提交 8a83d699 编写于 作者: S sneaxiy

delete data_balance unittest

test=develop
上级 9c2cbfb8
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed 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.
from __future__ import print_function
import unittest
import paddle.fluid as fluid
import paddle
import numpy as np
class TestDataBalance(unittest.TestCase):
def prepare_data(self):
def fake_data_generator():
for n in range(self.total_ins_num):
yield np.ones((3, 4)) * n, n
# Prepare data
with fluid.program_guard(fluid.Program(), fluid.Program()):
reader = paddle.batch(
fake_data_generator, batch_size=self.batch_size)
feeder = fluid.DataFeeder(
feed_list=[
fluid.layers.data(
name='image', shape=[3, 4], dtype='float32'),
fluid.layers.data(
name='label', shape=[1], dtype='int64'),
],
place=fluid.CPUPlace())
self.num_batches = fluid.recordio_writer.convert_reader_to_recordio_file(
self.data_file_name, reader, feeder)
def prepare_lod_data(self):
def fake_data_generator():
for n in range(1, self.total_ins_num + 1):
d1 = (np.ones((n, 3)) * n).astype('float32')
d2 = (np.array(n).reshape((1, 1))).astype('int32')
yield d1, d2
# Prepare lod data
with fluid.program_guard(fluid.Program(), fluid.Program()):
with fluid.recordio_writer.create_recordio_writer(
filename=self.lod_data_file_name) as writer:
eof = False
generator = fake_data_generator()
while (not eof):
data_batch = [
np.array([]).reshape((0, 3)), np.array([]).reshape(
(0, 1))
]
lod = [0]
for _ in range(self.batch_size):
try:
ins = next(generator)
except StopIteration:
eof = True
break
for i, d in enumerate(ins):
data_batch[i] = np.concatenate(
(data_batch[i], d), axis=0)
lod.append(lod[-1] + ins[0].shape[0])
if data_batch[0].shape[0] > 0:
for i, d in enumerate(data_batch):
t = fluid.LoDTensor()
t.set(data_batch[i], fluid.CPUPlace())
if i == 0:
t.set_lod([lod])
writer.append_tensor(t)
writer.complete_append_tensor()
def setUp(self):
self.use_cuda = fluid.core.is_compiled_with_cuda()
self.data_file_name = './data_balance_test.recordio'
self.lod_data_file_name = './data_balance_with_lod_test.recordio'
self.total_ins_num = 50
self.batch_size = 12
self.prepare_data()
self.prepare_lod_data()
def main(self):
main_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(main_prog, startup_prog):
data_reader = fluid.layers.io.open_files(
filenames=[self.data_file_name],
shapes=[[-1, 3, 4], [-1, 1]],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
if self.use_cuda:
data_reader = fluid.layers.double_buffer(data_reader)
image, label = fluid.layers.read_file(data_reader)
place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
build_strategy = fluid.BuildStrategy()
build_strategy.enable_data_balance = True
parallel_exe = fluid.ParallelExecutor(
use_cuda=self.use_cuda,
main_program=main_prog,
build_strategy=build_strategy)
if (parallel_exe.device_count > self.batch_size):
print("WARNING: Unittest TestDataBalance skipped. \
For the result is not correct when device count \
is larger than batch size.")
return
fetch_list = [image.name, label.name]
data_appeared = [False] * self.total_ins_num
while (True):
try:
image_val, label_val = parallel_exe.run(fetch_list,
return_numpy=True)
except fluid.core.EOFException:
break
ins_num = image_val.shape[0]
broadcasted_label = np.ones(
(ins_num, 3, 4)) * label_val.reshape((ins_num, 1, 1))
self.assertEqual(image_val.all(), broadcasted_label.all())
for l in label_val:
self.assertFalse(data_appeared[l[0]])
data_appeared[l[0]] = True
for i in data_appeared:
self.assertTrue(i)
def main_lod(self):
main_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(main_prog, startup_prog):
data_reader = fluid.layers.io.open_files(
filenames=[self.lod_data_file_name],
shapes=[[-1, 3], [-1, 1]],
lod_levels=[1, 0],
dtypes=['float32', 'int32'])
ins, label = fluid.layers.read_file(data_reader)
place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
build_strategy = fluid.BuildStrategy()
build_strategy.enable_data_balance = True
parallel_exe = fluid.ParallelExecutor(
use_cuda=self.use_cuda,
main_program=main_prog,
build_strategy=build_strategy)
if parallel_exe.device_count > self.batch_size:
print("WARNING: Unittest TestDataBalance skipped. \
For the result is not correct when device count \
is larger than batch size.")
exit(0)
fetch_list = [ins.name, label.name]
data_appeared = [False] * self.total_ins_num
while (True):
try:
ins_tensor, label_tensor = parallel_exe.run(
fetch_list, return_numpy=False)
except fluid.core.EOFException:
break
ins_val = np.array(ins_tensor)
label_val = np.array(label_tensor)
ins_lod = ins_tensor.lod()[0]
self.assertEqual(ins_val.shape[1], 3)
self.assertEqual(label_val.shape[1], 1)
self.assertEqual(len(ins_lod) - 1, label_val.shape[0])
for i in range(0, len(ins_lod) - 1):
ins_elem = ins_val[ins_lod[i]:ins_lod[i + 1]][:]
label_elem = label_val[i][0]
self.assertEqual(ins_elem.all(), label_elem.all())
self.assertFalse(data_appeared[int(label_elem - 1)])
data_appeared[int(label_elem - 1)] = True
for i in data_appeared:
self.assertTrue(i)
def test_all(self):
self.main()
self.main_lod()
if __name__ == '__main__':
unittest.main()
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