diff options
| -rw-r--r-- | examples/equations/content.png | bin | 0 -> 2650 bytes | |||
| -rw-r--r-- | neural_style.py | 14 |
2 files changed, 7 insertions, 7 deletions
diff --git a/examples/equations/content.png b/examples/equations/content.png Binary files differnew file mode 100644 index 0000000..d68663c --- /dev/null +++ b/examples/equations/content.png diff --git a/neural_style.py b/neural_style.py index 756d19b..374b4c1 100644 --- a/neural_style.py +++ b/neural_style.py @@ -440,8 +440,8 @@ def get_longterm_weights(i, j): c_max = tf.maximum(c - c_sum, 0.) return c_max -def sum_longterm_temporal_losses(sess, net, frame, x): - x = sess.run(net['input'].assign(x)) +def sum_longterm_temporal_losses(sess, net, frame, input_img): + x = sess.run(net['input'].assign(input_img)) loss = 0. for j in range(args.prev_frame_indices): prev_frame = frame - j @@ -450,8 +450,8 @@ def sum_longterm_temporal_losses(sess, net, frame, x): loss += temporal_loss(x, w, c) return loss -def sum_shortterm_temporal_losses(sess, net, frame, x): - x = sess.run(net['input'].assign(x)) +def sum_shortterm_temporal_losses(sess, net, frame, input_img): + x = sess.run(net['input'].assign(input_img)) prev_frame = frame - 1 w = get_prev_warped_frame(frame) c = get_content_weights(frame, prev_frame) @@ -463,9 +463,9 @@ def sum_shortterm_temporal_losses(sess, net, frame, x): remark: not sure this does anything significant. ''' -def sum_total_variation_losses(sess, net, x): - b, h, w, d = x.shape - x = sess.run(net['input'].assign(x)) +def sum_total_variation_losses(sess, net, input_img): + b, h, w, d = input_img.shape + x = sess.run(net['input'].assign(input_img)) tv_y_size = b * (h-1) * w * d tv_x_size = b * h * (w-1) * d loss_y = tf.nn.l2_loss(x[:,1:,:,:] - x[:,:h-1,:,:]) |
