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utils.py
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utils.py
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# Copyright 2020 Google LLC
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import torch
def makedirs_if_not_found(*dirs):
for d in dirs:
assert isinstance(d, str)
if not os.path.exists(d):
os.makedirs(d)
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
class LinearScheduler(object):
def __init__(self, iters, maxval=1.0):
iters = max(1, iters)
self.val = maxval / iters
self.maxval = maxval
self.iters = iters
def step(self):
self.val = min(self.maxval, self.val + self.maxval / self.iters)
def __call__(self):
return self.val
class EMAMetric(object):
def __init__(self, gamma=.99):
super(EMAMetric, self).__init__()
self.prev_metric = 0.
self.gamma = gamma
def step(self, x):
with torch.no_grad():
self.prev_metric = (1. - self.gamma) * self.prev_metric + self.gamma * x
def val(self):
return self.prev_metric