debug aam

nnet/loss.py

@@ -282,7 +282,7 @@ class ArcMarginProduct(torch.nn.Module):
         output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
         output = output * self.s
 
-        return output
+        return output, cosine * self.s
 
 
 class SoftmaxAngularProto(torch.nn.Module):

nnet/xvector.py

@@ -1342,6 +1342,7 @@ def xtrain(dataset_description,
         torch.backends.cudnn.deterministic = True
 
     # Set all the seeds
+    random.seed(training_opts["random_seed"])
     numpy.random.seed(training_opts["numpy_seed"]) # Set the random seed of numpy for the data split.
     torch.manual_seed(training_opts["torch_seed"])
     torch.cuda.manual_seed(training_opts["torch_seed"])
@@ -1521,6 +1522,7 @@ def train_epoch(model,
 
     accuracy = 0.0
     running_loss = 0.0
+    batch_count = 0
     for batch_idx, (data, target) in enumerate(training_loader):
         data = data.squeeze().to(device)