correction

Configuration/config.ini

@@ -4,7 +4,7 @@ transformers = 4.16.2
 [DATASET_PARAMS]
 symbols_vocab_size=26
 atom_vocab_size=18
-max_len_sentence=83
+max_len_sentence=290
 max_atoms_in_sentence=875
 max_atoms_in_one_type=324
 
@@ -12,7 +12,7 @@ max_atoms_in_one_type=324
 dim_encoder = 768
 
 [MODEL_LINKER]
-nhead=16
+nhead=8
 dim_emb_atom = 256
 dim_feedforward_transformer = 768
 num_layers=3
@@ -25,6 +25,6 @@ sinkhorn_iters=5
 
 [MODEL_TRAINING]
 batch_size=32
-epoch=25
+epoch=30
 seed_val=42
 learning_rate=2e-3
\ No newline at end of file

Linker/DataParallelLinker.py

-import datetime
-
-from torch.nn import DataParallel, Module
-from Linker import *
-
-
-class DataParallelModel(Module):
-
-    def __init__(self):
-        super().__init__()
-        self.linker = DataParallel(Linker("models/flaubert_super_98_V2_50e.pt"))
-
-    def forward(self, x):
-        x = self.linker(x)
-        return x
-
-    def train_linker(self, df_axiom_links, validation_rate=0.1, epochs=20,
-                     batch_size=32, checkpoint=True, tensorboard=False):
-        r"""
-        Args:
-            df_axiom_links : pandas dataFrame containing the atoms anoted with _i
-            validation_rate : float
-            epochs : int
-            batch_size : int
-            checkpoint : boolean
-            tensorboard : boolean
-        Returns:
-            Final accuracy and final loss
-        """
-        training_dataloader, validation_dataloader = self.__preprocess_data(batch_size, df_axiom_links,
-                                                                            validation_rate)
-        if checkpoint or tensorboard:
-            checkpoint_dir, writer = output_create_dir()
-
-        for epoch_i in range(epochs):
-            print("")
-            print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))
-            print('Training...')
-            avg_train_loss, avg_accuracy_train, training_time = self.train_epoch(training_dataloader)
-
-            print("")
-            print(f'Epoch: {epoch_i + 1:02} | Epoch Time: {training_time}')
-            print(f'\tTrain Loss: {avg_train_loss:.3f} | Train Acc: {avg_accuracy_train * 100:.2f}%')
-
-            if validation_rate > 0.0:
-                loss_test, accuracy_test = self.eval_epoch(validation_dataloader)
-                print(f'\tVal Loss: {loss_test:.3f} | Val Acc: {accuracy_test * 100:.2f}%')
-
-            if checkpoint:
-                self.__checkpoint_save(
-                    path=os.path.join("Output", 'linker' + datetime.today().strftime('%d-%m_%H-%M') + '.pt'))
-
-            if tensorboard:
-                writer.add_scalars(f'Accuracy', {
-                    'Train': avg_accuracy_train}, epoch_i)
-                writer.add_scalars(f'Loss', {
-                    'Train': avg_train_loss}, epoch_i)
-                if validation_rate > 0.0:
-                    writer.add_scalars(f'Accuracy', {
-                        'Validation': accuracy_test}, epoch_i)
-                    writer.add_scalars(f'Loss', {
-                        'Validation': loss_test}, epoch_i)
-
-            print('\n')
\ No newline at end of file

Linker/Linker.py

@@ -22,7 +22,7 @@ from Linker.Sinkhorn import sinkhorn_fn_no_exp as sinkhorn
 from Linker.AtomTokenizer import AtomTokenizer
 from Linker.atom_map import atom_map, atom_map_redux
 from Linker.eval import mesure_accuracy, SinkhornLoss
-from Linker.utils_linker import FFN, get_axiom_links, get_GOAL, get_pos_idx, get_num_atoms_batch
+from Linker.utils_linker import FFN, get_axiom_links, get_GOAL, get_pos_idx, get_num_atoms_batch, get_neg_idx
 from Supertagger import SuperTagger
 from utils import pad_sequence
 
@@ -149,7 +149,7 @@ class Linker(Module):
         num_atoms_per_word = get_num_atoms_batch(df_axiom_links["Z"], self.max_len_sentence)
 
         pos_idx = get_pos_idx(atoms_batch, atoms_polarity_batch, self.max_atoms_in_one_type, self.max_atoms_in_sentence)
-        neg_idx = get_pos_idx(atoms_batch, atoms_polarity_batch, self.max_atoms_in_one_type, self.max_atoms_in_sentence)
+        neg_idx = get_neg_idx(atoms_batch, atoms_polarity_batch, self.max_atoms_in_one_type, self.max_atoms_in_sentence)
 
         truth_links_batch = get_axiom_links(self.max_atoms_in_one_type, atoms_polarity_batch,
                                             df_axiom_links["Y"])