grounding-dino/run_eval_and_vis.sh

#!/bin/bash
# ================================================================
# Grounding DINO 评估 + 可视化 一体化脚本
# 功能:
#   1. 用 test.py 跑推理，保存 pkl 结果 + 可视化图片
#   2. 用自定义脚本计算 Precision / Recall / F1 (per-class + overall)
#   3. 用 analyze_results.py 展示 topk 好/坏样本
#   4. 用 confusion_matrix.py 生成混淆矩阵
# ================================================================
set -euo pipefail

resolve_python() {
    if [ -n "${PYTHON:-}" ]; then
        echo "${PYTHON}"
    elif command -v python3 >/dev/null 2>&1; then
        command -v python3
    elif command -v python >/dev/null 2>&1; then
        command -v python
    else
        echo ""
    fi
}

detect_gpus() {
    if [ -n "${GPUS:-}" ]; then
        echo "${GPUS}"
        return
    fi

    if [ -n "${CUDA_VISIBLE_DEVICES:-}" ]; then
        awk -F',' '{print NF}' <<< "${CUDA_VISIBLE_DEVICES}"
        return
    fi

    if command -v nvidia-smi >/dev/null 2>&1; then
        local n
        n=$(nvidia-smi -L 2>/dev/null | wc -l | tr -d ' ')
        if [ "${n}" -gt 0 ]; then
            echo "${n}"
            return
        fi
    fi

    echo 1
}

resolve_checkpoint() {
    local requested_path="${1:-}"
    local work_dir="${2}"
    local best_checkpoint

    if [ -n "${requested_path}" ] && [ -f "${requested_path}" ]; then
        echo "${requested_path}"
        return
    fi

    best_checkpoint=$(find "${work_dir}" -maxdepth 1 -type f -name 'best*.pth' | sort | tail -n 1)
    if [ -n "${best_checkpoint}" ] && [ -f "${best_checkpoint}" ]; then
        echo "${best_checkpoint}"
        return
    fi

    if [ -f "${work_dir}/last_checkpoint" ]; then
        local last_checkpoint
        last_checkpoint=$(cat "${work_dir}/last_checkpoint")
        if [ -f "${last_checkpoint}" ]; then
            echo "${last_checkpoint}"
            return
        fi
    fi

    local latest_checkpoint
    latest_checkpoint=$(find "${work_dir}" -maxdepth 1 -type f -name '*.pth' | sort | tail -n 1)
    if [ -n "${latest_checkpoint}" ] && [ -f "${latest_checkpoint}" ]; then
        echo "${latest_checkpoint}"
        return
    fi

    echo ""
}

# ============== 路径配置 ==============
PYTHON="$(resolve_python)"
BASE_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
MMDET_DIR="${BASE_DIR}/mmdetection"
WORK_DIR="${BASE_DIR}/work_dirs/mm_grounding_dino_traffic"

CONFIG="${CONFIG:-${WORK_DIR}/grounding_dino_swin-t_finetune_traffic.py}"
CHECKPOINT="${CHECKPOINT:-${WORK_DIR}/best_coco_traffic_sign_precision_epoch_18.pth}"
CHECKPOINT="$(resolve_checkpoint "${CHECKPOINT}" "${WORK_DIR}")"

OUTPUT_DIR="${OUTPUT_DIR:-${BASE_DIR}/eval_output}"
PKL_FILE="${OUTPUT_DIR}/predictions.pkl"
VIS_DIR="${OUTPUT_DIR}/vis_images"
ANALYZE_DIR="${OUTPUT_DIR}/analyze_results"
CM_DIR="${OUTPUT_DIR}/confusion_matrix"

SCORE_THR="${SCORE_THR:-0.3}"
TOPK="${TOPK:-20}"
GPUS="$(detect_gpus)"
BATCH_SIZE="${BATCH_SIZE:-4}"

if [ -z "${PYTHON}" ]; then
    echo "Error: python executable not found."
    exit 1
fi

if [ ! -f "${CONFIG}" ]; then
    echo "Error: config file not found: ${CONFIG}"
    exit 1
fi

if [ -z "${CHECKPOINT}" ] || [ ! -f "${CHECKPOINT}" ]; then
    echo "Error: checkpoint file not found."
    echo "Tried default path and fallback lookup under: ${WORK_DIR}"
    exit 1
fi

mkdir -p "$OUTPUT_DIR" "$VIS_DIR" "$ANALYZE_DIR" "$CM_DIR"

# ============== Step 1: 推理 + 保存pkl + 可视化 ==============
echo "========================================"
echo "STEP 1: 运行推理 (保存pkl + 可视化)"
echo "========================================"
echo "CONFIG: ${CONFIG}"
echo "CHECKPOINT: ${CHECKPOINT}"
echo "GPUS: ${GPUS}"
echo "BATCH_SIZE: ${BATCH_SIZE}"

cd "${MMDET_DIR}"
if [ "${GPUS}" -le 1 ]; then
    "${PYTHON}" tools/test.py \
        "$CONFIG" \
        "$CHECKPOINT" \
        --out "$PKL_FILE" \
        --show-dir "$VIS_DIR" \
        --cfg-options test_dataloader.batch_size="${BATCH_SIZE}"
else
    bash tools/dist_test.sh \
        "$CONFIG" \
        "$CHECKPOINT" \
        "${GPUS}" \
        --out "$PKL_FILE" \
        --show-dir "$VIS_DIR" \
        --cfg-options test_dataloader.batch_size="${BATCH_SIZE}"
fi

echo ">>> pkl 结果已保存到: $PKL_FILE"
echo ">>> 可视化结果已保存到: $VIS_DIR"

# ============== Step 2: 计算 Precision / Recall / F1 ==============
echo "========================================"
echo "STEP 2: 计算 Precision / Recall / F1"
echo "========================================"

"${PYTHON}" ${BASE_DIR}/compute_recall_f1.py \
    --config "$CONFIG" \
    --pkl "$PKL_FILE" \
    --score-thr $SCORE_THR \
    --output "${OUTPUT_DIR}/metrics_report.txt"

# ============== Step 3: analyze_results (好/坏样本可视化) ==============
echo "========================================"
echo "STEP 3: 好/坏样本可视化 (topk=${TOPK})"
echo "========================================"

"${PYTHON}" ${MMDET_DIR}/tools/analysis_tools/analyze_results.py \
    "$CONFIG" \
    "$PKL_FILE" \
    "$ANALYZE_DIR" \
    --topk $TOPK \
    --show-score-thr $SCORE_THR \
    --cfg-options launcher=none

echo ">>> 好样本保存到: ${ANALYZE_DIR}/good/"
echo ">>> 坏样本保存到: ${ANALYZE_DIR}/bad/"

# ============== Step 4: 混淆矩阵 ==============
echo "========================================"
echo "STEP 4: 生成混淆矩阵"
echo "========================================"

"${PYTHON}" ${MMDET_DIR}/tools/analysis_tools/confusion_matrix.py \
    "$CONFIG" \
    "$PKL_FILE" \
    "$CM_DIR" \
    --score-thr $SCORE_THR \
    --tp-iou-thr 0.5

echo ">>> 混淆矩阵保存到: $CM_DIR"

echo ""
echo "========================================"
echo "全部完成! 结果目录结构:"
echo "  ${OUTPUT_DIR}/"
echo "  ├── predictions.pkl          (推理结果)"
echo "  ├── metrics_report.txt       (Precision/Recall/F1)"
echo "  ├── vis_images/              (检测可视化)"
echo "  ├── analyze_results/         (好坏样本对比)"
echo "  │   ├── good/                (检测效果好的样本)"
echo "  │   └── bad/                 (检测效果差的样本)"
echo "  └── confusion_matrix/        (混淆矩阵)"
echo "========================================"