3.2. Object-Oriented Design for Implementation¶ Open the notebook in SageMaker Studio Lab
In our introduction to linear regression, we walked through various components including the data, the model, the loss function, and the optimization algorithm. Indeed, linear regression is one of the simplest machine learning models. Training it, however, uses many of the same components as other models in this book require. Therefore, before diving into the implementation details it is worth designing some of the APIs used throughout this book. Treating components in deep learning as objects, we can start by defining classes for these objects and their interactions. This object-oriented design for implementation will greatly streamline the presentation and you might even want to use it in your projects.
Inspired by open-source libraries such as PyTorch
Lightning, on a high level we wish
to have three classes: (i) Module contains models, losses, and
optimization methods; (ii) DataModule provides data loaders for
training and validation; (iii) both classes are combined using the
Trainer class, which allows us to train models on a variety of
hardware platforms. Most code in this book adapts Module and
DataModule. We will touch upon the Trainer class only when we
discuss GPUs, CPUs, parallel training, and optimization algorithms.
import time
import numpy as np
import torch
from torch import nn
from d2l import torch as d2l
import time
import numpy as np
from mxnet.gluon import nn
from d2l import mxnet as d2l
import time
import numpy as np
import tensorflow as tf
from d2l import torch as d2l
3.2.1. Utilities¶
We need a few utilities to simplify object-oriented programming in Jupyter notebooks. One of the challenges is that class definitions tend to be fairly long blocks of code. Notebook readability demands short code fragments, interspersed with explanations, a requirement incompatible with the style of programming common for Python libraries. The first utility function allows us to register functions as methods in a class after the class has been created. In fact, we can do so even after we’ve created instances of the class! It allows us to split the implementation of a class into multiple code blocks.
def add_to_class(Class): #@save
def wrapper(obj):
setattr(Class, obj.__name__, obj)
return wrapper
Let’s have a quick look at how to use it. We plan to implement a class
A with a method do. Instead of having code for both A and
do in the same code block, we can first declare the class A and
create an instance a.
class A:
def __init__(self):
self.b = 1
a = A()
Next we define the method do as we normally would, but not in class
A’s scope. Instead, we decorate this method by add_to_class with
class A as its argument. In doing so, the method is able to access
the member variables of A as we would expect if it had been defined
as part of A’s definition. Let’s see what happens when we invoke it
for the instance a.
@add_to_class(A)
def do(self):
print('Class attribute "b" is', self.b)
a.do()
Class attribute "b" is 1
@add_to_class(A)
def do(self):
print('Class attribute "b" is', self.b)
a.do()
Class attribute "b" is 1
@add_to_class(A)
def do(self):
print('Class attribute "b" is', self.b)
a.do()
Class attribute "b" is 1
The second one is a utility class that saves all arguments in a class’s
__init__ method as class attributes. This allows us to extend
constructor call signatures implicitly without additional code.
class HyperParameters: #@save
def save_hyperparameters(self, ignore=[]):
raise NotImplemented
We defer its implementation into Section 20.7. To use it, we
define our class that inherits from HyperParameters and calls
save_hyperparameters in the __init__ method.
# Call the fully implemented HyperParameters class saved in d2l
class B(d2l.HyperParameters):
def __init__(self, a, b, c):
self.save_hyperparameters(ignore=['c'])
print('self.a =', self.a, 'self.b =', self.b)
print('There is no self.c =', not hasattr(self, 'c'))
b = B(a=1, b=2, c=3)
self.a = 1 self.b = 2
There is no self.c = True
# Call the fully implemented HyperParameters class saved in d2l
class B(d2l.HyperParameters):
def __init__(self, a, b, c):
self.save_hyperparameters(ignore=['c'])
print('self.a =', self.a, 'self.b =', self.b)
print('There is no self.c =', not hasattr(self, 'c'))
b = B(a=1, b=2, c=3)
self.a = 1 self.b = 2
There is no self.c = True
# Call the fully implemented HyperParameters class saved in d2l
class B(d2l.HyperParameters):
def __init__(self, a, b, c):
self.save_hyperparameters(ignore=['c'])
print('self.a =', self.a, 'self.b =', self.b)
print('There is no self.c =', not hasattr(self, 'c'))
b = B(a=1, b=2, c=3)
self.a = 1 self.b = 2
There is no self.c = True
The last utility allows us to plot experiment progress interactively
while it is going on. In deference to the much more powerful (and
complex) TensorBoard we
name it ProgressBoard. The implementation is deferred to
Section 20.7. For now, let’s simply see it in action.
The draw function plots a point (x, y) in the figure, with
label specified in the legend. The optional every_n smooths the
line by only showing \(1/n\) points in the figure. Their values are
averaged from the \(n\) neighbor points in the original figure.
class ProgressBoard(d2l.HyperParameters): #@save
"""Plot data points in animation."""
def __init__(self, xlabel=None, ylabel=None, xlim=None,
ylim=None, xscale='linear', yscale='linear',
ls=['-', '--', '-.', ':'], colors=['C0', 'C1', 'C2', 'C3'],
fig=None, axes=None, figsize=(3.5, 2.5), display=True):
self.save_hyperparameters()
def draw(self, x, y, label, every_n=1):
raise NotImplemented
In the following example, we draw sin and cos with a different
smoothness. If you run this code block, you will see the lines grow in
animation.
board = d2l.ProgressBoard('x')
for x in np.arange(0, 10, 0.1):
board.draw(x, np.sin(x), 'sin', every_n=2)
board.draw(x, np.cos(x), 'cos', every_n=10)
board = d2l.ProgressBoard('x')
for x in np.arange(0, 10, 0.1):
board.draw(x, np.sin(x), 'sin', every_n=2)
board.draw(x, np.cos(x), 'cos', every_n=10)
board = d2l.ProgressBoard('x')
for x in np.arange(0, 10, 0.1):
board.draw(x, np.sin(x), 'sin', every_n=2)
board.draw(x, np.cos(x), 'cos', every_n=10)
3.2.2. Models¶
The Module class is the base class of all models we will implement.
At a minimum we need to define three methods. The __init__ method
stores the learnable parameters, the training_step method accepts a
data batch to return the loss value, the configure_optimizers method
returns the optimization method, or a list of them, that is used to
update the learnable parameters. Optionally we can define
validation_step to report the evaluation measures. Sometimes we put
the code to compute the output into a separate forward method to
make it more reusable.
class Module(nn.Module, d2l.HyperParameters): #@save
def __init__(self, plot_train_per_epoch=2, plot_valid_per_epoch=1):
super().__init__()
self.save_hyperparameters()
self.board = ProgressBoard()
def loss(self, y_hat, y):
raise NotImplementedError
def forward(self, X):
assert hasattr(self, 'net'), 'Neural network is defined'
return self.net(X)
def plot(self, key, value, train):
"""Plot a point in animation."""
assert hasattr(self, 'trainer'), 'Trainer is not inited'
self.board.xlabel = 'epoch'
if train:
x = self.trainer.train_batch_idx / \
self.trainer.num_train_batches
n = self.trainer.num_train_batches / \
self.plot_train_per_epoch
else:
x = self.trainer.epoch + 1
n = self.trainer.num_val_batches / \
self.plot_valid_per_epoch
self.board.draw(x, value.to(d2l.cpu()).detach().numpy(),
('train_' if train else 'val_') + key,
every_n=int(n))
def training_step(self, batch):
l = self.loss(self(*batch[:-1]), batch[-1])
self.plot('loss', l, train=True)
return l
def validation_step(self, batch):
l = self.loss(self(*batch[:-1]), batch[-1])
self.plot('loss', l, train=False)
def configure_optimizers(self):
raise NotImplementedError
You may notice that Module is a subclass of nn.Module, the base
class of neural networks in PyTorch. It provides convenient features to
handle neural networks. For example, if we define a forward method,
such as forward(self, X), then for an instance a we can invoke
this function by a(X). This works since it calls the forward
method in the built-in __call__ method. You can find more details
and examples about nn.Module in Section 6.1.
class Module(nn.Block, d2l.HyperParameters): #@save
def __init__(self, plot_train_per_epoch=2, plot_valid_per_epoch=1):
super().__init__()
self.save_hyperparameters()
self.board = ProgressBoard()
def loss(self, y_hat, y):
raise NotImplementedError
def forward(self, X):
assert hasattr(self, 'net'), 'Neural network is defined'
return self.net(X)
def plot(self, key, value, train):
"""Plot a point in animation."""
assert hasattr(self, 'trainer'), 'Trainer is not inited'
self.board.xlabel = 'epoch'
if train:
x = self.trainer.train_batch_idx / \
self.trainer.num_train_batches
n = self.trainer.num_train_batches / \
self.plot_train_per_epoch
else:
x = self.trainer.epoch + 1
n = self.trainer.num_val_batches / \
self.plot_valid_per_epoch
self.board.draw(x, value.asnumpy(), (
'train_' if train else 'val_') + key, every_n=int(n))
def training_step(self, batch):
l = self.loss(self(*batch[:-1]), batch[-1])
self.plot('loss', l, train=True)
return l
def validation_step(self, batch):
l = self.loss(self(*batch[:-1]), batch[-1])
self.plot('loss', l, train=False)
def configure_optimizers(self):
raise NotImplementedError
You may notice that Module is a subclass of nn.Block, the base
class of neural networks in Gluon. It provides convenient features to
handle neural networks. For example, if we define a forward method,
such as forward(self, X), then for an instance a we can invoke
this function by a(X). This works since it calls the forward
method in the built-in __call__ method. You can find more details
and examples about nn.Block in Section 6.1.
class Module(tf.keras.Model, d2l.HyperParameters): #@save
def __init__(self, plot_train_per_epoch=2, plot_valid_per_epoch=1):
super().__init__()
self.save_hyperparameters()
self.board = ProgressBoard()
self.training = None
def loss(self, y_hat, y):
raise NotImplementedError
def forward(self, X):
assert hasattr(self, 'net'), 'Neural network is defined'
return self.net(X)
def call(self, X, *args, **kwargs):
if kwargs and "training" in kwargs:
self.training = kwargs['training']
return self.forward(X, *args)
def plot(self, key, value, train):
"""Plot a point in animation."""
assert hasattr(self, 'trainer'), 'Trainer is not inited'
self.board.xlabel = 'epoch'
if train:
x = self.trainer.train_batch_idx / \
self.trainer.num_train_batches
n = self.trainer.num_train_batches / \
self.plot_train_per_epoch
else:
x = self.trainer.epoch + 1
n = self.trainer.num_val_batches / \
self.plot_valid_per_epoch
self.board.draw(x, value.numpy(), (
'train_' if train else 'val_') + key, every_n=int(n))
def training_step(self, batch):
l = self.loss(self(*batch[:-1]), batch[-1])
self.plot('loss', l, train=True)
return l
def validation_step(self, batch):
l = self.loss(self(*batch[:-1]), batch[-1])
self.plot('loss', l, train=False)
def configure_optimizers(self):
raise NotImplementedError
You may notice that Module is a subclass of tf.keras.Model, the
base class of neural networks in TensorFlow. It provides convenient
features to handle neural networks. For example, it invokes the call
method in the built-in __call__ method. Here we redirect call to
the forward function, saving its arguments as a class attribute. We
do this to make our code more similar to other framework
implementations.
3.2.3. Data¶
The DataModule class is the base class for data. Quite frequently
the __init__ method is used to prepare the data. This includes
downloading and preprocessing if needed. The train_dataloader
returns the data loader for the training dataset. A data loader is a
(Python) generator that yields a data batch each time it is used. This
batch is then fed into the training_step method of Module to
compute the loss. There is an optional val_dataloader to return the
validation dataset loader. It behaves in the same manner, except that it
yields data batches for the validation_step method in Module.
class DataModule(d2l.HyperParameters): #@save
def __init__(self, root='../data', num_workers=4):
self.save_hyperparameters()
def get_dataloader(self, train):
raise NotImplementedError
def train_dataloader(self):
return self.get_dataloader(train=True)
def val_dataloader(self):
return self.get_dataloader(train=False)
class DataModule(d2l.HyperParameters): #@save
def __init__(self, root='../data', num_workers=4):
self.save_hyperparameters()
def get_dataloader(self, train):
raise NotImplementedError
def train_dataloader(self):
return self.get_dataloader(train=True)
def val_dataloader(self):
return self.get_dataloader(train=False)
class DataModule(d2l.HyperParameters): #@save
def __init__(self, root='../data'):
self.save_hyperparameters()
def get_dataloader(self, train):
raise NotImplementedError
def train_dataloader(self):
return self.get_dataloader(train=True)
def val_dataloader(self):
return self.get_dataloader(train=False)
3.2.4. Training¶
The Trainer class trains the learnable parameters in the Module
class with data specified in DataModule. The key method is fit,
which accepts two arguments: model, an instance of Module, and
data, an instance of DataModule. It then iterates over the
entire dataset max_epochs times to train the model. As before, we
will defer the implementation of this function to later chapters.
class Trainer(d2l.HyperParameters): #@save
def __init__(self, max_epochs, num_gpus=0, gradient_clip_val=0):
self.save_hyperparameters()
assert num_gpus == 0, 'No GPU support yet'
def prepare_data(self, data):
self.train_dataloader = data.train_dataloader()
self.val_dataloader = data.val_dataloader()
self.num_train_batches = len(self.train_dataloader)
self.num_val_batches = (len(self.val_dataloader)
if self.val_dataloader is not None else 0)
def prepare_model(self, model):
model.trainer = self
model.board.xlim = [0, self.max_epochs]
self.model = model
def fit(self, model, data):
self.prepare_data(data)
self.prepare_model(model)
self.optim = model.configure_optimizers()
self.epoch = 0
self.train_batch_idx = 0
self.val_batch_idx = 0
for self.epoch in range(self.max_epochs):
self.fit_epoch()
def fit_epoch(self):
raise NotImplementedError
3.2.5. Summary¶
To highlight the object-oriented design for our future deep learning
implementation, the above classes just show how their objects store data
and interact with each other. We will keep enriching implementations of
these classes, such as via @add_to_class, in the rest of the book.
Moreover, these fully implemented classes are saved in the d2l
library, a
lightweight toolkit that makes structured modeling for deep learning
easy. In particular, it facilitates reusing many components between
projects without changing much at all. For instance, we can replace just
the optimizer, just the model, just the dataset, etc.; this degree of
modularity pays dividends throughout the book in terms of conciseness
and simplicity (this is why we added it) and it can do the same for your
own projects.
3.2.6. Exercises¶
Locate full implementations of the above classes that are saved in the d2l library. We strongly recommend that you look at the implementation in detail once you have gained some more familiarity with deep learning modeling.
Remove the
save_hyperparametersstatement in theBclass. Can you still printself.aandself.b? Optional: if you have dived into the full implementation of theHyperParametersclass, can you explain why?