edit image captioning code

Author: yunjey

tutorials/03-advanced/image_captioning/README.md

@@ -56,4 +56,4 @@ $ python sample.py --image='png/example.png'
 <br>
 
 ## Pretrained model
-If you do not want to train the model from scratch, you can use a pretrained model. I have provided the pretrained model as a zip file. You can download the file [here](https://www.dropbox.com/s/bmo30z81a4v7m0r/pretrained_model.zip?dl=0) and extract it to `./models/` directory using `unzip pretrained_model.zip`.
+If you do not want to train the model from scratch, you can use a pretrained model. I have provided the pretrained model as a zip file. You can download the pretrained model [here](https://www.dropbox.com/s/ne0ixz5d58ccbbz/pretrained_model.zip?dl=0) and vocabulary file [here](https://www.dropbox.com/s/26adb7y9m98uisa/vocap.zip?dl=0). Note that you should extract pretrained_model.zip to `./models/` and vocab.pkl to `./data/`.

tutorials/03-advanced/image_captioning/build_vocab.py

@@ -59,15 +59,15 @@ def main(args):
                         threshold=args.threshold)
     vocab_path = args.vocab_path
     with open(vocab_path, 'wb') as f:
-        pickle.dump(vocab, f, pickle.HIGHEST_PROTOCOL)
+        pickle.dump(vocab, f)
     print("Total vocabulary size: %d" %len(vocab))
     print("Saved the vocabulary wrapper to '%s'" %vocab_path)
 
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--caption_path', type=str, 
-                        default='./data/annotations/captions_train2014.json', 
+                        default='/usr/share/mscoco/annotations/captions_train2014.json', 
                         help='path for train annotation file')
     parser.add_argument('--vocab_path', type=str, default='./data/vocab.pkl', 
                         help='path for saving vocabulary wrapper')

tutorials/03-advanced/image_captioning/sample.py

@@ -47,7 +47,7 @@ def main(args):
     encoder.load_state_dict(torch.load(args.encoder_path))
     decoder.load_state_dict(torch.load(args.decoder_path))
 
-    # Prepare Image       
+    # Prepare Image
     image = load_image(args.image, transform)
     image_tensor = to_var(image, volatile=True)
 
@@ -72,6 +72,7 @@ def main(args):
 
     # Print out image and generated caption.
     print (sentence)
+    image = Image.open(args.image)
     plt.imshow(np.asarray(image))
 
 if __name__ == '__main__':