Spaces:
Runtime error
Runtime error
File size: 6,190 Bytes
f670afc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, check out LICENSE.md
import os
import time
import math
import subprocess
import shutil
from os.path import join
import numpy as np
from inspect import isclass
from pytz import timezone
from datetime import datetime
import inspect
import torch
def datestr():
pacific = timezone('US/Pacific')
now = datetime.now(pacific)
return '{}{:02}{:02}_{:02}{:02}'.format(
now.year, now.month, now.day, now.hour, now.minute)
def module_to_dict(module, exclude=[]):
return dict([(x, getattr(module, x)) for x in dir(module)
if isclass(getattr(module, x))
and x not in exclude
and getattr(module, x) not in exclude])
class TimerBlock:
def __init__(self, title):
print(("{}".format(title)))
def __enter__(self):
self.start = time.clock()
return self
def __exit__(self, exc_type, exc_value, traceback):
self.end = time.clock()
self.interval = self.end - self.start
if exc_type is not None:
self.log("Operation failed\n")
else:
self.log("Operation finished\n")
def log(self, string):
duration = time.clock() - self.start
units = 's'
if duration > 60:
duration = duration / 60.
units = 'm'
print((" [{:.3f}{}] {}".format(duration, units, string)))
def log2file(self, fid, string):
fid = open(fid, 'a')
fid.write("%s\n" % (string))
fid.close()
def add_arguments_for_module(
parser,
module,
argument_for_class,
default,
skip_params=[],
parameter_defaults={}):
argument_group = parser.add_argument_group(argument_for_class.capitalize())
module_dict = module_to_dict(module)
argument_group.add_argument(
'--' + argument_for_class,
type=str,
default=default,
choices=list(
module_dict.keys()))
args, unknown_args = parser.parse_known_args()
class_obj = module_dict[vars(args)[argument_for_class]]
argspec = inspect.getargspec(class_obj.__init__)
defaults = argspec.defaults[::-1] if argspec.defaults else None
args = argspec.args[::-1]
for i, arg in enumerate(args):
cmd_arg = '{}_{}'.format(argument_for_class, arg)
if arg not in skip_params + ['self', 'args']:
if arg in list(parameter_defaults.keys()):
argument_group.add_argument(
'--{}'.format(cmd_arg),
type=type(
parameter_defaults[arg]),
default=parameter_defaults[arg])
elif (defaults is not None and i < len(defaults)):
argument_group.add_argument(
'--{}'.format(cmd_arg),
type=type(
defaults[i]),
default=defaults[i])
else:
print(("[Warning]: non-default argument '{}' "
"detected on class '{}'. This argument "
"cannot be modified via the command line"
.format(arg, module.__class__.__name__)))
# We don't have a good way of dealing with
# inferring the type of the argument
# TODO: try creating a custom action and using ast's infer type?
# else:
# argument_group.add_argument('--{}'.format(
# cmd_arg), required=True)
def kwargs_from_args(args, argument_for_class):
argument_for_class = argument_for_class + '_'
return {key[len(argument_for_class):]: value for key, value in list(vars(
args).items()) if
argument_for_class in key and key != argument_for_class + 'class'}
def format_dictionary_of_losses(labels, values):
try:
string = ', '.join([('{}: {:' +
('.3f' if value >= 0.001 else '.1e') +
'}').format(name, value) for name, value in
zip(labels, values)])
except (TypeError, ValueError) as e:
print((list(zip(labels, values))))
string = '[Log Error] ' + str(e)
return string
class IteratorTimer():
def __init__(self, iterable):
self.iterable = iterable
self.iterator = self.iterable.__iter__()
def __iter__(self):
return self
def __len__(self):
return len(self.iterable)
def __next__(self):
start = time.time()
n = next(self.iterator)
self.last_duration = (time.time() - start)
return n
next = __next__
def gpumemusage():
gpu_mem = subprocess.check_output(
"nvidia-smi | grep MiB | cut -f 3 -d '|'",
shell=True).replace(
' ',
'').replace(
'\n',
'').replace(
'i',
'')
all_stat = [float(a) for a in gpu_mem.replace('/', '').split('MB')[:-1]]
gpu_mem = ''
for i in range(len(all_stat) / 2):
curr, tot = all_stat[2 * i], all_stat[2 * i + 1]
util = "%1.2f" % (100 * curr / tot) + '%'
cmem = str(int(math.ceil(curr / 1024.))) + 'GB'
gmem = str(int(math.ceil(tot / 1024.))) + 'GB'
gpu_mem += util + '--' + join(cmem, gmem) + ' '
return gpu_mem
def update_hyperparameter_schedule(args, epoch, global_iteration, optimizer):
if args.schedule_lr_frequency > 0:
for param_group in optimizer.param_groups:
if (global_iteration + 1) % args.schedule_lr_frequency == 0:
param_group['lr'] /= float(args.schedule_lr_fraction)
param_group['lr'] = float(
np.maximum(param_group['lr'], 0.000001))
def save_checkpoint(state, is_best, path, prefix,
filename='checkpoint.pth.tar'):
prefix_save = os.path.join(path, prefix)
name = prefix_save + '_' + filename
torch.save(state, name)
if is_best:
shutil.copyfile(name, prefix_save + '_model_best.pth.tar')
|