less crazy iterations... and sorting

4 files changed, 11 insertions, 9 deletions

diff --git a/cli/app/commands/biggan/extract_dense_vectors.py b/cli/app/commands/biggan/extract_dense_vectors.py
index 4e7952a..b4300b5 100644
--- a/cli/app/commands/biggan/extract_dense_vectors.py
+++ b/cli/app/commands/biggan/extract_dense_vectors.py
@@ -9,9 +9,9 @@ from app.search.json import params_dense_dict
 @click.command('')
 @click.option('-f', '--folder_id', 'opt_folder_id', type=int,
   help='Folder ID to process')
-@click.option('-ls', '--latent_steps', 'opt_latent_steps', default=2000, type=int,
+@click.option('-ls', '--latent_steps', 'opt_latent_steps', default=1000, type=int,
   help='Number of optimization iterations')
-@click.option('-ds', '--dense_steps', 'opt_dense_steps', default=2000, type=int,
+@click.option('-ds', '--dense_steps', 'opt_dense_steps', default=1000, type=int,
   help='Number of optimization iterations')
 @click.option('-v', '--video', 'opt_video', is_flag=True,
   help='Export a video for each dataset')
@@ -55,7 +55,7 @@ def cli(ctx, opt_folder_id, opt_latent_steps, opt_dense_steps, opt_video, opt_re
   find_nearest_vector_for_images(
     paths=paths,
     opt_dims=512,
-    opt_steps=opt_dense_steps,
+    opt_steps=opt_latent_steps,
     opt_video=opt_video,
     opt_tag=tag,
     opt_limit=-1,
diff --git a/cli/app/search/json.py b/cli/app/search/json.py
index ea70fd6..c50d716 100644
--- a/cli/app/search/json.py
+++ b/cli/app/search/json.py
@@ -16,7 +16,7 @@ def make_params_latent(tag):
     "out_dataset": os.path.join(app_cfg.DIR_INVERSES, tag, "dataset.latent.hdf5"),
     "inv_layer": "latent", 
     "decay_lr": True, 
-    "inv_it": 15000, 
+    "inv_it": 5000, 
     "generator_path": "https://tfhub.dev/deepmind/biggan-512/2",
     "attention_map_layer": "Generator_2/attention/Softmax:0",
     "pre_trained_latent": False, 
@@ -68,7 +68,7 @@ def make_params_dense(tag, folder_id):
     "dataset": os.path.join(app_cfg.DIR_INVERSES, tag, "dataset.latent.hdf5"),
     "inv_layer": "Generator_2/G_Z/Reshape:0", 
     "decay_lr": False, 
-    "inv_it": 15000, 
+    "inv_it": 5000, 
     "generator_path": "https://tfhub.dev/deepmind/biggan-512/2", 
     "attention_map_layer": "Generator_2/attention/Softmax:0",
     "pre_trained_latent": True, 
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py
index 6fba70d..066d946 100644
--- a/cli/app/search/search_dense.py
+++ b/cli/app/search/search_dense.py
@@ -327,20 +327,20 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op
     sample_labels = in_file['ytrain'][()]
     sample_fns = in_file['fn'][()]
     NUM_IMGS = sample_images.shape[0] # number of images to be inverted.
+    INFILL_IMGS = NUM_IMGS
     print("Number of images: {}".format(NUM_IMGS))
     print("Batch size: {}".format(BATCH_SIZE))
     def sample_images_gen():
-      for i in range(int(NUM_IMGS / BATCH_SIZE)):
+      for i in range(int(INFILL_IMGS / BATCH_SIZE)):
         i_1, i_2 = i*BATCH_SIZE, (i+1)*BATCH_SIZE
         yield sample_images[i_1:i_2], sample_labels[i_1:i_2]
     image_gen = sample_images_gen()
     sample_latents = in_file['latent']
     def sample_latent_gen():
-      for i in range(int(NUM_IMGS / BATCH_SIZE)):
+      for i in range(int(INFILL_IMGS / BATCH_SIZE)):
         i_1, i_2 = i*BATCH_SIZE, (i+1)*BATCH_SIZE
         yield sample_latents[i_1:i_2]
     latent_gen = sample_latent_gen()
-    INFILL_IMGS = NUM_IMGS
     while INFILL_IMGS % BATCH_SIZE != 0:
       REMAINDER = 1 # BATCH_SIZE - (NUM_IMGS % BATCH_SIZE)
       INFILL_IMGS += REMAINDER
@@ -444,7 +444,9 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op
     for i in range(BATCH_SIZE):
       out_i = out_pos + i
       if out_i >= NUM_IMGS:
+        print("{} >= {}, skipping...".format(out_i, NUM_IMGS))
         continue
+      print("{}: {}".format(out_i, sample_fn))
       sample_fn, ext = os.path.splitext(sample_fns[out_i])
       image = Image.fromarray(images[i])
       fp = BytesIO()
diff --git a/cli/app/utils/cortex_utils.py b/cli/app/utils/cortex_utils.py
index 1413f9a..26878fd 100644
--- a/cli/app/utils/cortex_utils.py
+++ b/cli/app/utils/cortex_utils.py
@@ -51,7 +51,7 @@ def find_unprocessed_files(files, reprocess=False):
       dataset = fn.split('-')[0]
       if dataset not in datasets or reprocess == True:
         unprocessed_files.append(file)
-  return unprocessed_files
+  return sorted(unprocessed_files, key=lambda x: x['name'])
 
 def fetch_json(url, **kwargs):
   """HTTP GET some JSON"""