configs/default.yaml

# configs/default.yaml — unified config for inference + artery classifier

# ===== General =====
seed: 42
device: auto                 # 'cpu' | 'cuda' | 'mps' | 'auto'
precision: bf16              # 'bf16' | 'fp16' | 'fp32'

# Video clip packing for SYNTAX inference
num_classes: 2               # [p(stenosis>=thr), log(1+score)]
frames_per_clip: 32
video_size: [256, 256]

# ===== Backbone / Head =====
backbone: r3d_18             # torchvision.models.video.r3d_18
backbone_weights: DEFAULT    # torchvision default Kinetics weights

# Head variant: mean_out | mean | lstm_mean | lstm_last | gru_mean | gru_last | bert_mean | bert_cls | bert_cls2
variant: lstm_mean

a_rnn:
  hidden_div: 4              # hidden_size = in_features // hidden_div
  dropout: 0.2

bert:
  nhead: 4
  num_layers: 1
  ff_div: 4
  dropout: 0.2

# ===== Thresholds (reporting) =====
thresholds:
  left: 15.0
  right: 5.0
  both: 22.0

# ===== Ensemble weights (leave empty to auto-discover in weights/{left,right}) =====
weights:
  left: []                    # e.g. ["weights/left/Left_fold00.pt", ...]
  right: []                   # e.g. ["weights/right/Right_fold00.pt", ...]

# Optional: HF model repo to auto-fetch weights if local not found.
# Can be overridden by env var WEIGHTS_REPO.
weights_repo: "MesserMMP/syntax-video-weights"

# ===== Artery classifier (do NOT change preprocessing; keep identical to training) =====
classifier:
  # path to Lightning/pt weights; must exist in the runtime
  weights: "assets/r3d_art.pt"
  # subdir in weights_repo
  hf_subdir: "classifier"

  # preprocessing params used in the classifier pipeline
  video_size: [224, 224]
  mean: [0.485, 0.456, 0.406]
  std:  [0.229, 0.224, 0.225]

  # routing thresholds on sigmoid probability:
  #   prob <= left_max  -> LEFT
  #   prob >= right_min -> RIGHT
  thresholds:
    left_max: 0.10
    right_min: 0.90

# ===== Inference loader (not strictly used, kept for completeness) =====
batch_size: 1