just adding sine

1 files changed, 38 insertions, 36 deletions

diff --git a/cli/app/search/live.py b/cli/app/search/live.py
index e626e25..31358e1 100644
--- a/cli/app/search/live.py
+++ b/cli/app/search/live.py
@@ -223,16 +223,17 @@ class Interpolator:
     disentangled = z_mix.output + zoom + shiftx + shifty + luminance
 
     # Latent - stored vector
-    # latent_stored = LerpParam(name='latent_stored', shape=[BATCH_SIZE, Z_DIM], datatype="noise")
-    latent_stored = InterpolatorParam(name='latent_stored', shape=[BATCH_SIZE, Z_DIM], datatype="noise")
-    latent_stored_mix = LerpParam('latent_stored_mix', a_in=disentangled, b_in=latent_stored.variable, shape=[BATCH_SIZE, Z_DIM], datatype="input")
+    latent_stored = LerpParam(name='latent_stored', shape=[BATCH_SIZE, Z_DIM], datatype="noise")
+    # latent_stored = InterpolatorParam(name='latent_stored', shape=[BATCH_SIZE, Z_DIM], datatype="noise")
+    latent_stored_mix = LerpParam('latent_stored_mix', a_in=disentangled, b_in=latent_stored.output, shape=[BATCH_SIZE, Z_DIM], datatype="input")
 
     # Label
     lerp_label = LerpParam('label', shape=[BATCH_SIZE, N_CLASS], datatype="label")
 
     # Latent - stored vector
-    label_stored = InterpolatorParam(name='label_stored', shape=[BATCH_SIZE, N_CLASS], datatype="label")
-    label_stored_mix = LerpParam('label_stored_mix', a_in=lerp_label.output, b_in=label_stored.variable, shape=[BATCH_SIZE, Z_DIM], datatype="input")
+    # label_stored = InterpolatorParam(name='label_stored', shape=[BATCH_SIZE, N_CLASS], datatype="label")
+    label_stored = LerpParam(name='label_stored', shape=[BATCH_SIZE, N_CLASS], datatype="label")
+    label_stored_mix = LerpParam('label_stored_mix', a_in=lerp_label.output, b_in=label_stored.output, shape=[BATCH_SIZE, Z_DIM], datatype="input")
 
     # Generator
     gen_in = {}
@@ -249,9 +250,10 @@ class Interpolator:
     encoding_shape_np = tuple([1,] + encoding_shape[1:])
 
     encoding_latent_placeholder = tf.constant(np.zeros(encoding_shape_np, dtype=np.float32))
-    encoding_stored = InterpolatorParam('encoding_stored', shape=encoding_shape_np, datatype="noise")
+    # encoding_stored = InterpolatorParam('encoding_stored', shape=encoding_shape_np, datatype="noise")
+    encoding_stored = LerpParam('encoding_stored', shape=encoding_shape_np, datatype="noise")
     encoding_stored_sin = SinParam('encoding_orbit', shape=encoding_shape_np, datatype="noise")
-    encoding_stored_sum = encoding_stored.variable + encoding_stored_sin.output
+    encoding_stored_sum = encoding_stored.output + encoding_stored_sin.output
     encoding_stored_mix = LerpParam('encoding_stored_mix', a_in=encoding_latent_placeholder, b_in=encoding_stored_sum, shape=encoding_shape_np, datatype="encoding")
 
     # Use the placeholder to redirect parts of the graph.
@@ -262,8 +264,8 @@ class Interpolator:
     tf.contrib.graph_editor.swap_ts(encoding_stored_mix.output, encoding_latent_placeholder)
 
     # Make all the stored lerps use the same interpolation amount.
-    # tf.contrib.graph_editor.reroute_ts(encoding_stored.n.variable, latent_stored.n.variable)
-    # tf.contrib.graph_editor.reroute_ts(encoding_stored.n.variable, label_stored.n.variable)
+    tf.contrib.graph_editor.reroute_ts(encoding_stored.n.variable, latent_stored.n.variable)
+    tf.contrib.graph_editor.reroute_ts(encoding_stored.n.variable, label_stored.n.variable)
     tf.contrib.graph_editor.reroute_ts(encoding_stored_mix.n.variable, latent_stored_mix.n.variable)
     tf.contrib.graph_editor.reroute_ts(encoding_stored_mix.n.variable, label_stored_mix.n.variable)
 
@@ -327,34 +329,34 @@ class Interpolator:
     new_label = np.expand_dims(data['label'], axis=0)
     new_encoding = np.expand_dims(data['encoding'], axis=0)
 
-    self.opts['latent_stored'].assign(new_latent)
-    self.opts['label_stored'].assign(new_label)
-    self.opts['encoding_stored'].assign(new_encoding)
-    encoding_stored_mix.switch()
-    # latent_stored = self.lerp_params['latent_stored']
-    # label_stored = self.lerp_params['label_stored']
-    # encoding_stored = self.lerp_params['encoding_stored']
-    # encoding_stored_mix = self.lerp_params['encoding_stored_mix']
+    # self.opts['latent_stored'].assign(new_latent)
+    # self.opts['label_stored'].assign(new_label)
+    # self.opts['encoding_stored'].assign(new_encoding)
+    # encoding_stored_mix.switch()
+    latent_stored = self.lerp_params['latent_stored']
+    label_stored = self.lerp_params['label_stored']
+    encoding_stored = self.lerp_params['encoding_stored']
+    encoding_stored_mix = self.lerp_params['encoding_stored_mix']
 
-    # # if we're showing an encoding already, lerp to the next one
-    # if encoding_stored_mix.n.value > 0:
-    #   encoding_stored.switch(target_value=new_encoding)
-    #   label_stored.switch(target_value=new_label)
-    #   latent_stored.switch(target_value=new_latent)
-    # # otherwise (we're showing the latent)...
-    # else:
-    #   # jump to the stored encoding, then switch
-    #   if encoding_stored.n.value < 0.5:
-    #     encoding_stored.n.assign(0)
-    #     encoding_stored.a.assign(new_encoding)
-    #     latent_stored.a.assign(new_latent)
-    #     label_stored.a.assign(new_label)
-    #   else:
-    #     encoding_stored.n.assign(1)
-    #     encoding_stored.b.assign(new_encoding)
-    #     latent_stored.b.assign(new_latent)
-    #     label_stored.b.assign(new_label)
-    #   encoding_stored_mix.switch()
+    # if we're showing an encoding already, lerp to the next one
+    if encoding_stored_mix.n.value > 0:
+      encoding_stored.switch(target_value=new_encoding)
+      label_stored.switch(target_value=new_label)
+      latent_stored.switch(target_value=new_latent)
+    # otherwise (we're showing the latent)...
+    else:
+      # jump to the stored encoding, then switch
+      if encoding_stored.n.value < 0.5:
+        encoding_stored.n.assign(0)
+        encoding_stored.a.assign(new_encoding)
+        latent_stored.a.assign(new_latent)
+        label_stored.a.assign(new_label)
+      else:
+        encoding_stored.n.assign(1)
+        encoding_stored.b.assign(new_encoding)
+        latent_stored.b.assign(new_latent)
+        label_stored.b.assign(new_label)
+      encoding_stored_mix.switch()
 
   def on_step(self, i, sess):
     for param in self.sin_params.values():