.0.130_cudnn7.4.1_1 readline=7.0=h7b6447c_5 setuptools=42.0.2=py36_0 six=1.13.0=py36_0 sqlite=3.30.1=h7b6447c_0 tbb=2019.8=hfd86e86_0 tbb4py=2019.8=py36hfd86e86_0 tk=8.6.8=hbc83047_0 wheel=0.33.6=py36_0 xz=5.2.4=h14c3975_4 zlib=1.2.11=h7b6447c_3 zstd=1.3.3=h84994c4_0 pip: channelnorm-cuda==0.0.0 chardet==3.0.4 correlation-cuda==0.0.0 cycler==0.10.0 decorator==4.4.1 dominate==2.4.0 idna==2.8 imageio==2.8.0 kiwisolver==1.1.0 matplotlib==3.1.3 networkx==2.4 opencv-python==4.1.2.30 pillow==6.1.0 pyparsing==2.4.6 python-dateutil==2.8.1 pytz==2019.3 pywavelets==1.1.1 requests==2.22.0 resample2d-cuda==0.0.0 scikit-image==0.16.2 scipy==1.4.1 torchvision==0.2.1 tqdm==4.19.9 urllib3==1.25.7