class Trainer(OptunaMlFlow):
def __init__(self, args, search_space):
super().__init__(args, search_space)
self.args=args
self.best_pred = 0.0
# 再現性を上げるためrandomを使用している場合はrandom.seed()でseedを設定する
random.seed(self.args.seed)
# numpyで再現性を上げるためのの設定
np.random.seed(self.args.seed)
# pytorchで再現性を上げるための設定
torch.manual_seed(self.args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
self.scaler = GradScaler()
self.evaluator=Evaluator(self.args.nclass)
def save_checkpoint(self, state, filename='checkpoint.pth.tar'):
"""Saves checkpoint to disk"""
torch.save(state, filename)#, _use_new_zipfile_serialization=False)
mlflow.log_artifact(filename)
def training(self, epoch):
train_loss = 0.0
self.model.train()
tbar = tqdm(self.train_loader)
num_img_tr = len(self.train_loader)
for i, sample in enumerate(tbar):
image, target = sample
if self.args.cuda:
image, target = image.cuda(), target.cuda()
# self.scheduler(self.optimizer, i, epoch, self.best_pred)
self.optimizer.zero_grad()
# for amp
with autocast():
output = self.model(image)
loss = self.criterion(output, target)
self.scaler.scale(loss).backward()
# needed for horovod+amp
# self.optimizer.synchronize()
# with self.optimizer.skip_synchronize():
self.scaler.step(self.optimizer)
self.scaler.update()
train_loss += loss.item()
tbar.set_description('Train loss: %.3f' % (train_loss / (i + 1)))
mlflow.log_metric('train/total_loss_iter', loss.item(), i + num_img_tr * epoch)
if i > self.args.proc_batch_count:
break
mlflow.log_metric('train/total_loss_epoch', train_loss, epoch)
# print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0]))
print('Loss: %.3f' % train_loss)
if self.args.no_val:
# save checkpoint every epoch
self.save_checkpoint(self.model.state_dict())
def validating(self, epoch):
self.model.eval()
self.evaluator.reset()
tbar = tqdm(self.val_loader, desc='\r')
test_loss = 0.0
for i, sample in enumerate(tbar):
image, target = sample
if self.args.cuda:
image, target = image.cuda(), target.cuda()
with autocast():
with torch.no_grad():
output = self.model(image)
output=output.float()
loss = self.criterion(output, target)
test_loss += loss.item()
tbar.set_description('Test loss: %.3f' % (test_loss / (i + 1)))
# pred = output.data.cpu().detach().numpy()
pred = torch.argmax(output.data, dim=1).cpu().detach().numpy()
target = target.cpu().detach().numpy()
# target = nn.functional.one_hot(target, num_classes=10).cpu().detach().numpy().astype(np.float32)
# Add batch sample into evaluator
self.evaluator.add_batch(target, pred)
if i > self.args.proc_batch_count:
break
# Fast test during the training
Acc = self.evaluator.Pixel_Accuracy()
"""
Acc_class = self.evaluator.Pixel_Accuracy_Class()
"""
print('Validation:')
# print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0]))
new_pred = Acc
if new_pred > self.best_pred:
is_best = True
self.best_pred = new_pred
self.save_checkpoint(self.model.state_dict())
# print("Acc:{}, Acc_class:{}, mIoU:{}, fwIoU: {}".format(Acc, Acc_class, mIoU, FWIoU))
mlflow.log_metric('val/total_loss_epoch', test_loss, epoch)
mlflow.log_metric("val/best_Acc", self.best_pred, epoch)
mlflow.log_metric('val/Acc', Acc, epoch)
"""
mlflow.log_metric('val/Acc_class', Acc_class, epoch)
"""
return self.best_pred
# ここで一つのパラメータの組み合わせについて評価する
def trial_process(self,
trial,
optimizer,
learning_rate,
horizontal_flip,
horizontal_shift_ratio,
vertical_shift_ratio,
random_erasing):
self.best_pred=0.0
self.start_run(trial)
# mlflowにtrialごとの情報をロギング
self.log_trial(trial)
self.model = EfficientNet.from_pretrained(self.args.backbone)
# Unfreeze model weights
for param in self.model.parameters():
param.requires_grad = True
num_ftrs = self.model._fc.in_features
self.model._fc = nn.Linear(num_ftrs, self.args.nclass)
if self.args.smry_viz:
from torchinfo import summary
from torchviz import make_dot
dummy_image=torch.zeros((2, 3, 32,32))
dummy_output=self.model(dummy_image)
make_dot(dummy_output,params=dict(self.model.named_parameters())).render("torchviz", format="png")
summary(self.model, (1,3, 32,32))
import sys;sys.exit()
if self.args.cuda:
self.model = self.model.to('cuda')
if optimizer=='SGD':
self.optimizer = optim.SGD(self.model.parameters(), lr=learning_rate)
elif optimizer=='Adam':
self.optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)
pipeline = [
T.ToTensor(),
T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
if strtobool(horizontal_flip) == 1:
pipeline.append(T.RandomHorizontalFlip(p=0.5))
pipeline.append(T.RandomAffine(0,translate=(horizontal_shift_ratio,vertical_shift_ratio)))
if strtobool(random_erasing) == 1:
pipeline.append(T.RandomErasing())
transform = T.Compose(pipeline)
train_set = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
self.train_loader = torch.utils.data.DataLoader(train_set, batch_size=self.args.batch_size,
shuffle=True, num_workers=self.args.workers)
val_set = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
self.val_loader = torch.utils.data.DataLoader(val_set, batch_size=self.args.batch_size,
shuffle=False, num_workers=self.args.workers)
self.criterion = nn.CrossEntropyLoss()
for epoch in range(self.args.start_epoch, self.args.epochs):
self.training(epoch)
if not self.args.no_val and epoch % self.args.eval_interval == (self.args.eval_interval - 1):
best_score=self.validating(epoch)
self.end_run()
# scoring by best
return 1.0 - best_score
# ランダムおよびTPEサーチを行うための目的関数
def objective_no_grid(self, trial):
'''
# Categorical parameter
optimizer = trial.suggest_categorical('optimizer', self.args.optimizer)
# Int parameter
num_layers = trial.suggest_int('num_layers', self.args.num_layers[0], self.args.num_layers[1])
# Uniform parameter
dropout_rate = trial.suggest_uniform('dropout_rate', self.args.dropout_rate[0], self.args.dropout_rate[1])
# Loguniform parameter
learning_rate = trial.suggest_loguniform('learning_rate', self.args.learning_rate[0], self.args.learning_rate[1])
# Discrete-uniform parameter
drop_path_rate = trial.suggest_discrete_uniform('drop_path_rate', self.args.drop_path_rate[0], self.args.drop_path_rate[1], self.args.drop_path_rate[2])
'''
# Int parameter
# num_layers = trial.suggest_int('num_layers', self.args.num_layers[0], self.args.num_layers[1])
optimizer = trial.suggest_categorical('optimizer', self.args.optimizer)
learning_rate = trial.suggest_loguniform('learning_rate', self.args.learning_rate[0], self.args.learning_rate[1])
horizontal_flip = trial.suggest_categorical('horizontal_flip', self.args.horizontal_flip)
horizontal_shift_ratio = trial.suggest_uniform('horizontal_shift_ratio', self.args.horizontal_shift_ratio[0], self.args.horizontal_shift_ratio[1])
vertical_shift_ratio = trial.suggest_uniform('vertical_shift_ratio', self.args.vertical_shift_ratio[0], self.args.vertical_shift_ratio[1])
random_erasing = trial.suggest_categorical('random_erasing', self.args.random_erasing)
# ここで一つのパラメータの組み合わせについて評価する
result=self.trial_process(trial,
optimizer,
learning_rate,
horizontal_flip,
horizontal_shift_ratio,
vertical_shift_ratio,
random_erasing)
return result
# 固定パラメータおよびグリッドサーチを行うための目的関数
def objective_grid(self, trial):
'''
パラメータは原則trial,suggest_categorical()で指定する。
'''
optimizer = trial.suggest_categorical('optimizer', self.args.optimizer)
learning_rate = trial.suggest_categorical('learning_rate', self.args.learning_rate)
horizontal_flip = trial.suggest_categorical('horizontal_flip', self.args.horizontal_flip)
horizontal_shift_ratio = trial.suggest_categorical('horizontal_shift_ratio', self.args.horizontal_shift_ratio)
vertical_shift_ratio = trial.suggest_categorical('vertical_shift_ratio', self.args.vertical_shift_ratio)
random_erasing = trial.suggest_categorical('random_erasing', self.args.random_erasing)
# ここで一つのパラメータの組み合わせについて評価する
result=self.trial_process(trial,
optimizer,
learning_rate,
horizontal_flip,
horizontal_shift_ratio,
vertical_shift_ratio,
random_erasing)
return result
class Trainer(object):
def __init__(self, args):
self.args = args
# Define Saver
self.saver = Saver(args)
self.saver.save_experiment_config()
# Define Tensorboard Summary
self.summary = TensorboardSummary(args.logdir)
self.writer = self.summary.create_summary()
# Define Dataloader
kwargs = {'num_workers': args.workers, 'pin_memory': True}
dltrain = DLDataset('trainval', "./data/pascal_voc_seg/tfrecord/")
dlval = DLDataset('val', "./data/pascal_voc_seg/tfrecord/")
# dltrain = DLDataset('trainval', "./data/pascal_voc_seg/VOCdevkit/VOC2012/")
# dlval = DLDataset('val', "./data/pascal_voc_seg/VOCdevkit/VOC2012/")
self.train_loader = DataLoader(dltrain,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
self.val_loader = DataLoader(dlval,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
# Define network
model = Deeplab()
train_params = [{
'params': model.get_1x_lr_params(),
'lr': args.lr
}, {
'params': model.get_10x_lr_params(),
'lr': args.lr * 10
}]
# Define Optimizer
optimizer = torch.optim.SGD(train_params,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=args.nesterov)
# Define Criterion
# whether to use class balanced weights
self.criterion = nn.CrossEntropyLoss(ignore_index=255).cuda()
self.model, self.optimizer = model, optimizer
# Define Evaluator
self.evaluator = Evaluator(21)
# Define lr scheduler
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(
optimizer=optimizer)
# Using cuda
# if args.cuda:
# self.model = torch.nn.DataParallel(self.model)
self.model = self.model.cuda()
# Resuming checkpoint
self.best_pred = 0.0
if args.resume is not None:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
if args.cuda:
self.model.module.load_state_dict(checkpoint['state_dict'])
else:
self.model.load_state_dict(checkpoint['state_dict'])
if not args.ft:
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.best_pred = checkpoint['best_pred']
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
# Clear start epoch if fine-tuning
if args.ft:
args.start_epoch = 0
def training(self, epoch):
train_loss = 0.0
self.model.train()
tbar = tqdm(self.train_loader)
num_img_tr = len(self.train_loader)
for i, (image, target) in enumerate(tbar):
if self.args.cuda:
image, target = image.cuda(), target.cuda()
self.optimizer.zero_grad()
output = self.model(image)
loss = self.criterion(output, target.long())
loss.backward()
self.optimizer.step()
train_loss += loss.item()
tbar.set_description('Train loss: %.3f' % (train_loss / (i + 1)))
self.writer.add_scalar('train/total_loss_iter', loss.item(),
i + num_img_tr * epoch)
# Show 10 * 3 inference results each epoch
# if i % (num_img_tr // 10) == 0:
if i % 10 == 0:
global_step = i + num_img_tr * epoch
self.summary.visualize_image(self.writer, self.args.dataset,
image, target, output,
global_step)
self.scheduler.step(train_loss)
self.writer.add_scalar('train/total_loss_epoch', train_loss, epoch)
print('[Epoch: %d, numImages: %5d]' %
(epoch, i * self.args.batch_size + image.data.shape[0]))
print('Loss: %.3f' % train_loss)
if self.args.no_val:
# save checkpoint every epoch
is_best = False
self.saver.save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': self.model.module.state_dict(),
'optimizer': self.optimizer.state_dict(),
'best_pred': self.best_pred,
}, is_best)
def validation(self, epoch):
self.model.eval()
self.evaluator.reset()
tbar = tqdm(self.val_loader, desc='\r')
test_loss = 0.0
for i, (image, target) in enumerate(tbar):
if self.args.cuda:
image, target = image.cuda(), target.cuda()
with torch.no_grad():
output = self.model(image)
loss = self.criterion(output, target.long())
test_loss += loss.item()
tbar.set_description('Test loss: %.3f' % (test_loss / (i + 1)))
pred = output.data.cpu().numpy()
target = target.cpu().numpy()
pred = np.argmax(pred, axis=1)
# Add batch sample into evaluator
self.evaluator.add_batch(target, pred)
# Fast test during the training
Acc = self.evaluator.Pixel_Accuracy()
Acc_class = self.evaluator.Pixel_Accuracy_Class()
mIoU = self.evaluator.Mean_Intersection_over_Union()
FWIoU = self.evaluator.Frequency_Weighted_Intersection_over_Union()
self.writer.add_scalar('val/total_loss_epoch', test_loss, epoch)
self.writer.add_scalar('val/mIoU', mIoU, epoch)
self.writer.add_scalar('val/Acc', Acc, epoch)
self.writer.add_scalar('val/Acc_class', Acc_class, epoch)
self.writer.add_scalar('val/fwIoU', FWIoU, epoch)
print('Validation:')
print('[Epoch: %d, numImages: %5d]' %
(epoch, i * self.args.batch_size + image.data.shape[0]))
print("Acc:{}, Acc_class:{}, mIoU:{}, fwIoU: {}".format(
Acc, Acc_class, mIoU, FWIoU))
print('Loss: %.3f' % test_loss)
new_pred = mIoU
if new_pred > self.best_pred:
is_best = True
self.best_pred = new_pred
self.saver.save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'best_pred': self.best_pred,
}, is_best)
