runway model

requirements.txt

+absl-py==0.7.0
+astor==0.7.1
+certifi==2018.11.29
+chardet==3.0.4
+Click==7.0
+Flask==1.0.2
+Flask-Cors==3.0.7
+gast==0.2.2
+gevent==1.4.0
+greenlet==0.4.15
+grpcio==1.19.0
+h5py==2.9.0
+idna==2.8
+itsdangerous==1.1.0
+Jinja2==2.10
+Keras-Applications==1.0.7
+Keras-Preprocessing==1.0.9
+Markdown==3.0.1
+MarkupSafe==1.1.1
+mock==2.0.0
+numpy==1.16.2
+pbr==5.1.2
+Pillow==5.4.1
+protobuf==3.6.1
+requests==2.21.0
+six==1.12.0
+tensorflow-gpu==1.15.0
+termcolor==1.1.0
+urllib3==1.24.1
+Werkzeug==0.14.1
+wget==3.2
+runway-python
\ No newline at end of file

runway.yml

+entrypoint: python runway_model.py
+python: 3.6
+cuda: 10.0
+spec:
+  gpu: True
+  cpu: False
+files:
+  ignore:
+    - checkpoints/*
+build_steps:
+  - pip install -r requirements.txt
\ No newline at end of file

runway_model.py

+import pickle
+import numpy as np
+import tensorflow as tf
+import dnnlib.tflib as tflib
+import runway
+
+fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
+
+@runway.setup(options={'checkpoint': runway.file(extension='.pkl')})
+def setup(opts):
+    global Gs
+    tflib.init_tf()
+    with open(opts['checkpoint'], 'rb') as file:
+        _G, _D, Gs = pickle.load(file, encoding='latin1')
+    noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
+    tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars})
+    return Gs
+
+
+generate_inputs = {
+    'z': runway.vector(512, sampling_std=0.5),
+    'truncation': runway.number(min=0, max=1, default=0.8, step=0.01)
+}
+
+@runway.command('generate', inputs=generate_inputs, outputs={'image': runway.image})
+def convert(model, inputs):
+    z = inputs['z']
+    truncation = inputs['truncation']
+    latents = z.reshape((1, 512))
+    images = model.run(latents, None, truncation_psi=truncation, randomize_noise=False, output_transform=fmt)
+    output = np.clip(images[0], 0, 255).astype(np.uint8)
+    return {'image': output}
+
+
+if __name__ == '__main__':
+    runway.run()