scripts/ablation_sam/debug_sam_gsc_eva_vitb16_coco.sh

#!/bin/bash
# SAM-GSC 消融实验：EVA-CLIP-B/16 单卡调试

data_root=/opt/tiger/xiaomoguhzz/standard_coco
pretrain_ckpt=/opt/tiger/xiaomoguhzz/EVA02_CLIP_B_psz16_s8B.pt
exp_name=Debug_SAM-GSC_EVA-B_DINOv2-B_csa_560
vfm_type=dinov2-B
dataset_type=ablation_sam
version=ablation_sam
mode=csa_vfm_distill

# 单卡调试
CUDA_VISIBLE_DEVICES=0 python -m training.main \
    --batch-size=2 \
    --lr=1e-5 \
    --wd=0.1 \
    --epochs=1 \
    --workers=2 \
    --model EVA02-CLIP-B-16 \
    --pretrained eva \
    --warmup 100 \
    --zeroshot-frequency 1 \
    --dataset-type ${dataset_type} \
    --test-type coco_panoptic \
    --train-data ${data_root}/annotations/instances_train2017.json \
    --val-data ${data_root}/annotations/panoptic_val2017.json \
    --embed-path metadata/coco_panoptic_clip_hand_craft_EVACLIP_ViTB16.npy \
    --train-image-root ${data_root}/train2017 \
    --val-image-root ${data_root}/val2017 \
    --cache-dir ${pretrain_ckpt} \
    --log-every-n-steps 10 \
    --lock-image \
    --save-frequency 1 \
    --lock-image-unlocked-groups 12 \
    --name ${exp_name} \
    --downsample-factor 16 \
    --det-image-size 560 \
    --val-segm-root ${data_root}/annotations/panoptic_val2017 \
    --alpha 0.7 \
    --mode ${mode} \
    --use_vfm ${vfm_type} \
    --loss_context_weight 0.25 \
    --loss_content_weight 1.0 \
    --loss_region_weight 0.05 \
    --skip-first-eval \
    --repa_layer_idx -1 \
    --sd-refine-weight 0.3 \
    --version ${version} \
    --train-ratio 0.01