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

we add a very simple method (conveniently named to()) that allows the user to
specify a device.
Our constructor (__init__()) method will initially look like this:
class StepByStep(object):
def __init__(self, model, loss_fn, optimizer):
# Here we define the attributes of our class
# We start by storing the arguments as attributes
# to use later
self.model = model
self.loss_fn = loss_fn
self.optimizer = optimizer
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Let's send the model to the specified device right away
self.model.to(self.device)
def to(self, device):
# This method allows the user to specify a different device
# It sets the corresponding attribute (to be used later in
# the mini-batches) and sends the model to the device
try:
self.device = device
self.model.to(self.device)
except RuntimeError:
self.device = ('cuda' if torch.cuda.is_available()
else 'cpu')
print(f"Couldn't send it to {device}, \
sending it to {self.device} instead.")
self.model.to(self.device)
Placeholders
Next, let’s tackle the placeholders or delayed arguments. We expect the user to
eventually provide some of these, as they are not necessarily required. There are
another three elements that fall into that category: train and validation data
loaders and a summary writer to interface with TensorBoard.
We need to append the following code to the constructor method above (I am not
reproducing the rest of the method here for the sake of simplicity; in the Jupyter
notebook you’ll find the full code):
178 | Chapter 2.1: Going Classy