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[Fix] Fix scale factor order of `LetterResize` and `YOLOv5KeepRatioResize` (#305) 

* Fix bug

* Fix bug

* Fix lint

* Fix scale from hw -> wh

* Fix scale from hw -> wh
pull/294/head
HinGwenWoong 2022-11-21 19:44:33 +08:00 committed by GitHub
parent 82d1680b4d
commit 1773439b83
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2 changed files with 37 additions and 30 deletions
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10
mmyolo/datasets/transforms/transforms.py
View File

@ -104,8 +104,7 @@ class YOLOv5KeepRatioResize(MMDET_Resize):
resized_h, resized_w = image.shape[:2]
scale_ratio = resized_h / original_h
scale_factor = np.array([scale_ratio, scale_ratio],
dtype=np.float32)
scale_factor = (scale_ratio, scale_ratio)
results['img'] = image
results['img_shape'] = image.shape[:2]
@ -208,10 +207,13 @@ class LetterResize(MMDET_Resize):
interpolation=self.interpolation,
backend=self.backend)
scale_factor = np.array([ratio[0], ratio[1]], dtype=np.float32)
scale_factor = (ratio[1], ratio[0]) # mmcv scale factor is (w, h)
if 'scale_factor' in results:
results['scale_factor'] = results['scale_factor'] * scale_factor
results['scale_factor'] = (results['scale_factor'][0] *
scale_factor[0],
results['scale_factor'][1] *
scale_factor[1])
else:
results['scale_factor'] = scale_factor

57
tests/test_datasets/test_transforms/test_transforms.py
View File

@ -27,59 +27,62 @@ class TestLetterResize(unittest.TestCase):
self.data_info1 = dict(
img=np.random.random((300, 400, 3)),
gt_bboxes=np.array([[0, 0, 150, 150]], dtype=np.float32),
batch_shape=np.array([460, 672], dtype=np.int64),
batch_shape=np.array([192, 672], dtype=np.int64),
gt_masks=BitmapMasks(rng.rand(1, 300, 400), height=300, width=400))
self.data_info2 = dict(
img=np.random.random((300, 400, 3)),
gt_bboxes=np.array([[0, 0, 150, 150]], dtype=np.float32))
self.data_info3 = dict(
img=np.random.random((300, 400, 3)),
batch_shape=np.array([460, 672], dtype=np.int64))
batch_shape=np.array([192, 672], dtype=np.int64))
self.data_info4 = dict(img=np.random.random((300, 400, 3)))
def test_letter_resize(self):
# Test allow_scale_up
transform = LetterResize(scale=(640, 640), allow_scale_up=False)
results = transform(copy.deepcopy(self.data_info1))
self.assertEqual(results['img_shape'], (460, 672, 3))
self.assertEqual(results['img_shape'], (192, 672, 3))
self.assertTrue(
(results['gt_bboxes'] == np.array([[136., 80., 286.,
230.]])).all())
self.assertTrue((results['batch_shape'] == np.array([460, 672])).all())
(results['gt_bboxes'] == np.array([[208., 0., 304., 96.]])).all())
self.assertTrue((results['batch_shape'] == np.array([192, 672])).all())
self.assertTrue((results['pad_param'] == np.array([0., 0., 208.,
208.])).all())
self.assertTrue(
(results['pad_param'] == np.array([80., 80., 136., 136.])).all())
self.assertTrue((results['scale_factor'] <= 1.).all())
(np.array(results['scale_factor'], dtype=np.float32) <= 1.).all())
# Test pad_val
transform = LetterResize(scale=(640, 640), pad_val=dict(img=144))
results = transform(copy.deepcopy(self.data_info1))
self.assertEqual(results['img_shape'], (460, 672, 3))
self.assertEqual(results['img_shape'], (192, 672, 3))
self.assertTrue(
(results['gt_bboxes'] == np.array([[29., 0., 259., 230.]])).all())
self.assertTrue((results['batch_shape'] == np.array([460, 672])).all())
self.assertTrue((results['pad_param'] == np.array([0., 0., 29.,
30.])).all())
self.assertTrue((results['scale_factor'] > 1.).all())
(results['gt_bboxes'] == np.array([[208., 0., 304., 96.]])).all())
self.assertTrue((results['batch_shape'] == np.array([192, 672])).all())
self.assertTrue((results['pad_param'] == np.array([0., 0., 208.,
208.])).all())
self.assertTrue(
(np.array(results['scale_factor'], dtype=np.float32) <= 1.).all())
# Test use_mini_pad
transform = LetterResize(scale=(640, 640), use_mini_pad=True)
results = transform(copy.deepcopy(self.data_info1))
self.assertEqual(results['img_shape'], (460, 640, 3))
self.assertEqual(results['img_shape'], (192, 256, 3))
self.assertTrue((results['gt_bboxes'] == np.array([[0., 0., 96.,
96.]])).all())
self.assertTrue((results['batch_shape'] == np.array([192, 672])).all())
self.assertTrue((results['pad_param'] == np.array([0., 0., 0.,
0.])).all())
self.assertTrue(
(results['gt_bboxes'] == np.array([[13., 0., 243., 230.]])).all())
self.assertTrue((results['batch_shape'] == np.array([460, 672])).all())
self.assertTrue((results['pad_param'] == np.array([0., 0., 13.,
14.])).all())
self.assertTrue((results['scale_factor'] > 1.).all())
(np.array(results['scale_factor'], dtype=np.float32) <= 1.).all())
# Test stretch_only
transform = LetterResize(scale=(640, 640), stretch_only=True)
results = transform(copy.deepcopy(self.data_info1))
self.assertEqual(results['img_shape'], (460, 672, 3))
self.assertEqual(results['img_shape'], (192, 672, 3))
self.assertTrue((results['gt_bboxes'] == np.array(
[[0., 0., 230., 251.99998474121094]])).all())
self.assertTrue((results['batch_shape'] == np.array([460, 672])).all())
self.assertTrue((results['pad_param'] == np.array([0, 0, 0, 0])).all())
[[0., 0., 251.99998474121094, 96.]])).all())
self.assertTrue((results['batch_shape'] == np.array([192, 672])).all())
self.assertTrue((results['pad_param'] == np.array([0., 0., 0.,
0.])).all())
# Test
transform = LetterResize(scale=(640, 640), pad_val=dict(img=144))
@ -150,13 +153,15 @@ class TestYOLOv5KeepRatioResize(unittest.TestCase):
self.assertEqual(results['img_shape'], (480, 640))
self.assertTrue(
(results['gt_bboxes'] == np.array([[0., 0., 240., 240.]])).all())
self.assertTrue((results['scale_factor'] == 1.6).all())
self.assertTrue((np.array(results['scale_factor'],
dtype=np.float32) == 1.6).all())
# Test only img
transform = YOLOv5KeepRatioResize(scale=(640, 640))
results = transform(copy.deepcopy(self.data_info2))
self.assertEqual(results['img_shape'], (480, 640))
self.assertTrue((results['scale_factor'] == 1.6).all())
self.assertTrue((np.array(results['scale_factor'],
dtype=np.float32) == 1.6).all())
class TestYOLOv5HSVRandomAug(unittest.TestCase):