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run_calculate_stats.py
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run_calculate_stats.py
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import csv
import itertools
import argparse
import numpy as np
import os
eval_specs = [
{"exp_name": "num_frames_12", "num_frames": 12},
]
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--metadata",
type=str,
default="/scratch-ssd/vivid123/scripts/gso_metadata_object_prompt_100.csv",
)
args = parser.parse_args()
psnr_save_dir = f"exps/stats/total_result_metrics_psnr"
lpips_save_dir = f"exps/stats/total_result_metrics_lpips"
ssim_save_dir = f"exps/stats/total_result_metrics_ssim"
for_8_save_dir = f"exps/stats/total_result_metrics_for_8"
for_16_save_dir = f"exps/stats/total_result_metrics_for_16"
os.makedirs(psnr_save_dir, exist_ok=True)
os.makedirs(lpips_save_dir, exist_ok=True)
os.makedirs(ssim_save_dir, exist_ok=True)
os.makedirs(for_8_save_dir, exist_ok=True)
os.makedirs(for_16_save_dir, exist_ok=True)
for eval_spec in eval_specs:
exp_name = eval_spec["exp_name"]
num_views = eval_spec["num_frames"]
eval_dir = f"exps/evaluations/{exp_name}"
# Aggregate the evaluation results to the final stats
csv_total_file_psnr = f"{psnr_save_dir}/{exp_name}.csv"
csv_total_columns_psnr = ["exp_name", "psnr"] + [
f"psnr_{i}" for i in range(num_views)
]
with open(csv_total_file_psnr, "w") as csvtotalfile:
writer = csv.DictWriter(csvtotalfile, fieldnames=csv_total_columns_psnr)
writer.writeheader()
csv_total_file_lpips = f"{lpips_save_dir}/{exp_name}.csv"
csv_total_columns_lpips = ["exp_name", "lpips"] + [
f"lpips_{i}" for i in range(num_views)
]
with open(csv_total_file_lpips, "w") as csvtotalfile:
writer = csv.DictWriter(csvtotalfile, fieldnames=csv_total_columns_lpips)
writer.writeheader()
csv_total_file_ssim = f"{ssim_save_dir}/{exp_name}.csv"
csv_total_columns_ssim = ["exp_name", "ssim"] + [
f"ssim_{i}" for i in range(num_views)
]
with open(csv_total_file_ssim, "w") as csvtotalfile:
writer = csv.DictWriter(csvtotalfile, fieldnames=csv_total_columns_ssim)
writer.writeheader()
csv_total_file_for_8 = f"{for_8_save_dir}/{exp_name}.csv"
csv_total_columns_for_8 = ["exp_name", "for_8"] + [
f"for_8_{i}" for i in range(num_views)
]
with open(csv_total_file_for_8, "w") as csvtotalfile:
writer = csv.DictWriter(csvtotalfile, fieldnames=csv_total_columns_for_8)
writer.writeheader()
csv_total_file_for_16 = f"{for_16_save_dir}/{exp_name}.csv"
csv_total_columns_for_16 = ["exp_name", "for_16"] + [
f"for_16_{i}" for i in range(num_views)
]
with open(csv_total_file_for_16, "w") as csvtotalfile:
writer = csv.DictWriter(csvtotalfile, fieldnames=csv_total_columns_for_16)
writer.writeheader()
results_psnr = {"psnr": []}
results_lpips = {"lpips": []}
results_ssim = {"ssim": []}
results_for_8 = {"for_8": []}
results_for_16 = {"for_16": []}
for i in range(num_views):
results_psnr[f"psnr_{i}"] = []
results_lpips[f"lpips_{i}"] = []
results_ssim[f"ssim_{i}"] = []
results_for_8[f"for_8_{i}"] = []
results_for_16[f"for_16_{i}"] = []
count = 0
with open(args.metadata, newline="") as csvmetadatafile:
csv_lines = csv.reader(csvmetadatafile, delimiter=",", quotechar='"')
for csv_line in csv_lines:
object_identifier = csv_line[0]
if not os.path.isfile(f"{eval_dir}/{object_identifier}.csv"):
print(
f"WARNING: {exp_name} doesn't have {object_identifier}! Skipping this object..."
)
continue
count += 1
with open(
f"{eval_dir}/{object_identifier}.csv", newline=""
) as csv_object_metric_file:
reader = csv.DictReader(csv_object_metric_file, delimiter=",")
row = reader.__next__()
print(row)
results_psnr["psnr"].append(float(row["psnr"]))
results_lpips["lpips"].append(float(row["lpips"]))
results_ssim["ssim"].append(float(row["ssim"]))
results_for_8["for_8"].append(float(row["for_8"]))
results_for_16["for_16"].append(float(row["for_16"]))
for i in range(num_views):
results_psnr[f"psnr_{i}"].append(float(row[f"psnr_{i}"]))
results_lpips[f"lpips_{i}"].append(float(row[f"lpips_{i}"]))
results_ssim[f"ssim_{i}"].append(float(row[f"ssim_{i}"]))
results_for_8[f"for_8_{i}"].append(float(row[f"for_8_{i}"]))
results_for_16[f"for_16_{i}"].append(float(row[f"for_16_{i}"]))
print(f"{exp_name} has {count} objects finished")
# write into csv file
with open(csv_total_file_psnr, "a") as csvtotalfile:
writer = csv.DictWriter(csvtotalfile, fieldnames=csv_total_columns_psnr)
row_to_write = {}
row_to_write["exp_name"] = exp_name
row_to_write["psnr"] = np.mean(results_psnr["psnr"])
for i in range(num_views):
row_to_write[f"psnr_{i}"] = np.mean(results_psnr[f"psnr_{i}"])
writer.writerow(row_to_write)
with open(csv_total_file_lpips, "a") as csvtotalfile:
writer = csv.DictWriter(
csvtotalfile, fieldnames=csv_total_columns_lpips
)
row_to_write = {}
row_to_write["exp_name"] = exp_name
row_to_write["lpips"] = np.mean(results_lpips["lpips"])
for i in range(num_views):
row_to_write[f"lpips_{i}"] = np.mean(results_lpips[f"lpips_{i}"])
writer.writerow(row_to_write)
with open(csv_total_file_ssim, "a") as csvtotalfile:
writer = csv.DictWriter(csvtotalfile, fieldnames=csv_total_columns_ssim)
row_to_write = {}
row_to_write["exp_name"] = exp_name
row_to_write["ssim"] = np.mean(results_ssim["ssim"])
for i in range(num_views):
row_to_write[f"ssim_{i}"] = np.mean(results_ssim[f"ssim_{i}"])
writer.writerow(row_to_write)
with open(csv_total_file_for_8, "a") as csvtotalfile:
writer = csv.DictWriter(
csvtotalfile, fieldnames=csv_total_columns_for_8
)
row_to_write = {}
row_to_write["exp_name"] = exp_name
row_to_write["for_8"] = np.mean(results_for_8["for_8"])
for i in range(num_views):
row_to_write[f"for_8_{i}"] = np.mean(results_for_8[f"for_8_{i}"])
writer.writerow(row_to_write)
with open(csv_total_file_for_16, "a") as csvtotalfile:
writer = csv.DictWriter(
csvtotalfile, fieldnames=csv_total_columns_for_16
)
row_to_write = {}
row_to_write["exp_name"] = exp_name
row_to_write["for_16"] = np.mean(results_for_16["for_16"])
for i in range(num_views):
row_to_write[f"for_16_{i}"] = np.mean(results_for_16[f"for_16_{i}"])
writer.writerow(row_to_write)