#!/usr/bin/env bash
# SUM Parts - run inference on the converted Korean tile
#
# This answers one question: does a SUM Parts model accept our data and emit
# per-point predictions? It does NOT measure anything. The checkpoint is the
# 1-epoch smoke-test model and the tile carries label=0 everywhere, so the
# reported mIoU/OA are meaningless by construction. Look at whether it runs and
# what the predicted class histogram looks like, nothing else.
set -euo pipefail

CONDA_ROOT="$HOME/miniconda3"
ENV_NAME="sumparts"
REPO="$HOME/sum-parts/semantic_segmentation/PointNeXt_bundle"
SEG="$REPO/examples/segmentation"

TILE="$HOME/sum-parts/data/korea_poc/seosan_BlockYBA_tile0.ply"
TRACK="$HOME/sum-parts/data/korea_poc_track"
LOG="${LOG:-/tmp/sumparts_poc_infer.log}"

source "$CONDA_ROOT/etc/profile.d/conda.sh"
conda activate "$ENV_NAME"
export WANDB_MODE=disabled WANDB_SILENT=true
export CUDA_HOME="$CONDA_PREFIX"

[ -f "$TILE" ] || { echo "error: $TILE missing -- run poc_korea.sh first" >&2; exit 1; }

# The dataset class globs {train,val,test}/*.ply and errors on a missing split,
# so all three have to exist even for a test-only run.
for split in train val test; do
    mkdir -p "$TRACK/$split"
    ln -f "$TILE" "$TRACK/$split/$(basename "$TILE")"
done
rm -rf "$TRACK/processed"

# The cfg has to match the architecture that wrote the checkpoint; hardcoding
# one here loads the weights into the wrong model and torch raises on the
# state_dict.
source "/mnt/d/MYCLAUDE_PROJECT/sum-parts-test/scripts/resolve_ckpt.sh"

cd "$SEG"

set +e
python -u main.py \
    --cfg "../../cfgs/sumv2_triangle/${CKPT_CFG}.yaml" \
    mode=test \
    --pretrained_path "$CKPT" \
    dataset.common.data_root="$TRACK" \
    wandb.use_wandb=False \
    batch_size=2 \
    val_batch_size=1 \
    > "$LOG" 2>&1
rc=$?
set -e

echo "=== exit=$rc | last 25 lines ==="
tail -25 "$LOG"
[ "$rc" -eq 0 ] && echo "POC INFER DONE" || echo "POC INFER FAILED (rc=$rc)"
exit "$rc"