Merge pull request #309 from MatthewInkawhich/rc1

ericnakagawa · web-flow · commit 5dc364f29cef · 2018-09-18T17:14:27.000-07:00
Added hosted model download link and fixed model saving
diff --git a/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py b/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py
@@ -117,9 +117,8 @@
 import unicodedata
 import numpy as np
 
+device = torch.device("cpu")
 
-USE_CUDA = torch.cuda.is_available()
-device = torch.device("cuda" if USE_CUDA else "cpu")
 
 MAX_LENGTH = 10  # Maximum sentence length
 
@@ -677,7 +676,7 @@ def evaluateExample(sentence, encoder, decoder, searcher, voc):
 #
 # To load the hosted model:
 #
-# 1) Download the model `here <>`__.
+# 1) Download the model `here <https://download.pytorch.org/models/tutorials/4000_checkpoint.tar>`__.
 #
 # 2) Set the ``loadFilename`` variable to the path to the downloaded
 #    checkpoint file.
@@ -728,18 +727,19 @@ def evaluateExample(sentence, encoder, decoder, searcher, voc):
 dropout = 0.1
 batch_size = 64
 
+# If you're loading your own model
 # Set checkpoint to load from
 checkpoint_iter = 4000
-loadFilename = os.path.join(save_dir, model_name, corpus_name,
-                            '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size),
-                            '{}_checkpoint.tar'.format(checkpoint_iter))
+# loadFilename = os.path.join(save_dir, model_name, corpus_name,
+#                             '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size),
+#                             '{}_checkpoint.tar'.format(checkpoint_iter))
 
+# If you're loading the hosted model
+loadFilename = '4000_checkpoint.tar'
 
 # Load model
-# If loading on same machine the model was trained on
-checkpoint = torch.load(loadFilename)
-# If loading a model trained on GPU to CPU
-#checkpoint = torch.load(loadFilename, map_location=torch.device('cpu'))
+# Force CPU device options (to match tensors in this tutorial)
+checkpoint = torch.load(loadFilename, map_location=torch.device('cpu'))
 encoder_sd = checkpoint['en']
 decoder_sd = checkpoint['de']
 encoder_optimizer_sd = checkpoint['en_opt']
@@ -874,7 +874,8 @@ def evaluateExample(sentence, encoder, decoder, searcher, voc):
 # will serialize it for use in a non-Python deployment environment. To do
 # this, we can simply save our ``scripted_searcher`` module, as this is
 # the user-facing interface for running inference against the chatbot
-# model.
+# model. When saving a Script module, use script_module.save(PATH) instead
+# of torch.save(model, PATH).
 #
 
-torch.save(scripted_searcher.state_dict(), "scripted_chatbot.pth")
+scripted_searcher.save("scripted_chatbot.pth")
