from __future__ import print_function
import os, os.path as osp
import math
import argparse
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data.distributed
from torchvision import datasets, transforms
import horovod.torch as hvd
import tensorboardX
from tqdm import tqdm
import net224x224 as models
from utils.bags_of_tricks import cross_encropy_with_label_smoothing
import subprocess
subprocess.call("ulimit -n 65536", shell=True)
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
# Training settings
parser = argparse.ArgumentParser(description='PyTorch ImageNet Example',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
choices=model_names,
help='model architecture: ' +
' | '.join(model_names) +
' (default: resnet50)')
parser.add_argument('--num-classes', type=int, default=1000,
help='The number of classes in the dataset.')
parser.add_argument('--train-dir', default=os.path.expanduser('/ssd/dataset/imagenet/train'),
help='path to training data')
parser.add_argument('--val-dir', default=os.path.expanduser('/ssd/dataset/imagenet/val'),
help='path to validation data')
parser.add_argument('--log-dir', default='./logs',
help='tensorboard log directory')
parser.add_argument('--format', default='./checkpoint-{epoch}.pth.tar',
help='checkpoint file format')
parser.add_argument('--fp16-allreduce', action='store_true', default=False,
help='use fp16 compression during allreduce')
# Default settings from https://arxiv.org/abs/1706.02677.
parser.add_argument('--batch-size', type=int, default=64,
help='input batch size for training')
parser.add_argument('--val-batch-size', type=int, default=64,
help='input batch size for validation')
parser.add_argument('--epochs', type=int, default=150,
help='number of epochs to train')
parser.add_argument('--base-lr', type=float, default=0.0125,
help='learning rate for a single GPU')
parser.add_argument('--warmup-epochs', type=float, default=5,
help='number of warmup epochs')
parser.add_argument('--momentum', type=float, default=0.9,
help='SGD momentum')
parser.add_argument('--wd', type=float, default=0.00005,
help='weight decay')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=42,
help='random seed')
# tricks to boost accuracy
parser.add_argument('--lr-scheduler', type=str, default="cosine", choices=["linear", "cosine"],
help='how to schedule learning rate')
parser.add_argument("--color-jitter", action='store_true', default=False,
help="To apply color augmentation or not.")
parser.add_argument("--label-smoothing", action='store_true', default=False,
help="To use label smoothing or not.")
parser.add_argument("--no-wd-bn", action='store_true', default=False,
help="Whether to remove the weight decay on BN")
args = parser.parse_args()
name_componenets = [args.arch, str(args.epochs), args.lr_scheduler]
if args.color_jitter:
name_componenets.append("color_jitter")
if args.label_smoothing:
name_componenets.append("label_smoothing")
args.log_dir = osp.join(args.log_dir, "-".join(name_componenets))
args.checkpoint_format = osp.join(args.log_dir, args.format)
# linearly scale the learning rate.
args.base_lr = args.base_lr * (args.batch_size / 64)
args.cuda = not args.no_cuda and torch.cuda.is_available()
hvd.init()
torch.manual_seed(args.seed)
if args.cuda:
# Horovod: pin GPU to local rank.
torch.cuda.set_device(hvd.local_rank())
torch.cuda.manual_seed(args.seed)
cudnn.benchmark = True
# If set > 0, will resume training from a given checkpoint.
resume_from_epoch = 0
for try_epoch in range(args.epochs, 0, -1):
if os.path.exists(args.checkpoint_format.format(epoch=try_epoch)):
resume_from_epoch = try_epoch
break
# Horovod: broadcast resume_from_epoch from rank 0 (which will have
# checkpoints) to other ranks.
resume_from_epoch = hvd.broadcast(torch.tensor(resume_from_epoch), root_rank=0,
name='resume_from_epoch').item()
# Horovod: print logs on the first worker.
verbose = 1 if hvd.rank() == 0 else 0
# Horovod: write TensorBoard logs on first worker.
log_writer = tensorboardX.SummaryWriter(args.log_dir) if hvd.rank() == 0 else None
best_val_acc = 0.0
kwargs = {'num_workers': 5, 'pin_memory': True} if args.cuda else {}
# Training transform
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
pre_process = [
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
]
if args.color_jitter:
pre_process += [transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1)]
pre_process += [
transforms.ToTensor(),
normalize
]
train_dataset = datasets.ImageFolder(args.train_dir,
transform=transforms.Compose(pre_process))
# 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.batch_size, sampler=train_sampler, **kwargs)
val_dataset = datasets.ImageFolder(args.val_dir,
transform=transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize
]))
val_sampler = torch.utils.data.distributed.DistributedSampler(
val_dataset, num_replicas=hvd.size(), rank=hvd.rank())
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.val_batch_size,
sampler=val_sampler, **kwargs)
# Set up standard ResNet-50 model.
# model = models.resnet50()
model = models.__dict__[args.arch](num_classes=args.num_classes)
if args.cuda:
# Move model to GPU.
model.cuda()
# Horovod: scale learning rate by the number of GPUs.
optimizer = optim.SGD(model.parameters(), lr=args.base_lr * hvd.size(),
momentum=args.momentum, weight_decay=args.wd)
# Horovod: (optional) compression algorithm.
compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none
# Horovod: wrap optimizer with DistributedOptimizer.
optimizer = hvd.DistributedOptimizer(optimizer,
named_parameters=model.named_parameters(),
compression=compression)
# Restore from a previous checkpoint, if initial_epoch is specified.
# Horovod: restore on the first worker which will broadcast weights to other workers.
if resume_from_epoch > 0 and hvd.rank() == 0:
filepath = args.checkpoint_format.format(epoch=resume_from_epoch)
checkpoint = torch.load(filepath)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
if args.label_smoothing:
criterion = cross_encropy_with_label_smoothing
else:
criterion = nn.CrossEntropyLoss()
# Horovod: broadcast parameters & optimizer state.
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
def train(epoch):
model.train()
train_sampler.set_epoch(epoch)
train_loss = Metric('train_loss')
train_accuracy = Metric('train_accuracy')
with tqdm(total=len(train_loader),
desc='Train Epoch #{}'.format(epoch + 1),
disable=not verbose) as t:
for batch_idx, (data, target) in enumerate(train_loader):
lr_cur = adjust_learning_rate(epoch, batch_idx, type=args.lr_scheduler)
if args.cuda:
data, target = data.cuda(), target.cuda()
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss.update(loss)
train_accuracy.update(accuracy(output, target))
t.set_postfix({'loss': train_loss.avg.item(),
'accuracy': 100. * train_accuracy.avg.item(),
'lr': lr_cur})
t.update(1)
if log_writer:
log_writer.add_scalar('train/loss', train_loss.avg, epoch)
log_writer.add_scalar('train/accuracy', train_accuracy.avg, epoch)
def validate(epoch, ):
global best_val_acc
model.eval()
val_loss = Metric('val_loss')
val_accuracy = Metric('val_accuracy')
with tqdm(total=len(val_loader),
desc='Validate Epoch #{}'.format(epoch + 1),
disable=not verbose) as t:
with torch.no_grad():
for data, target in val_loader:
if args.cuda:
data, target = data.cuda(), target.cuda()
output = model(data)
val_loss.update(criterion(output, target))
val_accuracy.update(accuracy(output, target))
t.set_postfix({'loss': val_loss.avg.item(),
'accuracy': 100. * val_accuracy.avg.item()})
t.update(1)
if log_writer:
log_writer.add_scalar('val/loss', val_loss.avg, epoch)
log_writer.add_scalar('val/accuracy', val_accuracy.avg, epoch)
best_val_acc = max(best_val_acc, val_accuracy.avg)
log_writer.add_scalar('val/best_acc', best_val_acc, epoch)
return val_accuracy.avg
import torch.optim.lr_scheduler as lr_scheduler
# Horovod: using `lr = base_lr * hvd.size()` from the very beginning leads to worse final
# accuracy. Scale the learning rate `lr = base_lr` ---> `lr = base_lr * hvd.size()` during
# the first five epochs. See https://arxiv.org/abs/1706.02677 for details.
# After the warmup reduce learning rate by 10 on the 30th, 60th and 80th epochs.
def adjust_learning_rate(epoch, batch_idx, type="cosine"):
if epoch < args.warmup_epochs:
epoch += float(batch_idx + 1) / len(train_loader)
lr_adj = 1. / hvd.size() * (epoch * (hvd.size() - 1) / args.warmup_epochs + 1)
elif type == "linear":
if epoch < 30:
lr_adj = 1.
elif epoch < 60:
lr_adj = 1e-1
elif epoch < 90:
lr_adj = 1e-2
else:
lr_adj = 1e-3
elif type == "cosine":
# self.init_lr * 0.5 * (1 + math.cos(math.pi * T_cur / T_total))
run_epochs = epoch - args.warmup_epochs
total_epochs = args.epochs - args.warmup_epochs
T_cur = float(run_epochs * len(train_loader)) + batch_idx
T_total = float(total_epochs * len(train_loader))
lr_adj = 0.5 * (1 + math.cos(math.pi * T_cur / T_total))
for param_group in optimizer.param_groups:
param_group['lr'] = args.base_lr * hvd.size() * lr_adj
return args.base_lr * hvd.size() * lr_adj
def accuracy(output, target):
# get the index of the max log-probability
pred = output.max(1, keepdim=True)[1]
return pred.eq(target.view_as(pred)).cpu().float().mean()
def save_checkpoint(epoch):
if hvd.rank() == 0:
os.remove(args.checkpoint_format.format(epoch=epoch))
filepath = args.checkpoint_format.format(epoch=epoch + 1)
state = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
torch.save(state, filepath)
# Horovod: average metrics from distributed training.
class Metric(object):
def __init__(self, name):
self.name = name
self.sum = torch.tensor(0.)
self.n = torch.tensor(0.)
def update(self, val):
self.sum += hvd.allreduce(val.detach().cpu(), name=self.name)
self.n += 1
@property
def avg(self):
return self.sum / self.n
best_acc = 0.0
last_saved_epoch = None
for epoch in range(resume_from_epoch, args.epochs):
train(epoch)
val_acc = validate(epoch)
# save checkpoint for the master
if hvd.rank() == 0:
if last_saved_epoch is not None:
os.remove(args.checkpoint_format.format(epoch=last_saved_epoch))
filepath = args.checkpoint_format.format(epoch=epoch)
state = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
torch.save(state, filepath)
last_saved_epoch = epoch
