up

1 files changed, 7 insertions, 5 deletions

diff --git a/cli/app/search/search_class.py b/cli/app/search/search_class.py
index cd53a71..134a139 100644
--- a/cli/app/search/search_class.py
+++ b/cli/app/search/search_class.py
@@ -46,7 +46,7 @@ feature_layer_names = {
 
 def find_nearest_vector_for_images(paths, opt_dims, opt_steps, opt_video, opt_tag,
   opt_limit=-1, opt_stochastic_clipping=0, opt_label_clipping=0,
-  opt_use_feature_detector=False, opt_feature_layers=[1,2,4,7], opt_snapshot_interval=20):
+  opt_use_feature_detector=False, opt_feature_layers=[1,2,4,7], opt_snapshot_interval=20, opt_clip_interval=500):
   tf.reset_default_graph()
   sess = tf.compat.v1.Session()
   print("Initializing generator...")
@@ -66,13 +66,13 @@ def find_nearest_vector_for_images(paths, opt_dims, opt_steps, opt_video, opt_ta
       break
     out_fns[index] = os.path.basename(path)
     fp_frames = find_nearest_vector(sess, generator, path, opt_dims, out_images, out_labels, out_latent, opt_steps, index,
-      opt_stochastic_clipping, opt_label_clipping, opt_use_feature_detector, opt_feature_layers, opt_snapshot_interval)
+      opt_stochastic_clipping, opt_label_clipping, opt_use_feature_detector, opt_feature_layers, opt_snapshot_interval, opt_clip_interval)
     if opt_video:
       export_video(fp_frames)
   sess.close()
 
 def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_labels, out_latent, opt_steps, index,
-  opt_stochastic_clipping, opt_label_clipping, opt_use_feature_detector, opt_feature_layers, opt_snapshot_interval):
+  opt_stochastic_clipping, opt_label_clipping, opt_use_feature_detector, opt_feature_layers, opt_snapshot_interval, opt_clip_interval):
   """
   Find the closest latent and class vectors for an image. Store the class vector in an HDF5.
   """
@@ -122,8 +122,10 @@ def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_la
     feature_extractor = hub.Module("https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1")
 
     # Convert images from range [-1, 1] channels_first to [0, 1] channels_last.
-    gen_img_1 = tf.transpose(output / 2.0 + 0.5, [0, 2, 3, 1])
-    target_img_1 = tf.transpose(target / 2.0 + 0.5, [0, 2, 3, 1])
+    # gen_img_1 = tf.transpose(output / 2.0 + 0.5, [0, 2, 3, 1])
+    # target_img_1 = tf.transpose(target / 2.0 + 0.5, [0, 2, 3, 1])
+    gen_img_1 = output / 2.0 + 0.5
+    target_img_1 = target / 2.0 + 0.5
 
     # Convert images to appropriate size for feature extraction.
     height, width = hub.get_expected_image_size(feature_extractor)