104 lines
3.1 KiB
Python
104 lines
3.1 KiB
Python
# Copyright 2020-2022 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.context as context
|
|
from mindspore import Tensor
|
|
import mindspore.nn as nn
|
|
from mindspore.ops import operations as P
|
|
|
|
|
|
class SqrtNet(nn.Cell):
|
|
def __init__(self):
|
|
super(SqrtNet, self).__init__()
|
|
self.ops = P.Sqrt()
|
|
|
|
def construct(self, x):
|
|
return self.ops(x)
|
|
|
|
|
|
def sqrt(nptype):
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: a function to test sqrt accuracy.
|
|
Expectation: the result match to numpy
|
|
"""
|
|
np.random.seed(0)
|
|
x_np = np.random.rand(2, 3, 4, 4).astype(nptype)
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
output_ms = P.Sqrt()(Tensor(x_np))
|
|
output_np = np.sqrt(x_np)
|
|
assert np.allclose(output_ms.asnumpy(), output_np)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_sqrt_float16():
|
|
sqrt(np.float16)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_sqrt_float32():
|
|
sqrt(np.float32)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_sqrt_float64():
|
|
sqrt(np.float64)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_rsqrt():
|
|
np.random.seed(0)
|
|
x_np = np.random.rand(2, 3, 4, 4).astype(np.float32)
|
|
|
|
output_ms = P.Rsqrt()(Tensor(x_np))
|
|
output_np = 1 / np.sqrt(x_np)
|
|
assert np.allclose(output_ms.asnumpy(), output_np)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
|
|
def test_sqrt_dy_shape(dtype):
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: test cases for Sqrt dynamic shape
|
|
Expectation: the result match to numpy
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
input_x_np = np.abs(np.random.randn(2, 3, 3, 4)).astype(dtype)
|
|
benchmark_output = np.sqrt(input_x_np)
|
|
loss = 1e-6
|
|
sqrt_net = SqrtNet()
|
|
real_input = Tensor(input_x_np)
|
|
dy_shape = [None for _ in input_x_np.shape]
|
|
input_dyn = Tensor(shape=dy_shape, dtype=real_input.dtype)
|
|
sqrt_net.set_inputs(input_dyn)
|
|
ms_result = sqrt_net(real_input)
|
|
np.testing.assert_allclose(benchmark_output, ms_result.asnumpy(), rtol=loss, atol=loss)
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
ms_result = sqrt_net(real_input)
|
|
np.testing.assert_allclose(benchmark_output, ms_result.asnumpy(), rtol=loss, atol=loss)
|