update template code

mmp/a2/main.py

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+from typing import Tuple
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+
+# these are the labels from the Cifar10 dataset:
+CLASSES = (
+    "plane",
+    "car",
+    "bird",
+    "cat",
+    "deer",
+    "dog",
+    "frog",
+    "horse",
+    "ship",
+    "truck",
+)
+
+
+class MmpNet(nn.Module):
+    """Exercise 2.1"""
+
+    def __init__(self, num_classes: int):
+        raise NotImplementedError()
+
+    def forward(self, x: torch.Tensor):
+        raise NotImplementedError()
+
+
+def get_dataloader(
+    is_train: bool, data_root: str, batch_size: int, num_workers: int
+) -> DataLoader:
+    """Exercise 2.2
+
+    @param is_train: Whether this is the training or validation split
+    @param data_root: Where to download the dataset to
+    @param batch_size: Batch size for the data loader
+    @param num_workers: Number of workers for the data loader
+    """
+    raise NotImplementedError()
+
+
+def get_criterion_optimizer(model: nn.Module) -> Tuple[nn.Module, optim.Optimizer]:
+    """Exercise 2.3a
+
+    @param model: The model that is being trained.
+    @return: Returns a tuple of the criterion and the optimizer.
+    """
+    raise NotImplementedError()
+
+
+def train_epoch(
+    model: nn.Module,
+    loader: DataLoader,
+    criterion: nn.Module,
+    optimizer: optim.Optimizer,
+    device: torch.device,
+):
+    """Exercise 2.3b
+
+    @param model: The model that should be trained
+    @param loader: The DataLoader that contains the training data
+    @param criterion: The criterion that is used to calculate the loss for backpropagation
+    @param optimizer: Executes the update step
+    @param device: The device where the epoch should run on
+    """
+    raise NotImplementedError()
+
+
+def eval_epoch(model: nn.Module, loader: DataLoader, device: torch.device) -> float:
+    """Exercise 2.3c
+
+    @param model: The model that should be evaluated
+    @param loader: The DataLoader that contains the evaluation data
+    @param device: The device where the epoch should run on
+
+    @return: Returns the accuracy over the full validation dataset as a float."""
+    raise NotImplementedError()
+
+
+def main():
+    """Exercise 2.3d"""
+    raise NotImplementedError()
+
+
+if __name__ == "__main__":
+    main()