问如何使用Tensorflow进行分布式预测/推理

EN

# https://github.com/tensorflow/tensorflow/issues/37686
# https://www.tensorflow.org/tutorials/distribute/custom_training
def compute_and_write_ious_multi_gpu(path: str, filename_csv: str, include_sampled: bool):
    strategy = tf.distribute.MirroredStrategy()
    util.log('Number of devices: {}'.format(strategy.num_replicas_in_sync))
    (ds, s, n) = dataset(path, shuffle=False, repeat=False, mask_as_input=True)
    dist_ds = strategy.experimental_distribute_dataset(ds)

    def predict_step(inputs):
        images, labels = inputs
        return model(images, training=False)

    @tf.function
    def distributed_predict_step(dataset_inputs):
        per_replica_losses = strategy.run(predict_step, args=(dataset_inputs,))
        return per_replica_losses  # unwrap!?

    # https://stackoverflow.com/questions/57549448/how-to-convert-perreplica-to-tensor
    def unwrap(per_replica):  # -> list of numpy arrays
        if strategy.num_replicas_in_sync > 1:
            out = per_replica.values
        else:
            out = (per_replica,)
        return list(map(lambda x: x.numpy(), out))

    with strategy.scope():
        model = wrap_model()

    util.log(f'Starting distributed prediction for {filename_csv}')
    ious = [unwrap(distributed_predict_step(x)) for x in dist_ds]
    t = ious
    ious = [item for sublist in t for item in
            sublist]  # https://stackoverflow.com/questions/952914/how-to-make-a-flat-list-out-of-list-of-lists
    util.log(f'Distributed prediction done for {filename_csv}')
    ious = np.concatenate(ious).ravel().tolist()
    ious = round_ious(ious)
    ious = list(zip(ious, ds.all_image_paths))
    ious.sort()
    write_ious(ious, filename_csv, include_sampled)