From a874cd6dc34c2e29c05b7a5a513af62f4e6e1264 Mon Sep 17 00:00:00 2001
From: Caroline DE POURTALES <cdepourt@montana.irit.fr>
Date: Tue, 10 May 2022 17:24:25 +0200
Subject: [PATCH] it runs, some corrections needed next
---
SuperTagger/Linker/Linker.py | 6 +++---
.../Linker/__pycache__/Linker.cpython-38.pyc | Bin 5697 -> 5536 bytes
.../Linker/__pycache__/utils.cpython-38.pyc | Bin 6078 -> 5906 bytes
SuperTagger/Linker/utils.py | 18 ++++++++++--------
SuperTagger/__pycache__/eval.cpython-38.pyc | Bin 1686 -> 1775 bytes
SuperTagger/eval.py | 6 ++++--
6 files changed, 17 insertions(+), 13 deletions(-)
diff --git a/SuperTagger/Linker/Linker.py b/SuperTagger/Linker/Linker.py
index f2f7192..b8dbf8c 100644
--- a/SuperTagger/Linker/Linker.py
+++ b/SuperTagger/Linker/Linker.py
@@ -90,13 +90,13 @@ class Linker(Module):
if (self.atom_map[atom_type] in atoms_batch_tokenized[s_idx] and
atoms_batch_tokenized[s_idx][i] == self.atom_map[atom_type] and
atoms_polarity_batch[s_idx][i])] + [torch.zeros(self.dim_embedding_atoms)])
- for s_idx in range(len(atoms_polarity_batch))], padding_value=19, max_len=self.max_atoms_in_one_type//2)
+ for s_idx in range(len(atoms_polarity_batch))], padding_value=0, max_len=self.max_atoms_in_one_type//2)
neg_encoding = pad_sequence([torch.stack([x for i, x in enumerate(atoms_encoding[s_idx])
if (self.atom_map[atom_type] in atoms_batch_tokenized[s_idx] and
atoms_batch_tokenized[s_idx][i] == self.atom_map[atom_type] and
not atoms_polarity_batch[s_idx][i])] + [torch.zeros(self.dim_embedding_atoms)])
- for s_idx in range(len(atoms_polarity_batch))], padding_value=19, max_len=self.max_atoms_in_one_type//2)
+ for s_idx in range(len(atoms_polarity_batch))], padding_value=0, max_len=self.max_atoms_in_one_type//2)
pos_encoding = self.pos_transformation(pos_encoding)
neg_encoding = self.neg_transformation(neg_encoding)
@@ -104,7 +104,7 @@ class Linker(Module):
weights = torch.bmm(pos_encoding, neg_encoding.transpose(2, 1))
link_weights.append(sinkhorn(weights, iters=3))
- return torch.cat([link_weights[i].unsqueeze(0) for i in range(len(link_weights))])
+ return torch.stack(link_weights)
def eval_batch(self, batch, cross_entropy_loss):
batch_atoms = batch[0].to("cuda" if torch.cuda.is_available() else "cpu")
diff --git a/SuperTagger/Linker/__pycache__/Linker.cpython-38.pyc b/SuperTagger/Linker/__pycache__/Linker.cpython-38.pyc
index db616de9a03c6a9f098f7797b6db73b69aea4751..d796c7a4452db32c4eb9d9237b80fc4bab334428 100644
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diff --git a/SuperTagger/Linker/__pycache__/utils.cpython-38.pyc b/SuperTagger/Linker/__pycache__/utils.cpython-38.pyc
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diff --git a/SuperTagger/Linker/utils.py b/SuperTagger/Linker/utils.py
index 7e12ea2..898a921 100644
--- a/SuperTagger/Linker/utils.py
+++ b/SuperTagger/Linker/utils.py
@@ -27,11 +27,12 @@ def get_axiom_links(max_atoms_in_one_type, atoms_polarity, batch_axiom_links):
linking_plus_to_minus = pad_sequence(
[torch.as_tensor([l_polarity_minus[s_idx].index(x) for i, x in enumerate(l_polarity_plus[s_idx])], dtype=torch.long)
- for s_idx in range(len(atoms_batch))], max_len=max_atoms_in_one_type//2, padding_value=0)
+ for s_idx in range(len(atoms_batch))], max_len=max_atoms_in_one_type//2, padding_value=-1)
linking_plus_to_minus_all_types.append(linking_plus_to_minus)
- return torch.cat([linking_plus_to_minus_all_types[i].unsqueeze(0) for i in range(len(linking_plus_to_minus_all_types))])
+ return torch.stack(linking_plus_to_minus_all_types)
+
def category_to_atoms_axiom_links(category, categories_to_atoms):
@@ -97,7 +98,7 @@ def category_to_atoms_polarity(category, polarity):
category_to_polarity = []
res = [bool(re.match(r''+atom_type+"_\d+", category)) for atom_type in atom_map.keys()]
if True in res or category.startswith("dia") or category.startswith("box"):
- category_to_polarity.append(polarity)
+ category_to_polarity.append(not polarity)
else:
# dr = /
if category.startswith("dr"):
@@ -106,7 +107,7 @@ def category_to_atoms_polarity(category, polarity):
left_side, right_side = category_cut[0], category_cut[1]
# for the left side
- category_to_polarity += category_to_atoms_polarity(left_side, polarity)
+ category_to_polarity += category_to_atoms_polarity(left_side, not polarity)
# for the right side
res = [bool(re.match(r'' + atom_type + "_\d+", right_side)) for atom_type in atom_map.keys()]
@@ -129,7 +130,7 @@ def category_to_atoms_polarity(category, polarity):
category_to_polarity += category_to_atoms_polarity(left_side, not polarity)
# for the right side
- category_to_polarity += category_to_atoms_polarity(right_side, polarity)
+ category_to_polarity += category_to_atoms_polarity(right_side, not polarity)
return category_to_polarity
@@ -145,13 +146,14 @@ def find_pos_neg_idexes(max_atoms_in_sentence, atoms_batch):
list_batch = []
for sentence in atoms_batch:
list_atoms = []
- polarity = False
for category in sentence:
+ polarity = True
for at in category_to_atoms_polarity(category, polarity):
list_atoms.append(at)
list_batch.append(torch.as_tensor(list_atoms))
return pad_sequence([list_batch[i] for i in range(len(list_batch))],
max_len=max_atoms_in_sentence, padding_value=0)
-
-print(find_pos_neg_idexes(9, [['dr(0,dl(0,dr(0,pp_52,np_53),dl(0,np_41,np_32)),dr(0,s_54,dia(1,box(1,pp_55))))', 'dr(0,dl(0,np_58,s_59),pp_55)']]))
\ No newline at end of file
+atoms_pol = find_pos_neg_idexes(10, [['dr(1,np_1,s_1)', 'dl(1,np_1,s_1)']])
+print(atoms_pol)
+print(get_axiom_links(10, atoms_pol, [['dr(1,np_1,s_1)', 'dl(1,np_1,s_1)']]))
diff --git a/SuperTagger/__pycache__/eval.cpython-38.pyc b/SuperTagger/__pycache__/eval.cpython-38.pyc
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diff --git a/SuperTagger/eval.py b/SuperTagger/eval.py
index b486e6a..9bfdc85 100644
--- a/SuperTagger/eval.py
+++ b/SuperTagger/eval.py
@@ -14,7 +14,7 @@ class SinkhornLoss(Module):
super(SinkhornLoss, self).__init__()
def forward(self, predictions, truths):
- return sum(nll_loss(link.flatten(0, 1), perm.flatten(), reduction='mean')
+ return sum(nll_loss(link.flatten(0, 1), perm.flatten(), reduction='mean', ignore_index=-1)
for link, perm in zip(predictions, truths))
@@ -25,7 +25,9 @@ def mesure_accuracy(linking_plus_to_minus, axiom_links_pred):
"""
correct_links = torch.ones(axiom_links_pred.size())
correct_links[axiom_links_pred != linking_plus_to_minus] = 0
+ correct_links[linking_plus_to_minus == -1] = 1
num_correct_links = correct_links.sum().item()
+ num_masked_atoms = len(linking_plus_to_minus[linking_plus_to_minus == -1])
# diviser par nombre de links
- return num_correct_links/(axiom_links_pred.size()[0] * axiom_links_pred.size()[1] * axiom_links_pred.size()[2])
+ return (num_correct_links - num_masked_atoms)/(axiom_links_pred.size()[0] * axiom_links_pred.size()[1] * axiom_links_pred.size()[2] - num_masked_atoms)
--
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