scope

1 files changed, 17 insertions, 16 deletions

diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py
index e5769ab..b2e05a4 100644
--- a/cli/app/search/search_dense.py
+++ b/cli/app/search/search_dense.py
@@ -566,8 +566,8 @@ def feature_loss_tfhub(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a,
       img_feat_err += tf.reduce_mean(feat_square_diff, axis=1)
   return feat_loss / len(opt_feature_layers), img_feat_err / len(opt_feature_layers)
 
-scope_index = 0
-vgg_model = tf.make_template('vgg16', nets.vgg.vgg_16, is_training=False)
+# scope_index = 0
+# vgg_model = tf.make_template('vgg16', nets.vgg.vgg_16, is_training=False)
 def feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a, img_b, y, x, height, width, resize_height=None, resize_width=None):
   height = int(height)
   width = int(width)
@@ -585,26 +585,27 @@ 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_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'
+  # 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_fc, gen_feat_ex = vgg_model(img_a) #, reuse=True)
-  target_fc, target_feat_ex = vgg_model(img_b) #, reuse=True)
+  with slim.arg_scope(nets.vgg.vgg_arg_scope()):
+    gen_fc, gen_feat_ex = vgg.vgg_16(img_a) #, reuse=True)
+    target_fc, target_feat_ex = vgg.vgg_16(img_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')
 
-  feat_loss = tf.constant(0.0)
-  img_feat_err = tf.constant(0.0)
+    feat_loss = tf.constant(0.0)
+    img_feat_err = tf.constant(0.0)
 
-  for layer_name in opt_feature_layers:
-    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)
-  return feat_loss / len(opt_feature_layers), img_feat_err / len(opt_feature_layers)
+    for layer_name in opt_feature_layers:
+      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)
+    return feat_loss / len(opt_feature_layers), img_feat_err / len(opt_feature_layers)