pytorch_transforms.py

'''
Shows a small example of how to use transformations (perhaps unecessarily many)
on CIFAR10 dataset and training on a small CNN toy network.

Video explanation: https://youtu.be/Zvd276j9sZ8
Got any questions leave a comment I'm pretty good at responding on youtube

Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
*    2020-04-09 Initial coding
'''

# Imports
import torch
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc.
import torch.nn.functional as F # All functions that don't have any parameters
from torch.utils.data import DataLoader # Gives easier dataset managment and creates mini batches
import torchvision.datasets as datasets # Has standard datasets we can import in a nice way
import torchvision.transforms as transforms # Transformations we can perform on our dataset

# Simple CNN
class CNN(nn.Module):
    def __init__(self, in_channels, num_classes):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=8, kernel_size=(3,3), stride=(1,1), padding=(1,1))
        self.pool = nn.MaxPool2d(kernel_size=(2,2), stride = (2,2))
        self.conv2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(3,3), stride=(1,1), padding=(1,1))
        self.fc1 = nn.Linear(16*8*8, num_classes)
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool(x)
        x = F.relu(self.conv2(x))
        x = self.pool(x)
        x = x.reshape(x.shape[0], -1)
        x = self.fc1(x)
        
        return x
    
# Set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Hyperparameters
learning_rate = 1e-4
batch_size = 64
num_epochs = 5

    
# Load pretrain model & modify it
model = CNN(in_channels=3, num_classes=10)
model.classifier = nn.Sequential(nn.Linear(512, 100),
                                 nn.ReLU(),
                                 nn.Linear(100, 10))
model.to(device)

# Load Data
my_transforms = transforms.Compose([ #Compose makes it possible to have many transforms
    transforms.Resize((36,36)), # Resizes (32,32) to (36,36)
    transforms.RandomCrop((32,32)), # Takes a random (32,32) crop
    transforms.ColorJitter(brightness=0.5), # Change brightness of image
    transforms.RandomRotation(degrees=45), # Perhaps a random rotation from -45 to 45 degrees
    transforms.RandomHorizontalFlip(p=0.5), # Flips the image horizontally with probability 0.5
    transforms.RandomVerticalFlip(p=0.05), # Flips image vertically with probability 0.05
    transforms.RandomGrayscale(p=0.2), # Converts to grayscale with probability 0.2
    transforms.ToTensor(), # Finally converts PIL image to tensor so we can train w. pytorch
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) # Note: these values aren't optimal
    ])


train_dataset = datasets.CIFAR10(root='dataset/', train=True, 
                                 transform=my_transforms, download=True)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)

# Loss and optimizer
criterion = nn.CrossEntropyLoss() 
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Train Network
for epoch in range(num_epochs):
    losses = []
    
    for batch_idx, (data, targets) in enumerate(train_loader):
        # Get data to cuda if possible
        data = data.to(device=device)
        targets = targets.to(device=device)
        
        # forward
        scores = model(data)
        loss = criterion(scores, targets)
        
        losses.append(loss.item())
        # backward
        optimizer.zero_grad()
        loss.backward()
        
        # gradient descent or adam step
        optimizer.step()
        
    
    print(f'Cost at epoch {epoch} is {sum(losses)/len(losses):.5f}')

# Check accuracy on training & test to see how good our model

def check_accuracy(loader, model):
    if loader.dataset.train:
        print("Checking accuracy on training data")
    else:
        print("Checking accuracy on test data")
        
    num_correct = 0
    num_samples = 0
    model.eval()
    
    with torch.no_grad():
        for x, y in loader:
            x = x.to(device=device)
            y = y.to(device=device)
            
            scores = model(x)
            _, predictions = scores.max(1)
            num_correct += (predictions == y).sum()
            num_samples += predictions.size(0)
        
        print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}') 
    
    model.train()

check_accuracy(train_loader, model)