time.perf_counter()的文档表明它是系统范围的
时间。 perf_counter（）→浮动
返回性能计数器的值（以秒为单位），即a 具有最高可用分辨率的时钟来测量短时间。 它的确包含整个系统的睡眠时间。的 返回值的参考点是不确定的，因此只有 连续调用结果之间的差异是有效的。
我在解释系统范围内的以包括跨进程的一致性方面是否正确？
如下所示，它在Linux上似乎是一致的，但在Windows上却不一致。此外，使用Python 3.6的Windows行为与3.7明显不同。
如果有人能指出有关此行为的文档或错误报告，我将不胜感激。
import concurrent.futures
import time
def worker():
return time.perf_counter()
if __name__ == '__main__':
pool = concurrent.futures.ProcessPoolExecutor()
futures = []
for i in range(3):
print('Submitting worker {:d} at time.perf_counter() == {:.3f}'.format(i, time.perf_counter()))
futures.append(pool.submit(worker))
time.sleep(1)
for i, f in enumerate(futures):
print('Worker {:d} started at time.perf_counter() == {:.3f}'.format(i, f.result()))
C:\...>Python36\python.exe -VV
Python 3.6.8 (tags/v3.6.8:3c6b436a57, Dec 24 2018, 00:16:47) [MSC v.1916 64 bit (AMD64)]
C:\...>Python36\python.exe perf_counter_across_processes.py
Submitting worker 0 at time.perf_counter() == 0.000
Submitting worker 1 at time.perf_counter() == 1.169
Submitting worker 2 at time.perf_counter() == 2.170
Worker 0 started at time.perf_counter() == 0.000
Worker 1 started at time.perf_counter() == 0.533
Worker 2 started at time.perf_counter() == 0.000
C:\...>Python37\python.exe -VV
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD64)]
C:\...>Python37\python.exe perf_counter_across_processes.py
Submitting worker 0 at time.perf_counter() == 0.376
Submitting worker 1 at time.perf_counter() == 1.527
Submitting worker 2 at time.perf_counter() == 2.529
Worker 0 started at time.perf_counter() == 0.380
Worker 1 started at time.perf_counter() == 0.956
Worker 2 started at time.perf_counter() == 1.963
为简洁起见，我在Windows上省略了进一步的结果，但是在Windows 8.1上观察到了相同的行为。此外，Python 3.6.7的行为与3.6.8相同，而Python 3.7.1的行为与3.7.3相同。
$ python3 -VV
Python 3.6.7 (default, Oct 22 2018, 11:32:17)
[GCC 8.2.0]
$ python3 perf_counter_across_processes.py
Submitting worker 0 at time.perf_counter() == 2075.896
Submitting worker 1 at time.perf_counter() == 2076.900
Submitting worker 2 at time.perf_counter() == 2077.903
Worker 0 started at time.perf_counter() == 2075.900
Worker 1 started at time.perf_counter() == 2076.902
Worker 2 started at time.perf_counter() == 2077.905
$ python3.7 -VV
Python 3.7.1 (default, Oct 22 2018, 11:21:55)
[GCC 8.2.0]
$ python3.7 perf_counter_across_processes.py
Submitting worker 0 at time.perf_counter() == 1692.514
Submitting worker 1 at time.perf_counter() == 1693.518
Submitting worker 2 at time.perf_counter() == 1694.520
Worker 0 started at time.perf_counter() == 1692.517
Worker 1 started at time.perf_counter() == 1693.519
Worker 2 started at time.perf_counter() == 1694.522
答案 0 :(得分：1)
在Windows中，time.perf_counter基于WINAPI QueryPerformanceCounter。该计数器是系统范围的。有关更多信息，请参见acquiring high-resolution time stamps。
也就是说，Windows中的perf_counter返回一个相对于进程启动值的值。因此，它不是系统范围的值。这样做是为了减少将整数值转换为float时的精度损失，perf_counter_ns仅具有15个十进制数字的精度。在大多数情况下，不需要相对值，只需要微秒的精度即可。应该有一个可选参数来查询真实的QPC计数器值，尤其是对于3.7+中的perf_counter。
关于perf_counter在3.6和3.7中返回的不同初始值，实现随时间变化了一点。在3.6.8中，time是在Modules/timemodule.c中实现的，因此初始值是在time.perf_counter()模块首次导入和初始化时存储的，这就是为什么看到第一个结果为0.000秒的原因。在最新版本中，它是在Python的C API中单独实现的。例如，请参见最新的3.8 beta版中的"Python/pytime.c"。在这种情况下，到Python代码调用import sys
if sys.platform != 'win32':
from time import perf_counter
try:
from time import perf_counter_ns
except ImportError:
def perf_counter_ns():
"""perf_counter_ns() -> int
Performance counter for benchmarking as nanoseconds.
"""
return int(perf_counter() * 10**9)
else:
import ctypes
from ctypes import wintypes
kernel32 = ctypes.WinDLL('kernel32', use_last_error=True)
kernel32.QueryPerformanceFrequency.argtypes = (
wintypes.PLARGE_INTEGER,) # lpFrequency
kernel32.QueryPerformanceCounter.argtypes = (
wintypes.PLARGE_INTEGER,) # lpPerformanceCount
_qpc_frequency = wintypes.LARGE_INTEGER()
if not kernel32.QueryPerformanceFrequency(ctypes.byref(_qpc_frequency)):
raise ctypes.WinError(ctypes.get_last_error())
_qpc_frequency = _qpc_frequency.value
def perf_counter_ns():
"""perf_counter_ns() -> int
Performance counter for benchmarking as nanoseconds.
"""
count = wintypes.LARGE_INTEGER()
if not kernel32.QueryPerformanceCounter(ctypes.byref(count)):
raise ctypes.WinError(ctypes.get_last_error())
return (count.value * 10**9) // _qpc_frequency
def perf_counter():
"""perf_counter() -> float
Performance counter for benchmarking.
"""
count = wintypes.LARGE_INTEGER()
if not kernel32.QueryPerformanceCounter(ctypes.byref(count)):
raise ctypes.WinError(ctypes.get_last_error())
return count.value / _qpc_frequency
时，计数器的增量已经远远超过了启动值。
这是基于ctypes的替代实现，它使用系统范围的QPC值而不是相对值。
floatperf_counter的精度为15个十进制数字。因此，import tensorflow as tf
import numpy as np
X = np.array(X)
y = np.array(y)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(2, activation='relu'))
model.compile(optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.1), loss='categorical_crossentropy', metrics=['categorical_accuracy'])
model.fit(X, y, epochs=100)
_, acc = model.evaluate(X, y)
print('acc = ' + str(acc))
的这种实现在QPC决议范围内，可正常运行约3年。