fengjiayi
7 years ago
parent
ba5380129c
commit
4b950951d3
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# 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 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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import numpy as np
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class TestDataBalance(unittest.TestCase):
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def prepare_data(self):
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def fake_data_generator():
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for n in xrange(self.total_ins_num):
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yield np.ones((3, 4)) * n, n
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# Prepare data
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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reader = paddle.batch(
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fake_data_generator, batch_size=self.batch_size)
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feeder = fluid.DataFeeder(
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feed_list=[
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fluid.layers.data(
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name='image', shape=[3, 4], dtype='float32'),
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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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self.data_file_name, reader, feeder)
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def prepare_lod_data(self):
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def fake_data_generator():
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for n in xrange(1, self.total_ins_num + 1):
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d1 = (np.ones((n, 3)) * n).astype('float32')
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d2 = (np.array(n).reshape((1, 1))).astype('int32')
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yield d1, d2
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# Prepare lod data
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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with fluid.recordio_writer.create_recordio_writer(
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filename=self.lod_data_file_name) as writer:
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eof = False
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generator = fake_data_generator()
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while (not eof):
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data_batch = [
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np.array([]).reshape((0, 3)), np.array([]).reshape(
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(0, 1))
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]
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lod = [0]
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for _ in xrange(self.batch_size):
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try:
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ins = generator.next()
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except StopIteration:
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eof = True
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break
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for i, d in enumerate(ins):
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data_batch[i] = np.concatenate(
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(data_batch[i], d), axis=0)
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lod.append(lod[-1] + ins[0].shape[0])
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if data_batch[0].shape[0] > 0:
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for i, d in enumerate(data_batch):
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t = fluid.LoDTensor()
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t.set(data_batch[i], fluid.CPUPlace())
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if i == 0:
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t.set_lod([lod])
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writer.append_tensor(t)
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writer.complete_append_tensor()
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def setUp(self):
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self.use_cuda = fluid.core.is_compiled_with_cuda()
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self.data_file_name = './data_balance_test.recordio'
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self.lod_data_file_name = './data_balance_with_lod_test.recordio'
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self.total_ins_num = 50
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self.batch_size = 10
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self.prepare_data()
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self.prepare_lod_data()
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def main(self):
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main_prog = fluid.Program()
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startup_prog = fluid.Program()
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with fluid.program_guard(main_prog, startup_prog):
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data_reader = fluid.layers.io.open_files(
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filenames=[self.data_file_name],
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shapes=[[-1, 3, 4], [-1, 1]],
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lod_levels=[0, 0],
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dtypes=['float32', 'int64'])
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if self.use_cuda:
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data_reader = fluid.layers.double_buffer(data_reader)
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image, label = fluid.layers.read_file(data_reader)
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place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup_prog)
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parallel_exe = fluid.ParallelExecutor(
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use_cuda=self.use_cuda, main_program=main_prog)
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if (parallel_exe.device_count > self.batch_size):
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print("WARNING: Unittest TestDataBalance skipped. \
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For the result is not correct when device count \
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is larger than batch size.")
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exit(0)
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fetch_list = [image.name, label.name]
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data_appeared = [False] * self.total_ins_num
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while (True):
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try:
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image_val, label_val = parallel_exe.run(fetch_list,
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return_numpy=True)
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except fluid.core.EnforceNotMet as ex:
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self.assertIn("There is no next data.", ex.message)
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break
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ins_num = image_val.shape[0]
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broadcasted_label = np.ones(
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(ins_num, 3, 4)) * label_val.reshape((ins_num, 1, 1))
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self.assertEqual(image_val.all(), broadcasted_label.all())
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for l in label_val:
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self.assertFalse(data_appeared[l[0]])
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data_appeared[l[0]] = True
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for i in data_appeared:
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self.assertTrue(i)
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def main_lod(self):
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main_prog = fluid.Program()
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startup_prog = fluid.Program()
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with fluid.program_guard(main_prog, startup_prog):
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data_reader = fluid.layers.io.open_files(
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filenames=[self.lod_data_file_name],
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shapes=[[-1, 3], [-1, 1]],
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lod_levels=[1, 0],
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dtypes=['float32', 'int32'],
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thread_num=1)
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ins, label = fluid.layers.read_file(data_reader)
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place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup_prog)
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parallel_exe = fluid.ParallelExecutor(
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use_cuda=self.use_cuda, main_program=main_prog)
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if (parallel_exe.device_count > self.batch_size):
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print("WARNING: Unittest TestDataBalance skipped. \
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For the result is not correct when device count \
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is larger than batch size.")
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exit(0)
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fetch_list = [ins.name, label.name]
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data_appeared = [False] * self.total_ins_num
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while (True):
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try:
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ins_tensor, label_tensor = parallel_exe.run(
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fetch_list, return_numpy=False)
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except fluid.core.EnforceNotMet as ex:
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self.assertIn("There is no next data.", ex.message)
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break
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ins_val = np.array(ins_tensor)
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label_val = np.array(label_tensor)
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ins_lod = ins_tensor.lod()[0]
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self.assertEqual(ins_val.shape[1], 3)
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self.assertEqual(label_val.shape[1], 1)
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self.assertEqual(len(ins_lod) - 1, label_val.shape[0])
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for i in range(0, len(ins_lod) - 1):
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ins_elem = ins_val[ins_lod[i]:ins_lod[i + 1]][:]
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label_elem = label_val[i][0]
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self.assertEqual(ins_elem.all(), label_elem.all())
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self.assertFalse(data_appeared[int(label_elem - 1)])
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data_appeared[int(label_elem - 1)] = True
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for i in data_appeared:
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self.assertTrue(i)
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def test_all(self):
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self.main()
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self.main_lod()
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