zhoufeng
5 years ago
parent
ea475637a1
commit
18bc81e88c
@ -0,0 +1,189 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
""" test_ascend_control_sink """
|
||||
import pytest
|
||||
import numpy as np
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as op
|
||||
from mindspore.common import dtype as mstype
|
||||
from mindspore.common.tensor import Tensor
|
||||
from mindspore.common.parameter import Parameter
|
||||
from mindspore.common.initializer import initializer
|
||||
|
||||
|
||||
class ControlSimpleIf(nn.Cell):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.addn = op.AddN()
|
||||
|
||||
def construct(self, x, y, z, input1, input2):
|
||||
addn1 = self.addn([input1, input1, input1])
|
||||
addn2 = self.addn([input2, input2, input2])
|
||||
addn11 = self.addn([addn1, addn1, addn1])
|
||||
addn22 = self.addn([addn2, addn2, addn2])
|
||||
cond1 = x > y
|
||||
cond2 = y > z
|
||||
# dodge pylint
|
||||
if cond1 and cond2:
|
||||
out = self.addn([addn11, addn11])
|
||||
else:
|
||||
out = self.addn([addn22, addn22])
|
||||
out_me = self.addn([out, input1])
|
||||
return out_me
|
||||
|
||||
|
||||
class ControlSimpleIfWithAssign(nn.Cell):
|
||||
def __init__(self, input_shape):
|
||||
super().__init__()
|
||||
self.addn = op.AddN()
|
||||
self.assign = op.Assign()
|
||||
self.input_data = Parameter(initializer(1, input_shape, mstype.float32), name="var")
|
||||
|
||||
def construct(self, x, y, input_data):
|
||||
if x > y:
|
||||
out = self.addn([input_data, input_data, input_data])
|
||||
else:
|
||||
out = self.assign(self.input_data, input_data)
|
||||
return out
|
||||
|
||||
|
||||
class ControlIfinIf(nn.Cell):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def construct(self, x, y):
|
||||
if x > y:
|
||||
x = x + 1
|
||||
if y < 0:
|
||||
y = y + 1
|
||||
else:
|
||||
y = y + 2
|
||||
else:
|
||||
x = x + 2
|
||||
x = x + y
|
||||
return x
|
||||
|
||||
|
||||
class ControlIfbyIfbyIf(nn.Cell):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.addn = op.AddN()
|
||||
|
||||
def construct(self, x, y, cond1, cond2, input_data):
|
||||
tri_in = self.addn([input_data, input_data, input_data])
|
||||
if x > y:
|
||||
addn_1 = self.addn([tri_in, tri_in])
|
||||
else:
|
||||
addn_1 = self.addn([tri_in, tri_in, tri_in])
|
||||
if cond1:
|
||||
addn_2 = self.addn([addn_1, addn_1])
|
||||
else:
|
||||
addn_2 = self.addn([addn_1, addn_1, addn_1])
|
||||
if cond2:
|
||||
out = self.addn([addn_2, addn_2, addn_2])
|
||||
else:
|
||||
out = self.addn([addn_2, addn_2])
|
||||
return out
|
||||
|
||||
|
||||
class ControlMixedWhileIf(nn.Cell):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def construct(self, x, y):
|
||||
y = y + 4
|
||||
while x < y:
|
||||
if 2 * x < y:
|
||||
x = x + 1
|
||||
else:
|
||||
x = x + 2
|
||||
x = x + 3
|
||||
return x
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_simple_if():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
x = np.array(3).astype(np.float32)
|
||||
y = np.array(2).astype(np.float32)
|
||||
z = np.array(3).astype(np.float32)
|
||||
input_shape = (127, 7, 53, 31)
|
||||
input1 = np.random.randn(*input_shape).astype(np.float32)
|
||||
input2 = np.random.randn(*input_shape).astype(np.float32)
|
||||
net = ControlSimpleIf()
|
||||
output = net(Tensor(x), Tensor(y), Tensor(z), Tensor(input1), Tensor(input2))
|
||||
expect = input2 * 3 * 3 * 2 + input1
|
||||
assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_simple_if_with_assign():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
x = np.array(0).astype(np.float32)
|
||||
y = np.array(1).astype(np.float32)
|
||||
input_shape = (127, 7, 53, 31)
|
||||
input_data = np.random.randn(*input_shape).astype(np.float32)
|
||||
net = ControlSimpleIfWithAssign(input_shape)
|
||||
output = net(Tensor(x), Tensor(y), Tensor(input_data))
|
||||
expect = input_data
|
||||
assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_if_in_if():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
x = np.array(2.345678).astype(np.float32)
|
||||
y = np.array(1.234567).astype(np.float32)
|
||||
net = ControlIfinIf()
|
||||
output = net(Tensor(x), Tensor(y))
|
||||
expect = x + y + 3
|
||||
assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_if_by_if_by_if():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
x = np.array(2.345678).astype(np.float32)
|
||||
y = np.array(1.234567).astype(np.float32)
|
||||
cond1 = np.array(True).astype(np.bool)
|
||||
cond2 = np.array(False).astype(np.bool)
|
||||
input_shape = (127, 7, 53, 31)
|
||||
input_data = np.random.randn(*input_shape).astype(np.float32)
|
||||
net = ControlIfbyIfbyIf()
|
||||
output = net(Tensor(x), Tensor(y), Tensor(cond1), Tensor(cond2), Tensor(input_data))
|
||||
expect = input_data * 3 * 2 * 2 * 2
|
||||
assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_mixed_while_if():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
x = np.array(2).astype(np.int32)
|
||||
y = np.array(14).astype(np.int32)
|
||||
net = ControlMixedWhileIf()
|
||||
output = net(Tensor(x), Tensor(y))
|
||||
expect = np.array(22).astype(np.int32)
|
||||
assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
|
||||
Loading…
Reference in new issue