pre-commit

Striveworks · Dec 12, 2024 · f07a71e · f07a71e
1 parent 2f3ccab
commit f07a71e
Show file tree

Hide file tree

Showing 8 changed files with 38 additions and 38 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -29,14 +29,13 @@ repos:
         args: [--line-length=79]
 
   - repo: https://github.com/RobertCraigie/pyright-python
-    rev: v1.1.376
+    rev: v1.1.390
     hooks:
       - id: pyright
         additional_dependencies: [
             "numpy",
             "pytest",
             "python-dotenv",
-            "importlib_metadata; python_version < '3.8'",
             "tqdm",
             "packaging",
             "openai",

diff --git a/src/pyproject.toml b/src/pyproject.toml
@@ -6,15 +6,12 @@ readme = "README.md"
 requires-python = ">=3.10"
 license = { file = "LICENSE" }
 dependencies = [
+    "numpy",
+    "tqdm",
+    "shapely",
     "evaluate",
-    "importlib_metadata; python_version < '3.8'",
     "nltk",
-    "numpy",
-    "Pillow >= 9.1.0",
-    "requests",
     "rouge_score",
-    "shapely",
-    "tqdm",
 ]
 
 [project.urls]

diff --git a/src/valor_lite/classification/computation.py b/src/valor_lite/classification/computation.py
@@ -212,7 +212,7 @@ def _count_with_examples(
     data: NDArray[np.float64],
     unique_idx: int | list[int],
     label_idx: int | list[int],
-) -> tuple[NDArray[np.float64], NDArray[np.int32], NDArray[np.int32]]:
+) -> tuple[NDArray[np.float64], NDArray[np.int32], NDArray[np.intp]]:
     """
     Helper function for counting occurences of unique detailed pairs.
 
@@ -231,7 +231,7 @@ def _count_with_examples(
         Examples drawn from the data input.
     NDArray[np.int32]
         Unique label indices.
-    NDArray[np.int32]
+    NDArray[np.intp]
         Counts for each unique label index.
     """
     unique_rows, indices = np.unique(
@@ -288,12 +288,14 @@ def compute_confusion_matrix(
     n_labels = label_metadata.shape[0]
     n_scores = score_thresholds.shape[0]
 
-    confusion_matrix = -1 * np.ones(
+    confusion_matrix = np.full(
         (n_scores, n_labels, n_labels, 2 * n_examples + 1),
-        dtype=np.float32,
+        fill_value=-1.0,
+        dtype=np.float64,
     )
-    unmatched_ground_truths = -1 * np.ones(
+    unmatched_ground_truths = np.full(
         (n_scores, n_labels, n_examples + 1),
+        fill_value=-1,
         dtype=np.int32,
     )
 

diff --git a/src/valor_lite/classification/manager.py b/src/valor_lite/classification/manager.py
@@ -39,7 +39,7 @@
 
 @dataclass
 class Filter:
-    indices: NDArray[np.int32]
+    indices: NDArray[np.intp]
     label_metadata: NDArray[np.int32]
     n_datums: int
 
@@ -170,8 +170,7 @@ def create_filter(
         label_metadata_per_datum = self._label_metadata_per_datum.copy()
         label_metadata_per_datum[:, ~mask] = 0
 
-        label_metadata = np.zeros_like(self._label_metadata, dtype=np.int32)
-        label_metadata = np.transpose(
+        label_metadata: NDArray[np.int32] = np.transpose(
             np.sum(
                 label_metadata_per_datum,
                 axis=1,

diff --git a/src/valor_lite/object_detection/computation.py b/src/valor_lite/object_detection/computation.py
@@ -381,9 +381,9 @@ def compute_precion_recall(
             _, indices_gt_unique = np.unique(
                 tp_candidates[:, [0, 1, 4]], axis=0, return_index=True
             )
-            mask_gt_unique = np.zeros(tp_candidates.shape[0], dtype=bool)
+            mask_gt_unique = np.zeros(tp_candidates.shape[0], dtype=np.bool_)
             mask_gt_unique[indices_gt_unique] = True
-            true_positives_mask = np.zeros(n_rows, dtype=bool)
+            true_positives_mask = np.zeros(n_rows, dtype=np.bool_)
             true_positives_mask[mask_tp_inner] = mask_gt_unique
 
             # calculate intermediates
@@ -452,9 +452,9 @@ def compute_precion_recall(
         _, indices_gt_unique = np.unique(
             tp_candidates[:, [0, 1, 4]], axis=0, return_index=True
         )
-        mask_gt_unique = np.zeros(tp_candidates.shape[0], dtype=bool)
+        mask_gt_unique = np.zeros(tp_candidates.shape[0], dtype=np.bool_)
         mask_gt_unique[indices_gt_unique] = True
-        true_positives_mask = np.zeros(n_rows, dtype=bool)
+        true_positives_mask = np.zeros(n_rows, dtype=np.bool_)
         true_positives_mask[mask_tp_outer] = mask_gt_unique
 
         # count running tp and total for AP
@@ -501,8 +501,8 @@ def compute_precion_recall(
         )
 
     # calculate average precision
-    running_max_precision = np.zeros((n_ious, n_labels))
-    running_max_score = np.zeros((n_labels))
+    running_max_precision = np.zeros((n_ious, n_labels), dtype=np.float64)
+    running_max_score = np.zeros((n_labels), dtype=np.float64)
     for recall in range(100, -1, -1):
 
         # running max precision
@@ -528,8 +528,12 @@ def compute_precion_recall(
 
     # calculate mAP and mAR
     if unique_pd_labels.size > 0:
-        mAP = average_precision[:, unique_pd_labels].mean(axis=1)
-        mAR = average_recall[:, unique_pd_labels].mean(axis=1)
+        mAP: NDArray[np.float64] = average_precision[:, unique_pd_labels].mean(
+            axis=1
+        )
+        mAR: NDArray[np.float64] = average_recall[:, unique_pd_labels].mean(
+            axis=1
+        )
     else:
         mAP = np.zeros(n_ious, dtype=np.float64)
         mAR = np.zeros(n_scores, dtype=np.float64)
@@ -561,14 +565,14 @@ def compute_precion_recall(
         accuracy,
         counts,
         pr_curve,
-    )
+    )  # type: ignore[reportReturnType]
 
 
 def _count_with_examples(
     data: NDArray[np.float64],
     unique_idx: int | list[int],
     label_idx: int | list[int],
-) -> tuple[NDArray[np.float64], NDArray[np.int32], NDArray[np.int32]]:
+) -> tuple[NDArray[np.float64], NDArray[np.int32], NDArray[np.intp]]:
     """
     Helper function for counting occurences of unique detailed pairs.
 
@@ -587,7 +591,7 @@ def _count_with_examples(
         Examples drawn from the data input.
     NDArray[np.int32]
         Unique label indices.
-    NDArray[np.int32]
+    NDArray[np.intp]
         Counts for each unique label index.
     """
     unique_rows, indices = np.unique(
@@ -681,12 +685,12 @@ def compute_confusion_matrix(
     confusion_matrix = -1 * np.ones(
         # (datum idx, gt idx, pd idx, pd score) * n_examples + count
         (n_ious, n_scores, n_labels, n_labels, 4 * n_examples + 1),
-        dtype=np.float32,
+        dtype=np.float64,
     )
     unmatched_predictions = -1 * np.ones(
         # (datum idx, pd idx, pd score) * n_examples + count
         (n_ious, n_scores, n_labels, 3 * n_examples + 1),
-        dtype=np.float32,
+        dtype=np.float64,
     )
     unmatched_ground_truths = -1 * np.ones(
         # (datum idx, gt idx) * n_examples + count
@@ -907,4 +911,4 @@ def compute_confusion_matrix(
         confusion_matrix,
         unmatched_predictions,
         unmatched_ground_truths,
-    )
+    )  # type: ignore[reportReturnType]
diff --git a/src/valor_lite/object_detection/manager.py b/src/valor_lite/object_detection/manager.py
@@ -43,8 +43,8 @@
 
 @dataclass
 class Filter:
-    ranked_indices: NDArray[np.int32]
-    detailed_indices: NDArray[np.int32]
+    ranked_indices: NDArray[np.intp]
+    detailed_indices: NDArray[np.intp]
     label_metadata: NDArray[np.int32]
 
 
@@ -570,7 +570,8 @@ def add_bounding_boxes(
                         [gt.extrema, pd.extrema]
                         for pd in detection.predictions
                         for gt in detection.groundtruths
-                    ]
+                    ],
+                    dtype=np.float64,
                 )
             ).reshape(len(detection.predictions), len(detection.groundtruths))
             for detection in detections

diff --git a/src/valor_lite/object_detection/utilities.py b/src/valor_lite/object_detection/utilities.py
@@ -137,10 +137,8 @@ def unpack_precision_recall_into_metric_lists(
 
     metrics[MetricType.PrecisionRecallCurve] = [
         Metric.precision_recall_curve(
-            precisions=pr_curves[iou_idx, label_idx, :, 0]
-            .astype(float)
-            .tolist(),
-            scores=pr_curves[iou_idx, label_idx, :, 1].astype(float).tolist(),
+            precisions=pr_curves[iou_idx, label_idx, :, 0].tolist(),  # type: ignore[reportArgumentType]
+            scores=pr_curves[iou_idx, label_idx, :, 1].tolist(),  # type: ignore[reportArgumentType]
             iou_threshold=iou_threshold,
             label=label,
         )

diff --git a/src/valor_lite/semantic_segmentation/manager.py b/src/valor_lite/semantic_segmentation/manager.py
@@ -38,7 +38,7 @@
 
 @dataclass
 class Filter:
-    indices: NDArray[np.int32]
+    indices: NDArray[np.intp]
     label_metadata: NDArray[np.int32]
     n_pixels: int