From d1c8b81386fcf1e578c6db288af0ee5f2b335990 Mon Sep 17 00:00:00 2001
From: Caroline DE POURTALES <cdepourt@montana.irit.fr>
Date: Thu, 23 Jun 2022 17:30:00 +0200
Subject: [PATCH] change padding handling
---
Configuration/config.ini | 6 ++--
Linker/DataParallelLinker.py | 64 ++++++++++++++++++++++++++++++++++++
2 files changed, 67 insertions(+), 3 deletions(-)
create mode 100644 Linker/DataParallelLinker.py
diff --git a/Configuration/config.ini b/Configuration/config.ini
index 61872f4..b33d6df 100644
--- a/Configuration/config.ini
+++ b/Configuration/config.ini
@@ -4,7 +4,7 @@ transformers = 4.16.2
[DATASET_PARAMS]
symbols_vocab_size=26
atom_vocab_size=18
-max_len_sentence=290
+max_len_sentence=83
max_atoms_in_sentence=875
max_atoms_in_one_type=324
@@ -12,10 +12,10 @@ max_atoms_in_one_type=324
dim_encoder = 768
[MODEL_LINKER]
-nhead=8
+nhead=16
dim_emb_atom = 256
dim_feedforward_transformer = 768
-num_layers=2
+num_layers=3
dim_cat_inter=512
dim_cat_out=256
dim_intermediate_FFN=128
diff --git a/Linker/DataParallelLinker.py b/Linker/DataParallelLinker.py
new file mode 100644
index 0000000..5885845
--- /dev/null
+++ b/Linker/DataParallelLinker.py
@@ -0,0 +1,64 @@
+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
--
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