File size: 5,744 Bytes
59b7eeb | 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 | import os
import struct
import logging
import torch
import math
import numpy as np
import random
import yaml
import torch.distributed as dist
import torch.nn.functional as F
# ------------------------------ Logger ------------------------------
# log to console or a file
def get_logger(
name,
format_str="%(asctime)s [%(pathname)s:%(lineno)s - %(levelname)s ] %(message)s",
date_format="%Y-%m-%d %H:%M:%S",
file=False):
"""
Get python logger instance
"""
logger = logging.getLogger(name)
logger.setLevel(logging.INFO)
# file or console
handler = logging.StreamHandler() if not file else logging.FileHandler(
name)
handler.setLevel(logging.INFO)
formatter = logging.Formatter(fmt=format_str, datefmt=date_format)
handler.setFormatter(formatter)
logger.addHandler(handler)
return logger
# log to concole and file at the same time
def get_logger_2(
name,
format_str="%(asctime)s [%(pathname)s:%(lineno)s - %(levelname)s ] %(message)s",
date_format="%Y-%m-%d %H:%M:%S"):
logger = logging.getLogger(name)
logger.setLevel(logging.INFO)
# Create handlers
c_handler = logging.StreamHandler()
f_handler = logging.FileHandler(name)
c_handler.setLevel(logging.INFO)
f_handler.setLevel(logging.INFO)
# Create formatters and add it to handlers
c_format = logging.Formatter(fmt=format_str, datefmt=date_format)
f_format = logging.Formatter(fmt=format_str, datefmt=date_format)
c_handler.setFormatter(c_format)
f_handler.setFormatter(f_format)
# Add handlers to the logger
logger.addHandler(c_handler)
logger.addHandler(f_handler)
return logger
# ------------------------------ Logger ------------------------------
# ------------------------------ Pytorch Distributed Training ------------------------------
def getoneNode():
nodelist = os.environ['SLURM_JOB_NODELIST']
nodelist = nodelist.strip().split(',')[0]
import re
text = re.split('[-\[\]]', nodelist)
if ('' in text):
text.remove('')
return text[0] + '-' + text[1] + '-' + text[2]
def dist_init(host_addr, rank, local_rank, world_size, port=23456):
host_addr_full = 'tcp://' + host_addr + ':' + str(port)
dist.init_process_group("nccl", init_method=host_addr_full,
rank=rank, world_size=world_size)
num_gpus = torch.cuda.device_count()
# torch.cuda.set_device(local_rank)
assert dist.is_initialized()
def cleanup():
dist.destroy_process_group()
def average_gradients(model, world_size):
size = float(world_size)
for param in model.parameters():
if (param.requires_grad and param.grad is not None):
dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
param.grad.data /= size
def data_reduce(data):
dist.all_reduce(data, op=dist.ReduceOp.SUM)
return data / torch.distributed.get_world_size()
# ------------------------------ Pytorch Distributed Training ------------------------------
# ------------------------------ Hyper-parameter Dynamic Change ------------------------------
def reduce_lr(optimizer, initial_lr, final_lr, current_iter, max_iter, coeff=1.0):
current_lr = coeff * math.exp((current_iter / max_iter) * math.log(final_lr / initial_lr)) * initial_lr
for param_group in optimizer.param_groups:
param_group['lr'] = current_lr
def get_reduce_lr(initial_lr, final_lr, current_iter, max_iter):
current_lr = math.exp((current_iter / max_iter) * math.log(final_lr / initial_lr)) * initial_lr
return current_lr
def set_lr(optimizer, lr):
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# ------------------------------ Hyper-parameter Dynamic Change ------------------------------
# ---------------------- About Configuration --------------------
def parse_config_or_kwargs(config_file, **kwargs):
with open(config_file) as con_read:
yaml_config = yaml.load(con_read, Loader=yaml.FullLoader)
# passed kwargs will override yaml config
return dict(yaml_config, **kwargs)
def store_yaml(config_file, store_path, **kwargs):
with open(config_file, 'r') as f:
config_lines = f.readlines()
keys_list = list(kwargs.keys())
with open(store_path, 'w') as f:
for line in config_lines:
if ':' in line and line.split(':')[0] in keys_list:
key = line.split(':')[0]
line = '{}: {}\n'.format(key, kwargs[key])
f.write(line)
# ---------------------- About Configuration --------------------
def check_dir(dir):
if not os.path.exists(dir):
os.mkdir(dir)
def set_seed(seed=66):
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# when store the model wrongly with "module" involved,
# we remove it here
def correct_key(state_dict):
keys = list(state_dict.keys())
if 'module' not in keys[0]:
return state_dict
else:
new_state_dict = {}
for key in keys:
new_key = '.'.join(key.split('.')[1:])
new_state_dict[new_key] = state_dict[key]
return new_state_dict
def validate_path(dir_name):
"""
:param dir_name: Create the directory if it doesn't exist
:return: None
"""
dir_name = os.path.dirname(dir_name) # get the path
if not os.path.exists(dir_name) and (dir_name != ''):
os.makedirs(dir_name)
def get_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
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