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test_sparse_utils_op.py
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test_sparse_utils_op.py
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# Copyright (c) 2022 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 numpy as np
import paddle
import paddle.fluid.core as core
from paddle.fluid.framework import _test_eager_guard
class TestSparseCreate(unittest.TestCase):
def test_create_coo_by_tensor(self):
with _test_eager_guard():
non_zero_indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
non_zero_elements = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
dense_indices = paddle.to_tensor(non_zero_indices)
dense_elements = paddle.to_tensor(
non_zero_elements, dtype='float32')
coo = paddle.sparse.sparse_coo_tensor(
dense_indices, dense_elements, dense_shape, stop_gradient=False)
assert np.array_equal(non_zero_indices,
coo.non_zero_indices().numpy())
assert np.array_equal(non_zero_elements,
coo.non_zero_elements().numpy())
def test_create_coo_by_np(self):
with _test_eager_guard():
indices = [[0, 1, 2], [1, 2, 0]]
values = [1.0, 2.0, 3.0]
dense_shape = [2, 3]
coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
print(coo)
assert np.array_equal(indices, coo.non_zero_indices().numpy())
assert np.array_equal(values, coo.non_zero_elements().numpy())
def test_create_csr_by_tensor(self):
with _test_eager_guard():
non_zero_crows = [0, 2, 3, 5]
non_zero_cols = [1, 3, 2, 0, 1]
non_zero_elements = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
dense_crows = paddle.to_tensor(non_zero_crows)
dense_cols = paddle.to_tensor(non_zero_cols)
dense_elements = paddle.to_tensor(
non_zero_elements, dtype='float32')
stop_gradient = False
csr = paddle.sparse.sparse_csr_tensor(
dense_crows,
dense_cols,
dense_elements,
dense_shape,
stop_gradient=stop_gradient)
print(csr)
def test_create_csr_by_np(self):
with _test_eager_guard():
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1, 2, 3, 4, 5]
dense_shape = [3, 4]
csr = paddle.sparse.sparse_csr_tensor(crows, cols, values,
dense_shape)
assert np.array_equal(crows, csr.non_zero_crows().numpy())
assert np.array_equal(cols, csr.non_zero_cols().numpy())
assert np.array_equal(values, csr.non_zero_elements().numpy())
def test_place(self):
with _test_eager_guard():
place = core.CPUPlace()
indices = [[0, 1], [0, 1]]
values = [1.0, 2.0]
dense_shape = [2, 2]
coo = paddle.sparse.sparse_coo_tensor(
indices, values, dense_shape, place=place)
assert coo.place.is_cpu_place()
assert coo.non_zero_elements().place.is_cpu_place()
assert coo.non_zero_indices().place.is_cpu_place()
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
csr = paddle.sparse.sparse_csr_tensor(
crows, cols, values, [3, 5], place=place)
assert csr.place.is_cpu_place()
assert csr.non_zero_crows().place.is_cpu_place()
assert csr.non_zero_cols().place.is_cpu_place()
assert csr.non_zero_elements().place.is_cpu_place()
def test_dtype(self):
with _test_eager_guard():
indices = [[0, 1], [0, 1]]
values = [1.0, 2.0]
dense_shape = [2, 2]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
coo = paddle.sparse.sparse_coo_tensor(
indices, values, dense_shape, dtype='float64')
assert coo.dtype == paddle.float64
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1.0, 2.0, 3.0, 4.0, 5.0]
csr = paddle.sparse.sparse_csr_tensor(
crows, cols, values, [3, 5], dtype='float16')
assert csr.dtype == paddle.float16
def test_create_coo_no_shape(self):
with _test_eager_guard():
indices = [[0, 1], [0, 1]]
values = [1.0, 2.0]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
coo = paddle.sparse.sparse_coo_tensor(indices, values)
assert [2, 2] == coo.shape
class TestSparseConvert(unittest.TestCase):
def test_to_sparse_coo(self):
with _test_eager_guard():
x = [[0, 1, 0, 2], [0, 0, 3, 0], [4, 5, 0, 0]]
non_zero_indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
non_zero_elements = [1, 2, 3, 4, 5]
dense_x = paddle.to_tensor(x)
out = dense_x.to_sparse_coo(2)
print(out)
assert np.array_equal(out.non_zero_indices().numpy(),
non_zero_indices)
assert np.array_equal(out.non_zero_elements().numpy(),
non_zero_elements)
dense_tensor = out.to_dense()
assert np.array_equal(dense_tensor.numpy(), x)
def test_to_sparse_csr(self):
with _test_eager_guard():
x = [[0, 1, 0, 2], [0, 0, 3, 0], [4, 5, 0, 0]]
non_zero_crows = [0, 2, 3, 5]
non_zero_cols = [1, 3, 2, 0, 1]
non_zero_elements = [1, 2, 3, 4, 5]
dense_x = paddle.to_tensor(x)
out = dense_x.to_sparse_csr()
print(out)
assert np.array_equal(out.non_zero_crows().numpy(), non_zero_crows)
assert np.array_equal(out.non_zero_cols().numpy(), non_zero_cols)
assert np.array_equal(out.non_zero_elements().numpy(),
non_zero_elements)
dense_tensor = out.to_dense()
print(dense_tensor)
assert np.array_equal(dense_tensor.numpy(), x)
if __name__ == "__main__":
unittest.main()