# -*- coding: utf-8 -*-
"""
Training and Saving a Classifier
=====================

This is adapted from **PyTorch Official** [Training a Classifier](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py)
tutorial:

Dealing with data
---

Generally, when you have to deal with image, text, audio or video data,
you can use standard python packages that load data into a numpy array.
Then you can convert this array into a ``torch.*Tensor``.

-  For images, packages such as Pillow, OpenCV are useful
-  For audio, packages such as scipy and librosa
-  For text, either raw Python or Cython based loading, or NLTK and
   SpaCy are useful

Specifically for vision, we have created a package called
``torchvision``, that has data loaders for common datasets such as
Imagenet, CIFAR10, MNIST, etc. and data transformers for images, viz.,
``torchvision.datasets`` and ``torch.utils.data.DataLoader``.

This provides a huge convenience and avoids writing boilerplate code.

For this tutorial, we will use the CIFAR10 dataset.
It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,
‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of
size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.

.. figure:: /_static/img/cifar10.png
   :alt: cifar10

   cifar10


Training an image classifier
----------------------------

We will do the following steps in order:

1. Load and normalizing the CIFAR10 training and test datasets using
   ``torchvision``
2. Define a Convolution Neural Network
3. Define a loss function
4. Train the network on the training data
5. Test the network on the test data

1. Loading and normalizing CIFAR10
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Using ``torchvision``, it’s extremely easy to load CIFAR10.
"""
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

########################################################################
# The output of torchvision datasets are PILImage images of range [0, 1].
# We transform them to Tensors of normalized range [-1, 1].

transform = transforms.Compose(
    [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
)

trainset = torchvision.datasets.CIFAR10(
    root="./data", train=True, download=True, transform=transform
)
trainloader = torch.utils.data.DataLoader(
    trainset, batch_size=4, shuffle=True, num_workers=0
)

testset = torchvision.datasets.CIFAR10(
    root="./data", train=False, download=True, transform=transform
)
testloader = torch.utils.data.DataLoader(
    testset, batch_size=4, shuffle=False, num_workers=0
)

classes = (
    "plane",
    "car",
    "bird",
    "cat",
    "deer",
    "dog",
    "frog",
    "horse",
    "ship",
    "truck",
)

########################################################################
# Let us show some of the training images, for fun.


# functions to show an image


def imshow(img):
    img = img / 2 + 0.5  # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))


# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()

# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(" ".join("%5s" % classes[labels[j]] for j in range(4)))


########################################################################
# 2. Define a Convolution Neural Network
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Copy the neural network from the Neural Networks section before and modify it to
# take 3-channel images (instead of 1-channel images as it was defined).


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = Net()
# net.to(device)
########################################################################
# 3. Define a Loss function and optimizer
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Let's use a Classification Cross-Entropy loss and SGD with momentum.


criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

########################################################################
# 4. Train the network
# ^^^^^^^^^^^^^^^^^^^^
#
# This is when things start to get interesting.
# We simply have to loop over our data iterator, and feed the inputs to the
# network and optimize.

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:  # print every 2000 mini-batches
            print("[%d, %5d] loss: %.3f" % (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print("Finished Training")

########################################################################
# 5. Test the network on the test data
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# We have trained the network for 2 passes over the training dataset.
# But we need to check if the network has learnt anything at all.
#
# We will check this by predicting the class label that the neural network
# outputs, and checking it against the ground-truth. If the prediction is
# correct, we add the sample to the list of correct predictions.
#
# Okay, first step. Let us display an image from the test set to get familiar.

dataiter = iter(testloader)
images, labels = dataiter.next()

# print images
imshow(torchvision.utils.make_grid(images))
print("GroundTruth: ", " ".join("%5s" % classes[labels[j]] for j in range(4)))

########################################################################
# Okay, now let us see what the neural network thinks these examples above are:

outputs = net(images)

########################################################################
# The outputs are energies for the 10 classes.
# Higher the energy for a class, the more the network
# thinks that the image is of the particular class.
# So, let's get the index of the highest energy:
_, predicted = torch.max(outputs, 1)

print("Predicted: ", " ".join("%5s" % classes[predicted[j]] for j in range(4)))

########################################################################
# The results seem pretty good.
#
# Let us look at how the network performs on the whole dataset.

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(
    "Accuracy of the network on the 10000 test images: %d %%" % (100 * correct / total)
)

########################################################################
# That looks waaay better than chance, which is 10% accuracy (randomly picking
# a class out of 10 classes).
# Seems like the network learnt something.
#
# Hmmm, what are the classes that performed well, and the classes that did
# not perform well:

class_correct = list(0.0 for i in range(10))
class_total = list(0.0 for i in range(10))
with torch.no_grad():
    for data in testloader:
        images, labels = data
        # inputs, labels = inputs.to(device), labels.to(device)
        outputs = net(images)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()
        for i in range(4):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1


for i in range(10):
    print(
        "Accuracy of %5s : %2d %%"
        % (classes[i], 100 * class_correct[i] / class_total[i])
    )

#########################################
# save model to disk
#########################################
print("Saving model to disk")

PATH = Path("models") / "cifar-weights.pt"
PATH = PATH.resolve().absolute()
torch.save(net.state_dict(), str(PATH))
print(f"Successful: Model Weights Saved at {PATH}")
