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utils.py
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utils.py
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import os
import csv
import logging
import math
import numpy as np
import random
import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler
_LOGGER = None
def get_rank():
return dist.get_rank()
def get_world_size():
return dist.get_world_size()
def mkdir(path):
os.makedirs(path, exist_ok=True)
def random_seed(seed_value):
np.random.seed(seed_value)
torch.manual_seed(seed_value)
random.seed(seed_value)
os.environ['PYTHONHASHSEED'] = str(seed_value)
torch.cuda.manual_seed(seed_value)
torch.cuda.manual_seed_all(seed_value)
def parameters_string(module):
lines = [
"",
"List of model parameters:",
"=" * 105,
]
row_format = "{name:<60} {shape:>27} ={total_size:>15,d}"
params = list(module.named_parameters())
for name, param in params:
lines.append(row_format.format(
name=name,
shape=" * ".join(str(p) for p in param.size()),
total_size=param.numel()
))
lines.append("=" * 105)
lines.append(row_format.format(
name="all parameters",
shape="sum of above",
total_size=sum(int(param.numel()) for name, param in params)
))
lines.append("")
return "\n".join(lines)
def create_logger(log_file, level=logging.INFO):
global _LOGGER
if _LOGGER is not None:
return _LOGGER
l = logging.getLogger('global')
formatter = logging.Formatter('[%(asctime)s][%(filename)15s][line:%(lineno)4d][%(levelname)8s] %(message)s')
fh = logging.FileHandler(log_file)
fh.setFormatter(formatter)
sh = logging.StreamHandler()
sh.setFormatter(formatter)
l.setLevel(level)
l.addHandler(fh)
l.addHandler(sh)
l.propagate = False
_LOGGER = l
return l
def get_logger():
return _LOGGER
class Logger(object):
def __init__(self, path, header):
self.log_file = open(path, 'w')
self.logger = csv.writer(self.log_file, delimiter='\t')
self.logger.writerow(header)
self.header = header
def __del(self):
self.log_file.close()
def log(self, values):
write_values = []
for col in self.header:
assert col in values
write_values.append(values[col])
self.logger.writerow(write_values)
self.log_file.flush()
class AverageMeter(object):
def __init__(self, length=0):
self.length = length
self.reset()
def reset(self):
if self.length > 0:
self.history, self.history_num = [], []
else:
self.count = 0
self.sum = 0.0
self.val = 0.0
self.avg = 0.0
def update(self, val, num=1):
assert num > 0
if self.length > 0:
self.history.append(val * num)
self.history_num.append(num)
if len(self.history) > self.length:
del self.history[0]
del self.history_num[0]
self.val = val
self.avg = np.sum(self.history) / np.sum(self.history_num)
else:
self.val = val
self.sum += val * num
self.count += num
self.avg = self.sum / self.count
class DistributedSampler(Sampler):
def __init__(self, dataset, world_size=None, rank=None, round_down=False):
if world_size is None:
world_size = get_world_size()
if rank is None:
rank = get_rank()
self.dataset = dataset
self.world_size = world_size
self.rank = rank
self.round_down = round_down
self.epoch = 0
self.total_size = len(self.dataset)
if self.round_down:
self.num_samples = int(math.floor(len(self.dataset) / self.world_size))
else:
self.num_samples = int(math.ceil(len(self.dataset) / self.world_size))
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = list(torch.randperm(len(self.dataset), generator=g))
assert len(indices) == self.total_size
# subsample
offset = self.num_samples * self.rank
indices = indices[offset:offset + self.num_samples]
if self.round_down or (not self.round_down and self.rank < self.world_size-1):
assert len(indices) == self.num_samples
return iter(indices)
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
def load_pretrain(pretrain_opt, net):
checkpoint = torch.load(pretrain_opt.path, map_location=lambda storage, loc: storage.cuda())
if pretrain_opt.get('state_dict_key', None) is not None:
checkpoint = checkpoint[pretrain_opt.state_dict_key]
if pretrain_opt.get('delete_prefix', None):
keys = set(checkpoint.keys())
for k in keys:
if k.startswith(pretrain_opt.delete_prefix):
checkpoint.pop(k)
if pretrain_opt.get('replace_prefix', None) is not None:
keys = set(checkpoint.keys())
for k in keys:
if k.startswith(pretrain_opt.replace_prefix):
new_k = pretrain_opt.get('replace_to', '') + k[len(pretrain_opt.replace_prefix):]
checkpoint[new_k] = checkpoint.pop(k)
net.load_state_dict(checkpoint, strict=False)
if get_rank() == 0:
ckpt_keys = set(checkpoint.keys())
own_keys = set(net.state_dict().keys())
missing_keys = own_keys - ckpt_keys
ignore_keys = ckpt_keys - own_keys
loaded_keys = own_keys - missing_keys
logger = get_logger()
for k in missing_keys:
logger.info('Caution: missing key {}'.format(k))
for k in ignore_keys:
logger.info('Caution: redundant key {}'.format(k))
logger.info('Loaded {} key(s) from pre-trained model at {}'.format(len(loaded_keys), pretrain_opt.path))