def main(args):
checkpoint = torch.load(args.model_path)
plfd_backbone = PFLDInference().cuda()
auxiliarynet = AuxiliaryNet().cuda()
plfd_backbone.load_state_dict(checkpoint['plfd_backbone'])
auxiliarynet.load_state_dict(checkpoint['auxiliarynet'])
transform = transforms.Compose([transforms.ToTensor()])
wlfw_val_dataset = WLFWDatasets(args.test_dataset, transform)
wlfw_val_dataloader = DataLoader(
wlfw_val_dataset, batch_size=8, shuffle=False, num_workers=0)
validate(wlfw_val_dataloader, plfd_backbone, auxiliarynet)
def main(args):
# Step 1: parse args config
logging.basicConfig(
format=
'[%(asctime)s] [p%(process)s] [%(pathname)s:%(lineno)d] [%(levelname)s] %(message)s',
level=logging.INFO,
handlers=[
logging.FileHandler(args.log_file, mode='w'),
logging.StreamHandler()
])
print_args(args)
# Step 2: model, criterion, optimizer, scheduler
plfd_backbone = PFLDInference().cuda()
auxiliarynet = AuxiliaryNet().cuda()
criterion = PFLDLoss()
optimizer = torch.optim.Adam([{
'params': plfd_backbone.parameters()
}, {
'params': auxiliarynet.parameters()
}],
lr=args.base_lr,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='min', patience=args.lr_patience, verbose=True)
# step 3: data
# argumetion
transform = transforms.Compose([transforms.ToTensor()])
wlfwdataset = WLFWDatasets(args.dataroot, transform)
dataloader = DataLoader(wlfwdataset,
batch_size=args.train_batchsize,
shuffle=True,
num_workers=args.workers,
drop_last=False)
wlfw_val_dataset = WLFWDatasets(args.val_dataroot, transform)
wlfw_val_dataloader = DataLoader(wlfw_val_dataset,
batch_size=args.val_batchsize,
shuffle=False,
num_workers=args.workers)
# step 4: run
writer = SummaryWriter(args.tensorboard)
for epoch in range(args.start_epoch, args.end_epoch + 1):
weighted_train_loss, train_loss = train(dataloader, plfd_backbone,
auxiliarynet, criterion,
optimizer, epoch)
filename = os.path.join(str(args.snapshot),
"checkpoint_epoch_" + str(epoch) + '.pth.tar')
save_checkpoint(
{
'epoch': epoch,
'plfd_backbone': plfd_backbone.state_dict(),
'auxiliarynet': auxiliarynet.state_dict()
}, filename)
val_loss = validate(wlfw_val_dataloader, plfd_backbone, auxiliarynet,
criterion, epoch)
scheduler.step(val_loss)
writer.add_scalar('data/weighted_loss', weighted_train_loss, epoch)
writer.add_scalars('data/loss', {
'val loss': val_loss,
'train loss': train_loss
}, epoch)
writer.close()
def main(args):
# Step 1: parse args config
logging.basicConfig(format='[%(asctime)s] [%(levelname)s] %(message)s',
level=logging.INFO,
handlers=[
logging.FileHandler(args.log_file, mode='w'),
logging.StreamHandler()
])
print_args(args)
# Step 2: model, criterion, optimizer, scheduler
plfd_backbone = PFLDInference().to(device)
auxiliarynet = AuxiliaryNet().to(device)
# criterion = PFLDLoss()
criterion = LandmarkLoss()
optimizer = torch.optim.Adam([{
'params': plfd_backbone.parameters()
}, {
'params': auxiliarynet.parameters()
}],
lr=args.base_lr,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='min', patience=args.lr_patience, verbose=True)
if args.resume_path:
print('loading checkpoint {}'.format(args.resume_path))
checkpoint = torch.load(str(args.resume_path))
args.start_epoch = checkpoint['epoch']
plfd_backbone.load_state_dict(checkpoint['plfd_backbone'])
auxiliarynet.load_state_dict(checkpoint['auxiliarynet'])
if 'optimizer' in checkpoint.keys():
optimizer.load_state_dict(checkpoint['optimizer'])
# step 3: data
# argumetion
train_transform = transforms.Compose([
AugCrop(output_size=112, is_training=True),
HorizontalFlip(mirror=args.mirror_file),
RandomRotate(max_angle=30),
Affine(max_strength=30, output_size=112),
ColorDistort()
])
val_transform = transforms.Compose([AugCrop(output_size=112)])
ibugdataset = IBUGDatasets(args.train_json,
transform=train_transform,
is_train=True)
train_dataset_size = ibugdataset.get_dataset_size()
sampler = RandomSampler(ibugdataset,
replacement=True,
num_samples=train_dataset_size)
dataloader = DataLoader(ibugdataset,
batch_size=args.train_batchsize,
sampler=sampler,
num_workers=args.workers,
drop_last=False)
ibug_val_dataset = IBUGDatasets(args.val_json, transform=val_transform)
val_dataset_size = ibug_val_dataset.get_dataset_size()
val_sampler = RandomSampler(ibug_val_dataset,
replacement=True,
num_samples=val_dataset_size)
ibug_val_dataloader = DataLoader(ibug_val_dataset,
batch_size=args.val_batchsize,
sampler=val_sampler,
num_workers=args.workers)
# step 4: run
writer = SummaryWriter(args.tensorboard)
for epoch in range(args.start_epoch, args.end_epoch + 1):
weighted_train_loss, train_loss = train(dataloader, plfd_backbone,
auxiliarynet, criterion,
optimizer, epoch)
filename = os.path.join(str(args.snapshot),
"checkpoint_epoch_" + str(epoch) + '.pth.tar')
save_checkpoint(
{
'epoch': epoch,
'plfd_backbone': plfd_backbone.state_dict(),
'auxiliarynet': auxiliarynet.state_dict(),
'optimizer': optimizer.state_dict()
}, filename)
val_loss = validate(ibug_val_dataloader, plfd_backbone, auxiliarynet,
criterion, epoch)
scheduler.step(val_loss)
writer.add_scalar('data/weighted_loss', weighted_train_loss, epoch)
writer.add_scalars('data/loss', {
'val loss': val_loss,
'train loss': train_loss
}, epoch)
writer.close()
def main(args):
# Step 1: parse args config
logging.basicConfig(
format=
'[%(asctime)s] [p%(process)s] [%(pathname)s:%(lineno)d] [%(levelname)s] %(message)s',
level=logging.INFO,
handlers=[
logging.FileHandler(args.log_file, mode='w'),
logging.StreamHandler()
])
print_args(args)
if args.backbone == "v2":
from models.pfld import PFLDInference, AuxiliaryNet
elif args.backbone == "v3":
from models.mobilev3_pfld import PFLDInference, AuxiliaryNet
elif args.backbone == "ghost":
from models.ghost_pfld import PFLDInference, AuxiliaryNet
elif args.backbone == "lite":
from models.lite import PFLDInference, AuxiliaryNet
else:
raise ValueError("backbone is not implemented")
plfd_backbone = PFLDInference()
auxiliarynet = AuxiliaryNet()
if os.path.exists(args.resume) and args.resume.endswith('.pth'):
logging.info("loading the checkpoint from {}".format(args.resume))
check = torch.load(args.resume, map_location=torch.device('cpu'))
plfd_backbone.load_state_dict(check["plfd_backbone"])
auxiliarynet.load_state_dict(check["auxiliarynet"])
args.start_epoch = check["epoch"]
# Step 2: model, criterion, optimizer, scheduler
plfd_backbone = plfd_backbone.to(device)
auxiliarynet = auxiliarynet.to(device)
criterion = LandMarkLoss()
optimizer = torch.optim.Adam([{
'params': plfd_backbone.parameters()
}, {
'params': auxiliarynet.parameters()
}],
lr=args.base_lr,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='min', patience=args.lr_patience, verbose=True)
# step 3: data
# argumetion
transform = transforms.Compose([transforms.ToTensor()])
wlfwdataset = PFLDDatasets(args.dataroot,
transform,
img_root=os.path.realpath('./data'),
img_size=args.img_size)
dataloader = DataLoader(wlfwdataset,
batch_size=args.train_batchsize,
shuffle=True,
num_workers=args.workers,
drop_last=False)
wlfw_val_dataset = PFLDDatasets(args.val_dataroot,
transform,
img_root=os.path.realpath('./data'),
img_size=args.img_size)
wlfw_val_dataloader = DataLoader(wlfw_val_dataset,
batch_size=args.val_batchsize,
shuffle=False,
num_workers=args.workers)
# step 4: run
weighted_losses = []
train_losses = []
val_losses = []
val_nme = 1e6
for epoch in range(args.start_epoch, args.end_epoch + 1):
weighted_train_loss, train_loss = train(dataloader, plfd_backbone,
auxiliarynet, criterion,
optimizer, epoch)
if epoch % args.epoch_interval == 0:
filename = os.path.join(str(args.snapshot),
"checkpoint_epoch_" + str(epoch) + '.pth')
save_checkpoint(
{
'epoch': epoch,
'plfd_backbone': plfd_backbone.state_dict(),
'auxiliarynet': auxiliarynet.state_dict()
}, filename)
val_loss, cur_val_nme = validate(wlfw_val_dataloader, plfd_backbone,
auxiliarynet, criterion)
if cur_val_nme < val_nme:
filename = os.path.join(str(args.snapshot),
"checkpoint_min_nme.pth")
save_checkpoint(
{
'epoch': epoch,
'plfd_backbone': plfd_backbone.state_dict(),
'auxiliarynet': auxiliarynet.state_dict()
}, filename)
val_nme = cur_val_nme
scheduler.step(val_loss)
weighted_losses.append(weighted_train_loss.item())
train_losses.append(train_loss.item())
val_losses.append(val_loss.item())
logging.info(
"epoch: {}, weighted_train_loss: {:.4f}, trainset loss: {:.4f} valset loss: {:.4f} best val "
"nme: {:.4f}\n ".format(epoch, weighted_train_loss, train_loss,
val_loss, val_nme))
weighted_losses = " ".join(list(map(str, weighted_losses)))
train_losses = " ".join(list(map(str, train_losses)))
val_losses = " ".join(list(map(str, val_losses)))
logging.info(weighted_losses)
logging.info(train_losses)
logging.info(val_losses)
def main(args):
# Step 1: parse args config
logging.basicConfig(
format=
'[%(asctime)s] [p%(process)s] [%(pathname)s:%(lineno)d] [%(levelname)s] %(message)s',
level=logging.INFO,
handlers=[
logging.FileHandler(args.log_file, mode='w'),
logging.StreamHandler()
])
print_args(args)
# Step 2: model, criterion, optimizer, scheduler
if wandb.config.pfld_backbone == "GhostNet":
plfd_backbone = CustomizedGhostNet(width=wandb.config.ghostnet_width, dropout=0.2)
logger.info(f"Using GHOSTNET with width={wandb.config.ghostnet_width} as backbone of PFLD backbone")
# If using pretrained weight from ghostnet model trained on image net
if (wandb.config.ghostnet_with_pretrained_weight_image_net == True):
logger.info(f"Using pretrained weights of ghostnet model trained on image net data ")
plfd_backbone = load_pretrained_weight_imagenet_for_ghostnet_backbone(
plfd_backbone, "./checkpoint_imagenet/state_dict_93.98.pth")
else:
plfd_backbone = PFLDInference().to(device) # MobileNet2 defaut
logger.info("Using MobileNet2 as backbone of PFLD backbone")
auxiliarynet = AuxiliaryNet().to(device)
# Watch model by wandb
wandb.watch(plfd_backbone)
wandb.watch(auxiliarynet)
criterion = PFLDLoss()
optimizer = torch.optim.Adam(
[{
'params': plfd_backbone.parameters()
}, {
'params': auxiliarynet.parameters()
}],
lr=args.base_lr,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', patience=args.lr_patience, verbose=True)
# step 3: data
# argumetion
transform = transforms.Compose([transforms.ToTensor()])
wlfwdataset = WLFWDatasets(args.dataroot, transform)
dataloader = DataLoader(
wlfwdataset,
batch_size=args.train_batchsize,
shuffle=True,
num_workers=args.workers,
drop_last=False)
wlfw_val_dataset = WLFWDatasets(args.val_dataroot, transform)
wlfw_val_dataloader = DataLoader(
wlfw_val_dataset,
batch_size=args.val_batchsize,
shuffle=False,
num_workers=args.workers)
# step 4: run
writer = SummaryWriter(args.tensorboard)
for epoch in range(args.start_epoch, args.end_epoch + 1):
weighted_train_loss, train_loss = train(dataloader, plfd_backbone, auxiliarynet,
criterion, optimizer, epoch)
filename = os.path.join(
str(args.snapshot), "checkpoint_epoch_" + str(epoch) + '.pth.tar')
save_checkpoint({
'epoch': epoch,
'plfd_backbone': plfd_backbone.state_dict(),
'auxiliarynet': auxiliarynet.state_dict()
}, filename)
val_loss = validate(wlfw_val_dataloader, plfd_backbone, auxiliarynet,
criterion)
wandb.log({"metric/val_loss": val_loss})
scheduler.step(val_loss)
writer.add_scalar('data/weighted_loss', weighted_train_loss, epoch)
writer.add_scalars('data/loss', {'val loss': val_loss, 'train loss': train_loss}, epoch)
writer.close()