revise fast test & fix aug test bug

mmdet/models/detectors/fast_rcnn.py

@@ -14,12 +14,33 @@ class FastRCNN(TwoStageDetector):
                  mask_head=None,
                  pretrained=None):
         super(FastRCNN, self).__init__(
-                    backbone=backbone,
-                    neck=neck,
-                    bbox_roi_extractor=bbox_roi_extractor,
-                    bbox_head=bbox_head,
-                    train_cfg=train_cfg,
-                    test_cfg=test_cfg,
-                    mask_roi_extractor=mask_roi_extractor,
-                    mask_head=mask_head,
-                    pretrained=pretrained)
+            backbone=backbone,
+            neck=neck,
+            bbox_roi_extractor=bbox_roi_extractor,
+            bbox_head=bbox_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            mask_roi_extractor=mask_roi_extractor,
+            mask_head=mask_head,
+            pretrained=pretrained)
+
+    def forward_test(self, imgs, img_metas, proposals, **kwargs):
+        for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:
+            if not isinstance(var, list):
+                raise TypeError('{} must be a list, but got {}'.format(
+                    name, type(var)))
+
+        num_augs = len(imgs)
+        if num_augs != len(img_metas):
+            raise ValueError(
+                'num of augmentations ({}) != num of image meta ({})'.format(
+                    len(imgs), len(img_metas)))
+        # TODO: remove the restriction of imgs_per_gpu == 1 when prepared
+        imgs_per_gpu = imgs[0].size(0)
+        assert imgs_per_gpu == 1
+
+        if num_augs == 1:
+            return self.simple_test(imgs[0], img_metas[0], proposals[0],
+                                    **kwargs)
+        else:
+            return self.aug_test(imgs, img_metas, proposals, **kwargs)

mmdet/models/detectors/test_mixins.py

@@ -135,6 +135,11 @@ class MaskTestMixin(object):
 
             ori_shape = img_metas[0][0]['ori_shape']
             segm_result = self.mask_head.get_seg_masks(
-                merged_masks, det_bboxes, det_labels, self.test_cfg.rcnn,
-                ori_shape)
+                merged_masks,
+                det_bboxes,
+                det_labels,
+                self.test_cfg.rcnn,
+                ori_shape,
+                scale_factor=1.0,
+                rescale=False)
         return segm_result

mmdet/models/detectors/two_stage.py

@@ -147,7 +147,7 @@ class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
 
         proposal_list = self.simple_test_rpn(
             x, img_meta,
-            self.test_cfg.rpn) if proposals is None else proposals[0]
+            self.test_cfg.rpn) if proposals is None else proposals
 
         det_bboxes, det_labels = self.simple_test_bboxes(
             x, img_meta, proposal_list, self.test_cfg.rcnn, rescale=rescale)