Fix modules

detection_metrics/pycocotools/coco.py → coco.py

@@ -54,6 +54,7 @@ __version__ = "2.0"
 import copy
 import itertools
 import json
+
 # from . import mask as maskUtils
 import os
 import sys

detection_metrics/coco_evaluate.py → coco_evaluate.py

@@ -6,10 +6,14 @@ from typing import Dict, List, Union
 import numpy as np
 import torch
 
-from detection_metrics.pycocotools.coco import COCO
-from detection_metrics.pycocotools.cocoeval import COCOeval
-from detection_metrics.utils import (_TYPING_BOX, _TYPING_PREDICTIONS, convert_to_xywh,
-                       create_common_coco_eval)
+from .coco import COCO
+from .cocoeval import COCOeval
+from .utils import (
+    _TYPING_BOX,
+    _TYPING_PREDICTIONS,
+    convert_to_xywh,
+    create_common_coco_eval,
+)
 
 _SUPPORTED_TYPES = ["bbox"]
 
@@ -64,7 +68,6 @@ class COCOEvaluator(object):
             eval_imgs = coco_eval.evaluate()
             self.eval_imgs[iou_type].append(eval_imgs)
 
-    
     def synchronize_between_processes(self) -> None:
         """
         Synchronizes evaluation images between processes.

detection_metrics.py

@@ -4,10 +4,9 @@ import datasets
 import torch
 import evaluate
 import json
-from tqdm import tqdm
-from detection_metrics.pycocotools.coco import COCO 
-from detection_metrics.coco_evaluate import COCOEvaluator 
-from detection_metrics.utils import _TYPING_PREDICTION, _TYPING_REFERENCE
+from .coco import COCO
+from .coco_evaluate import COCOEvaluator
+from .utils import _TYPING_PREDICTION, _TYPING_REFERENCE
 
 _DESCRIPTION = "This class evaluates object detection models using the COCO dataset \
     and its evaluation metrics."
@@ -37,6 +36,7 @@ Args:
     **kwargs: Additional keyword arguments forwarded to evaluate.Metrics.
 """
 
+
 class EvaluateObjectDetection(evaluate.Metric):
     """
     Class for evaluating object detection models.
@@ -73,8 +73,10 @@ class EvaluateObjectDetection(evaluate.Metric):
         self.coco_evaluator.coco_eval["bbox"].cocoGt.cats = cats
         self.coco_evaluator.coco_gt.cats = cats
         self.coco_evaluator.coco_gt.dataset["categories"] = list(cats.values())
-        self.coco_evaluator.coco_eval["bbox"].params.catIds = [c["id"] for c in cats.values()]
-        
+        self.coco_evaluator.coco_eval["bbox"].params.catIds = [
+            c["id"] for c in cats.values()
+        ]
+
     def _info(self):
         """
         Returns the MetricInfo object with information about the module.
@@ -147,15 +149,15 @@ class EvaluateObjectDetection(evaluate.Metric):
         for prediction in predictions:
             ret.append({k: v for k, v in prediction.items() if k in required})
         return ret
-    
+
     def _clear_references(self, references):
         required = [""]
         ret = []
         for ref in references:
             ret.append({k: v for k, v in ref.items() if k in required})
         return ret
-                
-    def add(self, *, prediction = None, reference = None, **kwargs):
+
+    def add(self, *, prediction=None, reference=None, **kwargs):
         """
         Preprocesses the predictions and references and calls the parent class function.
 
@@ -167,13 +169,15 @@ class EvaluateObjectDetection(evaluate.Metric):
         if prediction is not None:
             prediction = self._clear_predictions(prediction)
             prediction = self._preprocess(prediction)
-        
+
         res = {}  # {image_id} : prediction
         for output, target in zip(prediction, reference):
             res[target["image_id"][0]] = output
         self.coco_evaluator.update(res)
 
-        super(evaluate.Metric, self).add(prediction=prediction, references=reference, **kwargs)
+        super(evaluate.Metric, self).add(
+            prediction=prediction, references=reference, **kwargs
+        )
 
     def _compute(
         self,
@@ -192,12 +196,12 @@ class EvaluateObjectDetection(evaluate.Metric):
         """
         print("Synchronizing processes")
         self.coco_evaluator.synchronize_between_processes()
-        
+
         print("Accumulating values")
         self.coco_evaluator.accumulate()
-        
+
         print("Summarizing results")
         self.coco_evaluator.summarize()
-        
+
         stats = self.coco_evaluator.get_results()
         return stats

detection_metrics/pycocotools/mask.py → mask.py

@@ -1,6 +1,6 @@
-__author__ = 'tsungyi'
+__author__ = "tsungyi"
 
-from detection_metrics.pycocotools import _mask
+from pycocotools import _mask
 
 # Interface for manipulating masks stored in RLE format.
 #
@@ -73,22 +73,25 @@ from detection_metrics.pycocotools import _mask
 # Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
 # Licensed under the Simplified BSD License [see coco/license.txt]
 
-iou         = _mask.iou
-merge       = _mask.merge
+iou = _mask.iou
+merge = _mask.merge
 frPyObjects = _mask.frPyObjects
 
+
 def encode(bimask):
     if len(bimask.shape) == 3:
         return _mask.encode(bimask)
     elif len(bimask.shape) == 2:
         h, w = bimask.shape
-        return _mask.encode(bimask.reshape((h, w, 1), order='F'))[0]
+        return _mask.encode(bimask.reshape((h, w, 1), order="F"))[0]
+
 
 def decode(rleObjs):
     if type(rleObjs) == list:
         return _mask.decode(rleObjs)
     else:
-        return _mask.decode([rleObjs])[:,:,0]
+        return _mask.decode([rleObjs])[:, :, 0]
+
 
 def area(rleObjs):
     if type(rleObjs) == list:
@@ -96,8 +99,9 @@ def area(rleObjs):
     else:
         return _mask.area([rleObjs])[0]
 
+
 def toBbox(rleObjs):
     if type(rleObjs) == list:
         return _mask.toBbox(rleObjs)
     else:
-        return _mask.toBbox([rleObjs])[0]
+        return _mask.toBbox([rleObjs])[0]

detection_metrics/utils.py → utils.py

@@ -1,13 +1,12 @@
 import copy
 import pickle
 from typing import Dict, List, Tuple, Union
-from tqdm import tqdm
 import numpy as np
 import torch
 import torch.distributed as dist
 from datasets import Dataset
 
-from detection_metrics.pycocotools.cocoeval import COCOeval
+from .cocoeval import COCOeval
 
 # Typings
 _TYPING_BOX = Tuple[float, float, float, float]
@@ -19,6 +18,7 @@ _TYPING_PREDICTION = Dict[str, _TYPING_PRED_REF]
 _TYPING_REFERENCE = Dict[str, _TYPING_PRED_REF]
 _TYPING_PREDICTIONS = Dict[int, _TYPING_PREDICTION]
 
+
 def convert_to_xywh(boxes: torch.Tensor) -> torch.Tensor:
     """
     Convert bounding boxes from (xmin, ymin, xmax, ymax) format to (x, y, width, height) format.