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# Copyright (C) 2022-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
import torch
import torchvision.transforms
import torchvision.transforms.functional as F
# "Pair": apply a transform on a pair
# "Both": apply the exact same transform to both images
class ComposePair(torchvision.transforms.Compose):
def __call__(self, img1, img2):
for t in self.transforms:
img1, img2 = t(img1, img2)
return img1, img2
class NormalizeBoth(torchvision.transforms.Normalize):
def forward(self, img1, img2):
img1 = super().forward(img1)
img2 = super().forward(img2)
return img1, img2
class ToTensorBoth(torchvision.transforms.ToTensor):
def __call__(self, img1, img2):
img1 = super().__call__(img1)
img2 = super().__call__(img2)
return img1, img2
class RandomCropPair(torchvision.transforms.RandomCrop):
# the crop will be intentionally different for the two images with this class
def forward(self, img1, img2):
img1 = super().forward(img1)
img2 = super().forward(img2)
return img1, img2
class ColorJitterPair(torchvision.transforms.ColorJitter):
# can be symmetric (same for both images) or assymetric (different jitter params for each image) depending on assymetric_prob
def __init__(self, assymetric_prob, **kwargs):
super().__init__(**kwargs)
self.assymetric_prob = assymetric_prob
def jitter_one(self, img, fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor):
for fn_id in fn_idx:
if fn_id == 0 and brightness_factor is not None:
img = F.adjust_brightness(img, brightness_factor)
elif fn_id == 1 and contrast_factor is not None:
img = F.adjust_contrast(img, contrast_factor)
elif fn_id == 2 and saturation_factor is not None:
img = F.adjust_saturation(img, saturation_factor)
elif fn_id == 3 and hue_factor is not None:
img = F.adjust_hue(img, hue_factor)
return img
def forward(self, img1, img2):
fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor = self.get_params(
self.brightness, self.contrast, self.saturation, self.hue
)
img1 = self.jitter_one(img1, fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor)
if torch.rand(1) < self.assymetric_prob: # assymetric:
fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor = self.get_params(
self.brightness, self.contrast, self.saturation, self.hue
)
img2 = self.jitter_one(img2, fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor)
return img1, img2
def get_pair_transforms(transform_str, totensor=True, normalize=True):
# transform_str is eg crop224+color
trfs = []
for s in transform_str.split('+'):
if s.startswith('crop'):
size = int(s[len('crop'):])
trfs.append(RandomCropPair(size))
elif s=='acolor':
trfs.append(ColorJitterPair(assymetric_prob=1.0, brightness=(0.6, 1.4), contrast=(0.6, 1.4), saturation=(0.6, 1.4), hue=0.0))
elif s=='': # if transform_str was ""
pass
else:
raise NotImplementedError('Unknown augmentation: '+s)
if totensor:
trfs.append( ToTensorBoth() )
if normalize:
trfs.append( NormalizeBoth(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) )
if len(trfs)==0:
return None
elif len(trfs)==1:
return trfs
else:
return ComposePair(trfs)
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