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| author | Jules Laplace <julescarbon@gmail.com> | 2020-01-06 16:24:27 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-01-06 16:24:27 +0100 |
| commit | 1084fad3e5fc2a2d70276fbe8cba5e6dfea10dff (patch) | |
| tree | 4c9c708229638be5191078dd7731d5c2ea7facb8 /cli | |
| parent | 87a89a18604199696599ee227a17d03fcafeec7b (diff) | |
sigmoid
Diffstat (limited to 'cli')
| -rw-r--r-- | cli/app/commands/biggan/search_class.py | 7 |
1 files changed, 4 insertions, 3 deletions
diff --git a/cli/app/commands/biggan/search_class.py b/cli/app/commands/biggan/search_class.py index 58f0d86..cbf39b2 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=5000, type=int, +@click.option('-s', '--steps', 'opt_steps', default=2000, 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') @@ -87,6 +87,7 @@ def find_nearest_vector(sess, opt_fp_in, opt_dims, out_images, out_labels, out_l vocab_size = 1000 img_size = 512 num_channels = 3 + save_step = 20 z_initial = truncated_z_sample(batch_size, z_dim, truncation/2) y_initial = create_labels(batch_size, vocab_size, 10) @@ -144,10 +145,10 @@ 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) - if i % 20 == 0: + if i % save_step == 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(int(i / 20))), guess_im) + imwrite(join(app_cfg.DIR_OUTPUTS, fp_frames, 'frame_{:04d}.png'.format(int(i / save_step))), guess_im) print('iter: {}, loss: {}'.format(i, curr_loss)) except KeyboardInterrupt: pass |