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| author | Jules Laplace <julescarbon@gmail.com> | 2020-02-14 16:23:59 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-02-14 16:23:59 +0100 |
| commit | 0a7725b4e1ee2d10e7fe6c99c643bd5c25b325c8 (patch) | |
| tree | 7ba86c0e1b8d2c3f0eb44b8a1f4f5104c6223d3c | |
| parent | f2fa78a23ca62db4f52486fad5a55a266e78ceb8 (diff) | |
vgg feature loss
| -rw-r--r-- | cli/app/search/search_dense.py | 4 |
1 files changed, 3 insertions, 1 deletions
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py index e65a6c9..fba393a 100644 --- a/cli/app/search/search_dense.py +++ b/cli/app/search/search_dense.py @@ -203,6 +203,7 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op if 'http' in params.feature_extractor_path: feature_extractor = hub.Module(str(params.feature_extractor_path)) feature_loss = feature_loss_tfhub + height, width = hub.get_expected_image_size(feature_extractor) elif 'vgg' in params.feature_extractor_path: if params.feature_extractor_path == 'vgg_16': model_path = os.path.join(app_cfg.DIR_NETS, 'vgg_16.ckpt') @@ -215,6 +216,8 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op 'vgg_16/conv4/conv4_2', ] feature_loss = feature_loss_vgg + height = 224 + width = 224 else: print("Unknown feature extractor") return @@ -229,7 +232,6 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op target_img_ch = tf.transpose(target_img / 2.0 + 0.5, [0, 2, 3, 1]) # Convert images to appropriate size for feature extraction. - height, width = hub.get_expected_image_size(feature_extractor) img_w = 512 feat_loss, img_feat_err = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, None, None, height, width) |