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ImageNet training in PyTorch with logger
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import logging | |
import os | |
import sys | |
class NoOp: | |
def __getattr__(self, *args): | |
def no_op(*args, **kwargs): | |
"""Accept every signature by doing non-operation.""" | |
pass | |
return no_op | |
def get_logger(log_dir, log_name=None, resume="", is_rank0=True): | |
"""Get the program logger. | |
Args: | |
log_dir (str): The directory to save the log file. | |
log_name (str, optional): The log filename. If None, it will use the main | |
filename with ``.log`` extension. Default is None. | |
resume (str): If False, open the log file in writing and reading mode. | |
Else, open the log file in appending and reading mode; Default is "". | |
is_rank0 (boolean): If True, create the normal logger; If False, create the null | |
logger, which is useful in DDP training. Default is True. | |
""" | |
if is_rank0: | |
logger = logging.getLogger(__name__) | |
logger.setLevel(level=logging.INFO) | |
# StreamHandler | |
stream_handler = logging.StreamHandler(sys.stdout) | |
stream_handler.setLevel(level=logging.INFO) | |
logger.addHandler(stream_handler) | |
# FileHandler | |
mode = "w+" if resume == "False" else "a+" | |
if log_name is None: | |
log_name = os.path.basename(sys.argv[0]).split(".")[0] + (".log") | |
file_handler = logging.FileHandler(os.path.join(log_dir, log_name), mode=mode) | |
file_handler.setLevel(level=logging.INFO) | |
logger.addHandler(file_handler) | |
else: | |
logger = NoOp() | |
return logger |
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import argparse | |
import os | |
import random | |
import shutil | |
import time | |
import warnings | |
import torch | |
import torch.nn as nn | |
import torch.nn.parallel | |
import torch.backends.cudnn as cudnn | |
import torch.distributed as dist | |
import torch.optim | |
import torch.multiprocessing as mp | |
import torch.utils.data | |
import torch.utils.data.distributed | |
import torchvision.transforms as transforms | |
import torchvision.datasets as datasets | |
import torchvision.models as models | |
from log import get_logger | |
model_names = sorted(name for name in models.__dict__ | |
if name.islower() and not name.startswith("__") | |
and callable(models.__dict__[name])) | |
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') | |
parser.add_argument('data', metavar='DIR', | |
help='path to dataset') | |
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', | |
choices=model_names, | |
help='model architecture: ' + | |
' | '.join(model_names) + | |
' (default: resnet18)') | |
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', | |
help='number of data loading workers (default: 4)') | |
parser.add_argument('--epochs', default=90, type=int, metavar='N', | |
help='number of total epochs to run') | |
parser.add_argument('--start-epoch', default=0, type=int, metavar='N', | |
help='manual epoch number (useful on restarts)') | |
parser.add_argument('-b', '--batch-size', default=256, type=int, | |
metavar='N', | |
help='mini-batch size (default: 256), this is the total ' | |
'batch size of all GPUs on the current node when ' | |
'using Data Parallel or Distributed Data Parallel') | |
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float, | |
metavar='LR', help='initial learning rate', dest='lr') | |
parser.add_argument('--momentum', default=0.9, type=float, metavar='M', | |
help='momentum') | |
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float, | |
metavar='W', help='weight decay (default: 1e-4)', | |
dest='weight_decay') | |
parser.add_argument('-p', '--print-freq', default=10, type=int, | |
metavar='N', help='print frequency (default: 10)') | |
parser.add_argument('--resume', default='', type=str, metavar='PATH', | |
help='path to latest checkpoint (default: none)') | |
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', | |
help='evaluate model on validation set') | |
parser.add_argument('--pretrained', dest='pretrained', action='store_true', | |
help='use pre-trained model') | |
parser.add_argument('--world-size', default=-1, type=int, | |
help='number of nodes for distributed training') | |
parser.add_argument('--rank', default=-1, type=int, | |
help='node rank for distributed training') | |
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str, | |
help='url used to set up distributed training') | |
parser.add_argument('--dist-backend', default='nccl', type=str, | |
help='distributed backend') | |
parser.add_argument('--seed', default=None, type=int, | |
help='seed for initializing training. ') | |
parser.add_argument('--gpu', default=None, type=int, | |
help='GPU id to use.') | |
parser.add_argument('--multiprocessing-distributed', action='store_true', | |
help='Use multi-processing distributed training to launch ' | |
'N processes per node, which has N GPUs. This is the ' | |
'fastest way to use PyTorch for either single node or ' | |
'multi node data parallel training') | |
best_acc1 = 0 | |
def main(): | |
args = parser.parse_args() | |
if args.seed is not None: | |
random.seed(args.seed) | |
torch.manual_seed(args.seed) | |
cudnn.deterministic = True | |
warnings.warn('You have chosen to seed training. ' | |
'This will turn on the CUDNN deterministic setting, ' | |
'which can slow down your training considerably! ' | |
'You may see unexpected behavior when restarting ' | |
'from checkpoints.') | |
if args.gpu is not None: | |
warnings.warn('You have chosen a specific GPU. This will completely ' | |
'disable data parallelism.') | |
if args.dist_url == "env://" and args.world_size == -1: | |
args.world_size = int(os.environ["WORLD_SIZE"]) | |
args.distributed = args.world_size > 1 or args.multiprocessing_distributed | |
ngpus_per_node = torch.cuda.device_count() | |
if args.multiprocessing_distributed: | |
# Since we have ngpus_per_node processes per node, the total world_size | |
# needs to be adjusted accordingly | |
args.world_size = ngpus_per_node * args.world_size | |
# Use torch.multiprocessing.spawn to launch distributed processes: the | |
# main_worker process function | |
mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args)) | |
else: | |
# Simply call main_worker function | |
main_worker(args.gpu, ngpus_per_node, args) | |
def main_worker(gpu, ngpus_per_node, args): | |
global best_acc1 | |
args.gpu = gpu | |
logger = get_logger("./log_dir", resume="False", is_rank0=(gpu==0)) | |
if args.gpu is not None: | |
logger.info("Use GPU: {} for training".format(args.gpu)) | |
if args.distributed: | |
if args.dist_url == "env://" and args.rank == -1: | |
args.rank = int(os.environ["RANK"]) | |
if args.multiprocessing_distributed: | |
# For multiprocessing distributed training, rank needs to be the | |
# global rank among all the processes | |
args.rank = args.rank * ngpus_per_node + gpu | |
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, | |
world_size=args.world_size, rank=args.rank) | |
# create model | |
if args.pretrained: | |
logger.info("=> using pre-trained model '{}'".format(args.arch)) | |
model = models.__dict__[args.arch](pretrained=True) | |
else: | |
logger.info("=> creating model '{}'".format(args.arch)) | |
model = models.__dict__[args.arch]() | |
if not torch.cuda.is_available(): | |
logger.info('using CPU, this will be slow') | |
elif args.distributed: | |
# For multiprocessing distributed, DistributedDataParallel constructor | |
# should always set the single device scope, otherwise, | |
# DistributedDataParallel will use all available devices. | |
if args.gpu is not None: | |
torch.cuda.set_device(args.gpu) | |
model.cuda(args.gpu) | |
# When using a single GPU per process and per | |
# DistributedDataParallel, we need to divide the batch size | |
# ourselves based on the total number of GPUs we have | |
args.batch_size = int(args.batch_size / ngpus_per_node) | |
args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node) | |
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) | |
else: | |
model.cuda() | |
# DistributedDataParallel will divide and allocate batch_size to all | |
# available GPUs if device_ids are not set | |
model = torch.nn.parallel.DistributedDataParallel(model) | |
elif args.gpu is not None: | |
torch.cuda.set_device(args.gpu) | |
model = model.cuda(args.gpu) | |
else: | |
# DataParallel will divide and allocate batch_size to all available GPUs | |
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'): | |
model.features = torch.nn.DataParallel(model.features) | |
model.cuda() | |
else: | |
model = torch.nn.DataParallel(model).cuda() | |
# define loss function (criterion) and optimizer | |
criterion = nn.CrossEntropyLoss().cuda(args.gpu) | |
optimizer = torch.optim.SGD(model.parameters(), args.lr, | |
momentum=args.momentum, | |
weight_decay=args.weight_decay) | |
# optionally resume from a checkpoint | |
if args.resume: | |
if os.path.isfile(args.resume): | |
logger.info("=> loading checkpoint '{}'".format(args.resume)) | |
if args.gpu is None: | |
checkpoint = torch.load(args.resume) | |
else: | |
# Map model to be loaded to specified single gpu. | |
loc = 'cuda:{}'.format(args.gpu) | |
checkpoint = torch.load(args.resume, map_location=loc) | |
args.start_epoch = checkpoint['epoch'] | |
best_acc1 = checkpoint['best_acc1'] | |
if args.gpu is not None: | |
# best_acc1 may be from a checkpoint from a different GPU | |
best_acc1 = best_acc1.to(args.gpu) | |
model.load_state_dict(checkpoint['state_dict']) | |
optimizer.load_state_dict(checkpoint['optimizer']) | |
logger.info("=> loaded checkpoint '{}' (epoch {})" | |
.format(args.resume, checkpoint['epoch'])) | |
else: | |
logger.info("=> no checkpoint found at '{}'".format(args.resume)) | |
cudnn.benchmark = True | |
# Data loading code | |
traindir = os.path.join(args.data, 'train') | |
valdir = os.path.join(args.data, 'val') | |
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], | |
std=[0.229, 0.224, 0.225]) | |
train_dataset = datasets.ImageFolder( | |
traindir, | |
transforms.Compose([ | |
transforms.RandomResizedCrop(224), | |
transforms.RandomHorizontalFlip(), | |
transforms.ToTensor(), | |
normalize, | |
])) | |
if args.distributed: | |
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) | |
else: | |
train_sampler = None | |
train_loader = torch.utils.data.DataLoader( | |
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), | |
num_workers=args.workers, pin_memory=True, sampler=train_sampler) | |
val_loader = torch.utils.data.DataLoader( | |
datasets.ImageFolder(valdir, transforms.Compose([ | |
transforms.Resize(256), | |
transforms.CenterCrop(224), | |
transforms.ToTensor(), | |
normalize, | |
])), | |
batch_size=args.batch_size, shuffle=False, | |
num_workers=args.workers, pin_memory=True) | |
if args.evaluate: | |
validate(val_loader, model, criterion, args) | |
return | |
for epoch in range(args.start_epoch, args.epochs): | |
if args.distributed: | |
train_sampler.set_epoch(epoch) | |
adjust_learning_rate(optimizer, epoch, args) | |
# train for one epoch | |
train(train_loader, model, criterion, optimizer, epoch, logger, args) | |
# evaluate on validation set | |
acc1 = validate(val_loader, model, criterion, args) | |
# remember best acc@1 and save checkpoint | |
is_best = acc1 > best_acc1 | |
best_acc1 = max(acc1, best_acc1) | |
if not args.multiprocessing_distributed or (args.multiprocessing_distributed | |
and args.rank % ngpus_per_node == 0): | |
save_checkpoint({ | |
'epoch': epoch + 1, | |
'arch': args.arch, | |
'state_dict': model.state_dict(), | |
'best_acc1': best_acc1, | |
'optimizer' : optimizer.state_dict(), | |
}, is_best) | |
def train(train_loader, model, criterion, optimizer, epoch, logger, args): | |
batch_time = AverageMeter('Time', ':6.3f') | |
data_time = AverageMeter('Data', ':6.3f') | |
losses = AverageMeter('Loss', ':.4e') | |
top1 = AverageMeter('Acc@1', ':6.2f') | |
top5 = AverageMeter('Acc@5', ':6.2f') | |
progress = ProgressMeter( | |
len(train_loader), | |
[batch_time, data_time, losses, top1, top5], | |
logger, | |
prefix="Epoch: [{}]".format(epoch)) | |
# switch to train mode | |
model.train() | |
end = time.time() | |
for i, (images, target) in enumerate(train_loader): | |
# measure data loading time | |
data_time.update(time.time() - end) | |
if args.gpu is not None: | |
images = images.cuda(args.gpu, non_blocking=True) | |
if torch.cuda.is_available(): | |
target = target.cuda(args.gpu, non_blocking=True) | |
# compute output | |
output = model(images) | |
loss = criterion(output, target) | |
# measure accuracy and record loss | |
acc1, acc5 = accuracy(output, target, topk=(1, 5)) | |
losses.update(loss.item(), images.size(0)) | |
top1.update(acc1[0], images.size(0)) | |
top5.update(acc5[0], images.size(0)) | |
# compute gradient and do SGD step | |
optimizer.zero_grad() | |
loss.backward() | |
optimizer.step() | |
# measure elapsed time | |
batch_time.update(time.time() - end) | |
end = time.time() | |
if i % args.print_freq == 0: | |
progress.display(i) | |
def validate(val_loader, model, criterion, args): | |
batch_time = AverageMeter('Time', ':6.3f') | |
losses = AverageMeter('Loss', ':.4e') | |
top1 = AverageMeter('Acc@1', ':6.2f') | |
top5 = AverageMeter('Acc@5', ':6.2f') | |
progress = ProgressMeter( | |
len(val_loader), | |
[batch_time, losses, top1, top5], | |
prefix='Test: ') | |
# switch to evaluate mode | |
model.eval() | |
with torch.no_grad(): | |
end = time.time() | |
for i, (images, target) in enumerate(val_loader): | |
if args.gpu is not None: | |
images = images.cuda(args.gpu, non_blocking=True) | |
if torch.cuda.is_available(): | |
target = target.cuda(args.gpu, non_blocking=True) | |
# compute output | |
output = model(images) | |
loss = criterion(output, target) | |
# measure accuracy and record loss | |
acc1, acc5 = accuracy(output, target, topk=(1, 5)) | |
losses.update(loss.item(), images.size(0)) | |
top1.update(acc1[0], images.size(0)) | |
top5.update(acc5[0], images.size(0)) | |
# measure elapsed time | |
batch_time.update(time.time() - end) | |
end = time.time() | |
if i % args.print_freq == 0: | |
progress.display(i) | |
# TODO: this should also be done with the ProgressMeter | |
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}' | |
.format(top1=top1, top5=top5)) | |
return top1.avg | |
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'): | |
torch.save(state, filename) | |
if is_best: | |
shutil.copyfile(filename, 'model_best.pth.tar') | |
class AverageMeter(object): | |
"""Computes and stores the average and current value""" | |
def __init__(self, name, fmt=':f'): | |
self.name = name | |
self.fmt = fmt | |
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 __str__(self): | |
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' | |
return fmtstr.format(**self.__dict__) | |
class ProgressMeter(object): | |
def __init__(self, num_batches, meters, logger, prefix=""): | |
self.batch_fmtstr = self._get_batch_fmtstr(num_batches) | |
self.meters = meters | |
self.prefix = prefix | |
self.logger = logger | |
def display(self, batch): | |
entries = [self.prefix + self.batch_fmtstr.format(batch)] | |
entries += [str(meter) for meter in self.meters] | |
self.logger.info('\t'.join(entries)) | |
def _get_batch_fmtstr(self, num_batches): | |
num_digits = len(str(num_batches // 1)) | |
fmt = '{:' + str(num_digits) + 'd}' | |
return '[' + fmt + '/' + fmt.format(num_batches) + ']' | |
def adjust_learning_rate(optimizer, epoch, args): | |
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs""" | |
lr = args.lr * (0.1 ** (epoch // 30)) | |
for param_group in optimizer.param_groups: | |
param_group['lr'] = lr | |
def accuracy(output, target, topk=(1,)): | |
"""Computes the accuracy over the k top predictions for the specified values of k""" | |
with torch.no_grad(): | |
maxk = max(topk) | |
batch_size = target.size(0) | |
_, pred = output.topk(maxk, 1, True, True) | |
pred = pred.t() | |
correct = pred.eq(target.view(1, -1).expand_as(pred)) | |
res = [] | |
for k in topk: | |
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) | |
res.append(correct_k.mul_(100.0 / batch_size)) | |
return res | |
if __name__ == '__main__': | |
main() |
Author
hkunzhe
commented
Jul 5, 2021
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