@ -14,9 +14,14 @@ from ultralytics.utils.checks import check_version
from ultralytics . utils . instance import Instances
from ultralytics . utils . metrics import bbox_ioa
from ultralytics . utils . ops import segment2box
from ultralytics . utils . torch_utils import TORCHVISION_0_10 , TORCHVISION_0_11 , TORCHVISION_0_13
from . utils import polygons2masks , polygons2masks_overlap
DEFAULT_MEAN = ( 0.0 , 0.0 , 0.0 )
DEFAULT_STD = ( 1.0 , 1.0 , 1.0 )
DEFAULT_CROP_FTACTION = 1.0
# TODO: we might need a BaseTransform to make all these augments be compatible with both classification and semantic
class BaseTransform :
@ -982,65 +987,144 @@ def v8_transforms(dataset, imgsz, hyp, stretch=False):
# Classification augmentations -----------------------------------------------------------------------------------------
def classify_transforms ( size = 224 , rect = False , mean = ( 0.0 , 0.0 , 0.0 ) , std = ( 1.0 , 1.0 , 1.0 ) ) : # IMAGENET_MEAN, IMAGENET_STD
""" Transforms to apply if albumentations not installed. """
def classify_transforms (
size = 224 ,
mean = DEFAULT_MEAN ,
std = DEFAULT_STD ,
interpolation : T . InterpolationMode = T . InterpolationMode . BILINEAR ,
crop_fraction : float = DEFAULT_CROP_FTACTION ,
) :
"""
Classification transforms for evaluation / inference . Inspired by timm / data / transforms_factory . py .
Args :
size ( int ) : image size
mean ( tuple ) : mean values of RGB channels
std ( tuple ) : std values of RGB channels
interpolation ( T . InterpolationMode ) : interpolation mode . default is T . InterpolationMode . BILINEAR .
crop_fraction ( float ) : fraction of image to crop . default is 1.0 .
Returns :
T . Compose : torchvision transforms
"""
if isinstance ( size , ( tuple , list ) ) :
assert len ( size ) == 2
scale_size = tuple ( [ math . floor ( x / crop_fraction ) for x in size ] )
else :
scale_size = math . floor ( size / crop_fraction )
scale_size = ( scale_size , scale_size )
# aspect ratio is preserved, crops center within image, no borders are added, image is lost
if scale_size [ 0 ] == scale_size [ 1 ] :
# simple case, use torchvision built-in Resize w/ shortest edge mode (scalar size arg)
tfl = [ T . Resize ( scale_size [ 0 ] , interpolation = interpolation ) ]
else :
# resize shortest edge to matching target dim for non-square target
tfl = [ T . Resize ( scale_size ) ]
tfl + = [ T . CenterCrop ( size ) ]
tfl + = [ T . ToTensor ( ) , T . Normalize (
mean = torch . tensor ( mean ) ,
std = torch . tensor ( std ) ,
) ]
return T . Compose ( tfl )
# Classification augmentations train ---------------------------------------------------------------------------------------
def classify_augmentations (
size = 224 ,
mean = DEFAULT_MEAN ,
std = DEFAULT_STD ,
scale = None ,
ratio = None ,
hflip = 0.5 ,
vflip = 0.0 ,
auto_augment = None ,
hsv_h = 0.015 , # image HSV-Hue augmentation (fraction)
hsv_s = 0.4 , # image HSV-Saturation augmentation (fraction)
hsv_v = 0.4 , # image HSV-Value augmentation (fraction)
force_color_jitter = False ,
erasing = 0. ,
interpolation : T . InterpolationMode = T . InterpolationMode . BILINEAR ,
) :
"""
Classification transforms with augmentation for training . Inspired by timm / data / transforms_factory . py .
Args :
size ( int ) : image size
scale ( tuple ) : scale range of the image . default is ( 0.08 , 1.0 )
ratio ( tuple ) : aspect ratio range of the image . default is ( 3. / 4. , 4. / 3. )
mean ( tuple ) : mean values of RGB channels
std ( tuple ) : std values of RGB channels
hflip ( float ) : probability of horizontal flip
vflip ( float ) : probability of vertical flip
auto_augment ( str ) : auto augmentation policy . can be ' randaugment ' , ' augmix ' , ' autoaugment ' or None .
hsv_h ( float ) : image HSV - Hue augmentation ( fraction )
hsv_s ( float ) : image HSV - Saturation augmentation ( fraction )
hsv_v ( float ) : image HSV - Value augmentation ( fraction )
contrast ( float ) : image contrast augmentation ( fraction )
force_color_jitter ( bool ) : force to apply color jitter even if auto augment is enabled
erasing ( float ) : probability of random erasing
interpolation ( T . InterpolationMode ) : interpolation mode . default is T . InterpolationMode . BILINEAR .
Returns :
T . Compose : torchvision transforms
"""
# Transforms to apply if albumentations not installed
if not isinstance ( size , int ) :
raise TypeError ( f ' classify_transforms() size { size } must be integer, not (list, tuple) ' )
transforms = [ ClassifyLetterBox ( size , auto = True ) if rect else CenterCrop ( size ) , ToTensor ( ) ]
if any ( mean ) or any ( std ) :
transforms . append ( T . Normalize ( mean , std , inplace = True ) )
return T . Compose ( transforms )
def hsv2colorjitter ( h , s , v ) :
""" Map HSV (hue, saturation, value) jitter into ColorJitter values (brightness, contrast, saturation, hue) """
return v , v , s , h
def classify_albumentations (
augment = True ,
size = 224 ,
scale = ( 0.08 , 1.0 ) ,
hflip = 0.5 ,
vflip = 0.0 ,
hsv_h = 0.015 , # image HSV-Hue augmentation (fraction)
hsv_s = 0.7 , # image HSV-Saturation augmentation (fraction)
hsv_v = 0.4 , # image HSV-Value augmentation (fraction)
mean = ( 0.0 , 0.0 , 0.0 ) , # IMAGENET_MEAN
std = ( 1.0 , 1.0 , 1.0 ) , # IMAGENET_STD
auto_aug = False ,
) :
""" YOLOv8 classification Albumentations (optional, only used if package is installed). """
prefix = colorstr ( ' albumentations: ' )
try :
import albumentations as A
from albumentations . pytorch import ToTensorV2
check_version ( A . __version__ , ' 1.0.3 ' , hard = True ) # version requirement
if augment : # Resize and crop
T = [ A . RandomResizedCrop ( height = size , width = size , scale = scale ) ]
if auto_aug :
# TODO: implement AugMix, AutoAug & RandAug in albumentations
LOGGER . info ( f ' { prefix } auto augmentations are currently not supported ' )
scale = tuple ( scale or ( 0.08 , 1.0 ) ) # default imagenet scale range
ratio = tuple ( ratio or ( 3. / 4. , 4. / 3. ) ) # default imagenet ratio range
primary_tfl = [ T . RandomResizedCrop ( size , scale = scale , ratio = ratio , interpolation = interpolation ) ]
if hflip > 0. :
primary_tfl + = [ T . RandomHorizontalFlip ( p = hflip ) ]
if vflip > 0. :
primary_tfl + = [ T . RandomVerticalFlip ( p = vflip ) ]
secondary_tfl = [ ]
disable_color_jitter = False
if auto_augment :
assert isinstance ( auto_augment , str )
# color jitter is typically disabled if AA/RA on,
# this allows override without breaking old hparm cfgs
disable_color_jitter = not force_color_jitter
if auto_augment == ' randaugment ' :
if TORCHVISION_0_11 :
secondary_tfl + = [ T . RandAugment ( interpolation = interpolation ) ]
else :
if hflip > 0 :
T + = [ A . HorizontalFlip ( p = hflip ) ]
if vflip > 0 :
T + = [ A . VerticalFlip ( p = vflip ) ]
if any ( ( hsv_h , hsv_s , hsv_v ) ) :
T + = [ A . ColorJitter ( * hsv2colorjitter ( hsv_h , hsv_s , hsv_v ) ) ] # brightness, contrast, saturation, hue
else : # Use fixed crop for eval set (reproducibility)
T = [ A . SmallestMaxSize ( max_size = size ) , A . CenterCrop ( height = size , width = size ) ]
T + = [ A . Normalize ( mean = mean , std = std ) , ToTensorV2 ( ) ] # Normalize and convert to Tensor
LOGGER . info ( prefix + ' , ' . join ( f ' { x } ' . replace ( ' always_apply=False, ' , ' ' ) for x in T if x . p ) )
return A . Compose ( T )
except ImportError : # package not installed, skip
pass
except Exception as e :
LOGGER . info ( f ' { prefix } { e } ' )
LOGGER . warning ( ' " auto_augment=randaugment " requires torchvision >= 0.11.0. Disabling it. ' )
elif auto_augment == ' augmix ' :
if TORCHVISION_0_13 :
secondary_tfl + = [ T . AugMix ( interpolation = interpolation ) ]
else :
LOGGER . warning ( ' " auto_augment=augmix " requires torchvision >= 0.13.0. Disabling it. ' )
elif auto_augment == ' autoaugment ' :
if TORCHVISION_0_10 :
secondary_tfl + = [ T . AutoAugment ( interpolation = interpolation ) ]
else :
LOGGER . warning ( ' " auto_augment=autoaugment " requires torchvision >= 0.10.0. Disabling it. ' )
else :
raise ValueError ( f ' Invalid auto_augment policy: { auto_augment } . Should be one of " randaugment " , '
f ' " augmix " , " autoaugment " or None ' )
if not disable_color_jitter :
secondary_tfl + = [ T . ColorJitter ( brightness = hsv_v , contrast = hsv_v , saturation = hsv_s , hue = hsv_h ) ]
final_tfl = [
T . ToTensor ( ) ,
T . Normalize ( mean = torch . tensor ( mean ) , std = torch . tensor ( std ) ) ,
T . RandomErasing ( p = erasing , inplace = True ) ]
return T . Compose ( primary_tfl + secondary_tfl + final_tfl )
# NOTE: keep this class for backward compatibility
class ClassifyLetterBox :
"""
YOLOv8 LetterBox class for image preprocessing , designed to be part of a transformation pipeline , e . g . ,
@ -1091,6 +1175,7 @@ class ClassifyLetterBox:
return im_out
# NOTE: keep this class for backward compatibility
class CenterCrop :
""" YOLOv8 CenterCrop class for image preprocessing, designed to be part of a transformation pipeline, e.g.,
T . Compose ( [ CenterCrop ( size ) , ToTensor ( ) ] ) .
@ -1117,6 +1202,7 @@ class CenterCrop:
return cv2 . resize ( im [ top : top + m , left : left + m ] , ( self . w , self . h ) , interpolation = cv2 . INTER_LINEAR )
# NOTE: keep this class for backward compatibility
class ToTensor :
""" YOLOv8 ToTensor class for image preprocessing, i.e., T.Compose([LetterBox(size), ToTensor()]). """
fatih
11 months ago
committed by
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