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baselines/run_all.sh

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+#!/bin/bash
+# =============================================================================
+# Run all deterministic baselines: train, predict, evaluate
+# =============================================================================
+set -e
+
+DATA_ROOT="data"
+EPOCHS=100
+BATCH_SIZE=32
+LR=0.001
+
+echo "============================================================"
+echo "Deterministic Segmentation Baselines for LIDC-IDRI"
+echo "============================================================"
+
+# Step 1: Prepare data (if not already done)
+if [ ! -d "${DATA_ROOT}/flat_train/images" ]; then
+    echo ""
+    echo "[Step 1] Preparing flat dataset with majority-vote masks..."
+    python baselines/prepare_data.py --data_root ${DATA_ROOT} --skip_nnunet
+else
+    echo "[Step 1] Flat dataset already exists, skipping..."
+fi
+
+# Step 2: Train and predict for each model
+MODELS=("unet" "attention_unet" "unetpp" "transunet")
+
+for MODEL in "${MODELS[@]}"; do
+    echo ""
+    echo "============================================================"
+    echo "[Step 2] Training ${MODEL}..."
+    echo "============================================================"
+    
+    python baselines/train.py \
+        --model ${MODEL} \
+        --data_dir ${DATA_ROOT}/flat_train \
+        --epochs ${EPOCHS} \
+        --batch_size ${BATCH_SIZE} \
+        --lr ${LR} \
+        --checkpoint_dir checkpoints
+    
+    echo ""
+    echo "[Step 3] Predicting with ${MODEL}..."
+    python baselines/predict.py \
+        --model ${MODEL} \
+        --checkpoint checkpoints/${MODEL}_best.pth \
+        --test_dir ${DATA_ROOT}/flat_test \
+        --output_dir results/${MODEL} \
+        --num_samples 16
+    
+    echo ""
+    echo "[Step 4] Evaluating ${MODEL}..."
+    python evaluate.py \
+        --samples_dir results/${MODEL} \
+        --gt_dir ${DATA_ROOT}/testing \
+        --results_file results/${MODEL}_eval.csv
+    
+    echo ""
+    echo ">>> ${MODEL} complete!"
+    echo ""
+done
+
+# Step 5: Print comparison summary
+echo ""
+echo "============================================================"
+echo "FINAL RESULTS COMPARISON"
+echo "============================================================"
+python -c "
+import pandas as pd
+import os
+
+models = ['unet', 'attention_unet', 'unetpp', 'transunet']
+results = []
+
+for m in models:
+    f = f'results/{m}_eval.csv'
+    if os.path.exists(f):
+        df = pd.read_csv(f)
+        avg = df[df['image_id'] == 'AVERAGE'].iloc[0]
+        results.append({
+            'Method': m,
+            'GED ↓': f\"{avg['ged_iou_paper']:.4f}\",
+            'CI ↑': f\"{avg['CI_Score_Paper']:.4f}\",
+            'Dmax ↑': f\"{avg['D_max_Paper']:.4f}\",
+            'Dice': f\"{avg['avg_dice']:.4f}\",
+            'IoU': f\"{avg['avg_iou']:.4f}\",
+        })
+
+if results:
+    df = pd.DataFrame(results)
+    print(df.to_string(index=False))
+else:
+    print('No results found!')
+"
+
+echo ""
+echo "All baselines complete!"