offer support for chinese

README.md

@@ -440,6 +440,20 @@ ex.
 $ python train_dalle.py --image_text_folder ./path/to/data --bpe_path ./path/to/bpe.json
 ```
 
+#### Chinese
+
+You can train with a <a href="https://huggingface.co/bert-base-chinese">pretrained chinese tokenizer</a> offered by Huggingface 🤗 by simply passing in an extra flag `--chinese`
+
+ex.
+
+```sh
+$ python train_dalle.py --chinese --image_text_folder ./path/to/data
+```
+
+```sh
+$ python generate.py --chinese --text '追老鼠的猫'
+```
+
 ## Citations
 
 ```bibtex

dalle_pytorch/tokenizer.py

@@ -2,7 +2,9 @@
 # to give users a quick easy start to training DALL-E without doing BPE
 
 import torch
+
 from tokenizers import Tokenizer
+from transformers import BertTokenizer
 
 import html
 import os
@@ -123,6 +125,7 @@ def encode(self, text):
     def decode(self, tokens, remove_start_end = True):
         if torch.is_tensor(tokens):
             tokens = tokens.tolist()
+
         if remove_start_end:
             tokens = [token for token in tokens if token not in (49406, 40407, 0)]
         text = ''.join([self.decoder[token] for token in tokens])
@@ -160,7 +163,10 @@ def __init__(self, bpe_path = None):
         self.vocab_size = tokenizer.get_vocab_size()
 
     def decode(self, tokens):
-        tokens = [token for token in tokens.tolist() if token not in (0,)]
+        if torch.is_tensor(tokens):
+            tokens = tokens.tolist()
+
+        tokens = [token for token in tokens if token not in (0,)]
         return self.tokenizer.decode(tokens, skip_special_tokens = True)
 
     def encode(self, text):
@@ -182,3 +188,38 @@ def tokenize(self, texts, context_length = 256, truncate_text = False):
             result[i, :len(tokens)] = torch.tensor(tokens)
 
         return result
+
+# chinese tokenizer
+
+class ChineseTokenizer:
+    def __init__(self):
+        tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
+        self.tokenizer = tokenizer
+        self.vocab_size = tokenizer.vocab_size
+
+    def decode(self, tokens):
+        if torch.is_tensor(tokens):
+            tokens = tokens.tolist()
+
+        tokens = [token for token in tokens if token not in (0,)]
+        return self.tokenizer.decode(tokens)
+
+    def encode(self, text):
+        return torch.tensor(self.tokenizer.encode(text, add_special_tokens = False))
+
+    def tokenize(self, texts, context_length = 256, truncate_text = False):
+        if isinstance(texts, str):
+            texts = [texts]
+
+        all_tokens = [self.encode(text) for text in texts]
+
+        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
+        for i, tokens in enumerate(all_tokens):
+            if len(tokens) > context_length:
+                if truncate_text:
+                    tokens = tokens[:context_length]
+                else:
+                    raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
+            result[i, :len(tokens)] = torch.tensor(tokens)
+
+        return result

generate.py

@@ -43,14 +43,23 @@
 parser.add_argument('--bpe_path', type = str,
                     help='path to your huggingface BPE json file')
 
+parser.add_argument('--chinese', dest='chinese', action = 'store_true')
+
 parser.add_argument('--taming', dest='taming', action='store_true')
 
 args = parser.parse_args()
 
+# helper fns
+
+def exists(val):
+    return val is not None
+
 # tokenizer
 
-if args.bpe_path is not None:
+if exists(args.bpe_path):
     tokenizer = HugTokenizer(args.bpe_path)
+elif args.chinese:
+    tokenizer = ChineseTokenizer()
 
 # load DALL-E
 

setup.py

@@ -4,7 +4,7 @@
   name = 'dalle-pytorch',
   packages = find_packages(),
   include_package_data = True,
-  version = '0.10.1',
+  version = '0.10.2',
   license='MIT',
   description = 'DALL-E - Pytorch',
   author = 'Phil Wang',
@@ -27,6 +27,7 @@
     'tokenizers',
     'torch>=1.6',
     'torchvision',
+    'transformers',
     'tqdm'
   ],
   classifiers=[

train_dalle.py

@@ -21,7 +21,7 @@
 
 from dalle_pytorch import distributed_utils
 from dalle_pytorch import OpenAIDiscreteVAE, VQGanVAE1024, DiscreteVAE, DALLE
-from dalle_pytorch.tokenizer import tokenizer, HugTokenizer
+from dalle_pytorch.tokenizer import tokenizer, HugTokenizer, ChineseTokenizer
 
 # argument parsing
 
@@ -41,6 +41,8 @@
 parser.add_argument('--truncate_captions', dest='truncate_captions',
                     help='Captions passed in which exceed the max token length will be truncated if this is set.')
 
+parser.add_argument('--chinese', dest='chinese', action = 'store_true')
+
 parser.add_argument('--taming', dest='taming', action='store_true')
 
 parser.add_argument('--bpe_path', type = str,
@@ -89,6 +91,8 @@ def exists(val):
 
 if exists(args.bpe_path):
     tokenizer = HugTokenizer(args.bpe_path)
+elif args.chinese:
+    tokenizer = ChineseTokenizer()
 
 # reconstitute vae