diff options
| author | Jules Laplace <julescarbon@gmail.com> | 2019-12-15 02:17:03 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2019-12-15 02:17:03 +0100 |
| commit | ad466e9594feb51b0584901b6429fd6fa93a3e24 (patch) | |
| tree | 25e71006fc7790d470635724b9711de4a615e455 /inversion/image_inversion_inception.py | |
| parent | 92ddbd73d20e3292de517bbfaaf66078c4d47314 (diff) | |
try 12347
Diffstat (limited to 'inversion/image_inversion_inception.py')
| -rw-r--r-- | inversion/image_inversion_inception.py | 22 |
1 files changed, 14 insertions, 8 deletions
diff --git a/inversion/image_inversion_inception.py b/inversion/image_inversion_inception.py index 570c1ac..a97bf0f 100644 --- a/inversion/image_inversion_inception.py +++ b/inversion/image_inversion_inception.py @@ -254,26 +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) * 0.25 - img_feat_err = tf.reduce_mean(feat_square_diff, axis=1) * 0.25 + 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_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) * 0.25 - img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.25 + 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) * 0.25 - img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.25 + 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) * 0.25 - img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.25 + 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/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 else: feat_loss = tf.constant(0.0) |