predict links with drawing possible

Linker/Linker.py

@@ -440,7 +440,7 @@ class Linker(Module):
         with torch.no_grad():
             self.cpu()
             self.device = torch.device("cpu")
-            sentences_tokens, sentences_mask = self.Supertagger.sent_tokenizer.fit_transform_tensors([sentence])
+            sentences_tokens, sentences_mask = self.Supertagger.sent_tokenizer.fit_transform_tensors(sentence)
             nb_sentence, len_sentence = sentences_tokens.shape
 
             hidden_state, categories = self.Supertagger.predict(sentence)

postprocessing.py

@@ -113,7 +113,8 @@ def draw_sentence_output(sentence, categories, links):
     return dot.source
 
 
-sentence = ["Le", "chat", "est", "noir", "bleu"]
-categories = ["dr(0,s,n)", "dl(0,s,n)", "dr(0,dl(0,n,np),n)", "dl(0,np,n)", "n"]
-links = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 2, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
-draw_sentence_output(sentence, categories, links)
+if __name__== '__main__':
+    sentence = ["Le", "chat", "est", "noir", "bleu"]
+    categories = ["dr(0,s,n)", "dl(0,s,n)", "dr(0,dl(0,n,np),n)", "dl(0,np,n)", "n"]
+    links = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 2, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
+    draw_sentence_output(sentence, categories, links)

predict_links.py

 from NeuralProofNet.NeuralProofNet import NeuralProofNet
+from postprocessing import draw_sentence_output
 
 # region data
-a_s = "( 1 ) parmi les huit \" partants \" acquis ou potentiels , MM. Lacombe , Koehler et Laroze ne sont pas membres " \
-      "du PCF . "
+a_s = ["( 1 ) parmi les huit \" partants \" acquis ou potentiels , MM. Lacombe , Koehler et Laroze ne sont pas membres du PCF ."]
 tags_s = ['let', 'dr(0,s,s)', 'let', 'dr(0,dr(0,s,s),np)', 'dr(0,np,n)', 'dr(0,n,n)', 'let', 'n', 'let', 'dl(0,n,n)',
            'dr(0,dl(0,dl(0,n,n),dl(0,n,n)),dl(0,n,n))', 'dl(0,n,n)', 'let', 'dr(0,np,np)', 'np', 'dr(0,dl(0,np,np),np)',
            'np', 'dr(0,dl(0,np,np),np)', 'np', 'dr(0,dl(0,np,s),dl(0,np,s))', 'dr(0,dl(0,np,s),np)', 'dl(1,s,s)', 'np',
@@ -20,7 +20,10 @@ neuralproofnet.linker.load_weights(model)
 
 # region prediction
 linker = neuralproofnet.linker
-links = linker.predict_without_categories(a_s)
+categories, links = linker.predict_without_categories(a_s)
 #links = linker.predict_with_categories(a_s, tags_s)
-print(links)
-# endregion
\ No newline at end of file
+# endregion
+
+if __name__== '__main__':
+      idx=0
+      draw_sentence_output(a_s[idx].split(" "), categories[idx], links[:,idx,:].numpy())
\ No newline at end of file

predict_supertags.py

@@ -2,12 +2,8 @@ from SuperTagger.SuperTagger.SuperTagger import SuperTagger
 from SuperTagger.SuperTagger.eval import categorical_accuracy
 
 # region data
-a_s = "( 1 ) parmi les huit \" partants \" acquis ou potentiels , MM. Lacombe , Koehler et Laroze ne sont pas membres " \
-      "du PCF . "
-tags_s = [['let', 'dr(0,s,s)', 'let', 'dr(0,dr(0,s,s),np)', 'dr(0,np,n)', 'dr(0,n,n)', 'let', 'n', 'let', 'dl(0,n,n)',
-           'dr(0,dl(0,dl(0,n,n),dl(0,n,n)),dl(0,n,n))', 'dl(0,n,n)', 'let', 'dr(0,np,np)', 'np', 'dr(0,dl(0,np,np),np)',
-           'np', 'dr(0,dl(0,np,np),np)', 'np', 'dr(0,dl(0,np,s),dl(0,np,s))', 'dr(0,dl(0,np,s),np)', 'dl(1,s,s)', 'np',
-           'dr(0,dl(0,np,np),n)', 'n', 'dl(0,s,txt)']]
+a_s = "85,6 % de membres du PCF ."
+tags_s = [['dr(0,np,n)', 'n', 'dr(0,dl(0,np,np),n)', 'n', 'dr(0,dl(0,n,n),n)', 'n', 'dl(0,np,txt)']]
 # endregion