1247

1 files changed, 13 insertions, 13 deletions

diff --git a/inversion/image_inversion_inception.py b/inversion/image_inversion_inception.py
index a97bf0f..b220a55 100644
--- a/inversion/image_inversion_inception.py
+++ b/inversion/image_inversion_inception.py
@@ -254,32 +254,32 @@ if params.features:
   gen_feat = gen_feat_ex["InceptionV3/Conv2d_1a_3x3"]
   target_feat = target_feat_ex["InceptionV3/Conv2d_1a_3x3"]
   feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-  feat_loss = tf.reduce_mean(feat_square_diff) * 0.2
-  img_feat_err = tf.reduce_mean(feat_square_diff, axis=1) * 0.2
+  feat_loss = tf.reduce_mean(feat_square_diff) * 0.25
+  img_feat_err = tf.reduce_mean(feat_square_diff, axis=1) * 0.25
 
   gen_feat = gen_feat_ex["InceptionV3/Conv2d_2a_3x3"]
   target_feat = target_feat_ex["InceptionV3/Conv2d_2a_3x3"]
   feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-  feat_loss += tf.reduce_mean(feat_square_diff) * 0.2
-  img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.2
+  feat_loss += tf.reduce_mean(feat_square_diff) * 0.25
+  img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.25
 
-  gen_feat = gen_feat_ex["InceptionV3/Conv2d_3b_1x1"]
-  target_feat = target_feat_ex["InceptionV3/Conv2d_3b_1x1"]
-  feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-  feat_loss += tf.reduce_mean(feat_square_diff) * 0.2
-  img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.2
+  # gen_feat = gen_feat_ex["InceptionV3/Conv2d_3b_1x1"]
+  # target_feat = target_feat_ex["InceptionV3/Conv2d_3b_1x1"]
+  # feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
+  # feat_loss += tf.reduce_mean(feat_square_diff) * 0.2
+  # img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.2
 
   gen_feat = gen_feat_ex["InceptionV3/Conv2d_4a_3x3"]
   target_feat = target_feat_ex["InceptionV3/Conv2d_4a_3x3"]
   feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-  feat_loss += tf.reduce_mean(feat_square_diff) * 0.2
-  img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.2
+  feat_loss += tf.reduce_mean(feat_square_diff) * 0.25
+  img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.25
 
   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) * 0.2
-  img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.2
+  feat_loss += tf.reduce_mean(feat_square_diff) * 0.25
+  img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.25
 
 else:
   feat_loss = tf.constant(0.0)