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94 lines
3.7 KiB
94 lines
3.7 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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import paddle.v2 as paddle
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import paddle.v2.dataset.mnist as mnist
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class TestPreprocessor(unittest.TestCase):
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def setUp(self):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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reader = paddle.batch(mnist.train(), batch_size=32)
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feeder = fluid.DataFeeder(
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feed_list=[ # order is image and label
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fluid.layers.data(
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name='image', shape=[784]),
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fluid.layers.data(
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name='label', shape=[1], dtype='int64'),
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],
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place=fluid.CPUPlace())
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self.num_batches = fluid.recordio_writer.convert_reader_to_recordio_file(
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'./mnist_for_preprocessor_test.recordio', reader, feeder)
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def test_main(self):
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N = 10
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img_expected_res = []
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lbl_expected_res = []
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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data_file = fluid.layers.io.open_recordio_file(
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'./mnist_for_preprocessor_test.recordio',
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shapes=[[-1, 784], [-1, 1]],
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lod_levels=[0, 0],
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dtypes=['float32', 'int64'])
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img, lbl = fluid.layers.io.read_file(data_file)
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if fluid.core.is_compiled_with_cuda():
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place = fluid.CUDAPlace(0)
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else:
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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for _ in range(N):
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img_v, lbl_v = exe.run(fetch_list=[img, lbl])
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img_expected_res.append(img_v / 2)
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lbl_expected_res.append(lbl_v + 1)
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img_actual_res = []
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lbl_actual_res = []
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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data_file = fluid.layers.io.open_recordio_file(
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'./mnist_for_preprocessor_test.recordio',
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shapes=[[-1, 784], [-1, 1]],
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lod_levels=[0, 0],
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dtypes=['float32', 'int64'])
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preprocessor = fluid.layers.io.Preprocessor(reader=data_file)
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with preprocessor.block():
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img, lbl = preprocessor.inputs()
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img_out = img / 2
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lbl_out = lbl + 1
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preprocessor.outputs(img_out, lbl_out)
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data_file = fluid.layers.io.double_buffer(preprocessor())
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img, lbl = fluid.layers.io.read_file(data_file)
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if fluid.core.is_compiled_with_cuda():
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place = fluid.CUDAPlace(0)
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else:
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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for _ in range(N):
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img_v, lbl_v = exe.run(fetch_list=[img, lbl])
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img_actual_res.append(img_v)
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lbl_actual_res.append(lbl_v)
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for idx in range(N):
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np.allclose(img_expected_res[idx], img_actual_res[idx])
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np.allclose(lbl_expected_res[idx], lbl_actual_res[idx])
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