def main():
# PyTorch Part
model = vision.models.segmentation.deeplabv3_resnet50(
num_classes=1, pretrained_backbone=True)
train_dst = vision.datasets.NYUv2(
'data/NYUv2',
split='train',
target_type='depth',
transforms=sT.Compose([
sT.Multi(sT.Resize(240), sT.Resize(240)),
sT.Sync(sT.RandomRotation(5), sT.RandomRotation(5)),
sT.Multi(sT.ColorJitter(0.2, 0.2, 0.2), None),
sT.Sync(sT.RandomCrop(240), sT.RandomCrop(240)),
sT.Sync(sT.RandomHorizontalFlip(), sT.RandomHorizontalFlip()),
sT.Multi(sT.ToTensor(),
sT.ToTensor(normalize=False, dtype=torch.float)),
sT.Multi(
sT.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
sT.Lambda(lambda x: x / 1000))
]))
val_dst = vision.datasets.NYUv2(
'data/NYUv2',
split='test',
target_type='depth',
transforms=sT.Compose([
sT.Multi(sT.Resize(240), sT.Resize(240)),
sT.Multi(sT.ToTensor(),
sT.ToTensor(normalize=False, dtype=torch.float)),
sT.Multi(
sT.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
sT.Lambda(lambda x: x / 1000))
]))
train_loader = torch.utils.data.DataLoader(train_dst,
batch_size=16,
shuffle=True,
num_workers=4)
val_loader = torch.utils.data.DataLoader(val_dst,
batch_size=16,
num_workers=4)
TOTAL_ITERS = len(train_loader) * 200
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
optim = torch.optim.SGD(model.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=1e-4)
sched = torch.optim.lr_scheduler.CosineAnnealingLR(optim,
T_max=TOTAL_ITERS)
# KAE Part
metric = kamal.tasks.StandardMetrics.monocular_depth()
evaluator = engine.evaluator.BasicEvaluator(dataloader=val_loader,
metric=metric,
progress=False)
task = kamal.tasks.StandardTask.monocular_depth()
trainer = engine.trainer.BasicTrainer(
logger=kamal.utils.logger.get_logger('nyuv2_depth_deeplab'),
tb_writer=SummaryWriter(log_dir='run/nyuv2_depth_deeplab-%s' %
(time.asctime().replace(' ', '_'))))
trainer.setup(model=model,
task=task,
dataloader=train_loader,
optimizer=optim,
device=device)
trainer.add_callback(engine.DefaultEvents.AFTER_STEP(every=10),
callbacks=callbacks.MetricsLogging(keys=('total_loss',
'lr')))
trainer.add_callback(
engine.DefaultEvents.AFTER_STEP,
callbacks=callbacks.LRSchedulerCallback(schedulers=[sched]))
trainer.add_callback(engine.DefaultEvents.AFTER_EPOCH,
callbacks=[
callbacks.EvalAndCkpt(model=model,
evaluator=evaluator,
metric_name='rmse',
metric_mode='min',
ckpt_prefix='nyuv2_depth'),
callbacks.VisualizeDepth(
model=model,
dataset=val_dst,
idx_list_or_num_vis=10,
max_depth=10,
normalizer=kamal.utils.Normalizer(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
reverse=True),
)
])
trainer.run(start_iter=0, max_iter=TOTAL_ITERS)
def main():
# PyTorch Part
num_classes = 11
model = vision.models.segmentation.deeplabv3_resnet50(num_classes=num_classes, pretrained_backbone=True)
train_dst = vision.datasets.CamVid(
'data/CamVid11', split='trainval', transforms=sT.Compose([
sT.Multi( sT.Resize(240), sT.Resize(240, interpolation=Image.NEAREST)),
sT.Sync( sT.RandomRotation(5), sT.RandomRotation(5)),
sT.Multi( sT.ColorJitter(0.2, 0.2, 0.2), None),
sT.Sync( sT.RandomCrop(240), sT.RandomCrop(240)),
sT.Sync( sT.RandomHorizontalFlip(), sT.RandomHorizontalFlip() ),
sT.Multi( sT.ToTensor(), sT.ToTensor( normalize=False, dtype=torch.long) ),
sT.Multi( sT.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ), sT.Lambda(lambd=lambda x: x.squeeze()) )
]) )
val_dst = vision.datasets.CamVid(
'data/CamVid11', split='test', transforms=sT.Compose([
sT.Multi( sT.Resize(240), sT.Resize(240, interpolation=Image.NEAREST)),
sT.Multi( sT.ToTensor(), sT.ToTensor( normalize=False, dtype=torch.long ) ),
sT.Multi( sT.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ), sT.Lambda(lambd=lambda x: x.squeeze()) )
]) )
train_loader = torch.utils.data.DataLoader( train_dst, batch_size=16, shuffle=True, num_workers=4 )
val_loader = torch.utils.data.DataLoader( val_dst, batch_size=16, num_workers=4 )
TOTAL_ITERS=len(train_loader) * 200
device = torch.device( 'cuda' if torch.cuda.is_available() else 'cpu' )
optim = torch.optim.SGD( model.parameters(), lr=0.01, momentum=0.9, weight_decay=1e-4 )
sched = torch.optim.lr_scheduler.CosineAnnealingLR( optim, T_max=TOTAL_ITERS )
# KAE Part
metric = kamal.tasks.StandardMetrics.segmentation(num_classes=num_classes)
evaluator = engine.evaluator.BasicEvaluator( dataloader=val_loader, metric=metric, progress=False )
task = kamal.tasks.StandardTask.segmentation()
trainer = engine.trainer.BasicTrainer(
logger=kamal.utils.logger.get_logger('camvid_seg_deeplab'),
tb_writer=SummaryWriter( log_dir='run/camvid_seg_deeplab-%s'%( time.asctime().replace( ' ', '_' ) ) )
)
trainer.setup( model=model,
task=task,
dataloader=train_loader,
optimizer=optim,
device=device )
trainer.add_callback(
engine.DefaultEvents.AFTER_STEP(every=10),
callbacks=callbacks.MetricsLogging(keys=('total_loss', 'lr')))
trainer.add_callback(
engine.DefaultEvents.AFTER_STEP,
callbacks=callbacks.LRSchedulerCallback(schedulers=[sched]))
trainer.add_callback(
engine.DefaultEvents.AFTER_EPOCH,
callbacks=[
callbacks.EvalAndCkpt(model=model, evaluator=evaluator, metric_name='miou', ckpt_prefix='camvid_seg_deeplabv3_resnet50'),
callbacks.VisualizeSegmentation(
model=model,
dataset=val_dst,
idx_list_or_num_vis=10,
normalizer=kamal.utils.Normalizer( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], reverse=True),
)])
#import matplotlib.pyplot as plt
#lr_finder = kamal.engine.lr_finder.LRFinder()
#best_lr = lr_finder.find( optim, model, trainer, lr_range=[1e-8, 1.0], max_iter=100, smooth_momentum=0.9 )
#fig = lr_finder.plot(polyfit=4)
#plt.savefig('lr_finder_deeplab.png')
#lr_finder.adjust_learning_rate(optim, best_lr)
trainer.run( start_iter=0, max_iter=TOTAL_ITERS )
def main():
# Seg + Depth
model = MultiTaskSegNet(out_channel_list=[13, 1])
seg_teacher = vision.models.segmentation.segnet_vgg16_bn(
num_classes=13, pretrained_backbone=True)
depth_teacher = vision.models.segmentation.segnet_vgg16_bn(
num_classes=1, pretrained_backbone=True)
seg_teacher.load_state_dict(torch.load(args.seg_ckpt))
depth_teacher.load_state_dict(torch.load(args.depth_ckpt))
seg_train_dst = vision.datasets.NYUv2('../data/NYUv2',
split='train',
target_type='semantic')
seg_val_dst = vision.datasets.NYUv2('../data/NYUv2',
split='test',
target_type='semantic')
depth_train_dst = vision.datasets.NYUv2('../data/NYUv2',
split='train',
target_type='depth')
depth_val_dst = vision.datasets.NYUv2('../data/NYUv2',
split='test',
target_type='depth')
train_dst = vision.datasets.LabelConcatDataset(
datasets=[seg_train_dst, depth_train_dst],
transforms=sT.Compose([
sT.Multi(sT.Resize(240), sT.Resize(240,
interpolation=Image.NEAREST),
sT.Resize(240)),
sT.Sync(sT.RandomCrop(240), sT.RandomCrop(240),
sT.RandomCrop(240)),
sT.Sync(sT.RandomHorizontalFlip(), sT.RandomHorizontalFlip(),
sT.RandomHorizontalFlip()),
sT.Multi(sT.ToTensor(),
sT.ToTensor(normalize=False, dtype=torch.long),
sT.ToTensor(normalize=False, dtype=torch.float)),
sT.Multi(
sT.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
sT.Lambda(lambd=lambda x: x.squeeze()),
sT.Lambda(lambd=lambda x: x / 1e3))
]))
val_dst = vision.datasets.LabelConcatDataset(
datasets=[seg_val_dst, depth_val_dst],
transforms=sT.Compose([
sT.Multi(sT.Resize(240), sT.Resize(240,
interpolation=Image.NEAREST),
sT.Resize(240)),
sT.Multi(sT.ToTensor(),
sT.ToTensor(normalize=False, dtype=torch.long),
sT.ToTensor(normalize=False, dtype=torch.float)),
sT.Multi(
sT.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
sT.Lambda(lambd=lambda x: x.squeeze()),
sT.Lambda(lambd=lambda x: x / 1e3))
]))
train_loader = torch.utils.data.DataLoader(train_dst,
batch_size=16,
shuffle=True,
num_workers=4)
val_loader = torch.utils.data.DataLoader(val_dst,
batch_size=16,
num_workers=4)
TOTAL_ITERS = len(train_loader) * 200
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
optim = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-4)
sched = torch.optim.lr_scheduler.CosineAnnealingLR(optim,
T_max=TOTAL_ITERS)
confusion_matrix = metrics.ConfusionMatrix(num_classes=13,
ignore_idx=255,
attach_to=0)
metric = metrics.MetricCompose(
metric_dict={
'acc': metrics.Accuracy(attach_to=0),
'cm': confusion_matrix,
'mIoU': metrics.mIoU(confusion_matrix),
'rmse': metrics.RootMeanSquaredError(attach_to=1)
})
evaluator = engine.evaluator.BasicEvaluator(dataloader=val_loader,
metric=metric,
progress=False)
task = [
kamal.tasks.StandardTask.distillation(attach_to=[0, 0]),
kamal.tasks.StandardTask.monocular_depth(attach_to=[1, 1])
]
trainer = engine.trainer.KDTrainer(
logger=kamal.utils.logger.get_logger('nyuv2_simple_kd'),
tb_writer=SummaryWriter(log_dir='run/nyuv2_simple_kd-%s' %
(time.asctime().replace(' ', '_'))))
trainer.setup(student=model,
teacher=[seg_teacher, depth_teacher],
task=task,
dataloader=train_loader,
optimizer=optim,
device=device)
trainer.add_callback(engine.DefaultEvents.AFTER_STEP(every=10),
callbacks=callbacks.MetricsLogging(keys=('total_loss',
'lr')))
trainer.add_callback(
engine.DefaultEvents.AFTER_STEP,
callbacks=callbacks.LRSchedulerCallback(schedulers=[sched]))
trainer.add_callback(
engine.DefaultEvents.AFTER_EPOCH,
callbacks=[
callbacks.EvalAndCkpt(model=model,
evaluator=evaluator,
metric_name='rmse',
metric_mode='min',
ckpt_prefix='nyuv2_simple_kd'),
callbacks.VisualizeSegmentation(
model=model,
dataset=val_dst,
idx_list_or_num_vis=10,
attach_to=0,
normalizer=kamal.utils.Normalizer(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
reverse=True),
),
callbacks.VisualizeDepth(
model=model,
dataset=val_dst,
idx_list_or_num_vis=10,
max_depth=10,
attach_to=1,
normalizer=kamal.utils.Normalizer(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
reverse=True),
),
])
trainer.run(start_iter=0, max_iter=TOTAL_ITERS)