update template code

mmp/a6/evallib.py

@@ -0,0 +1,102 @@
+import numpy as np
+from ..a4.label_grid import iou
+
+
+def _assign_detections(det_boxes_scores, gt_boxes, min_iou):
+    """Returns a list of 3-tuples (det_box, det_score, closest_gt_box)"""
+
+    assert min_iou > 0, "min_iou should be greater than zero"
+    det2gt = []
+    for det_box, det_score in det_boxes_scores:
+        # get all GTs that overlap at least by 0.5 and sort descending by overlap
+        best_iou = -1.0
+        closest_gt_box = None
+        for gt in gt_boxes:
+            curr_iou = iou(gt, det_box)
+            if curr_iou > best_iou:
+                best_iou = curr_iou
+                closest_gt_box = gt
+
+        if best_iou >= min_iou:
+            det2gt.append((det_box, det_score, closest_gt_box))
+        else:
+            det2gt.append((det_box, det_score, None))
+
+    return det2gt
+
+
+def calculate_ap_pr(det_boxes_scores: dict, gt_boxes: dict, min_iou=0.5):
+    """
+    Calculates average precision for the given detections and ground truths.
+    This function also calculates precision and recall values that can be used to plot a PR curve
+
+    @param det_boxes_scores: A dictionary that maps image numbers to a list of tuples,
+    containing detected AnnotationRect objects and a score.
+    @param gt_boxes: A dictionary that maps image numbers to the list of ground truth AnnotationRect objects.
+    """
+
+    for dets in det_boxes_scores.values():
+        assert all(
+            isinstance(d, tuple) and len(d) == 2 for d in dets
+        ), "Your detection boxes must have scores"
+
+    assert (
+        len(set(gt_boxes.keys()).intersection(det_boxes_scores.keys())) > 0
+    ), "The two dictionaries have no common keys. Maybe you have selected the wrong dataset?"
+
+    gts_total = 0
+
+    dets2gts_flat = []
+    for img in gt_boxes:
+        gts_total += len(gt_boxes[img])
+        det2gts = _assign_detections(
+            det_boxes_scores.get(img, []), gt_boxes[img], min_iou
+        )
+        dets2gts_flat.extend(det2gts)
+
+    # sort by detection confidence
+    # p[1] is the score
+    dets2gts_flat.sort(key=lambda p: p[1], reverse=True)
+
+    tp = np.zeros(len(dets2gts_flat), dtype=np.float32)
+    fp = np.zeros(len(dets2gts_flat), dtype=np.float32)
+    fn_cum = np.zeros(len(dets2gts_flat), dtype=np.float32)
+    gts_seen = {None}
+    for idx, (_det, _det_score, gt) in enumerate(dets2gts_flat):
+        gt_prev_seen = gt in gts_seen
+        # If detection was assigned to GT that was not previously assigned to anything-> TP
+        tp[idx] = gt is not None and not gt_prev_seen
+        # If detection was not assigned to GT or detection was assigned to a previously assigned GT -> FP
+        fp[idx] = (gt is None) or gt_prev_seen
+        # all gts that have not been assigned yet -> FN
+        gts_seen.add(gt)
+        fn_cum[idx] = gts_total - (len(gts_seen) - 1)
+
+    # dets2gts_flat i sorted by score, therefore we can calculate tp and fp with cumsum
+    tp_cum = np.cumsum(tp)
+    fp_cum = np.cumsum(fp)
+
+    precision = tp_cum / (tp_cum + fp_cum)
+    recall = tp_cum / (tp_cum + fn_cum)
+    average_prevision = np.trapz(precision, recall)
+    return average_prevision, precision, recall
+
+
+# def example():
+#     dboxes = {
+#         "31": [
+#             (AnnotationRect(1, 2, 3, 4), 0.234),
+#             (AnnotationRect(9, 8, 7, 6), 2.431093),
+#         ],
+#         "32": [
+#             (AnnotationRect(1, 2, 3, 4), 0.234),
+#             (AnnotationRect(9, 8, 7, 6), 2.431093),
+#         ],
+#         # ...
+#     }
+#     gboxes = {
+#         "31": [AnnotationRect(0, 3, 2, 3)],
+#         "32": [AnnotationRect(9, 9, 12, 23), AnnotationRect(1, 2, 40, 40)],
+#     }
+#     ap, _, _ = calculate_ap_pr(dboxes, gboxes)
+#     print("ap is", ap)

mmp/a6/main.py

@@ -0,0 +1,40 @@
+from typing import List, Tuple
+import torch
+import numpy as np
+
+from ..a5.model import MmpNet
+from ..a3.annotation import AnnotationRect
+
+
+def batch_inference(
+    model: MmpNet, images: torch.Tensor, device: torch.device, anchor_grid: np.ndarray
+) -> List[List[Tuple[AnnotationRect, float]]]:
+    raise NotImplementedError()
+
+
+def evaluate() -> float:  # feel free to change the arguments
+    """Evaluates a specified model on the whole validation dataset.
+
+    @return: AP for the validation set as a float.
+
+    You decide which arguments this function should receive
+    """
+    raise NotImplementedError()
+
+
+def evaluate_test():  # feel free to change the arguments
+    """Generates predictions on the provided test dataset.
+    This function saves the predictions to a text file.
+
+    You decide which arguments this function should receive
+    """
+    raise NotImplementedError()
+
+
+def main():
+    """Put the surrounding training code here. The code will probably look very similar to last assignment"""
+    raise NotImplementedError()
+
+
+if __name__ == "__main__":
+    main()

mmp/a6/model_output.txt

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mmp/a6/nms.py

@@ -0,0 +1,15 @@
+from typing import List, Sequence, Tuple
+
+from ..a3.annotation import AnnotationRect
+
+
+def non_maximum_suppression(
+    boxes_scores: Sequence[Tuple[AnnotationRect, float]], threshold: float
+) -> List[Tuple[AnnotationRect, float]]:
+    """Exercise 6.1
+    @param boxes_scores: Sequence of tuples of annotations and scores
+    @param threshold: Threshold for NMS
+
+    @return: A list of tuples of the remaining boxes after NMS together with their scores
+    """
+    raise NotImplementedError()