update template code

mmp/a4/anchor_grid.py

@@ -0,0 +1,12 @@
+from typing import Sequence
+import numpy as np
+
+
+def get_anchor_grid(
+    num_rows: int,
+    num_cols: int,
+    scale_factor: float,
+    anchor_widths: Sequence[float],
+    aspect_ratios: Sequence[float],
+) -> np.ndarray:
+    raise NotImplementedError()

mmp/a4/dataset.py

@@ -0,0 +1,50 @@
+from typing import Tuple
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+
+
+class MMP_Dataset(torch.utils.data.Dataset):
+    def __init__(
+        self,
+        path_to_data: str,
+        image_size: int,
+        anchor_grid: np.ndarray,
+        min_iou: float,
+        is_test: bool,
+    ):
+        """
+        @param anchor_grid: The anchor grid to be used for every image
+        @param min_iou: The minimum IoU that is required for an overlap for the label grid.
+        @param is_test: Whether this is the test set (True) or the validation/training set (False)
+        """
+        raise NotImplementedError()
+
+    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, int]:
+        """
+        @return: 3-tuple of image tensor, label grid, and image (file-)number
+        """
+        raise NotImplementedError()
+
+    def __len__(self) -> int:
+        raise NotImplementedError()
+
+
+def get_dataloader(
+    path_to_data: str,
+    image_size: int,
+    batch_size: int,
+    num_workers: int,
+    anchor_grid: np.ndarray,
+    is_test: bool,
+) -> DataLoader:
+    raise NotImplementedError()
+
+
+def calculate_max_coverage(loader: DataLoader, min_iou: float) -> float:
+    """
+    @param loader: A DataLoader object, generated with the get_dataloader function.
+    @param min_iou: Minimum IoU overlap that is required to count a ground truth box as covered.
+    @return: Ratio of how mamy ground truth boxes are covered by a label grid box. Must be a value between 0 and 1.
+    """
+    raise NotImplementedError()

mmp/a4/label_grid.py

@@ -0,0 +1,14 @@
+from typing import Sequence
+import numpy as np
+
+from ..a3.annotation import AnnotationRect
+
+
+def iou(rect1: AnnotationRect, rect2: AnnotationRect) -> float:
+    raise NotImplementedError()
+
+
+def get_label_grid(
+    anchor_grid: np.ndarray, gts: Sequence[AnnotationRect], min_iou: float
+) -> tuple[np.ndarray, ...]:
+    raise NotImplementedError()