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.
272 lines
8.8 KiB
272 lines
8.8 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")
|
|
|
|
|
|
axis0 = 0
|
|
axis1 = 1
|
|
axis2 = 2
|
|
axis3 = 3
|
|
axis4 = 4
|
|
axis5 = -1
|
|
axis6 = -2
|
|
|
|
x0 = np.random.rand(3, 3, 4, 5, 3).astype(np.float32)
|
|
x1 = np.random.rand(2, 3, 4, 5, 3).astype(np.float16)
|
|
x2 = np.random.randint(-10000, 10000, size=(2, 3, 4, 5, 3)).astype(np.int32)
|
|
x3 = np.random.randint(-5, 5, size=(2, 3, 4, 5, 3)).astype(np.int8)
|
|
x4 = np.random.randint(0, 10, size=(2, 3, 4, 5, 3)).astype(np.uint8)
|
|
x5 = np.random.rand(3).astype(np.float32)
|
|
|
|
list1 = [x0, x1, x2, x3, x4]
|
|
list2 = [axis0, axis1, axis2, axis3, axis4, axis5, axis6]
|
|
|
|
class CumSum(nn.Cell):
|
|
def __init__(self, exclusive=False, reverse=False):
|
|
super(CumSum, self).__init__()
|
|
self.cumsum_op = P.CumSum(exclusive, reverse)
|
|
|
|
self.x0 = Tensor(x0)
|
|
self.axis0 = axis0
|
|
self.x1 = Tensor(x0)
|
|
self.axis1 = axis1
|
|
self.x2 = Tensor(x0)
|
|
self.axis2 = axis2
|
|
self.x3 = Tensor(x0)
|
|
self.axis3 = axis3
|
|
self.x4 = Tensor(x0)
|
|
self.axis4 = axis4
|
|
self.x5 = Tensor(x0)
|
|
self.axis5 = axis5
|
|
self.x6 = Tensor(x0)
|
|
self.axis6 = axis6
|
|
|
|
self.x7 = Tensor(x1)
|
|
self.axis7 = axis0
|
|
self.x8 = Tensor(x1)
|
|
self.axis8 = axis1
|
|
self.x9 = Tensor(x1)
|
|
self.axis9 = axis2
|
|
self.x10 = Tensor(x1)
|
|
self.axis10 = axis3
|
|
self.x11 = Tensor(x1)
|
|
self.axis11 = axis4
|
|
self.x12 = Tensor(x1)
|
|
self.axis12 = axis5
|
|
self.x13 = Tensor(x1)
|
|
self.axis13 = axis6
|
|
|
|
self.x14 = Tensor(x2)
|
|
self.axis14 = axis0
|
|
self.x15 = Tensor(x2)
|
|
self.axis15 = axis1
|
|
self.x16 = Tensor(x2)
|
|
self.axis16 = axis2
|
|
self.x17 = Tensor(x2)
|
|
self.axis17 = axis3
|
|
self.x18 = Tensor(x2)
|
|
self.axis18 = axis4
|
|
self.x19 = Tensor(x2)
|
|
self.axis19 = axis5
|
|
self.x20 = Tensor(x2)
|
|
self.axis20 = axis6
|
|
|
|
self.x21 = Tensor(x3)
|
|
self.axis21 = axis0
|
|
self.x22 = Tensor(x3)
|
|
self.axis22 = axis1
|
|
self.x23 = Tensor(x3)
|
|
self.axis23 = axis2
|
|
self.x24 = Tensor(x3)
|
|
self.axis24 = axis3
|
|
self.x25 = Tensor(x3)
|
|
self.axis25 = axis4
|
|
self.x26 = Tensor(x3)
|
|
self.axis26 = axis5
|
|
self.x27 = Tensor(x3)
|
|
self.axis27 = axis6
|
|
|
|
self.x28 = Tensor(x4)
|
|
self.axis28 = axis0
|
|
self.x29 = Tensor(x4)
|
|
self.axis29 = axis1
|
|
self.x30 = Tensor(x4)
|
|
self.axis30 = axis2
|
|
self.x31 = Tensor(x4)
|
|
self.axis31 = axis3
|
|
self.x32 = Tensor(x4)
|
|
self.axis32 = axis4
|
|
self.x33 = Tensor(x4)
|
|
self.axis33 = axis5
|
|
self.x34 = Tensor(x4)
|
|
self.axis34 = axis6
|
|
|
|
self.x35 = Tensor(x5)
|
|
self.axis35 = axis0
|
|
|
|
def construct(self):
|
|
return (self.cumsum_op(self.x0, self.axis0),
|
|
self.cumsum_op(self.x1, self.axis1),
|
|
self.cumsum_op(self.x2, self.axis2),
|
|
self.cumsum_op(self.x3, self.axis3),
|
|
self.cumsum_op(self.x4, self.axis4),
|
|
self.cumsum_op(self.x5, self.axis5),
|
|
self.cumsum_op(self.x6, self.axis6),
|
|
self.cumsum_op(self.x7, self.axis7),
|
|
self.cumsum_op(self.x8, self.axis8),
|
|
self.cumsum_op(self.x9, self.axis9),
|
|
self.cumsum_op(self.x10, self.axis10),
|
|
self.cumsum_op(self.x11, self.axis11),
|
|
self.cumsum_op(self.x12, self.axis12),
|
|
self.cumsum_op(self.x13, self.axis13),
|
|
self.cumsum_op(self.x14, self.axis14),
|
|
self.cumsum_op(self.x15, self.axis15),
|
|
self.cumsum_op(self.x16, self.axis16),
|
|
self.cumsum_op(self.x17, self.axis17),
|
|
self.cumsum_op(self.x18, self.axis18),
|
|
self.cumsum_op(self.x19, self.axis19),
|
|
self.cumsum_op(self.x20, self.axis20),
|
|
self.cumsum_op(self.x21, self.axis21),
|
|
self.cumsum_op(self.x22, self.axis22),
|
|
self.cumsum_op(self.x23, self.axis23),
|
|
self.cumsum_op(self.x24, self.axis24),
|
|
self.cumsum_op(self.x25, self.axis25),
|
|
self.cumsum_op(self.x26, self.axis26),
|
|
self.cumsum_op(self.x27, self.axis27),
|
|
self.cumsum_op(self.x28, self.axis28),
|
|
self.cumsum_op(self.x29, self.axis29),
|
|
self.cumsum_op(self.x30, self.axis30),
|
|
self.cumsum_op(self.x31, self.axis31),
|
|
self.cumsum_op(self.x32, self.axis32),
|
|
self.cumsum_op(self.x33, self.axis33),
|
|
self.cumsum_op(self.x34, self.axis34),
|
|
self.cumsum_op(self.x35, self.axis35))
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_cumsum():
|
|
cumsum = CumSum()
|
|
output = cumsum()
|
|
|
|
k = 0
|
|
|
|
for i in list1:
|
|
for j in list2:
|
|
expect = np.cumsum(i, axis=j)
|
|
diff = abs(output[k].asnumpy() - expect)
|
|
error = np.ones(shape=expect.shape) * 1.0e-5
|
|
assert np.all(diff < error)
|
|
assert output[k].shape == expect.shape
|
|
k += 1
|
|
|
|
expect = np.cumsum(x5, axis=axis0)
|
|
diff = abs(output[k].asnumpy() - expect)
|
|
error = np.ones(shape=expect.shape) * 1.0e-5
|
|
assert np.all(diff < error)
|
|
assert output[k].shape == expect.shape
|
|
|
|
|
|
def test_cumsum2():
|
|
cumsum = CumSum(exclusive=False, reverse=True)
|
|
output = cumsum()
|
|
|
|
k = 0
|
|
|
|
for i in list1:
|
|
for j in list2:
|
|
result1 = np.flip(i, axis=j)
|
|
result2 = np.cumsum(result1, axis=j)
|
|
expect = np.flip(result2, axis=j)
|
|
diff = abs(output[k].asnumpy() - expect)
|
|
error = np.ones(shape=expect.shape) * 1.0e-5
|
|
assert np.all(diff < error)
|
|
assert output[k].shape == expect.shape
|
|
k += 1
|
|
|
|
result1 = np.flip(x5, axis=axis0)
|
|
result2 = np.cumsum(result1, axis=axis0)
|
|
expect = np.flip(result2, axis=axis0)
|
|
diff = abs(output[k].asnumpy() - expect)
|
|
error = np.ones(shape=expect.shape) * 1.0e-5
|
|
assert np.all(diff < error)
|
|
assert output[k].shape == expect.shape
|
|
|
|
|
|
def test_cumsum3():
|
|
cumsum = CumSum(exclusive=True, reverse=False)
|
|
output = cumsum()
|
|
|
|
k = 0
|
|
|
|
for i in list1:
|
|
for j in list2:
|
|
result1 = np.insert(i, 0, [0], axis=j)
|
|
result2 = np.delete(result1, -1, axis=j)
|
|
expect = np.cumsum(result2, axis=j)
|
|
diff = abs(output[k].asnumpy() - expect)
|
|
error = np.ones(shape=expect.shape) * 1.0e-5
|
|
assert np.all(diff < error)
|
|
assert output[k].shape == expect.shape
|
|
k += 1
|
|
|
|
result1 = np.insert(x5, 0, [0], axis=axis0)
|
|
result2 = np.delete(result1, -1, axis=axis0)
|
|
expect = np.cumsum(result2, axis=axis0)
|
|
diff = abs(output[k].asnumpy() - expect)
|
|
error = np.ones(shape=expect.shape) * 1.0e-5
|
|
assert np.all(diff < error)
|
|
assert output[k].shape == expect.shape
|
|
|
|
|
|
def test_cumsum4():
|
|
cumsum = CumSum(exclusive=True, reverse=True)
|
|
output = cumsum()
|
|
|
|
k = 0
|
|
|
|
for i in list1:
|
|
for j in list2:
|
|
result1 = np.flip(i, axis=j)
|
|
result2 = np.insert(result1, 0, [0], axis=j)
|
|
result3 = np.delete(result2, -1, axis=j)
|
|
result4 = np.cumsum(result3, axis=j)
|
|
expect = np.flip(result4, axis=j)
|
|
diff = abs(output[k].asnumpy() - expect)
|
|
error = np.ones(shape=expect.shape) * 1.0e-5
|
|
assert np.all(diff < error)
|
|
assert output[k].shape == expect.shape
|
|
k += 1
|
|
|
|
result1 = np.flip(x5, axis=axis0)
|
|
result2 = np.insert(result1, 0, [0], axis=axis0)
|
|
result3 = np.delete(result2, -1, axis=axis0)
|
|
result4 = np.cumsum(result3, axis=axis0)
|
|
expect = np.flip(result4, axis=axis0)
|
|
diff = abs(output[k].asnumpy() - expect)
|
|
error = np.ones(shape=expect.shape) * 1.0e-5
|
|
assert np.all(diff < error)
|
|
assert output[k].shape == expect.shape
|