getting random gan images

2 files changed, 46 insertions, 5 deletions

diff --git a/.gitignore b/.gitignore
index 68bd48a..fd07da9 100755
--- a/.gitignore
+++ b/.gitignore
@@ -58,4 +58,7 @@ data_store/imagenet/imagenet_images/
 *.gif
 *.jpeg
 *.png
+*.mp4
+
+*.swp
 
diff --git a/cli/app/commands/process/random.py b/cli/app/commands/process/random.py
index 994afce..a1e5aff 100644
--- a/cli/app/commands/process/random.py
+++ b/cli/app/commands/process/random.py
@@ -5,9 +5,18 @@ 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')
@@ -20,20 +29,49 @@ def cli(ctx):
   import tensorflow as tf
   import tensorflow_hub as hub
 
-  module = hub.Module('https://tfhub.dev/deepmind/bigbigan-resnet50/1')
+  #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
-  gen_samples = module(z, signature='generate', as_dict=True)['upsampled']
+  outputs = module(z, signature='generate', as_dict=True)
 
-  print(gen_samples)
+  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 gen_samples:
+  for sample in results['upsampled']:
+    sample = image_to_uint8(sample)
     img = Image.fromarray(sample, "RGB")
-    fp_img_out = int(time.time() * 1000) + '.png'
+    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).