commiting py file

console_play.py

@@ -0,0 +1,24 @@
+import os
+from story.utils import *
+from google.cloud import storage
+import json
+from story.story_manager import *
+from generator.tf_local.generator_local import *
+import tensorflow as tf
+
+if __name__ == '__main__':
+
+    sess = tf.Session()
+    generator = LocalGenerator(sess)
+    prompt = "You enter a dungeon with your trusty sword and shield. You are searching for the evil necromancer who killed your family. You've heard that he resides at the bottom of the dungeon, guarded by legions of the undead. You enter the first door and see"
+    story_manager = UnconstrainedStoryManager(generator, prompt)
+    print(story_manager.story)
+    while(True):
+        action = input("> ")
+        action = "You " + action
+        print(action)
+        result = story_manager.act(action)
+        print(result)
+
+
+

generator/tf_local/generator_local.py

@@ -3,17 +3,16 @@ import os
 import numpy as np
 import tensorflow as tf
 
-import gpt2.src.model as model
+from src.model import *
 from tensorflow.contrib import predictor
-import gpt2.src.sample as sample
-import gpt2.src.encoder as encoder
-from story.utils import *
+from src.sample import *
+from src.encoder import *
 import pdb
 
 pos_action_starts = ["You attack", "You tell", "You use", "You go"]
 
 
-class StoryGenerator():
+class LocalGenerator():
 
     def __init__(self, sess, length=75, temperature=0.9, top_k=40):
     
@@ -51,51 +50,25 @@ class StoryGenerator():
         text = self.enc.decode(out[0])
         return text
 
-    def generate_story_block(self, prompt):
-        block = self.generate(prompt)
-        block = cut_trailing_sentence(block)
-        block = story_replace(block)
-
-        return block
-
-    def generate_action_options(self, prompt, action_starts=pos_action_starts):
-
-        possible_actions = []
-        for phrase in action_starts:
-            action = phrase + self.generate(prompt + phrase)
-            action = first_sentence(action)
-            possible_actions.append(action)
-
-        return possible_actions
-
-    def generate_action_result(self, prompt, phrase):
-        action = phrase + self.generate(prompt + phrase)
-        action_result = cut_trailing_sentence(action)
-        action_result = story_replace(action_result)
-
-        action = first_sentence(action)
-
-        return action, action_result
-
-
 def save_model():
     length=75
     temperature=0.9
     top_k=40
+    os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
     
     with tf.Session() as sess:
         seed = None
         batch_size=None
-        model_path='gpt2/models/117M'
+        model_path='models/774M'
 
-        hparams = model.default_hparams()
+        hparams = default_hparams()
         with open(os.path.join(model_path, 'hparams.json')) as f:
             hparams.override_from_dict(json.load(f))  
 
         context = tf.placeholder(tf.int32, [batch_size, None])
         np.random.seed(seed)
         tf.set_random_seed(seed)
-        output = sample.sample_sequence(
+        output = sample_sequence(
             hparams=hparams, length=length,
             context=context,
             batch_size=batch_size,
@@ -106,14 +79,20 @@ def save_model():
         ckpt = tf.train.latest_checkpoint(model_path)
         saver.restore(sess, ckpt)
 
-        tf.saved_model.simple_save(sess, "./saved2", inputs={"context": context}, outputs={"output": output})
+        tf.saved_model.simple_save(sess, "./saved_model", inputs={"context": context}, outputs={"output": output})
 
 def load_model():
-    # Set your memory fraction equal to a value less than 1, 0.6 is a good starting point.
-    # If no fraction is defined, the tensorflow algorithm may run into gpu out of memory problems.
     fraction = 0.6
+    config = config = generate_gpu_config(fraction)
     path_to_graph = "./saved"
 
+    # tf.saved_model.loader.load(
+    #   session,
+    #  [tf.saved_model.tag_constants.SERVING],
+    # path_to_graph)
+
+    # output = session.graph.get_tensor_by_name('output:0')
+    # context = session.graph.get_tensor_by_name('context:0')
     model_path = 'gpt2/models/117M'
     enc = encoder.get_encoder(model_path)
 
@@ -125,6 +104,7 @@ def load_model():
 
     return (output, session)
 
+
 if __name__ == '__main__':
    save_model()
     

story/story_manager.py

@@ -31,9 +31,14 @@ class Story():
         return sum(story_list)
 
 
-class UnconstrainedStoryGenerator():
+class UnconstrainedStoryManager():
+
+    def __init__(self, generator, story_prompt):
+        block = self.generator.generate(story_prompt)
+        block = cut_trailing_sentence(block)
+        block = story_replace(block)
+        story_start = story_prompt + block
 
-    def __init__(self, generator, story_start):
         self.story = Story(story_start)
         self.generator = generator
 
@@ -53,9 +58,14 @@ class UnconstrainedStoryGenerator():
         return block
 
 
-class ConstrainedStoryGenerator():
+class ConstrainedStoryManager():
+
+    def __init__(self, generator, story_prompt):
+        block = self.generator.generate(story_prompt)
+        block = cut_trailing_sentence(block)
+        block = story_replace(block)
+        story_start = story_prompt + block
 
-    def __init__(self, generator, story_start):
         self.story = Story(story_start)
         self.generator = generator
         self.possible_action_results = self.get_action_results()