Add faster-coco-eval metric

mmdet/evaluation/metrics/coco_metric.py

@@ -18,6 +18,13 @@
 from mmdet.structures.mask import encode_mask_results
 from ..functional import eval_recalls
 
+try:
+    from faster_coco_eval import COCO as FasterCOCO
+    from faster_coco_eval import COCOeval_faster
+except ImportError:
+    FasterCOCO = None
+    COCOeval_faster = None
+
 
 @METRICS.register_module()
 class CocoMetric(BaseMetric):
@@ -64,6 +71,7 @@ class CocoMetric(BaseMetric):
         sort_categories (bool): Whether sort categories in annotations. Only
             used for `Objects365V1Dataset`. Defaults to False.
         use_mp_eval (bool): Whether to use mul-processing evaluation
+        use_faster_coco_eval (bool): Whether to use Faster-COCO-Eval evaluation
     """
     default_prefix: Optional[str] = 'coco'
 
@@ -81,7 +89,8 @@ def __init__(self,
                  collect_device: str = 'cpu',
                  prefix: Optional[str] = None,
                  sort_categories: bool = False,
-                 use_mp_eval: bool = False) -> None:
+                 use_mp_eval: bool = False,
+                 use_faster_coco_eval: bool = False) -> None:
         super().__init__(collect_device=collect_device, prefix=prefix)
         # coco evaluation metrics
         self.metrics = metric if isinstance(metric, list) else [metric]
@@ -96,6 +105,10 @@ def __init__(self,
         self.classwise = classwise
         # whether to use multi processing evaluation, default False
         self.use_mp_eval = use_mp_eval
+        # whether to use Faster Coco Eval, default False
+        self.use_faster_coco_eval = use_faster_coco_eval
+        if FasterCOCO is None:
+            raise RuntimeError('faster-coco-eval is not installed')
 
         # proposal_nums used to compute recall or precision.
         self.proposal_nums = list(proposal_nums)
@@ -127,7 +140,10 @@ def __init__(self,
         if ann_file is not None:
             with get_local_path(
                     ann_file, backend_args=self.backend_args) as local_path:
-                self._coco_api = COCO(local_path)
+                if self.use_faster_coco_eval:
+                    self._coco_api = FasterCOCO(local_path)
+                else:
+                    self._coco_api = COCO(local_path)
                 if sort_categories:
                     # 'categories' list in objects365_train.json and
                     # objects365_val.json is inconsistent, need sort
@@ -410,7 +426,10 @@ def compute_metrics(self, results: list) -> Dict[str, float]:
             logger.info('Converting ground truth to coco format...')
             coco_json_path = self.gt_to_coco_json(
                 gt_dicts=gts, outfile_prefix=outfile_prefix)
-            self._coco_api = COCO(coco_json_path)
+            if self.use_faster_coco_eval:
+                self._coco_api = FasterCOCO(coco_json_path)
+            else:
+                self._coco_api = COCO(coco_json_path)
 
         # handle lazy init
         if self.cat_ids is None:
@@ -468,6 +487,13 @@ def compute_metrics(self, results: list) -> Dict[str, float]:
 
             if self.use_mp_eval:
                 coco_eval = COCOevalMP(self._coco_api, coco_dt, iou_type)
+            elif self.use_faster_coco_eval:
+                coco_eval = COCOeval_faster(
+                    self._coco_api,
+                    coco_dt,
+                    iou_type,
+                    print_function=logger.info,
+                )
             else:
                 coco_eval = COCOeval(self._coco_api, coco_dt, iou_type)
 

requirements/optional.txt

@@ -1,5 +1,6 @@
 cityscapesscripts
 emoji
 fairscale
+faster-coco-eval
 imagecorruptions
 scikit-learn

tests/test_evaluation/test_metrics/test_coco_metric.py

@@ -1,14 +1,21 @@
 import os.path as osp
 import tempfile
+import unittest
 from unittest import TestCase
 
 import numpy as np
 import pycocotools.mask as mask_util
 import torch
 from mmengine.fileio import dump
+from parameterized import parameterized
 
 from mmdet.evaluation import CocoMetric
 
+try:
+    from faster_coco_eval import COCO as FasterCOCO
+except ImportError:
+    FasterCOCO = None
+
 
 class TestCocoMetric(TestCase):
 
@@ -111,7 +118,11 @@ def test_init(self):
         with self.assertRaisesRegex(KeyError, 'metric should be one of'):
             CocoMetric(ann_file=fake_json_file, metric='unknown')
 
-    def test_evaluate(self):
+    @parameterized.expand([False, True])
+    def test_evaluate(self, use_faster_coco_eval):
+        if use_faster_coco_eval and (FasterCOCO is None):
+            return unittest.skip('faster-coco-eval is not installed')
+
         # create dummy data
         fake_json_file = osp.join(self.tmp_dir.name, 'fake_data.json')
         self._create_dummy_coco_json(fake_json_file)
@@ -121,7 +132,9 @@ def test_evaluate(self):
         coco_metric = CocoMetric(
             ann_file=fake_json_file,
             classwise=False,
-            outfile_prefix=f'{self.tmp_dir.name}/test')
+            outfile_prefix=f'{self.tmp_dir.name}/test',
+            use_faster_coco_eval=use_faster_coco_eval,
+        )
         coco_metric.dataset_meta = dict(classes=['car', 'bicycle'])
         coco_metric.process(
             {},
@@ -144,7 +157,9 @@ def test_evaluate(self):
             ann_file=fake_json_file,
             metric=['bbox', 'segm'],
             classwise=False,
-            outfile_prefix=f'{self.tmp_dir.name}/test')
+            outfile_prefix=f'{self.tmp_dir.name}/test',
+            use_faster_coco_eval=use_faster_coco_eval,
+        )
         coco_metric.dataset_meta = dict(classes=['car', 'bicycle'])
         coco_metric.process(
             {},
@@ -174,7 +189,10 @@ def test_evaluate(self):
         with self.assertRaisesRegex(KeyError,
                                     'metric item "invalid" is not supported'):
             coco_metric = CocoMetric(
-                ann_file=fake_json_file, metric_items=['invalid'])
+                ann_file=fake_json_file,
+                metric_items=['invalid'],
+                use_faster_coco_eval=use_faster_coco_eval,
+            )
             coco_metric.dataset_meta = dict(classes=['car', 'bicycle'])
             coco_metric.process({}, [
                 dict(
@@ -184,7 +202,10 @@ def test_evaluate(self):
 
         # test custom metric_items
         coco_metric = CocoMetric(
-            ann_file=fake_json_file, metric_items=['mAP_m'])
+            ann_file=fake_json_file,
+            metric_items=['mAP_m'],
+            use_faster_coco_eval=use_faster_coco_eval,
+        )
         coco_metric.dataset_meta = dict(classes=['car', 'bicycle'])
         coco_metric.process(
             {},
@@ -195,17 +216,24 @@ def test_evaluate(self):
         }
         self.assertDictEqual(eval_results, target)
 
-    def test_classwise_evaluate(self):
+    @parameterized.expand([False, True])
+    def test_classwise_evaluate(self, use_faster_coco_eval):
+        if use_faster_coco_eval and (FasterCOCO is None):
+            return unittest.skip('faster-coco-eval is not installed')
+
         # create dummy data
         fake_json_file = osp.join(self.tmp_dir.name, 'fake_data.json')
         self._create_dummy_coco_json(fake_json_file)
         dummy_pred = self._create_dummy_results()
 
         # test single coco dataset evaluation
         coco_metric = CocoMetric(
-            ann_file=fake_json_file, metric='bbox', classwise=True)
-        # coco_metric1 = CocoMetric(
-        #     ann_file=fake_json_file, metric='bbox', classwise=True)
+            ann_file=fake_json_file,
+            metric='bbox',
+            classwise=True,
+            use_faster_coco_eval=use_faster_coco_eval,
+        )
+
         coco_metric.dataset_meta = dict(classes=['car', 'bicycle'])
         coco_metric.process(
             {},
@@ -223,26 +251,41 @@ def test_classwise_evaluate(self):
         }
         self.assertDictEqual(eval_results, target)
 
-    def test_manually_set_iou_thrs(self):
+    @parameterized.expand([False, True])
+    def test_manually_set_iou_thrs(self, use_faster_coco_eval):
+        if use_faster_coco_eval and (FasterCOCO is None):
+            return unittest.skip('faster-coco-eval is not installed')
+
         # create dummy data
         fake_json_file = osp.join(self.tmp_dir.name, 'fake_data.json')
         self._create_dummy_coco_json(fake_json_file)
 
         # test single coco dataset evaluation
         coco_metric = CocoMetric(
-            ann_file=fake_json_file, metric='bbox', iou_thrs=[0.3, 0.6])
+            ann_file=fake_json_file,
+            metric='bbox',
+            iou_thrs=[0.3, 0.6],
+            use_faster_coco_eval=use_faster_coco_eval,
+        )
         coco_metric.dataset_meta = dict(classes=['car', 'bicycle'])
         self.assertEqual(coco_metric.iou_thrs, [0.3, 0.6])
 
-    def test_fast_eval_recall(self):
+    @parameterized.expand([False, True])
+    def test_fast_eval_recall(self, use_faster_coco_eval):
+        if use_faster_coco_eval and (FasterCOCO is None):
+            return unittest.skip('faster-coco-eval is not installed')
+
         # create dummy data
         fake_json_file = osp.join(self.tmp_dir.name, 'fake_data.json')
         self._create_dummy_coco_json(fake_json_file)
         dummy_pred = self._create_dummy_results()
 
         # test default proposal nums
         coco_metric = CocoMetric(
-            ann_file=fake_json_file, metric='proposal_fast')
+            ann_file=fake_json_file,
+            metric='proposal_fast',
+            use_faster_coco_eval=use_faster_coco_eval,
+        )
         coco_metric.dataset_meta = dict(classes=['car', 'bicycle'])
         coco_metric.process(
             {},
@@ -264,13 +307,21 @@ def test_fast_eval_recall(self):
         target = {'coco/AR@2': 0.5, 'coco/AR@4': 1.0}
         self.assertDictEqual(eval_results, target)
 
-    def test_evaluate_proposal(self):
+    @parameterized.expand([False, True])
+    def test_evaluate_proposal(self, use_faster_coco_eval):
+        if use_faster_coco_eval and (FasterCOCO is None):
+            return unittest.skip('faster-coco-eval is not installed')
+
         # create dummy data
         fake_json_file = osp.join(self.tmp_dir.name, 'fake_data.json')
         self._create_dummy_coco_json(fake_json_file)
         dummy_pred = self._create_dummy_results()
 
-        coco_metric = CocoMetric(ann_file=fake_json_file, metric='proposal')
+        coco_metric = CocoMetric(
+            ann_file=fake_json_file,
+            metric='proposal',
+            use_faster_coco_eval=use_faster_coco_eval,
+        )
         coco_metric.dataset_meta = dict(classes=['car', 'bicycle'])
         coco_metric.process(
             {},
@@ -287,11 +338,19 @@ def test_evaluate_proposal(self):
         }
         self.assertDictEqual(eval_results, target)
 
-    def test_empty_results(self):
+    @parameterized.expand([False, True])
+    def test_empty_results(self, use_faster_coco_eval):
+        if use_faster_coco_eval and (FasterCOCO is None):
+            return unittest.skip('faster-coco-eval is not installed')
+
         # create dummy data
         fake_json_file = osp.join(self.tmp_dir.name, 'fake_data.json')
         self._create_dummy_coco_json(fake_json_file)
-        coco_metric = CocoMetric(ann_file=fake_json_file, metric='bbox')
+        coco_metric = CocoMetric(
+            ann_file=fake_json_file,
+            metric='bbox',
+            use_faster_coco_eval=use_faster_coco_eval,
+        )
         coco_metric.dataset_meta = dict(classes=['car', 'bicycle'])
         bboxes = np.zeros((0, 4))
         labels = np.array([])
@@ -308,7 +367,11 @@ def test_empty_results(self):
         # coco api Index error will be caught
         coco_metric.evaluate(size=1)
 
-    def test_evaluate_without_json(self):
+    @parameterized.expand([False, True])
+    def test_evaluate_without_json(self, use_faster_coco_eval):
+        if use_faster_coco_eval and (FasterCOCO is None):
+            return unittest.skip('faster-coco-eval is not installed')
+
         dummy_pred = self._create_dummy_results()
 
         dummy_mask = np.zeros((10, 10), order='F', dtype=np.uint8)
@@ -340,7 +403,8 @@ def test_evaluate_without_json(self):
             ann_file=None,
             metric=['bbox', 'segm'],
             classwise=False,
-            outfile_prefix=f'{self.tmp_dir.name}/test')
+            outfile_prefix=f'{self.tmp_dir.name}/test',
+            use_faster_coco_eval=use_faster_coco_eval)
         coco_metric.dataset_meta = dict(classes=['car', 'bicycle'])
         coco_metric.process({}, [
             dict(
@@ -373,7 +437,11 @@ def test_evaluate_without_json(self):
         self.assertTrue(
             osp.isfile(osp.join(self.tmp_dir.name, 'test.gt.json')))
 
-    def test_format_only(self):
+    @parameterized.expand([False, True])
+    def test_format_only(self, use_faster_coco_eval):
+        if use_faster_coco_eval and (FasterCOCO is None):
+            return unittest.skip('faster-coco-eval is not installed')
+
         # create dummy data
         fake_json_file = osp.join(self.tmp_dir.name, 'fake_data.json')
         self._create_dummy_coco_json(fake_json_file)
@@ -384,7 +452,8 @@ def test_format_only(self):
                 ann_file=fake_json_file,
                 classwise=False,
                 format_only=True,
-                outfile_prefix=None)
+                outfile_prefix=None,
+                use_faster_coco_eval=use_faster_coco_eval)
 
         coco_metric = CocoMetric(
             ann_file=fake_json_file,