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-rw-r--r--cli/app/search/search_dense.py14
1 files changed, 8 insertions, 6 deletions
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py
index 4341130..8b45f60 100644
--- a/cli/app/search/search_dense.py
+++ b/cli/app/search/search_dense.py
@@ -221,7 +221,7 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op
else:
print("Unknown feature extractor")
return
- variables_to_restore = slim.get_variables_to_restore()
+ variables_to_restore = slim.get_variables_to_restore(include=['vgg_16'])
restorer = tf.train.Saver(variables_to_restore)
else:
print("Unknown feature extractor")
@@ -581,11 +581,13 @@ 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
- 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)
+ # scope_index += 1
+ # scope_a = 'vgg_16_{}_a'.format(scope_index)
+ # scope_b = 'vgg_16_{}_b'.format(scope_index)
+ scope_a = 'vgg_16'
+ scope_b = 'vgg_16'
+ gen_fc, gen_feat_ex = nets.vgg.vgg_16(img_a, scope=scope_a, reuse=True)
+ target_fc, target_feat_ex = nets.vgg.vgg_16(img_b, scope=scope_b, reuse=True)
# 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')