gt_image = np.expand_dims(gt_image, axis=0)
input_image = np.where(mask == 1, 1, gt_image)
prediction_coarse, prediction_refine = generator(
[input_image, mask], training=False)
prediction_refine = prediction_refine * mask + gt_image * (
1 - mask)
save_images(input_image[0, ...], gt_image[0, ...],
prediction_coarse[0, ...], prediction_refine[0,
...],
os.path.join(config.testing_dir, file))
count += 1
if count == config.test_num:
return
print('-' * 20)
if __name__ == '__main__':
# Loading the arguments
config = TestOptions().parse()
model = Model()
generator = model.build_generator()
checkpoint = tf.train.Checkpoint(generator=generator)
checkpoint.restore(
os.path.join(config.pretrained_model_dir, config.checkpoint_prefix))
test(config)
