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pytorch-horovod-imagenet.py
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pytorch-horovod-imagenet.py
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"""
Based on https://github.com/pytorch/examples/blob/master/imagenet/main.py
"""
import argparse
import os
import time
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import horovod.torch as hvd
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
parser = argparse.ArgumentParser()
parser.add_argument(
'--arch', metavar='ARCH', default='resnet50',
choices=model_names,
help='model architecture: ' +
' | '.join(model_names) +
' (default: resnet50)')
parser.add_argument(
'--bs', type=int, required=True, metavar='N',
help='Mini-batch size.')
parser.add_argument(
'--lr', '--learning-rate', default=0.05, type=float, metavar='LR',
help='Initial learning rate', dest='lr')
parser.add_argument(
'--epochs', default=1, type=int, metavar='N',
help='Number of total epochs to run. (default: 1)')
parser.add_argument(
'--num_warmup_epochs', type=int, required=False, default=0, metavar='N',
help='Number of warmup epochs. (default: 0)')
parser.add_argument(
'--num_loader_threads', type=int, required=False, default=4, metavar='N',
help='Number of dataset loader threads. (default: 4)')
parser.add_argument(
'--data', metavar='DIR',
help='Path to dataset.')
best_acc1 = 0
# Initialize Horovod.
hvd.init()
# Horovod: pin GPU to local rank.
torch.cuda.set_device(hvd.local_rank())
def main():
args = parser.parse_args()
if hvd.rank() == 0:
print("torch.version.__version__=%s" % torch.version.__version__)
print("torch.cuda.is_available()=%s" % torch.cuda.is_available())
print("torch.version.cuda=%s" % torch.version.cuda)
print("torch.backends.cudnn.version()=%s" % torch.backends.cudnn.version())
main_worker(args)
def main_worker(args):
global best_acc1
if hvd.rank() == 0:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
# Move model to GPU.
model.cuda()
# Use CrossEntropyLoss with multi-class classification.
criterion = nn.CrossEntropyLoss().cuda()
# Default hyperparameters based on PyTorch Imagenet examples.
optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=0.9, weight_decay=1e-4)
# Horovod: wrap optimizer with DistributedOptimizer.
optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters(), compression=hvd.Compression.none)
cudnn.benchmark = True
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
# Default normalizations based on PyTorch Imagenet examples.
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# Default transforms based on PyTorch Imagenet examples.
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
)
# The sampler defines the strategy to draw samples from the dataset. If specified, shuffle must be False.
# Horovod: use DistributedSampler to partition data among workers. Manually specify
# `num_replicas=hvd.size()` and `rank=hvd.rank()`.
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.bs, shuffle=(train_sampler is None),
num_workers=args.num_loader_threads, 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.bs, shuffle=False,
num_workers=args.num_loader_threads, pin_memory=True)
# Horovod: broadcast parameters & optimizer state.
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
for epoch in range(0, args.epochs):
train_sampler.set_epoch(epoch)
adjust_learning_rate(optimizer, epoch, args)
# Train the model.
train(train_loader, model, criterion, optimizer, epoch, args)
# Validate accuracy on valdation set.
acc1 = validate(val_loader, model, criterion, args)
# Remember best acc@1 and save best model.
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
# Horovod: only save model on rank 0
if is_best and (hvd.rank() == 0):
state = {
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer' : optimizer.state_dict(),
}
torch.save(state, 'model_best.pth.tar')
def adjust_learning_rate(optimizer, epoch, args):
"""Adjusts the learning rate per https://arxiv.org/pdf/1706.02677.pdf"""
# Apply gradual warmup for num_warmup_epochs if global batch size >= 512.
if ((epoch < args.num_warmup_epochs) and (args.bs * hvd.size() >= 128)):
base_lr = (args.bs / 256) * 0.1
step_size = (args.lr - base_lr) / args.num_warmup_epochs
if hvd.rank() == 0:
print("Applying gradual warmup for epoch {}. ".format(epoch) + "(batch_size={}, ".format(args.bs) + "world_size={})".format(hvd.size()))
lr = base_lr + (step_size * epoch)
else:
if epoch < 30:
p = 0
elif epoch < 60:
p = 1
elif epoch < 80:
p = 2
else:
p = 3
lr = args.lr * (0.1 ** p)
if hvd.rank() == 0:
print("Setting learning rate for epoch {}".format(epoch) + ' ' + "to {}".format(lr))
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].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
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 train(train_loader, model, criterion, optimizer, epoch, args):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# Switch to train mode.
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# Measure data loading time.
data_time.update(time.time() - end)
input = input.cuda()
target = target.cuda(non_blocking=True)
# Compute output.
output = model(input)
loss = criterion(output, target)
# Measure accuracy and record loss.
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(acc1[0], input.size(0))
top5.update(acc5[0], input.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 hvd.rank() == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1, top5=top5))
def validate(val_loader, model, criterion, args):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# Switch to evaluate mode.
model.eval()
with torch.no_grad():
end = time.time()
for i, (input, target) in enumerate(val_loader):
input = input.cuda()
target = target.cuda(non_blocking=True)
# Compute output.
output = model(input)
loss = criterion(output, target)
# Measure accuracy and record loss.
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(acc1[0], input.size(0))
top5.update(acc5[0], input.size(0))
# Measure elapsed time.
batch_time.update(time.time() - end)
end = time.time()
if hvd.rank() == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1, top5=top5))
if hvd.rank() == 0:
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(top1=top1, top5=top5))
return top1.avg
if __name__ == '__main__':
main()