implemented most of the basic functionality from the prototype script and created some messy tests

1 files changed, 50 insertions, 0 deletions

diff --git a/test/latent_space_test.py b/test/latent_space_test.py
new file mode 100644
index 0000000..0655923
--- /dev/null
+++ b/test/latent_space_test.py
@@ -0,0 +1,50 @@
+import unittest
+import numpy as np
+from scipy.stats import truncnorm
+from gantools import latent_space
+from gantools import biggan
+import PIL.Image
+
+def create_random_keyframe(n_vector, n_label):
+    truncation = (0.9 - 0.1)*np.random.random() + 0.1
+    random_state = np.random.RandomState()
+    vectors = truncnorm.rvs(-2, 2, size=(n_vector,), random_state=random_state)
+    keyframe = {
+            'vector': vectors.tolist(),
+            'label': latent_space.one_hot(np.random.randint(0, n_label), n_label),
+            'truncation': truncation,
+            }
+    return keyframe
+
+#### TMP
+def save_image(arr, fp):
+    image = PIL.Image.fromarray(arr)
+    image.save(fp, format='JPEG', quality=90)
+
+def save_ims(ims):
+    i = 0
+    for im in ims:
+        path = './GAN_'+str(i).zfill(3)+'.jpeg'
+        save_image(im, path)
+        i += 1
+####
+
+class TestLatentSpace(unittest.TestCase):
+    def test_sequence_keyframes(self):
+        num_frames = 20
+        batch_size = 3
+        keyframe_count = 3
+        dim_z = 128
+        dim_label = 1000
+        keyframes = [create_random_keyframe(dim_z,dim_label) for i in range(keyframe_count)]
+        z_seq, label_seq, truncation_seq = latent_space.sequence_keyframes(keyframes, num_frames, batch_size)
+        self.assertIs(len(z_seq), num_frames)
+        self.assertIs(len(label_seq), num_frames)
+        gan = biggan.BigGAN()
+        ims = gan.sample(z_seq, label_seq, truncation_seq, batch_size)
+        save_ims(ims)
+
+
+
+if __name__ == '__main__':
+    unittest.main()