diff options
| -rw-r--r-- | cli/app/search/search_dense.py | 26 |
1 files changed, 13 insertions, 13 deletions
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py index 7c0c728..fe0c1aa 100644 --- a/cli/app/search/search_dense.py +++ b/cli/app/search/search_dense.py @@ -253,32 +253,32 @@ def find_dense_embedding_for_images(params): 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) * 0.15 - img_feat_err = tf.reduce_mean(feat_square_diff, axis=1) * 0.15 + 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) * 0.15 - img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.15 + 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) * 0.15 - img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.15 + # 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 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) * 0.15 - img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.15 + 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) * 0.4 - img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.4 + 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) |