class Trainer():
def __init__(self,
model,
dataset,
ctx=-1,
batch_size=128,
optimizer='sgd',
lambdas=[0.1, 0.1],
print_freq=32):
self.model = model
self.dataset = dataset
self.batch_size = batch_size
self.optbb = optim.SGD(chain(self.model.age_classifier.parameters(),
self.model.RFM.parameters(),
self.model.margin_fc.parameters(),
self.model.backbone.parameters()),
lr=0.01,
momentum=0.9)
self.optDAL = optim.SGD(self.model.DAL.parameters(),
lr=0.01,
momentum=0.9)
self.lambdas = lambdas
self.print_freq = print_freq
self.id_recorder = Recorder()
self.age_recorder = Recorder()
self.trainingDAL = False
if ctx < 0:
self.ctx = torch.device('cpu')
else:
self.ctx = torch.device(f'cuda:{ctx}')
def train(self, epochs, start_epoch, save_path=None):
self.train_ds = ImageFolderWithAgeGroup(self.dataset['pat'], self.dataset['pos'], \
age_cutoffs, self.dataset['train_root'], transform=transforms.Compose(\
[transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor(), \
transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))]))
self.train_ld = DataLoader(self.train_ds,
shuffle=True,
batch_size=self.batch_size)
if self.dataset['val_root'] is not None:
self.val_ds = ImageFolderWithAgeGroup(self.dataset['pat'], self.dataset['pos'], age_cutoffs, self.dataset['val_root'], \
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))]))
self.val_ld = DataLoader(self.val_ds,
shuffle=True,
batch_size=self.batch_size)
self.model = self.model.to(self.ctx)
for epoch in range(epochs):
print(f'---- epoch {epoch} ----')
self.update()
if self.dataset['val_root'] is not None:
acc = self.validate()
else:
acc = -1.
if save_path is not None:
torch.save(
self.model.state_dict(),
os.path.join(save_path,
f'{start_epoch+epoch}_{acc:.4f}.state'))
def update(self):
print(' -- Training --')
self.model.train()
self.id_recorder.reset()
self.age_recorder.reset()
for i, (xs, ys, agegrps) in enumerate(self.train_ld):
if i % 70 == 0: # canonical maximization procesure
self.set_train_mode(False)
elif i % 70 == 20: # RFM optimize procesure
self.set_train_mode(True)
xs, ys, agegrps = xs.to(self.ctx), ys.to(self.ctx), agegrps.to(
self.ctx)
idLoss, id_acc, ageLoss, age_acc, cc = self.model(xs, ys, agegrps)
#print(f' ---\n{idLoss}\n{id_acc}\n{ageLoss}\n{age_acc}\n{cc}')
total_loss = idLoss + ageLoss * self.lambdas[
0] + cc * self.lambdas[1]
self.id_recorder.gulp(len(agegrps), idLoss.item(), id_acc.item())
self.age_recorder.gulp(len(agegrps), ageLoss.item(),
age_acc.item())
if i % self.print_freq == 0:
print(
f' iter: {i} {i%70} total loss: {total_loss.item():.4f} ({idLoss.item():.4f}, {id_acc.item():.4f}, {ageLoss.item():.4f}, {age_acc.item():.4f}, {cc.item():.8f})'
)
if self.trainingDAL:
self.optDAL.zero_grad()
total_loss.backward()
Trainer.flip_grads(self.model.DAL)
self.optDAL.step()
else:
self.optbb.zero_grad()
total_loss.backward()
self.optbb.step()
# show average training meta after epoch
print(f' {self.id_recorder.excrete().result_as_string()}')
print(f' {self.age_recorder.excrete().result_as_string()}')
def validate(self):
print(' -- Validating --')
self.model.eval()
self.id_recorder.reset()
self.age_recorder.reset()
for i, (xs, ys, agegrps) in enumerate(self.val_ld):
xs, ys, agegrps = xs.to(self.ctx), ys.to(self.ctx), agegrps.to(
self.ctx)
with torch.no_grad():
idLoss, id_acc, ageLoss, age_acc, cc = self.model(
xs, ys, agegrps)
total_loss = idLoss + ageLoss * self.lambdas[
0] + cc * self.lambdas[1]
self.id_recorder.gulp(len(agegrps), idLoss.item(),
id_acc.item())
self.age_recorder.gulp(len(agegrps), ageLoss.item(),
age_acc.item())
# show average validation meta after epoch
print(f' {self.id_recorder.excrete().result_as_string()}')
print(f' {self.age_recorder.excrete().result_as_string()}')
return self.id_recorder.acc
def set_train_mode(self, state):
self.trainingDAL = not state
Trainer.set_grads(self.model.RFM, state)
Trainer.set_grads(self.model.backbone, state)
Trainer.set_grads(self.model.margin_fc, state)
Trainer.set_grads(self.model.age_classifier, state)
Trainer.set_grads(self.model.DAL, not state)
@staticmethod
def set_grads(mod, state):
for para in mod.parameters():
para.requires_grad = state
@staticmethod
def flip_grads(mod):
for para in mod.parameters():
if para.requires_grad:
para.grad = -para.grad
class Trainer():
def __init__(
self, model, dataset, ctx=-1, batch_size=128, optimizer='sgd',
grad_accu=1, lambdas=[0.05, 0.1], print_freq=32, train_head_only=True
):
self.model = model
self.dataset = dataset
self.batch_size = batch_size
self.finetune_layers = (
# self.model.backbone.repeat_3[-1:],
self.model.backbone.last_bn,
self.model.backbone.last_linear, self.model.backbone.block8
)
first_group = [
{
"params": chain(
self.model.age_classifier.parameters(),
self.model.RFM.parameters(),
self.model.margin_fc.parameters(),
),
"lr": 5e-4
}
]
if not train_head_only:
# first_group[0]["lr"] = 1e-4
first_group.append(
{
"params": chain(
*(x.parameters() for x in self.finetune_layers)
),
"lr": 5e-5
}
)
self.optbb = RAdam(first_group)
self.optDAL = RAdam(self.model.DAL.parameters(), lr=5e-4)
self.lambdas = lambdas
self.print_freq = print_freq
self.id_recorder = Recorder()
self.age_recorder = Recorder()
self.trainingDAL = False
if ctx < 0:
self.ctx = torch.device('cpu')
else:
self.ctx = torch.device(f'cuda:{ctx}')
self.scaler1 = GradScaler()
self.scaler2 = GradScaler()
self.grad_accu = grad_accu
self.train_head_only = train_head_only
def train(self, epochs, start_epoch, save_path=None):
self.train_ds = ImageFolderWithAges(
self.dataset['pat'], self.dataset['pos'],
transforms=Compose(
[
HorizontalFlip(p=0.5),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.25),
Resize(200, 200, cv2.INTER_AREA),
ToTensor(normalize=dict(
mean=[0.5, 0.5, 0.5], std=[0.50196, 0.50196, 0.50196])
)
]
),
root=self.dataset['train_root'],
)
self.train_ld = DataLoader(
self.train_ds, shuffle=True, batch_size=self.batch_size, num_workers=2, drop_last=True, pin_memory=True
)
print("# Batches:", len(self.train_ld))
if self.dataset['val_root'] is not None:
self.val_ds = ImageFolderWithAges(
self.dataset['pat'], self.dataset['pos'],
root=self.dataset['val_root'],
transforms=Compose([
Resize(200, 200, cv2.INTER_AREA),
ToTensor(normalize=dict(
mean=[0.5, 0.5, 0.5], std=[0.50196, 0.50196, 0.50196])
)
])
)
self.val_ld = DataLoader(self.val_ds, shuffle=False, batch_size=self.batch_size,
pin_memory=True, num_workers=1)
self.model = self.model.to(self.ctx)
total_steps = len(self.train_ld) * epochs
lr_durations = [
int(total_steps*0.05),
int(np.ceil(total_steps*0.95))
]
break_points = [0] + list(np.cumsum(lr_durations))[:-1]
self.schedulers = [
MultiStageScheduler(
[
LinearLR(self.optbb, 0.01, lr_durations[0]),
CosineAnnealingLR(self.optbb, lr_durations[1], eta_min=1e-6)
],
start_at_epochs=break_points
),
MultiStageScheduler(
[
LinearLR(self.optDAL, 0.01, lr_durations[0]),
CosineAnnealingLR(self.optDAL, lr_durations[1], eta_min=1e-6)
],
start_at_epochs=break_points
)
]
if self.train_head_only:
set_trainable(self.model.backbone, False)
for module in self.model.backbone.modules():
if isinstance(module, (nn.BatchNorm2d, nn.BatchNorm1d)):
module.track_running_stats = False
else:
set_trainable(self.model.backbone, False)
# for module in self.model.backbone.modules():
# if isinstance(module, (nn.BatchNorm2d, nn.BatchNorm1d)):
# module.track_running_stats = False
for module in self.finetune_layers:
set_trainable(module, True)
# for submodule in chain([module], module.modules()):
# if isinstance(submodule, (nn.BatchNorm2d, nn.BatchNorm1d)):
# submodule.track_running_stats = True
count_model_parameters(self.model)
# print(self.optbb.param_groups[-1]["lr"])
# print(self.optDAL.param_groups[-1]["lr"])
for epoch in range(epochs):
print(f'---- epoch {epoch} ----')
self.update()
if self.dataset['val_root'] is not None:
acc = self.validate()
else:
acc = -1.
if save_path is not None:
torch.save(self.model.state_dict(), os.path.join(save_path, f'{start_epoch+epoch}_{acc:.4f}.pth'))
def update(self):
print(' -- Training --')
self.model.train()
self.model.backbone.eval()
if not self.train_head_only:
for module in self.finetune_layers:
module.train()
# for submodule in chain([module], module.modules()):
# if isinstance(submodule, (nn.BatchNorm2d, nn.BatchNorm1d)):
# submodule.eval()
self.id_recorder.reset()
self.age_recorder.reset()
for i, (xs, ys, agegrps) in enumerate(self.train_ld):
if i % 80 == 0: # canonical maximization procesure
self.set_train_mode(False)
elif i % 80 == 28: # RFM optimize procesure
self.set_train_mode(True)
xs, ys, agegrps = xs.to(self.ctx), ys.to(self.ctx), agegrps.to(self.ctx)
with autocast():
self.model(xs, ys, agegrps=agegrps)
idLoss, id_acc, ageLoss, age_acc, cc = self.model(xs, ys, agegrps=agegrps)
#print(f' ---\n{idLoss}\n{id_acc}\n{ageLoss}\n{age_acc}\n{cc}')
total_loss = idLoss + ageLoss*self.lambdas[0] + cc*self.lambdas[1]
total_loss /= self.grad_accu
self.id_recorder.gulp(len(agegrps), idLoss.detach().item(), id_acc.detach().item())
self.age_recorder.gulp(len(agegrps), ageLoss.detach().item(), age_acc.detach().item())
if i % self.print_freq == 0:
print(
f' iter: {i} {i%70} total loss: {total_loss.item():.4f} ({idLoss.item():.4f}, {id_acc.item():.4f}, {ageLoss.item():.4f}, {age_acc.item():.4f}, {cc.item():.8f}) {self.optbb.param_groups[-1]["lr"]:.6f}')
if self.trainingDAL:
self.scaler1.scale(-1 * cc*self.lambdas[1]).backward()
# total_loss.backward()
# Trainer.flip_grads(self.model.DAL)
if (i + 1) % self.grad_accu == 0:
# self.optDAL.step()
self.scaler1.step(self.optDAL)
self.scaler1.update()
self.optDAL.zero_grad()
else:
self.scaler2.scale(total_loss).backward()
# total_loss.backward()
# self.optbb.step()
if (i + 1) % self.grad_accu == 0:
self.scaler2.step(self.optbb)
self.scaler2.update()
self.optbb.zero_grad()
for scheduler in self.schedulers:
scheduler.step()
# show average training meta after epoch
print(f' {self.id_recorder.excrete().result_as_string()}')
print(f' {self.age_recorder.excrete().result_as_string()}')
def validate(self):
print(' -- Validating --')
self.model.eval()
self.id_recorder.reset()
self.age_recorder.reset()
for i, (xs, ys, agegrps) in enumerate(self.val_ld):
xs, ys, agegrps = xs.to(self.ctx), ys.to(self.ctx), agegrps.to(self.ctx)
with torch.no_grad():
with autocast():
idLoss, id_acc, ageLoss, age_acc, cc = self.model(xs, ys, agegrps)
# total_loss = idLoss + ageLoss*self.lambdas[0] + cc*self.lambdas[1]
self.id_recorder.gulp(len(agegrps), idLoss.item(), id_acc.item())
self.age_recorder.gulp(len(agegrps), ageLoss.item(), age_acc.item())
# show average validation meta after epoch
print(f' {self.id_recorder.excrete().result_as_string()}')
print(f' {self.age_recorder.excrete().result_as_string()}')
return self.id_recorder.acc
def set_train_mode(self, state):
self.trainingDAL = not state
# Trainer.set_grads(self.model.RFM, state)
# # Trainer.set_grads(self.model.backbone, state)
# Trainer.set_grads(self.model.margin_fc, state)
# Trainer.set_grads(self.model.age_classifier, state)
# Trainer.set_grads(self.model.DAL, not state)
@staticmethod
def set_grads(mod, state):
for para in mod.parameters():
para.requires_grad = state
@staticmethod
def flip_grads(mod):
for para in mod.parameters():
if para.requires_grad:
para.grad = - para.grad
