src/pixelsplat_src/benchmarker.py

import json
from collections import defaultdict
from contextlib import contextmanager
from pathlib import Path
from time import time

import numpy as np
import torch
import os


class Benchmarker:
 def __init__(self):
 self.execution_times = defaultdict(list)

 @contextmanager
 def time(self, tag: str, num_calls: int = 1):
 try:
 start_time = time()
 yield
 finally:
 end_time = time()
 for _ in range(num_calls):
 self.execution_times[tag].append((end_time - start_time) / num_calls)

 def dump(self, path: str) -> None:
 parent = os.path.abspath(os.path.join(path, os.pardir))
 os.makedirs(parent, exist_ok=True)
 # path.parent.mkdir(exist_ok=True, parents=True)
 with open(path, "w") as f:
 json.dump(dict(self.execution_times), f)
 # with path.open("w") as f:
 # json.dump(dict(self.execution_times), f)

 def dump_memory(self, path: Path) -> None:
 path.parent.mkdir(exist_ok=True, parents=True)
 with path.open("w") as f:
 json.dump(torch.cuda.memory_stats()["allocated_bytes.all.peak"], f)

 def summarize(self) -> None:
 for tag, times in self.execution_times.items():
 print(f"{tag}: {len(times)} calls, avg. {np.mean(times)} seconds per call")