PermissionError: [Errno 13] Permission denied:

[sunsunshark]

When I started training using my data，such an error occurred
data:
centered: false
dataset: AAPM
image_size: 256
is_complex: false
is_multi: false
num_channels: 1
random_flip: true
root: /media/sun/tomo/AAPM_data/256
uniform_dequantization: false
device: !!python/object/apply:torch.device

• cuda
• 0
  eval:
  batch_size: 8
  begin_ckpt: 50
  bpd_dataset: test
  enable_bpd: false
  enable_loss: true
  enable_sampling: true
  end_ckpt: 96
  num_samples: 50000
  model:
  attention_type: ddpm
  attn_resolutions: !!python/tuple
  • 16
    beta_max: 20.0
    beta_min: 0.1
    ch_mult: !!python/tuple
  • 1
  • 2
  • 2
  • 2
    conditional: true
    conv_size: 3
    dropout: 0.0
    ema_rate: 0.999
    embedding_type: fourier
    fir: true
    fir_kernel:
  • 1
  • 3
  • 3
  • 1
    fourier_scale: 16
    init_scale: 0.0
    name: ncsnpp
    nf: 128
    nonlinearity: swish
    normalization: GroupNorm
    num_res_blocks: 4
    num_scales: 2000
    progressive: none
    progressive_combine: sum
    progressive_input: residual
    resamp_with_conv: true
    resblock_type: biggan
    scale_by_sigma: true
    sigma_max: 378
    sigma_min: 0.01
    skip_rescale: true
    optim:
    beta1: 0.9
    eps: 1.0e-08
    grad_clip: 1.0
    lr: 0.0002
    optimizer: Adam
    warmup: 5000
    weight_decay: 0
    sampling:
    corrector: langevin
    method: pc
    n_steps_each: 1
    noise_removal: true
    predictor: reverse_diffusion
    probability_flow: false
    snr: 0.075
    seed: 42
    training:
    batch_size: 1
    continuous: true
    epochs: 1000
    eval_freq: 100
    likelihood_weighting: false
    log_freq: 25
    reduce_mean: false
    sde: vesde
    snapshot_freq: 50000
    snapshot_freq_for_preemption: 5000
    snapshot_sampling: true

Traceback (most recent call last):
File "E:\py2\DiffusionMBIR-main\main.py", line 71, in
app.run(main)
File "D:\Anaconda\envs\diffusion-mbir\lib\site-packages\absl\app.py", line 312, in run
_run_main(main, args)
File "D:\Anaconda\envs\diffusion-mbir\lib\site-packages\absl\app.py", line 258, in _run_main
sys.exit(main(argv))
File "E:\py2\DiffusionMBIR-main\main.py", line 62, in main
run_lib.train(FLAGS.config, FLAGS.workdir)
File "E:\py2\DiffusionMBIR-main\run_lib.py", line 82, in train
state = restore_checkpoint(checkpoint_meta_dir, state, config.device)
File "E:\py2\DiffusionMBIR-main\utils.py", line 38, in restore_checkpoint
loaded_state = torch.load(ckpt_dir, map_location=device)
File "D:\Anaconda\envs\diffusion-mbir\lib\site-packages\torch\serialization.py", line 594, in load
with _open_file_like(f, 'rb') as opened_file:
File "D:\Anaconda\envs\diffusion-mbir\lib\site-packages\torch\serialization.py", line 230, in _open_file_like
return _open_file(name_or_buffer, mode)
File "D:\Anaconda\envs\diffusion-mbir\lib\site-packages\torch\serialization.py", line 211, in init
super(_open_file, self).init(open(name, mode))
PermissionError: [Errno 13] Permission denied: 'workdir\AAPM256\checkpoints-meta'

Process finished with exit code 1

How can I solve the problem?

[JoanNig]

When I started training using my data，such an error occurred data: centered: false dataset: AAPM image_size: 256 is_complex: false is_multi: false num_channels: 1 random_flip: true root: /media/sun/tomo/AAPM_data/256 uniform_dequantization: false device: !!python/object/apply:torch.device

• cuda

• 0
  eval:
  batch_size: 8
  begin_ckpt: 50
  bpd_dataset: test
  enable_bpd: false
  enable_loss: true
  enable_sampling: true
  end_ckpt: 96
  num_samples: 50000
  model:
  attention_type: ddpm
  attn_resolutions: !!python/tuple

  • 16
    beta_max: 20.0
    beta_min: 0.1
    ch_mult: !!python/tuple
  • 1
  • 2
  • 2
  • 2
    conditional: true
    conv_size: 3
    dropout: 0.0
    ema_rate: 0.999
    embedding_type: fourier
    fir: true
    fir_kernel:
  • 1
  • 3
  • 3
  • 1
    fourier_scale: 16
    init_scale: 0.0
    name: ncsnpp
    nf: 128
    nonlinearity: swish
    normalization: GroupNorm
    num_res_blocks: 4
    num_scales: 2000
    progressive: none
    progressive_combine: sum
    progressive_input: residual
    resamp_with_conv: true
    resblock_type: biggan
    scale_by_sigma: true
    sigma_max: 378
    sigma_min: 0.01
    skip_rescale: true
    optim:
    beta1: 0.9
    eps: 1.0e-08
    grad_clip: 1.0
    lr: 0.0002
    optimizer: Adam
    warmup: 5000
    weight_decay: 0
    sampling:
    corrector: langevin
    method: pc
    n_steps_each: 1
    noise_removal: true
    predictor: reverse_diffusion
    probability_flow: false
    snr: 0.075
    seed: 42
    training:
    batch_size: 1
    continuous: true
    epochs: 1000
    eval_freq: 100
    likelihood_weighting: false
    log_freq: 25
    reduce_mean: false
    sde: vesde
    snapshot_freq: 50000
    snapshot_freq_for_preemption: 5000
    snapshot_sampling: true

Traceback (most recent call last): File "E:\py2\DiffusionMBIR-main\main.py", line 71, in app.run(main) File "D:\Anaconda\envs\diffusion-mbir\lib\site-packages\absl\app.py", line 312, in run _run_main(main, args) File "D:\Anaconda\envs\diffusion-mbir\lib\site-packages\absl\app.py", line 258, in _run_main sys.exit(main(argv)) File "E:\py2\DiffusionMBIR-main\main.py", line 62, in main run_lib.train(FLAGS.config, FLAGS.workdir) File "E:\py2\DiffusionMBIR-main\run_lib.py", line 82, in train state = restore_checkpoint(checkpoint_meta_dir, state, config.device) File "E:\py2\DiffusionMBIR-main\utils.py", line 38, in restore_checkpoint loaded_state = torch.load(ckpt_dir, map_location=device) File "D:\Anaconda\envs\diffusion-mbir\lib\site-packages\torch\serialization.py", line 594, in load with _open_file_like(f, 'rb') as opened_file: File "D:\Anaconda\envs\diffusion-mbir\lib\site-packages\torch\serialization.py", line 230, in _open_file_like return _open_file(name_or_buffer, mode) File "D:\Anaconda\envs\diffusion-mbir\lib\site-packages\torch\serialization.py", line 211, in init super(_open_file, self).init(open(name, mode)) PermissionError: [Errno 13] Permission denied: 'workdir\AAPM256\checkpoints-meta'

Process finished with exit code 1

How can I solve the problem?

Hello Sunshark, I have also encountered the same problem as you. Have you solved this problem? May I ask for your advice on a solution?