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Paddle/python/paddle/fluid/tests/unittests/test_slice_var.py

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2.3 KiB

# 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.
import math
import unittest
from paddle.fluid.transpiler.distribute_transpiler import slice_variable
import paddle.fluid as fluid
import paddle.fluid.core as core
import random
class TestSliceVar(unittest.TestCase):
def check_slice_output(self, shapes, expected_sizes, min_size):
var_list = []
program = fluid.Program()
for shape in shapes:
var = program.global_block().create_var(
name=str(random.randint(10000, 99999)),
persistable=True,
# dtype=core.VarDesc.VarType.LOD_TENSOR,
shape=shape)
var_list.append(var)
blocks = slice_variable(var_list, 10, min_size)
all_sizes = []
for s in expected_sizes:
for s2 in s:
all_sizes.append(s2)
for i, block_str in enumerate(blocks):
varname, block_id, size = block_str.split(":")
self.assertEqual(int(size), all_sizes[i])
def test_1k(self):
shapes = [[3, 5], [1024], [28, 784], [8, 1020], [800, 10]]
expected_sizes = [
[15], [1024],
[2352, 2352, 2352, 2352, 2352, 2352, 2352, 2352, 2352, 784],
[2040, 2040, 2040, 2040],
[1150, 1150, 1150, 1150, 1150, 1150, 1100]
]
self.check_slice_output(shapes, expected_sizes, 1024)
def test_check_output_8k(self):
shapes = [[3, 5], [1024], [28, 784], [8, 1020], [800, 10],
[6, 33, 33, 33]]
expected_sizes = [[15], [1024], [10976, 10976], [8160], [8000],
[35937, 35937, 35937, 35937, 35937, 35937]]
self.check_slice_output(shapes, expected_sizes, 8192)
if __name__ == '__main__':
unittest.main()