From 1f3e27344b59788fa7debb174efdfc041b3612e2 Mon Sep 17 00:00:00 2001
From: Wenwei Zhang <40779233+ZwwWayne@users.noreply.github.com>
Date: Fri, 25 Oct 2019 19:47:37 +0800
Subject: [PATCH] Support to view AP for each class (#1549)
* also support to view ap
* change string format
* eval class_wise in coco_eval
* reformat
* class_wise API from detectron
* reformat
* change code source
* reformat, use terminaltable
---
mmdet/core/evaluation/coco_utils.py | 39 ++++++++++++++++++++++++++++-
tools/coco_eval.py | 4 ++-
2 files changed, 41 insertions(+), 2 deletions(-)
diff --git a/mmdet/core/evaluation/coco_utils.py b/mmdet/core/evaluation/coco_utils.py
index f6f5ac0..ef44940 100644
--- a/mmdet/core/evaluation/coco_utils.py
+++ b/mmdet/core/evaluation/coco_utils.py
@@ -1,12 +1,19 @@
+import itertools
+
import mmcv
import numpy as np
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
+from terminaltables import AsciiTable
from .recall import eval_recalls
-def coco_eval(result_files, result_types, coco, max_dets=(100, 300, 1000)):
+def coco_eval(result_files,
+ result_types,
+ coco,
+ max_dets=(100, 300, 1000),
+ classwise=False):
for res_type in result_types:
assert res_type in [
'proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'
@@ -43,6 +50,36 @@ def coco_eval(result_files, result_types, coco, max_dets=(100, 300, 1000)):
cocoEval.accumulate()
cocoEval.summarize()
+ if classwise:
+ # Compute per-category AP
+ # from https://github.com/facebookresearch/detectron2/blob/03064eb5bafe4a3e5750cc7a16672daf5afe8435/detectron2/evaluation/coco_evaluation.py#L259-L283 # noqa
+ precisions = cocoEval.eval['precision']
+ catIds = coco.getCatIds()
+ # precision has dims (iou, recall, cls, area range, max dets)
+ assert len(catIds) == precisions.shape[2]
+
+ results_per_category = []
+ for idx, catId in enumerate(catIds):
+ # area range index 0: all area ranges
+ # max dets index -1: typically 100 per image
+ nm = coco.loadCats(catId)[0]
+ precision = precisions[:, :, idx, 0, -1]
+ precision = precision[precision > -1]
+ ap = np.mean(precision) if precision.size else float('nan')
+ results_per_category.append(
+ ('{}'.format(nm['name']),
+ '{:0.3f}'.format(float(ap * 100))))
+
+ N_COLS = min(6, len(results_per_category) * 2)
+ results_flatten = list(itertools.chain(*results_per_category))
+ headers = ['category', 'AP'] * (N_COLS // 2)
+ results_2d = itertools.zip_longest(
+ *[results_flatten[i::N_COLS] for i in range(N_COLS)])
+ table_data = [headers]
+ table_data += [result for result in results_2d]
+ table = AsciiTable(table_data)
+ print(table.table)
+
def fast_eval_recall(results,
coco,
diff --git a/tools/coco_eval.py b/tools/coco_eval.py
index 65e114c..bc3c96b 100644
--- a/tools/coco_eval.py
+++ b/tools/coco_eval.py
@@ -20,8 +20,10 @@ def main():
nargs='+',
default=[100, 300, 1000],
help='proposal numbers, only used for recall evaluation')
+ parser.add_argument(
+ '--classwise', action='store_true', help='whether eval class wise ap')
args = parser.parse_args()
- coco_eval(args.result, args.types, args.ann, args.max_dets)
+ coco_eval(args.result, args.types, args.ann, args.max_dets, args.classwise)
if __name__ == '__main__':
--
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