#!/usr/bin/env python3
# coding: utf-8

# convolution neural net
# source: 
# https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html


# What about 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`.
# 
# Specifically for vision `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`.
# 
# The CIFAR10 dataset used here 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.
# 
# 
# Training an image classifier
# ----------------------------
# 
# 1.  Load and normalize the CIFAR10 training and test datasets 
#     using `torchvision`
# 2.  Define a Convolutional Neural Network
# 3.  Define a loss function
# 4.  Train the network on the training data
# 5.  Test the network on the test data


# ### 1. Load and normalize CIFAR10
# 
import torch
import torchvision
import torchvision.transforms as transforms


# The output of torchvision datasets,
# PILImage images of range [0, 1], are transformed 
# 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))])

batch_size = 4

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                         shuffle=False, num_workers=2)

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


#
# Let us show some of the training images, for fun.
# 
import matplotlib.pyplot as plt
import numpy as np

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


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

# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))


# 2. Define a Convolutional 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).
# 
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super().__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 = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()


# 3. Define a Loss function and optimizer
# =======================================
# 
# Let\'s use a Classification Cross-Entropy loss and SGD with momentum.
# 
import torch.optim as optim

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; data is a list of [inputs, labels]
        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(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
            running_loss = 0.0

print('Finished Training')


# Save trained model, compare
# https://pytorch.org/docs/stable/notes/serialization.html
PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)


# 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 = next(dataiter)

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


# Load back saved model, for illustation,
# saving and re-loading # the model wasn't necessary.
# 
net = Net()
net.load_state_dict(torch.load(PATH))


# What does the neural network thinks these examples above are?
outputs = net(images)


# The outputs are energies for the 10 classes. The 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(f'{classes[predicted[j]]:5s}'
                              for j in range(4)))

#
# Network performs on the whole dataset.
# 
correct = 0
total = 0
# since we're not training, we don't need to calculate 
# the gradients for our outputs
with torch.no_grad():
    for data in testloader:
        images, labels = data
        # calculate outputs by running images through the network
        outputs = net(images)
        # the class with the highest energy is what we choose as prediction
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')


#
# Count predictions for each class.
#
correct_pred = {classname: 0 for classname in classes}
total_pred = {classname: 0 for classname in classes}

# again, no gradients needed
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predictions = torch.max(outputs, 1)
        # collect the correct predictions for each class
        for label, prediction in zip(labels, predictions):
            if label == prediction:
                correct_pred[classes[label]] += 1
            total_pred[classes[label]] += 1


# print accuracy for each class
for classname, correct_count in correct_pred.items():
    accuracy = 100 * float(correct_count) / total_pred[classname]
    print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')


# Assuming that we are on a CUDA machine, this should print a CUDA device:
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(device)

del dataiter