params.yml

output_path: "./logs/output_${SLURM_JOB_ID}_${NOW}"

cudnn:
    benchmark: yes
    enabled: yes

dataset_file: datasets.lidc

dataset_pipeline_train: ["flip",  "resize", "colorjitter", "torchvision_normalise"]
dataset_pipeline_train_settings:
    target_size:  [128, 256]

dataset_pipeline_val: ["resize", "torchvision_normalise"]
dataset_pipeline_val_settings:
    target_size: [128, 256]

dataset_val_max_size: 100  # One of "null" (=full val size), or any number
class_weights: "uniform"

multigpu: no
distributed: no
mp_loaders: 4
batch_size: 16
samples: 12 # For GED calculation
max_epochs: 2000

optim:
    name: "Adam"
    learning_rate: 1.0e-4
    lr_function: "polynomial"
    lr_params:
        power: 1.0     # setting to 1.0 means linear decay
        min_lr: 1.0e-6 # learning rate value for the final step of training
    epochs: 2000 # total number of epochs to train for if missing then trains for max_epochs (for step > epochs lr = min_lr)

polyak_alpha: 0.9999
beta_schedule: "cosine" # One of ["cosine", "linear"]
beta_schedule_params:
    s: 0.008

backbone: "unet_openai"

diffusion_type: "categorical"
time_steps: 250

feature_cond_encoder:
    type: 'none' # ['dino', 'none']
    model: 'dino_vits8' # 'dino_vitb8
    channels: 384 # 768 for vitb, 384 for vits
    conditioning: "concat_pixels_concat_features"
    output_stride: 8  # at what output_stride in the unet to plug features
    scale: 'single'
    train: no
    source_layer: 11 # layer 11 is always the last layer of any dino vit model
    target_layer: 10 # at what output_stride in the unet to plug features

validation_freq: 2500
display_freq: 100
n_validation_predictions: 3
n_validation_images: 3

wandb: no 
wandb_mode: 'online'  # ['online', 'offline']
wandb_project: ccdm


unet_openai:
    base_channels: 32  # 64
    channel_mult: null  # [1, 2, 3.5, 4, 5]
    attention_resolutions: [32, 16, 8]
    num_heads: 1  # Ignored if num_head_channels is not -1
    num_head_channels: 32  # If not -1, num_heads is automatically set to channels//num_head_channels
    softmax_output: yes # this is the default for build_model

load_from: null