File size: 5,201 Bytes
b944fa1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
import warnings
import annotator.uniformer.mmcv as mmcv
from ..builder import PIPELINES
from .compose import Compose
@PIPELINES.register_module()
class MultiScaleFlipAug(object):
"""Test-time augmentation with multiple scales and flipping.
An example configuration is as followed:
.. code-block::
img_scale=(2048, 1024),
img_ratios=[0.5, 1.0],
flip=True,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
]
After MultiScaleFLipAug with above configuration, the results are wrapped
into lists of the same length as followed:
.. code-block::
dict(
img=[...],
img_shape=[...],
scale=[(1024, 512), (1024, 512), (2048, 1024), (2048, 1024)]
flip=[False, True, False, True]
...
)
Args:
transforms (list[dict]): Transforms to apply in each augmentation.
img_scale (None | tuple | list[tuple]): Images scales for resizing.
img_ratios (float | list[float]): Image ratios for resizing
flip (bool): Whether apply flip augmentation. Default: False.
flip_direction (str | list[str]): Flip augmentation directions,
options are "horizontal" and "vertical". If flip_direction is list,
multiple flip augmentations will be applied.
It has no effect when flip == False. Default: "horizontal".
"""
def __init__(self,
transforms,
img_scale,
img_ratios=None,
flip=False,
flip_direction='horizontal'):
self.transforms = Compose(transforms)
if img_ratios is not None:
img_ratios = img_ratios if isinstance(img_ratios,
list) else [img_ratios]
assert mmcv.is_list_of(img_ratios, float)
if img_scale is None:
# mode 1: given img_scale=None and a range of image ratio
self.img_scale = None
assert mmcv.is_list_of(img_ratios, float)
elif isinstance(img_scale, tuple) and mmcv.is_list_of(
img_ratios, float):
assert len(img_scale) == 2
# mode 2: given a scale and a range of image ratio
self.img_scale = [(int(img_scale[0] * ratio),
int(img_scale[1] * ratio))
for ratio in img_ratios]
else:
# mode 3: given multiple scales
self.img_scale = img_scale if isinstance(img_scale,
list) else [img_scale]
assert mmcv.is_list_of(self.img_scale, tuple) or self.img_scale is None
self.flip = flip
self.img_ratios = img_ratios
self.flip_direction = flip_direction if isinstance(
flip_direction, list) else [flip_direction]
assert mmcv.is_list_of(self.flip_direction, str)
if not self.flip and self.flip_direction != ['horizontal']:
warnings.warn(
'flip_direction has no effect when flip is set to False')
if (self.flip
and not any([t['type'] == 'RandomFlip' for t in transforms])):
warnings.warn(
'flip has no effect when RandomFlip is not in transforms')
def __call__(self, results):
"""Call function to apply test time augment transforms on results.
Args:
results (dict): Result dict contains the data to transform.
Returns:
dict[str: list]: The augmented data, where each value is wrapped
into a list.
"""
aug_data = []
if self.img_scale is None and mmcv.is_list_of(self.img_ratios, float):
h, w = results['img'].shape[:2]
img_scale = [(int(w * ratio), int(h * ratio))
for ratio in self.img_ratios]
else:
img_scale = self.img_scale
flip_aug = [False, True] if self.flip else [False]
for scale in img_scale:
for flip in flip_aug:
for direction in self.flip_direction:
_results = results.copy()
_results['scale'] = scale
_results['flip'] = flip
_results['flip_direction'] = direction
data = self.transforms(_results)
aug_data.append(data)
# list of dict to dict of list
aug_data_dict = {key: [] for key in aug_data[0]}
for data in aug_data:
for key, val in data.items():
aug_data_dict[key].append(val)
return aug_data_dict
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(transforms={self.transforms}, '
repr_str += f'img_scale={self.img_scale}, flip={self.flip})'
repr_str += f'flip_direction={self.flip_direction}'
return repr_str
|