def parse_transform(self, transform_type):
if transform_type=='ImageJitter':
method = add_transforms.ImageJitter( self.jitter_param )
return method
method = getattr(transforms, transform_type)
if transform_type=='RandomSizedCrop':
return method(self.image_size)
elif transform_type=='CenterCrop':
return method(self.image_size)
elif transform_type=='Scale':
return method([int(self.image_size*1.15), int(self.image_size*1.15)])
elif transform_type=='Normalize':
return method(**self.normalize_param )
else:
return method()
def parse_transform(self, transform_type):
if transform_type == "ImageJitter":
method = add_transforms.ImageJitter(self.jitter_param)
return method
method = getattr(transforms, transform_type)
if transform_type == "RandomResizedCrop":
return method(self.image_size, scale=(0.8, 1.0))
elif transform_type == "CenterCrop":
return method(self.image_size)
elif transform_type == "Resize":
return method(
[int(self.image_size * 1.15),
int(self.image_size * 1.15)])
elif transform_type == "Normalize":
return method(**self.normalize_param)
else:
return method()
def parse_transform(self, transform_type):
if isinstance(transform_type, tuple) and transform_type[0] == 'Noise':
_, confound_noise, confound_noise_class_weight = transform_type
method = add_transforms.Noiser(confound_noise,
confound_noise_class_weight)
return method
if transform_type == 'ImageJitter':
method = add_transforms.ImageJitter(self.jitter_param)
return method
method = getattr(transforms, transform_type)
if transform_type == 'RandomResizedCrop':
return method(self.image_size)
elif transform_type == 'CenterCrop':
return method(self.image_size)
elif transform_type == 'Resize':
return method(
[int(self.image_size * 1.15),
int(self.image_size * 1.15)])
elif transform_type == 'Normalize':
return method(**self.normalize_param)
else:
return method()
