evaluation for synthetic nerf

examples/benchmarks/compression/mcmc_syn.sh

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+SCENE_DIR="data/nerf_synthetic"
+SCENE_LIST="chair drums ficus hotdog lego materials mic ship"
+
+# # 0.36M GSs
+# RESULT_DIR="results/benchmark_syn_mcmc_0_36M_png_compression"
+# CAP_MAX=360000
+
+# # 0.49M GSs
+# RESULT_DIR="results/benchmark_syn_mcmc_0_49M_png_compression"
+# CAP_MAX=490000
+
+# 1M GSs
+RESULT_DIR="results/benchmark_syn_mcmc_1M_png_compression"
+CAP_MAX=1000000
+
+# # 4M GSs
+# RESULT_DIR="results/benchmark_syn_mcmc_4M_png_compression"
+# CAP_MAX=4000000
+
+for SCENE in $SCENE_LIST;
+do
+    echo "Running $SCENE"
+
+    # train without eval
+    CUDA_VISIBLE_DEVICES=0 python simple_trainer.py mcmc --eval_steps -1 --disable_viewer --data_factor 1 \
+        --strategy.cap-max $CAP_MAX \
+        --data_dir $SCENE_DIR/$SCENE/ \
+        --result_dir $RESULT_DIR/$SCENE/
+
+    # eval: use vgg for lpips to align with other benchmarks
+    CUDA_VISIBLE_DEVICES=0 python simple_trainer.py mcmc --disable_viewer --data_factor 1 \
+        --strategy.cap-max $CAP_MAX \
+        --data_dir $SCENE_DIR/$SCENE/ \
+        --result_dir $RESULT_DIR/$SCENE/ \
+        --lpips_net vgg \
+        --compression png \
+        --ckpt $RESULT_DIR/$SCENE/ckpts/ckpt_29999_rank0.pt
+done
+
+# Zip the compressed files and summarize the stats
+if command -v zip &> /dev/null
+then
+    echo "Zipping results"
+    python benchmarks/compression/summarize_stats.py --results_dir $RESULT_DIR --scenes $SCENE_LIST
+else
+    echo "zip command not found, skipping zipping"
+fi