You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
245 lines
8.1 KiB
245 lines
8.1 KiB
# 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 numpy as np
|
|
import pytest
|
|
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.ops import operations as P
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
|
|
|
|
class OpNetWrapper(nn.Cell):
|
|
def __init__(self, op):
|
|
super(OpNetWrapper, self).__init__()
|
|
self.op = op
|
|
|
|
def construct(self, *inputs):
|
|
return self.op(*inputs)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_out1_axis0():
|
|
op = P.Split(0, 1)
|
|
op_wrapper = OpNetWrapper(op)
|
|
|
|
input_x = Tensor(np.arange(24).astype(np.int32).reshape((2, 2, 6)))
|
|
outputs = op_wrapper(input_x)
|
|
|
|
print(outputs)
|
|
assert outputs[0].shape == (2, 2, 6)
|
|
assert np.allclose(outputs[0].asnumpy()[0, 0, :], [0, 1, 2, 3, 4, 5])
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_out2_axis2():
|
|
op = P.Split(2, 2)
|
|
op_wrapper = OpNetWrapper(op)
|
|
|
|
input_x = Tensor(np.arange(24).astype(np.int32).reshape((2, 2, 6)))
|
|
outputs = op_wrapper(input_x)
|
|
|
|
print(outputs)
|
|
assert outputs[0].shape == (2, 2, 3)
|
|
assert outputs[1].shape == (2, 2, 3)
|
|
assert np.allclose(outputs[0].asnumpy()[0, 0, :], [0, 1, 2])
|
|
assert np.allclose(outputs[1].asnumpy()[0, 0, :], [3, 4, 5])
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_out2_axis1neg():
|
|
op = P.Split(-1, 2)
|
|
op_wrapper = OpNetWrapper(op)
|
|
|
|
input_x = Tensor(np.arange(24).astype(np.float32).reshape((2, 2, 6)))
|
|
outputs = op_wrapper(input_x)
|
|
|
|
print(outputs)
|
|
assert np.allclose(outputs[0].asnumpy()[0, :, :], [[0., 1., 2.], [6., 7., 8.]])
|
|
assert np.allclose(outputs[1].asnumpy()[0, :, :], [[3., 4., 5.], [9., 10., 11.]])
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_out_float32():
|
|
op = P.Split(5, 2)
|
|
op_wrapper = OpNetWrapper(op)
|
|
|
|
input_x = Tensor(np.arange(192).astype(np.float32).reshape((2, 2, 2, 2, 2, 6)))
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[0, 0, 0, 0, 0, :], [0., 1., 2.])
|
|
assert np.allclose(outputs[1].asnumpy()[0, 0, 0, 0, 0, :], [3., 4., 5.])
|
|
|
|
op = P.Split(5, 3)
|
|
op_wrapper = OpNetWrapper(op)
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[0, 0, 0, 0, 0, :], [0., 1.])
|
|
assert np.allclose(outputs[1].asnumpy()[0, 0, 0, 0, 0, :], [2., 3.])
|
|
assert np.allclose(outputs[2].asnumpy()[0, 0, 0, 0, 0, :], [4., 5.])
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_out_float64():
|
|
op = P.Split(5, 2)
|
|
op_wrapper = OpNetWrapper(op)
|
|
|
|
input_x = Tensor(np.arange(192).astype(np.float64).reshape((2, 2, 2, 2, 2, 6)))
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[0, 0, 0, 0, 0, :], [0., 1., 2.])
|
|
assert np.allclose(outputs[1].asnumpy()[0, 0, 0, 0, 0, :], [3., 4., 5.])
|
|
|
|
op = P.Split(5, 3)
|
|
op_wrapper = OpNetWrapper(op)
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[0, 0, 0, 0, 0, :], [0., 1.])
|
|
assert np.allclose(outputs[1].asnumpy()[0, 0, 0, 0, 0, :], [2., 3.])
|
|
assert np.allclose(outputs[2].asnumpy()[0, 0, 0, 0, 0, :], [4., 5.])
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_out_float16():
|
|
op = P.Split(-1, 2)
|
|
op_wrapper = OpNetWrapper(op)
|
|
|
|
input_x = Tensor(np.arange(320).astype(np.float16).reshape((2, 2, 2, 2, 2, 10)))
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[0, 0, 0, 0, 0, :], [0., 1., 2., 3., 4.])
|
|
assert np.allclose(outputs[1].asnumpy()[0, 0, 0, 0, 0, :], [5., 6., 7., 8., 9.])
|
|
|
|
op = P.Split(-1, 5)
|
|
op_wrapper = OpNetWrapper(op)
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[0, 0, 0, 0, 0, :], [0., 1.])
|
|
assert np.allclose(outputs[1].asnumpy()[0, 0, 0, 0, 0, :], [2., 3.])
|
|
assert np.allclose(outputs[2].asnumpy()[0, 0, 0, 0, 0, :], [4., 5.])
|
|
assert np.allclose(outputs[3].asnumpy()[0, 0, 0, 0, 0, :], [6., 7.])
|
|
assert np.allclose(outputs[4].asnumpy()[0, 0, 0, 0, 0, :], [8., 9.])
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_out_int32():
|
|
op = P.Split(5, 2)
|
|
op_wrapper = OpNetWrapper(op)
|
|
|
|
input_x = Tensor(np.arange(192).astype(np.int32).reshape((2, 2, 2, 2, 2, 6)))
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[0, 0, 0, 0, 0, :], [0, 1, 2])
|
|
assert np.allclose(outputs[1].asnumpy()[0, 0, 0, 0, 0, :], [3, 4, 5])
|
|
|
|
op = P.Split(5, 3)
|
|
op_wrapper = OpNetWrapper(op)
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[1, 0, 0, 0, 0, :], [96, 97])
|
|
assert np.allclose(outputs[1].asnumpy()[1, 0, 0, 0, 0, :], [98, 99])
|
|
assert np.allclose(outputs[2].asnumpy()[1, 0, 0, 0, 0, :], [100, 101])
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_out_int64():
|
|
op = P.Split(5, 2)
|
|
op_wrapper = OpNetWrapper(op)
|
|
|
|
input_x = Tensor(np.arange(192).astype(np.int64).reshape((2, 2, 2, 2, 2, 6)))
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[0, 0, 0, 0, 0, :], [0, 1, 2])
|
|
assert np.allclose(outputs[1].asnumpy()[0, 0, 0, 0, 0, :], [3, 4, 5])
|
|
|
|
op = P.Split(5, 3)
|
|
op_wrapper = OpNetWrapper(op)
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[1, 0, 0, 0, 0, :], [96, 97])
|
|
assert np.allclose(outputs[1].asnumpy()[1, 0, 0, 0, 0, :], [98, 99])
|
|
assert np.allclose(outputs[2].asnumpy()[1, 0, 0, 0, 0, :], [100, 101])
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_out_uint32():
|
|
op = P.Split(-1, 2)
|
|
op_wrapper = OpNetWrapper(op)
|
|
|
|
input_x = Tensor(np.arange(320).astype(np.uint32).reshape((2, 2, 2, 2, 2, 10)))
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[0, 0, 0, 0, 0, :], [0, 1, 2, 3, 4])
|
|
assert np.allclose(outputs[1].asnumpy()[0, 0, 0, 0, 0, :], [5, 6, 7, 8, 9])
|
|
|
|
op = P.Split(-1, 5)
|
|
op_wrapper = OpNetWrapper(op)
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[1, 1, 1, 1, 1, :], [310, 311])
|
|
assert np.allclose(outputs[1].asnumpy()[1, 1, 1, 1, 1, :], [312, 313])
|
|
assert np.allclose(outputs[2].asnumpy()[1, 1, 1, 1, 1, :], [314, 315])
|
|
assert np.allclose(outputs[3].asnumpy()[1, 1, 1, 1, 1, :], [316, 317])
|
|
assert np.allclose(outputs[4].asnumpy()[1, 1, 1, 1, 1, :], [318, 319])
|
|
|
|
op = P.Split(-2, 2)
|
|
op_wrapper = OpNetWrapper(op)
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy()[0, 0, 0, 0, :, 0], [0])
|
|
assert np.allclose(outputs[1].asnumpy()[0, 0, 0, 0, :, 1], [11])
|
|
assert np.allclose(outputs[0].asnumpy()[1, 0, 0, 0, :, 2], [162])
|
|
assert np.allclose(outputs[1].asnumpy()[1, 0, 0, 0, :, 3], [173])
|
|
assert np.allclose(outputs[0].asnumpy()[1, 1, 0, 0, :, 4], [244])
|
|
assert np.allclose(outputs[1].asnumpy()[1, 1, 0, 0, :, 5], [255])
|
|
assert np.allclose(outputs[0].asnumpy()[1, 1, 1, 0, :, 6], [286])
|
|
assert np.allclose(outputs[1].asnumpy()[1, 1, 1, 0, :, 7], [297])
|
|
assert np.allclose(outputs[0].asnumpy()[1, 1, 1, 1, :, 8], [308])
|
|
assert np.allclose(outputs[1].asnumpy()[1, 1, 1, 1, :, 9], [319])
|
|
|
|
op = P.Split(-1, 1)
|
|
op_wrapper = OpNetWrapper(op)
|
|
input_x = Tensor(np.arange(1).astype(np.uint32))
|
|
outputs = op_wrapper(input_x)
|
|
|
|
assert np.allclose(outputs[0].asnumpy(), [0])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_out1_axis0()
|
|
test_out2_axis2()
|
|
test_out2_axis1neg()
|