from aim import Run
from aim.pytorch import track_gradients_dists, track_params_dists
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from tqdm import tqdm

# Hyperparameters
batch_size = 64
epochs = 10
learning_rate = 0.01

aim_run = Run()

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 7 * 7, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = torch.flatten(x, 1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

train_dataset = datasets.MNIST(root='./data',
                               train=True,
                               transform=transforms.ToTensor(),
                               download=True)

test_dataset = datasets.MNIST(root='./data',
                              train=False,
                              transform=transforms.ToTensor())


train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=False)


model = CNN()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()

# Training loop
for epoch in range(epochs):
    model.train()
    train_loss = 0
    correct = 0
    total = 0

    for batch_idx, (data, target) in enumerate(tqdm(train_loader, desc="Training", leave=False)):
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = torch.max(output.data, 1)
        total += target.size(0)
        correct += (predicted == target).sum().item()

    # Track training metrics and distributions
    acc = correct / total
    items = {'accuracy': acc, 'loss': train_loss / len(train_loader)}
    aim_run.track(items, epoch=epoch, context={'subset': 'train'})

    track_params_dists(model, aim_run, epoch=epoch, context={'subset': 'train'})
    track_gradients_dists(model, aim_run, epoch=epoch, context={'subset': 'train'})

####################
    model.eval()
    test_loss = 0
    correct = 0
    total = 0
####################
    with torch.no_grad():
        for batch_idx, (data, target) in enumerate(tqdm(test_loader, desc="Testing", leave=False)):
            output = model(data)
            loss = criterion(output, target)
            test_loss += loss.item()
            _, predicted = torch.max(output.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()






##
    acc = correct / total
    items = {'accuracy': acc, 'loss': test_loss / len(test_loader)}
    aim_run.track(items, epoch=epoch, context={'subset': 'test'})

    track_params_dists(model, aim_run, epoch=epoch, context={'subset': 'test'})
    track_gradients_dists(model, aim_run, epoch=epoch, context={'subset': 'test'})
#
###
torch.save(model.state_dict(), 'mnist_cnn.pth')