diff options
| author | Jules Laplace <julescarbon@gmail.com> | 2020-01-14 20:10:45 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-01-14 20:10:45 +0100 |
| commit | 536dc2710d7a9449fe7e2da79d79feee62b7a828 (patch) | |
| tree | 24a64318b1cbb4ebadfa5c75b6db4ec93c540cb7 | |
| parent | f91ed4899d5706c7771327269e69fa9ae4a10f9f (diff) | |
fixes
| -rw-r--r-- | cli/app/commands/biggan/extract_dense_vectors.py | 3 | ||||
| -rw-r--r-- | cli/app/search/search_class.py | 5 | ||||
| -rw-r--r-- | cli/app/search/search_dense.py | 2 |
3 files changed, 6 insertions, 4 deletions
diff --git a/cli/app/commands/biggan/extract_dense_vectors.py b/cli/app/commands/biggan/extract_dense_vectors.py index 40b90f9..866104d 100644 --- a/cli/app/commands/biggan/extract_dense_vectors.py +++ b/cli/app/commands/biggan/extract_dense_vectors.py @@ -64,7 +64,8 @@ def cli(ctx, opt_folder_id, opt_latent_steps, opt_dense_steps, opt_video, opt_re opt_use_feature_detector=opt_use_feature_detector, opt_feature_layers=opt_feature_layers, opt_snapshot_interval=opt_snapshot_interval, - opt_clip_interval=opt_clip_interval + opt_clip_interval=opt_clip_interval, + opt_folder_id=folder['id'] ) params = params_dense_dict(tag, folder_id=folder['id']) diff --git a/cli/app/search/search_class.py b/cli/app/search/search_class.py index 40801d6..0c3fd29 100644 --- a/cli/app/search/search_class.py +++ b/cli/app/search/search_class.py @@ -48,7 +48,8 @@ feature_layer_names = { def find_nearest_vector_for_images(paths, opt_dims, opt_steps, opt_video, opt_tag, opt_limit=-1, opt_stochastic_clipping=True, opt_label_clipping=True, - opt_use_feature_detector=False, opt_feature_layers=[1,2,4,7], opt_snapshot_interval=20, opt_clip_interval=500): + opt_use_feature_detector=False, opt_feature_layers=[1,2,4,7], opt_snapshot_interval=20, opt_clip_interval=500, + opt_folder_id=59): tf.reset_default_graph() sess = tf.compat.v1.Session() print("Initializing generator...") @@ -57,7 +58,7 @@ def find_nearest_vector_for_images(paths, opt_dims, opt_steps, opt_video, opt_ta fp_inverses = os.path.join(app_cfg.DIR_INVERSES, opt_tag) os.makedirs(fp_inverses, exist_ok=True) # save_params_latent(fp_inverses, opt_tag) - save_params_dense(fp_inverses, opt_tag) + save_params_dense(fp_inverses, opt_tag, folder_id=opt_folder_id) out_file = h5py.File(join(fp_inverses, 'dataset.latent.hdf5'), 'w') out_images = out_file.create_dataset('xtrain', (len(paths), 3, 512, 512,), dtype='float32') out_labels = out_file.create_dataset('ytrain', (len(paths), 1000,), dtype='float32') diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py index 7d3fd2a..1f08e24 100644 --- a/cli/app/search/search_dense.py +++ b/cli/app/search/search_dense.py @@ -227,7 +227,7 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op # # feat_loss += tf.reduce_mean(feat_square_diff) * 0.17 # # img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.17 - feat_square_diff = tf.constant(0.0) + feat_loss = tf.constant(0.0) img_feat_err = tf.constant(0.0) for layer in opt_feature_layers: |