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80 lines
2.7 KiB
80 lines
2.7 KiB
#!/usr/bin/env bash
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# Step1 pretrain — 합성 데이터로 PGNet 학습
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#
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# 컨테이너 안 실행:
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# docker exec kr_lp_pgnet bash /workspace/kr_lp_pgnet/scripts/run_step1.sh
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#
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# 환경 변수:
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# DRY_RUN=1 2 epoch만 돌려 동작 검증
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# EPOCHS=N epoch 수 override (기본 config의 epoch_num)
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# NUM_SAMPLES=N 합성 데이터 수 (기본 50000)
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set -euo pipefail
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PADDLEOCR_DIR=/workspace/PaddleOCR
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KR_LP_DIR=/workspace/kr_lp_pgnet
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TRAIN_DATA=/workspace/train_data
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SYNTH_DIR="$TRAIN_DATA/kr_lp_synth"
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ASSET_DIR="$KR_LP_DIR/data_gen/Korean-license-plate-Generator"
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NUM_SAMPLES="${NUM_SAMPLES:-50000}"
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TS=$(date +%Y%m%d_%H%M)
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RUN_NAME="step1-${TS}"
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OUTPUT_DIR="$PADDLEOCR_DIR/output/kr_lp_pgnet_${TS}"
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LOG="$OUTPUT_DIR/run.log"
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echo "==========================="
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echo "RUN: $RUN_NAME"
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echo "OUTPUT: $OUTPUT_DIR"
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echo "==========================="
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# ── 1. 합성 데이터 생성 ──────────────────────────────────────────────────────
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echo "[1/3] 합성 데이터 생성 (${NUM_SAMPLES}장)"
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rm -rf "$SYNTH_DIR"
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python3.10 "$KR_LP_DIR/data_gen/generate_synthetic.py" \
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--asset_dir "$ASSET_DIR" \
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--out_dir "$SYNTH_DIR" \
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--num "$NUM_SAMPLES" \
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--dict "$KR_LP_DIR/dict/kr_lp_dict.txt"
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# ── 2. eval GT mat 생성 ─────────────────────────────────────────────────────
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echo "[2/3] eval GT mat 생성"
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python3.10 "$KR_LP_DIR/tools/make_gt_mat.py" \
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--label "$SYNTH_DIR/test/test.txt" \
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--out_dir "$SYNTH_DIR/gt"
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# ── 3. 학습 ─────────────────────────────────────────────────────────────────
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echo "[3/3] Step1 학습 시작"
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cd "$PADDLEOCR_DIR"
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if [ ! -e ./train_data ]; then
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ln -sf "$TRAIN_DATA" ./train_data
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fi
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mkdir -p "$OUTPUT_DIR"
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OVERRIDE=(
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-o Global.pretrained_model=./pretrain_models/train_step1/best_accuracy
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Global.load_static_weights=False
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Global.save_model_dir="${OUTPUT_DIR}/"
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Global.save_res_path="${OUTPUT_DIR}/predicts.txt"
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wandb.name="${RUN_NAME}"
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)
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if [ -n "${EPOCHS:-}" ]; then
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OVERRIDE+=(Global.epoch_num="$EPOCHS")
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fi
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if [ "${DRY_RUN:-0}" = "1" ]; then
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OVERRIDE+=(Global.epoch_num=2 Global.eval_batch_step="[0,200]")
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echo "DRY_RUN=1 → 2 epoch만 실행"
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fi
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echo " config: configs/e2e/kr_lp_pgnet.yml"
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echo " data: $SYNTH_DIR/"
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echo " output: $OUTPUT_DIR/"
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echo " wandb: $RUN_NAME"
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echo " log: $LOG"
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python3.10 tools/train.py -c configs/e2e/kr_lp_pgnet.yml "${OVERRIDE[@]}" 2>&1 | tee "$LOG"
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