Solved some bug

README.md

@@ -1,12 +1,12 @@
 # MaskGAN.pytorch
 
-A PyTorch attempt at reimplementing 
+A PyTorch attempt at reimplementing
 
 * MaskGAN: Better Text Generation via Filling in the _______ , William Fedus, Ian Goodfellow, Andrew M. Dai
   [[paper]](https://openreview.net/pdf?id=ByOExmWAb)
-
-**This is a work in progress.**
 
+==Solved some bugs in original repository==
+https://github.com/jerinphilip/MaskGAN.pytorch
 
 # Setting up
 
@@ -28,12 +28,12 @@ python3 -m pip install git+https://github.com/pytorch/fairseq
 
 #### IMDB Reviews Dataset
 ```
-mkdir datasets 
+mkdir datasets
 cd datasets
 IMDB_DATASET='http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'
 wget $IMDB_DATASET -O aclImdb_v1.tar.gz
 tar xvzf aclImdb_v1.tar.gz
-``` 
+```
 
 #### Training
 
@@ -48,6 +48,6 @@ Run the training script.
 
 ```
 python3 -m mgan.main \
-  --path datasets/aclImdb/train/ \
-  --spm_path datasets/aclImdb/train/imdb.model
+--path datasets/aclImdb/ \
+--spm_prefix datasets/aclImdb/train/imdb
 ```

mgan/data/imdb_tensor.py

@@ -22,7 +22,7 @@ def __init__(self, path, tokenizer, mask_builder, truncate_length, vocab=None):
     def _construct_vocabulary(self):
         if self.vocab is None:
             raw_dataset = IMDbDataset(self.path)
-            builder = VocabBuilder(raw_dataset, self.tokenizer, self.path)
+            builder = VocabBuilder(raw_dataset, self.tokenizer, self.path, self.mask_builder)
             self.vocab = builder.vocab()
 
     def __len__(self):
@@ -76,7 +76,7 @@ def collate(samples):
 
         lengths = torch.LongTensor(lengths)
         lengths, sort_order = lengths.sort(descending=True)
-        
+
         def _rearrange(tensor):
             return tensor.index_select(0, sort_order)
 

mgan/data/vocab_builder.py

@@ -1,31 +1,35 @@
 import os
+from tqdm import tqdm
 from fairseq.data.dictionary import Dictionary
 
 class VocabBuilder:
-    def __init__(self, dataset, tokenizer, save_path):
+    def __init__(self, dataset, tokenizer, save_path, mask_builder):
+        self.save_path = save_path
         self.dataset = dataset
         self.tokenizer = tokenizer
         self.vocab_path = os.path.join(save_path, 'vocab.pt')
         self._vocab = None
+        self.mask_builder = mask_builder
 
     def vocab(self):
         if self._vocab is None:
             self.build_vocab()
         return self._vocab
 
     def build_vocab(self):
+        print('vocab path:',self.vocab_path)
         if os.path.exists(self.vocab_path):
             self._vocab = Dictionary.load(self.vocab_path)
         else:
             self.rebuild_vocab()
-    
+
     def rebuild_vocab(self):
         self._vocab = Dictionary()
         self._vocab.add_symbol(self.mask_builder.mask_token)
         desc = 'build-vocab: {}'.format(self.save_path)
         pbar = tqdm(
-                range(len(self.dataset)), 
-                desc=desc, 
+                range(len(self.dataset)),
+                desc=desc,
                 leave=True
         )
 

mgan/main.py

@@ -37,36 +37,37 @@ def main(args):
     truncate_length = 20
     batch_size = int(max_tokens/truncate_length)
 
-    checkpoint_path = "/home/jerin/mgan-attempts/"
+    checkpoint_path = "/data/neil_noadmin/jerin/mgan-attempts/"
     saver = Saver(checkpoint_path)
 
     train_path = os.path.join(args.path, 'train')
+    print('train path:',train_path)
     dev_path = os.path.join(args.path, 'test')
 
     train_dataset = TensorIMDbDataset(
-            train_path, spm_tokenize, 
+            train_path, spm_tokenize,
             rmask, truncate_length
     )
 
     # Constructed vocabulary from train
     vocab = train_dataset.vocab
     Task = namedtuple('Task', 'source_dictionary target_dictionary')
-    task = Task(source_dictionary=vocab, 
+    task = Task(source_dictionary=vocab,
             target_dictionary=vocab)
 
     trainer = MGANTrainer(args, task, saver, visdom, vocab)
     def loader(dataset):
-        _loader = DataLoader(dataset, batch_size=batch_size, 
-                collate_fn=TensorIMDbDataset.collate, 
+        _loader = DataLoader(dataset, batch_size=batch_size,
+                collate_fn=TensorIMDbDataset.collate,
                 shuffle=True, num_workers=8)
         return _loader
 
     #trainer.validate_dataset(loader(train_dataset))
 
     dev_dataset = TensorIMDbDataset(
-            dev_path, spm_tokenize, 
+            dev_path, spm_tokenize,
             rmask, truncate_length,
-            vocab 
+            vocab
     )
 
     Datasets = namedtuple('Dataset', 'train dev')

mgan/models/critic.py

@@ -19,8 +19,8 @@ def __init__(self, *args, **kwargs):
         out_embed_dim = self.additional_fc.out_features if hasattr(self, "additional_fc") else self.hidden_size
         self.fc_out = nn.Linear(out_embed_dim, 1)
 
-    def forward(self, prev_output_tokens, encoder_out_dict, incremental_state=None):
-        x, attn_scores = super().forward(prev_output_tokens, encoder_out_dict, incremental_state)
+    def forward(self, prev_output_tokens, encoder_out, incremental_state=None):
+        x, attn_scores = super().forward(prev_output_tokens, encoder_out, incremental_state)
         return x, attn_scores
 
 
@@ -58,7 +58,7 @@ def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
                 )
             if args.encoder_embed_dim != args.decoder_embed_dim:
                 raise RuntimeError(
-                    '--share-all-embeddings requires --encoder-embed-dim to '
+                    '--share-all-embeddings requires --encoder_embed_dim to '
                     'match --decoder-embed-dim'
                 )
             pretrained_decoder_embed = pretrained_encoder_embed
@@ -99,7 +99,6 @@ def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
             dropout_in=args.decoder_dropout_in,
             dropout_out=args.decoder_dropout_out,
             attention=options.eval_bool(args.decoder_attention),
-            encoder_embed_dim=args.encoder_embed_dim,
             encoder_output_units=encoder.output_units,
             pretrained_embed=pretrained_decoder_embed,
             share_input_output_embed=args.share_decoder_input_output_embed,

mgan/models/discriminator.py

@@ -21,8 +21,8 @@ def __init__(self, *args, **kwargs):
         out_embed_dim = self.additional_fc.out_features if hasattr(self, "additional_fc") else self.hidden_size
         self.fc_out = nn.Linear(out_embed_dim, 1)
 
-    def forward(self, prev_output_tokens, encoder_out_dict, incremental_state=None):
-        x, attn_scores = super().forward(prev_output_tokens, encoder_out_dict, incremental_state)
+    def forward(self, prev_output_tokens, encoder_out, incremental_state=None):
+        x, attn_scores = super().forward(prev_output_tokens, encoder_out, incremental_state)
         # Do not apply sigmoid, numerically unstable while training.
         # Get logits and use BCEWithLogitsLoss() instead.
         # x = torch.sigmoid(x)
@@ -64,7 +64,7 @@ def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
                 )
             if args.encoder_embed_dim != args.decoder_embed_dim:
                 raise RuntimeError(
-                    '--share-all-embeddings requires --encoder-embed-dim to '
+                    '--share-all-embeddings requires --encoder_embed_dim to '
                     'match --decoder-embed-dim'
                 )
             pretrained_decoder_embed = pretrained_encoder_embed
@@ -105,7 +105,6 @@ def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
             dropout_in=args.decoder_dropout_in,
             dropout_out=args.decoder_dropout_out,
             attention=options.eval_bool(args.decoder_attention),
-            encoder_embed_dim=args.encoder_embed_dim,
             encoder_output_units=encoder.output_units,
             pretrained_embed=pretrained_decoder_embed,
             share_input_output_embed=args.share_decoder_input_output_embed,

mgan/preproc/tokenize.py

@@ -17,7 +17,7 @@ def __init__(self, model_prefix):
         for key in ['model', 'vocab']:
             self.path[key] = '{}.{}'.format(self.prefix, key)
 
-        self.sp = spm.SentencePieceProcessor() 
+        self.sp = spm.SentencePieceProcessor()
         self.sp.Load(self.path['model'])
 
         # Build vocabulary.
@@ -33,7 +33,8 @@ def build_vocabulary(self):
     def __call__(self, text):
         tokens = self.sp.EncodeAsPieces(text)
 
-        to_utf = lambda x: x.decode("utf-8") 
+        # to_utf = lambda x: x.decode("utf-8")
+        to_utf = lambda x: x
         stokens = list(map(to_utf, tokens))
 
         wanted = lambda s: s in self.vocab