manual

faster_coco_eval/core/cocoeval.py

@@ -335,13 +335,13 @@ def computeOks(self, imgId: int, catId: int) -> np.ndarray:
         return ious
 
     def evaluateImg(self, imgId, catId, aRng, maxDet):
-        raise DeprecationWarning("COCOeval.evaluateImg deprecated! Use COCOeval_faster.evaluateImg" " instead.")
+        raise DeprecationWarning("COCOeval.evaluateImg deprecated! Use COCOeval_faster.evaluateImg instead.")
 
     def accumulate(self, p=None):
-        raise DeprecationWarning("COCOeval.accumulate deprecated! Use COCOeval_faster.accumulate" " instead.")
+        raise DeprecationWarning("COCOeval.accumulate deprecated! Use COCOeval_faster.accumulate instead.")
 
     def evaluate(self):
-        raise DeprecationWarning("COCOeval.evaluate deprecated! Use COCOeval_faster.evaluate" " instead.")
+        raise DeprecationWarning("COCOeval.evaluate deprecated! Use COCOeval_faster.evaluate instead.")
 
     def _summarize(
         self,
@@ -353,7 +353,7 @@ def _summarize(
         catIds=None,
     ):
         p = self.params
-        iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} {}] =" " {:0.3f}"
+        iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} {}] = {:0.3f}"
 
         freq_str = "catIds={:>3s}".format("all") if self.lvis_style else ""
 
@@ -492,7 +492,7 @@ def _summarizeKps():
         elif iouType == "keypoints":
             summarize = _summarizeKps
         else:
-            ValueError("iouType must be bbox, segm, boundary or keypoints. Get" f" {iouType}")
+            ValueError(f"iouType must be bbox, segm, boundary or keypoints. Get {iouType}")
 
         self.all_stats = summarize()
         self.stats = self.all_stats[:12]

faster_coco_eval/core/faster_eval_api.py

@@ -51,7 +51,7 @@ def evaluate(self):
         elif p.iouType == "keypoints":
             computeIoU = self.computeOks
         else:
-            raise ValueError("p.iouType must be segm, bbox, boundary or keypoints. Get" f" {p.iouType}")
+            raise ValueError(f"p.iouType must be segm, bbox, boundary or keypoints. Get {p.iouType}")
 
         self.ious = {
             (imgId, catId): computeIoU(imgId, catId) for (imgId, catId) in itertools.product(p.imgIds, catIds)

faster_coco_eval/core/mask.py

@@ -83,7 +83,7 @@ def rleToBoundary(
         return _mask.toBoundary([rle], dilation_ratio)[0]
     else:
         if not opencv_available:
-            raise ImportError("OpenCV is not available. Please install OpenCV to use this" " function.")
+            raise ImportError("OpenCV is not available. Please install OpenCV to use this function.")
         return rleToBoundaryCV(rle, dilation_ratio)
 
 

faster_coco_eval/extra/display.py

@@ -116,9 +116,7 @@ def compute_confusion_matrix(self):
         assert self.eval is not None, "Run first self.evaluate()"
 
         if self.useCats:
-            logger.warning(
-                "The calculation may not be accurate. No intersection of" f" classes. useCats={self.useCats}"
-            )
+            logger.warning(f"The calculation may not be accurate. No intersection of classes. useCats={self.useCats}")
 
         y_true = []
         y_pred = []

faster_coco_eval/extra/utils.py

@@ -80,7 +80,7 @@ def convert_ann_rle_to_poly(ann: dict):
     """
     if type(ann["segmentation"]) is dict:
         if not opencv_available:
-            raise Exception("Your dataset needs to be converted to polygons. Install" " **opencv-python** for this.")
+            raise Exception("Your dataset needs to be converted to polygons. Install **opencv-python** for this.")
 
         ann["counts"] = ann["segmentation"]
         ann["segmentation"] = convert_rle_to_poly(ann["segmentation"], ann["bbox"])