forked from mindspore-Ecosystem/mindspore
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
# Copyright 2022 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.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore import context
|
|
from mindspore.ops import operations as P
|
|
|
|
|
|
class NetCosh(nn.Cell):
|
|
def __init__(self):
|
|
super(NetCosh, self).__init__()
|
|
self.cosh = P.Cosh()
|
|
|
|
def construct(self, x):
|
|
return self.cosh(x)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('dtype, tol', [(np.float16, 1.e-3), (np.float32, 1.e-5), (np.float64, 1.e-8)])
|
|
def test_cosh_graph(dtype, tol):
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: test cases for Cosh
|
|
Expectation: the result match to numpy
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
np_array = np.array([-1, -0.5, 0, 0.5, 1]).astype(dtype)
|
|
input_x = Tensor(np_array)
|
|
net = NetCosh()
|
|
output = net(input_x)
|
|
expect = np.cosh(np_array)
|
|
assert np.allclose(output.asnumpy(), expect, atol=tol, rtol=tol)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('dtype, tol', [(np.float16, 1.e-3), (np.float32, 1.e-5), (np.float64, 1.e-8)])
|
|
def test_cosh_py(dtype, tol):
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: test cases for Cosh
|
|
Expectation: the result match to numpy
|
|
"""
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
np_array = np.array([-1, -0.5, 0, 0.5, 1]).astype(dtype)
|
|
input_x = Tensor(np_array)
|
|
net = NetCosh()
|
|
output = net(input_x)
|
|
expect = np.cosh(np_array)
|
|
assert np.allclose(output.asnumpy(), expect, atol=tol, rtol=tol)
|