update template code

mmp/a3/annotation.py

@@ -0,0 +1,27 @@
+from typing import List
+import numpy as np
+
+
+class AnnotationRect:
+    """Exercise 3.1"""
+
+    def __init__(self, x1, y1, x2, y2):
+        raise NotImplementedError()
+
+    def area(self):
+        raise NotImplementedError()
+
+    def __array__(self) -> np.ndarray:
+        raise NotImplementedError()
+
+    @staticmethod
+    def fromarray(arr: np.ndarray):
+        raise NotImplementedError()
+
+
+def read_groundtruth_file(path: str) -> List[AnnotationRect]:
+    """Exercise 3.1b"""
+    raise NotImplementedError()
+
+
+# put your solution for exercise 3.1c wherever you deem it right

mmp/a3/dataset.py

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+from typing import Tuple
+import torch
+from torch.utils.data import DataLoader
+
+
+class MMP_Dataset(torch.utils.data.Dataset):
+    """Exercise 3.2"""
+
+    def __init__(self, path_to_data: str, image_size: int):
+        """
+        @param path_to_data: Path to the folder that contains the images and annotation files, e.g. dataset_mmp/train
+        @param image_size: Desired image size that this dataset should return
+        """
+        raise NotImplementedError()
+
+    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, int]:
+        """
+        @return: Tuple of image tensor and label. The label is 0 if there is one person and 1 if there a multiple people.
+        """
+        raise NotImplementedError()
+
+    def __len__(self) -> int:
+        raise NotImplementedError()
+
+def get_dataloader(
+        path_to_data: str, image_size: int, batch_size: int, num_workers: int, is_train: bool = True
+) -> DataLoader:
+
+    """Exercise 3.2d"""

mmp/a3/main.py

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+def main():
+    """Put your code for Exercise 3.3 in here"""
+    raise NotImplementedError()
+
+
+if __name__ == "__main__":
+    main()