From e79d73faec5783be8321ef2b6f05b04f8ffaa382 Mon Sep 17 00:00:00 2001 From: Bobholamovic Date: Tue, 13 Sep 2022 21:37:20 +0800 Subject: [PATCH] permute->F.permute --- paddlers/transforms/operators.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/paddlers/transforms/operators.py b/paddlers/transforms/operators.py index edd13ae..a902d74 100644 --- a/paddlers/transforms/operators.py +++ b/paddlers/transforms/operators.py @@ -1911,11 +1911,11 @@ class _Permute(Transform): super(_Permute, self).__init__() def apply(self, sample): - sample['image'] = permute(sample['image'], False) + sample['image'] = F.permute(sample['image'], False) if 'image2' in sample: - sample['image2'] = permute(sample['image2'], False) + sample['image2'] = F.permute(sample['image2'], False) if 'target' in sample: - sample['target'] = permute(sample['target'], False) + sample['target'] = F.permute(sample['target'], False) return sample @@ -2046,7 +2046,7 @@ class ArrangeSegmenter(Arrange): mask = sample['mask'] mask = mask.astype('int64') - image = permute(sample['image'], False) + image = F.permute(sample['image'], False) if self.mode == 'train': return image, mask if self.mode == 'eval': @@ -2061,8 +2061,8 @@ class ArrangeChangeDetector(Arrange): mask = sample['mask'] mask = mask.astype('int64') - image_t1 = permute(sample['image'], False) - image_t2 = permute(sample['image2'], False) + image_t1 = F.permute(sample['image'], False) + image_t2 = F.permute(sample['image2'], False) if self.mode == 'train': masks = [mask] if 'aux_masks' in sample: @@ -2079,7 +2079,7 @@ class ArrangeChangeDetector(Arrange): class ArrangeClassifier(Arrange): def apply(self, sample): - image = permute(sample['image'], False) + image = F.permute(sample['image'], False) if self.mode in ['train', 'eval']: return image, sample['label'] else: @@ -2096,8 +2096,8 @@ class ArrangeDetector(Arrange): class ArrangeRestorer(Arrange): def apply(self, sample): if 'target' in sample: - target = permute(sample['target'], False) - image = permute(sample['image'], False) + target = F.permute(sample['target'], False) + image = F.permute(sample['image'], False) if self.mode == 'train': return image, target if self.mode == 'eval':