Delete configs/c_absorption_L5_d200_s150_is30_r60_ws5_q03_nq256.yaml
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configs/c_absorption_L5_d200_s150_is30_r60_ws5_q03_nq256.yaml
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general:
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name: c_absorption_L5_d200_s150_is30_r60_ws5_q03_nq256
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root_dir: null
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dset:
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prior_sampler:
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cls: SubpriorParametricSampler
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kwargs:
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param_ranges:
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thicknesses: [1., 200.]
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roughnesses: [0., 60.]
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slds: [0., 150.]
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islds: [0., 30.]
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bound_width_ranges:
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thicknesses: [1.0e-2, 200.]
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roughnesses: [1.0e-2, 60.]
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slds: [1.0e-2, 5.]
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islds: [1.0e-2, 5.]
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model_name: model_with_absorption
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max_num_layers: 5
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constrained_roughness: true
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constrained_isld: true
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max_thickness_share: 0.5
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max_sld_share: 0.2
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logdist: false
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scale_params_by_ranges: false
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scaled_range: [-1., 1.]
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device: 'cuda'
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q_generator:
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cls: ConstantQ
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kwargs:
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q: [0.02, 0.3, 256]
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device: 'cuda'
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intensity_noise:
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cls: BasicExpIntensityNoise
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kwargs:
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relative_errors: [0.0, 0.2]
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abs_errors: 0.0
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consistent_rel_err: false
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logdist: false
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apply_shift: false
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shift_range: [-0.05, 0.05]
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apply_scaling: false
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scale_range: [-0.01, 0.01]
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curves_scaler:
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cls: LogAffineCurvesScaler
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kwargs:
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weight: 0.2
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bias: 1.0
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eps: 1.0e-10
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model:
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network:
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cls: NetworkWithPriorsConvEmb
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pretrained_name: null
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device: 'cuda'
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kwargs:
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in_channels: 1
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hidden_channels: [32, 64, 128, 256, 512]
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dim_embedding: 256
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dim_avpool: 1
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embedding_net_activation: 'gelu'
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use_batch_norm: true
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dim_out: 23
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layer_width: 1024
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num_blocks: 8
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repeats_per_block: 2
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mlp_activation: 'gelu'
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dropout_rate: 0.0
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pretrained_embedding_net: null
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training:
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num_iterations: 50000
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batch_size: 4096
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lr: 1.0e-4
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grad_accumulation_steps: 1
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clip_grad_norm_max: True
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train_with_q_input: False
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update_tqdm_freq: 1
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optimizer: AdamW
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trainer_kwargs:
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optim_kwargs:
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betas: [0.9, 0.999]
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weight_decay: 0.0005
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callbacks:
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save_best_model:
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enable: true
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freq: 100
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lr_scheduler:
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cls: StepLR
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kwargs:
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step_size: 2500
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gamma: 0.9
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logger:
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use_neptune: false
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