added fused classifier head
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@@ -1,34 +1,45 @@
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#!/usr/bin/env bash
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set -euo pipefail
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# Runs two back-to-back Hypertower mode comparisons with ROI cropping:
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# 1) GT masks
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# 2) UNet masks
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# Six V2HyperTower runs using GT ROI crop:
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# binary × {ensemble, bilateral} (runs 1-2)
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# multiclass × {ensemble, bilateral} (runs 3-4)
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# multiclass × {ensemble, bilateral} + balanced (runs 5-6)
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ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/../../.." && pwd)"
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cd "$ROOT_DIR"
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CROP_ARGS=(
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--img-crop-manifest manifest.csv
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--img-crop-gt
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)
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COMMON_ARGS=(
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--eval-modes binary multiclass
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--tower-modes single ensemble bilateral
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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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--img-crop-manifest manifest.csv
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)
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echo "[1/2] Starting GT ROI run..."
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# Runs 1-4: binary + multiclass, ensemble + bilateral, no balanced sampling
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echo "[1/2] GT ROI — binary + multiclass, ensemble + bilateral..."
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python3 scripts/basic_analysis/compare_hypertower_modes.py \
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"${COMMON_ARGS[@]}" \
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--img-crop-gt \
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--run-name v2_modes_full_40ep_5fold_roi_gt_holdout
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"${CROP_ARGS[@]}" \
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--eval-modes binary multiclass \
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--tower-modes ensemble bilateral \
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--run-name v2_modes_gt_40ep_5fold_no_single_v2
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echo "[2/2] Starting UNet ROI run..."
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# Runs 5-6: multiclass only, ensemble + bilateral, balanced sampling
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# (reuse the crop cache built during runs 1-4)
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echo "[2/2] GT ROI — multiclass, ensemble + bilateral, balanced sampling..."
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python3 scripts/basic_analysis/compare_hypertower_modes.py \
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"${COMMON_ARGS[@]}" \
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--img-crop-weights models/v2/refuge/segmentation/per_image_refuge_build/best.pt \
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--img-crop-normalize per_image \
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--run-name v2_modes_full_40ep_5fold_roi_unet_perimage_refugebuild_holdout
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"${CROP_ARGS[@]}" \
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--eval-modes multiclass \
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--tower-modes ensemble bilateral \
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--balanced-sampling \
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--persist-img-crop-cache \
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--run-name v2_modes_gt_40ep_5fold_multiclass_balanced_v2
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echo "All runs complete."
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