feed dict is the key

1 files changed, 44 insertions, 45 deletions

diff --git a/inversion/live.py b/inversion/live.py
index 4e42703..cf7f761 100644
--- a/inversion/live.py
+++ b/inversion/live.py
@@ -8,6 +8,7 @@ import tensorflow as tf
 import tensorflow_probability as tfp
 import tensorflow_hub as hub
 import time
+from PIL import Image
 import visualize as vs
 tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
 sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../live-cortex/rpc/'))
@@ -15,7 +16,7 @@ from rpc import CortexRPC
 
 from params import Params
 
-params = Params('params_dense.json')
+params = Params('params_dense-512.json')
 
 # --------------------------
 # Make directories.
@@ -28,10 +29,7 @@ if not os.path.exists(OUTPUT_DIR):
 # --------------------------
 # Load Graph.
 # --------------------------
-sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
-sess.run(tf.global_variables_initializer())
-sess.run(tf.tables_initializer())
-
+print("Loading module...")
 generator = hub.Module(str(params.generator_path))
 
 gen_signature = 'generator'
@@ -39,21 +37,17 @@ if 'generator' not in generator.get_signature_names():
   gen_signature = 'default'
 
 input_info = generator.get_input_info_dict(gen_signature)
-COND_GAN = True
 
 BATCH_SIZE = 1
 Z_DIM = input_info['z'].get_shape().as_list()[1]
 N_CLASS = input_info['y'].get_shape().as_list()[1]
 
-
 def sin(opts, key, shape):
   noise = lerp(opts, key + '_noise', shape)
   scale = InterpolatorParam(name=key + '_scale')
-  time = opts['global']['time'].variable
+  time = opts['time'].variable
   out = tf.sin(time + noise) * scale.variable
-  opts[key] = {
-    'scale': scale,
-  }
+  opts[key + '_scale'] = scale
   return out
 
 def lerp(opts, key, shape):
@@ -61,11 +55,9 @@ def lerp(opts, key, shape):
   b = InterpolatorParam(name=key + '_b', shape=shape)
   n = InterpolatorParam(name=key + '_n')
   out = a.variable * (1 - n.variable) + b.variable * n.variable
-  opts[key] = {
-    'a': a,
-    'b': b,
-    'n': n,
-  }
+  opts[key + '_a'] = a
+  opts[key + '_b'] = b
+  opts[key + '_n'] = n
   return out
 
 class InterpolatorParam:
@@ -73,11 +65,10 @@ class InterpolatorParam:
     self.scalar = shape == ()
     self.shape = shape
     self.value = value or np.zeros(shape)
-    self.variable = tf.Variable(self.value, name=name, dtype=dtype, shape=shape)
+    self.variable = tf.placeholder(dtype=dtype, shape=shape)
 
-  def assign(value):
+  def assign(self, value):
     self.value = value
-    return self.variable.assign(value)
 
   def randomize(self):
     return self.assign(np.random.normal(size=self.shape))
@@ -85,16 +76,14 @@ class InterpolatorParam:
 class Interpolator:
   def __init__(self):
     self.opts = {
-      'global': {
-        'time': InterpolatorParam(name='t', value=time.time())
-      },
+      'time': InterpolatorParam(name='t', value=time.time()),
+      'truncation' : InterpolatorParam(name='truncation', value=1.0),
     }
 
   def build(self):
     lerp_z = lerp(self.opts, 'latent', [BATCH_SIZE, Z_DIM])
     sin_z = sin(self.opts, 'sin_z', [BATCH_SIZE, Z_DIM])
     lerp_label = lerp(self.opts, 'label', [BATCH_SIZE, N_CLASS])
-    self.opts['truncation'] = InterpolatorParam('truncation', value=1.0)
 
     gen_in = {}
     gen_in['truncation'] = self.opts['truncation'].variable
@@ -105,29 +94,24 @@ class Interpolator:
     # Convert generated image to channels_first.
     self.gen_img = tf.transpose(gen_img, [0, 3, 1, 2])
 
-    for group, lookup in self.opts.items():
-      for key, param in group.items():
-        if param.scalar:
-          param.assign().eval(session=sess)
-        else:
-          param.randomize().eval(session=sess)
+  def get_feed_dict(self):
+    opt = {}
+    for key, param in self.opts.items():
+      opt[param.variable] = param.value
+    return opt
 
   def get_state(self):
     opt = {}
-    for group, lookup in self.opts.items():
-      for key, param in group.items():
-        if param.scalar:
-          opt[group][key] = param.value
+    for key, param in self.opts.items():
+      if param.scalar:
+        opt[key] = param.value
     return opt
 
   def set_value(self, key, value):
-    self.opts[key].assign(value).eval(session=sess)
+    return self.opts[key].assign(value)
 
-  def on_step(self, i):
-    gen_time = time.time()
-    self.opts['global']['time'].assign(gen_time).eval(session=sess)
-    gen_images  = sess.run(self.gen_img)
-    print("Generation time: {:.1f}s".format(time.time() - gen_time))
+  def on_step(self, i, sess):
+    gen_images = sess.run(self.gen_img, feed_dict=self.get_feed_dict())
     return gen_images
 
   def run(self, cmd, payload):
@@ -136,6 +120,7 @@ class Interpolator:
 
 class Listener:
   def __init__(self):
+    self.assign_ops = {}
     self.interpolator = Interpolator()
     self.interpolator.build()
 
@@ -143,7 +128,7 @@ class Listener:
     self.rpc_client = CortexRPC(self.on_get, self.on_set, self.on_ready, self.on_cmd)
 
   def on_set(self, key, value):
-    self.interpolator.set_value(key, value)
+    self.opts[key].assign(value)
 
   def on_get(self):
     return self.interpolator.get_state()
@@ -153,20 +138,34 @@ class Listener:
     self.interpolator.run(cmd, payload)
 
   def on_ready(self, rpc_client):
+    self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
+    self.sess.run(tf.global_variables_initializer())
+    self.sess.run(tf.tables_initializer())
     print("Ready!")
     self.rpc_client = rpc_client
     self.rpc_client.send_status('processing', True)
     for i in range(99999):
-      gen_images = self.interpolator.on_step(i)
+      print("Step {}".format(i))
+      gen_time = time.time()
+      self.interpolator.opts['time'].assign(gen_time)
+      gen_images = self.interpolator.on_step(i, self.sess)
       if gen_images is None:
+        print("Exiting...")
         break
+      print("Generation time: {:.1f}s".format(time.time() - gen_time))
       out_img = vs.data2pil(gen_images[0])
       if out_img is not None:
-        if out_img.resize_before_sending:
-          out_img.resize((256, 256), Image.BICUBIC)
-        self.rpc_client.send_pil_image("frame_{:05d}.png".format(i+1), meta, img_to_send, data_opt.output_format)
+        #if out_img.resize_before_sending:
+        img_to_send = out_img.resize((256, 256), Image.BICUBIC)
+        meta = {
+          'i': i,
+          'sequence_i': i,
+          'skip_i': 0,
+          'sequence_len': 99999,
+        }
+        self.rpc_client.send_pil_image("frame_{:05d}.png".format(i+1), meta, img_to_send, 'jpg')
     self.rpc_client.send_status('processing', False)
-    sess.close()
+    self.sess.close()
 
 if __name__ == '__main__':
   listener = Listener()