def test_model():
"""Test a model"""
utils.setup_distributed()
rank = int(os.environ["RANK"])
local_rank = int(os.environ["LOCAL_RANK"])
device = torch.device("cuda", local_rank)
utils.setup_logger(rank, local_rank)
try:
net = models.build_model(
arch=cfg.MODEL.ARCH,
pretrained=cfg.MODEL.PRETRAINED,
num_classes=cfg.MODEL.NUM_CLASSES,
)
except KeyError:
net = timm.create_model(
model_name=cfg.MODEL.ARCH,
pretrained=cfg.MODEL.PRETRAINED,
num_classes=cfg.MODEL.NUM_CLASSES,
)
net = net.to(device)
net = DDP(net, device_ids=[local_rank], output_device=local_rank)
val_loader = utils.construct_val_loader()
criterion = nn.CrossEntropyLoss().to(device)
if cfg.MODEL.WEIGHTS:
utils.load_checkpoint(cfg.MODEL.WEIGHTS, net)
acc1, acck = validate(val_loader, net, criterion)
if rank == 0:
logger.info(
f"ACCURACY: TOP1 {acc1:.3f} | TOP{cfg.TRAIN.TOPK} {acck:.3f}")
def train_model():
"""Train a model"""
# Set up distributed device
utils.setup_distributed()
rank = int(os.environ["RANK"])
local_rank = int(os.environ["LOCAL_RANK"])
device = torch.device("cuda", local_rank)
utils.setup_seed(rank)
utils.setup_logger(rank, local_rank)
net = models.build_model(arch=cfg.MODEL.ARCH,
pretrained=cfg.MODEL.PRETRAINED)
# SyncBN (https://pytorch.org/docs/stable/generated/torch.nn.SyncBatchNorm.html)
net = nn.SyncBatchNorm.convert_sync_batchnorm(
net) if cfg.MODEL.SYNCBN else net
net = net.to(device)
# DistributedDataParallel Wrapper
net = DDP(net, device_ids=[local_rank], output_device=local_rank)
train_loader = utils.construct_train_loader()
val_loader = utils.construct_val_loader()
criterion = nn.CrossEntropyLoss().to(device)
optimizer = utils.construct_optimizer(net)
# Resume from a specific checkpoint or the last checkpoint
best_acc1 = start_epoch = 0
if cfg.TRAIN.AUTO_RESUME and utils.has_checkpoint():
file = utils.get_last_checkpoint()
start_epoch, best_acc1 = utils.load_checkpoint(file, net, optimizer)
elif cfg.MODEL.WEIGHTS:
load_opt = optimizer if cfg.TRAIN.LOAD_OPT else None
start_epoch, best_acc1 = utils.load_checkpoint(cfg.MODEL.WEIGHTS, net,
load_opt)
if rank == 0:
# from torch.utils.collect_env import get_pretty_env_info
# logger.debug(get_pretty_env_info())
# logger.debug(net)
logger.info("\n\n\n ======= TRAINING ======= \n\n")
for epoch in range(start_epoch, cfg.OPTIM.MAX_EPOCH):
# Train one epoch
train_epoch(train_loader, net, criterion, optimizer, epoch)
# Validate
acc1, acck = validate(val_loader, net, criterion)
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
# Save model
checkpoint_file = utils.save_checkpoint(net, optimizer, epoch,
best_acc1, is_best)
if rank == 0:
logger.info(
f"ACCURACY: TOP1 {acc1:.3f}(BEST {best_acc1:.3f}) | TOP{cfg.TRAIN.TOPK} {acck:.3f} | SAVED {checkpoint_file}"
)
