41ef0bf4ece3daf13a02b14c79eb7ddebb42bc0c,facegen/generate.py,,generate_from_yaml,#,418

Before Change

    gen = model.predict(inputs, batch_size=batch_size)

    print("Writing to "{}"...".format(output_dir))

    for i in range(0, gen.shape[0]):
        if K.image_dim_ordering() == "th":
            image = np.empty(gen.shape[2:]+(3,))

After Change

    num_images = inputs["identity"].shape[0]
    count = 0

    for idx in tqdm(range(0, num_images, batch_size)):

        batch = {
            "identity":    inputs["identity"]   [idx:idx+batch_size,:],
            "emotion":     inputs["emotion"]    [idx:idx+batch_size,:],
            "orientation": inputs["orientation"][idx:idx+batch_size,:],
        }

        gen = model.predict_on_batch(batch)

        for i in range(0, gen.shape[0]):
            if K.image_dim_ordering() == "th":
                image = np.empty(gen.shape[2:]+(3,))
                for x in range(0, 3):
                    image[:,:,x] = gen[i,x,:,:]
            else:
                image = gen[i,:,:,:]
            image = np.array(255*np.clip(image,0,1), dtype=np.uint8)
            file_path = os.path.join(output_dir, "{:05}.png".format(count))
            scipy.misc.imsave(file_path, image)
            count += 1

Instances