import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor

# 공개 데이터셋에서 학습 데이터를 내려받습니다.
training_data = datasets.FashionMNIST(
  root="Fashion_MNIST_Data",
  train=True,
  download=True,
  transform=ToTensor(),
)

# 공개 데이터셋에서 테스트 데이터를 내려받습니다.
test_data = datasets.FashionMNIST(
  root="Fashion_MNIST_Data",
  train=False,
  download=True,
  transform=ToTensor(),
)

batch_size = 64

# 데이터로더를 생성합니다.
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)

for X, y in test_dataloader:
  print(f"Shape of X [N, C, H, W]: {X.shape}")
  print(f"Shape of y: {y.shape} {y.dtype}")
  break

# 학습에 사용할 CPU나 GPU, MPS 장치를 얻습니다.
device = (
  "cuda"
  if torch.cuda.is_available()
  else "mps"
  if torch.backends.mps.is_available()
  else "cpu"
)
print(f"Using {device} device")

# 모델을 정의합니다.
class NeuralNetwork(nn.Module):
  def __init__(self):
    super().__init__()
    self.flatten = nn.Flatten()
    self.linear_relu_stack = nn.Sequential(
        nn.Linear(28*28, 512),
        nn.ReLU(),
        nn.Linear(512, 512),
        nn.ReLU(),
        nn.Linear(512, 10)
    )

  def forward(self, x):
    x = self.flatten(x)
    logits = self.linear_relu_stack(x)
    return logits

model = NeuralNetwork().to(device)
print(model)

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

def train(dataloader, model, loss_fn, optimizer):
  size = len(dataloader.dataset)
  for batch, (X, y) in enumerate(dataloader):
    X, y = X.to(device), y.to(device)

    # 예측 오류 계산
    pred = model(X)
    loss = loss_fn(pred, y)

    # 역전파
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if batch % 100 == 0:
      loss, current = loss.item(), (batch + 1) * len(X)
      print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")

def test(dataloader, model, loss_fn):
  size = len(dataloader.dataset)
  num_batches = len(dataloader)
  model.eval()
  test_loss, correct = 0, 0
  with torch.no_grad():
    for X, y in dataloader:
      X, y = X.to(device), y.to(device)
      pred = model(X)
      test_loss += loss_fn(pred, y).item()
      correct += (pred.argmax(1) == y).type(torch.float).sum().item()
  test_loss /= num_batches
  correct /= size
  print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")

epochs = 5
for t in range(epochs):
    print(f"Epoch {t+1}\n-------------------------------")
    train(train_dataloader, model, loss_fn, optimizer)
    test(test_dataloader, model, loss_fn)
print("Done!")

torch.save(model.state_dict(), "model.pth")
print("Saved PyTorch Model State to model.pth")
