removing

2 files changed, 9 insertions, 9 deletions

diff --git a/cli/app/search/search_class.py b/cli/app/search/search_class.py
index fb672c3..f7b2136 100644
--- a/cli/app/search/search_class.py
+++ b/cli/app/search/search_class.py
@@ -157,7 +157,7 @@ def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_la
     train_step_z = tf.train.AdamOptimizer(z_lr).minimize(loss, var_list=[input_z], name='AdamOpterZ')
     train_step_y = tf.train.AdamOptimizer(y_lr).minimize(loss, var_list=[input_y], name='AdamOpterY')
 
-  target_im, fp_frames = load_target_image(opt_fp_in)
+  target_im, fp_frames, fn_base = load_target_image(opt_fp_in)
 
   # crop image and convert to format for next script
   phi_target_for_inversion = resize_and_crop_image(target_im, 512)
@@ -199,7 +199,7 @@ def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_la
 
   phi_guess = sess.run(output)
   guess_im = imgrid(imconvert_uint8(phi_guess), cols=1)
-  imwrite(join(app_cfg.DIR_OUTPUTS, 'frame_{}_final.png'.format(opt_tag)), guess_im)
+  imwrite(join(app_cfg.DIR_OUTPUTS, 'frame-{}-{}-final.png'.format(opt_tag, fn_base)), guess_im)
 
   z_guess, y_guess = sess.run([input_z, input_y])
   out_images[index] = phi_target_for_inversion
@@ -224,10 +224,10 @@ def export_video(fp_frames):
 def load_target_image(opt_fp_in):
   print("Loading {}".format(opt_fp_in))
   fn = os.path.basename(opt_fp_in)
-  fbase, ext = os.path.splitext(fn)
-  fp_frames = "frames_{}_{}".format(fbase, int(time.time() * 1000))
+  fn_base, ext = os.path.splitext(fn)
+  fp_frames = "frames_{}_{}".format(fn_base, int(time.time() * 1000))
   fp_frames_fullpath = join(app_cfg.DIR_OUTPUTS, fp_frames)
   print("Output to {}".format(fp_frames_fullpath))
   os.makedirs(fp_frames_fullpath, exist_ok=True)
   target_im = imread(opt_fp_in)
-  return target_im, fp_frames
+  return target_im, fp_frames, fn_base
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py
index fe0c1aa..ac4dc59 100644
--- a/cli/app/search/search_dense.py
+++ b/cli/app/search/search_dense.py
@@ -396,9 +396,9 @@ def find_dense_embedding_for_images(params):
     print("Total iterations: {}".format(params.inv_it))
     for _ in range(params.inv_it):
 
-      _inv_loss, _mse_loss, _feat_loss, _rec_loss, _reg_loss, _dist_loss,\
+      _inv_loss, _mse_loss, _feat_loss,\
           _lrate, _ = sess.run([inv_loss, mse_loss, feat_loss,
-          rec_loss, reg_loss, dist_loss, lrate, inv_train_op])
+          lrate, inv_train_op])
 
       if params.clipping or params.stochastic_clipping:
         sess.run(clip_latent)
@@ -408,9 +408,9 @@ def find_dense_embedding_for_images(params):
         # Log losses.
         etime = time.time() - start_time
         print('It [{:8d}] time [{:5.1f}] total [{:.4f}] mse [{:.4f}] '
-              'feat [{:.4f}] rec [{:.4f}] reg [{:.4f}] dist [{:.4f}] '
+              'feat [{:.4f}] '
               'lr [{:.4f}]'.format(it, etime, _inv_loss, _mse_loss,
-              _feat_loss, _rec_loss, _reg_loss, _dist_loss, _lrate))
+              _feat_loss, _lrate))
 
         sys.stdout.flush()