|
|
general:
|
|
|
name: NR-1layer-basic-v1
|
|
|
root_dir: null
|
|
|
|
|
|
dset:
|
|
|
cls: ReflectivityDataLoader
|
|
|
prior_sampler:
|
|
|
cls: SubpriorParametricSampler
|
|
|
kwargs:
|
|
|
param_ranges:
|
|
|
thicknesses: [1., 1500.]
|
|
|
roughnesses: [0., 60.]
|
|
|
slds: [-8., 16.]
|
|
|
r_scale: [0.9, 1.1]
|
|
|
log10_background: [-10.0, -4.0]
|
|
|
bound_width_ranges:
|
|
|
thicknesses: [1.0e-2, 1500.]
|
|
|
roughnesses: [1.0e-2, 60.]
|
|
|
slds: [1.0e-2, 5.]
|
|
|
r_scale: [1.0e-3, 0.2]
|
|
|
log10_background: [1.0e-2, 6.0]
|
|
|
shift_param_config:
|
|
|
r_scale: true
|
|
|
log10_background: true
|
|
|
model_name: standard_model
|
|
|
max_num_layers: 1
|
|
|
constrained_roughness: true
|
|
|
max_thickness_share: 0.5
|
|
|
logdist: false
|
|
|
scale_params_by_ranges: false
|
|
|
scaled_range: [-1., 1.]
|
|
|
device: 'cuda'
|
|
|
|
|
|
q_generator:
|
|
|
cls: MaskedVariableQ
|
|
|
kwargs:
|
|
|
q_min_range: [0.001, 0.02]
|
|
|
q_max_range: [0.05, 0.4]
|
|
|
n_q_range: [50, 256]
|
|
|
mode: 'mixed'
|
|
|
shuffle_mask: False
|
|
|
total_thickness_constraint: True
|
|
|
min_points_per_fringe: 4
|
|
|
device: 'cuda'
|
|
|
|
|
|
intensity_noise:
|
|
|
cls: GaussianExpIntensityNoise
|
|
|
kwargs:
|
|
|
relative_errors: [0.01, 0.3]
|
|
|
add_to_context: true
|
|
|
|
|
|
smearing:
|
|
|
cls: Smearing
|
|
|
kwargs:
|
|
|
sigma_range: [0.01, 0.12]
|
|
|
gauss_num: 17
|
|
|
share_smeared: 1.0
|
|
|
|
|
|
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: 'integral_conv'
|
|
|
embedding_net_kwargs:
|
|
|
z_num: [64, 128, 256]
|
|
|
z_range: [0., 0.41]
|
|
|
dim_embedding: 256
|
|
|
in_dim: 1
|
|
|
num_blocks: 4
|
|
|
kernel_coef: 16
|
|
|
use_layer_norm: true
|
|
|
conv_dims: [32, 64, 128]
|
|
|
pretrained_embedding_net: null
|
|
|
dim_out: 7
|
|
|
dim_conditioning_params: 1
|
|
|
layer_width: 1024
|
|
|
num_blocks: 8
|
|
|
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: 'film'
|
|
|
concat_condition_first_layer: false
|
|
|
|
|
|
training:
|
|
|
trainer_cls: PointEstimatorTrainer
|
|
|
num_iterations: 300000
|
|
|
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
|
|
|
condition_on_q_resolutions: true
|
|
|
rescale_loss_interval_width: true
|
|
|
use_l1_loss: true
|
|
|
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: 500
|
|
|
total_iters: 300000 |
