diff --git a/configs/mc-o1.yaml b/configs/mc-o1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..275ba1694cb31d58304c95c29d860f945c07110e --- /dev/null +++ b/configs/mc-o1.yaml @@ -0,0 +1,102 @@ +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 \ No newline at end of file diff --git a/configs/mc-o10.yaml b/configs/mc-o10.yaml new file mode 100644 index 0000000000000000000000000000000000000000..07b49dd06d2e76847424270eca2ddaea6b7648dd --- /dev/null +++ b/configs/mc-o10.yaml @@ -0,0 +1,102 @@ +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 \ No newline at end of file diff --git a/configs/mc-o11.yaml b/configs/mc-o11.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3e324ec96252e31878455d7c151512c331181dd2 --- /dev/null +++ b/configs/mc-o11.yaml @@ -0,0 +1,102 @@ +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 \ No newline at end of file diff --git a/configs/mc-o12.yaml b/configs/mc-o12.yaml new file mode 100644 index 0000000000000000000000000000000000000000..78470352afa54f39041fd1baceca6de9336209ed --- /dev/null +++ b/configs/mc-o12.yaml @@ -0,0 +1,102 @@ +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 \ No newline at end of file diff --git a/configs/mc-o13.yaml b/configs/mc-o13.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3fe4f720118e96a6494d393837f29878ad107087 --- /dev/null +++ b/configs/mc-o13.yaml @@ -0,0 +1,102 @@ +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 \ No newline at end of file diff --git a/configs/mc-o14.yaml b/configs/mc-o14.yaml new file mode 100644 index 0000000000000000000000000000000000000000..18449b1b96963a50eb1cd967ee3de5aa93be7882 --- /dev/null +++ b/configs/mc-o14.yaml @@ -0,0 +1,102 @@ +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 \ No newline at end of file diff --git a/configs/mc-o15.yaml b/configs/mc-o15.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e3e17d4cbf86724d0fa63732cecd3d7fce3d7089 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc-o16.yaml b/configs/mc-o16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e77fa5d8e21abca8e08e62d5fa88ae3cef45496f --- /dev/null +++ b/configs/mc-o16.yaml @@ -0,0 +1,102 @@ +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 \ No newline at end of file diff --git a/configs/mc-o2.yaml b/configs/mc-o2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f780159ed655e582cbe5b9738ab351a85f195349 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc-o3.yaml b/configs/mc-o3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3c13b0dfbd8356e02c1372c9dd5f1173a2d4e9da --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc-o4.yaml b/configs/mc-o4.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c9ac3f5c0f6bee0a179f82f4f311533745f42159 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc-o5.yaml b/configs/mc-o5.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e1ec6e6822d878fa8f0132d5b7865e2be3acc1d5 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc-o7.yaml b/configs/mc-o7.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9f295f1845431ab3259fe4f24990d8c46ae8d944 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc-o8.yaml b/configs/mc-o8.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e5e55f48a2552107a83fd34032f934ac767263e9 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc-o9.yaml b/configs/mc-o9.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b2d44b6a48f13b5bae2d5947d6e85cc4d2b4ea23 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc1.yaml b/configs/mc1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..61b93d1d9dce95e6fd405a427aa0d7eb4224a793 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc10.yaml b/configs/mc10.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3854ec3a415d7a41d890d93c2d33a6e58063744a --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc11.yaml b/configs/mc11.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a5fd628b21e6905025c10f7c265aa6a9c089715d --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc12.yaml b/configs/mc12.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cc2c04fd4b58ae55a09d65aa497c734aeff71c39 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc13.yaml b/configs/mc13.yaml new file mode 100644 index 0000000000000000000000000000000000000000..287a3a2607f5ba26c2aeeb8c9d5820eef108b425 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc14.yaml b/configs/mc14.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0fb885bbcea1bff8fd88ba635ab58d9651b8925a --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc15.yaml b/configs/mc15.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2b58e4daa1fe8cd4e4e872549558262b066c3ec2 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc16.yaml b/configs/mc16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f711ff7f47e1947bcc05294df3fcf12a15ee953e --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc17.yaml b/configs/mc17.yaml new file mode 100644 index 0000000000000000000000000000000000000000..704aa6ec411c1bfc263ebb988132f779315e1ed0 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc18.yaml b/configs/mc18.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b23800189b0f2807182fa181b031a4cce5c811d7 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc19.yaml b/configs/mc19.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e6aefec896dfbfe5c01faeec4daecc93d1dc3e19 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc2.yaml b/configs/mc2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b9764be2c52396af854c5376afe5643680fc9a01 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc20.yaml b/configs/mc20.yaml new file mode 100644 index 0000000000000000000000000000000000000000..52c8b8295ff0ae4596f4930d4d2cf7b2f734262c --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc21.yaml b/configs/mc21.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2e640ace25ca27a9c149ee5953caf3892d330312 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc22.yaml b/configs/mc22.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e54da49c33cdf8022d51cbbdaf1b7958c3cbd21e --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc23.yaml b/configs/mc23.yaml new file mode 100644 index 0000000000000000000000000000000000000000..26bd01dad33080059a9a229920deacc5bc4a1c30 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc24.yaml b/configs/mc24.yaml new file mode 100644 index 0000000000000000000000000000000000000000..469884615fc6e5247d477d13d1c9cba8edb0197d --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc25.yaml b/configs/mc25.yaml new file mode 100644 index 0000000000000000000000000000000000000000..69fd590534a18cbe79ca075b1466cb5fb646f319 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc26.yaml b/configs/mc26.yaml new file mode 100644 index 0000000000000000000000000000000000000000..71518c39bbf358516f714bb6008e7685254e23b5 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc27.yaml b/configs/mc27.yaml new file mode 100644 index 0000000000000000000000000000000000000000..db1377cb4a292ff94d4930f34dddce584441d5d0 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc28.yaml b/configs/mc28.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5af6d03717d2fca41a64fb2c1afaf2d80776df15 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc29.yaml b/configs/mc29.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f6c3f24f75c7d6f82ce5cc19b8636d1ece9ecdd8 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc3.yaml b/configs/mc3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..163952f95428f6eac014b04f1e35cfa43d4b879a --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc30.yaml b/configs/mc30.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aa1554744e6842ee0935f3570ef00dd0d9abecd2 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc31.yaml b/configs/mc31.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0e259d262526f87a29f6a24201cbbe79f0ffe5d4 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc32.yaml b/configs/mc32.yaml new file mode 100644 index 0000000000000000000000000000000000000000..81cb89693659fee85cbefcef53071f8c11f3161d --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc33.yaml b/configs/mc33.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e338c1fa80ffbc86cb1f71f6c999870ba70e74c0 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc34.yaml b/configs/mc34.yaml new file mode 100644 index 0000000000000000000000000000000000000000..afdcfd7fa619a5ae2cde5d36b0a7c1a3d0a66b9a --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc35.yaml b/configs/mc35.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4ce3d60c2eb933d900036f1d6c697b50b03f059c --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc36.yaml b/configs/mc36.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a19e3088fe38eeabc583fd7233ff0adc84b27427 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc37.yaml b/configs/mc37.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9d11fe8bc902a27b066b29b47fd00ab95d6cbfa5 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc38.yaml b/configs/mc38.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c1b5146ceb46ec3b32b75527bf80e95d4c315d73 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc39.yaml b/configs/mc39.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d26e2056a7967e6628b4a987f95ab9a78585f901 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc4.yaml b/configs/mc4.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bd74d8670d02fb984a4d86a076af9e33077f8229 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc40.yaml b/configs/mc40.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b9a214397f4e68eacbbe1edad57004d6516ec83c --- /dev/null +++ b/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 \ No newline at end of file diff --git a/configs/mc41.yaml b/configs/mc41.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2607e5e3ebaf31f44dae5f140c6d178eb7201229 --- /dev/null +++ b/configs/mc41.yaml @@ -0,0 +1,105 @@ +general: + name: mc41 + 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, 0.3, 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 \ No newline at end of file diff --git a/configs/mc42.yaml b/configs/mc42.yaml new file mode 100644 index 0000000000000000000000000000000000000000..014a7eccfe6d5200e8df2bc9b826e81cfa1cf033 --- /dev/null +++ b/configs/mc42.yaml @@ -0,0 +1,105 @@ +general: + name: mc42 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 300.] + roughnesses: [0., 20.] + slds: [0., 150.] + 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 \ No newline at end of file diff --git a/configs/mc43.yaml b/configs/mc43.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4a27f6bb1b2ef10fd6125d12b90a2a7fa00a0af8 --- /dev/null +++ b/configs/mc43.yaml @@ -0,0 +1,105 @@ +general: + name: mc43 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 300.] + roughnesses: [0., 20.] + slds: [0., 150.] + 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 \ No newline at end of file diff --git a/configs/mc44.yaml b/configs/mc44.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4b78b506438a363aab078d3071dc957eca1b74b9 --- /dev/null +++ b/configs/mc44.yaml @@ -0,0 +1,105 @@ +general: + name: mc44 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 300.] + roughnesses: [0., 60.] + slds: [0., 150.] + 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 \ No newline at end of file diff --git a/configs/mc45.yaml b/configs/mc45.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1b82a14ea74d3a196896789050eb79b267340d49 --- /dev/null +++ b/configs/mc45.yaml @@ -0,0 +1,105 @@ +general: + name: mc45 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 300.] + roughnesses: [0., 60.] + slds: [0., 150.] + 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, 0.3, 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 \ No newline at end of file diff --git a/configs/mc46.yaml b/configs/mc46.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f581bc79507e9710bf47193b7eea0b4fba1e6e59 --- /dev/null +++ b/configs/mc46.yaml @@ -0,0 +1,105 @@ +general: + name: mc46 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 300.] + roughnesses: [0., 20.] + slds: [-20., 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 \ No newline at end of file diff --git a/configs/mc47.yaml b/configs/mc47.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3ac7e23dbbbdbe922607793bfbd0ed385b05246b --- /dev/null +++ b/configs/mc47.yaml @@ -0,0 +1,105 @@ +general: + name: mc47 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 300.] + roughnesses: [0., 20.] + slds: [-20., 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 \ No newline at end of file diff --git a/configs/mc48.yaml b/configs/mc48.yaml new file mode 100644 index 0000000000000000000000000000000000000000..73c5114302de7924e1c71ad002dd80576c87a9a6 --- /dev/null +++ b/configs/mc48.yaml @@ -0,0 +1,105 @@ +general: + name: mc48 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 300.] + roughnesses: [0., 60.] + slds: [-20., 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 \ No newline at end of file diff --git a/configs/mc49.yaml b/configs/mc49.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a4f24da07d814594aa3d30d74dab25d14e1b67e --- /dev/null +++ b/configs/mc49.yaml @@ -0,0 +1,105 @@ +general: + name: mc49 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 300.] + roughnesses: [0., 60.] + slds: [-20., 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, 0.3, 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 \ No newline at end of file diff --git a/configs/mc5.yaml b/configs/mc5.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6b65de58596e8cb464b6e14ec6913eb27ba54dcd --- /dev/null +++ b/configs/mc5.yaml @@ -0,0 +1,105 @@ +general: + name: mc5 + 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: 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 \ No newline at end of file diff --git a/configs/mc50.yaml b/configs/mc50.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f7cfacc6811e2d2a0e178e69e4e18d67775dbdf4 --- /dev/null +++ b/configs/mc50.yaml @@ -0,0 +1,105 @@ +general: + name: mc50 + 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: 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 \ No newline at end of file diff --git a/configs/mc51.yaml b/configs/mc51.yaml new file mode 100644 index 0000000000000000000000000000000000000000..06cba513535a3eddd8f73dcbe259b2705eace7f5 --- /dev/null +++ b/configs/mc51.yaml @@ -0,0 +1,105 @@ +general: + name: mc51 + 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: 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 \ No newline at end of file diff --git a/configs/mc52.yaml b/configs/mc52.yaml new file mode 100644 index 0000000000000000000000000000000000000000..37c28e8518acb8fec37861ca97a9e444367cdbfa --- /dev/null +++ b/configs/mc52.yaml @@ -0,0 +1,105 @@ +general: + name: mc52 + 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: 4 + 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: 14 + 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 \ No newline at end of file diff --git a/configs/mc53.yaml b/configs/mc53.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bda29feddef595865c918cf86a12123e894295d4 --- /dev/null +++ b/configs/mc53.yaml @@ -0,0 +1,105 @@ +general: + name: mc53 + 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: 4 + 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: 14 + 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 \ No newline at end of file diff --git a/configs/mc54.yaml b/configs/mc54.yaml new file mode 100644 index 0000000000000000000000000000000000000000..048fdb17bb7b9d22c852da766c58664d6a0b313a --- /dev/null +++ b/configs/mc54.yaml @@ -0,0 +1,105 @@ +general: + name: mc54 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 300.] + roughnesses: [0., 20.] + slds: [0., 150.] + 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: 4 + 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: 14 + 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 \ No newline at end of file diff --git a/configs/mc55.yaml b/configs/mc55.yaml new file mode 100644 index 0000000000000000000000000000000000000000..793701b15d2bf715f3443f63589abd48768c0b50 --- /dev/null +++ b/configs/mc55.yaml @@ -0,0 +1,105 @@ +general: + name: mc55 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 300.] + roughnesses: [0., 60.] + slds: [0., 150.] + 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: 4 + 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: 14 + 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 \ No newline at end of file diff --git a/configs/mc56.yaml b/configs/mc56.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4a71b958c537c4e4214adcff559931ae3def378e --- /dev/null +++ b/configs/mc56.yaml @@ -0,0 +1,105 @@ +general: + name: mc56 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 300.] + roughnesses: [0., 20.] + slds: [-20., 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: 4 + 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: 14 + 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 \ No newline at end of file diff --git a/configs/mc57.yaml b/configs/mc57.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ad98f5c4d654bc85b3ca6e2ebc8e7edb339b09a8 --- /dev/null +++ b/configs/mc57.yaml @@ -0,0 +1,105 @@ +general: + name: mc57 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 300.] + roughnesses: [0., 60.] + slds: [-20., 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: 4 + 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: 14 + 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 \ No newline at end of file diff --git a/configs/mc58.yaml b/configs/mc58.yaml new file mode 100644 index 0000000000000000000000000000000000000000..20811d98ffa4c98d0eee1a4fdec564972914c62c --- /dev/null +++ b/configs/mc58.yaml @@ -0,0 +1,105 @@ +general: + name: mc58 + 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: 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: 17 + 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 \ No newline at end of file diff --git a/configs/mc59.yaml b/configs/mc59.yaml new file mode 100644 index 0000000000000000000000000000000000000000..33392d9d0fbed18cec03ae49221699c810a05ad8 --- /dev/null +++ b/configs/mc59.yaml @@ -0,0 +1,105 @@ +general: + name: mc59 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 200.] + roughnesses: [0., 60.] + slds: [0., 50.] + bound_width_ranges: + thicknesses: [1.0e-2, 200.] + roughnesses: [1.0e-2, 60.] + 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: 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: 17 + 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 \ No newline at end of file diff --git a/configs/mc6.yaml b/configs/mc6.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a2fee8f89ad097aef783bd6ce40d71baddb31d53 --- /dev/null +++ b/configs/mc6.yaml @@ -0,0 +1,105 @@ +general: + name: mc6 + 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: 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 \ No newline at end of file diff --git a/configs/mc60.yaml b/configs/mc60.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fd4bb79e3d26a92b9fb7bc66d5ff727b41bff2f9 --- /dev/null +++ b/configs/mc60.yaml @@ -0,0 +1,105 @@ +general: + name: mc60 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 200.] + roughnesses: [0., 20.] + slds: [0., 150.] + 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: 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: 17 + 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 \ No newline at end of file diff --git a/configs/mc61.yaml b/configs/mc61.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ad9d14284e4bb4373ee87e579b8396c6374225f0 --- /dev/null +++ b/configs/mc61.yaml @@ -0,0 +1,105 @@ +general: + name: mc61 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 200.] + roughnesses: [0., 60.] + slds: [0., 150.] + bound_width_ranges: + thicknesses: [1.0e-2, 200.] + roughnesses: [1.0e-2, 60.] + 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: 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: 17 + 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 \ No newline at end of file diff --git a/configs/mc62.yaml b/configs/mc62.yaml new file mode 100644 index 0000000000000000000000000000000000000000..44489834a8ab72e6c4da808f4803606e64c59d74 --- /dev/null +++ b/configs/mc62.yaml @@ -0,0 +1,105 @@ +general: + name: mc62 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 200.] + roughnesses: [0., 20.] + slds: [-20., 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: 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: 17 + 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 \ No newline at end of file diff --git a/configs/mc63.yaml b/configs/mc63.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d8457c5053eed786139ec0a987efc87d04241a61 --- /dev/null +++ b/configs/mc63.yaml @@ -0,0 +1,105 @@ +general: + name: mc63 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 200.] + roughnesses: [0., 60.] + slds: [-20., 50.] + bound_width_ranges: + thicknesses: [1.0e-2, 200.] + roughnesses: [1.0e-2, 60.] + 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: 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: 17 + 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 \ No newline at end of file diff --git a/configs/mc64.yaml b/configs/mc64.yaml new file mode 100644 index 0000000000000000000000000000000000000000..92be917ed99d3a35d9c5b62c2868eac5f3e5b8dc --- /dev/null +++ b/configs/mc64.yaml @@ -0,0 +1,105 @@ +general: + name: mc64 + 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: 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: ConstantQ + kwargs: + q: [0.02, 0.3, 1024] + 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: 17 + 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 \ No newline at end of file diff --git a/configs/mc65.yaml b/configs/mc65.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cf093eaf76181d0caca470e4f42eaccbefac90c2 --- /dev/null +++ b/configs/mc65.yaml @@ -0,0 +1,105 @@ +general: + name: mc65 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 200.] + roughnesses: [0., 60.] + slds: [0., 50.] + bound_width_ranges: + thicknesses: [1.0e-2, 200.] + roughnesses: [1.0e-2, 60.] + 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: 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: 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: 17 + 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 \ No newline at end of file diff --git a/configs/mc66.yaml b/configs/mc66.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fd80885530735d60f7b6e23a79967f9f0fda3c20 --- /dev/null +++ b/configs/mc66.yaml @@ -0,0 +1,105 @@ +general: + name: mc66 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 200.] + roughnesses: [0., 60.] + slds: [0., 50.] + bound_width_ranges: + thicknesses: [1.0e-2, 200.] + roughnesses: [1.0e-2, 60.] + 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: 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: 17 + 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 \ No newline at end of file diff --git a/configs/mc67.yaml b/configs/mc67.yaml new file mode 100644 index 0000000000000000000000000000000000000000..23b320efd6606262f71fbf71389da5eff53a084d --- /dev/null +++ b/configs/mc67.yaml @@ -0,0 +1,105 @@ +general: + name: mc67 + root_dir: null + +dset: + prior_sampler: + cls: SubpriorParametricSampler + kwargs: + param_ranges: + thicknesses: [0., 200.] + roughnesses: [0., 60.] + slds: [0., 50.] + bound_width_ranges: + thicknesses: [1.0e-2, 200.] + roughnesses: [1.0e-2, 60.] + 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: 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: 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: 17 + 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 \ No newline at end of file diff --git a/configs/mc7.yaml b/configs/mc7.yaml new file mode 100644 index 0000000000000000000000000000000000000000..97323572495e5f4ccdf774f4e4c929b379f5c39b --- /dev/null +++ b/configs/mc7.yaml @@ -0,0 +1,105 @@ +general: + name: mc7 + 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, 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 \ No newline at end of file diff --git a/configs/mc8.yaml b/configs/mc8.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f1dcf047e414d335f03844877a03e9d236955723 --- /dev/null +++ b/configs/mc8.yaml @@ -0,0 +1,105 @@ +general: + name: mc8 + 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, 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 \ No newline at end of file diff --git a/configs/mc9.yaml b/configs/mc9.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2cf24f08260d0f53ceaee332ddffdd479829c4e8 --- /dev/null +++ b/configs/mc9.yaml @@ -0,0 +1,105 @@ +general: + name: mc9 + 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, 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 \ No newline at end of file