import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models
from omegaconf import DictConfig, OmegaConf
from data.dataset import get_dataset
from utils import get_model
import time

import hydra

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

 

@hydra.main(version_base=None, config_path="./config", config_name="config")
def main(cfg: DictConfig) -> None:
    model=get_model(cfg)
    model.train()
    train_dataloader,test_dataloader,train_dataset,test_dataset=get_dataset(cfg)
    num_epochs = cfg.classifier.num_epochs
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.parameters(), lr=cfg.classifier.lr, momentum=cfg.classifier.momentum)
    start_time = time.time()

    for epoch in range(num_epochs):
        model.train()

        running_loss = 0
        running_corrects = 0

        for i, (inputs, labels) in enumerate(train_dataloader):
            inputs = inputs.to(device)
            labels = labels.to(device)

            optimizer.zero_grad()
            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)

            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            running_loss += loss.item() * inputs.size(0)
            running_corrects += torch.sum(preds == labels.data)

        epoch_loss = running_loss / len(train_dataset)
        epoch_acc = running_corrects / len(train_dataset) * 100.
        print('[Train #{}] Loss: {:.4f} Acc: {:.4f}% Time: {:.4f}s'.format(epoch, epoch_loss, epoch_acc, time.time() - start_time))

    
        model.eval()

        with torch.no_grad():
            running_loss = 0.
            running_corrects = 0

            for inputs, labels in test_dataloader:
                inputs = inputs.to(device)
                labels = labels.to(device)

                outputs = model(inputs)
                _, preds = torch.max(outputs, 1)
                loss = criterion(outputs, labels)

                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / len(test_dataset)
            epoch_acc = running_corrects / len(test_dataset) * 100.
            print('[Test #{}] Loss: {:.4f} Acc: {:.4f}% Time: {:.4f}s'.format(epoch, epoch_loss, epoch_acc, time.time() - start_time))

    save_path = '{}/{}_{}.pth'.format(cfg.paths.classifier, cfg.classifier.model, cfg.dataset)
    torch.save(model.state_dict(), save_path)













if __name__ == "__main__":
    main()