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208 | import json
from pathlib import Path
from typing import List
import pandas as pd
ROOT_FOLDER = Path(__file__).parent
METHODS_FOLDER = ROOT_FOLDER / "existing_methods"
DEFORM_SCENES = [
"nerf_lego",
"nerf_chair",
"nerf_ficus",
"nerf_hotdog"
]
DEFORMING_NERF_SCENES = [
"nerf_lego",
"nerf_chair",
"nsvf_robot",
"nsvf_toad"
]
CAGE_TYPE = ["broxy", "deforming_nerf"]
def read_deforming_nerf_training_stats(scenes: List[str]) -> pd.DataFrame:
table = []
for scene in scenes:
p = METHODS_FOLDER / "deforming_nerf" / "opt" / "ckpt" / scene / "time_secs.txt"
time_sec = float(p.read_text(encoding="UTF-8"))
table.append(("deforming_nerf", scene, time_sec))
return pd.DataFrame(table, columns=['method', 'scene', 'time_sec'])
def read_deforming_nerf_deformation_stats(scenes: List[str], cage_type: str) -> pd.DataFrame:
table = []
ctype = cage_type
for scene in scenes:
p = METHODS_FOLDER / "results" / "deforming_nerf" / f"benchmark_{scene}_{ctype}.json"
d = json.loads(p.read_text(encoding="UTF-8"))
table.append(("deforming_nerf", ctype, scene, "preprocess", d["preprocess_ms_avg"]))
table.append(("deforming_nerf", ctype, scene, "deform", d["deform_ms_avg"]))
table.append(("deforming_nerf", ctype, scene, "render", d["render_ms_avg"]))
return pd.DataFrame(table, columns=['method', 'cage_type', 'scene', 'stage', 'time_ms'])
def read_games_training_stats(scenes: List[str]) -> pd.DataFrame:
table = []
for scene in scenes:
p = METHODS_FOLDER / "gaussian_mesh_splatting" / "output" / scene / "training_time.json"
time_sec = json.loads(p.read_text(encoding="UTF-8"))["training_time_sec"]
table.append(("games", scene, time_sec))
return pd.DataFrame(table, columns=['method', 'scene', 'time_sec'])
def read_games_deformation_stats(scenes: List[str], cage_type: str) -> pd.DataFrame:
table = []
ctype = cage_type
for scene in scenes:
p = METHODS_FOLDER / "results" / "games" / f"benchmark_{scene}_{ctype}.json"
d = json.loads(p.read_text(encoding="UTF-8"))
preprocess = d["avg_preprocess_time_ms"] + d["avg_preprocess_time2_ms"]
table.append(("games", ctype, scene, "preprocess", preprocess))
table.append(("games", ctype, scene, "deform", d["avg_deform_time_ms"]))
table.append(("games", ctype, scene, "render", d["avg_render_time_ms"]))
return pd.DataFrame(table, columns=['method', 'cage_type', 'scene', 'stage', 'time_ms'])
def read_sugar_training_stats(scenes: List[str]) -> pd.DataFrame:
table = []
for scene in scenes:
base = METHODS_FOLDER / "sugar" / "output" / scene
p = base / "training_time_coarse.json"
coarse_time_sec = json.loads(p.read_text(encoding="UTF-8"))["training_time_sec"]
p = base / "fine_benchmark.json"
fine_time_sec = json.loads(p.read_text(encoding="UTF-8"))["training_time_sec"]
table.append(("sugar", scene, coarse_time_sec + fine_time_sec))
return pd.DataFrame(table, columns=['method', 'scene', 'time_sec'])
def read_sugar_deformation_stats(scenes: List[str], cage_type: str) -> pd.DataFrame:
table = []
ctype = cage_type
for scene in scenes:
p = METHODS_FOLDER / "results" / "sugar" / f"benchmark_{scene}_{ctype}.json"
d = json.loads(p.read_text(encoding="UTF-8"))
table.append(("sugar", ctype, scene, "preprocess", d["avg_time_preproc_ms"]))
table.append(("sugar", ctype, scene, "deform", d["avg_time_deform_ms"]))
table.append(("sugar", ctype, scene, "render", d["avg_time_render_ms"]))
return pd.DataFrame(table, columns=['method', 'cage_type', 'scene', 'stage', 'time_ms'])
def read_vanilla_training_stats(scenes: List[str]) -> pd.DataFrame:
table = []
for scene in scenes:
base = ROOT_FOLDER / "gs3d" / "output" / scene
p = base / "training_time.json"
time_sec = json.loads(p.read_text(encoding="UTF-8"))["training_time_sec"]
table.append(("vanilla", scene, time_sec))
return pd.DataFrame(table, columns=['method', 'scene', 'time_sec'])
def read_ours_deformation_stats(scenes: List[str], cage_type: str) -> pd.DataFrame:
table = []
ctype = cage_type
for scene in scenes:
p = ROOT_FOLDER / f"exp-quant-benchmark-{ctype}" / f"{scene}_all_benchmark.json"
d = json.loads(p.read_text(encoding="UTF-8"))
table.append(("ours", ctype, scene, "preprocess", d["preprocess_time_ms_avg"]))
table.append(("ours", ctype, scene, "deform", d["deform_time_ms_avg"]))
table.append(("ours", ctype, scene, "render", d["render_time_ms_avg"]))
return pd.DataFrame(table, columns=['method', 'cage_type', 'scene', 'stage', 'time_ms'])
def read_frosting_training_stats(scenes: List[str]) -> pd.DataFrame:
table = []
for scene in scenes:
base = METHODS_FOLDER / "frosting" / "output" / "vanilla_gs" / scene
p = base / "training_time_coarse.json"
coarse_time_sec = json.loads(p.read_text(encoding="UTF-8"))["training_time_sec"]
p = base / "fine_benchmark.json"
fine_time_sec = json.loads(p.read_text(encoding="UTF-8"))["training_time_sec"]
table.append(("frosting", scene, coarse_time_sec + fine_time_sec))
return pd.DataFrame(table, columns=['method', 'scene', 'time_sec'])
def read_frosting_deformation_stats(scenes: List[str], cage_type: str) -> pd.DataFrame:
table = []
ctype = cage_type
for scene in scenes:
p = METHODS_FOLDER / "results" / "frosting" / f"benchmark_{scene}_{ctype}.json"
d = json.loads(p.read_text(encoding="UTF-8"))
table.append(("frosting", ctype, scene, "preprocess", d["avg_time_preproc_ms"]))
table.append(("frosting", ctype, scene, "deform", d["avg_time_deform_ms"]))
table.append(("frosting", ctype, scene, "render", d["avg_time_render_ms"]))
return pd.DataFrame(table, columns=['method', 'cage_type', 'scene', 'stage', 'time_ms'])
def main():
for cage_type in ["broxy", "deforming_nerf"]:
if cage_type == "broxy":
training_scenes = DEFORM_SCENES.copy()
benchmark_scenes = DEFORM_SCENES
elif cage_type == "deforming_nerf":
training_scenes = DEFORMING_NERF_SCENES
benchmark_scenes = DEFORMING_NERF_SCENES
else:
assert False
method_ordering = ["ours", "vanilla", "deforming_nerf", "sugar", "games", "frosting"]
all_train_stats = pd.concat([
read_deforming_nerf_training_stats(training_scenes),
read_games_training_stats(training_scenes),
read_sugar_training_stats(training_scenes),
read_frosting_training_stats(training_scenes),
read_vanilla_training_stats(training_scenes)
], ignore_index=True)
all_train_stats['method'] = pd.Categorical(all_train_stats['method'], categories=method_ordering, ordered=True)
print("--- writing training stats & averages ---")
all_train_stats["time_sec"] = all_train_stats["time_sec"].round(2)
all_train_stats.to_csv(f"exp_{cage_type}_all_train_stats.csv")
avg = all_train_stats.groupby("method").agg({'time_sec': 'mean'}).reset_index()
avg = avg.sort_values('method')
for col in avg.columns:
if col != 'method':
avg[col] = avg[col].round(2)
avg.to_csv(f"exp_{cage_type}_all_train_stats_avg.csv")
all_deform_stats = pd.concat([
read_deforming_nerf_deformation_stats(benchmark_scenes, cage_type),
read_games_deformation_stats(benchmark_scenes, cage_type),
read_sugar_deformation_stats(benchmark_scenes, cage_type),
read_frosting_deformation_stats(benchmark_scenes, cage_type),
read_ours_deformation_stats(benchmark_scenes, cage_type)
], ignore_index=True)
all_deform_stats['method'] = pd.Categorical(all_deform_stats['method'], categories=method_ordering, ordered=True)
print("--- writing deformation stats & averages ---")
all_deform_stats["time_ms"] = all_deform_stats["time_ms"].round(2)
all_deform_stats.to_csv(f"exp_{cage_type}_all_deform_stats.csv")
avg = all_deform_stats.groupby(["method", "cage_type", "stage"]).agg({'time_ms': 'mean'}).reset_index()
avg = avg.sort_values('method')
avg.to_csv(f"exp_{cage_type}_all_deform_stats_avg.csv")
broxy_avg = avg[avg['cage_type'] == cage_type].drop('cage_type', axis=1)
broxy_avg_pivoted = broxy_avg.pivot(index='method', columns='stage', values='time_ms').reset_index()
broxy_avg_pivoted.columns = ['method', 'deform_time_ms', 'preprocess_time_ms', 'render_time_ms']
broxy_avg_pivoted = broxy_avg_pivoted[['method', 'preprocess_time_ms', 'deform_time_ms', 'render_time_ms']]
for col in broxy_avg_pivoted.columns:
if col != 'method':
broxy_avg_pivoted[col] = broxy_avg_pivoted[col].round(2)
deform_time_fps = (1000 / broxy_avg_pivoted["deform_time_ms"]).round(2)
broxy_avg_pivoted["deform_time_summary"] = [
f"{avg:.2f} ({fps:.2f}FPS)"
for avg, fps in zip(broxy_avg_pivoted["deform_time_ms"], deform_time_fps)
]
render_time_fps = (1000 / broxy_avg_pivoted["render_time_ms"]).round(2)
broxy_avg_pivoted["render_time_summary"] = [
f"{avg:.2f} ({fps:.2f}FPS)"
for avg, fps in zip(broxy_avg_pivoted["render_time_ms"], render_time_fps)
]
broxy_avg_pivoted = broxy_avg_pivoted.sort_values('method')
broxy_avg_pivoted.to_csv(f"exp_{cage_type}_all_deform_stats_pivoted.csv")
if __name__ == '__main__':
main()
|