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OCDC
author
Francois Fleuret
<francois@fleuret.org>
Wed, 27 Jul 2022 14:42:28 +0000
(16:42 +0200)
committer
Francois Fleuret
<francois@fleuret.org>
Wed, 27 Jul 2022 14:42:28 +0000
(16:42 +0200)
main.py
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diff --git
a/main.py
b/main.py
index
339d185
..
5f3e8cf
100755
(executable)
--- a/
main.py
+++ b/
main.py
@@
-156,7
+156,7
@@
import picoclvr
class TaskPicoCLVR(Task):
class TaskPicoCLVR(Task):
- def
descr2tensor
(self, descr):
+ def
tensorize
(self, descr):
t = [ [ self.token2id[u] for u in s ] for s in descr ]
return torch.tensor(t, device = self.device)
t = [ [ self.token2id[u] for u in s ] for s in descr ]
return torch.tensor(t, device = self.device)
@@
-173,8
+173,8
@@
class TaskPicoCLVR(Task):
descr = [ s.strip().split(' ') for s in descr ]
l = max([ len(s) for s in descr ])
descr = [ s.strip().split(' ') for s in descr ]
l = max([ len(s) for s in descr ])
- #descr = [ [ '<
unk
>' ] * (l - len(s)) + s for s in descr ]
- descr = [ s + [ '<
unk
>' ] * (l - len(s)) for s in descr ]
+ #descr = [ [ '<
nul
>' ] * (l - len(s)) + s for s in descr ]
+ descr = [ s + [ '<
nul
>' ] * (l - len(s)) for s in descr ]
return descr
return descr
@@
-196,8
+196,8
@@
class TaskPicoCLVR(Task):
self.id2token = dict([ (n, t) for n, t in enumerate(tokens) ])
# Tokenize the train and test sets
self.id2token = dict([ (n, t) for n, t in enumerate(tokens) ])
# Tokenize the train and test sets
- self.train_input =
descr2tensor
(self.train_descr)
- self.test_input =
descr2tensor
(self.test_descr)
+ self.train_input =
self.tensorize
(self.train_descr)
+ self.test_input =
self.tensorize
(self.test_descr)
def batches(self, split = 'train'):
assert split in { 'train', 'test' }
def batches(self, split = 'train'):
assert split in { 'train', 'test' }
@@
-208,14
+208,6
@@
class TaskPicoCLVR(Task):
def vocabulary_size(self):
return len(self.token2id)
def vocabulary_size(self):
return len(self.token2id)
- def generate(self, primer_descr, model, nb_tokens):
- results = autoregression(
- model, self.batch_size,
- nb_samples = 1, nb_tokens = nb_tokens, primer = descr2tensor(primer_descr),
- device = self.device
- )
- return ' '.join([ self.id2token[t.item()] for t in results.flatten() ])
-
def produce_results(self, n_epoch, model):
nb_tokens = self.height * self.width + 3
result_descr = [ ]
def produce_results(self, n_epoch, model):
nb_tokens = self.height * self.width + 3
result_descr = [ ]
@@
-229,10
+221,20
@@
class TaskPicoCLVR(Task):
]:
for k in range(nb_per_primer):
]:
for k in range(nb_per_primer):
- result_descr.append(self.generate(primer_descr, model, nb_tokens))
+ results = autoregression(
+ model, self.batch_size,
+ nb_samples = 1, nb_tokens = nb_tokens,
+ primer = self.tensorize(primer_descr),
+ device = self.device
+ )
+ r = ' '.join([ self.id2token[t.item()] for t in results.flatten() ])
+ result_descr.append(r)
+
+ img = [
+ picoclvr.descr2img(d, height = self.height, width = self.width)
+ for d in result_descr
+ ]
- img = [ picoclvr.descr2img(d, height = self.height, width = self.width)
- for d in result_descr ]
img = torch.cat(img, 0)
image_name = f'result_picoclvr_{n_epoch:04d}.png'
torchvision.utils.save_image(
img = torch.cat(img, 0)
image_name = f'result_picoclvr_{n_epoch:04d}.png'
torchvision.utils.save_image(
@@
-276,7
+278,7
@@
class TaskWiki103(Task):
self.vocab = torchtext.vocab.build_vocab_from_iterator(
yield_tokens(),
self.vocab = torchtext.vocab.build_vocab_from_iterator(
yield_tokens(),
- specials = [ '<unk>', '<n
on
>' ],
+ specials = [ '<unk>', '<n
ul
>' ],
min_freq = self.min_freq
)
min_freq = self.min_freq
)
@@
-284,7
+286,7
@@
class TaskWiki103(Task):
def tensorize(self, s):
a = max(len(x) for x in s)
def tensorize(self, s):
a = max(len(x) for x in s)
- return torch.tensor([ self.vocab(x + [ '<n
on
>' ] * (a - len(x))) for x in s ])
+ return torch.tensor([ self.vocab(x + [ '<n
ul
>' ] * (a - len(x))) for x in s ])
def yield_batches(self, ds):
s = [ ]
def yield_batches(self, ds):
s = [ ]
@@
-342,7
+344,7
@@
class TaskWiki103(Task):
else:
t_next = logits.argmax()
t_generated.append(self.vocab.lookup_token(t_next))
else:
t_next = logits.argmax()
t_generated.append(self.vocab.lookup_token(t_next))
- if t_generated[-1] == '<n
on
>': break
+ if t_generated[-1] == '<n
ul
>': break
s = ' '.join(t_generated)
s = ' '.join(t_generated)