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| author | Jules Laplace <julescarbon@gmail.com> | 2020-02-23 02:58:50 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-02-23 02:58:50 +0100 |
| commit | 036a0c608e4e43f5da3883c6324a7fca860b908b (patch) | |
| tree | 27a3c5c1e0e49fed9769e63e606adf3b17e72fe9 | |
| parent | 1688b044e7aa042d85226e9597b250b3d0a51aa9 (diff) | |
more vgg
| -rw-r--r-- | cli/app/search/json.py | 2 | ||||
| -rw-r--r-- | cli/app/search/search_dense.py | 5 |
2 files changed, 2 insertions, 5 deletions
diff --git a/cli/app/search/json.py b/cli/app/search/json.py index 69ce7a5..9b82578 100644 --- a/cli/app/search/json.py +++ b/cli/app/search/json.py @@ -71,7 +71,7 @@ def make_params_dense(tag, folder_id): # "inv_layer": "latent", "decay_lr": True, # "inv_it": 10000, - "inv_it": 12000, + "inv_it": 10000, "generator_path": "https://tfhub.dev/deepmind/biggan-512/2", "attention_map_layer": "Generator_2/attention/Softmax:0", "pre_trained_latent": True, diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py index 53c548b..7b83952 100644 --- a/cli/app/search/search_dense.py +++ b/cli/app/search/search_dense.py @@ -419,7 +419,6 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op out_fns[:] = sample_fns[:NUM_IMGS_TO_PROCESS] # Gradient descent w.r.t. generator's inputs. - it = 0 out_pos = 0 start_time = time.time() @@ -450,7 +449,7 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op # Main optimization loop. print("Beginning dense iteration...") - for _ in range(params.inv_it): + for it in range(params.inv_it): if it < params.inv_it * 0.7: n = 0.0 @@ -498,8 +497,6 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op inv_batch = vs.grid_transform(inv_batch) vs.save_image('{}/progress_{}_{:04d}.png'.format(SAMPLES_DIR, opt_tag, int(it / 500)), inv_batch) - it += 1 - # Save images that are ready. label_trained, latent_trained = sess.run([label, latent]) if params.inv_layer != 'latent': |