three mixed layers

1 files changed, 17 insertions, 17 deletions

diff --git a/inversion/image_inversion_inception.py b/inversion/image_inversion_inception.py
index 7cadfe6..c4eb5b5 100644
--- a/inversion/image_inversion_inception.py
+++ b/inversion/image_inversion_inception.py
@@ -223,14 +223,14 @@ if params.features:
   gen_feat = gen_feat_ex["InceptionV3/Mixed_7a"]
   target_feat = target_feat_ex["InceptionV3/Mixed_7a"]
   feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-  feat_loss = tf.reduce_mean(feat_square_diff) * 0.5
-  img_feat_err = tf.reduce_mean(feat_square_diff, axis=1) * 0.5
+  feat_loss = tf.reduce_mean(feat_square_diff) * 0.34
+  img_feat_err = tf.reduce_mean(feat_square_diff, axis=1) * 0.34
 
-  gen_feat = gen_feat_ex["InceptionV3/Mixed_6a"]
-  target_feat = target_feat_ex["InceptionV3/Mixed_6a"]
-  feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-  feat_loss += tf.reduce_mean(feat_square_diff) * 0.5
-  img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.5
+  # gen_feat = gen_feat_ex["InceptionV3/Mixed_6a"]
+  # target_feat = target_feat_ex["InceptionV3/Mixed_6a"]
+  # feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
+  # feat_loss += tf.reduce_mean(feat_square_diff) * 0.5
+  # img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.5
 
   # gen_feat = gen_feat_ex["InceptionV3/Mixed_5a"]
   # target_feat = target_feat_ex["InceptionV3/Mixed_5a"]
@@ -238,17 +238,17 @@ if params.features:
   # feat_loss += tf.reduce_mean(feat_square_diff)
   # img_feat_err += tf.reduce_mean(feat_square_diff, axis=1)
 
-  # gen_feat = gen_feat_ex["InceptionV3/Mixed_7b"]
-  # target_feat = target_feat_ex["InceptionV3/Mixed_7b"]
-  # feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-  # feat_loss += tf.reduce_mean(feat_square_diff)
-  # img_feat_err += tf.reduce_mean(feat_square_diff, axis=1)
+  gen_feat = gen_feat_ex["InceptionV3/Mixed_7b"]
+  target_feat = target_feat_ex["InceptionV3/Mixed_7b"]
+  feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
+  feat_loss += tf.reduce_mean(feat_square_diff) * 0.33
+  img_feat_err += tf.reduce_mean(feat_square_diff, axis=1)
 
-  # gen_feat = gen_feat_ex["InceptionV3/Mixed_7c"]
-  # target_feat = target_feat_ex["InceptionV3/Mixed_7c"]
-  # feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-  # feat_loss += tf.reduce_mean(feat_square_diff)
-  # img_feat_err += tf.reduce_mean(feat_square_diff, axis=1)
+  gen_feat = gen_feat_ex["InceptionV3/Mixed_7c"]
+  target_feat = target_feat_ex["InceptionV3/Mixed_7c"]
+  feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
+  feat_loss += tf.reduce_mean(feat_square_diff) * 0.33
+  img_feat_err += tf.reduce_mean(feat_square_diff, axis=1)
 
 else:
   feat_loss = tf.constant(0.0)