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