Python config.random_seed() Examples

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Example #1
Source File: predict.py    From Keras-progressive_growing_of_gans with MIT License 5 votes vote down vote up 
def predict_gan():
    separate_funcs          = False
    drange_net              = [-1,1]
    drange_viz              = [-1,1]
    image_grid_size         = None
    image_grid_type         = 'default'
    resume_network          = 'pre-trained_weight'
    
    np.random.seed(config.random_seed)

    if resume_network:
        print("Resuming weight from:"+resume_network)
        G = Generator(num_channels=3, resolution=128, label_size=0, **config.G)
        G = load_G_weights(G,resume_network,True)

    print(G.summary())

    # Misc init.

    if image_grid_type == 'default':
        if image_grid_size is None:
            w, h = G.output_shape[1], G.output_shape[2]
            print("w:%d,h:%d"%(w,h))
            image_grid_size = np.clip(int(1920 // w), 3, 16).astype('int'), np.clip(1080 / h, 2, 16).astype('int')
        
        print("image_grid_size:",image_grid_size)
    else:
        raise ValueError('Invalid image_grid_type', image_grid_type)

    result_subdir = misc.create_result_subdir('pre-trained_result', config.run_desc)

    for i in range(1,6):
        snapshot_fake_latents = random_latents(np.prod(image_grid_size), G.input_shape)
        snapshot_fake_images = G.predict_on_batch(snapshot_fake_latents)
        misc.save_image_grid(snapshot_fake_images, os.path.join(result_subdir, 'pre-trained_%03d.png'%i), drange=drange_viz, grid_size=image_grid_size)