allow models to be in a separate folder via models_dir argument (#129)

* models_dir argument to allow models in a separate folder

* default value for models_dir to be same as before

* allow environment variables and user home in models_dir

src/encoder.py

@@ -105,10 +105,10 @@ class Encoder:
         text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
         return text
 
-def get_encoder(model_name):
-    with open(os.path.join('models', model_name, 'encoder.json'), 'r') as f:
+def get_encoder(model_name, models_dir):
+    with open(os.path.join(models_dir, model_name, 'encoder.json'), 'r') as f:
         encoder = json.load(f)
-    with open(os.path.join('models', model_name, 'vocab.bpe'), 'r', encoding="utf-8") as f:
+    with open(os.path.join(models_dir, model_name, 'vocab.bpe'), 'r', encoding="utf-8") as f:
         bpe_data = f.read()
     bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]]
     return Encoder(

src/generate_unconditional_samples.py

@@ -16,6 +16,7 @@ def sample_model(
     length=None,
     temperature=1,
     top_k=0,
+    models_dir='models',
 ):
     """
     Run the sample_model
@@ -35,10 +36,13 @@ def sample_model(
      considered for each step (token), resulting in deterministic completions,
      while 40 means 40 words are considered at each step. 0 (default) is a
      special setting meaning no restrictions. 40 generally is a good value.
+     :models_dir : path to parent folder containing model subfolders
+     (i.e. contains the <model_name> folder)
     """
-    enc = encoder.get_encoder(model_name)
+    models_dir = os.path.expanduser(os.path.expandvars(models_dir))
+    enc = encoder.get_encoder(model_name, models_dir)
     hparams = model.default_hparams()
-    with open(os.path.join('models', model_name, 'hparams.json')) as f:
+    with open(os.path.join(models_dir, model_name, 'hparams.json')) as f:
         hparams.override_from_dict(json.load(f))
 
     if length is None:
@@ -58,7 +62,7 @@ def sample_model(
         )[:, 1:]
 
         saver = tf.train.Saver()
-        ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name))
+        ckpt = tf.train.latest_checkpoint(os.path.join(models_dir, model_name))
         saver.restore(sess, ckpt)
 
         generated = 0

src/interactive_conditional_samples.py

@@ -16,6 +16,7 @@ def interact_model(
     length=None,
     temperature=1,
     top_k=0,
+    models_dir='models',    
 ):
     """
     Interactively run the model
@@ -34,14 +35,17 @@ def interact_model(
      considered for each step (token), resulting in deterministic completions,
      while 40 means 40 words are considered at each step. 0 (default) is a
      special setting meaning no restrictions. 40 generally is a good value.
+     :models_dir : path to parent folder containing model subfolders
+     (i.e. contains the <model_name> folder)     
     """
+    models_dir = os.path.expanduser(os.path.expandvars(models_dir))
     if batch_size is None:
         batch_size = 1
     assert nsamples % batch_size == 0
 
-    enc = encoder.get_encoder(model_name)
+    enc = encoder.get_encoder(model_name, models_dir)
     hparams = model.default_hparams()
-    with open(os.path.join('models', model_name, 'hparams.json')) as f:
+    with open(os.path.join(models_dir, model_name, 'hparams.json')) as f:
         hparams.override_from_dict(json.load(f))
 
     if length is None:
@@ -61,7 +65,7 @@ def interact_model(
         )
 
         saver = tf.train.Saver()
-        ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name))
+        ckpt = tf.train.latest_checkpoint(os.path.join(models_dir, model_name))
         saver.restore(sess, ckpt)
 
         while True: