Hi everyone,
I have a question about the segm generator as part of the tiramisu network:
My understanding is that the paper says to train and then fine-tune, where training is on random crops and flips and fine-tuning is on the full sized-images - but I can’t see where this is implemented in Jeremy’s (awesome!) notebook…
It may just be that I don’t really understand how segm generator works but where do you turn on/off the random crops?
class segm_generator(object):
def __init__(self, x, y, bs=64, out_sz=(224,224), train=True):
self.x, self.y, self.bs, self.train = x,y,bs,train
self.n, self.ri, self.ci, _ = x.shape
self.idx_gen = BatchIndices(self.n, bs, train)
self.ro, self.co = out_sz
self.ych = self.y.shape[-1] if len(y.shape)==4 else 1
def get_slice(self, i,o):
start = random.randint(0, i-o) if self.train else (i-o)
return slice(start, start+o)
def get_item(self, idx):
slice_r = self.get_slice(self.ri, self.ro)
slice_c = self.get_slice(self.ci, self.co)
x = self.x[idx, slice_r, slice_c]
y = self.y[idx, slice_r, slice_c]
if self.train and (random.random()>0.5):
y = y[:,::-1]
x = x[:,::-1]
return x, y
def __next__(self):
idxs = next(self.idx_gen)
items = (self.get_item(idx) for idx in idxs)
xs,ys = zip(*items)
return np.stack(xs), np.stack(ys).reshape(len(ys), -1, self.ych)
As far as I can tell, setting train=true only shuffles the order of the images returned by the generator, it doesn’t turn cropping on/off?
Thanks in advance!
p.s. bonus question: to do the train phase then the fine-tune phase, do I just setup the generator with cropping then set up a new generator and train the model some more with that new generator?