2 files changed, 103 insertions, 0 deletions
diff --git a/cli/app/commands/bigbigan/fetch.py b/cli/app/commands/bigbigan/fetch.py
new file mode 100644
index 0000000..5b6c102
--- /dev/null
+++ b/cli/app/commands/bigbigan/fetch.py
@@ -0,0 +1,57 @@
+import click
+
+from app.utils import click_utils
+from app.settings import app_cfg
+
+from os.path import join
+from subprocess import call
+
+@click.command('')
+# @click.option('-i', '--input', 'opt_dir_in', required=True, 
+#   help='Path to input image glob directory')
+# @click.option('-r', '--recursive', 'opt_recursive', is_flag=True)
+@click.pass_context
+def cli(ctx):
+  """
+  """
+
+  # app_cfg.MODELZOO_CFG
+  import gensim
+  
+  # from nltk.corpus import wordnet as wn
+  # synsets = wordnet.synsets("fir_tree")
+  # synonyms = [ lemma.name() for lemma in synsets[0].lemmas() ]
+
+  imagenet = Imagenet()
+
+  sentence = "The quick brown fox jumps over the lazy dog"
+  tokens = gensim.utils.simple_preprocess(sentence)
+
+class Imagenet:
+  def __init__():
+    tokens = {}
+    with open(app_cfg.FP_IMAGENET_WORDS, "r") as fp:
+      for line in fp.readlines():
+        wordnet_id, word_list = line.split('\t')
+        words = [word.trim() for word in word_list.split(',')]
+        for word in words:
+          tokens[word] = wordnet_id
+    self.tokens = tokens
+ 
+  def get_wordnet_ids_for_words(tokens):
+    # for token in tokens:
+      # if token in tokens:
+    pass
+
+  def images_from_wordnet_id(wordnet_id):
+    """
+    Given a Wordnet ID, download images for this class
+    """
+    call([
+      "python",
+      join(app_cfg.DIR_APP, "../ImageNet-Datasets-Downloader/downloader.py"),
+      '-data_root', app_cfg.FP_IMAGENET,
+      '-use_class_list', 'True',
+      '-class_list', wordnet_id,
+      '-images_per_class', app_cfg.IMAGENET_IMAGES_PER_CLASS
+    ])
diff --git a/cli/app/commands/bigbigan/random.py b/cli/app/commands/bigbigan/random.py
new file mode 100644
index 0000000..a1fd65f
--- /dev/null
+++ b/cli/app/commands/bigbigan/random.py
@@ -0,0 +1,46 @@
+import click
+
+from app.utils import click_utils
+from app.settings import app_cfg
+
+from os.path import join
+import time
+import numpy as np
+
+from PIL import Image
+
+def image_to_uint8(x):
+  """Converts [-1, 1] float array to [0, 255] uint8."""
+  x = np.asarray(x)
+  x = (256. / 2.) * (x + 1.)
+  x = np.clip(x, 0, 255)
+  x = x.astype(np.uint8)
+  return x
+
+@click.command('')
+# @click.option('-i', '--input', 'opt_dir_in', required=True, 
+#   help='Path to input image glob directory')
+# @click.option('-r', '--recursive', 'opt_recursive', is_flag=True)
+@click.pass_context
+def cli(ctx):
+  """
+  """
+  import tensorflow as tf
+  import tensorflow_hub as hub
+
+  print("Loading module...")
+  module = hub.Module('https://tfhub.dev/deepmind/bigbigan-resnet50/1')
+  z = tf.random.normal([8, 120])  # latent samples
+  outputs = module(z, signature='generate', as_dict=True)
+
+  with tf.Session() as sess:
+    sess.run(tf.compat.v1.global_variables_initializer())
+    sess.run(tf.compat.v1.tables_initializer())
+    results = sess.run(outputs)
+
+  for sample in results['default']:
+    sample = image_to_uint8(sample)
+    img = Image.fromarray(sample, "RGB")
+    fp_img_out = "{}.png".format(int(time.time() * 1000))
+    img.save(join(app_cfg.DIR_OUTPUTS, fp_img_out))
+