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configs/mc-o1.yaml

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+general:
+  name: mc-o1
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 16
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o10.yaml

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+general:
+  name: mc-o10
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 1024
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 16
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o11.yaml

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+general:
+  name: mc-o11
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 256
+      n_fno_blocks : 6
+      modes: 16
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o12.yaml

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+general:
+  name: mc-o12
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [-20., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 16
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o13.yaml

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+general:
+  name: mc-o13
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 200.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 200.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 5
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 16
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 17
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o14.yaml

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+general:
+  name: mc-o14
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 200.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 200.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 5
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 32
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 17
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o15.yaml

@@ -0,0 +1,102 @@
+general:
+  name: mc-o15
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 200.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 200.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 5
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 16
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 17
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 512
+  lr: 1.0e-4
+  grad_accumulation_steps: 10
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o16.yaml

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+general:
+  name: mc-o16
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 200.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 200.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 5
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 16
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 17
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-4
+  grad_accumulation_steps: 20
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o2.yaml

@@ -0,0 +1,102 @@
+general:
+  name: mc-o2
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 16
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-4
+  grad_accumulation_steps: 10
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o3.yaml

@@ -0,0 +1,102 @@
+general:
+  name: mc-o3
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 16
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-4
+  grad_accumulation_steps: 50
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o4.yaml

@@ -0,0 +1,102 @@
+general:
+  name: mc-o4
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 16
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-3
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o5.yaml

@@ -0,0 +1,102 @@
+general:
+  name: mc-o5
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 16
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 512
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o7.yaml

@@ -0,0 +1,102 @@
+general:
+  name: mc-o7
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 8
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o8.yaml

@@ -0,0 +1,102 @@
+general:
+  name: mc-o8
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 256
+      width_fno: 128
+      n_fno_blocks : 6
+      modes: 32
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc-o9.yaml

@@ -0,0 +1,102 @@
+general:
+  name: mc-o9
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: VariableQ
+    kwargs:
+      q_min_range: [0.01, 0.05]
+      q_max_range: [0.15, 0.4]
+      n_q_range: [128, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  q_noise:
+    cls: BasicQNoiseGenerator
+    kwargs:
+      shift_std: 1.0e-3
+      noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsFnoEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 2
+      dim_embedding: 512
+      width_fno: 128
+      n_fno_blocks : 1
+      modes: 16
+      embedding_net_activation: 'gelu'
+      use_batch_norm: True
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+       
+training:
+  num_iterations: 50000
+  batch_size: 1024
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: 1.0
+  train_with_q_input: True
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 500
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false

configs/mc1.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc1
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc10.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc10
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: true
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc11.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc11
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: true
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.2, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc12.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc12
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: true
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc13.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc13
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: true
+      shift_range: [-0.3, 0.3]
+      apply_scaling: true
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc14.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc14
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.2, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: true
+      shift_range: [-0.3, 0.3]
+      apply_scaling: true
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc15.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc15
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: true
+      shift_range: [-0.3, 0.3]
+      apply_scaling: true
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc16.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc16
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: true
+      shift_range: [-0.3, 0.3]
+      apply_scaling: true
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc17.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc17
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.2, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: true
+      shift_range: [-0.3, 0.3]
+      apply_scaling: true
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc18.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc18
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: true
+      shift_range: [-0.3, 0.3]
+      apply_scaling: true
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc19.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc19
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc2.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc2
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.2, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc20.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc20
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.2, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc21.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc21
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc22.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc22
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc23.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc23
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.2, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc24.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc24
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc25.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc25
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc26.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc26
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 150.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc27.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc27
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 150.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc28.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc28
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc29.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc29
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 150.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc3.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc3
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc30.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc30
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [0., 150.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc31.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc31
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 10.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc32.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc32
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 1.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc33.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc33
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 50.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc34.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc34
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [-20., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc35.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc35
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [-20., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc36.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc36
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [-20., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 128]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc37.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc37
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 20.]
+        slds: [-20., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc38.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc38
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 300.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 300.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 3
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 11
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc39.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc39
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 300.]
+        roughnesses: [0., 20.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 300.]
+        roughnesses: [1.0e-2, 20.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 3
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.3, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 11
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc4.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc4
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 500.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 500.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 2
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: false
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 8
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani

configs/mc40.yaml

@@ -0,0 +1,105 @@
+general:
+  name: mc40
+  root_dir: null
+  
+dset:
+  prior_sampler:
+    cls: SubpriorParametricSampler
+    kwargs:
+      param_ranges:
+        thicknesses: [0., 300.]
+        roughnesses: [0., 60.]
+        slds: [0., 50.]
+      bound_width_ranges:
+        thicknesses: [1.0e-2, 300.]
+        roughnesses: [1.0e-2, 60.]
+        slds: [ 1.0e-2, 5.]
+      model_name: standard_model
+      max_num_layers: 3
+      constrained_roughness: true
+      max_thickness_share: 0.5
+      logdist: false
+      scale_params_by_ranges: false
+      scaled_range: [-1., 1.]
+      device: 'cuda'
+      
+  q_generator:
+    cls: ConstantQ
+    kwargs:
+      q: [0.02, 0.15, 256]
+      device: 'cuda'
+      
+  intensity_noise:
+    cls: BasicExpIntensityNoise
+    kwargs:
+      relative_errors: [0.0, 0.2]
+      abs_errors: 0.0
+      consistent_rel_err: true
+      logdist: false
+      apply_shift: false
+      shift_range: [-0.3, 0.3]
+      apply_scaling: false
+      scale_range: [-0.02, 0.02]
+
+  # q_noise:
+  #   cls: BasicQNoiseGenerator
+  #   kwargs:
+  #     shift_std: 1.0e-3
+  #     noise_std: [0., 1.0e-3]
+      
+  curves_scaler:
+    cls: LogAffineCurvesScaler
+    kwargs:
+      weight: 0.2
+      bias: 1.0
+      eps: 1.0e-10
+
+model:
+  network:
+    cls: NetworkWithPriorsConvEmb
+    pretrained_name: null
+    device: 'cuda'
+    kwargs:
+      in_channels: 1
+      hidden_channels: [32, 64, 128, 256, 512]
+      dim_embedding: 128
+      dim_avpool: 1
+      embedding_net_activation: 'gelu'
+      use_batch_norm: true
+      dim_out: 11
+      layer_width: 1024
+      num_blocks: 6
+      repeats_per_block: 2
+      mlp_activation: 'gelu'
+      dropout_rate: 0.0 
+      pretrained_embedding_net: null
+       
+training:
+  num_iterations: 50000
+  batch_size: 4096
+  lr: 1.0e-4
+  grad_accumulation_steps: 1
+  clip_grad_norm_max: null
+  train_with_q_input: False
+  update_tqdm_freq: 1
+  optimizer: AdamW
+  trainer_kwargs:
+    optim_kwargs:
+      betas: [0.9, 0.999]
+      weight_decay: 0.0005
+  callbacks:
+    save_best_model:
+      enable: true
+      freq: 100
+    lr_scheduler:
+      cls: StepLR
+      kwargs:
+        step_size: 2000
+        gamma: 0.9
+  logger:
+    use_neptune: false
+
+slurm:
+  cluster: 'tuebingen'
+  time: 0-05:00  #D-HH:MM 
+  partition: 2080-galvani