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| author | Jules Laplace <julescarbon@gmail.com> | 2020-02-14 16:38:46 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-02-14 16:38:46 +0100 |
| commit | 887feafc4a9bb8fb818bfed208fdafb1cd21b5fc (patch) | |
| tree | f4a6e2cebc6541b51ae9cde38c81b479ec091660 /cli/app | |
| parent | dfaac1a79fdc2219e2ec597d3781a81992716e24 (diff) | |
vgg feature loss
Diffstat (limited to 'cli/app')
| -rw-r--r-- | cli/app/search/search_dense.py | 21 |
1 files changed, 10 insertions, 11 deletions
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py index f453a44..267d7ff 100644 --- a/cli/app/search/search_dense.py +++ b/cli/app/search/search_dense.py @@ -210,10 +210,10 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op feature_extractor = slim.nets.vgg.vgg_16 # conv1_1, conv1_2, conv3_2, conv4_2 opt_feature_layers = [ - 'vgg_16/conv1/conv1_1', - 'vgg_16/conv1/conv1_2', - 'vgg_16/conv3/conv3_2', - 'vgg_16/conv4/conv4_2', + 'conv1/conv1_1', + 'conv1/conv1_2', + 'conv3/conv3_2', + 'conv4/conv4_2', ] feature_loss = feature_loss_vgg height = 224 @@ -581,11 +581,10 @@ def feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a, i img_b = tf.image.resize_images(img_b, [resize_height, resize_width]) global scope_index - scope_index += 1 - gen_fc, gen_feat_ex = nets.vgg.vgg_16(img_a, scope='vgg_16_{}'.format(scope_index)) - - scope_index += 1 - target_fc, target_feat_ex = nets.vgg.vgg_16(img_b, scope='vgg_16_{}'.format(scope_index)) + scope_a = 'vgg_16_{}_a'.format(scope_index) + scope_b = 'vgg_16_{}_b'.format(scope_index) + gen_fc, gen_feat_ex = nets.vgg.vgg_16(img_a, scope=scope_a) + target_fc, target_feat_ex = nets.vgg.vgg_16(img_b, scope=scope_b) # gen_feat_ex = feature_extractor(dict(images=img_a), as_dict=True, signature='image_feature_vector') # target_feat_ex = feature_extractor(dict(images=img_b), as_dict=True, signature='image_feature_vector') @@ -593,8 +592,8 @@ def feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a, i img_feat_err = tf.constant(0.0) for layer_name in opt_feature_layers: - gen_feat = gen_feat_ex[layer_name] - target_feat = target_feat_ex[layer_name] + gen_feat = gen_feat_ex[scope_a + '/' + layer_name] + target_feat = target_feat_ex[scope_b + '/' + layer_name] 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) |