update

Configuration/config.ini

@@ -6,7 +6,7 @@ symbols_vocab_size=26
 atom_vocab_size=20
 max_len_sentence=109
 max_atoms_in_sentence=1250
-max_atoms_in_one_type=250
+max_atoms_in_one_type=324
 
 [MODEL_ENCODER]
 dim_encoder = 768
@@ -26,8 +26,7 @@ dropout=0.1
 sinkhorn_iters=3
 
 [MODEL_TRAINING]
-device=cpu
 batch_size=16
-epoch=25
+epoch=30
 seed_val=42
 learning_rate=2e-4

Linker/Linker.py

@@ -22,9 +22,10 @@ from Linker.atom_map import atom_map
 from Linker.eval import mesure_accuracy, SinkhornLoss
 from Linker.utils_linker import find_pos_neg_idexes, get_atoms_batch, FFN, get_axiom_links, get_pos_encoding_for_s_idx, \
     get_neg_encoding_for_s_idx
-from SuperTagger import *
+from Supertagger import *
 from utils import pad_sequence
 
+
 def format_time(elapsed):
     '''
     Takes a time in seconds and returns a string hh:mm:ss
@@ -35,6 +36,7 @@ def format_time(elapsed):
     # Format as hh:mm:ss
     return str(datetime.timedelta(seconds=elapsed_rounded))
 
+
 def output_create_dir():
     """
     Create le output dir for tensorboard and checkpoint
@@ -99,16 +101,17 @@ class Linker(Module):
         Returns:
             the training dataloader and the validation dataloader. They contains the list of atoms, their polarities, the axiom links, the sentences tokenized, sentence mask
         """
-        sentences_batch = df_axiom_links["Sentences"].tolist()
+        print("Start preprocess Data")
+        sentences_batch = df_axiom_links["X"].tolist()
         sentences_tokens, sentences_mask = self.Supertagger.sent_tokenizer.fit_transform_tensors(sentences_batch)
 
-        atoms_batch = get_atoms_batch(df_axiom_links["sub_tree"])
+        atoms_batch = get_atoms_batch(df_axiom_links["Z"])
         atoms_batch_tokenized = self.atoms_tokenizer.convert_batchs_to_ids(atoms_batch)
 
-        atoms_polarity_batch = find_pos_neg_idexes(self.max_atoms_in_sentence, df_axiom_links["sub_tree"])
+        atoms_polarity_batch = find_pos_neg_idexes(self.max_atoms_in_sentence, df_axiom_links["Z"])
 
         truth_links_batch = get_axiom_links(self.max_atoms_in_one_type, atoms_polarity_batch,
-                                            df_axiom_links["sub_tree"])
+                                            df_axiom_links["Y"])
         truth_links_batch = truth_links_batch.permute(1, 0, 2)
 
         # Construction tensor dataset
@@ -125,6 +128,7 @@ class Linker(Module):
             train_dataset = dataset
 
         training_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=False)
+        print("End preprocess Data")
         return training_dataloader, validation_dataloader
 
     def make_decoder_mask(self, atoms_token):
@@ -202,7 +206,6 @@ class Linker(Module):
             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}%')
@@ -215,7 +218,6 @@ class Linker(Module):
                 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)

Linker/utils_linker.py

@@ -23,6 +23,7 @@ class FFN(Module):
         return self.ffn(x)
 
 
+regex_categories_axiom_links = r'\w+\(\d+,(?:((?R))|(\w+))*,?(?:((?R))|(\w+))*\)'
 regex_categories = r'\w+\(\d+,(?:((?R))|(\w+))*,?(?:((?R))|(\w+))*\)'
 
 
@@ -76,7 +77,7 @@ def category_to_atoms_axiom_links(category, categories_to_atoms):
     elif True in res:
         return [category]
     else:
-        category_cut = regex.match(regex_categories, category).groups()
+        category_cut = regex.match(regex_categories_axiom_links, category).groups()
         category_cut = [cat for cat in category_cut if cat is not None]
         for cat in category_cut:
             categories_to_atoms += category_to_atoms_axiom_links(cat, [])
@@ -111,14 +112,16 @@ def category_to_atoms(category, categories_to_atoms):
     Returns:
         List of atoms inside the category in prefix order
     """
-    res = [bool(re.match(r'' + atom_type + "_\d+", category)) for atom_type in atom_map.keys()]
+    res = [bool(re.match(r'' + atom_type, category)) for atom_type in atom_map.keys()]
     if category.startswith("GOAL:"):
         word, cat = category.split(':')
         return category_to_atoms(cat, categories_to_atoms)
     elif True in res:
-        category = re.match(r'([a-zA-Z|_]+)_\d+', category).group(1)
         return [category]
+    elif category == "let":
+        return []
     else:
+        print(category)
         category_cut = regex.match(regex_categories, category).groups()
         category_cut = [cat for cat in category_cut if cat is not None]
         for cat in category_cut:
@@ -155,21 +158,21 @@ def category_to_atoms_polarity(category, polarity):
         Boolean Tensor of shape max_symbols_in_word, containing 1 for pos indexes and 0 for neg indexes
     """
     category_to_polarity = []
-    res = [bool(re.match(r'' + atom_type + "_\d+", category)) for atom_type in atom_map.keys()]
+    res = [bool(re.match(r'' + atom_type, category)) for atom_type in atom_map.keys()]
 
     # mot final
     if category.startswith("GOAL:"):
         word, cat = category.split(':')
-        res = [bool(re.match(r'' + atom_type + "_\d+", cat)) for atom_type in atom_map.keys()]
+        res = [bool(re.match(r'' + atom_type, cat)) for atom_type in atom_map.keys()]
         if True in res:
             category_to_polarity.append(True)
         else:
             category_to_polarity += category_to_atoms_polarity(cat, True)
-
+    elif category == "let":
+        pass
     # le mot a une category atomique
     elif True in res or category.startswith("dia") or category.startswith("box"):
         category_to_polarity.append(not polarity)
-
     # sinon c'est une formule longue
     else:
         # dr = /

Supertagger @ eeb4774c

+Subproject commit eeb4774c071e03f460f48798ab8d6820395825c9

bash_GPU.sh

 #!/bin/sh
-#SBATCH --job-name=Deepgrail_Linker
+#SBATCH --job-name=Deepgrail_Linker_9000
 #SBATCH --partition=RTX6000Node
 #SBATCH --gres=gpu:1
 #SBATCH --mem=32000

command_line.txt

+scp -r cdepourt@osirim-slurm.irit.fr:projets/deepgrail2/deepgrail_RNN_with_linker/TensorBoard/Tranning_19-05_09-49/logs /home/cdepourt/Bureau/deepgrail_RNN_with_linker/logs
+
+rsync -av -e ssh --exclude="__pycache__" --exclude="venv" --exclude=".git" --exclude=".idea"  -r /home/cdepourt/Bureau/deepgrail_RNN_with_linker cdepourt@osirim-slurm.irit.fr:projets/deepgrail2
+

utils.py

@@ -37,7 +37,7 @@ def read_csv_pgbar(csv_path, nrows=float('inf'), chunksize=500):
         chunksize = nrows
 
     with tqdm(total=rows, desc='Rows read: ') as bar:
-        for chunk in pd.read_csv(csv_path, converters={'sub_tree': pd.eval, 'Z': pd.eval}, chunksize=chunksize, nrows=rows):
+        for chunk in pd.read_csv(csv_path, converters={'Y': pd.eval, 'Z': pd.eval}, chunksize=chunksize, nrows=rows):
             chunk_list.append(chunk)
             bar.update(len(chunk))