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tabulate.py
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tabulate.py
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import os
import json
import pandas as pd
import argparse
def tabulate_results(eval_dir, experiment_csv_fname, out_pivot_fname, out_all_results_fname):
exists = os.path.exists(eval_dir)
if not exists:
raise ValueError(f"eval_dir {eval_dir} does not exist")
print(f"eval_dir: {eval_dir}")
evals_order = [
## llava
# 'vqav2',
'gqa',
'vizwiz',
'scienceqa',
'textvqa',
'pope',
'mme',
'mmbench_en',
'mmbench_cn',
'seed',
# 'llava_w',
# 'mmvet', # submission
## Addtl
'mmmu',
'mathvista',
'ai2d',
'chartqa',
# 'docvqa', # submission
# 'infovqa', # submission
# 'stvqa', # submission
'ocrbench',
'mmstar',
'realworldqa',
'qbench',
'blink',
'mmvp',
'vstar',
'ade',
'omni',
'coco'
# 'synthdog', # seems broken?
]
evals_col_overrides = {
'scienceqa': '100x_multimodal_acc',
'mme': "Perception",
'mmbench_en': "100x_circular_accuracy",
'mmbench_cn': "100x_circular_accuracy",
'seed': "100x_accuracy",
'mmmu': "100x_accuracy",
'mathvista': "100x_accuracy",
'ocrbench': "total_accuracy['accuracy']",
'qbench': "100x_accuracy",
'blink': "100x_accuracy",
'ade': "100x_accuracy",
'omni': "100x_accuracy",
'coco': "100x_accuracy",
}
# gather results from each eval
dfs = []
for eval_name in evals_order:
results_path = os.path.join(eval_dir, eval_name, experiment_csv_fname)
if not os.path.exists(results_path):
print(f"Skipping {eval_name} as no results file found")
continue
try:
df = pd.read_csv(results_path)
except Exception as e:
print(f"Error reading {results_path}: {e}")
raise e
if eval_name in evals_col_overrides:
override = evals_col_overrides[eval_name]
if override.startswith("100x_"):
override = override[5:]
df["accuracy"] = df[override] * 100
elif override == "total_accuracy['accuracy']":
df["accuracy"] = df["total_accuracy"].apply(lambda x: json.loads(x.replace("'", '"'))["accuracy"])
else:
df["accuracy"] = df[override]
df["eval_name"] = eval_name
df = df.sort_values("time")
df = df.drop_duplicates("model", keep="last")
df = df[["time", "eval_name", "model", "accuracy"]]
dfs.append(df)
all_results = pd.concat(dfs)
all_results.sort_values("time").to_csv(out_all_results_fname, index=False)
print(f"Saved all results to {out_all_results_fname}")
pivot = all_results.pivot(index="model", columns="eval_name", values="accuracy")
pivot = pivot[evals_order]
# if .xlsx, to_excel, else to_csv
if out_pivot_fname.endswith(".xlsx"):
pivot.to_excel(out_pivot_fname)
print(f"Saved excel file pivot to {out_pivot_fname}")
else:
pivot.to_csv(out_pivot_fname)
print(f"Saved csv file pivot to {out_pivot_fname}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Tabulate experiment results")
parser.add_argument("--eval_dir", type=str, default="eval", help="Directory containing evaluation results")
parser.add_argument("--experiment_csv", type=str, default="experiments.csv", help="Name of the CSV file containing experiment results")
parser.add_argument("--out_pivot", type=str, default="pivot.xlsx", help="Name of the output file (Excel or CSV)")
parser.add_argument("--out_all_results", type=str, default="all_results.csv", help="Name of the CSV file to save all results")
args = parser.parse_args()
tabulate_results(args.eval_dir, args.experiment_csv, args.out_pivot, args.out_all_results)