DeepLearningExamples/PyTorch/Forecasting/TFT/tft_pyt/utils.py
2021-11-08 14:08:58 -08:00

47 lines
1.4 KiB
Python

# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import time
class PerformanceMeter():
def __init__(self):
self.reset()
def reset(self):
self.avg = 0
self.count = 0
self.total_time = 0
self.last_update_time = time.time()
self.intervals = []
def update(self, n, exclude_from_total=False):
delta = time.time() - self.last_update_time
self.intervals.append(delta)
if not exclude_from_total:
self.total_time += delta
self.count += n
self.avg = self.count / self.total_time
self.last_update_time = time.time()
return n/delta
def reset_current_lap(self):
self.last_update_time = time.time()
def p(self, i):
assert i <= 100
idx = int(len(self.intervals) * i / 100)
return sorted(self.intervals)[idx]