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# Copyright (c) OpenMMLab. All rights reserved.
import random
import mmengine
import numpy as np
from mmcv.transforms import BaseTransform, to_tensor
from mmengine.utils import digit_version
from mmaction.registry import TRANSFORMS
@TRANSFORMS.register_module()
class TorchVisionWrapper(BaseTransform):
"""Torchvision Augmentations, under torchvision.transforms.
Args:
op (str): The name of the torchvision transformation.
"""
def __init__(self, op, **kwargs):
try:
import torchvision
import torchvision.transforms as tv_trans
except ImportError:
raise RuntimeError('Install torchvision to use TorchvisionTrans')
if digit_version(torchvision.__version__) < digit_version('0.8.0'):
raise RuntimeError('The version of torchvision should be at least '
'0.8.0')
trans = getattr(tv_trans, op, None)
assert trans, f'Transform {op} not in torchvision'
self.trans = trans(**kwargs)
def transform(self, results):
"""Perform Torchvision augmentations.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
assert 'imgs' in results
imgs = [x.transpose(2, 0, 1) for x in results['imgs']]
imgs = to_tensor(np.stack(imgs))
imgs = self.trans(imgs).data.numpy()
imgs[imgs > 255] = 255
imgs[imgs < 0] = 0
imgs = imgs.astype(np.uint8)
imgs = [x.transpose(1, 2, 0) for x in imgs]
results['imgs'] = imgs
return results
@TRANSFORMS.register_module()
class PytorchVideoWrapper(BaseTransform):
"""PytorchVideoTrans Augmentations, under pytorchvideo.transforms.
Args:
op (str): The name of the pytorchvideo transformation.
"""
def __init__(self, op, **kwargs):
try:
import pytorchvideo.transforms as ptv_trans
import torch
except ImportError:
raise RuntimeError('Install pytorchvideo to use PytorchVideoTrans')
if digit_version(torch.__version__) < digit_version('1.8.0'):
raise RuntimeError(
'The version of PyTorch should be at least 1.8.0')
trans = getattr(ptv_trans, op, None)
assert trans, f'Transform {op} not in pytorchvideo'
supported_pytorchvideo_trans = ('AugMix', 'RandAugment',
'RandomResizedCrop', 'ShortSideScale',
'RandomShortSideScale')
assert op in supported_pytorchvideo_trans,\
f'PytorchVideo Transform {op} is not supported in MMAction2'
self.trans = trans(**kwargs)
self.op = op
def transform(self, results):
"""Perform PytorchVideoTrans augmentations.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
assert 'imgs' in results
assert 'gt_bboxes' not in results,\
f'PytorchVideo {self.op} doesn\'t support bboxes yet.'
assert 'proposals' not in results,\
f'PytorchVideo {self.op} doesn\'t support bboxes yet.'
if self.op in ('AugMix', 'RandAugment'):
# list[ndarray(h, w, 3)] -> torch.tensor(t, c, h, w)
imgs = [x.transpose(2, 0, 1) for x in results['imgs']]
imgs = to_tensor(np.stack(imgs))
else:
# list[ndarray(h, w, 3)] -> torch.tensor(c, t, h, w)
# uint8 -> float32
imgs = to_tensor((np.stack(results['imgs']).transpose(3, 0, 1, 2) /
255.).astype(np.float32))
imgs = self.trans(imgs).data.numpy()
if self.op in ('AugMix', 'RandAugment'):
imgs[imgs > 255] = 255
imgs[imgs < 0] = 0
imgs = imgs.astype(np.uint8)
# torch.tensor(t, c, h, w) -> list[ndarray(h, w, 3)]
imgs = [x.transpose(1, 2, 0) for x in imgs]
else:
# float32 -> uint8
imgs = imgs * 255
imgs[imgs > 255] = 255
imgs[imgs < 0] = 0
imgs = imgs.astype(np.uint8)
# torch.tensor(c, t, h, w) -> list[ndarray(h, w, 3)]
imgs = [x for x in imgs.transpose(1, 2, 3, 0)]
results['imgs'] = imgs
return results
@TRANSFORMS.register_module()
class ImgAug(BaseTransform):
"""Imgaug augmentation.
Adds custom transformations from imgaug library.
Please visit `https://imgaug.readthedocs.io/en/latest/index.html`
to get more information. Two demo configs could be found in tsn and i3d
config folder.
It's better to use uint8 images as inputs since imgaug works best with
numpy dtype uint8 and isn't well tested with other dtypes. It should be
noted that not all of the augmenters have the same input and output dtype,
which may cause unexpected results.
Required keys are "imgs", "img_shape"(if "gt_bboxes" is not None) and
"modality", added or modified keys are "imgs", "img_shape", "gt_bboxes"
and "proposals".
It is worth mentioning that `Imgaug` will NOT create custom keys like
"interpolation", "crop_bbox", "flip_direction", etc. So when using
`Imgaug` along with other mmaction2 pipelines, we should pay more attention
to required keys.
Two steps to use `Imgaug` pipeline:
1. Create initialization parameter `transforms`. There are three ways
to create `transforms`.
1) string: only support `default` for now.
e.g. `transforms='default'`
2) list[dict]: create a list of augmenters by a list of dicts, each
dict corresponds to one augmenter. Every dict MUST contain a key
named `type`. `type` should be a string(iaa.Augmenter's name) or
an iaa.Augmenter subclass.
e.g. `transforms=[dict(type='Rotate', rotate=(-20, 20))]`
e.g. `transforms=[dict(type=iaa.Rotate, rotate=(-20, 20))]`
3) iaa.Augmenter: create an imgaug.Augmenter object.
e.g. `transforms=iaa.Rotate(rotate=(-20, 20))`
2. Add `Imgaug` in dataset pipeline. It is recommended to insert imgaug
pipeline before `Normalize`. A demo pipeline is listed as follows.
```
pipeline = [
dict(
type='SampleFrames',
clip_len=1,
frame_interval=1,
num_clips=16,
),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(
type='MultiScaleCrop',
input_size=224,
scales=(1, 0.875, 0.75, 0.66),
random_crop=False,
max_wh_scale_gap=1,
num_fixed_crops=13),
dict(type='Resize', scale=(224, 224), keep_ratio=False),
dict(type='Flip', flip_ratio=0.5),
dict(type='Imgaug', transforms='default'),
# dict(type='Imgaug', transforms=[
# dict(type='Rotate', rotate=(-20, 20))
# ]),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs', 'label'])
]
```
Args:
transforms (str | list[dict] | :obj:`iaa.Augmenter`): Three different
ways to create imgaug augmenter.
"""
def __init__(self, transforms):
# Hack to fix incompatibility of ImgAug and latest Numpy
if digit_version(np.__version__) >= digit_version('1.24.0'):
np.bool = bool
import imgaug.augmenters as iaa
if transforms == 'default':
self.transforms = self.default_transforms()
elif isinstance(transforms, list):
assert all(isinstance(trans, dict) for trans in transforms)
self.transforms = transforms
elif isinstance(transforms, iaa.Augmenter):
self.aug = self.transforms = transforms
else:
raise ValueError('transforms must be `default` or a list of dicts'
' or iaa.Augmenter object')
if not isinstance(transforms, iaa.Augmenter):
self.aug = iaa.Sequential(
[self.imgaug_builder(t) for t in self.transforms])
@staticmethod
def default_transforms():
"""Default transforms for imgaug.
Implement RandAugment by imgaug.
Please visit `https://arxiv.org/abs/1909.13719` for more information.
Augmenters and hyper parameters are borrowed from the following repo:
https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py # noqa
Miss one augmenter ``SolarizeAdd`` since imgaug doesn't support this.
Returns:
dict: The constructed RandAugment transforms.
"""
# RandAugment hyper params
num_augmenters = 2
cur_magnitude, max_magnitude = 9, 10
cur_level = 1.0 * cur_magnitude / max_magnitude
return [
dict(
type='SomeOf',
n=num_augmenters,
children=[
dict(
type='ShearX',
shear=17.19 * cur_level * random.choice([-1, 1])),
dict(
type='ShearY',
shear=17.19 * cur_level * random.choice([-1, 1])),
dict(
type='TranslateX',
percent=.2 * cur_level * random.choice([-1, 1])),
dict(
type='TranslateY',
percent=.2 * cur_level * random.choice([-1, 1])),
dict(
type='Rotate',
rotate=30 * cur_level * random.choice([-1, 1])),
dict(type='Posterize', nb_bits=max(1, int(4 * cur_level))),
dict(type='Solarize', threshold=256 * cur_level),
dict(type='EnhanceColor', factor=1.8 * cur_level + .1),
dict(type='EnhanceContrast', factor=1.8 * cur_level + .1),
dict(
type='EnhanceBrightness', factor=1.8 * cur_level + .1),
dict(type='EnhanceSharpness', factor=1.8 * cur_level + .1),
dict(type='Autocontrast', cutoff=0),
dict(type='Equalize'),
dict(type='Invert', p=1.),
dict(
type='Cutout',
nb_iterations=1,
size=0.2 * cur_level,
squared=True)
])
]
def imgaug_builder(self, cfg):
"""Import a module from imgaug.
It follows the logic of :func:`build_from_cfg`. Use a dict object to
create an iaa.Augmenter object.
Args:
cfg (dict): Config dict. It should at least contain the key "type".
Returns:
obj:`iaa.Augmenter`: The constructed imgaug augmenter.
"""
import imgaug.augmenters as iaa
assert isinstance(cfg, dict) and 'type' in cfg
args = cfg.copy()
obj_type = args.pop('type')
if mmengine.is_str(obj_type):
obj_cls = getattr(iaa, obj_type) if hasattr(iaa, obj_type) \
else getattr(iaa.pillike, obj_type)
elif issubclass(obj_type, iaa.Augmenter):
obj_cls = obj_type
else:
raise TypeError(
f'type must be a str or valid type, but got {type(obj_type)}')
for aug_list_key in ['children', 'then_list', 'else_list']:
if aug_list_key in args:
args[aug_list_key] = [
self.imgaug_builder(child) for child in args[aug_list_key]
]
return obj_cls(**args)
def __repr__(self):
repr_str = self.__class__.__name__ + f'(transforms={self.aug})'
return repr_str
def transform(self, results):
"""Perform Imgaug augmentations.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
assert results['modality'] == 'RGB', 'Imgaug only support RGB images.'
in_type = results['imgs'][0].dtype
cur_aug = self.aug.to_deterministic()
results['imgs'] = [
cur_aug.augment_image(frame) for frame in results['imgs']
]
img_h, img_w, _ = results['imgs'][0].shape
out_type = results['imgs'][0].dtype
assert in_type == out_type, \
('Imgaug input dtype and output dtype are not the same. ',
f'Convert from {in_type} to {out_type}')
if 'gt_bboxes' in results:
from imgaug.augmentables import bbs
bbox_list = [
bbs.BoundingBox(
x1=bbox[0], y1=bbox[1], x2=bbox[2], y2=bbox[3])
for bbox in results['gt_bboxes']
]
bboxes = bbs.BoundingBoxesOnImage(
bbox_list, shape=results['img_shape'])
bbox_aug, *_ = cur_aug.augment_bounding_boxes([bboxes])
results['gt_bboxes'] = [[
max(bbox.x1, 0),
max(bbox.y1, 0),
min(bbox.x2, img_w),
min(bbox.y2, img_h)
] for bbox in bbox_aug.items]
if 'proposals' in results:
bbox_list = [
bbs.BoundingBox(
x1=bbox[0], y1=bbox[1], x2=bbox[2], y2=bbox[3])
for bbox in results['proposals']
]
bboxes = bbs.BoundingBoxesOnImage(
bbox_list, shape=results['img_shape'])
bbox_aug, *_ = cur_aug.augment_bounding_boxes([bboxes])
results['proposals'] = [[
max(bbox.x1, 0),
max(bbox.y1, 0),
min(bbox.x2, img_w),
min(bbox.y2, img_h)
] for bbox in bbox_aug.items]
results['img_shape'] = (img_h, img_w)
return results
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