From 77ac0b8697ea67c236efdfb11cea70b5fa0422cf Mon Sep 17 00:00:00 2001
From: Caroline DE POURTALES <cdepourt@montana.irit.fr>
Date: Tue, 17 May 2022 15:28:25 +0200
Subject: [PATCH] brouillon main predict
---
Linker/Linker.py | 3 +++
SuperTagger/__init__.py | 0
main.py | 16 +++++++++-------
3 files changed, 12 insertions(+), 7 deletions(-)
delete mode 100644 SuperTagger/__init__.py
diff --git a/Linker/Linker.py b/Linker/Linker.py
index 4d4eaaa..7f7462d 100644
--- a/Linker/Linker.py
+++ b/Linker/Linker.py
@@ -192,6 +192,7 @@ class Linker(Module):
self.scheduler.step()
avg_train_loss = epoch_loss / len(training_dataloader)
+ print("Average Loss on train dataset : ", avg_train_loss)
if checkpoint:
checkpoint_dir = os.path.join("Output", 'Tranning_' + datetime.today().strftime('%d-%m_%H-%M'))
@@ -200,6 +201,8 @@ class Linker(Module):
if validate:
with torch.no_grad():
accuracy, average_test_loss = self.eval_epoch(validation_dataloader, self.cross_entropy_loss)
+ print("Average Loss on test dataset : ", average_test_loss)
+ print("Average Accuracy on test dataset : ", accuracy)
return accuracy, avg_train_loss
diff --git a/SuperTagger/__init__.py b/SuperTagger/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/main.py b/main.py
index 723f0f1..55e8c52 100644
--- a/main.py
+++ b/main.py
@@ -1,5 +1,5 @@
import torch.nn.functional as F
-
+import torch
from Configuration import Configuration
from Linker.Linker import Linker
from Supertagger.SuperTagger.SuperTagger import SuperTagger
@@ -7,15 +7,17 @@ from Supertagger.SuperTagger.SuperTagger import SuperTagger
max_atoms_in_sentence = int(Configuration.datasetConfig['max_atoms_in_sentence'])
# categories tagger
-tagger = SuperTagger()
-tagger.load_weights("models/model_check.pt")
+supertagger = SuperTagger()
+supertagger.load_weights("models/model_supertagger.pt")
# axiom linker
-linker = Linker()
+linker = Linker(supertagger)
linker.load_weights("models/linker.pt")
# predict categories and links for this sentence
-sentence = [[]]
-categories, sentence_embedding = tagger.predict(sentence)
+sentence = ["le chat est noir"]
+sents_tokenized, sents_mask = supertagger.sent_tokenizer.fit_transform_tensors(sentence)
+logits, sentence_embedding = supertagger.foward(sents_tokenized, sents_mask)
+categories = torch.argmax(F.softmax(logits, dim=2), dim=2)
-axiom_links = linker.predict(categories, sentence_embedding)
+axiom_links = linker.predict(categories, sentence_embedding, sents_mask)
--
GitLab