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Segmentation fault (core dumped) training on 4 gpus #52

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robotzheng opened this issue Jun 28, 2019 · 5 comments
Closed

Segmentation fault (core dumped) training on 4 gpus #52

robotzheng opened this issue Jun 28, 2019 · 5 comments

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@robotzheng
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[root@A01-R04-I221-51 codes]# python3 train.py -opt options/train/train_EDVR_woTSA_M_vimeo90k.yml
export CUDA_VISIBLE_DEVICES=0,1,2,3
Disabled distributed training.
Path already exists. Rename it to [/home/zzt/EDVR/experiments/001_EDVRwoTSA_scratch_lr4e-4_600k_vimeo90k_LrCAR4S_archived_190628-144447]
19-06-28 14:44:47.113 - INFO: name: 001_EDVRwoTSA_scratch_lr4e-4_600k_vimeo90k_LrCAR4S
use_tb_logger: True
model: VideoSR_base
distortion: sr
scale: 4
gpu_ids: [0, 1, 2, 3]
datasets:[
train:[
name: Vimeo90K
mode: Vimeo90K
interval_list: [1]
random_reverse: False
border_mode: False
dataroot_GT: /home/zzt/EDVR/datasets/vimeo90k_train_GT.lmdb
dataroot_LQ: /home/zzt/EDVR/datasets/vimeo90k_train_LR7frames.lmdb
cache_keys: Vimeo90K_train_keys.pkl
N_frames: 5
use_shuffle: True
n_workers: 3
batch_size: 32
GT_size: 256
LQ_size: 64
use_flip: True
use_rot: True
color: RGB
phase: train
scale: 4
data_type: lmdb
]
]
network_G:[
which_model_G: EDVR
nf: 64
nframes: 5
groups: 8
front_RBs: 5
back_RBs: 10
predeblur: False
HR_in: False
w_TSA: False
scale: 4
]
path:[
pretrain_model_G: None
strict_load: True
resume_state: None
root: /home/zzt/EDVR
experiments_root: /home/zzt/EDVR/experiments/001_EDVRwoTSA_scratch_lr4e-4_600k_vimeo90k_LrCAR4S
models: /home/zzt/EDVR/experiments/001_EDVRwoTSA_scratch_lr4e-4_600k_vimeo90k_LrCAR4S/models
training_state: /home/zzt/EDVR/experiments/001_EDVRwoTSA_scratch_lr4e-4_600k_vimeo90k_LrCAR4S/training_state
log: /home/zzt/EDVR/experiments/001_EDVRwoTSA_scratch_lr4e-4_600k_vimeo90k_LrCAR4S
val_images: /home/zzt/EDVR/experiments/001_EDVRwoTSA_scratch_lr4e-4_600k_vimeo90k_LrCAR4S/val_images
]
train:[
lr_G: 0.0004
lr_scheme: CosineAnnealingLR_Restart
beta1: 0.9
beta2: 0.99
niter: 600000
warmup_iter: -1
T_period: [150000, 150000, 150000, 150000]
restarts: [150000, 300000, 450000]
restart_weights: [1, 1, 1]
eta_min: 1e-07
pixel_criterion: cb
pixel_weight: 1.0
val_freq: 2000.0
manual_seed: 0
]
logger:[
print_freq: 1
save_checkpoint_freq: 2000.0
]
is_train: True
dist: False

19-06-28 14:44:50.625 - INFO: Random seed: 0
19-06-28 14:44:50.628 - INFO: Temporal augmentation interval list: [1], with random reverse is False.
19-06-28 14:44:50.629 - INFO: Using cache keys: Vimeo90K_train_keys.pkl
19-06-28 14:44:50.629 - INFO: Using cache keys - Vimeo90K_train_keys.pkl.
19-06-28 14:44:50.638 - INFO: Dataset [Vimeo90KDataset - Vimeo90K] is created.
19-06-28 14:44:50.638 - INFO: Number of train images: 64,612, iters: 2,020
19-06-28 14:44:50.638 - INFO: Total epochs needed: 298 for iters 600,000
19-06-28 14:44:53.798 - INFO: Network G structure: DataParallel - EDVR, with parameters: 2,996,259
19-06-28 14:44:53.799 - INFO: EDVR(
(conv_first): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(feature_extraction): Sequential(
(0): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(fea_L2_conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(fea_L2_conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fea_L3_conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(fea_L3_conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(pcd_align): PCD_Align(
(L3_offset_conv1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(L3_offset_conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(L3_dcnpack): DCN_sep(
(conv_offset_mask): Conv2d(64, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(L2_offset_conv1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(L2_offset_conv2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(L2_offset_conv3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(L2_dcnpack): DCN_sep(
(conv_offset_mask): Conv2d(64, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(L2_fea_conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(L1_offset_conv1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(L1_offset_conv2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(L1_offset_conv3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(L1_dcnpack): DCN_sep(
(conv_offset_mask): Conv2d(64, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(L1_fea_conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(cas_offset_conv1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(cas_offset_conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(cas_dcnpack): DCN_sep(
(conv_offset_mask): Conv2d(64, 216, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(lrelu): LeakyReLU(negative_slope=0.1, inplace)
)
(tsa_fusion): Conv2d(320, 64, kernel_size=(1, 1), stride=(1, 1))
(recon_trunk): Sequential(
(0): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(2): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(3): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(5): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(6): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(7): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(8): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(9): ResidualBlock_noBN(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
(upconv1): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(upconv2): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(pixel_shuffle): PixelShuffle(upscale_factor=2)
(HRconv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv_last): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(lrelu): LeakyReLU(negative_slope=0.1, inplace)
)
19-06-28 14:44:53.801 - INFO: Model [VideoSRBaseModel] is created.
19-06-28 14:44:53.802 - INFO: Start training from epoch: 0, iter: 0
Segmentation fault (core dumped)
@XiZhang93
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I have met the same problem, how to solve it?

@xinntao
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xinntao commented Aug 5, 2019

I have not encountered this problem.
You may try to google it OR reinstall pytorch and reinstall the dcn package OR install the pytorch from source codes.

@llp1996
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llp1996 commented Sep 4, 2019

i meet the same problem ,,have you fixed it?

@xinntao
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xinntao commented Sep 6, 2019 •
edited

@llp1996
Try to use pytorch1.1 version？

@llp1996
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llp1996 commented Sep 11, 2019

@llp1996
Try to use pytorch1.1 version？

i have already use pytorch 1.1

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4 participants
@robotzheng @XiZhang93 @xinntao @llp1996