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3a464db | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | #!/usr/bin/env bash
# Fast-dDrive — official Waymo E2E ADE / RFS scoring launcher.
#
# Consumes the predictions.json written by `run_eval.sh` (i.e.
# `eval/batch_inference.py`) and computes ADE@3s, ADE@5s, and the Rater
# Feedback Score (RFS) against the Waymo Open Dataset ground truth.
#
# This step depends on `tensorflow` + `waymo_open_dataset`, which conflict
# with the inference stack — install them in a separate env (we use one
# called `autovla`). See `data/README.md` for setup details.
#
# Required env:
# PRED_JSON — predictions.json produced by run_eval.sh
# GT — either a TFRecord glob (e.g. '/path/to/val*.tfrecord*')
# or a pre-computed gt_dict pickle (.pkl) from an earlier run
#
# Optional env:
# OUTPUT_DIR — default: <dirname of PRED_JSON>/waymo_metrics
# PYTHON — interpreter to use (default: python3 on $PATH). Point this
# at the autovla env's python when running on a clean shell.
set -eo pipefail
FAST_DDRIVE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
: "${PRED_JSON:?Set PRED_JSON to the predictions.json written by run_eval.sh.}"
: "${GT:?Set GT to a Waymo TFRecord glob or a gt_dict pickle (.pkl).}"
OUTPUT_DIR="${OUTPUT_DIR:-$(dirname "${PRED_JSON}")/waymo_metrics}"
PYTHON="${PYTHON:-python3}"
echo "=========================================="
echo "Fast-dDrive Waymo metrics"
echo " PRED_JSON: ${PRED_JSON}"
echo " GT: ${GT}"
echo " OUTPUT: ${OUTPUT_DIR}"
echo "=========================================="
"${PYTHON}" "${FAST_DDRIVE_ROOT}/eval/evaluate_waymo_metrics.py" \
--pred_json "${PRED_JSON}" \
--gt "${GT}" \
--output_dir "${OUTPUT_DIR}" \
"$@"
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