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.
101 lines
3.0 KiB
101 lines
3.0 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
|
|
import mindspore.ops.operations.array_ops as P
|
|
from mindspore import Tensor
|
|
from mindspore.common.api import ms_function
|
|
from mindspore.common.initializer import initializer
|
|
from mindspore.common.parameter import Parameter
|
|
|
|
|
|
class PackNet(nn.Cell):
|
|
def __init__(self, nptype):
|
|
super(PackNet, self).__init__()
|
|
self.stack = P.Stack(axis=2)
|
|
self.data_np = np.array([0] * 16).astype(nptype)
|
|
self.data_np = np.reshape(self.data_np, (2, 2, 2, 2))
|
|
self.x1 = Parameter(initializer(
|
|
Tensor(self.data_np), [2, 2, 2, 2]), name='x1')
|
|
self.x2 = Parameter(initializer(
|
|
Tensor(np.arange(16).reshape(2, 2, 2, 2).astype(nptype)), [2, 2, 2, 2]), name='x2')
|
|
|
|
@ms_function
|
|
def construct(self):
|
|
return self.stack((self.x1, self.x2))
|
|
|
|
|
|
def pack(nptype):
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
pack_ = PackNet(nptype)
|
|
output = pack_()
|
|
expect = np.array([[[[[0, 0],
|
|
[0, 0]],
|
|
[[0, 1],
|
|
[2, 3]]],
|
|
[[[0, 0],
|
|
[0, 0]],
|
|
[[4, 5],
|
|
[6, 7]]]],
|
|
[[[[0, 0],
|
|
[0, 0]],
|
|
[[8, 9],
|
|
[10, 11]]],
|
|
[[[0, 0],
|
|
[0, 0]],
|
|
[[12, 13],
|
|
[14, 15]]]]]).astype(nptype)
|
|
assert (output.asnumpy() == expect).all()
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pack_graph_float32():
|
|
pack(np.float32)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pack_graph_float16():
|
|
pack(np.float16)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pack_graph_int32():
|
|
pack(np.int32)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pack_graph_int16():
|
|
pack(np.int16)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pack_graph_uint8():
|
|
pack(np.uint8)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pack_graph_bool():
|
|
pack(np.bool)
|