reworked iop_corr, added explainability tools and plotting tools, cleanup codebase
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#!/usr/bin/env bash
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set -euo pipefail
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# Image-only runs v2.21 (6 total):
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# No crop: binary | multiclass
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# GT crop: binary | multiclass
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# UNet crop: binary | multiclass
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#
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# Purpose: isolate the effect of ROI cropping at the single-CNN level,
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# without any MD tower contribution (bridge-mode=image_only).
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ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/../../.." && pwd)"
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cd "$ROOT_DIR"
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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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COMMON=(
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--epochs 40
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--n-splits 5
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--batch-size 8
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--backbone refugelike
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--tower-mode single
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--bridge-mode image_only
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--single-warmup-tower-epochs 4
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--single-warmup-fused-epochs 0
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--img-crop-manifest "$MANIFEST"
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)
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# ── No crop ──────────────────────────────────────────────────────────────────
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echo "[1/6] No crop — binary, image-only..."
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python3 scripts/main/v2/run_multifold_v2.py \
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"${COMMON[@]}" --eval-mode binary \
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--run-name v2.21_imgonly_binary_nocrop_40ep_5fold
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echo "[2/6] No crop — multiclass, image-only..."
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python3 scripts/main/v2/run_multifold_v2.py \
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"${COMMON[@]}" --eval-mode multiclass \
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--run-name v2.21_imgonly_multiclass_nocrop_40ep_5fold
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# ── GT crop ──────────────────────────────────────────────────────────────────
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echo "[3/6] GT crop — binary, image-only..."
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python3 scripts/main/v2/run_multifold_v2.py \
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"${COMMON[@]}" --eval-mode binary --img-crop-gt \
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--run-name v2.21_imgonly_binary_gt_40ep_5fold
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echo "[4/6] GT crop — multiclass, image-only..."
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python3 scripts/main/v2/run_multifold_v2.py \
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"${COMMON[@]}" --eval-mode multiclass --img-crop-gt \
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--run-name v2.21_imgonly_multiclass_gt_40ep_5fold
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# ── UNet crop ────────────────────────────────────────────────────────────────
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echo "[5/6] UNet crop — binary, image-only..."
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python3 scripts/main/v2/run_multifold_v2.py \
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"${COMMON[@]}" --eval-mode binary \
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--img-crop-weights "$UNET_WEIGHTS" \
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--run-name v2.21_imgonly_binary_unet_40ep_5fold
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echo "[6/6] UNet crop — multiclass, image-only..."
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python3 scripts/main/v2/run_multifold_v2.py \
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"${COMMON[@]}" --eval-mode multiclass \
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--img-crop-weights "$UNET_WEIGHTS" \
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--run-name v2.21_imgonly_multiclass_unet_40ep_5fold
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echo "Image-only v2.21 runs complete."
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