projects
/
picoclvr.git
/ commitdiff
commit
grep
author
committer
pickaxe
?
search:
re
summary
|
shortlog
|
log
|
commit
| commitdiff |
tree
raw
|
patch
|
inline
| side by side (parent:
38d3035
)
Update.
author
François Fleuret
<francois@fleuret.org>
Tue, 4 Jul 2023 15:26:24 +0000
(17:26 +0200)
committer
François Fleuret
<francois@fleuret.org>
Tue, 4 Jul 2023 15:26:24 +0000
(17:26 +0200)
main.py
patch
|
blob
|
history
diff --git
a/main.py
b/main.py
index
b774fce
..
324aeba
100755
(executable)
--- a/
main.py
+++ b/
main.py
@@
-1023,29
+1023,33
@@
class TaskExpr(Task):
self.char2id = dict(
[
(c, n)
self.char2id = dict(
[
(c, n)
- for n, c in enumerate(set("".join(train_sequences + test_sequences)))
+ for n, c in enumerate(set("
#"+"
".join(train_sequences + test_sequences)))
]
)
]
)
- self.id2char = dict([(n, c) for
n, c
in self.char2id.items()])
+ self.id2char = dict([(n, c) for
c, n
in self.char2id.items()])
len_max = max([len(x) for x in train_sequences + test_sequences])
self.train_input = torch.cat(
[
torch.tensor(
len_max = max([len(x) for x in train_sequences + test_sequences])
self.train_input = torch.cat(
[
torch.tensor(
- [char2id(c) for c in s + " " * (len_max - len(s))]
- for s in train_sequences
+ [
+ [self.char2id[c] for c in s + "#" * (len_max - len(s))]
+ for s in train_sequences
+ ]
)
],
0,
)
],
0,
- )
+ )
.to(device)
self.test_input = torch.cat(
[
torch.tensor(
self.test_input = torch.cat(
[
torch.tensor(
- [char2id(c) for c in s + " " * (len_max - len(s))]
- for s in test_sequences
+ [
+ [self.char2id[c] for c in s + "#" * (len_max - len(s))]
+ for s in test_sequences
+ ]
)
],
0,
)
],
0,
- )
+ )
.to(device)
self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
def batches(self, split="train", nb_to_use=-1, desc=None):
self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
def batches(self, split="train", nb_to_use=-1, desc=None):
@@
-1064,26
+1068,21
@@
class TaskExpr(Task):
return self.nb_codes
def produce_results(self, n_epoch, model):
return self.nb_codes
def produce_results(self, n_epoch, model):
- # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
with torch.autograd.no_grad():
t = model.training
model.eval()
def compute_nb_correct(input):
result = input.clone()
with torch.autograd.no_grad():
t = model.training
model.eval()
def compute_nb_correct(input):
result = input.clone()
- stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
- ar_mask = (result != input).long()
+ space = self.char2id["#"]
+ ar_mask = (result == space).long().cumsum(dim=1).clamp(max=1)
+ result = (1 - ar_mask) * result + space * ar_mask
masked_inplace_autoregression(
model, self.batch_size, result, ar_mask, device=self.device
)
masked_inplace_autoregression(
model, self.batch_size, result, ar_mask, device=self.device
)
- errors = ((result != input).long() * ar_mask).reshape(
- -1, 1 + self.nb_digits
- )
- ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
-
- nb_total = ar_mask.max(1).values.sum()
- nb_correct = nb_total - errors.max(1).values.sum()
+ nb_total = ar_mask.sum()
+ nb_correct = ((input == result).long() * ar_mask).sum()
return nb_total, nb_correct
return nb_total, nb_correct
@@
-1095,21
+1094,20
@@
class TaskExpr(Task):
##############################################################
# Log a few generated sequences
##############################################################
# Log a few generated sequences
- input = self.test_input[:10
, : 12 * (1 + self.nb_digits)
]
+ input = self.test_input[:10]
result = input.clone()
result = input.clone()
- stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
- ar_mask = (result != input).long()
+ space = self.char2id["#"]
+ ar_mask = (result == space).long().cumsum(dim=1).clamp(max=1)
+ result = (1 - ar_mask) * result + space * ar_mask
for n in range(result.size(0)):
for n in range(result.size(0)):
- log_string(
- f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
- )
+ s = "".join([self.id2char[k.item()] for k in result[n]])
+ log_string(f"test_before {s}")
masked_inplace_autoregression(
model, self.batch_size, result, ar_mask, device=self.device
)
for n in range(result.size(0)):
masked_inplace_autoregression(
model, self.batch_size, result, ar_mask, device=self.device
)
for n in range(result.size(0)):
- log_string(
- f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
- )
+ s = "".join([self.id2char[k.item()] for k in result[n]])
+ log_string(f"test_after {s}")
##############################################################
model.train(t)
##############################################################
model.train(t)