TypeError: expected str, bytes or os.PathLike object, not bool

[wjkbigface]

Traceback (most recent call last):
File "/root/autodl-fs/image-restoration-sde-main/codes/config/deblurring/train.py", line 319, in
main()
File "/root/autodl-fs/image-restoration-sde-main/codes/config/deblurring/train.py", line 52, in main
opt = option.parse(args.opt, is_train=True)
File "/root/autodl-fs/image-restoration-sde-main/codes/config/deblurring/options.py", line 62, in parse
opt["path"][key] = osp.expanduser(path)
File "/root/miniconda3/envs/irsde/lib/python3.10/posixpath.py", line 231, in expanduser
path = os.fspath(path)
TypeError: expected str, bytes or os.PathLike object, not bool

[Algolzw]

Hi, it seems a model path problem. Can you provide your config file used in training?

[wjkbigface]

general settings

name: ir-sde
use_tb_logger: true
model: denoising
distortion: deblur
gpu_ids: [0]

sde:
max_sigma: 10
T: 100
schedule: cosine # linear, cosine
eps: 0.005

degradation: # for some synthetic dataset that only have GTs

for denoising

sigma: 25
noise_type: G # Gaussian noise: G

for super-resolution

scale: 4

datasets

datasets:
train:
name: Train_Dataset
mode: LQGT
dataroot_GT: /root/autodl-fs/image-restoration-sde-main/datasets/blur/trainH/GT
dataroot_LQ: /root/autodl-fs/image-restoration-sde-main/datasets/blur/trainH/LQ

use_shuffle: true
n_workers: 4  # per GPU
batch_size: 4
GT_size: 128
LR_size: 128
use_flip: true
use_rot: true
color: RGB

val:
name: Val_Dataset
mode: LQGT
dataroot_GT: /root/autodl-fs/image-restoration-sde-main/datasets/blur/val/GT
dataroot_LQ: /root/autodl-fs/image-restoration-sde-main/datasets/blur/val/LQ

network structures

network_G:
which_model_G: ConditionalUNet
setting:
in_nc: 3
out_nc: 3
nf: 64
depth: 4

path

path:
pretrain_model_G: ~
strict_load: true
resume_state: true

training settings: learning rate scheme, loss

train:
optimizer: Adam # Adam, AdamW, Lion
lr_G: !!float 1e-4
lr_scheme: MultiStepLR
beta1: 0.9
beta2: 0.99
niter: 700000
warmup_iter: -1 # no warm up
lr_steps: [200000, 400000, 600000]
lr_gamma: 0.5
eta_min: !!float 1e-7

criterion

is_weighted: False
loss_type: l1
weight: 1.0

manual_seed: 0
val_freq: !!float 5e3

logger

logger:
print_freq: 100
save_checkpoint_freq: !!float 5e3

[Algolzw]

Hi, if you want to continue the training with the existing checkpoint please add the checkpoint path to resume_state (rather than true). Otherwise, you should set the resume_state to ~ (means none) to enable the training from scratch.

[wjkbigface]

谢谢，我试一下