Delete configs/a_base_point_neutron_conv_standard.yaml

configs/a_base_point_neutron_conv_standard.yaml

@@ -1,144 +0,0 @@
-general:
-  name: a_base_point_neutron_conv_standard
-  root_dir: null
-  
-dset:
-  cls: ReflectivityDataLoader
-  prior_sampler:
-    cls: SubpriorParametricSampler
-    kwargs:
-      param_ranges:
-        thicknesses: [1., 500.]
-        roughnesses: [0., 60.]
-        slds: [-8., 16.]
-      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'
-
-  # q_generator:
-  #   cls: VariableQ
-  #   kwargs:
-  #     q_min_range: [0.005, 0.03]
-  #     q_max_range: [0.15, 0.3]
-  #     n_q_range: [128, 128]
-  #     device: 'cuda'
-      
-  intensity_noise:
-    cls: GaussianExpIntensityNoise
-    kwargs:
-      relative_errors: [0.01, 0.3]
-      consistent_rel_err: false
-      apply_shift: false #true
-      shift_range: [-0.3, 0.3]
-      apply_background: false
-      background_range: [1.0e-10, 1.0e-4]
-      add_to_context: true
-
-  # q_noise:
-  #   cls: BasicQNoiseGenerator
-  #   kwargs:
-  #     apply_systematic_shifts: true 
-  #     shift_std: 1.0e-3
-  #     apply_gaussian_noise: false
-  #     noise_std: [0., 1.0e-3]
-  #     add_to_context: true
-
-  smearing:
-    cls: Smearing
-    kwargs:
-      sigma_range: [0.01, 0.08]
-      constant_dq: False
-      gauss_num: 17
-      share_smeared: 0.8
-      
-  curves_scaler:
-    cls: LogAffineCurvesScaler
-    kwargs:
-      weight: 0.2
-      bias: 1.0
-      eps: 1.0e-10
-
-model:
-  network:
-    cls: NetworkWithPriors
-    pretrained_name: null
-    device: 'cuda'
-    kwargs:
-      embedding_net_type: 'conv'
-      embedding_net_kwargs:
-        in_channels: 1
-        hidden_channels: [32, 64, 128, 256, 512]
-        kernel_size: 3
-        dim_embedding: 128
-        dim_avpool: 1
-        use_batch_norm: true
-        use_se: false
-        activation: 'gelu'
-      pretrained_embedding_net: null
-      dim_out: 8
-      dim_conditioning_params: 0
-      layer_width: 512
-      num_blocks: 6
-      repeats_per_block: 2
-      residual: true
-      use_batch_norm: true
-      use_layer_norm: false
-      mlp_activation: 'gelu'
-      dropout_rate: 0.0 
-      tanh_output: false
-      conditioning: 'concat' # 'concat', 'glu', 'film'
-      concat_condition_first_layer: false
-       
-training:
-  trainer_cls: PointEstimatorTrainer
-  num_iterations: 10000
-  batch_size: 4096
-  lr: 1.0e-3
-  grad_accumulation_steps: 1
-  clip_grad_norm_max: null
-  update_tqdm_freq: 1
-  optimizer: AdamW
-  trainer_kwargs:
-    train_with_q_input: false
-    train_with_sigmas: false
-    condition_on_q_resolutions: false
-    use_curve_reconstruction_loss: false
-    rescale_loss_interval_width: true #false
-    use_l1_loss: true #false
-    optim_kwargs:
-      betas: [0.9, 0.999]
-      weight_decay: 0.0005
-  callbacks:
-    save_best_model:
-      enable: true
-      freq: 500
-    lr_scheduler:
-      cls: CosineAnnealingWithWarmup
-      kwargs:
-        min_lr: 1.0e-6
-        warmup_iters: 100
-        total_iters: 10000
-      # cls: StepLR
-      # kwargs:
-      #   step_size: 10000
-      #   gamma: 0.5
-
-  # logger:
-  #   cls: TensorBoardLogger
-  #   kwargs:
-  #     log_dir: "tensorboard_runs/test_1"