From 4e1fd9bdbd9d25924bead494c46bb416f1be8d52 Mon Sep 17 00:00:00 2001
From: Kai Chen <chenkaidev@gmail.com>
Date: Mon, 26 Nov 2018 19:00:50 +0800
Subject: [PATCH] allow test multi-stage results
---
mmdet/models/detectors/cascade_rcnn.py | 21 ++++++++++++++-------
tools/test.py | 15 ++++++++++++---
2 files changed, 26 insertions(+), 10 deletions(-)
diff --git a/mmdet/models/detectors/cascade_rcnn.py b/mmdet/models/detectors/cascade_rcnn.py
index 16ad5fe..91d3eaf 100644
--- a/mmdet/models/detectors/cascade_rcnn.py
+++ b/mmdet/models/detectors/cascade_rcnn.py
@@ -248,7 +248,7 @@ class CascadeRCNN(BaseDetector, RPNTestMixin):
rois = bbox_head.regress_by_class(rois, bbox_label, bbox_pred,
img_meta[0])
- cls_score = sum(ms_scores) / len(ms_scores)
+ cls_score = sum(ms_scores) / self.num_stages
det_bboxes, det_labels = self.bbox_head[-1].get_det_bboxes(
rois,
cls_score,
@@ -286,14 +286,21 @@ class CascadeRCNN(BaseDetector, RPNTestMixin):
ms_segm_result['ensemble'] = segm_result
if not self.test_cfg.keep_all_stages:
- ms_bbox_result = ms_bbox_result['ensemble']
if self.with_mask:
- ms_segm_result = ms_segm_result['ensemble']
-
- if not self.with_mask:
- return ms_bbox_result
+ results = (ms_bbox_result['ensemble'],
+ ms_segm_result['ensemble'])
+ else:
+ results = ms_bbox_result['ensemble']
else:
- return ms_bbox_result, ms_segm_result
+ if self.with_mask:
+ results = {
+ stage: (ms_bbox_result[stage], ms_segm_result[stage])
+ for stage in ms_bbox_result
+ }
+ else:
+ results = ms_bbox_result
+
+ return results
def aug_test(self, img, img_meta, proposals=None, rescale=False):
raise NotImplementedError
diff --git a/tools/test.py b/tools/test.py
index dc8dc5e..9a599c2 100644
--- a/tools/test.py
+++ b/tools/test.py
@@ -104,10 +104,19 @@ def main():
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = args.out
+ coco_eval(result_file, eval_types, dataset.coco)
else:
- result_file = args.out + '.json'
- results2json(dataset, outputs, result_file)
- coco_eval(result_file, eval_types, dataset.coco)
+ if not isinstance(outputs[0], dict):
+ result_file = args.out + '.json'
+ results2json(dataset, outputs, result_file)
+ coco_eval(result_file, eval_types, dataset.coco)
+ else:
+ for name in outputs[0]:
+ print('\nEvaluating {}'.format(name))
+ outputs_ = [out[name] for out in outputs]
+ result_file = args.out + '.{}.json'.format(name)
+ results2json(dataset, outputs_, result_file)
+ coco_eval(result_file, eval_types, dataset.coco)
if __name__ == '__main__':
--
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