rpotter6298
112 lines
3.7 KiB
Bash
Executable File
112 lines
3.7 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Phase 2 overnight batch — image-only ResNet50, 10x5 rep-CV
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# Runs 6 configurations:
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# 1. Leaky CV (eye-level splits)
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# 2. Proper CV (patient-level, baseline)
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# 3. GT crop scale=1.1 (paper-matched tight crop)
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# 4. GT crop scale=2.5 (default generous crop)
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# 5. UNet crop scale=1.1
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# 6. UNet crop scale=2.5
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set -euo pipefail
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SCRIPT="python -m v3.scripts.main.run_cv"
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OUTROOT="v3/results/phase2"
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MANIFEST="manifest.csv"
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UNET_WEIGHTS="models/v2/refuge/segmentation/per_image/best.pt"
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BASE="--eval-mode binary \
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--tower-mode single \
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--bridge-mode image_only \
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--backbone resnet50 \
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--epochs 30 \
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--augment \
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--in-memory-cache \
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--reps 10 \
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--rep-seed-start 100 \
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--rep-seed-step 100 \
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--output-root ${OUTROOT}"
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echo "============================================================"
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echo " Phase 2 overnight batch"
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echo " $(date)"
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echo "============================================================"
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# ----------------------------------------------------------------
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# 1. Leaky CV (eye-level splits, no crop)
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# ----------------------------------------------------------------
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echo ""
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echo "=== [1/6] Leaky CV (eye-level) ==="
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$SCRIPT $BASE \
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--leaky-cv \
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--run-name imageonly_resnet50_leaky
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# ----------------------------------------------------------------
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# 2. Proper CV (patient-level, no crop) — baseline
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# ----------------------------------------------------------------
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echo ""
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echo "=== [2/6] Proper CV (patient-level, baseline) ==="
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$SCRIPT $BASE \
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--run-name imageonly_resnet50_proper
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# ----------------------------------------------------------------
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# 3. GT crop, scale=1.1 (paper-matched tight crop)
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# ----------------------------------------------------------------
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echo ""
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echo "=== [3/6] GT crop, scale=1.1 ==="
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$SCRIPT $BASE \
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--img-crop-gt \
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--img-crop-manifest ${MANIFEST} \
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--img-crop-scale 1.1 \
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--img-crop-size 200 \
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--run-name imageonly_resnet50_gtcrop_1.1
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# ----------------------------------------------------------------
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# 4. GT crop, scale=2.5 (default generous crop)
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# ----------------------------------------------------------------
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echo ""
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echo "=== [4/6] GT crop, scale=2.5 ==="
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$SCRIPT $BASE \
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--img-crop-gt \
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--img-crop-manifest ${MANIFEST} \
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--img-crop-scale 2.5 \
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--img-crop-size 200 \
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--run-name imageonly_resnet50_gtcrop_2.5
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# ----------------------------------------------------------------
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# 5. UNet crop, scale=1.1
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# ----------------------------------------------------------------
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echo ""
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echo "=== [5/6] UNet crop, scale=1.1 ==="
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$SCRIPT $BASE \
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--img-crop-weights ${UNET_WEIGHTS} \
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--img-crop-manifest ${MANIFEST} \
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--img-crop-scale 1.1 \
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--img-crop-size 200 \
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--run-name imageonly_resnet50_unetcrop_1.1
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# ----------------------------------------------------------------
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# 6. UNet crop, scale=2.5
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# ----------------------------------------------------------------
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echo ""
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echo "=== [6/6] UNet crop, scale=2.5 ==="
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$SCRIPT $BASE \
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--img-crop-weights ${UNET_WEIGHTS} \
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--img-crop-manifest ${MANIFEST} \
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--img-crop-scale 2.5 \
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--img-crop-size 200 \
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--run-name imageonly_resnet50_unetcrop_2.5
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# ----------------------------------------------------------------
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# 7. Refugelike backbone (proper CV, no crop) — pre-training effect
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# ----------------------------------------------------------------
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echo ""
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echo "=== [7/7] Refugelike backbone (proper CV, no crop) ==="
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$SCRIPT $BASE \
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--backbone refugelike \
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--run-name imageonly_refugelike_proper
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echo ""
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echo "============================================================"
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echo " All done — $(date)"
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echo "============================================================"
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