brouillon main predict

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
 

main.py

 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)