add replicas

nnet/xsets.py

@@ -62,6 +62,7 @@ class SideSampler(torch.utils.data.Sampler):
                  batch_size,
                  seed=0,
                  rank=0,
+                 num_process=1,
                  num_replicas=1):
         """[summary]
 
@@ -81,29 +82,34 @@ class SideSampler(torch.utils.data.Sampler):
         self.epoch = 0
         self.seed = seed
         self.rank = rank
+        self.num_process = num_process
         self.num_replicas = num_replicas
 
-        assert batch_size % examples_per_speaker == 0
-        assert (self.samples_per_speaker * self.spk_count * self.examples_per_speaker) % self.num_replicas == 0
+        assert batch_size % (examples_per_speaker * self.num_replicas) == 0
+        assert (self.samples_per_speaker * self.spk_count * self.examples_per_speaker) % self.num_process == 0
 
-        self.batch_size = batch_size // examples_per_speaker
+        self.batch_size = batch_size // (self.examples_per_speaker * self.num_replicas)
 
         # reference all segment indexes per speaker
         for idx in range(self.spk_count):
             self.labels_to_indices[idx] = list()
         for idx, value in enumerate(self.train_sessions):
             self.labels_to_indices[value].append(idx)
-        # suffle segments per speaker
+        # shuffle segments per speaker
         g = torch.Generator()
         g.manual_seed(self.seed + self.epoch)
         for idx, ldlist in enumerate(self.labels_to_indices.values()):
             ldlist = numpy.array(ldlist)
-            self.labels_to_indices[idx] = ldlist[torch.randperm(ldlist.shape[0]).numpy()]
+            self.labels_to_indices[idx] = ldlist[torch.randperm(ldlist.shape[0], generator=g).numpy()]
 
         self.segment_cursors = numpy.zeros((len(self.labels_to_indices),), dtype=numpy.int)
 
 
     def __iter__(self):
+        g = torch.Generator()
+        g.manual_seed(self.seed + self.epoch)
+        numpy.random.seed(self.seed + self.epoch)
+
         # Generate batches per speaker
         straight = numpy.arange(self.spk_count)
         indices = numpy.ones((self.samples_per_speaker, self.spk_count), dtype=numpy.int) * straight
@@ -134,9 +140,6 @@ class SideSampler(torch.utils.data.Sampler):
         # we want to convert the speaker indexes into segment indexes
         self.index_iterator = numpy.zeros_like(batch_matrix)
 
-        g = torch.Generator()
-        g.manual_seed(self.seed + self.epoch)
-
         # keep track of next segment index to sample for each speaker
         for idx, value in enumerate(batch_matrix):
             if self.segment_cursors[value] > len(self.labels_to_indices[value]) - 1:
@@ -144,13 +147,15 @@ class SideSampler(torch.utils.data.Sampler):
                 self.segment_cursors[value] = 0
             self.index_iterator[idx] = self.labels_to_indices[value][self.segment_cursors[value]]
             self.segment_cursors[value] += 1
-        self.index_iterator = self.index_iterator.reshape(-1, self.num_replicas * self.examples_per_speaker)[:, self.rank * self.examples_per_speaker:(self.rank + 1) * self.examples_per_speaker].flatten()
 
-        return iter(self.index_iterator)
+        self.index_iterator = numpy.repeat(self.index_iterator, self.num_replicas)
 
+        self.index_iterator = self.index_iterator.reshape(-1, self.num_process * self.examples_per_speaker * self.num_replicas)[:, self.rank * self.examples_per_speaker * self.num_replicas:(self.rank + 1) * self.examples_per_speaker * self.num_replicas].flatten()
+
+        return iter(self.index_iterator)
 
     def __len__(self) -> int:
-        return (self.samples_per_speaker * self.spk_count * self.examples_per_speaker) // self.num_replicas
+        return (self.samples_per_speaker * self.spk_count * self.examples_per_speaker * self.num_replicas) // self.num_process
 
     def set_epoch(self, epoch: int) -> None:
         self.epoch = epoch

nnet/xvector.py

@@ -1064,6 +1064,8 @@ def get_loaders(dataset_opts, training_opts, model_opts, local_rank=0):
                                    batch_size=batch_size,
                                    seed=training_opts['torch_seed'],
                                    rank=local_rank,
+                                   num_process=torch.cuda.device_count(),
+                                   num_replicas=dataset_opts["train"]["sampler"]["augmentation_replica"]
                                    )
     else:
         batch_size = dataset_opts["batch_size"]