############################
# WEATHER-TRANSFER validation for the multi-source phase runs, using ALL modalities.
#
# Clear (non-rain/snow) scenes from split_val_transfer (96 curated scenes) paired with
# RAIN/SNOW text prompts: tests whether the model transfers weather onto the clear scene's
# structure. Unlike adverse_weather_canny_val_transfer.yaml (labels:[rgb] only), this loads
# depth + semantic too -- depth/seg are GENERATED for these 96 clear scenes (Video-Depth-
# Anything + Mask2Former/Mapillary, same pipeline as the rain/snow train set) so the
# structured-noise phase can be injected from depth/canny/seg/rgb, matching training.
#
#   - path -> split_val_transfer.json (96 clear scenes)
#   - labels:[rgb,depth,semantic]; canny derived from rgb in the dataloader
#   - custom.weather_transfer_prompts -> rain/snow captions (cycled by index), override the
#     clear scene's own caption. PSNR/SSIM vs the clear GT are LOW by design; eval = visuals.
############################
dataset:
    name: Unified
    path: [/workspace/data/cv_unified_adverse_weather/videos/parquet_all_weather/split_val_transfer.json]
    labels: [rgb, depth, semantic]
    length: 1
    cameras_core: [front_wide,front_tele,left_side_forward,left_side_rearward,rear_tele,right_side_forward,right_side_rearward]
    cameras_core_mode: skip
    cameras_core_sample: 1
    augmentation:
        resize: [704,1280]
    custom:
        frame_rate: 10.0
        frame_stride: 1
        prompt: "a driving scene"
        reference_camera: false
        scale_factor: 0.125
        weather_transfer_prompts: /workspace/Any4D/custom/config/anydata/local/weather_transfer_prompts.json
############################
