I have been reading the new fastai book on github and came across an example of using a siamese network. However I can’t seem to figure out how to add augmentations into the process.
from fastai2.vision.all import *
path = untar_data(URLs.PETS)
files = get_image_files(path/"images")
class SiameseImage(Tuple):
def show(self, ctx=None, **kwargs):
img1,img2,same_breed = self
if not isinstance(img1, Tensor):
if img2.size != img1.size: img2 = img2.resize(img1.size)
t1,t2 = tensor(img1),tensor(img2)
t1,t2 = t1.permute(2,0,1),t2.permute(2,0,1)
else: t1,t2 = img1,img2
line = t1.new_zeros(t1.shape[0], t1.shape[1], 10)
return show_image(torch.cat([t1,line,t2], dim=2),
title=same_breed, ctx=ctx)
def label_func(fname):
return re.match(r'^(.*)_\d+.jpg$', fname.name).groups()[0]
class SiameseTransform(Transform):
def __init__(self, files, label_func, splits):
self.labels = files.map(label_func).unique()
self.lbl2files = {l: L(f for f in files if label_func(f) == l) for l in self.labels}
self.label_func = label_func
self.valid = {f: self._draw(f) for f in files[splits[1]]}
def encodes(self, f):
f2,t = self.valid.get(f, self._draw(f))
img1,img2 = PILImage.create(f),PILImage.create(f2)
return SiameseImage(img1, img2, t)
def _draw(self, f):
same = random.random() < 0.5
cls = self.label_func(f)
if not same: cls = random.choice(L(l for l in self.labels if l != cls))
return random.choice(self.lbl2files[cls]),same
splits = RandomSplitter()(files)
tfm = SiameseTransform(files, label_func, splits)
tls = TfmdLists(files, tfm, splits=splits)
dls = tls.dataloaders(after_item=[Resize(224), ToTensor],
after_batch=[IntToFloatTensor, Normalize.from_stats(*imagenet_stats)])
Elsewhere in the book it shows adding augmentations and it looks like this:
bears = bears.new(item_tfms=Resize(128), batch_tfms=aug_transforms(mult=2))
dls = bears.dataloaders(path)
How would I add aug_transforms into the siamese example?
if you run into issues feel free to ping me, I plan on working on what I described here in the next few days