Implementing inference functions

refactor/main.py

@@ -16,7 +16,7 @@ def main(args):
     _DATASET = '/home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle'
     EMBEDDINGS_PATH = '/home/andreapdr/gfun/embeddings'
     data = MultilingualDataset.load(_DATASET)
-    # data.set_view(languages=['it', 'fr'])
+    data.set_view(languages=['it', 'fr'])
     lX, ly = data.training()
     lXte, lyte = data.test()
 

refactor/models/pl_gru.py

@@ -126,7 +126,7 @@ class RecurrentModel(pl.LightningModule):
                         batch = lX[lang][i:i+batch_size]
                     max_pad_len = define_pad_length(batch)
                     batch = pad(batch, pad_index=l_pad[lang], max_pad_length=max_pad_len)
-                    X = torch.LongTensor(batch)
+                    X = torch.LongTensor(batch).to('cuda' if self.gpus else 'cpu')
                     _batch_size = X.shape[0]
                     X = self.embed(X, lang)
                     X = self.embedding_dropout(X, drop_range=self.drop_embedding_range, p_drop=self.drop_embedding_prop,

refactor/view_generators.py

@@ -229,6 +229,7 @@ class RecurrentGen(ViewGen):
         l_pad = self.multilingualIndex.l_pad()
         data = self.multilingualIndex.l_devel_index()
         # trainer = Trainer(gpus=self.gpus)
+        self.model.to('cuda' if self.gpus else 'cpu')
         self.model.eval()
         time_init = time()
         l_embeds = self.model.encode(data, l_pad, batch_size=256)