Deep-Learning-with-PyTorch

What does an ideal dataset look like?
339
consists of 10 equally weighted classes, and we want to instead have 1 class (say, “airplane”)
now make up 50% of all of the training images. We could decide to use
WeightedRandomSampler (http://mng.bz/8plK) and weight each of the “airplane”
sample indexes higher, but constructing the weights argument requires that we know
in advance which indexes are airplanes.
As we discussed, the Dataset API only specifies that subclasses provide __len__
and __getitem__, but there is nothing direct we can use to ask “Which samples are
airplanes?” We’d either have to load up every sample beforehand to inquire about the
class of that sample, or we’d have to break encapsulation and hope the information
we need is easily obtained from looking at the internal implementation of the Dataset
subclass.
Since neither of those options is particularly ideal in cases where we have control
over the dataset directly, the code for part 2 implements any needed data shaping
inside the Dataset subclasses instead of relying on an external sampler.
IMPLEMENTING CLASS BALANCING IN THE DATASET
We are going to directly change our LunaDataset to present a balanced, one-to-one
ratio of positive and negative samples for training. We will keep separate lists of negative
training samples and positive training samples, and alternate returning samples
from each of those two lists. This will prevent the degenerate behavior of the model
scoring well by simply answering “false” to every sample presented. In addition, the
positive and negative classes will be intermixed so that the weight updates are forced
to discriminate between the classes.
Let’s add a ratio_int to LunaDataset that will control the label for the Nth sample
as well as keep track of our samples separated by label.
Listing 12.6
dsets.py:217, class LunaDataset
class LunaDataset(Dataset):
def __init__(self,
val_stride=0,
isValSet_bool=None,
ratio_int=0,
):
self.ratio_int = ratio_int
# ... line 228
self.negative_list = [
nt for nt in self.candidateInfo_list if not nt.isNodule_bool
]
self.pos_list = [
nt for nt in self.candidateInfo_list if nt.isNodule_bool
]
# ... line 265
def shuffleSamples(self):
if self.ratio_int:
random.shuffle(self.negative_list)
random.shuffle(self.pos_list)
We will call this at the top of each
epoch to randomize the order of
samples being presented.