def wrapper(*args, **kwargs):
start_time = time.perf_counter() # 注意将记录time.sleep()的时间
return_values = function(*args, **kwargs)
end_time = time.perf_counter()
CLASSICML_LOGGER.info('耗时 {:.5f} s'.format(end_time - start_time))
return return_values
def wrapper(*args, **kwargs):
return_values = function(*args, **kwargs)
pid = os.getpid()
current_process = psutil.Process(pid)
process_memory = current_process.memory_full_info()
CLASSICML_LOGGER.info('占用内存 {:.5f} MB'.format(process_memory.uss /
1024 / 1024))
return return_values
import os
from classicML import CLASSICML_LOGGER
from classicML.backend.python import activations
from classicML.backend.python import callbacks
from classicML.backend.python import initializers
from classicML.backend.python import io
from classicML.backend.python import kernels
from classicML.backend.python import losses
if os.environ['CLASSICML_ENGINE'] == 'CC':
from classicML.backend.cc.metrics import __version__
from classicML.backend.cc import metrics
CLASSICML_LOGGER.info('后端版本是: {}'.format(__version__))
else:
from classicML.backend.python import metrics
from classicML.backend.python import optimizers
from classicML.backend.python import tree
if os.environ['CLASSICML_ENGINE'] == 'CC':
from classicML.backend.cc.ops import cc_calculate_error
from classicML.backend.cc.ops import cc_clip_alpha
from classicML.backend.cc.ops import cc_get_conditional_probability
from classicML.backend.cc.ops import cc_get_dependent_prior_probability
from classicML.backend.cc.ops import cc_get_prior_probability
from classicML.backend.cc.ops import cc_get_probability_density
from classicML.backend.cc.ops import cc_get_w
from classicML.backend.cc.ops import cc_get_within_class_scatter_matrix
from classicML.backend.cc.ops import cc_select_second_alpha
from classicML.backend.cc.ops import cc_type_of_target
