xtractor single

__init__.py

@@ -37,7 +37,7 @@ import importlib
 # Read environment variable if it exists
 SIDEKIT_CONFIG={"libsvm":True,
                 "mpi":False,
-                "cuda":False
+                "cuda":True
                 }
 
 if 'SIDEKIT' in os.environ:
@@ -165,9 +165,10 @@ if CUDA:
     from sidekit.nnet import StatDataset
     from sidekit.nnet import Xtractor
     from sidekit.nnet import xtrain
+    from sidekit.nnet import xtrain_single
     from sidekit.nnet import extract_idmap
     from sidekit.nnet import extract_parallel
-    from sidekit.nnet import SAD_RNN
+    #from sidekit.nnet import SAD_RNN
 else:
     print("Don't import Torch")
 

nnet/__init__.py

@@ -27,11 +27,11 @@ Copyright 2014-2019 Anthony Larcher and Sylvain Meignier
 :mod:`nnet` provides methods to manage Neural Networks using PyTorch
 """
 
-from sidekit.nnet.sad_rnn import SAD_RNN
+#from sidekit.nnet.sad_rnn import SAD_RNN
 from sidekit.nnet.feed_forward import FForwardNetwork
 from sidekit.nnet.feed_forward import kaldi_to_hdf5
 from sidekit.nnet.xsets import XvectorMultiDataset, XvectorDataset, StatDataset
-from sidekit.nnet.xvector import Xtractor, xtrain, extract_idmap, extract_parallel
+from sidekit.nnet.xvector import Xtractor, xtrain, extract_idmap, extract_parallel, xtrain_single
 
 
 __author__ = "Anthony Larcher and Sylvain Meignier"

nnet/xvector.py

@@ -469,19 +469,23 @@ def xtrain_single(args):
 
     for epoch in range(1, args.epochs + 1):
         # Process one epoch and return the current model
-        model = train_epoch_single(model, epoch, args, current_model_file_name)
+        model = train_epoch_single(model, epoch, args)
 
         # Add the cross validation here
         #accuracy = cross_validation_single(args, current_model_file_name)
         #print("*** Cross validation accuracy = {} %".format(accuracy))
 
         # Decrease learning rate after every epoch
-        #args.lr = args.lr * 0.9
-        #args.lr = args.lr * 0.9
-        #print("        Decrease learning rate: {}".format(args.lr))
+        args.lr = args.lr * 0.9
+        args.lr = args.lr * 0.9
+        print("        Decrease learning rate: {}".format(args.lr))
 
+        # return the file name of the new model
+        current_model_file_name = "{}/model_{}_epoch_{}".format(args.model_path, args.expe_id, epoch)
+        torch.save(model, current_model_file_name)
 
-def train_epoch_single(model, epoch, args, batch_list, output_queue):
+
+def train_epoch_single(model, epoch, args):
     """
 
     :param model:
@@ -494,6 +498,13 @@ def train_epoch_single(model, epoch, args, batch_list, output_queue):
     device =  device = torch.device("cuda:0")
 
     torch.manual_seed(args.seed)
+
+    # Get the list of batches
+    print(args.batch_training_list)
+
+    with open(args.batch_training_list, 'r') as fh:
+        batch_list = [l.rstrip() for l in fh]
+
     train_loader = XvectorMultiDataset(batch_list, args.batch_path)
 
     optimizer = optim.Adam([{'params': model.frame_conv0.parameters(), 'weight_decay': args.l2_frame},