Merge branch 'master' into minjean/torchserve_with_ipex_2

Author: min-jean-cho

beginner_source/fgsm_tutorial.py

@@ -17,7 +17,7 @@
 machine learning. You may be surprised to find that adding imperceptible
 perturbations to an image *can* cause drastically different model
 performance. Given that this is a tutorial, we will explore the topic
-via example on an image classifier. Specifically we will use one of the
+via example on an image classifier. Specifically, we will use one of the
 first and most popular attack methods, the Fast Gradient Sign Attack
 (FGSM), to fool an MNIST classifier.
 

beginner_source/translation_transformer.py

@@ -309,7 +309,7 @@ def train_epoch(model, optimizer):
         optimizer.step()
         losses += loss.item()
 
-    return losses / len(train_dataloader)
+    return losses / len(list(train_dataloader))
 
 
 def evaluate(model):
@@ -333,7 +333,7 @@ def evaluate(model):
         loss = loss_fn(logits.reshape(-1, logits.shape[-1]), tgt_out.reshape(-1))
         losses += loss.item()
 
-    return losses / len(val_dataloader)
+    return losses / len(list(val_dataloader))
 
 ######################################################################
 # Now we have all the ingredients to train our model. Let's do it!

intermediate_source/text_to_speech_with_torchaudio.py

@@ -293,11 +293,11 @@ def text_to_sequence(text):
 # Waveglow
 # ~~~~~~~~
 # 
-# Waveglow is a vocoder published by Nvidia. The pretrained weight is
-# publishe on Torch Hub. One can instantiate the model using ``torch.hub``
+# Waveglow is a vocoder published by Nvidia. The pretrained weights are
+# published on Torch Hub. One can instantiate the model using ``torch.hub``
 # module.
 # 
-if False:
+if torch.cuda.is_available():
   waveglow = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_waveglow', model_math='fp32')
 else:
   # Workaround to load model mapped on GPU
@@ -309,7 +309,7 @@ def text_to_sequence(text):
       pretrained=False,
   )
   checkpoint = torch.hub.load_state_dict_from_url(
-      "https://api.ngc.nvidia.com/v2/models/nvidia/waveglowpyt_fp32/versions/1/files/nvidia_waveglowpyt_fp32_20190306.pth",
+      "https://api.ngc.nvidia.com/v2/models/nvidia/waveglow_ckpt_fp32/versions/19.09.0/files/nvidia_waveglowpyt_fp32_20190427",
       progress=False,
       map_location=device,
   )