diff options
| -rw-r--r-- | cli/app/search/search_class.py | 8 | ||||
| -rw-r--r-- | cli/app/search/search_dense.py | 9 | ||||
| -rw-r--r-- | inversion/live.py | 7 |
3 files changed, 17 insertions, 7 deletions
diff --git a/cli/app/search/search_class.py b/cli/app/search/search_class.py index f7b2136..105fc9f 100644 --- a/cli/app/search/search_class.py +++ b/cli/app/search/search_class.py @@ -96,6 +96,8 @@ def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_la 'truncation': input_trunc, }) + target = tf.compat.v1.placeholder(tf.float32, shape=(batch_size, img_size, img_size, num_channels)) + ## clip the Z encoding opt_clip = 1.0 @@ -104,10 +106,10 @@ def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_la tf.random.uniform([batch_size, z_dim], minval=-opt_clip, maxval=opt_clip), input_z) clip_latent = tf.assign(input_z, clipped_encoding) - target = tf.compat.v1.placeholder(tf.float32, shape=(batch_size, img_size, img_size, num_channels)) - ## normalize the Y encoding - normalized_labels = tf.nn.l2_normalize(input_y) + # normalized_labels = tf.nn.l2_normalize(input_y) + # tf.reduce_mean(tf.abs(encoding - gen_encoding)) + normalized_labels = input_y / tf.reduce_sum(input_y) clip_labels = tf.assign(input_y, normalized_labels) ## if computing Feature loss, use these encoders diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py index ac4dc59..46183c7 100644 --- a/cli/app/search/search_dense.py +++ b/cli/app/search/search_dense.py @@ -124,6 +124,8 @@ def find_dense_embedding_for_images(params): else: gen_img = generator(latent, signature=gen_signature) + gen_img_orig = gen_img + # Convert generated image to channels_first. gen_img = tf.transpose(gen_img, [0, 3, 1, 2]) @@ -393,7 +395,7 @@ def find_dense_embedding_for_images(params): ]) # Main optimization loop. - print("Total iterations: {}".format(params.inv_it)) + print("Beginning dense iteration...") for _ in range(params.inv_it): _inv_loss, _mse_loss, _feat_loss,\ @@ -435,15 +437,14 @@ def find_dense_embedding_for_images(params): out_labels[out_pos:out_pos+BATCH_SIZE] = label_batch out_err[out_pos:out_pos+BATCH_SIZE] = rec_err_batch - gen_images = sess.run(gen_img) + gen_images = sess.run(gen_img_orig) images = vs.data2img(gen_images) # write encoding, latent to pkl file for i in range(BATCH_SIZE): out_i = out_pos + i sample_fn, ext = os.path.splitext(sample_fns[out_i]) - image = Image.fromarray(images[i]) - image = vs.grid_transform(image) + image = Image.fromarray(image) fp = BytesIO() image.save(fp, format='png') data = upload_bytes_to_cortex(params.folder_id, sample_fn + "-inverse.png", fp, "image/png") diff --git a/inversion/live.py b/inversion/live.py index ffac0af..e8001ef 100644 --- a/inversion/live.py +++ b/inversion/live.py @@ -212,6 +212,13 @@ class Interpolator: gen_in['y'] = lerp_label.output self.gen_img = generator(gen_in, signature=gen_signature) + gen_layer_name = 'module_apply_' + gen_signature + '/' + params.inv_layer + gen_encoding = tf.get_default_graph().get_tensor_by_name(gen_layer_name) + ENC_SHAPE = gen_encoding.get_shape().as_list()[1:] + set_encoding = tf.get_variable(name='encoding', dtype=tf.float32, shape=[BATCH_SIZE,] + ENC_SHAPE) + encoding_mix = LerpParam('encoding_mix', a_in=gen_encoding, b_in=set_encoding, shape=[BATCH_SIZE,] + ENC_SHAPE, datatype="input") + tf.contrib.graph_editor.swap_ts(gen_encoding, encoding_mix) + sys.stderr.write("Sin params: {}\n".format(", ".join(self.sin_params.keys()))) sys.stderr.write("Lerp params: {}\n".format(", ".join(self.lerp_params.keys()))) sys.stderr.write("Opts: {}\n".format(", ".join(self.opts.keys()))) |