| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| |
| import numpy as np |
| import pytest |
| |
| |
| def run_tensorflow_test_with_dtype(tf, plasma, plasma_store_name, |
| client, use_gpu, dtype): |
| FORCE_DEVICE = '/gpu' if use_gpu else '/cpu' |
| |
| object_id = np.random.bytes(20) |
| |
| data = np.random.randn(3, 244, 244).astype(dtype) |
| ones = np.ones((3, 244, 244)).astype(dtype) |
| |
| sess = tf.Session(config=tf.ConfigProto( |
| allow_soft_placement=True, log_device_placement=True)) |
| |
| def ToPlasma(): |
| data_tensor = tf.constant(data) |
| ones_tensor = tf.constant(ones) |
| return plasma.tf_plasma_op.tensor_to_plasma( |
| [data_tensor, ones_tensor], |
| object_id, |
| plasma_store_socket_name=plasma_store_name) |
| |
| def FromPlasma(): |
| return plasma.tf_plasma_op.plasma_to_tensor( |
| object_id, |
| dtype=tf.as_dtype(dtype), |
| plasma_store_socket_name=plasma_store_name) |
| |
| with tf.device(FORCE_DEVICE): |
| to_plasma = ToPlasma() |
| from_plasma = FromPlasma() |
| |
| z = from_plasma + 1 |
| |
| sess.run(to_plasma) |
| # NOTE(zongheng): currently it returns a flat 1D tensor. |
| # So reshape manually. |
| out = sess.run(from_plasma) |
| |
| out = np.split(out, 2) |
| out0 = out[0].reshape(3, 244, 244) |
| out1 = out[1].reshape(3, 244, 244) |
| |
| sess.run(z) |
| |
| assert np.array_equal(data, out0), "Data not equal!" |
| assert np.array_equal(ones, out1), "Data not equal!" |
| |
| # Try getting the data from Python |
| plasma_object_id = plasma.ObjectID(object_id) |
| obj = client.get(plasma_object_id) |
| |
| # Deserialized Tensor should be 64-byte aligned. |
| assert obj.ctypes.data % 64 == 0 |
| |
| result = np.split(obj, 2) |
| result0 = result[0].reshape(3, 244, 244) |
| result1 = result[1].reshape(3, 244, 244) |
| |
| assert np.array_equal(data, result0), "Data not equal!" |
| assert np.array_equal(ones, result1), "Data not equal!" |
| |
| |
| @pytest.mark.plasma |
| @pytest.mark.tensorflow |
| @pytest.mark.skip(reason='Until ARROW-4259 is resolved') |
| def test_plasma_tf_op(use_gpu=False): |
| import pyarrow.plasma as plasma |
| import tensorflow as tf |
| |
| plasma.build_plasma_tensorflow_op() |
| |
| if plasma.tf_plasma_op is None: |
| pytest.skip("TensorFlow Op not found") |
| |
| with plasma.start_plasma_store(10**8) as (plasma_store_name, p): |
| client = plasma.connect(plasma_store_name) |
| for dtype in [np.float32, np.float64, |
| np.int8, np.int16, np.int32, np.int64]: |
| run_tensorflow_test_with_dtype(tf, plasma, plasma_store_name, |
| client, use_gpu, dtype) |
| |
| # Make sure the objects have been released. |
| for _, info in client.list().items(): |
| assert info['ref_count'] == 0 |