Deep-Learning-with-PyTorch

288 CHAPTER 11 Training a classification model to detect suspected tumors
Luna Dataset
Data Loader
CT Files
.MHD
Sample tupleS
sample aRray
( ,
sample aRray
( ,
Batch tuple
5d fp32 aRray
( ,
.RAW
is nodule?
T,
is nodule?
F,
1d bOol aRray
[T, F...]
ANnotations
Series_uid
“1.2.3”,
Series_uid
“4.5.6”,
List of Strings
[“123”, “456”...],
.CSV
candidate
location
(I,R,C))
candidate
location
(I,R,C))
List of IRC
tuples
[IRC, IRC...])
Figure 11.4
Sample tuples being collated into a single batch tuple inside a data loader
Conveniently, we don’t have to implement any of this batching: the PyTorch Data-
Loader class will handle all of the collation work for us. We’ve already built the bridge
from the CT scans to PyTorch tensors with our LunaDataset class, so all that remains
is to plug our dataset into a data loader.
Listing 11.5
training.py:89, LunaTrainingApp.initTrainDl
def initTrainDl(self):
train_ds = LunaDataset(
val_stride=10,
isValSet_bool=False,
)
Our custom dataset
batch_size = self.cli_args.batch_size
if self.use_cuda:
batch_size *= torch.cuda.device_count()
train_dl = DataLoader(
train_ds,
batch_size=batch_size,
num_workers=self.cli_args.num_workers,
pin_memory=self.use_cuda,
)
An off-the-shelf class
Batching is done automatically.
Pinned memory transfers
to GPU quickly.