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| author | Jules Laplace <julescarbon@gmail.com> | 2020-02-14 16:19:27 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-02-14 16:19:27 +0100 |
| commit | f2fa78a23ca62db4f52486fad5a55a266e78ceb8 (patch) | |
| tree | 2cf9ac7a61c0840ddd6fffe1c96332eca62a304a /cli/app/search | |
| parent | abc27615421ebd63048a3450eb89e46c1f51e322 (diff) | |
vgg feature loss
Diffstat (limited to 'cli/app/search')
| -rw-r--r-- | cli/app/search/search_dense.py | 17 |
1 files changed, 9 insertions, 8 deletions
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py index 1281d64..e65a6c9 100644 --- a/cli/app/search/search_dense.py +++ b/cli/app/search/search_dense.py @@ -14,6 +14,7 @@ import tensorflow as tf import tensorflow_probability as tfp import tensorflow_hub as hub import tensorflow.contrib.slim as slim +import tensorflow.contrib.slim.nets as nets import time import app.search.visualize as vs tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) @@ -556,9 +557,9 @@ def feature_loss_tfhub(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a, gen_feat = gen_feat_ex[layer_name] target_feat = target_feat_ex[layer_name] feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1]) - feat_loss += tf.reduce_mean(feat_square_diff) / len(opt_feature_layers) - img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) / len(opt_feature_layers) - return feat_loss, img_feat_err + feat_loss += tf.reduce_mean(feat_square_diff) + img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) + return feat_loss / len(opt_feature_layers), img_feat_err / len(opt_feature_layers) def feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a, img_b, y, x, height, width, resize_height=None, resize_width=None): @@ -577,8 +578,8 @@ def feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a, i img_a = tf.image.resize_images(img_a, [resize_height, resize_width]) img_b = tf.image.resize_images(img_b, [resize_height, resize_width]) - gen_fc, gen_feat_ex = slim.nets.vgg.vgg_16(img_a) - target_fc, target_feat_ex = slim.nets.vgg.vgg_16(img_b) + gen_fc, gen_feat_ex = nets.vgg.vgg_16(img_a) + target_fc, target_feat_ex = nets.vgg.vgg_16(img_b) # gen_feat_ex = feature_extractor(dict(images=img_a), as_dict=True, signature='image_feature_vector') # target_feat_ex = feature_extractor(dict(images=img_b), as_dict=True, signature='image_feature_vector') @@ -589,6 +590,6 @@ def feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a, i gen_feat = gen_feat_ex[layer_name] target_feat = target_feat_ex[layer_name] feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1]) - feat_loss += tf.reduce_mean(feat_square_diff) / len(opt_feature_layers) - img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) / len(opt_feature_layers) - return feat_loss, img_feat_err + feat_loss += tf.reduce_mean(feat_square_diff) + img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) + return feat_loss / len(opt_feature_layers), img_feat_err / len(opt_feature_layers) |