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| author | Jules Laplace <julescarbon@gmail.com> | 2020-01-16 02:56:48 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-01-16 02:56:48 +0100 |
| commit | 887c6874ff8d7bf44ea0e039f777234007398b55 (patch) | |
| tree | 5260c1dfdc02edec96963e1554843e4930c1d3b3 /cli | |
| parent | 6e96d6198f5a7726e135ebae5228646ca8a22f2e (diff) | |
hopefully all fixed
Diffstat (limited to 'cli')
| -rw-r--r-- | cli/app/commands/biggan/extract_dense_vectors.py | 2 | ||||
| -rw-r--r-- | cli/app/search/search_dense.py | 11 |
2 files changed, 7 insertions, 6 deletions
diff --git a/cli/app/commands/biggan/extract_dense_vectors.py b/cli/app/commands/biggan/extract_dense_vectors.py index b4300b5..707b99d 100644 --- a/cli/app/commands/biggan/extract_dense_vectors.py +++ b/cli/app/commands/biggan/extract_dense_vectors.py @@ -11,7 +11,7 @@ from app.search.json import params_dense_dict help='Folder ID to process') @click.option('-ls', '--latent_steps', 'opt_latent_steps', default=1000, type=int, help='Number of optimization iterations') -@click.option('-ds', '--dense_steps', 'opt_dense_steps', default=1000, type=int, +@click.option('-ds', '--dense_steps', 'opt_dense_steps', default=2000, type=int, help='Number of optimization iterations') @click.option('-v', '--video', 'opt_video', is_flag=True, help='Export a video for each dataset') diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py index 066d946..df6edd7 100644 --- a/cli/app/search/search_dense.py +++ b/cli/app/search/search_dense.py @@ -431,11 +431,12 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op # Save images that are ready. label_trained, latent_trained, enc_trained, rec_err_trained = sess.run([label, latent, encoding, img_rec_err]) - out_lat[out_pos:out_pos+BATCH_SIZE] = latent_trained - out_enc[out_pos:out_pos+BATCH_SIZE] = enc_trained - out_images[out_pos:out_pos+BATCH_SIZE] = image_batch - out_labels[out_pos:out_pos+BATCH_SIZE] = label_trained - out_err[out_pos:out_pos+BATCH_SIZE] = rec_err_trained + count = len(latent_trained) + out_lat[out_pos:out_pos+count] = latent_trained + out_enc[out_pos:out_pos+count] = enc_trained + out_images[out_pos:out_pos+count] = image_batch + out_labels[out_pos:out_pos+count] = label_trained + out_err[out_pos:out_pos+count] = rec_err_trained gen_images = sess.run(gen_img_orig) images = vs.data2img(gen_images) |