| from time import perf_counter as timer |
| from collections import OrderedDict |
| import numpy as np |
|
|
|
|
| class Profiler: |
| def __init__(self, summarize_every=5, disabled=False): |
| self.last_tick = timer() |
| self.logs = OrderedDict() |
| self.summarize_every = summarize_every |
| self.disabled = disabled |
| |
| def tick(self, name): |
| if self.disabled: |
| return |
| |
| |
| if not name in self.logs: |
| self.logs[name] = [] |
| if len(self.logs[name]) >= self.summarize_every: |
| self.summarize() |
| self.purge_logs() |
| self.logs[name].append(timer() - self.last_tick) |
| |
| self.reset_timer() |
| |
| def purge_logs(self): |
| for name in self.logs: |
| self.logs[name].clear() |
| |
| def reset_timer(self): |
| self.last_tick = timer() |
| |
| def summarize(self): |
| n = max(map(len, self.logs.values())) |
| assert n == self.summarize_every |
| print("\nAverage execution time over %d steps:" % n) |
|
|
| name_msgs = ["%s (%d/%d):" % (name, len(deltas), n) for name, deltas in self.logs.items()] |
| pad = max(map(len, name_msgs)) |
| for name_msg, deltas in zip(name_msgs, self.logs.values()): |
| print(" %s mean: %4.0fms std: %4.0fms" % |
| (name_msg.ljust(pad), np.mean(deltas) * 1000, np.std(deltas) * 1000)) |
| print("", flush=True) |
| |
