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utils.py
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import logging
import os,sys
import torch
from tensorboardX import SummaryWriter
import data_loader.transform as T
from data_loader.randaugment import RandAugmentMC as RandomAugment
import torchvision.transforms as transforms
from torch.optim.lr_scheduler import LambdaLR
import math
class ConfigMapper(object):
def __init__(self, args):
for key in args:
self.__dict__[key] = args[key]
class AverageMeter(object):
"""
Computes and stores the average and current value
"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def get_cosine_schedule_with_warmup(optimizer,
num_warmup_steps,
num_training_steps,
num_cycles=7./16.,
last_epoch=-1):
def _lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
no_progress = float(current_step - num_warmup_steps) / \
float(max(1, num_training_steps - num_warmup_steps))
return max(0., math.cos(math.pi * num_cycles * no_progress))
return LambdaLR(optimizer, _lr_lambda, last_epoch)
def get_normalize(_dataset):
if _dataset == 'CIFAR10':
return (0.4914, 0.4822, 0.4465),(0.2471, 0.2435, 0.2616)
elif _dataset =='CIFAR100':
return (0.5071, 0.4867, 0.4408),(0.2675, 0.2565, 0.2761)
elif _dataset =='SVHN':
return (0.4377, 0.4438, 0.4728), (0.1980, 0.2010, 0.1970)
elif _dataset =='STL10':
return (0.4409, 0.4279, 0.3868), (0.2683, 0.2611, 0.2687)
else:
raise NotImplementedError
def get_mixmatch_transform(_dataset):
mean, std = get_normalize(_dataset)
if _dataset=='CIFAR10' or _dataset=='CIFAR100':
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomCrop(size=32,
padding=int(32*0.125),
padding_mode='reflect'),
transforms.ToTensor(),
transforms.Normalize(mean,std)
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean,std)
])
elif _dataset == 'SVHN':
train_transform = transforms.Compose([
transforms.RandomCrop(size=32,
padding=4,
padding_mode='reflect'),
transforms.ToTensor(),
transforms.Normalize(mean,std)
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean,std)
])
elif _dataset =='STL10':
train_transform = transforms.Compose([
transforms.RandomCrop(96, padding=int(96*0.125), padding_mode='reflect'),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean,std)
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
else:
raise NotImplementedError
return train_transform, test_transform
def get_transform(method, _dataset):
if method == 'Mixmatch':
return get_mixmatch_transform(_dataset)
else:
raise NotImplementedError
def mixmatch_interleave_offsets(batch, nu):
groups = [batch // (nu + 1)] * (nu + 1)
for x in range(batch - sum(groups)):
groups[-x - 1] += 1
offsets = [0]
for g in groups:
offsets.append(offsets[-1] + g)
assert offsets[-1] == batch
return offsets
def mixmatch_interleave(xy, batch):
nu = len(xy) - 1
offsets = mixmatch_interleave_offsets(batch, nu)
xy = [[v[offsets[p]:offsets[p + 1]] for p in range(nu + 1)] for v in xy]
for i in range(1, nu + 1):
xy[0][i], xy[i][i] = xy[i][i], xy[0][i]
return [torch.cat(v, dim=0) for v in xy]
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, largest=True, sorted=True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].contiguous().view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def create_logger(configs):
result_dir = os.path.join("results", configs.name) # "results/MixMatch"
os.makedirs(result_dir, exist_ok=True)
out_dir = os.path.join(result_dir, str(configs.dataset) + '_' + str(configs.depth) + '-' +str(configs.width) + '_' + str(configs.num_label))
os.makedirs(out_dir, exist_ok=True)
log_dir = os.path.join(result_dir, "log")
os.makedirs(log_dir, exist_ok=True)
writer = SummaryWriter(log_dir=log_dir)
log_file = '{}.log'.format(configs.name)
final_log_file = os.path.join(out_dir, log_file)
head = '%(asctime)-15s %(message)s'
logging.basicConfig(filename=str(final_log_file),
format=head)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setFormatter(logging.Formatter(head))
logger.addHandler(console_handler)
if configs.mode =='train':
logger.info(f" Desc = PyTorch Implementation of MixMatch")
logger.info(f" Task = {configs.dataset}@{configs.num_label}")
logger.info(f" Model = WideResNet {configs.depth}x{configs.width}")
logger.info(f" large model = {configs.large}")
logger.info(f" Batch size = {configs.batch_size}")
logger.info(f" Epoch = {configs.epochs}")
logger.info(f" Optim = {configs.optim}")
logger.info(f" lambda_u = {configs.lambda_u}")
logger.info(f" alpha = {configs.alpha}")
logger.info(f" T = {configs.T}")
logger.info(f" K = {configs.K}")
return logger, writer, out_dir