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#!/bin/bash
# OV-COCO Failure Case Analysis - 完整流程脚本
# 包含:推理 -> 转换 -> 对比分析 -> 可视化 -> 生成报告
#
# 重要:使用官方 F-ViT test.py 进行推理,确保结果与评估指标一致
#
# Usage:
# bash run_comparison.sh [BACKBONE] [NUM_GPUS]
#
# Arguments:
# BACKBONE: vitb16 或 vitl14 (默认: vitb16)
# NUM_GPUS: 使用的GPU数量 (默认: 8)
#
# Examples:
# bash run_comparison.sh vitb16 8 # 使用ViT-B/16,8卡
# bash run_comparison.sh vitl14 8 # 使用ViT-L/14,8卡
set -e
# 参数解析
BACKBONE=${1:-vitb16}
NUM_GPUS=${2:-8}
PORT=${PORT:-12346}
# 路径设置
BASE_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
CLIPSELF_DIR="$(cd "$BASE_DIR/../../CLIPSelf/F-ViT" && pwd)"
# 数据集路径
ANN_FILE="$CLIPSELF_DIR/data/coco/zero-shot/instances_val2017_all_2.json"
IMG_DIR="$CLIPSELF_DIR/data/coco/val2017"
SEEN_CLASSES="$CLIPSELF_DIR/datasets/mscoco_seen_classes.json"
# 模型配置
declare -A DECLIP_CKPTS=(
["vitb16"]="/opt/tiger/xiaomoguhzz/declip2_ovcoco_detector/EVAB_dinov2B_epoch2.pth"
["vitl14"]="/opt/tiger/xiaomoguhzz/declip2_ovcoco_detector/EVAL_dinov2L_epoch3.pth"
)
declare -A CLIPSELF_CKPTS=(
["vitb16"]="/opt/tiger/xiaomoguhzz/fvit_eva_vitb16_ovcoco_clipself_proposals.pth"
["vitl14"]="/opt/tiger/xiaomoguhzz/fvit_eva_vitl14_ovcoco_clipself_proposals.pth"
)
declare -A CLIP_CKPTS=(
["vitb16"]="$CLIPSELF_DIR/work_dirs/clearclip_ovcoco/epoch_3.pth"
["vitl14"]="$CLIPSELF_DIR/work_dirs/clearclip_ovcoco_large/epoch_3.pth"
)
echo "========================================"
echo "OV-COCO Failure Case Analysis Pipeline"
echo "========================================"
echo "Backbone: $BACKBONE"
echo "Num GPUs: $NUM_GPUS"
echo "Base Dir: $BASE_DIR"
echo ""
# 检查必要文件
echo "[Checking] Required files..."
if [ ! -f "$ANN_FILE" ]; then
echo " Warning: Annotation file not found (may be a symlink on dev machine)"
echo " Path: $ANN_FILE"
fi
if [ ! -f "$SEEN_CLASSES" ]; then
echo " Warning: Seen classes file not found"
echo " Path: $SEEN_CLASSES"
fi
# 结果目录
DECLIP_PKL="$BASE_DIR/results/declip_${BACKBONE}/predictions.pkl"
CLIPSELF_PKL="$BASE_DIR/results/clipself_${BACKBONE}/predictions.pkl"
CLIP_PKL="$BASE_DIR/results/clearclip_${BACKBONE}/predictions.pkl"
DECLIP_PRED="$BASE_DIR/results/declip_${BACKBONE}/predictions.json"
CLIPSELF_PRED="$BASE_DIR/results/clipself_${BACKBONE}/predictions.json"
CLIP_PRED="$BASE_DIR/results/clearclip_${BACKBONE}/predictions.json"
COMPARISON_OUTPUT="$BASE_DIR/analysis_output/comparison_${BACKBONE}"
VISUALIZATION_OUTPUT="$BASE_DIR/analysis_output/visualizations_${BACKBONE}"
REPORT_OUTPUT="$BASE_DIR/analysis_output/paper_report_${BACKBONE}"
# ============================================================================
# Step 1: 使用官方 F-ViT test.py 推理 (保存pkl)
# ============================================================================
echo ""
echo "========================================"
echo "Step 1: Official F-ViT Inference"
echo "========================================"
run_official_inference() {
local model_name=$1
local config=$2
local checkpoint=$3
local output_pkl=$4
if [ -f "$output_pkl" ]; then
echo " [$model_name] Results exist: $output_pkl, skipping..."
return
fi
if [ ! -f "$checkpoint" ]; then
echo " [$model_name] Error: Checkpoint not found: $checkpoint"
return
fi
echo " [$model_name] Running official inference..."
echo " Config: $config"
echo " Checkpoint: $checkpoint"
local work_dir="/opt/tiger/fvit_eval_${model_name}_${BACKBONE}"
local tmpdir="/opt/tiger/mmdet_tmpdir_${model_name}_${BACKBONE}"
mkdir -p "$work_dir" "$tmpdir" "$(dirname $output_pkl)"
cd "$CLIPSELF_DIR"
torchrun \
--nnodes=1 \
--node_rank=0 \
--nproc_per_node=$NUM_GPUS \
--master_addr=127.0.0.1 \
--master_port=$PORT \
"$CLIPSELF_DIR/test.py" \
"$config" \
"$checkpoint" \
--launcher pytorch \
--work-dir "$work_dir" \
--out "$output_pkl" \
--eval bbox \
--tmpdir "$tmpdir" \
--cfg-options data.test.ann_file="$ANN_FILE" data.test.img_prefix="$IMG_DIR"
echo " [$model_name] Done! Saved to $output_pkl"
# 更新端口避免冲突
PORT=$((PORT + 1))
}
# 运行三个模型的推理
run_official_inference "declip" \
"$BASE_DIR/configs/declip_${BACKBONE}_ovcoco.py" \
"${DECLIP_CKPTS[$BACKBONE]}" \
"$DECLIP_PKL"
run_official_inference "clipself" \
"$BASE_DIR/configs/clipself_${BACKBONE}_ovcoco.py" \
"${CLIPSELF_CKPTS[$BACKBONE]}" \
"$CLIPSELF_PKL"
run_official_inference "clearclip" \
"$BASE_DIR/configs/clearclip_${BACKBONE}_ovcoco.py" \
"${CLIP_CKPTS[$BACKBONE]}" \
"$CLIP_PKL"
# ============================================================================
# Step 1.5: 转换 pkl 到 json
# ============================================================================
echo ""
echo "========================================"
echo "Step 1.5: Convert pkl to json"
echo "========================================"
cd "$BASE_DIR"
python convert_pkl_to_json.py --backbone "$BACKBONE"
# ============================================================================
# Step 2: 跨模型对比分析
# ============================================================================
echo ""
echo "========================================"
echo "Step 2: Cross-Model Comparison Analysis"
echo "========================================"
if [ ! -f "$DECLIP_PRED" ] || [ ! -f "$CLIPSELF_PRED" ] || [ ! -f "$CLIP_PRED" ]; then
echo "Error: Some prediction files are missing. Please run inference first."
echo " DeCLIP: $DECLIP_PRED"
echo " CLIPSelf: $CLIPSELF_PRED"
echo " CLIP: $CLIP_PRED"
exit 1
fi
cd "$BASE_DIR"
python compare_models_v2.py \
--declip-pred "$DECLIP_PRED" \
--clipself-pred "$CLIPSELF_PRED" \
--clip-pred "$CLIP_PRED" \
--ann-file "$ANN_FILE" \
--seen-classes "$SEEN_CLASSES" \
--output "$COMPARISON_OUTPUT" \
--top-k 50
echo "Comparison analysis saved to: $COMPARISON_OUTPUT"
# ============================================================================
# Step 3: 可视化(优势 + 未解决)
# ============================================================================
echo ""
echo "========================================"
echo "Step 3: Visualization (Advantage + Unsolved)"
echo "========================================"
python visualize_comparison.py \
--cases "$COMPARISON_OUTPUT/top_advantage_cases.json" \
--ann-file "$ANN_FILE" \
--img-dir "$IMG_DIR" \
--output "$VISUALIZATION_OUTPUT/advantage" \
--case-type advantage \
--dpi 150
python visualize_comparison.py \
--cases "$COMPARISON_OUTPUT/unsolved_small_novel_cases.json" \
--ann-file "$ANN_FILE" \
--img-dir "$IMG_DIR" \
--output "$VISUALIZATION_OUTPUT/unsolved" \
--case-type unsolved \
--dpi 150
echo "Visualizations saved to:"
echo " - $VISUALIZATION_OUTPUT/advantage"
echo " - $VISUALIZATION_OUTPUT/unsolved"
# ============================================================================
# Step 4: 生成论文报告
# ============================================================================
echo ""
echo "========================================"
echo "Step 4: Generate Paper Report"
echo "========================================"
python generate_paper_report.py \
--statistics "$COMPARISON_OUTPUT/statistics.json" \
--output "$REPORT_OUTPUT" \
--dpi 300
echo "Report saved to: $REPORT_OUTPUT"
# ============================================================================
# 完成
# ============================================================================
echo ""
echo "========================================"
echo "Pipeline Complete!"
echo "========================================"
echo ""
echo "Output directories:"
echo " - Comparison analysis: $COMPARISON_OUTPUT"
echo " - Visualizations: $VISUALIZATION_OUTPUT"
echo " - Paper report: $REPORT_OUTPUT"
echo ""
echo "Key files:"
echo " - Top 50 advantage cases: $COMPARISON_OUTPUT/top_advantage_cases.json"
echo " - Advantage index: $VISUALIZATION_OUTPUT/advantage/index.html"
echo " - Unsolved index: $VISUALIZATION_OUTPUT/unsolved/index.html"
echo " - LaTeX tables: $REPORT_OUTPUT/tables.tex"
echo " - Classification accuracy chart: $REPORT_OUTPUT/cls_acc_comparison.pdf"
echo ""