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# -*- coding: utf-8 -*-
What is `torch.nn` *really*?
============================
**Authors:** Jeremy Howard, `fast.ai <https://www.fast.ai>`_. Thanks to Rachel Thomas and Francisco Ingham.
###############################################################################
# We recommend running this tutorial as a notebook, not a script. To download the notebook (``.ipynb``) file,
# click the link at the top of the page.
# PyTorch provides the elegantly designed modules and classes `torch.nn <https://pytorch.org/docs/stable/nn.html>`_ ,
# `torch.optim <https://pytorch.org/docs/stable/optim.html>`_ ,
# `Dataset <https://pytorch.org/docs/stable/data.html?highlight=dataset#torch.utils.data.Dataset>`_ ,
# and `DataLoader <https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader>`_
# to help you create and train neural networks.
# In order to fully utilize their power and customize
# them for your problem, you need to really understand exactly what they're
# doing. To develop this understanding, we will first train basic neural net
# on the MNIST data set without using any features from these models; we will
# initially only use the most basic PyTorch tensor functionality. Then, we will
# incrementally add one feature from ``torch.nn``, ``torch.optim``, ``Dataset``, or
# ``DataLoader`` at a time, showing exactly what each piece does, and how it
# works to make the code either more concise, or more flexible.
# **This tutorial assumes you already have PyTorch installed, and are familiar
# with the basics of tensor operations.** (If you're familiar with Numpy array
# operations, you'll find the PyTorch tensor operations used here nearly identical).
# MNIST data setup
# ----------------
# We will use the classic `MNIST <https://yann.lecun.com/exdb/mnist/index.html>`_ dataset,
# which consists of black-and-white images of hand-drawn digits (between 0 and 9).
# We will use `pathlib <https://docs.python.org/3/library/pathlib.html>`_
# for dealing with paths (part of the Python 3 standard library), and will
# download the dataset using
# `requests <http://docs.python-requests.org/en/master/>`_. We will only
# import modules when we use them, so you can see exactly what's being
# used at each point.
from pathlib import Path
import requests
DATA_PATH = Path("data")
PATH = DATA_PATH / "mnist"
PATH.mkdir(parents=True, exist_ok=True)
URL = "https://github.com/pytorch/tutorials/raw/main/_static/"
FILENAME = "mnist.pkl.gz"
if not (PATH / FILENAME).exists():
        content = requests.get(URL + FILENAME).content
        (PATH / FILENAME).open("wb").write(content)
###############################################################################
# This dataset is in numpy array format, and has been stored using pickle,
# a python-specific format for serializing data.
import pickle
import gzip
with gzip.open((PATH / FILENAME).as_posix(), "rb") as f:
        ((x_train, y_train), (x_valid, y_valid), _) = pickle.load(f, encoding="latin-1")
###############################################################################
# Each image is 28 x 28, and is being stored as a flattened row of length
# 784 (=28x28). Let's take a look at one; we need to reshape it to 2d
from matplotlib import pyplot
import numpy as np
pyplot.imshow(x_train[0].reshape((28, 28)), cmap="gray")
# ``pyplot.show()`` only if not on Colab
    import google.colab
except ImportError:
    pyplot.show()
print(x_train.shape)
###############################################################################
# PyTorch uses ``torch.tensor``, rather than numpy arrays, so we need to
# convert our data.
import torch
x_train, y_train, x_valid, y_valid = map(
    torch.tensor, (x_train, y_train, x_valid, y_valid)
n, c = x_train.shape
print(x_train, y_train)
print(x_train.shape)
print(y_train.min(), y_train.max())
###############################################################################
# Neural net from scratch (without ``torch.nn``)
# -----------------------------------------------
# Let's first create a model using nothing but PyTorch tensor operations. We're assuming
# you're already familiar with the basics of neural networks. (If you're not, you can
# learn them at `course.fast.ai <https://course.fast.ai>`_).
# PyTorch provides methods to create random or zero-filled tensors, which we will
# use to create our weights and bias for a simple linear model. These are just regular
# tensors, with one very special addition: we tell PyTorch that they require a
# gradient. This causes PyTorch to record all of the operations done on the tensor,
# so that it can calculate the gradient during back-propagation *automatically*!
# For the weights, we set ``requires_grad`` **after** the initialization, since we
# don't want that step included in the gradient. (Note that a trailing ``_`` in
# PyTorch signifies that the operation is performed in-place.)
# .. note:: We are initializing the weights here with
#    `Xavier initialisation <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
#    (by multiplying with ``1/sqrt(n)``).
import math
weights = torch.randn(784, 10) / math.sqrt(784)
weights.requires_grad_()
bias = torch.zeros(10, requires_grad=True)
###############################################################################
# Thanks to PyTorch's ability to calculate gradients automatically, we can
# use any standard Python function (or callable object) as a model! So
# let's just write a plain matrix multiplication and broadcasted addition
# to create a simple linear model. We also need an activation function, so
# we'll write `log_softmax` and use it. Remember: although PyTorch
# provides lots of prewritten loss functions, activation functions, and
# so forth, you can easily write your own using plain python. PyTorch will
# even create fast accelerator or vectorized CPU code for your function
# automatically.
def log_softmax(x):
    return x - x.exp().sum(-1).log().unsqueeze(-1)
def model(xb):
    return log_softmax(xb @ weights + bias)
######################################################################################
# In the above, the ``@`` stands for the matrix multiplication operation. We will call
# our function on one batch of data (in this case, 64 images).  This is
# one *forward pass*.  Note that our predictions won't be any better than
# random at this stage, since we start with random weights.
bs = 64  # batch size
xb = x_train[0:bs]  # a mini-batch from x
preds = model(xb)  # predictions
preds[0], preds.shape
print(preds[0], preds.shape)
###############################################################################
# As you see, the ``preds`` tensor contains not only the tensor values, but also a
# gradient function. We'll use this later to do backprop.
# Let's implement negative log-likelihood to use as the loss function
# (again, we can just use standard Python):
def nll(input, target):
    return -input[range(target.shape[0]), target].mean()
loss_func = nll
###############################################################################
# Let's check our loss with our random model, so we can see if we improve
# after a backprop pass later.
yb = y_train[0:bs]
print(loss_func(preds, yb))
###############################################################################
# Let's also implement a function to calculate the accuracy of our model.
# For each prediction, if the index with the largest value matches the
# target value, then the prediction was correct.
def accuracy(out, yb):
    preds = torch.argmax(out, dim=1)
    return (preds == yb).float().mean()
###############################################################################
# Let's check the accuracy of our random model, so we can see if our
# accuracy improves as our loss improves.
print(accuracy(preds, yb))
###############################################################################
# We can now run a training loop.  For each iteration, we will:
# - select a mini-batch of data (of size ``bs``)
# - use the model to make predictions
# - calculate the loss
# - ``loss.backward()`` updates the gradients of the model, in this case, ``weights``
#   and ``bias``.
# We now use these gradients to update the weights and bias.  We do this
# within the ``torch.no_grad()`` context manager, because we do not want these
# actions to be recorded for our next calculation of the gradient.  You can read
# more about how PyTorch's Autograd records operations
# `here <https://pytorch.org/docs/stable/notes/autograd.html>`_.
# We then set the
# gradients to zero, so that we are ready for the next loop.
# Otherwise, our gradients would record a running tally of all the operations
# that had happened (i.e. ``loss.backward()`` *adds* the gradients to whatever is
# already stored, rather than replacing them).
# .. tip:: You can use the standard python debugger to step through PyTorch
#    code, allowing you to check the various variable values at each step.
#    Uncomment ``set_trace()`` below to try it out.
from IPython.core.debugger import set_trace
lr = 0.5  # learning rate
epochs = 2  # how many epochs to train for
for epoch in range(epochs):
    for i in range((n - 1) // bs + 1):
        #         set_trace()
        start_i = i * bs
        end_i = start_i + bs
        xb = x_train[start_i:end_i]
        yb = y_train[start_i:end_i]
        pred = model(xb)
        loss = loss_func(pred, yb)
        loss.backward()
        with torch.no_grad():
            weights -= weights.grad * lr
            bias -= bias.grad * lr
            weights.grad.zero_()
            bias.grad.zero_()
###############################################################################
# That's it: we've created and trained a minimal neural network (in this case, a
# logistic regression, since we have no hidden layers) entirely from scratch!
# Let's check the loss and accuracy and compare those to what we got
# earlier. We expect that the loss will have decreased and accuracy to
# have increased, and they have.
print(loss_func(model(xb), yb), accuracy(model(xb), yb))
###############################################################################
# Using ``torch.nn.functional``
# ------------------------------
# We will now refactor our code, so that it does the same thing as before, only
# we'll start taking advantage of PyTorch's ``nn`` classes to make it more concise
# and flexible. At each step from here, we should be making our code one or more
# of: shorter, more understandable, and/or more flexible.
# The first and easiest step is to make our code shorter by replacing our
# hand-written activation and loss functions with those from ``torch.nn.functional``
# (which is generally imported into the namespace ``F`` by convention). This module
# contains all the functions in the ``torch.nn`` library (whereas other parts of the
# library contain classes). As well as a wide range of loss and activation
# functions, you'll also find here some convenient functions for creating neural
# nets, such as pooling functions. (There are also functions for doing convolutions,
# linear layers, etc, but as we'll see, these are usually better handled using
# other parts of the library.)
# If you're using negative log likelihood loss and log softmax activation,
# then Pytorch provides a single function ``F.cross_entropy`` that combines
# the two. So we can even remove the activation function from our model.
import torch.nn.functional as F
loss_func = F.cross_entropy
def model(xb):
    return xb @ weights + bias
###############################################################################
# Note that we no longer call ``log_softmax`` in the ``model`` function. Let's
# confirm that our loss and accuracy are the same as before:
print(loss_func(model(xb), yb), accuracy(model(xb), yb))
###############################################################################
# Refactor using ``nn.Module``
# -----------------------------
# Next up, we'll use ``nn.Module`` and ``nn.Parameter``, for a clearer and more
# concise training loop. We subclass ``nn.Module`` (which itself is a class and
# able to keep track of state).  In this case, we want to create a class that
# holds our weights, bias, and method for the forward step.  ``nn.Module`` has a
# number of attributes and methods (such as ``.parameters()`` and ``.zero_grad()``)
# which we will be using.
# .. note:: ``nn.Module`` (uppercase M) is a PyTorch specific concept, and is a
#    class we'll be using a lot. ``nn.Module`` is not to be confused with the Python
#    concept of a (lowercase ``m``) `module <https://docs.python.org/3/tutorial/modules.html>`_,
#    which is a file of Python code that can be imported.
from torch import nn
class Mnist_Logistic(nn.Module):
    def __init__(self):
        super().__init__()
        self.weights = nn.Parameter(torch.randn(784, 10) / math.sqrt(784))
        self.bias = nn.Parameter(torch.zeros(10))
    def forward(self, xb):
        return xb @ self.weights + self.bias
###############################################################################
# Since we're now using an object instead of just using a function, we
# first have to instantiate our model:
model = Mnist_Logistic()
###############################################################################
# Now we can calculate the loss in the same way as before. Note that
# ``nn.Module`` objects are used as if they are functions (i.e they are
# *callable*), but behind the scenes Pytorch will call our ``forward``
# method automatically.
print(loss_func(model(xb), yb))
###############################################################################
# Previously for our training loop we had to update the values for each parameter
# by name, and manually zero out the grads for each parameter separately, like this:
# .. code-block:: python
#    with torch.no_grad():
#        weights -= weights.grad * lr
#        bias -= bias.grad * lr
#        weights.grad.zero_()
#        bias.grad.zero_()
# Now we can take advantage of model.parameters() and model.zero_grad() (which
# are both defined by PyTorch for ``nn.Module``) to make those steps more concise
# and less prone to the error of forgetting some of our parameters, particularly
# if we had a more complicated model:
# .. code-block:: python
#    with torch.no_grad():
#        for p in model.parameters(): p -= p.grad * lr
#        model.zero_grad()
# We'll wrap our little training loop in a ``fit`` function so we can run it
# again later.
    for epoch in range(epochs):
        for i in range((n - 1) // bs + 1):
            start_i = i * bs
            end_i = start_i + bs
            xb = x_train[start_i:end_i]
            yb = y_train[start_i:end_i]
            pred = model(xb)
            loss = loss_func(pred, yb)
            loss.backward()
            with torch.no_grad():
                for p in model.parameters():
                    p -= p.grad * lr
                model.zero_grad()
###############################################################################
# Let's double-check that our loss has gone down:
print(loss_func(model(xb), yb))
###############################################################################
# Refactor using ``nn.Linear``
# ----------------------------
# We continue to refactor our code.  Instead of manually defining and
# initializing ``self.weights`` and ``self.bias``, and calculating ``xb  @
# self.weights + self.bias``, we will instead use the Pytorch class
# `nn.Linear <https://pytorch.org/docs/stable/nn.html#linear-layers>`_ for a
# linear layer, which does all that for us. Pytorch has many types of
# predefined layers that can greatly simplify our code, and often makes it
# faster too.
class Mnist_Logistic(nn.Module):
    def __init__(self):
        super().__init__()
        self.lin = nn.Linear(784, 10)
    def forward(self, xb):
        return self.lin(xb)
###############################################################################
# We instantiate our model and calculate the loss in the same way as before:
model = Mnist_Logistic()
print(loss_func(model(xb), yb))
###############################################################################
# We are still able to use our same ``fit`` method as before.
print(loss_func(model(xb), yb))
###############################################################################
# Refactor using ``torch.optim``
# ------------------------------
# Pytorch also has a package with various optimization algorithms, ``torch.optim``.
# We can use the ``step`` method from our optimizer to take a forward step, instead
# of manually updating each parameter.
# This will let us replace our previous manually coded optimization step:
# .. code-block:: python
#    with torch.no_grad():
#        for p in model.parameters(): p -= p.grad * lr
#        model.zero_grad()
# and instead use just:
# .. code-block:: python
#    opt.step()
#    opt.zero_grad()
# (``optim.zero_grad()`` resets the gradient to 0 and we need to call it before
# computing the gradient for the next minibatch.)
from torch import optim
###############################################################################
# We'll define a little function to create our model and optimizer so we
# can reuse it in the future.
def get_model():
    model = Mnist_Logistic()
    return model, optim.SGD(model.parameters(), lr=lr)
model, opt = get_model()
print(loss_func(model(xb), yb))
for epoch in range(epochs):
    for i in range((n - 1) // bs + 1):
        start_i = i * bs
        end_i = start_i + bs
        xb = x_train[start_i:end_i]
        yb = y_train[start_i:end_i]
        pred = model(xb)
        loss = loss_func(pred, yb)
        loss.backward()
        opt.step()
        opt.zero_grad()
print(loss_func(model(xb), yb))
###############################################################################
# Refactor using Dataset
# ------------------------------
# PyTorch has an abstract Dataset class.  A Dataset can be anything that has
# a ``__len__`` function (called by Python's standard ``len`` function) and
# a ``__getitem__`` function as a way of indexing into it.
# `This tutorial <https://pytorch.org/tutorials/beginner/data_loading_tutorial.html>`_
# walks through a nice example of creating a custom ``FacialLandmarkDataset`` class
# as a subclass of ``Dataset``.
# PyTorch's `TensorDataset <https://pytorch.org/docs/stable/_modules/torch/utils/data/dataset.html#TensorDataset>`_
# is a Dataset wrapping tensors. By defining a length and way of indexing,
# this also gives us a way to iterate, index, and slice along the first
# dimension of a tensor. This will make it easier to access both the
# independent and dependent variables in the same line as we train.
from torch.utils.data import TensorDataset
###############################################################################
# Both ``x_train`` and ``y_train`` can be combined in a single ``TensorDataset``,
# which will be easier to iterate over and slice.
train_ds = TensorDataset(x_train, y_train)
###############################################################################
# Previously, we had to iterate through minibatches of ``x`` and ``y`` values separately:
# .. code-block:: python
#    xb = x_train[start_i:end_i]
#    yb = y_train[start_i:end_i]
# Now, we can do these two steps together:
# .. code-block:: python
#    xb,yb = train_ds[i*bs : i*bs+bs]
model, opt = get_model()
for epoch in range(epochs):
    for i in range((n - 1) // bs + 1):
        xb, yb = train_ds[i * bs: i * bs + bs]
        pred = model(xb)
        loss = loss_func(pred, yb)
        loss.backward()
        opt.step()
        opt.zero_grad()
print(loss_func(model(xb), yb))
###############################################################################
# Refactor using ``DataLoader``
# ------------------------------
# PyTorch's ``DataLoader`` is responsible for managing batches. You can
# create a ``DataLoader`` from any ``Dataset``. ``DataLoader`` makes it easier
# to iterate over batches. Rather than having to use ``train_ds[i*bs : i*bs+bs]``,
# the ``DataLoader`` gives us each minibatch automatically.
from torch.utils.data import DataLoader
train_ds = TensorDataset(x_train, y_train)
train_dl = DataLoader(train_ds, batch_size=bs)
###############################################################################
# Previously, our loop iterated over batches ``(xb, yb)`` like this:
# .. code-block:: python
#    for i in range((n-1)//bs + 1):
#        xb,yb = train_ds[i*bs : i*bs+bs]
#        pred = model(xb)
# Now, our loop is much cleaner, as ``(xb, yb)`` are loaded automatically from the data loader:
# .. code-block:: python
#    for xb,yb in train_dl:
#        pred = model(xb)
model, opt = get_model()
for epoch in range(epochs):
    for xb, yb in train_dl:
        pred = model(xb)
        loss = loss_func(pred, yb)
        loss.backward()
        opt.step()
        opt.zero_grad()
print(loss_func(model(xb), yb))
###############################################################################
# Thanks to PyTorch's ``nn.Module``, ``nn.Parameter``, ``Dataset``, and ``DataLoader``,
# our training loop is now dramatically smaller and easier to understand. Let's
# now try to add the basic features necessary to create effective models in practice.
# Add validation
# -----------------------
# In section 1, we were just trying to get a reasonable training loop set up for
# use on our training data.  In reality, you **always** should also have
# a `validation set <https://www.fast.ai/2017/11/13/validation-sets/>`_, in order
# to identify if you are overfitting.
# Shuffling the training data is
# `important <https://www.quora.com/Does-the-order-of-training-data-matter-when-training-neural-networks>`_
# to prevent correlation between batches and overfitting. On the other hand, the
# validation loss will be identical whether we shuffle the validation set or not.
# Since shuffling takes extra time, it makes no sense to shuffle the validation data.
# We'll use a batch size for the validation set that is twice as large as
# that for the training set. This is because the validation set does not
# need backpropagation and thus takes less memory (it doesn't need to
# store the gradients). We take advantage of this to use a larger batch
# size and compute the loss more quickly.
train_ds = TensorDataset(x_train, y_train)
train_dl = DataLoader(train_ds, batch_size=bs, shuffle=True)
valid_ds = TensorDataset(x_valid, y_valid)
valid_dl = DataLoader(valid_ds, batch_size=bs * 2)
###############################################################################
# We will calculate and print the validation loss at the end of each epoch.
# (Note that we always call ``model.train()`` before training, and ``model.eval()``
# before inference, because these are used by layers such as ``nn.BatchNorm2d``
# and ``nn.Dropout`` to ensure appropriate behavior for these different phases.)
model, opt = get_model()
for epoch in range(epochs):
    model.train()
    for xb, yb in train_dl:
        pred = model(xb)
        loss = loss_func(pred, yb)
        loss.backward()
        opt.step()
        opt.zero_grad()
    model.eval()
    with torch.no_grad():
        valid_loss = sum(loss_func(model(xb), yb) for xb, yb in valid_dl)
    print(epoch, valid_loss / len(valid_dl))
###############################################################################
# Create fit() and get_data()
# ----------------------------------
# We'll now do a little refactoring of our own. Since we go through a similar
# process twice of calculating the loss for both the training set and the
# validation set, let's make that into its own function, ``loss_batch``, which
# computes the loss for one batch.
# We pass an optimizer in for the training set, and use it to perform
# backprop.  For the validation set, we don't pass an optimizer, so the
# method doesn't perform backprop.
def loss_batch(model, loss_func, xb, yb, opt=None):
    loss = loss_func(model(xb), yb)
    if opt is not None:
        loss.backward()
        opt.step()
        opt.zero_grad()
    return loss.item(), len(xb)
###############################################################################
# ``fit`` runs the necessary operations to train our model and compute the
# training and validation losses for each epoch.
import numpy as np
def fit(epochs, model, loss_func, opt, train_dl, valid_dl):
    for epoch in range(epochs):
        model.train()
        for xb, yb in train_dl:
            loss_batch(model, loss_func, xb, yb, opt)
        model.eval()
        with torch.no_grad():
            losses, nums = zip(
                *[loss_batch(model, loss_func, xb, yb) for xb, yb in valid_dl]
        val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)
        print(epoch, val_loss)
###############################################################################
# ``get_data`` returns dataloaders for the training and validation sets.
def get_data(train_ds, valid_ds, bs):
    return (
        DataLoader(train_ds, batch_size=bs, shuffle=True),
        DataLoader(valid_ds, batch_size=bs * 2),
###############################################################################
# Now, our whole process of obtaining the data loaders and fitting the
# model can be run in 3 lines of code:
train_dl, valid_dl = get_data(train_ds, valid_ds, bs)
model, opt = get_model()
fit(epochs, model, loss_func, opt, train_dl, valid_dl)
###############################################################################
# You can use these basic 3 lines of code to train a wide variety of models.
# Let's see if we can use them to train a convolutional neural network (CNN)!
# Switch to CNN
# -------------
# We are now going to build our neural network with three convolutional layers.
# Because none of the functions in the previous section assume anything about
# the model form, we'll be able to use them to train a CNN without any modification.
# We will use PyTorch's predefined
# `Conv2d <https://pytorch.org/docs/stable/nn.html#torch.nn.Conv2d>`_ class
# as our convolutional layer. We define a CNN with 3 convolutional layers.
# Each convolution is followed by a ReLU.  At the end, we perform an
# average pooling.  (Note that ``view`` is PyTorch's version of Numpy's
# ``reshape``)
class Mnist_CNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1)
        self.conv3 = nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1)
    def forward(self, xb):
        xb = xb.view(-1, 1, 28, 28)
        xb = F.relu(self.conv1(xb))
        xb = F.relu(self.conv2(xb))
        xb = F.relu(self.conv3(xb))
        xb = F.avg_pool2d(xb, 4)
        return xb.view(-1, xb.size(1))
###############################################################################
# `Momentum <https://cs231n.github.io/neural-networks-3/#sgd>`_ is a variation on
# stochastic gradient descent that takes previous updates into account as well
# and generally leads to faster training.
model = Mnist_CNN()
opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
fit(epochs, model, loss_func, opt, train_dl, valid_dl)
###############################################################################
# Using ``nn.Sequential``
# ------------------------
# ``torch.nn`` has another handy class we can use to simplify our code:
# `Sequential <https://pytorch.org/docs/stable/nn.html#torch.nn.Sequential>`_ .
# A ``Sequential`` object runs each of the modules contained within it, in a
# sequential manner. This is a simpler way of writing our neural network.
# To take advantage of this, we need to be able to easily define a
# **custom layer** from a given function.  For instance, PyTorch doesn't
# have a `view` layer, and we need to create one for our network. ``Lambda``
# will create a layer that we can then use when defining a network with
# ``Sequential``.
class Lambda(nn.Module):
    def __init__(self, func):
        super().__init__()
        self.func = func
    def forward(self, x):
        return self.func(x)
def preprocess(x):
    return x.view(-1, 1, 28, 28)
###############################################################################
# The model created with ``Sequential`` is simple:
model = nn.Sequential(
    Lambda(preprocess),
    nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.AvgPool2d(4),
    Lambda(lambda x: x.view(x.size(0), -1)),
opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
fit(epochs, model, loss_func, opt, train_dl, valid_dl)
###############################################################################
# Wrapping ``DataLoader``
# -----------------------------
# Our CNN is fairly concise, but it only works with MNIST, because:
#  - It assumes the input is a 28\*28 long vector
#  - It assumes that the final CNN grid size is 4\*4 (since that's the average pooling kernel size we used)
# Let's get rid of these two assumptions, so our model works with any 2d
# single channel image. First, we can remove the initial Lambda layer by
# moving the data preprocessing into a generator:
def preprocess(x, y):
    return x.view(-1, 1, 28, 28), y
class WrappedDataLoader:
    def __init__(self, dl, func):
        self.dl = dl
        self.func = func
    def __len__(self):
        return len(self.dl)
    def __iter__(self):
        for b in self.dl:
            yield (self.func(*b))
train_dl, valid_dl = get_data(train_ds, valid_ds, bs)
train_dl = WrappedDataLoader(train_dl, preprocess)
valid_dl = WrappedDataLoader(valid_dl, preprocess)
###############################################################################
# Next, we can replace ``nn.AvgPool2d`` with ``nn.AdaptiveAvgPool2d``, which
# allows us to define the size of the *output* tensor we want, rather than
# the *input* tensor we have. As a result, our model will work with any
# size input.
model = nn.Sequential(
    nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.Conv2d(16, 10, kernel_size=3, stride=2, padding=1),
    nn.ReLU(),
    nn.AdaptiveAvgPool2d(1),
    Lambda(lambda x: x.view(x.size(0), -1)),
opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
###############################################################################
# Let's try it out:
fit(epochs, model, loss_func, opt, train_dl, valid_dl)
###############################################################################
# Using your `Accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__
# ---------------
# If you're lucky enough to have access to an accelerator such as CUDA (you can
# rent one for about $0.50/hour from most cloud providers) you can
# use it to speed up your code. First check that your accelerator is working in
# If the current accelerator is available, we will use it. Otherwise, we use the CPU.
device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
print(f"Using {device} device")
###############################################################################
# Let's update ``preprocess`` to move batches to the accelerator:
def preprocess(x, y):
    return x.view(-1, 1, 28, 28).to(device), y.to(device)
train_dl, valid_dl = get_data(train_ds, valid_ds, bs)
train_dl = WrappedDataLoader(train_dl, preprocess)
valid_dl = WrappedDataLoader(valid_dl, preprocess)
###############################################################################
# Finally, we can move our model to the accelerator.
model.to(device)
opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
###############################################################################
# You should find it runs faster now:
fit(epochs, model, loss_func, opt, train_dl, valid_dl)
###############################################################################
# Closing thoughts
# -----------------
# We now have a general data pipeline and training loop which you can use for
# training many types of models using Pytorch. To see how simple training a model
# can now be, take a look at the `mnist_sample notebook <https://github.com/fastai/fastai_dev/blob/master/dev_nb/mnist_sample.ipynb>`__.
# Of course, there are many things you'll want to add, such as data augmentation,
# hyperparameter tuning, monitoring training, transfer learning, and so forth.
# These features are available in the fastai library, which has been developed
# using the same design approach shown in this tutorial, providing a natural
# next step for practitioners looking to take their models further.
# We promised at the start of this tutorial we'd explain through example each of
# ``torch.nn``, ``torch.optim``, ``Dataset``, and ``DataLoader``. So let's summarize
# what we've seen:
#  - ``torch.nn``:
#    + ``Module``: creates a callable which behaves like a function, but can also
#      contain state(such as neural net layer weights). It knows what ``Parameter`` (s) it
#      contains and can zero all their gradients, loop through them for weight updates, etc.
#    + ``Parameter``: a wrapper for a tensor that tells a ``Module`` that it has weights
#      that need updating during backprop. Only tensors with the `requires_grad` attribute set are updated
#    + ``functional``: a module(usually imported into the ``F`` namespace by convention)
#      which contains activation functions, loss functions, etc, as well as non-stateful
#      versions of layers such as convolutional and linear layers.
#  - ``torch.optim``: Contains optimizers such as ``SGD``, which update the weights
#    of ``Parameter`` during the backward step
#  - ``Dataset``: An abstract interface of objects with a ``__len__`` and a ``__getitem__``,
#    including classes provided with Pytorch such as ``TensorDataset``
#  - ``DataLoader``: Takes any ``Dataset`` and creates an iterator which returns batches of data.
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