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| author | Jules Laplace <julescarbon@gmail.com> | 2020-01-06 15:58:45 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-01-06 15:58:45 +0100 |
| commit | 79a24083c7db61ce056bf08cc5e2b8f872fd51b7 (patch) | |
| tree | 9e5a067f7ec06f6f4963e416c22f5ae65536f788 /cli/app | |
| parent | 6f90d4b5e0ac8b168e3ff35c4a3a79bb44f81235 (diff) | |
getting it running..?
Diffstat (limited to 'cli/app')
| -rw-r--r-- | cli/app/commands/biggan/search_class.py | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/cli/app/commands/biggan/search_class.py b/cli/app/commands/biggan/search_class.py index 14c3c39..9b9d466 100644 --- a/cli/app/commands/biggan/search_class.py +++ b/cli/app/commands/biggan/search_class.py @@ -31,7 +31,7 @@ from app.search.vector import truncated_z_sample, truncated_z_single, create_lab help='Path to input image') @click.option('-d', '--dims', 'opt_dims', default=512, type=int, help='Dimensions of BigGAN network (128, 256, 512)') -@click.option('-s', '--steps', 'opt_steps', default=500, type=int, +@click.option('-s', '--steps', 'opt_steps', default=5000, type=int, help='Number of optimization iterations') @click.option('-l', '--limit', 'opt_limit', default=1000, type=int, help='Limit the number of images to process') @@ -92,7 +92,7 @@ def find_nearest_vector(sess, opt_fp_in, opt_dims, out_images, out_labels, out_l y_initial = create_labels(batch_size, vocab_size, 10) z_lr = 0.001 - y_lr = 0.00001 + y_lr = 0.001 input_z = tf.compat.v1.Variable(z_initial, dtype=np.float32, constraint=lambda t: tf.clip_by_value(t, -2, 2)) input_y = tf.compat.v1.Variable(y_initial, dtype=np.float32, constraint=lambda t: tf.clip_by_value(t, 0, 1)) @@ -133,7 +133,7 @@ def find_nearest_vector(sess, opt_fp_in, opt_dims, out_images, out_labels, out_l } # feed_dict = {input_z: z, input_y: y, input_trunc: truncation} - phi_start = sess.run(output, feed_dict=feed_dict) + phi_start = sess.run(output) start_im = imgrid(imconvert_uint8(phi_start), cols=1) imwrite(join(app_cfg.DIR_OUTPUTS, fp_frames, 'frame_0000_start.png'), start_im) @@ -142,10 +142,10 @@ def find_nearest_vector(sess, opt_fp_in, opt_dims, out_images, out_labels, out_l for i in range(opt_steps): curr_loss, _, _ = sess.run([loss, train_step_z, train_step_y], feed_dict=feed_dict) - phi_guess = sess.run(output) - guess_im = imgrid(imconvert_uint8(phi_guess), cols=1) - imwrite(join(app_cfg.DIR_OUTPUTS, fp_frames, 'frame_{:04d}.png'.format(i)), guess_im) if i % 20 == 0: + phi_guess = sess.run(output) + guess_im = imgrid(imconvert_uint8(phi_guess), cols=1) + imwrite(join(app_cfg.DIR_OUTPUTS, fp_frames, 'frame_{:04d}.png'.format(i)), guess_im) print('iter: {}, loss: {}'.format(i, curr_loss)) except KeyboardInterrupt: pass |