fastai notebook - custom transform

from fastai.text.all import *
from fastai.vision.all import *
# !pip install albumentations

source = untar_data(URLs.PETS)
items = get_image_files(source/"images")

class AlbumentationsTransform(Transform):
    def __init__(self, aug): self.aug = aug
    def encodes(self, img: PILImage):
        aug_img = self.aug(image=np.array(img))['image']
        return PILImage.create(aug_img)

tfm = AlbumentationsTransform(ShiftScaleRotate(p=1))
a,b = tfm((img, 'dog'))
show_image(a, title=b);

cv_source = untar_data(URLs.CAMVID_TINY)
cv_items = get_image_files(cv_source/'images')
cv_splitter = RandomSplitter(seed=42)
cv_split = cv_splitter(cv_items)
cv_label = lambda o: cv_source/'labels'/f'{o.stem}_P{o.suffix}'

class ImageResizer(Transform):
    order=1
    "Resize image to `size` using `resample`"
    def __init__(self, size, resample=BILINEAR):
        if not is_listy(size): size=(size,size)
        self.size,self.resample = (size[1],size[0]),resample

    def encodes(self, o:PILImage): return o.resize(size=self.size, resample=self.resample)
    def encodes(self, o:PILMask):  return o.resize(size=self.size, resample=NEAREST)

tfms = [[PILImage.create], [cv_label, PILMask.create]]
cv_dsets = Datasets(cv_items, tfms, splits=cv_split)
dls = cv_dsets.dataloaders(bs=64, after_item=[ImageResizer(128), ToTensor(), IntToFloatTensor()])

# split_idx=0 (0 is for the training set, 1 for the validation set):
class SegmentationAlbumentationsTransform(ItemTransform):
    split_idx = 0
    def __init__(self, aug): self.aug = aug
    def encodes(self, x):
        img,mask = x
        aug = self.aug(image=np.array(img), mask=np.array(mask))
        return PILImage.create(aug["image"]), PILMask.create(aug["mask"])

cv_dsets = Datasets(cv_items, tfms, splits=cv_split)
dls = cv_dsets.dataloaders(bs=64, after_item=[ImageResizer(128), ToTensor(), IntToFloatTensor(), 
                                              SegmentationAlbumentationsTransform(ShiftScaleRotate(p=1))])

dls.show_batch(max_n=4)

class AlbumentationsTransform(RandTransform):
    "A transform handler for multiple `Albumentation` transforms"
    split_idx,order=None,2
    def __init__(self, train_aug, valid_aug): store_attr()
    
    def before_call(self, b, split_idx):
        self.idx = split_idx
    
    def encodes(self, img: PILImage):
        if self.idx == 0:
            aug_img = self.train_aug(image=np.array(img))['image']
        else:
            aug_img = self.valid_aug(image=np.array(img))['image']
        return PILImage.create(aug_img)

We changed our split_idx to be None, which allows for us to say when we’re setting our split_idx.
We also inherit from RandTransform, which allows for us to set that split_idx in our before_call.