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from abc import abstractmethod
import torchvision.transforms as transforms
from datasets import augmentations
class TransformsConfig(object):
def __init__(self, opts):
self.opts = opts
@abstractmethod
def get_transforms(self):
pass
class EncodeTransforms(TransformsConfig):
def __init__(self, opts):
super(EncodeTransforms, self).__init__(opts)
def get_transforms(self):
transforms_dict = {
'transform_gt_train': transforms.Compose([
transforms.Resize((320, 320)),
transforms.RandomHorizontalFlip(0.5),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_source': None,
'transform_test': transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_inference': transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
}
return transforms_dict
class FrontalizationTransforms(TransformsConfig):
def __init__(self, opts):
super(FrontalizationTransforms, self).__init__(opts)
def get_transforms(self):
transforms_dict = {
'transform_gt_train': transforms.Compose([
transforms.Resize((256, 256)),
transforms.RandomHorizontalFlip(0.5),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_source': transforms.Compose([
transforms.Resize((256, 256)),
transforms.RandomHorizontalFlip(0.5),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_test': transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_inference': transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
}
return transforms_dict
class SketchToImageTransforms(TransformsConfig):
def __init__(self, opts):
super(SketchToImageTransforms, self).__init__(opts)
def get_transforms(self):
transforms_dict = {
'transform_gt_train': transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_source': transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor()]),
'transform_test': transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_inference': transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor()]),
}
return transforms_dict
class SegToImageTransforms(TransformsConfig):
def __init__(self, opts):
super(SegToImageTransforms, self).__init__(opts)
def get_transforms(self):
transforms_dict = {
'transform_gt_train': transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_source': transforms.Compose([
transforms.Resize((320, 320)),
augmentations.ToOneHot(self.opts.label_nc),
transforms.ToTensor()]),
'transform_test': transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_inference': transforms.Compose([
transforms.Resize((320, 320)),
augmentations.ToOneHot(self.opts.label_nc),
transforms.ToTensor()])
}
return transforms_dict
class SuperResTransforms(TransformsConfig):
def __init__(self, opts):
super(SuperResTransforms, self).__init__(opts)
def get_transforms(self):
if self.opts.resize_factors is None:
self.opts.resize_factors = '1,2,4,8,16,32'
factors = [int(f) for f in self.opts.resize_factors.split(",")]
print("Performing down-sampling with factors: {}".format(factors))
transforms_dict = {
'transform_gt_train': transforms.Compose([
transforms.Resize((1280, 1280)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_source': transforms.Compose([
transforms.Resize((320, 320)),
augmentations.BilinearResize(factors=factors),
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_test': transforms.Compose([
transforms.Resize((1280, 1280)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_inference': transforms.Compose([
transforms.Resize((320, 320)),
augmentations.BilinearResize(factors=factors),
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
}
return transforms_dict
class SuperResTransforms_320(TransformsConfig):
def __init__(self, opts):
super(SuperResTransforms_320, self).__init__(opts)
def get_transforms(self):
if self.opts.resize_factors is None:
self.opts.resize_factors = '1,2,4,8,16,32'
factors = [int(f) for f in self.opts.resize_factors.split(",")]
print("Performing down-sampling with factors: {}".format(factors))
transforms_dict = {
'transform_gt_train': transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_source': transforms.Compose([
transforms.Resize((320, 320)),
augmentations.BilinearResize(factors=factors),
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_test': transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_inference': transforms.Compose([
transforms.Resize((320, 320)),
augmentations.BilinearResize(factors=factors),
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
}
return transforms_dict
class ToonifyTransforms(TransformsConfig):
def __init__(self, opts):
super(ToonifyTransforms, self).__init__(opts)
def get_transforms(self):
transforms_dict = {
'transform_gt_train': transforms.Compose([
transforms.Resize((1024, 1024)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_source': transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_test': transforms.Compose([
transforms.Resize((1024, 1024)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_inference': transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
}
return transforms_dict
class EditingTransforms(TransformsConfig):
def __init__(self, opts):
super(EditingTransforms, self).__init__(opts)
def get_transforms(self):
transforms_dict = {
'transform_gt_train': transforms.Compose([
transforms.Resize((1280, 1280)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_source': transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_test': transforms.Compose([
transforms.Resize((1280, 1280)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
'transform_inference': transforms.Compose([
transforms.Resize((320, 320)),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
}
return transforms_dict |