face-gan-gen-def.py

@st.cache(show_spinner=False, hash_funcs={tf.Session: id})
def generate_image(session, pg_gan_model, tl_gan_model, features, feature_names):

    # Create rescaled feature vector.
    feature_values = np.array([features[name] for name in feature_names])
    feature_values = (feature_values - 50) / 250

    # Multiply by Shaobo's matrix to get the latent variables.
    latents = np.dot(tl_gan_model, feature_values)
    latents = latents.reshape(1, -1)
    dummies = np.zeros([1] + pg_gan_model.input_shapes[1][1:])

    # Feed the latent vector to the GAN in TensorFlow.
    with session.as_default():
        images = pg_gan_model.run(latents, dummies)

    # Rescale and reorient the GAN's output to make an image.
    images = np.clip(np.rint((images + 1.0) / 2.0 * 255.0),
                              0.0, 255.0).astype(np.uint8)  # [-1,1] => [0,255]

    if USE_GPU:
        images = images.transpose(0, 2, 3, 1)  # NCHW => NHWC

    return images[0]