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436b829 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | #!/bin/bash
# Train LiDAR-Perfect Depth on top of a pretrained PPD checkpoint.
#
# Stage 1 (image pretrain @512²): trains only the sparse-prompt encoder + gate
# on Hypersim with simulated sparse-LiDAR; DiT backbone frozen.
# Stage 2 (image finetune @1024×768): unfreezes the DiT (optional) and mixes
# Hypersim + UrbanSyn + UnrealStereo4K + VKITTI2 + TartanAir.
# Stage 3 (video finetune): adds the temporal Kalman filter loop on short clips.
#
# Pre-reqs:
# * checkpoints/ppd.pth <- PPD pretrained weights
# * checkpoints/depth_anything_v2_vitl.pth <- DA-V2 ViT-L semantics
# * datasets extracted under /mnt/sig/datasets/ <- see datasets/README.md
set -e
# Stage 1: image pretrain (Hypersim only, 512×512)
python main.py --cfg_file ppd/configs/lpd_pretrain.yaml pl_trainer.devices=1
# Stage 2 (uncomment after stage 1 produces a checkpoint):
# python main.py --cfg_file ppd/configs/lpd_finetune.yaml pl_trainer.devices=8
# Stage 3 (uncomment for video):
# python main.py --cfg_file ppd/configs/lpd_video.yaml pl_trainer.devices=8
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