Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

# These attributes are defined here, but since they are
# not available at the moment of creation, we keep them None
self.train_loader = None
self.val_loader = None
self.writer = None
The train data loader is obviously required. How could we possibly train a model
without it?
"Why don’t we make the train data loader an argument then?"
Conceptually speaking, the data loader (and the dataset it contains) is not part of
the model. It is the input we use to train the model. Since we can specify a model
without it, it shouldn’t be made an argument of our class.
In other words, our StepByStep class is defined by a particular combination of
arguments (model, loss function, and optimizer), which can then be used to
perform model training on any (compatible) dataset.
The validation data loader is not required (although it is recommended), and the
summary writer is definitely optional.
The class should implement methods to allow the user to supply those at a later
time (both methods should be placed inside the StepByStep class, after the
constructor method):
def set_loaders(self, train_loader, val_loader=None):
# This method allows the user to define which train_loader
# (and val_loader, optionally) to use
# Both loaders are then assigned to attributes of the class
# So they can be referred to later
self.train_loader = train_loader
self.val_loader = val_loader
def set_tensorboard(self, name, folder='runs'):
# This method allows the user to create a SummaryWriter to
# interface with TensorBoard
suffix = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
self.writer = SummaryWriter(f'{folder}/{name}_{suffix}')
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