forked from huawei/mindspore2022
119 lines
3.7 KiB
Python
119 lines
3.7 KiB
Python
# Copyright 2020-2021 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
|
|
from mindspore.nn import Cell
|
|
import mindspore.ops.operations as P
|
|
|
|
|
|
class Net(Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.div = P.RealDiv()
|
|
self.sqrt = P.Sqrt()
|
|
self.pow = P.Pow()
|
|
self.neg = P.Neg()
|
|
self.reducemin = P.ReduceMin()
|
|
self.reducesum = P.ReduceSum(keep_dims=True)
|
|
self.reshape = P.Reshape()
|
|
|
|
def construct(self, x, y):
|
|
add_res1 = self.add(x, 4)
|
|
add_res2 = self.add(add_res1, 5)
|
|
sub_res = self.sub(y, 3)
|
|
mul_res = self.mul(self.sqrt(add_res2), self.sqrt(sub_res))
|
|
div_res = self.div(mul_res, self.sqrt(mul_res))
|
|
pow_res = self.pow(y, 2)
|
|
neg_res = self.neg(self.neg(pow_res))
|
|
add_res3 = self.add(neg_res, div_res)
|
|
resh_res = self.reshape(add_res3, (2, 12, 3))
|
|
neg_res = self.neg(resh_res)
|
|
red_res = self.reducesum(neg_res, 0)
|
|
return self.reducemin(self.reducemin(red_res, 1), 1)
|
|
|
|
|
|
class EmptyNet(Cell):
|
|
def __init__(self):
|
|
super(EmptyNet, self).__init__()
|
|
self.add = P.Add()
|
|
self.neg = P.Neg()
|
|
|
|
def construct(self, x, y):
|
|
add_res1 = self.add(x, y)
|
|
neg_res1 = self.neg(x)
|
|
add_res2 = self.add(add_res1, neg_res1)
|
|
return add_res2
|
|
|
|
|
|
def test_basic():
|
|
input_x = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
|
|
input_y = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
|
|
input_y = np.abs(input_y) + 3
|
|
add_res = input_x + 9
|
|
sub_res = input_y + (-3)
|
|
mul_res = np.sqrt(add_res * sub_res)
|
|
div_res = np.sqrt(mul_res)
|
|
pow_res = input_y * input_y
|
|
neg_res = pow_res
|
|
add_res3 = neg_res + div_res
|
|
neg_res = np.negative(add_res3)
|
|
red_res = np.sum(neg_res, axis=0, keepdims=True)
|
|
expect = np.min(red_res, (1, 2, 3))
|
|
|
|
net = Net()
|
|
result = net(Tensor(input_x), Tensor(input_y))
|
|
|
|
res = np.allclose(expect, result.asnumpy(), rtol=1.e-4,
|
|
atol=1.e-7, equal_nan=True)
|
|
assert res
|
|
|
|
|
|
def test_empty_graph():
|
|
input_x = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
|
|
input_y = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
|
|
expect = input_y
|
|
|
|
net = EmptyNet()
|
|
result = net(Tensor(input_x), Tensor(input_y))
|
|
|
|
res = np.allclose(expect, result.asnumpy(), rtol=1.e-4,
|
|
atol=1.e-7, equal_nan=True)
|
|
assert res
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_basic_gpu():
|
|
context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
|
|
test_basic()
|
|
test_empty_graph()
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_basic_ascend():
|
|
context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="Ascend")
|
|
test_basic()
|
|
test_empty_graph()
|