Index

_

__getitem__() (dataset.SegmentationDataset method)

__len__() (dataset.SegmentationDataset method)

B

bottleneck (unet.UNet attribute)

D

dataset module device (trainer.Trainer attribute)

double_conv (unet.DoubleConvolution attribute) DoubleConvolution (class in unet) downs (unet.UNet attribute)

F

final_conv (unet.UNet attribute)

forward() (unet.DoubleConvolution method), [1] (unet.UNet method), [1]

I

image_dir (dataset.SegmentationDataset attribute)

images (dataset.SegmentationDataset attribute) is_binary() (unet.UNet method), [1]

L

load_checkpoint() (trainer.Trainer method), [1]

loss_fn (trainer.Trainer attribute)

M

mask_dir (dataset.SegmentationDataset attribute) model (trainer.Trainer attribute) model_is_binary (trainer.Trainer attribute)

module dataset trainer unet

N

num_features (unet.UNet attribute)

O

optimizer (trainer.Trainer attribute)

P

pool (unet.UNet attribute)

S

save_checkpoint() (trainer.Trainer method), [1]

scaler (trainer.Trainer attribute) SegmentationDataset (class in dataset)

T

train() (trainer.Trainer method), [1] train_loader (trainer.Trainer attribute) train_loss (trainer.Trainer attribute) train_step() (trainer.Trainer method), [1]

trainer module Trainer (class in trainer) transform (dataset.SegmentationDataset attribute)

U

unet module

UNet (class in unet) ups (unet.UNet attribute)

V

val_accuracy (trainer.Trainer attribute) val_dice (trainer.Trainer attribute)

val_loader (trainer.Trainer attribute) val_loss (trainer.Trainer attribute) val_step() (trainer.Trainer method), [1]