forked from mindspore-Ecosystem/mindspore
68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
# 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
|
|
|
|
|
|
class NetLog(nn.Cell):
|
|
def __init__(self):
|
|
super(NetLog, self).__init__()
|
|
self.log = P.Log()
|
|
|
|
def construct(self, x):
|
|
return self.log(x)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_log():
|
|
x0_np = np.random.uniform(1, 2, (2, 3, 4, 4)).astype(np.float32)
|
|
x1_np = np.random.uniform(1, 2, 1).astype(np.float32)
|
|
x0 = Tensor(x0_np)
|
|
x1 = Tensor(x1_np)
|
|
expect0 = np.log(x0_np)
|
|
expect1 = np.log(x1_np)
|
|
error0 = np.ones(shape=expect0.shape) * 1.0e-5
|
|
error1 = np.ones(shape=expect1.shape) * 1.0e-5
|
|
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
log = NetLog()
|
|
output0 = log(x0)
|
|
output1 = log(x1)
|
|
diff0 = output0.asnumpy() - expect0
|
|
assert np.all(diff0 < error0)
|
|
assert output0.shape == expect0.shape
|
|
diff1 = output1.asnumpy() - expect1
|
|
assert np.all(diff1 < error1)
|
|
assert output1.shape == expect1.shape
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
log = NetLog()
|
|
output0 = log(x0)
|
|
output1 = log(x1)
|
|
diff0 = output0.asnumpy() - expect0
|
|
assert np.all(diff0 < error0)
|
|
assert output0.shape == expect0.shape
|
|
diff1 = output1.asnumpy() - expect1
|
|
assert np.all(diff1 < error1)
|
|
assert output1.shape == expect1.shape
|