iptionnal cuda

__init__.py

@@ -36,7 +36,8 @@ import importlib
 
 # Read environment variable if it exists
 SIDEKIT_CONFIG={"libsvm":True,
-                "mpi":False
+                "mpi":False,
+                "cuda":False
                 }
 
 if 'SIDEKIT' in os.environ:
@@ -47,7 +48,11 @@ if 'SIDEKIT' in os.environ:
                 SIDEKIT_CONFIG["libsvm"] = False 
         elif k == "mpi":
             if val == "true":
-               SIDEKIT_CONFIG["mpi"] = True 
+               SIDEKIT_CONFIG["mpi"] = True
+        if k == "cuda":
+            if val == "true":
+                SIDEKIT_CONFIG["libsvm"] = True
+
 
 PARALLEL_MODULE = 'multiprocessing'  # can be , threading, multiprocessing MPI is planned in the future
 PARAM_TYPE = numpy.float32
@@ -103,8 +108,6 @@ from sidekit.frontend.features import mfcc
 from sidekit.frontend.features import pca_dct
 from sidekit.frontend.features import shifted_delta_cepstral
 
-from sidekit.nnet import FForwardNetwork
-
 from sidekit.iv_scoring import cosine_scoring
 from sidekit.iv_scoring import mahalanobis_scoring
 from sidekit.iv_scoring import two_covariance_scoring
@@ -149,10 +152,29 @@ if libsvm_loaded:
     from sidekit.svm_training import *
 
 
+CUDA = False
+if SIDEKIT_CONFIG["cuda"]:
+    CUDA = True
+
+
+if CUDA:
+    from sidekit.nnet import FForwardNetwork
+    from sidekit.nnet import kaldi_to_hdf5
+    from sidekit.nnet import XvectorMultiDataset
+    from sidekit.nnet import XvectorDataset
+    from sidekit.nnet import StatDataset
+    from sidekit.nnet import Xtractor
+    from sidekit.nnet import xtrain
+    from sidekit.nnet import extract_idmap
+    from sidekit.nnet import extract_parallel
+
+
 if SIDEKIT_CONFIG["mpi"]:
     found_mpi4py = importlib.find_loader('mpi4py') is not None
     if found_mpi4py:
-        from sidekit.sidekit_mpi import EM_split, total_variability, extract_ivector
+        from sidekit.sidekit_mpi import EM_split
+        from sidekit.sidekit_mpi import total_variability
+        from sidekit.sidekit_mpi import extract_ivector
         print("Import MPI")
         
 

nnet/sad_rnn.py

@@ -114,37 +114,6 @@ class SAD_RNN():
         else:
             self.model = model
 
-
-    def _sad_generator(self, train_list, uem_list, features_server):
-        """
-        Internal method that generates batches of features
-        :param train_list:
-        :param uem_list: 
-        :param features_server:
-        """
-        batch_X = numpy.zeros((self.batch_size, self.duration, self.input_size))
-        batch_Y = numpy.zeros((self.batch_size, self.duration, 1))
-        batch_i = 0
-
-        for show in sorted(train_list.keys()):
-            features, _ = features_server.load(show)
-            labels = numpy.zeros((len(features), 1), dtype=numpy.int)
-            for seg in train_list[show]:
-                labels[seg['start']:seg['stop']] = 1
-
-            for seg in uem_list[show]:
-                start, stop = seg['start'], seg['stop']
-                for i in range(start, min(stop, len(features)) - self.duration, self.step):
-                    batch_X[batch_i] = features[i:i + self.duration]
-                    batch_Y[batch_i] = labels[i:i + self.duration]
-                    batch_i += 1
-
-                    if batch_i == self.batch_size:
-                        X = torch.Tensor(batch_X)
-                        Y = torch.Tensor(batch_Y)
-                        yield X, Y
-                        batch_i = 0
-
     def _fit_batch(self, optimizer, criterion, X, Y):
         """
         Internal method used to train the network