def seg_unet3d_test() -> cfg.ExperimentConfig:
"""Image segmentation on a dummy dataset with 3D UNet for testing purpose."""
train_batch_size = 2
eval_batch_size = 2
steps_per_epoch = 10
config = cfg.ExperimentConfig(
task=SemanticSegmentation3DTask(
model=SemanticSegmentationModel3D(
num_classes=2,
input_size=[32, 32, 32],
num_channels=2,
backbone=backbones.Backbone(
type='unet_3d', unet_3d=backbones.UNet3D(model_id=2)),
decoder=decoders.Decoder(
type='unet_3d_decoder',
unet_3d_decoder=decoders.UNet3DDecoder(model_id=2)),
head=SegmentationHead3D(num_convs=0, num_classes=2),
norm_activation=common.NormActivation(
activation='relu', use_sync_bn=False)),
train_data=DataConfig(
input_path='train.tfrecord',
num_classes=2,
input_size=[32, 32, 32],
num_channels=2,
is_training=True,
global_batch_size=train_batch_size),
validation_data=DataConfig(
input_path='val.tfrecord',
num_classes=2,
input_size=[32, 32, 32],
num_channels=2,
is_training=False,
global_batch_size=eval_batch_size),
losses=Losses(loss_type='adaptive')),
trainer=cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=10,
validation_steps=10,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
},
'learning_rate': {
'type': 'constant',
'constant': {
'learning_rate': 0.000001
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
class SemanticSegmentationModel3D(hyperparams.Config):
"""Semantic segmentation model config."""
num_classes: int = 0
num_channels: int = 1
input_size: List[int] = dataclasses.field(default_factory=list)
min_level: int = 3
max_level: int = 6
head: SegmentationHead3D = SegmentationHead3D()
backbone: backbones.Backbone = backbones.Backbone(
type='unet_3d', unet_3d=backbones.UNet3D())
decoder: decoders.Decoder = decoders.Decoder(
type='unet_3d_decoder', unet_3d_decoder=decoders.UNet3DDecoder())
norm_activation: common.NormActivation = common.NormActivation()
