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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 | |
# Log the time needed to execute that function | |
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) | |