From aba4f1cd12248ce6b21d275bf3ed8387dc812393 Mon Sep 17 00:00:00 2001
From: Schneider Leo <leo.schneider@etu.ec-lyon.fr>
Date: Tue, 15 Oct 2024 13:48:11 +0200
Subject: [PATCH] seq_length args
---
config_common.py | 3 +++
main_custom.py | 15 ++++++++-------
2 files changed, 11 insertions(+), 7 deletions(-)
diff --git a/config_common.py b/config_common.py
index 09dea8f..3b9a019 100644
--- a/config_common.py
+++ b/config_common.py
@@ -1,5 +1,7 @@
import argparse
+from tensorflow.python.keras.utils.generic_utils import default
+
def load_args():
parser = argparse.ArgumentParser()
@@ -27,6 +29,7 @@ def load_args():
parser.add_argument('--norm_first', action=argparse.BooleanOptionalAction)
parser.add_argument('--activation', type=str,default='relu')
parser.add_argument('--file', action=argparse.BooleanOptionalAction)
+ parser.add_argument('--seq_length', type=int, default=25)
args = parser.parse_args()
return args
diff --git a/main_custom.py b/main_custom.py
index 24d11ff..8aa621b 100644
--- a/main_custom.py
+++ b/main_custom.py
@@ -223,28 +223,28 @@ def main(args):
data_train, data_val, data_test = common_dataset.load_data(path_train=args.dataset_train,
path_val=args.dataset_val,
path_test=args.dataset_test,
- batch_size=args.batch_size, length=25, pad = True, convert=True, vocab='unmod')
+ batch_size=args.batch_size, length=args.seq_length, pad = True, convert=True, vocab='unmod')
elif args.forward == 'rt':
data_train, data_val, data_test = dataloader.load_data(data_sources=[args.dataset_train,args.dataset_val,args.dataset_test],
- batch_size=args.batch_size, length=25)
+ batch_size=args.batch_size, length=args.seq_length)
elif args.forward == 'transfer':
data_train, _, _ = dataloader.load_data(data_sources=[args.dataset_train,'database/data_holdout.csv','database/data_holdout.csv'],
- batch_size=args.batch_size, length=25)
+ batch_size=args.batch_size, length=args.seq_length)
_, data_val, data_test = common_dataset.load_data(path_train=args.dataset_val,
path_val=args.dataset_val,
path_test=args.dataset_test,
- batch_size=args.batch_size, length=25, pad = True, convert=True, vocab='unmod')
+ batch_size=args.batch_size, length=args.seq_length, pad = True, convert=True, vocab='unmod')
elif args.forward == 'reverse':
_, data_val, data_test = dataloader.load_data(data_sources=['database/data_train.csv',args.dataset_val,args.dataset_test],
- batch_size=args.batch_size, length=25)
+ batch_size=args.batch_size, length=args.seq_length)
data_train, _, _ = common_dataset.load_data(path_train=args.dataset_train,
path_val=args.dataset_train,
path_test=args.dataset_train,
- batch_size=args.batch_size, length=25, pad = True, convert=True, vocab='unmod')
+ batch_size=args.batch_size, length=args.seq_length, pad = True, convert=True, vocab='unmod')
print('\nData loaded')
@@ -253,7 +253,8 @@ def main(args):
, n_head=args.n_head, encoder_num_layer=args.encoder_num_layer,
decoder_int_num_layer=args.decoder_int_num_layer,
decoder_rt_num_layer=args.decoder_rt_num_layer, drop_rate=args.drop_rate,
- embedding_dim=args.embedding_dim, acti=args.activation, norm=args.norm_first)
+ embedding_dim=args.embedding_dim, acti=args.activation, norm=args.norm_first,
+ seq_length=args.seq_length)
if torch.cuda.is_available():
model = model.cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
--
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