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.
265 lines
9.7 KiB
265 lines
9.7 KiB
# Copyright 2019 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
|
|
from mindspore.ops import composite as C
|
|
|
|
class StridedSliceNet(nn.Cell):
|
|
def __init__(self, begin, end, stride, begin_mask=0, end_mask=0, ellipsis_mask=0):
|
|
super(StridedSliceNet, self).__init__()
|
|
self.begin = begin
|
|
self.end = end
|
|
self.strides = stride
|
|
self.slice = P.StridedSlice(begin_mask, end_mask, ellipsis_mask)
|
|
|
|
def construct(self, x):
|
|
return self.slice(x, self.begin, self.end, self.strides)
|
|
|
|
class GradData(nn.Cell):
|
|
def __init__(self, network):
|
|
super(GradData, self).__init__()
|
|
self.grad = C.GradOperation(get_all=True, sens_param=False)
|
|
self.network = network
|
|
|
|
def construct(self, x):
|
|
return self.grad(self.network)(x)
|
|
|
|
|
|
def strided_slice_grad(nptype):
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
|
|
x = Tensor(np.arange(0, 2*3*4*5).reshape(2, 3, 4, 5).astype(nptype))
|
|
net = StridedSliceNet((1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1))
|
|
dx = GradData(net)(x)
|
|
expect = np.array([[[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]]],
|
|
|
|
|
|
[[[0., 0., 1., 1., 0.],
|
|
[0., 0., 1., 1., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 1., 1., 0.],
|
|
[0., 0., 1., 1., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]]]]).astype(nptype)
|
|
assert np.allclose(dx[0].asnumpy(), expect)
|
|
|
|
net = StridedSliceNet((1, 0, 0, 5), (2, 2, 2, 1), (1, 1, 1, -2))
|
|
dx = GradData(net)(x)
|
|
expect = np.array([[[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]]],
|
|
|
|
|
|
[[[0., 0., 1., 0., 1.],
|
|
[0., 0., 1., 0., 1.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 1., 0., 1.],
|
|
[0., 0., 1., 0., 1.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]]]]).astype(nptype)
|
|
assert np.allclose(dx[0].asnumpy(), expect)
|
|
|
|
|
|
net = StridedSliceNet((1, 0, 0, -1), (2, 2, 2, 1), (1, 1, 1, -1))
|
|
dx = GradData(net)(x)
|
|
expect = np.array([[[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]]],
|
|
|
|
|
|
[[[0., 0., 1., 1., 1.],
|
|
[0., 0., 1., 1., 1.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 1., 1., 1.],
|
|
[0., 0., 1., 1., 1.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]]]]).astype(nptype)
|
|
assert np.allclose(dx[0].asnumpy(), expect)
|
|
|
|
|
|
net = StridedSliceNet((1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1),
|
|
begin_mask=0b1000, end_mask=0b0010, ellipsis_mask=0b0100)
|
|
dx = GradData(net)(x)
|
|
expect = np.array([[[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]]],
|
|
|
|
|
|
[[[1., 1., 1., 1., 0.],
|
|
[1., 1., 1., 1., 0.],
|
|
[1., 1., 1., 1., 0.],
|
|
[1., 1., 1., 1., 0.]],
|
|
|
|
[[1., 1., 1., 1., 0.],
|
|
[1., 1., 1., 1., 0.],
|
|
[1., 1., 1., 1., 0.],
|
|
[1., 1., 1., 1., 0.]],
|
|
|
|
[[1., 1., 1., 1., 0.],
|
|
[1., 1., 1., 1., 0.],
|
|
[1., 1., 1., 1., 0.],
|
|
[1., 1., 1., 1., 0.]]]]).astype(nptype)
|
|
assert np.allclose(dx[0].asnumpy(), expect)
|
|
|
|
x = Tensor(np.arange(0, 3*4*5).reshape(3, 4, 5).astype(np.float32))
|
|
net = StridedSliceNet((1, 0, 0), (2, -3, 3), (1, 1, 3))
|
|
dx = GradData(net)(x)
|
|
expect = np.array([[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[1., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]]]).astype(nptype)
|
|
assert np.allclose(dx[0].asnumpy(), expect)
|
|
|
|
x = Tensor(np.arange(0, 1 * 1 * 1 * 2 * 3 * 4 * 5).reshape(1, 1, 1, 2, 3, 4, 5).astype(nptype))
|
|
net = StridedSliceNet((0, 0, 0, 1, 1, 2, 2), (1, 1, 1, 2, 3, 3, 4), (1, 1, 1, 1, 1, 1, 1))
|
|
dx = GradData(net)(x)
|
|
expect = np.array([[[[[[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]]],
|
|
|
|
[[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 1., 1., 0.],
|
|
[0., 0., 0., 0., 0.]],
|
|
|
|
[[0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0., 0.],
|
|
[0., 0., 1., 1., 0.],
|
|
[0., 0., 0., 0., 0.]]]]]]]).astype(nptype)
|
|
assert np.allclose(dx[0].asnumpy(), expect)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_strided_slice_grad_float32():
|
|
strided_slice_grad(np.float32)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_strided_slice_grad_int16():
|
|
strided_slice_grad(np.int16)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_strided_slice_grad_uint8():
|
|
strided_slice_grad(np.uint8)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_strided_slice_grad_bool():
|
|
strided_slice_grad(np.bool)
|