1 files changed, 0 insertions, 91 deletions
diff --git a/cli/app/commands/process/random.py b/cli/app/commands/process/random.py
deleted file mode 100644
index a1e5aff..0000000
--- a/cli/app/commands/process/random.py
+++ /dev/null
@@ -1,91 +0,0 @@
-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):
-  """
-  """
-  print("Loading Tensorflow....")
-  import tensorflow as tf
-  import tensorflow_hub as hub
-
-  #tf.compat.v1.disable_eager_execution()
-  #g = tf.compat.v1.get_default_graph()
-
-  # Sample a batch of 8 random latent vectors (z) from the Gaussian prior. Then
-  # call the generator on the latent samples to generate a batch of images with
-  # shape [8, 128, 128, 3] and range [-1, 1].
-  #recons = module(z, signature='generate', as_dict=True)['upsampled']
-
-  #info = module.get_input_info_dict('encode')['x']
-  #enc_ph = tf.placeholder(dtype=info.dtype, shape=info.get_shape())
-
-  #z = bigbigan.encode(enc_ph, return_all_features=True)['z_mean']
-  #recons = bigbigan.generate(z, upsample=True)
-  #recons = outputs['upsampled']
-
-  #if return_all_features else outputs['z_sample']
-
-  #fp_img_out = "{}.png".format(int(time.time() * 1000))
-  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['upsampled']:
-    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))
-    #print(result)
-
-  #tf.keras.preprocessing.image.save_img(
-  #  join(app_cfg.DIR_OUTPUTS, fp_img_out),
-  #  gen_samples,
-  #)
-  #with tf.Session() as sess:
-  #  gen_samples = gen_samples.eval()
-  #  print(gen_samples)
-
-  # # Given a batch of 256x256 RGB images in range [-1, 1], call the encoder to
-  # # compute predicted latents z and other features (e.g. for use in downstream
-  # # recognition tasks).
-  # images = tf.placeholder(tf.float32, shape=[None, 256, 256, 3])
-  # features = module(images, signature='encode', as_dict=True)
-
-  # # Get the predicted latent sample `z_sample` from the dict of features.
-  # # Other available features include `avepool_feat` and `bn_crelu_feat`, used in
-  # # the representation learning results.
-  # z_sample = features['z_sample']  # shape [?, 120]
-
-  # # Compute reconstructions of the input `images` by passing the encoder's output
-  # # `z_sample` back through the generator. Note that raw generator outputs are
-  # # half the resolution of encoder inputs (128x128). To get upsampled generator
-  # # outputs matching the encoder input resolution (256x256), instead use:
-  # #     recons = module(z_sample, signature='generate', as_dict=True)['upsampled']
-  # recons = module(z_sample, signature='generate')  # shape [?, 128, 128, 3]