def training_loop_lars(model,
criterion,
train_loader,
valid_loader,
epochs,
device,
print_every=1):
'''
Function defining the entire training loop
'''
# set objects for storing metrics
train_losses = []
valid_losses = []
train_accuracy = []
valid_accuracy = []
# Train model
for epoch in range(0, epochs):
#training
if epoch < 10:
optimizer = LARS(model.parameters(),
lr=0.1 * (epoch + 1) / 10,
momentum=0.9)
elif epoch < 15:
optimizer = LARS(model.parameters(), lr=0.1, momentum=0.9)
else:
optimizer = LARS(model.parameters(),
lr=0.1 * (0.95**(epoch - 15)),
momentum=0.9)
model, optimizer, train_loss = train(train_loader, model, criterion,
optimizer, device)
train_losses.append(train_loss)
# validation
with torch.no_grad():
model, valid_loss = validate(valid_loader, model, criterion,
device)
valid_losses.append(valid_loss)
if epoch % print_every == (print_every - 1):
train_acc = get_accuracy(model, train_loader, device=device)
valid_acc = get_accuracy(model, valid_loader, device=device)
train_accuracy.append(float(train_acc))
valid_accuracy.append(float(valid_acc))
print(f'{datetime.now().time().replace(microsecond=0)} --- '
f'Epoch: {epoch}\t'
f'Train loss: {train_loss:.4f}\t'
f'Valid loss: {valid_loss:.4f}\t'
f'Train accuracy: {100 * train_acc:.2f}\t'
f'Valid accuracy: {100 * valid_acc:.2f}\n')
#plot_losses(train_losses, valid_losses)
return model, [train_losses, valid_losses, train_accuracy, valid_accuracy]
def load_model(args):
model = SimCLR(backbone=args.backbone,
projection_dim=args.projection_dim,
pretrained=args.pretrained,
normalize=args.normalize)
if args.inference:
model.load_state_dict(
torch.load("SimCLR_{}_epoch90.pth".format(args.backbone)))
model = model.to(args.device)
scheduler = None
if args.optimizer == "Adam":
optimizer = Adam(model.parameters(), lr=3e-4) # TODO: LARS
elif args.optimizer == "LARS":
# optimized using LARS with linear learning rate scaling
# (i.e. LearningRate = 0.3 × BatchSize/256) and weight decay of 10−6.
learning_rate = 0.3 * args.batch_size / 256
optimizer = LARS(
model.parameters(),
lr=learning_rate,
weight_decay=args.weight_decay,
exclude_from_weight_decay=["batch_normalization", "bias"],
)
# "decay the learning rate with the cosine decay schedule without restarts"
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
args.epochs,
eta_min=0,
last_epoch=-1)
else:
raise NotImplementedError
return model, optimizer, scheduler
def build_byol_optimizer(hparams: AttrDict, model: nn.Module) -> Optimizer:
"""
Build optimizer for BYOL self-supervised network, including backbone.
"""
regular_parameters = []
excluded_parameters = []
for name, parameter in model.named_parameters():
if parameter.requires_grad is False:
continue
if any(x in name for x in [".bn", ".bias"]):
excluded_parameters.append(parameter)
else:
regular_parameters.append(parameter)
param_groups = [
{
"params": regular_parameters,
"use_lars": True
},
{
"params": excluded_parameters,
"use_lars": False,
"weight_decay": 0,
},
]
return LARS(
param_groups,
lr=hparams.self_supervised.learning_rate.base,
eta=hparams.self_supervised.lars_eta,
momentum=hparams.self_supervised.momentum,
weight_decay=hparams.self_supervised.weight_decay,
)
def configure_optimizers(self):
#With this thing we get only params, which requires grad (weights needed to train)
params = filter(lambda p: p.requires_grad, self.model.parameters())
if self.hparams.optimizer == "SGD":
self.optimizer = torch.optim.SGD(params, self.hparams.lr, momentum = self.hparams.momentum, weight_decay=self.hparams.wd)
elif self.hparams.optimizer == "LARS":
self.optimizer = LARS(params, lr=self.hparams.lr, momentum=self.hparams.momentum, weight_decay=self.hparams.wd, max_epoch=self.hparams.epochs)
self.scheduler = torch.optim.lr_scheduler.OneCycleLR(self.optimizer, self.hparams.lr, epochs=self.hparams.epochs, steps_per_epoch=1, pct_start=self.hparams.pct_start)
sched_dict = {'scheduler': self.scheduler}
return [self.optimizer], [sched_dict]
def do_training(args):
trainloader, testloader = build_dataset(
args.dataset,
dataroot=args.dataroot,
batch_size=args.batch_size,
eval_batch_size=args.eval_batch_size,
num_workers=2)
model = build_model(args.arch, num_classes=num_classes(args.dataset))
if args.cuda:
model = torch.nn.DataParallel(model).cuda()
# Calculate total number of model parameters
num_params = sum(p.numel() for p in model.parameters())
track.metric(iteration=0, num_params=num_params)
num_chunks = max(1, args.batch_size // args.max_samples_per_gpu)
optimizer = LARS(params=model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
eta=args.eta,
max_epoch=args.epochs)
criterion = torch.nn.CrossEntropyLoss()
best_acc = 0.0
for epoch in range(args.epochs):
track.debug("Starting epoch %d" % epoch)
train_loss, train_acc = train(trainloader,
model,
criterion,
optimizer,
epoch,
args.cuda,
num_chunks=num_chunks)
test_loss, test_acc = test(testloader, model, criterion, epoch,
args.cuda)
track.debug('Finished epoch %d... | train loss %.3f | train acc %.3f '
'| test loss %.3f | test acc %.3f' %
(epoch, train_loss, train_acc, test_loss, test_acc))
# Save model
model_fname = os.path.join(track.trial_dir(),
"model{}.ckpt".format(epoch))
torch.save(model, model_fname)
if test_acc > best_acc:
best_acc = test_acc
best_fname = os.path.join(track.trial_dir(), "best.ckpt")
track.debug("New best score! Saving model")
torch.save(model, best_fname)
class ApexDistributeModel(object):
def __init__(self, model, criterion, optimizer, args, gpu=None):
super(ApexDistributeModel, self).__init__()
self.model = model
self.args = args
self.sync_bn = self.args.sync_bn
self.gpu = gpu
if self.gpu is not None:
assert isinstance(self.gpu, int), "GPU should is a int type."
self.criterion = criterion
self.optimizer = optimizer
self.opt_level = None
def convert(self, opt_level='O0'):
self.opt_level = opt_level
if self.sync_bn:
# synchronization batch normal
self.model = apex.parallel.convert_syncbn_model(self.model)
# assign specific gpu
self.model = self.model.cuda(self.gpu)
self.criterion = self.criterion.cuda(self.gpu)
# init model and optimizer by apex
self.model, self.optimizer = apex.amp.initialize(self.model, self.optimizer,
opt_level=self.opt_level)
# apex parallel
self.model = apex.parallel.DistributedDataParallel(self.model, delay_allreduce=True)
# self.model = apex.parallel.DistributedDataParallel(self.model)
self.model = nn.parallel.DistributedDataParallel(self.model, device_ids=[self.gpu])
return self.model, self.criterion, self.optimizer
def lars(self):
print("Enable LARS Optimizer Algorithm")
self.optimizer = LARS(self.optimizer)
def train(self, epoch, train_loader, max_batches, train_index):
"""
you must run it after the 'convert' function.
:param epoch:
:param train_loader:
:return:
"""
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
self.model.train()
train_log_file = 'train_log'
end = time.time()
epoch_batch = epoch * (len(train_loader.dataset) / self.args.batch_size)
for i, (inputs, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
if train_index != -1 and i < train_index:
continue
adjust_learning_rate_epoch_poly(self.optimizer, self.args, burn_in=Darknet53.burn_in, power=Darknet53.power,
batch_num=i + epoch_batch, max_batches=max_batches)
inputs = inputs.cuda(self.gpu, non_blocking=True)
target = target.cuda(self.gpu, non_blocking=True)
# compute output
output = self.model(inputs)
loss = self.criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(acc1[0], inputs.size(0))
top5.update(acc5[0], inputs.size(0))
# compute gradient and do SGD step
self.optimizer.zero_grad()
# loss.backward()
time1 = time.time()
with apex.amp.scale_loss(loss, self.optimizer) as scale_loss:
scale_loss.backward()
time2 = time.time()
self.optimizer.step()
time3 = time.time()
print("step cost time: {}, backward cost time: {}".format(time3-time2, time2-time1))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % self.args.print_freq == 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))
if i % 1000 == 0 and i:
save_checkpoint({
'epoch': epoch,
'index': i,
'arch': "Darknet_53",
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
}, False, filename='darknet_53_init_55_pytorch_train_tmp.pth.tar')
with open(train_log_file, 'a+') as log_file:
log_file.write('Epoch:{0}, Loss{loss.avg:.4f}, Top1:{top1.avg:.3f},Top5:{top5.avg:.3f}\n'.format(
epoch, loss=losses, top1=top1, top5=top5))
def main(lr=0.1):
global best_acc
args.lr = lr
device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
trainset = torchvision.datasets.CIFAR10(root='/tmp/cifar10',
train=True,
download=True,
transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=2)
testset = torchvision.datasets.CIFAR10(root='/tmp/cifar10',
train=False,
download=True,
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset,
batch_size=100,
shuffle=False,
num_workers=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
# Model
print('==> Building model..')
# net = VGG('VGG19')
# net = ResNet18()
# net = PreActResNet18()
# net = GoogLeNet()
# net = DenseNet121()
# net = ResNeXt29_2x64d()
# net = MobileNet()
# net = MobileNetV2()
# net = DPN92()
# net = ShuffleNetG2()
# net = SENet18()
# net = ShuffleNetV2(1)
# net = EfficientNetB0()
# net = RegNetX_200MF()
net = ResNet50()
net = net.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
ckpt = './checkpoint/' + args.optimizer + str(lr) + '_ckpt.pth'
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isdir(
'checkpoint'), 'Error: no checkpoint directory found!'
checkpoint = torch.load(ckpt)
net.load_state_dict(checkpoint['net'])
best_acc = checkpoint['acc']
start_epoch = checkpoint['epoch']
criterion = nn.CrossEntropyLoss()
if args.optimizer.lower() == 'sgd':
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
if args.optimizer.lower() == 'sgdwm':
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'adam':
optimizer = torch.optim.Adam(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'rmsprop':
optimizer = optim.RMSprop(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'adagrad':
optimizer = optim.Adagrad(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'radam':
from radam import RAdam
optimizer = RAdam(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'lars': #no tensorboardX
from lars import LARS
optimizer = LARS(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'lamb':
from lamb import Lamb
optimizer = Lamb(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'novograd':
from novograd import NovoGrad
optimizer = NovoGrad(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
else:
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# lrs = create_lr_scheduler(args.warmup_epochs, args.lr_decay)
# lr_scheduler = LambdaLR(optimizer,lrs)
# lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.lr_decay, gamma=0.1)
train_acc = []
valid_acc = []
# Training
def train(epoch):
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
print(batch_idx)
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
# lr_scheduler.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
print(100. * correct / total)
train_acc.append(correct / total)
def test(epoch):
global best_acc
net.eval()
test_loss = 0
correct = 0
total = 0
print('test')
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
print(batch_idx)
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
# Save checkpoint.
acc = 100. * correct / total
print(acc)
valid_acc.append(correct / total)
if acc > best_acc:
print('Saving..')
state = {
'net': net.state_dict(),
'acc': acc,
'epoch': epoch,
}
if not os.path.isdir('checkpoint'):
os.mkdir('checkpoint')
torch.save(state, ckpt)
best_acc = acc
for epoch in range(200):
if epoch in args.lr_decay:
checkpoint = torch.load(ckpt)
net.load_state_dict(checkpoint['net'])
best_acc = checkpoint['acc']
args.lr *= 0.1
if args.optimizer.lower() == 'sgd':
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
if args.optimizer.lower() == 'sgdwm':
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'adam':
optimizer = optim.Adam(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'rmsprop':
optimizer = optim.RMSprop(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'adagrad':
optimizer = optim.Adagrad(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'radam':
from radam import RAdam
optimizer = RAdam(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'lars': # no tensorboardX
optimizer = LARS(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
dampening=args.damping)
elif args.optimizer.lower() == 'lamb':
optimizer = Lamb(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'novograd':
optimizer = NovoGrad(net.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
else:
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
train(epoch)
test(epoch)
file = open(args.optimizer + str(lr) + 'log.json', 'w+')
json.dump([train_acc, valid_acc], file)
return best_acc
lr=args.base_lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'adagrad':
optimizer = optim.Adagrad(model.parameters(),
lr=args.base_lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'radam':
from radam import RAdam
optimizer = RAdam(model.parameters(),
lr=args.base_lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'lars': #no tensorboardX
from lars import LARS
optimizer = LARS(model.parameters(),
lr=args.base_lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'lamb':
from lamb import Lamb
optimizer = Lamb(model.parameters(),
lr=args.base_lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'novograd':
from novograd import NovoGrad
optimizer = NovoGrad(model.parameters(),
lr=args.base_lr,
weight_decay=args.weight_decay)
lr_scheduler = [
optim.lr_scheduler.CosineAnnealingLR(optimizer, 3 * len(train_loader),
1e-4)
]
epochs = 400
stop_at_epoch = 100
batch_size = 64
image_size = (92, 92)
train_loader, mem_loader, test_loader = get_train_mem_test_dataloaders(
batch_size=batch_size)
train_transform, test_transform = gpu_transformer(image_size)
loss_ls = []
acc_ls = []
model = BYOL().to(device)
optimizer = LARS(model.named_modules(),
lr=lr,
momentum=momentum,
weight_decay=weight_decay)
scheduler = LR_Scheduler(optimizer,
warmup_epochs,
warmup_lr * batch_size / 8,
epochs,
lr * batch_size / 8,
final_lr * batch_size / 8,
len(train_loader),
constant_predictor_lr=True)
min_loss = np.inf
accuracy = 0
# start training
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer.lower()=='adam':
optimizer = torch.optim.Adam(net.parameters(), lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'rmsprop':
optimizer = optim.RMSprop(net.parameters(),lr=args.lr, momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'adagrad':
optimizer = optim.Adagrad(net.parameters(), lr=args.lr, weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'radam':
from radam import RAdam
optimizer = RAdam(net.parameters(),lr=args.lr,weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'lars':#no tensorboardX
from lars import LARS
optimizer = LARS(net.parameters(), lr=args.lr,momentum=args.momentum,weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'lamb':
from lamb import Lamb
optimizer = Lamb(net.parameters(),lr=args.lr,weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'novograd':
from novograd import NovoGrad
optimizer = NovoGrad(net.parameters(), lr=args.lr,weight_decay=args.weight_decay)
else:
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum,
weight_decay=args.weight_decay)
# lrs = create_lr_scheduler(args.warmup_epochs, args.lr_decay)
# lr_scheduler = LambdaLR(optimizer,lrs)
# lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.lr_decay, gamma=0.1)
train_acc = []
valid_acc = []
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer.lower()=='adam':
optimizer = torch.optim.Adam(net.parameters(), lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'rmsprop':
optimizer = optim.RMSprop(net.parameters(),lr=args.lr, momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'adagrad':
optimizer = optim.Adagrad(net.parameters(), lr=args.lr, weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'radam':
from radam import RAdam
optimizer = RAdam(net.parameters(),lr=args.lr,weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'lars':#no tensorboardX
from lars import LARS
optimizer = LARS(net.parameters(), lr=args.lr,momentum=args.momentum,weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'lamb':
from lamb import Lamb
optimizer = Lamb(net.parameters(),lr=args.lr,weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'novograd':
from novograd import NovoGrad
optimizer = NovoGrad(net.parameters(), lr=args.lr,weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'dyna':
from dyna import Dyna
optimizer = Dyna(net.parameters(), lr=args.lr, weight_decay=args.weight_decay)
else:
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum,
weight_decay=args.weight_decay)
# lrs = create_lr_scheduler(args.warmup_epochs, args.lr_decay)
# lr_scheduler = LambdaLR(optimizer,lrs)
# lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.lr_decay, gamma=0.1)
def lars(self):
self.optimizer = LARS(self.optimizer)
class ApexDistributeModel(object):
def __init__(self, model, criterion, optimizer, config, gpu=None):
super(ApexDistributeModel, self).__init__()
self.model = model
self.config = config
self.sync_bn = self.config['sync_bn']
self.gpu = gpu
if self.gpu is not None:
assert isinstance(self.gpu, int), "GPU should is a int type."
self.criterion = criterion
self.optimizer = optimizer
self.opt_level = None
def convert(self, opt_level='O0'):
self.opt_level = opt_level
if self.sync_bn:
# synchronization batch normal
self.model = apex.parallel.convert_syncbn_model(self.model)
# assign specific gpu
self.model = self.model.cuda(self.gpu)
self.criterion = self.criterion.cuda(self.gpu)
# init model and optimizer by apex
self.model, self.optimizer = apex.amp.initialize(
self.model, self.optimizer, opt_level=self.opt_level)
# apex parallel
self.model = apex.parallel.DistributedDataParallel(
self.model, delay_allreduce=True)
return self.model, self.criterion, self.optimizer
def lars(self):
self.optimizer = LARS(self.optimizer)
def train(self, epoch, train_loader):
"""
you must run it after the 'convert' function.
:param epoch:
:param train_loader:
:return:
"""
self.model.train()
print("Epoch is {}".format(epoch))
train_iter = iter(train_loader)
inputs, target = next(train_iter)
step = 0
start_time = time.time()
while inputs is not None:
step += 1
inputs = inputs.cuda(self.gpu, non_blocking=True)
target = target.cuda(self.gpu, non_blocking=True)
output = self.model(inputs)
loss = self.criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
self.optimizer.zero_grad()
with apex.amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
self.optimizer.step()
inputs, target = next(train_iter, (None, None))
if step % 10 == 0:
end_time = time.time()
print("Step is {}, cost time: {}, loss: {}, acc1: {}, acc5:{}".
format(step, (end_time - start_time), loss.item(),
acc1.item(), acc5.item()))
start_time = time.time()
class ApexDistributeModel(object):
def __init__(self, model, criterion, optimizer, args, gpu=None):
super(ApexDistributeModel, self).__init__()
self.model = model
self.args = args
self.sync_bn = self.args.sync_bn
self.gpu = gpu
if self.gpu is not None:
assert isinstance(self.gpu, int), "GPU should is a int type."
self.criterion = criterion
self.optimizer = optimizer
self.opt_level = None
def convert(self, opt_level='O0'):
self.opt_level = opt_level
if self.sync_bn:
# synchronization batch normal
self.model = apex.parallel.convert_syncbn_model(self.model)
# assign specific gpu
self.model = self.model.cuda(self.gpu)
# self.criterion = self.criterion.cuda(self.gpu)
# init model and optimizer by apex
# self.model, self.optimizer = apex.amp.initialize(self.model, self.optimizer,
# opt_level=self.opt_level)
# apex parallel
# self.model = apex.parallel.DistributedDataParallel(self.model, delay_allreduce=True)
# self.model = apex.parallel.DistributedDataParallel(self.model)
self.model = nn.parallel.DistributedDataParallel(self.model,
device_ids=[self.gpu],
bucket_cap_mb=10)
return self.model, self.criterion, self.optimizer
def lars(self):
print("Enable LARS Optimizer Algorithm")
self.optimizer = LARS(self.optimizer)
def train(self, epoch, train_loader, max_batches, train_index):
"""
you must run it after the 'convert' function.
:param epoch:
:param train_loader:
:return:
"""
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
self.model.train()
train_log_file = 'train_log'
end = time.time()
epoch_batch = epoch * (len(train_loader.dataset) /
self.args.batch_size)
for i, (inputs, target) in enumerate(train_loader):
data_time.update(time.time() - end)
print("data iteration cost time: {}ms".format(data_time.val *
1000))
# print(target)
gpu_1 = time.time()
inputs = inputs.cuda(self.gpu, non_blocking=True)
target = target.cuda(self.gpu, non_blocking=True)
gpu_2 = time.time()
print("convert datasets to gpu cost time: {}ms".format(
(gpu_2 - gpu_1) * 1000))
inference_1 = time.time()
output = self.model(inputs)
inference_2 = time.time()
print("inference time cost: {}ms".format(
(inference_2 - inference_1) * 1000))
loss_1 = time.time()
loss = self.criterion(output, target)
loss_2 = time.time()
print("loss cost time: {}ms".format((loss_2 - loss_1) * 1000))
zero_1 = time.time()
self.optimizer.zero_grad()
zero_2 = time.time()
print("zero cost time: {}ms".format((zero_2 - zero_1) * 1000))
backward_1 = time.time()
loss.backward()
backward_2 = time.time()
print("backward cost time: {}ms".format(
(backward_2 - backward_1) * 1000))
step_1 = time.time()
self.optimizer.step()
step_2 = time.time()
print("step cost time: {}ms".format((step_2 - step_1) * 1000))
batch_time.update(time.time() - end)
print("total cost time is: {}s".format(batch_time.val))
# item_1 = time.time()
# print("loss is {}".format(loss.item()))
# item_2 = time.time()
# print("loss item cost: {}s".format(item_2 - item_1))
print("==================================")
end = time.time()
#we are going to try to find the sum(n_releant_orders)
#important predictors. first compute all X possible products
X_aug = poly_order_of_cols(X,orders,include_cross_terms)
print 'shape of X_aug ',X_aug.shape
B = linear_regression(X_aug,Y[:,0],True)
r_sq = lin_reg_coefficient_of_determination(X_aug,Y[:,0],B)
print 'r_squared for brute force fit ',r_sq
lin_reg_statistics(X_aug,Y[:,0],B,False)
print 'full linreg coefs'
print B
print 'beginning LARS test'
lars_obj = LARS(X_aug,Y[:,0],0)
lars_obj.train()
print 'beginning LASSO test'
lars_obj = LARS(X_aug,Y[:,0],1)
lars_obj.train()
#Cross Validation
#cvn = 10
#print 'performing cross validation CV('+str(cvn)+') for linear regression'
#cvn_lr = n_cross_validation(X_aug,Y,linear_regression_train_test,None,cvn)
#print 'linear regression cvn mse is ',cvn_lr
#Now we will compute cvn for LASSO with a number of lambdas
#if m==1:
if False:
def lars(self):
print("Enable LARS Optimizer Algorithm")
self.optimizer = LARS(self.optimizer)
if len(sys.argv) == 1:
optimizer = optim.SGD(model.parameters(), lr=0.01)
elif sys.argv[1] == 'adam':
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
elif sys.argv[1] == 'sgd':
optimizer = optim.SGD(model.parameters(), lr=0.01)
elif sys.argv[1] == 'sgdwm':
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
elif sys.argv[1] == 'rmsprop':
optimizer = optim.RMSprop(model.parameters(), lr=0.001, momentum=0.9)
elif sys.argv[1] == 'adagrad':
optimizer = optim.Adagrad(model.parameters(), lr=0.01)
elif sys.argv[1] == 'radam':
optimizer = RAdam(model.parameters())
elif sys.argv[1] == 'lars': #no tensorboardX
optimizer = LARS(model.parameters(), lr=0.1, momentum=0.9)
elif sys.argv[1] == 'lamb':
optimizer = Lamb(model.parameters())
elif sys.argv[1] == 'novograd':
optimizer = NovoGrad(model.parameters(), lr=0.01, weight_decay=0.001)
schedular = optim.lr_scheduler.CosineAnnealingLR(optimizer,
3 * len(train_loader),
1e-4)
def train(train_loader, model, criterion, optimizer, schedular, device):
'''
Function for the training step of the training loop
'''
model.train()
running_loss = 0
def main():
args = parse_args()
if args.name is None:
args.name = '%s_WideResNet%s-%s_%d' % (args.dataset, args.depth,
args.width, args.batch_size)
if args.linear_scaling:
args.name += '_wLS'
if args.lars:
args.name += '_wLARS'
if not os.path.exists('models/%s' % args.name):
os.makedirs('models/%s' % args.name)
print('Config -----')
for arg in vars(args):
print('%s: %s' % (arg, getattr(args, arg)))
print('------------')
with open('models/%s/args.txt' % args.name, 'w') as f:
for arg in vars(args):
print('%s: %s' % (arg, getattr(args, arg)), file=f)
joblib.dump(args, 'models/%s/args.pkl' % args.name)
criterion = nn.CrossEntropyLoss().cuda()
cudnn.benchmark = True
# data loading code
if args.dataset == 'cifar10':
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
train_set = datasets.CIFAR10(root='~/data',
train=True,
download=True,
transform=transform_train)
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=8)
test_set = datasets.CIFAR10(root='~/data',
train=False,
download=True,
transform=transform_test)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=128,
shuffle=False,
num_workers=8)
num_classes = 10
elif args.dataset == 'cifar100':
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
train_set = datasets.CIFAR100(root='~/data',
train=True,
download=True,
transform=transform_train)
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=128,
shuffle=True,
num_workers=8)
test_set = datasets.CIFAR100(root='~/data',
train=False,
download=True,
transform=transform_test)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=128,
shuffle=False,
num_workers=8)
num_classes = 100
# create model
model = WideResNet(args.depth, args.width, num_classes=num_classes)
model = model.cuda()
if args.lars:
optimizer = LARS(filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
else:
optimizer = optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.linear_scaling:
scheduler = WarmupMultiStepLR(
optimizer,
milestones=[int(e) for e in args.milestones.split(',')],
target_lr=args.lr * args.batch_size / base_batch_size,
gamma=args.gamma)
else:
scheduler = lr_scheduler.MultiStepLR(
optimizer,
milestones=[int(e) for e in args.milestones.split(',')],
gamma=args.gamma)
log = pd.DataFrame(
index=[],
columns=['epoch', 'lr', 'loss', 'acc', 'val_loss', 'val_acc'])
best_acc = 0
for epoch in range(args.epochs):
print('Epoch [%d/%d]' % (epoch + 1, args.epochs))
scheduler.step()
# train for one epoch
train_log = train(args, train_loader, model, criterion, optimizer,
epoch)
# evaluate on validation set
val_log = validate(args, test_loader, model, criterion)
print('loss %.4f - acc %.4f - val_loss %.4f - val_acc %.4f' %
(train_log['loss'], train_log['acc'], val_log['loss'],
val_log['acc']))
tmp = pd.Series(
[
epoch,
scheduler.get_lr()[0],
train_log['loss'],
train_log['acc'],
val_log['loss'],
val_log['acc'],
],
index=['epoch', 'lr', 'loss', 'acc', 'val_loss', 'val_acc'])
log = log.append(tmp, ignore_index=True)
log.to_csv('models/%s/log.csv' % args.name, index=False)
if val_log['acc'] > best_acc:
torch.save(model.state_dict(), 'models/%s/model.pth' % args.name)
best_acc = val_log['acc']
print("=> saved best model")
args = parser.parse_args()
if args.optimizer.lower() == 'adam':
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
elif args.optimizer.lower() == 'sgd':
optimizer = optim.SGD(model.parameters(), lr=args.lr)
elif args.optimizer.lower() == 'sgdwm':
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9)
elif args.optimizer.lower() == 'rmsprop':
optimizer = optim.RMSprop(model.parameters(), lr=args.lr, momentum=0.9)
elif args.optimizer.lower() == 'adagrad':
optimizer = optim.Adagrad(model.parameters(), lr=args.lr)
elif args.optimizer.lower() == 'radam':
optimizer = RAdam(model.parameters(), lr=args.lr)
elif args.optimizer.lower() == 'lars': #no tensorboardX
optimizer = LARS(model.parameters(), lr=args.lr, momentum=0.9)
elif args.optimizer.lower() == 'lamb':
optimizer = Lamb(model.parameters(), lr=args.lr)
elif args.optimizer.lower() == 'novograd':
optimizer = NovoGrad(model.parameters(), lr=args.lr, weight_decay=0.0001)
else:
optimizer = optim.SGD(model.parameters(), lr=0.01)
optname = args.optimizer if len(sys.argv) >= 2 else 'sgd'
# log = open(optname+'log.txt','w+')
log = None
criterion = nn.CrossEntropyLoss()
def main(is_distributed, rank, ip, sync_bn):
world_size = 1
if is_distributed:
world_size = 2
torch.distributed.init_process_group(backend='nccl',
init_method=ip,
world_size=world_size,
rank=rank)
assert torch.backends.cudnn.enabled, "Amp requires cudnn backend to be enabled."
print("Connect")
# set hyper parameters
batch_size = 128
lr = 0.01 # base on batch size 256
momentum = 0.9
weight_decay = 0.0001
epoch = 100
# recompute lr
lr = lr * world_size
# create model
model = AlexNet(10)
# synchronization batch normal
if sync_bn:
model = apex.parallel.convert_syncbn_model(model)
model = model.cuda()
# define loss function
criterion = nn.CrossEntropyLoss().cuda()
# define optimizer strategy
optimizer = torch.optim.SGD(model.parameters(),
lr,
momentum=momentum,
weight_decay=weight_decay)
model, optimizer = apex.amp.initialize(model, optimizer, opt_level='O0')
optimizer = LARS(optimizer)
if is_distributed:
# for distribute training
model = nn.parallel.DistributedDataParallel(model)
# model = apex.parallel.DistributedDataParallel(model, delay_allreduce=True)
# load train data
data_path = '~/datasets/cifar10/train'
train_set = LoadClassifyDataSets(data_path, 227)
train_sampler = None
if is_distributed:
train_sampler = distributed.DistributedSampler(train_set)
train_loader = DataLoader(train_set,
batch_size,
shuffle=(train_sampler is None),
num_workers=4,
pin_memory=True,
sampler=train_sampler,
collate_fn=collate_fn)
for epoch in range(100):
# for distribute
if is_distributed:
train_sampler.set_epoch(epoch)
model.train()
# train_iter = iter(train_loader)
# inputs, target = next(train_iter)
prefetcher = DataPrefetcher(train_loader)
inputs, target = prefetcher.next()
step = 0
print("Epoch is {}".format(epoch))
while inputs is not None:
step += 1
print("Step is {}".format(step))
time_model_1 = time.time()
output = model(inputs)
time_model_2 = time.time()
print("model time: {}".format(time_model_2 - time_model_1))
time_loss_1 = time.time()
loss = criterion(output, target.cuda(async=True))
time_loss_2 = time.time()
print("loss time: {}".format(time_loss_2 - time_loss_1))
optimizer.zero_grad()
time_back_1 = time.time()
# loss.backward()
with apex.amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
time_back_2 = time.time()
print("back time: {}".format(time_back_2 - time_back_1))
optimizer.step()
# if step % 10 == 0:
# print("loss is : {}", loss.item())
# inputs, target = next(train_iter, (None, None))
inputs, target = prefetcher.next()
