feature loss on quadrants

2 files changed, 8 insertions, 8 deletions

diff --git a/cli/app/commands/biggan/extract_dense_vectors.py b/cli/app/commands/biggan/extract_dense_vectors.py
index 54f9762..0f61528 100644
--- a/cli/app/commands/biggan/extract_dense_vectors.py
+++ b/cli/app/commands/biggan/extract_dense_vectors.py
@@ -24,7 +24,7 @@ from app.search.params import timestamp
   help='Normalize labels every N steps')
 @click.option('-feat', '--use_feature_detector', 'opt_use_feature_detector', is_flag=True,
   help='Compute feature loss')
-@click.option('-ll', '--feature_layers', 'opt_feature_layers', default="1a,2a,3a,4a,7a",
+@click.option('-ll', '--feature_layers', 'opt_feature_layers', default="1a,2a,4a,7a",
   help='Feature layers used for loss')
 @click.option('-snap', '--snapshot_interval', 'opt_snapshot_interval', default=20,
   help='Interval to store sample images')
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py
index 1065edb..a296264 100644
--- a/cli/app/search/search_dense.py
+++ b/cli/app/search/search_dense.py
@@ -43,7 +43,7 @@ feature_layer_names = {
   '7c': "InceptionV3/Mixed_7c",
 }
 
-def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), opt_feature_layers=["1a,2a,3a,4a,7a"], opt_save_progress=True):
+def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), opt_feature_layers=["1a,2a,4a,7a"], opt_save_progress=True):
   # --------------------------
   # Global directories.
   # --------------------------
@@ -188,12 +188,12 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op
     height, width = hub.get_expected_image_size(feature_extractor)
     img_w = IMG_SHAPE[0]
 
-    feat_loss, img_feat_err = feature_loss(feature_extractor, gen_img_ch, target_img_ch, None, None, height, width)
+    feat_loss, img_feat_err = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, None, None, height, width)
 
-    feat_loss_a, feat_err_a = feature_loss(feature_extractor, gen_img_ch, target_img_ch, 0, 0, height, width)
-    feat_loss_b, feat_err_b = feature_loss(feature_extractor, gen_img_ch, target_img_ch, img_w - width, 0, height, width)
-    feat_loss_c, feat_err_c = feature_loss(feature_extractor, gen_img_ch, target_img_ch, 0, img_w - width, height, width)
-    feat_loss_d, feat_err_d = feature_loss(feature_extractor, gen_img_ch, target_img_ch, img_w - width, img_w - width, height, width)
+    feat_loss_a, feat_err_a = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, 0, 0, height, width)
+    feat_loss_b, feat_err_b = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, img_w - width, 0, height, width)
+    feat_loss_c, feat_err_c = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, 0, img_w - width, height, width)
+    feat_loss_d, feat_err_d = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, img_w - width, img_w - width, height, width)
 
     feat_loss_quad = feat_loss_a + feat_loss_b + feat_loss_c + feat_loss_d
     img_feat_err_quad = feat_err_a + feat_err_b + feat_err_c + feat_err_d
@@ -400,7 +400,7 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op
   out_file.close()
   sess.close()
 
-def feature_loss(feature_extractor, img_a, img_b, y, x, height, width):
+def feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a, img_b, y, x, height, width):
   if y is not None:
     img_a = tf.image.crop_to_bounding_box(img_a, y, x, height, width)
     img_b = tf.image.crop_to_bounding_box(img_b, y, x, height, width)