class StrCategorize(Transform):
    def __init__(self, vocab):
        self.vocab = vocab
        self.s2i = {s:i for i,s in enumerate(vocab)}
        self.i2s = {i:s for i,s in enumerate(vocab)}
    def encodes(self, s:str): return self.s2i[s]
    def decodes(self, i:int): return self.i2s[i]
    
transforms_ft = Pipeline([
   load_img_and_label,
   Resize(256,method="squish"),
   Resize(224,method="crop"),
   ToTensor(),
   IntToFloatTensor(),
   Normalize.from_stats(*imagenet_stats, cuda=False),
   StrCategorize(vocab=['husky','wolf']), # <-- Transform is just for the target label
])

out = transforms_ft(fpath)
print((out[0][0,:2,:2,:2], out[1]))