diff options
Diffstat (limited to 'cli')
| -rw-r--r-- | cli/app/search/search_dense.py | 32 |
1 files changed, 25 insertions, 7 deletions
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py index 7e144c8..b2c77f4 100644 --- a/cli/app/search/search_dense.py +++ b/cli/app/search/search_dense.py @@ -239,13 +239,13 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op feature_loss = feature_loss_tfhub height, width = hub.get_expected_image_size(feature_extractor) - feat_loss_inception, img_feat_err = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, None, None, height, width) + feat_loss_inception_q, img_feat_err = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, None, None, height, width) # feat_loss_a, feat_err_a = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, 0, 0, height, width) - # feat_loss_b, feat_err_b = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, img_w - width, 0, height, width) - # feat_loss_c, feat_err_c = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, 0, img_w - width, height, width) - # feat_loss_d, feat_err_d = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, img_w - width, img_w - width, height, width) - # feat_loss_e, feat_err_e = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, int((img_w - width) / 2), int((img_w - width) / 2), height, width) + feat_loss_inception_a, feat_err_inception_a = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, img_w - width, 0, height, width) + feat_loss_inception_b, feat_err_inception_b = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, 0, img_w - width, height, width) + feat_loss_inception_c, feat_err_inception_c = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, img_w - width, img_w - width, height, width) + feat_loss_inception_d, feat_err_inception_d = feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, int((img_w - width) / 2), int((img_w - width) / 2), height, width) ################################################ # VGG feature extractor @@ -261,8 +261,19 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op height = 224 width = 224 - feat_loss_vgg, img_feat_err_vgg = feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, None, None, height, width) + feat_loss_vgg_q, img_feat_err_vgg = feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, None, None, height, width) + feat_loss_vgg_a, feat_err_vgg_a = feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, 16, 16, height, width) + feat_loss_vgg_b, feat_err_vgg_b = feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, img_w - width - 16, 16, height, width) + feat_loss_vgg_c, feat_err_vgg_c = feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, 16, img_w - width - 16, height, width) + feat_loss_vgg_d, feat_err_vgg_d = feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, img_w - width - 16, img_w - width - 16, height, width) + # feat_loss_vgg_e, feat_err_vgg_e = feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, gen_img_ch, target_img_ch, int((img_w - width) / 2), int((img_w - width) / 2), height, width) + + vgg_small_n = tf.placeholder(tf.float32, shape=(), name="vgg") + inception_small_n = tf.placeholder(tf.float32, shape=(), name="vgg") + + feat_loss_vgg = (feat_loss_vgg_a + feat_loss_vgg_b + feat_loss_vgg_c + feat_loss_vgg_d) * (vgg_small_n) / 4 + feat_loss_vgg_q * (1 - vgg_small_n) + feat_loss_inception = (feat_loss_inception_a + feat_loss_inception_b + feat_loss_inception_c + feat_loss_inception_d) * (inception_small_n) / 4 + feat_loss_inception_q * (1 - vgg_small_n) feat_loss = feat_loss_vgg + 10.0 * feat_loss_inception # mse_loss_a = mse_loss_crop(target_img_ch, gen_img_ch, 0, 0, img_w / 2, img_w / 2) @@ -441,7 +452,14 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op print("Beginning dense iteration...") for _ in range(params.inv_it): - _inv_loss, _mse_loss, _feat_loss, _feat_loss_vgg, _feat_loss_inception, _lrate, _ = sess.run([inv_loss, mse_loss, feat_loss, feat_loss_vgg, feat_loss_inception, lrate, inv_train_op]) + if it < params.inv_it * 0.7: + n = 0.0 + elif it < params.inv_it * 0.75: + n = (it - params.inv_it * 0.70) / (params.inv_it * 0.05) + else: + n = 1.0 + + _inv_loss, _mse_loss, _feat_loss, _feat_loss_vgg, _feat_loss_inception, _lrate, _ = sess.run([inv_loss, mse_loss, feat_loss, feat_loss_vgg, feat_loss_inception, lrate, inv_train_op], feed_dict={ vgg_small_n: n, inception_small_n: n }) # if it < params.inv_it * 0.5: # _inv_loss, _mse_loss, _feat_loss, _lrate, _ = sess.run([inv_loss, mse_loss, feat_loss, lrate, inv_train_op]) # elif it < params.inv_it * 0.75: |