def train_and_eval(tag,
dataroot,
test_ratio=0.0,
cv_fold=0,
reporter=None,
metric='last',
save_path=None,
only_eval=False,
local_rank=-1,
evaluation_interval=5):
total_batch = C.get()["batch"]
if local_rank >= 0:
dist.init_process_group(backend='nccl',
init_method='env://',
world_size=int(os.environ['WORLD_SIZE']))
device = torch.device('cuda', local_rank)
torch.cuda.set_device(device)
C.get()['lr'] *= dist.get_world_size()
logger.info(
f'local batch={C.get()["batch"]} world_size={dist.get_world_size()} ----> total batch={C.get()["batch"] * dist.get_world_size()}'
)
total_batch = C.get()["batch"] * dist.get_world_size()
is_master = local_rank < 0 or dist.get_rank() == 0
if is_master:
add_filehandler(logger, 'master' + '.log')
if not reporter:
reporter = lambda **kwargs: 0
max_epoch = C.get()['epoch']
trainsampler, trainloader, validloader, testloader_ = get_dataloaders(
C.get()['dataset'],
C.get()['batch'],
dataroot,
test_ratio,
split_idx=cv_fold,
multinode=(local_rank >= 0))
# create a model & an optimizer
model = get_model(C.get()['model'],
num_class(C.get()['dataset']),
local_rank=local_rank)
model_ema = get_model(C.get()['model'],
num_class(C.get()['dataset']),
local_rank=-1)
model_ema.eval()
criterion_ce = criterion = CrossEntropyLabelSmooth(
num_class(C.get()['dataset']),
C.get().conf.get('lb_smooth', 0))
if C.get().conf.get('mixup', 0.0) > 0.0:
criterion = CrossEntropyMixUpLabelSmooth(
num_class(C.get()['dataset']),
C.get().conf.get('lb_smooth', 0))
if C.get()['optimizer']['type'] == 'sgd':
optimizer = optim.SGD(
model.parameters(),
lr=C.get()['lr'],
momentum=C.get()['optimizer'].get('momentum', 0.9),
weight_decay=0.0,
nesterov=C.get()['optimizer'].get('nesterov', True))
elif C.get()['optimizer']['type'] == 'rmsprop':
optimizer = RMSpropTF(model.parameters(),
lr=C.get()['lr'],
weight_decay=0.0,
alpha=0.9,
momentum=0.9,
eps=0.001)
else:
raise ValueError('invalid optimizer type=%s' %
C.get()['optimizer']['type'])
lr_scheduler_type = C.get()['lr_schedule'].get('type', 'cosine')
if lr_scheduler_type == 'cosine':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=C.get()['epoch'], eta_min=0.)
elif lr_scheduler_type == 'resnet':
scheduler = adjust_learning_rate_resnet(optimizer)
elif lr_scheduler_type == 'efficientnet':
scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer,
lr_lambda=lambda x: 0.97**int(
(x + C.get()['lr_schedule']['warmup']['epoch']) / 2.4))
else:
raise ValueError('invalid lr_schduler=%s' % lr_scheduler_type)
if C.get()['lr_schedule'].get(
'warmup', None) and C.get()['lr_schedule']['warmup']['epoch'] > 0:
scheduler = GradualWarmupScheduler(
optimizer,
multiplier=C.get()['lr_schedule']['warmup']['multiplier'],
total_epoch=C.get()['lr_schedule']['warmup']['epoch'],
after_scheduler=scheduler)
if not tag or not is_master:
from FastAutoAugment.metrics import SummaryWriterDummy as SummaryWriter
logger.warning('tag not provided, no tensorboard log.')
else:
from tensorboardX import SummaryWriter
writers = [
SummaryWriter(log_dir='./logs/%s/%s' % (tag, x))
for x in ['train', 'valid', 'test']
]
if C.get()['optimizer']['ema'] > 0.0 and is_master:
# https://discuss.pytorch.org/t/how-to-apply-exponential-moving-average-decay-for-variables/10856/4?u=ildoonet
ema = EMA(C.get()['optimizer']['ema'])
else:
ema = None
result = OrderedDict()
epoch_start = 1
#TODO: change only eval=False when without save_path ??
if save_path != 'test.pth': # and is_master: --> should load all data(not able to be broadcasted)
if save_path and not os.path.exists(save_path):
import torch.utils.model_zoo as model_zoo
data = model_zoo.load_url(
'https://download.pytorch.org/models/resnet50-19c8e357.pth',
model_dir=os.path.join(os.getcwd(), 'FastAutoAugment/models'))
if C.get()['dataset'] == 'cifar10':
data.pop('fc.weight')
data.pop('fc.bias')
model_dict = model.state_dict()
model_dict.update(data)
model.load_state_dict(model_dict)
torch.save(model_dict, save_path)
logger.info('%s file found. loading...' % save_path)
data = torch.load(save_path)
key = 'model' if 'model' in data else 'state_dict'
if 'epoch' not in data:
model.load_state_dict(data)
else:
logger.info('checkpoint epoch@%d' % data['epoch'])
if not isinstance(model, (DataParallel, DistributedDataParallel)):
model.load_state_dict({
k.replace('module.', ''): v
for k, v in data[key].items()
})
else:
model.load_state_dict({
k if 'module.' in k else 'module.' + k: v
for k, v in data[key].items()
})
logger.info('optimizer.load_state_dict+')
optimizer.load_state_dict(data['optimizer'])
if data['epoch'] < C.get()['epoch']:
epoch_start = data['epoch']
else:
only_eval = True
if ema is not None:
ema.shadow = data.get('ema', {}) if isinstance(
data.get('ema', {}), dict) else data['ema'].state_dict()
del data
if local_rank >= 0:
for name, x in model.state_dict().items():
dist.broadcast(x, 0)
logger.info(
f'multinode init. local_rank={dist.get_rank()} is_master={is_master}'
)
torch.cuda.synchronize()
tqdm_disabled = bool(os.environ.get(
'TASK_NAME', '')) and local_rank != 0 # KakaoBrain Environment
if only_eval:
logger.info('evaluation only+')
model.eval()
rs = dict()
rs['train'] = run_epoch(model,
trainloader,
criterion,
None,
desc_default='train',
epoch=0,
writer=writers[0],
is_master=is_master)
with torch.no_grad():
rs['valid'] = run_epoch(model,
validloader,
criterion,
None,
desc_default='valid',
epoch=0,
writer=writers[1],
is_master=is_master)
rs['test'] = run_epoch(model,
testloader_,
criterion,
None,
desc_default='*test',
epoch=0,
writer=writers[2],
is_master=is_master)
if ema is not None and len(ema) > 0:
model_ema.load_state_dict({
k.replace('module.', ''): v
for k, v in ema.state_dict().items()
})
rs['valid'] = run_epoch(model_ema,
validloader,
criterion_ce,
None,
desc_default='valid(EMA)',
epoch=0,
writer=writers[1],
verbose=is_master,
tqdm_disabled=tqdm_disabled)
rs['test'] = run_epoch(model_ema,
testloader_,
criterion_ce,
None,
desc_default='*test(EMA)',
epoch=0,
writer=writers[2],
verbose=is_master,
tqdm_disabled=tqdm_disabled)
for key, setname in itertools.product(['loss', 'top1', 'top5'],
['train', 'valid', 'test']):
if setname not in rs:
continue
result['%s_%s' % (key, setname)] = rs[setname][key]
result['epoch'] = 0
return result
# train loop
best_top1 = 0
for epoch in range(epoch_start, max_epoch + 1):
if local_rank >= 0:
trainsampler.set_epoch(epoch)
model.train()
rs = dict()
rs['train'] = run_epoch(model,
trainloader,
criterion,
optimizer,
desc_default='train',
epoch=epoch,
writer=writers[0],
verbose=(is_master and local_rank <= 0),
scheduler=scheduler,
ema=ema,
wd=C.get()['optimizer']['decay'],
tqdm_disabled=tqdm_disabled)
model.eval()
if math.isnan(rs['train']['loss']):
raise Exception('train loss is NaN.')
if ema is not None and C.get(
)['optimizer']['ema_interval'] > 0 and epoch % C.get(
)['optimizer']['ema_interval'] == 0:
logger.info(f'ema synced+ rank={dist.get_rank()}')
if ema is not None:
model.load_state_dict(ema.state_dict())
for name, x in model.state_dict().items():
# print(name)
dist.broadcast(x, 0)
torch.cuda.synchronize()
logger.info(f'ema synced- rank={dist.get_rank()}')
if is_master and (epoch % evaluation_interval == 0
or epoch == max_epoch):
with torch.no_grad():
rs['valid'] = run_epoch(model,
validloader,
criterion_ce,
None,
desc_default='valid',
epoch=epoch,
writer=writers[1],
verbose=is_master,
tqdm_disabled=tqdm_disabled)
rs['test'] = run_epoch(model,
testloader_,
criterion_ce,
None,
desc_default='*test',
epoch=epoch,
writer=writers[2],
verbose=is_master,
tqdm_disabled=tqdm_disabled)
if ema is not None:
model_ema.load_state_dict({
k.replace('module.', ''): v
for k, v in ema.state_dict().items()
})
rs['valid'] = run_epoch(model_ema,
validloader,
criterion_ce,
None,
desc_default='valid(EMA)',
epoch=epoch,
writer=writers[1],
verbose=is_master,
tqdm_disabled=tqdm_disabled)
rs['test'] = run_epoch(model_ema,
testloader_,
criterion_ce,
None,
desc_default='*test(EMA)',
epoch=epoch,
writer=writers[2],
verbose=is_master,
tqdm_disabled=tqdm_disabled)
logger.info(
f'epoch={epoch} '
f'[train] loss={rs["train"]["loss"]:.4f} top1={rs["train"]["top1"]:.4f} '
f'[valid] loss={rs["valid"]["loss"]:.4f} top1={rs["valid"]["top1"]:.4f} '
f'[test] loss={rs["test"]["loss"]:.4f} top1={rs["test"]["top1"]:.4f} '
)
if metric == 'last' or rs[metric]['top1'] > best_top1:
if metric != 'last':
best_top1 = rs[metric]['top1']
for key, setname in itertools.product(
['loss', 'top1', 'top5'], ['train', 'valid', 'test']):
result['%s_%s' % (key, setname)] = rs[setname][key]
result['epoch'] = epoch
writers[1].add_scalar('valid_top1/best', rs['valid']['top1'],
epoch)
writers[2].add_scalar('test_top1/best', rs['test']['top1'],
epoch)
reporter(loss_valid=rs['valid']['loss'],
top1_valid=rs['valid']['top1'],
loss_test=rs['test']['loss'],
top1_test=rs['test']['top1'])
# save checkpoint
if is_master and save_path:
logger.info('save model@%d to %s, err=%.4f' %
(epoch, save_path, 1 - best_top1))
torch.save(
{
'epoch': epoch,
'log': {
'train': rs['train'].get_dict(),
'valid': rs['valid'].get_dict(),
'test': rs['test'].get_dict(),
},
'optimizer': optimizer.state_dict(),
'model': model.state_dict(),
'ema':
ema.state_dict() if ema is not None else None,
}, save_path)
del model
result['top1_test'] = best_top1
return result
def train_and_eval(
args_save,
tag,
dataroot,
test_ratio=0.0,
cv_fold=0,
reporter=None,
metric="last",
save_path=None,
only_eval=False,
local_rank=-1,
evaluation_interval=5,
get_dataloaders=None,
num_class=None,
get_model=None,
):
assert get_dataloaders is not None
assert num_class is not None
assert get_model is not None
total_batch = C.get()["batch"]
if local_rank >= 0:
dist.init_process_group(
backend="nccl",
init_method="env://",
world_size=int(os.environ["WORLD_SIZE"]),
)
device = torch.device("cuda", local_rank)
torch.cuda.set_device(device)
C.get()["lr"] *= dist.get_world_size()
logger.info(
f'local batch={C.get()["batch"]} world_size={dist.get_world_size()} ----> total batch={C.get()["batch"] * dist.get_world_size()}'
)
total_batch = C.get()["batch"] * dist.get_world_size()
is_master = local_rank < 0 or dist.get_rank() == 0
if is_master:
add_filehandler(logger, args_save + ".log")
if not reporter:
reporter = lambda **kwargs: 0
max_epoch = C.get()["epoch"]
trainsampler, trainloader, validloader, testloader_ = get_dataloaders(
C.get()["dataset"],
C.get()["batch"],
dataroot,
test_ratio,
split_idx=cv_fold,
multinode=(local_rank >= 0),
)
# create a model & an optimizer
model = get_model(
C.get()["model"], num_class(C.get()["dataset"]), local_rank=local_rank
)
model_ema = get_model(
C.get()["model"], num_class(C.get()["dataset"]), local_rank=-1
)
model_ema.eval()
criterion_ce = criterion = CrossEntropyLabelSmooth(
num_class(C.get()["dataset"]), C.get().conf.get("lb_smooth", 0)
)
if C.get().conf.get("mixup", 0.0) > 0.0:
criterion = CrossEntropyMixUpLabelSmooth(
num_class(C.get()["dataset"]), C.get().conf.get("lb_smooth", 0)
)
if C.get()["optimizer"]["type"] == "sgd":
optimizer = optim.SGD(
model.parameters(),
lr=C.get()["lr"],
momentum=C.get()["optimizer"].get("momentum", 0.9),
weight_decay=0.0,
nesterov=C.get()["optimizer"].get("nesterov", True),
)
elif C.get()["optimizer"]["type"] == "rmsprop":
optimizer = RMSpropTF(
model.parameters(),
lr=C.get()["lr"],
weight_decay=0.0,
alpha=0.9,
momentum=0.9,
eps=0.001,
)
else:
raise ValueError("invalid optimizer type=%s" % C.get()["optimizer"]["type"])
lr_scheduler_type = C.get()["lr_schedule"].get("type", "cosine")
if lr_scheduler_type == "cosine":
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=C.get()["epoch"], eta_min=0.0
)
elif lr_scheduler_type == "resnet":
scheduler = adjust_learning_rate_resnet(optimizer)
elif lr_scheduler_type == "efficientnet":
scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer,
lr_lambda=lambda x: 0.97
** int((x + C.get()["lr_schedule"]["warmup"]["epoch"]) / 2.4),
)
else:
raise ValueError("invalid lr_schduler=%s" % lr_scheduler_type)
if (
C.get()["lr_schedule"].get("warmup", None)
and C.get()["lr_schedule"]["warmup"]["epoch"] > 0
):
scheduler = GradualWarmupScheduler(
optimizer,
multiplier=C.get()["lr_schedule"]["warmup"]["multiplier"],
total_epoch=C.get()["lr_schedule"]["warmup"]["epoch"],
after_scheduler=scheduler,
)
if not tag or not is_master:
from FastAutoAugment.metrics import SummaryWriterDummy as SummaryWriter
logger.warning("tag not provided, no tensorboard log.")
else:
from tensorboardX import SummaryWriter
writers = [
SummaryWriter(log_dir="./logs/%s/%s" % (tag, x))
for x in ["train", "valid", "test"]
]
if C.get()["optimizer"]["ema"] > 0.0 and is_master:
# https://discuss.pytorch.org/t/how-to-apply-exponential-moving-average-decay-for-variables/10856/4?u=ildoonet
ema = EMA(C.get()["optimizer"]["ema"])
else:
ema = None
result = OrderedDict()
epoch_start = 1
if (
save_path != "test.pth"
): # and is_master: --> should load all data(not able to be broadcasted)
if save_path and os.path.exists(save_path):
logger.info("%s file found. loading..." % save_path)
data = torch.load(save_path)
key = "model" if "model" in data else "state_dict"
if "epoch" not in data:
model.load_state_dict(data)
else:
logger.info("checkpoint epoch@%d" % data["epoch"])
if not isinstance(model, (DataParallel, DistributedDataParallel)):
model.load_state_dict(
{k.replace("module.", ""): v for k, v in data[key].items()}
)
else:
model.load_state_dict(
{
k if "module." in k else "module." + k: v
for k, v in data[key].items()
}
)
logger.info("optimizer.load_state_dict+")
optimizer.load_state_dict(data["optimizer"])
if data["epoch"] < C.get()["epoch"]:
epoch_start = data["epoch"]
else:
only_eval = True
if ema is not None:
ema.shadow = (
data.get("ema", {})
if isinstance(data.get("ema", {}), dict)
else data["ema"].state_dict()
)
del data
else:
logger.info('"%s" file not found. skip to pretrain weights...' % save_path)
if only_eval:
logger.warning(
"model checkpoint not found. only-evaluation mode is off."
)
only_eval = False
if local_rank >= 0:
for name, x in model.state_dict().items():
dist.broadcast(x, 0)
logger.info(
f"multinode init. local_rank={dist.get_rank()} is_master={is_master}"
)
torch.cuda.synchronize()
tqdm_disabled = (
bool(os.environ.get("TASK_NAME", "")) and local_rank != 0
) # KakaoBrain Environment
if only_eval:
logger.info("evaluation only+")
model.eval()
rs = dict()
rs["train"] = run_epoch(
model,
trainloader,
criterion,
None,
desc_default="train",
epoch=0,
writer=writers[0],
is_master=is_master,
)
with torch.no_grad():
rs["valid"] = run_epoch(
model,
validloader,
criterion,
None,
desc_default="valid",
epoch=0,
writer=writers[1],
is_master=is_master,
)
rs["test"] = run_epoch(
model,
testloader_,
criterion,
None,
desc_default="*test",
epoch=0,
writer=writers[2],
is_master=is_master,
)
if ema is not None and len(ema) > 0:
model_ema.load_state_dict(
{k.replace("module.", ""): v for k, v in ema.state_dict().items()}
)
rs["valid"] = run_epoch(
model_ema,
validloader,
criterion_ce,
None,
desc_default="valid(EMA)",
epoch=0,
writer=writers[1],
verbose=is_master,
tqdm_disabled=tqdm_disabled,
)
rs["test"] = run_epoch(
model_ema,
testloader_,
criterion_ce,
None,
desc_default="*test(EMA)",
epoch=0,
writer=writers[2],
verbose=is_master,
tqdm_disabled=tqdm_disabled,
)
for key, setname in itertools.product(
["loss", "top1", "top5"], ["train", "valid", "test"]
):
if setname not in rs:
continue
result["%s_%s" % (key, setname)] = rs[setname][key]
result["epoch"] = 0
return result
# train loop
best_top1 = 0
for epoch in range(epoch_start, max_epoch + 1):
if local_rank >= 0:
trainsampler.set_epoch(epoch)
model.train()
rs = dict()
rs["train"] = run_epoch(
model,
trainloader,
criterion,
optimizer,
desc_default="train",
epoch=epoch,
writer=writers[0],
verbose=(is_master and local_rank <= 0),
scheduler=scheduler,
ema=ema,
wd=C.get()["optimizer"]["decay"],
tqdm_disabled=tqdm_disabled,
)
model.eval()
if math.isnan(rs["train"]["loss"]):
raise Exception("train loss is NaN.")
if (
ema is not None
and C.get()["optimizer"]["ema_interval"] > 0
and epoch % C.get()["optimizer"]["ema_interval"] == 0
):
logger.info(f"ema synced+ rank={dist.get_rank()}")
if ema is not None:
model.load_state_dict(ema.state_dict())
for name, x in model.state_dict().items():
# print(name)
dist.broadcast(x, 0)
torch.cuda.synchronize()
logger.info(f"ema synced- rank={dist.get_rank()}")
if is_master and (epoch % evaluation_interval == 0 or epoch == max_epoch):
with torch.no_grad():
rs["valid"] = run_epoch(
model,
validloader,
criterion_ce,
None,
desc_default="valid",
epoch=epoch,
writer=writers[1],
verbose=is_master,
tqdm_disabled=tqdm_disabled,
)
rs["test"] = run_epoch(
model,
testloader_,
criterion_ce,
None,
desc_default="*test",
epoch=epoch,
writer=writers[2],
verbose=is_master,
tqdm_disabled=tqdm_disabled,
)
if ema is not None:
model_ema.load_state_dict(
{
k.replace("module.", ""): v
for k, v in ema.state_dict().items()
}
)
rs["valid"] = run_epoch(
model_ema,
validloader,
criterion_ce,
None,
desc_default="valid(EMA)",
epoch=epoch,
writer=writers[1],
verbose=is_master,
tqdm_disabled=tqdm_disabled,
)
rs["test"] = run_epoch(
model_ema,
testloader_,
criterion_ce,
None,
desc_default="*test(EMA)",
epoch=epoch,
writer=writers[2],
verbose=is_master,
tqdm_disabled=tqdm_disabled,
)
logger.info(
f"epoch={epoch} "
f'[train] loss={rs["train"]["loss"]:.4f} top1={rs["train"]["top1"]:.4f} '
f'[valid] loss={rs["valid"]["loss"]:.4f} top1={rs["valid"]["top1"]:.4f} '
f'[test] loss={rs["test"]["loss"]:.4f} top1={rs["test"]["top1"]:.4f} '
)
if metric == "last" or rs[metric]["top1"] > best_top1:
if metric != "last":
best_top1 = rs[metric]["top1"]
for key, setname in itertools.product(
["loss", "top1", "top5"], ["train", "valid", "test"]
):
result["%s_%s" % (key, setname)] = rs[setname][key]
result["epoch"] = epoch
writers[1].add_scalar("valid_top1/best", rs["valid"]["top1"], epoch)
writers[2].add_scalar("test_top1/best", rs["test"]["top1"], epoch)
reporter(
loss_valid=rs["valid"]["loss"],
top1_valid=rs["valid"]["top1"],
loss_test=rs["test"]["loss"],
top1_test=rs["test"]["top1"],
)
# save checkpoint
if is_master and save_path:
logger.info(
"save model@%d to %s, err=%.4f"
% (epoch, save_path, 1 - best_top1)
)
torch.save(
{
"epoch": epoch,
"log": {
"train": rs["train"].get_dict(),
"valid": rs["valid"].get_dict(),
"test": rs["test"].get_dict(),
},
"optimizer": optimizer.state_dict(),
"model": model.state_dict(),
"ema": ema.state_dict() if ema is not None else None,
},
save_path,
)
del model
result["top1_test"] = best_top1
return result