forked from mindspore-Ecosystem/mindspore
90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# 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 pytest
|
|
import numpy as np
|
|
import mindspore
|
|
from mindspore import Tensor
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
import mindspore.ops.operations.sparse_ops as P
|
|
|
|
|
|
class SparseSparseMaximumNet(nn.Cell):
|
|
def __init__(self):
|
|
super(SparseSparseMaximumNet, self).__init__()
|
|
self.sparsesparsemaximum = P.SparseSparseMaximum()
|
|
|
|
def construct(self, x1_indices, x1_values, x1_shape, x2_indices, x2_values, x2_shape):
|
|
return self.sparsesparsemaximum(x1_indices, x1_values, x1_shape, x2_indices, x2_values, x2_shape)
|
|
|
|
|
|
def sparse_sparse_maximum(loss):
|
|
loss1 = loss
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
indices1 = Tensor([[0, 1], [0, 1], [2, 2], [0, 2]])
|
|
values1 = Tensor([4, 2, 3, 4], dtype=mindspore.float32)
|
|
shape1 = Tensor([3, 4])
|
|
indices2 = Tensor([[0, 1], [2, 3]])
|
|
values2 = Tensor([2, 3], dtype=mindspore.float32)
|
|
shape2 = Tensor([3, 4])
|
|
net = SparseSparseMaximumNet()
|
|
m, n = net(indices1, values1, shape1, indices2, values2, shape2)
|
|
expected_m = np.array([[0, 1], [0, 1], [2, 2], [0, 2], [2, 3]], dtype=np.int64)
|
|
expected_n = np.array([4, 2, 3, 4, 3], dtype=np.float32)
|
|
assert np.allclose(m.asnumpy(), expected_m, loss, loss)
|
|
assert np.allclose(n.asnumpy(), expected_n, loss1, loss1)
|
|
|
|
|
|
def sparse_sparse_maximum_pynative(loss):
|
|
loss1 = loss
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
|
|
indices1 = Tensor([[0, 1], [0, 1], [2, 2], [0, 2]])
|
|
values1 = Tensor([4, 2, 3, 4], dtype=mindspore.float32)
|
|
shape1 = Tensor([3, 4])
|
|
indices2 = Tensor([[0, 1], [2, 3]])
|
|
values2 = Tensor([2, 3], dtype=mindspore.float32)
|
|
shape2 = Tensor([3, 4])
|
|
net = SparseSparseMaximumNet()
|
|
m, n = net(indices1, values1, shape1, indices2, values2, shape2)
|
|
print(m)
|
|
expected_m = np.array([[0, 1], [0, 1], [2, 2], [0, 2], [2, 3]], dtype=np.int64)
|
|
expected_n = np.array([4, 2, 3, 4, 3], dtype=np.float32)
|
|
assert np.allclose(m.asnumpy(), expected_m, loss, loss)
|
|
assert np.allclose(n.asnumpy(), expected_n, loss1, loss1)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_sparse_sparse_maximum_graph_float32():
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: test cases for SparseSparseMaximum
|
|
Expectation: the result match to tensorflow
|
|
"""
|
|
sparse_sparse_maximum(loss=1.0e-4)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_sparse_sparse_maximum_pynative_float32():
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: test cases for SparseSparseMaximum
|
|
Expectation: the result match to tensorflow
|
|
"""
|
|
sparse_sparse_maximum_pynative(loss=1.0e-5)
|