forked from mindspore-Ecosystem/mindspore
1042 lines
29 KiB
Python
1042 lines
29 KiB
Python
# 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 control ops """
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore as ms
|
|
from mindspore import Tensor
|
|
from mindspore import context
|
|
from mindspore import nn
|
|
from mindspore.common import dtype as mstype
|
|
from mindspore.ops import composite as C
|
|
from mindspore.ops import functional as F
|
|
from mindspore.ops import operations as P
|
|
from mindspore.common.parameter import Parameter, ParameterTuple
|
|
from mindspore.common import ms_function
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
|
|
grad_by_list = C.GradOperation(get_by_list=True)
|
|
grad_all = C.GradOperation(get_all=True)
|
|
grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
|
|
|
|
|
|
def cond_data_test(x_init, y_init):
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.square = P.Square()
|
|
self.add = P.TensorAdd()
|
|
self.value = Tensor(3, dtype=ms.float32)
|
|
self.switch = P.GeSwitch()
|
|
self.merge = P.Merge()
|
|
self.less = P.Less()
|
|
|
|
def construct(self, x, y):
|
|
cond = self.less(x, y)
|
|
st1, _ = self.switch(x, cond)
|
|
st2, _ = self.switch(y, cond)
|
|
add_ret = self.add(st1, st2)
|
|
_, sf3 = self.switch(self.value, cond)
|
|
sq_ret = self.square(sf3)
|
|
ret = self.merge((add_ret, sq_ret))
|
|
return ret[0]
|
|
|
|
x = Tensor(x_init, dtype=ms.float32)
|
|
y = Tensor(y_init, dtype=ms.float32)
|
|
net = Net()
|
|
output = net(x, y)
|
|
return output
|
|
|
|
|
|
def test_cond_data_true():
|
|
output = cond_data_test(3, 8)
|
|
print("test_cond_data_true:", output)
|
|
|
|
|
|
def test_cond_data_false():
|
|
output = cond_data_test(8, 3)
|
|
print("test_cond_data_false:", output)
|
|
|
|
|
|
def if_compile_test(x_init, y_init):
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.square = P.Square()
|
|
self.add = P.TensorAdd()
|
|
self.value = Tensor(3, dtype=ms.float32)
|
|
self.switch = P.GeSwitch()
|
|
self.merge = P.Merge()
|
|
self.less = P.Less()
|
|
|
|
def construct(self, x, y):
|
|
cond = self.less(x, y)
|
|
ret = self.value
|
|
if cond:
|
|
ret = self.add(x, ret)
|
|
ret = self.add(y, ret)
|
|
else:
|
|
ret = self.square(self.value)
|
|
return ret
|
|
|
|
x = Tensor(x_init, dtype=ms.float32)
|
|
y = Tensor(y_init, dtype=ms.float32)
|
|
net = Net()
|
|
output = net(x, y)
|
|
return output
|
|
|
|
|
|
def test_if_none():
|
|
class Net(nn.Cell):
|
|
def __init__(self, z: None):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.z = z
|
|
|
|
def construct(self, x, y):
|
|
if self.z:
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
return ret
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.zeros([3, 4, 5], np.int32))
|
|
z = None
|
|
net = Net(z)
|
|
assert np.all(net(x, y).asnumpy() == y.asnumpy())
|
|
|
|
|
|
def test_if_str_is_not_none_right():
|
|
class Net(nn.Cell):
|
|
def __init__(self, z: str):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.z = z
|
|
|
|
def construct(self, x, y):
|
|
if self.z is None:
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
return ret
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.zeros([3, 4, 5], np.int32))
|
|
z = "ok"
|
|
net = Net(z)
|
|
assert np.all(net(x, y).asnumpy() == y.asnumpy())
|
|
|
|
|
|
def test_if_str_is_not_none_left():
|
|
class Net(nn.Cell):
|
|
def __init__(self, z: str):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.z = z
|
|
|
|
def construct(self, x, y):
|
|
if self.z is None:
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
return ret
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.zeros([3, 4, 5], np.int32))
|
|
z = "ok"
|
|
net = Net(z)
|
|
assert np.all(net(x, y).asnumpy() == y.asnumpy())
|
|
|
|
|
|
def test_if_none_equal_none():
|
|
class Net(nn.Cell):
|
|
def __init__(self, z: None):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.z = z
|
|
|
|
def construct(self, x, y):
|
|
if self.z is None:
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
return ret
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.zeros([3, 4, 5], np.int32))
|
|
z = None
|
|
net = Net(z)
|
|
assert np.all(net(x, y).asnumpy() == x.asnumpy())
|
|
|
|
|
|
def test_if_str_is_null():
|
|
class Net(nn.Cell):
|
|
def __init__(self, z: str):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.z = z
|
|
|
|
def construct(self, x, y):
|
|
if self.z:
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
return ret
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.zeros([3, 4, 5], np.int32))
|
|
z = ""
|
|
net = Net(z)
|
|
assert np.all(net(x, y).asnumpy() == y.asnumpy())
|
|
|
|
|
|
def test_if_str_is_true():
|
|
class Net(nn.Cell):
|
|
def __init__(self, z: str):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.z = z
|
|
|
|
def construct(self, x, y):
|
|
if self.z:
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
return ret
|
|
|
|
x = Tensor(np.ones([6, 9, 10], np.int32))
|
|
y = Tensor(np.zeros([3, 4, 5], np.int32))
|
|
z = "ok"
|
|
net = Net(z)
|
|
assert np.all(net(x, y).asnumpy() == x.asnumpy())
|
|
|
|
|
|
def test_if_str_equal():
|
|
class Net(nn.Cell):
|
|
def __init__(self, z: str):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.z = z
|
|
|
|
def construct(self, x, y):
|
|
if self.z == "ok":
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
return ret
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.zeros([3, 4, 5], np.int32))
|
|
z = "ok"
|
|
net = Net(z)
|
|
assert np.all(net(x, y).asnumpy() == x.asnumpy())
|
|
|
|
|
|
def test_if_tuple_is_null():
|
|
class Net(nn.Cell):
|
|
def __init__(self, z: tuple):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.z = z
|
|
|
|
def construct(self, x, y):
|
|
if self.z:
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
return ret
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.zeros([3, 4, 5], np.int32))
|
|
z = ()
|
|
net = Net(z)
|
|
assert np.all(net(x, y).asnumpy() == y.asnumpy())
|
|
|
|
|
|
def test_if_tuple_is_not_null():
|
|
class Net(nn.Cell):
|
|
def __init__(self, z: tuple):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.z = z
|
|
|
|
def construct(self, x, y):
|
|
if self.z:
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
return ret
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.zeros([3, 4, 5], np.int32))
|
|
z = (1, 2, 3)
|
|
net = Net(z)
|
|
assert np.all(net(x, y).asnumpy() == x.asnumpy())
|
|
|
|
|
|
def test_if_dict_is_null():
|
|
class Net(nn.Cell):
|
|
def __init__(self, z: dict):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.z = z
|
|
|
|
def construct(self, x, y):
|
|
if self.z:
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
return ret
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.zeros([3, 4, 5], np.int32))
|
|
z = {}
|
|
net = Net(z)
|
|
assert np.all(net(x, y).asnumpy() == y.asnumpy())
|
|
|
|
|
|
def test_if_dict_is_not_null():
|
|
class Net(nn.Cell):
|
|
def __init__(self, z: dict):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.z = z
|
|
|
|
def construct(self, x, y):
|
|
if self.z:
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
return ret
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.zeros([3, 4, 5], np.int32))
|
|
z = {"one": 1, "two": 2}
|
|
net = Net(z)
|
|
assert np.all(net(x, y).asnumpy() == x.asnumpy())
|
|
|
|
|
|
def test_if_else_assign():
|
|
class Net(nn.Cell):
|
|
def __init__(self, m: list):
|
|
""""""
|
|
super(Net, self).__init__()
|
|
self.m = m
|
|
self.n = [4, 5, 6]
|
|
|
|
def construct(self, x, y):
|
|
exp_1 = self.m if self.m else self.n
|
|
exp_2 = self.m if exp_1 == self.n else self.n
|
|
if exp_2 == self.m:
|
|
if self.m:
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
else:
|
|
if self.m:
|
|
ret = x
|
|
else:
|
|
ret = y
|
|
return ret
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.zeros([3, 4, 5], np.int32))
|
|
z = [1, 2]
|
|
net = Net(z)
|
|
assert np.all(net(x, y).asnumpy() == x.asnumpy())
|
|
|
|
|
|
def test_if_compile_true():
|
|
output = if_compile_test(3, 8)
|
|
print("test_if_compile_true:", output)
|
|
|
|
|
|
def test_if_compile_false():
|
|
output = if_compile_test(8, 3)
|
|
print("test_if_compile_false:", output)
|
|
|
|
|
|
def test_switch_layer():
|
|
class Layer1(nn.Cell):
|
|
def __init__(self):
|
|
super(Layer1, self).__init__()
|
|
self.z1 = Parameter(
|
|
Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z1')
|
|
|
|
def construct(self, x):
|
|
return x * self.z1
|
|
|
|
class Layer2(nn.Cell):
|
|
def __init__(self):
|
|
super(Layer2, self).__init__()
|
|
self.z2 = Parameter(
|
|
Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z2')
|
|
|
|
def construct(self, x):
|
|
return x * self.z2
|
|
|
|
class SwitchLayerCell(nn.Cell):
|
|
def __init__(self):
|
|
super(SwitchLayerCell, self).__init__()
|
|
self.layers = (Layer1(), Layer2())
|
|
self.z3 = Parameter(
|
|
Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z3')
|
|
|
|
def construct(self, index, x):
|
|
ret = F.switch_layer(index, self.layers)(x) * self.z3
|
|
return ret
|
|
|
|
index = Tensor(0, dtype=mstype.int32)
|
|
net = SwitchLayerCell()
|
|
net(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
|
|
grad_by_list(net, ParameterTuple(net.trainable_params()))(index,
|
|
Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
|
|
grad_all(net)(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
|
|
|
|
|
|
def test_index_to_switch_layer():
|
|
class Layer1(nn.Cell):
|
|
def __init__(self):
|
|
super(Layer1, self).__init__()
|
|
self.z1 = Parameter(
|
|
Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z1')
|
|
|
|
def construct(self, x):
|
|
return x * self.z1
|
|
|
|
class Layer2(nn.Cell):
|
|
def __init__(self):
|
|
super(Layer2, self).__init__()
|
|
self.z2 = Parameter(
|
|
Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z2')
|
|
|
|
def construct(self, x):
|
|
return x * self.z2
|
|
|
|
class SwitchLayerCell(nn.Cell):
|
|
def __init__(self):
|
|
super(SwitchLayerCell, self).__init__()
|
|
self.layers = (Layer1(), Layer2())
|
|
self.z3 = Parameter(
|
|
Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z3')
|
|
|
|
def construct(self, index, x):
|
|
ret = self.layers[index](x) * self.z3
|
|
return ret
|
|
|
|
index = Tensor(0, dtype=mstype.int32)
|
|
net = SwitchLayerCell()
|
|
net(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
|
|
grad_by_list(net, ParameterTuple(net.trainable_params()))(index,
|
|
Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
|
|
grad_all(net)(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
|
|
|
|
|
|
def test_parser_switch_layer_switch_in_bprop():
|
|
class OneInputBprop(nn.Cell):
|
|
def __init__(self, funcs):
|
|
super(OneInputBprop, self).__init__()
|
|
self.op = P.ReLU()
|
|
self.funcs = funcs
|
|
|
|
def construct(self, i, x):
|
|
return self.op(x)
|
|
|
|
def bprop(self, i, x, out, dout):
|
|
return i, self.funcs[i](x, dout)
|
|
|
|
class Add(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.op = P.TensorAdd()
|
|
|
|
def construct(self, x, y):
|
|
return self.op(x, y)
|
|
|
|
class Mul(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.op = P.Mul()
|
|
|
|
def construct(self, x, y):
|
|
return self.op(x, y)
|
|
|
|
func1 = Add()
|
|
func2 = Mul()
|
|
funcs = (func1, func2)
|
|
net = OneInputBprop(funcs)
|
|
input1 = Tensor(np.ones([2, 2]).astype(np.float32))
|
|
grad = Tensor(np.random.randn(2, 2).astype(np.float32))
|
|
i = Tensor(1, mstype.int32)
|
|
grad_net = grad_all_with_sens(net)
|
|
grad_net(i, input1, grad)
|
|
|
|
|
|
def test_parser_switch_layer_inputs_tuple():
|
|
class TwoInputTupleFinalNet(nn.Cell):
|
|
def __init__(self, funcs):
|
|
super().__init__()
|
|
self.funcs = funcs
|
|
|
|
def construct(self, i, inputa, inputb):
|
|
inputs = (inputa, inputb)
|
|
x = self.funcs[i](inputs)
|
|
return x
|
|
|
|
class Add(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.op = P.TensorAdd()
|
|
|
|
def construct(self, x):
|
|
y = self.op(x[0], x[1])
|
|
return self.op(x[0], y)
|
|
|
|
class Mul(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.op = P.Mul()
|
|
|
|
def construct(self, x):
|
|
y = self.op(x[0], x[1])
|
|
return self.op(x[0], y)
|
|
|
|
func1 = Add()
|
|
func2 = Mul()
|
|
|
|
funcs = (func1, func2)
|
|
net = TwoInputTupleFinalNet(funcs)
|
|
|
|
input1 = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
|
|
input2 = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
|
|
i = Tensor(1, mstype.int32)
|
|
grad = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
|
|
back_net = grad_all_with_sens(net)
|
|
back_out = back_net(i, input1, input2, grad)
|
|
|
|
|
|
def test_switch_layer_with_single_prim():
|
|
class SwitchLayerCell(nn.Cell):
|
|
def __init__(self):
|
|
super(SwitchLayerCell, self).__init__()
|
|
self.layers = (nn.ReLU(), nn.ReLU())
|
|
self.z3 = Parameter(
|
|
Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z3')
|
|
|
|
def construct(self, index, x):
|
|
ret = self.layers[index](x) * self.z3
|
|
return ret
|
|
|
|
index = Tensor(0, dtype=mstype.int32)
|
|
net = SwitchLayerCell()
|
|
net(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
|
|
grad_by_list(net, ParameterTuple(net.trainable_params()))(index,
|
|
Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
|
|
grad_all(net)(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
|
|
|
|
|
|
def test_switch_layer_env_eliminate():
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.conv = nn.Conv2d(1, 1, 3, pad_mode='same')
|
|
self.conv2 = nn.Conv2d(1, 1, 5, pad_mode='same')
|
|
self.funs = (self.conv, self.conv2)
|
|
|
|
def construct(self, x, index):
|
|
x = self.funs[index](x)
|
|
return x
|
|
|
|
class NetGrad(nn.Cell):
|
|
def __init__(self, net):
|
|
super(NetGrad, self).__init__()
|
|
self.grad_op = C.GradOperation(get_by_list=True, sens_param=False)
|
|
self.net = net
|
|
self.weights = ParameterTuple(self.net.trainable_params())
|
|
|
|
def construct(self, x, index):
|
|
weights = self.weights
|
|
grad = self.grad_op(self.net, weights)(x, index)
|
|
return grad
|
|
|
|
net = Net()
|
|
net2 = NetGrad(net)
|
|
x = Tensor(np.ones((3, 1, 12, 12)), ms.float32)
|
|
i = Tensor(1, ms.int32)
|
|
net2(x, i)
|
|
|
|
|
|
def test_switch_layer_single_layer():
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.conv = nn.Conv2d(1, 1, 3, pad_mode='same')
|
|
self.funs = (self.conv,)
|
|
|
|
def construct(self, x, index):
|
|
x = self.funs[index](x)
|
|
return x
|
|
|
|
class NetGrad(nn.Cell):
|
|
def __init__(self, net):
|
|
super(NetGrad, self).__init__()
|
|
self.grad_op = C.GradOperation(get_by_list=True, sens_param=False)
|
|
self.net = net
|
|
self.weights = ParameterTuple(self.net.trainable_params())
|
|
|
|
def construct(self, x, index):
|
|
weights = self.weights
|
|
grad = self.grad_op(self.net, weights)(x, index)
|
|
return grad
|
|
|
|
net = Net()
|
|
net2 = NetGrad(net)
|
|
x = Tensor(np.ones((3, 1, 12, 12)), ms.float32)
|
|
i = Tensor(1, ms.int32)
|
|
net2(x, i)
|
|
|
|
|
|
def test_control_depend_check():
|
|
with pytest.raises(TypeError) as e:
|
|
P.ControlDepend(0.0)
|
|
print(e)
|
|
with pytest.raises(ValueError) as e:
|
|
P.ControlDepend(2)
|
|
print(e)
|
|
with pytest.raises(TypeError) as e:
|
|
P.ControlDepend((2,))
|
|
print(e)
|
|
|
|
|
|
def test_if_nested_compile():
|
|
class Net(nn.Cell):
|
|
def __init__(self, auto_prefix=True):
|
|
super().__init__(auto_prefix=auto_prefix)
|
|
self.squre = P.Square()
|
|
self.value = Tensor(3, dtype=ms.float32)
|
|
|
|
def construct(self, x, y):
|
|
res = self.value
|
|
if x <= y:
|
|
res = x + res
|
|
res = y + res
|
|
else:
|
|
if x == y:
|
|
res = self.squre(self.value * y)
|
|
else:
|
|
res = self.squre(self.value)
|
|
return res
|
|
|
|
x = Tensor(1.0, dtype=ms.float32)
|
|
y = Tensor(2.0, dtype=ms.float32)
|
|
net = Net()
|
|
net(x, y)
|
|
|
|
|
|
def test_if_inside_for():
|
|
class Net(nn.Cell):
|
|
def __init__(self, auto_prefix=True):
|
|
super().__init__(auto_prefix=auto_prefix)
|
|
self.squre = P.Square()
|
|
self.value = Tensor(3, dtype=ms.float32)
|
|
self.count = 4
|
|
|
|
def construct(self, x, y):
|
|
res = 0
|
|
for i in range(self.count):
|
|
if i == x:
|
|
res = res + x
|
|
else:
|
|
res = res - y
|
|
return res
|
|
|
|
c1 = Tensor(1, dtype=ms.int32)
|
|
c2 = Tensor(1, dtype=ms.int32)
|
|
net = Net()
|
|
net(c1, c2)
|
|
|
|
|
|
def test_while_in_while():
|
|
c1 = Tensor(1, dtype=ms.int32)
|
|
c2 = Tensor(2, dtype=ms.int32)
|
|
c3 = Tensor(3, dtype=ms.int32)
|
|
c4 = Tensor(4, dtype=ms.int32)
|
|
|
|
@ms_function
|
|
def while_in_while(x, y, z, u):
|
|
out = c4
|
|
while x < y:
|
|
z = c4 + c4
|
|
while z < y:
|
|
z = z + 1
|
|
out = out + 1
|
|
x = x + 1
|
|
|
|
out = out + 3
|
|
return out
|
|
|
|
while_in_while(c1, c2, c3, c4)
|
|
|
|
|
|
def test_tensor_cond():
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.t = Tensor(np.array(0, np.bool))
|
|
self.t1 = Tensor(np.array([True], np.bool))
|
|
|
|
def construct(self, x, y):
|
|
t = 0
|
|
if self.t:
|
|
t = t - x * y
|
|
else:
|
|
t = t - x / y
|
|
if self.t1:
|
|
t = t + x / y
|
|
else:
|
|
t = t + x * y
|
|
return t
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.ones([6, 8, 10], np.int32))
|
|
net = Net()
|
|
out = net(x, y)
|
|
|
|
|
|
def test_tensor_cond_exception():
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.t = Tensor(np.array([True, False], np.bool))
|
|
|
|
def construct(self, x, y):
|
|
t = 0
|
|
if self.t:
|
|
t = t - x * y
|
|
else:
|
|
t = t - x / y
|
|
return t
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.ones([6, 8, 10], np.int32))
|
|
net = Net()
|
|
with pytest.raises(ValueError):
|
|
out = net(x, y)
|
|
|
|
|
|
def test_while_scalar():
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.x = 10
|
|
|
|
def construct(self, x, y):
|
|
i = 0
|
|
t = 0
|
|
while (i < 10):
|
|
t = t + x + y
|
|
i = i + 1
|
|
return t
|
|
|
|
net = Net()
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.ones([6, 8, 10], np.int32))
|
|
out = net(x, y)
|
|
|
|
|
|
def test_while_tensor():
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.t = Tensor(np.ones([6, 8, 10], np.int32))
|
|
self.count = Tensor(np.array([10], np.int32))
|
|
|
|
def construct(self, x, y):
|
|
i = 0
|
|
t = self.t
|
|
while (i < self.count):
|
|
t = t + x + y
|
|
i = i + 1
|
|
return t
|
|
|
|
net = Net()
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
|
|
y = Tensor(np.ones([6, 8, 10], np.int32))
|
|
out = net(x, y)
|
|
|
|
|
|
def test_large_for_loop():
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.flatten = P.ReLU() # nn.Flatten()
|
|
|
|
def construct(self, x):
|
|
for elem in range(1, 1900):
|
|
x = self.flatten(x + elem)
|
|
return x
|
|
|
|
t = Tensor(np.ones([2, 3], dtype=np.float32))
|
|
net = Net()
|
|
old_max_call_depth = context.get_context('max_call_depth')
|
|
context.set_context(max_call_depth=60)
|
|
with pytest.raises(RuntimeError) as err:
|
|
net(t)
|
|
context.set_context(max_call_depth=old_max_call_depth)
|
|
assert 'Exceed function call depth limit 60' in str(err.value)
|
|
|
|
|
|
def test_large_for_loop_with_continue_break():
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.flatten = P.ReLU() # nn.Flatten()
|
|
|
|
def construct(self, x):
|
|
idx = 0
|
|
for elem1 in range(200):
|
|
idx = idx + 1
|
|
if idx < 10:
|
|
x = x + 0.5
|
|
continue
|
|
if idx > 500:
|
|
break
|
|
x = self.flatten(x + elem1)
|
|
return x
|
|
|
|
old_max_call_depth = context.get_context('max_call_depth')
|
|
context.set_context(max_call_depth=2000)
|
|
t = Tensor(np.ones([2, 3], dtype=np.float32))
|
|
net = Net()
|
|
net(t)
|
|
context.set_context(max_call_depth=old_max_call_depth)
|
|
|
|
|
|
def test_mixed_precision_cast():
|
|
x = Tensor(np.ones([2, 3], dtype=np.float32))
|
|
z = F.mixed_precision_cast(mstype.float16, x)
|
|
assert z.dtype == mstype.float16
|
|
|
|
|
|
def test_while_concat():
|
|
class Net(nn.Cell):
|
|
def __init__(self, data):
|
|
super(Net, self).__init__()
|
|
self.start = Tensor(0, dtype=mstype.int32)
|
|
self.end = Tensor(2, dtype=mstype.int32)
|
|
self.out = Tensor(np.zeros([2, 3], dtype=np.float32))
|
|
self.concat = P.Concat()
|
|
|
|
def construct(self, inputs):
|
|
idx = self.start
|
|
end = self.end
|
|
out = self.out
|
|
while idx < end:
|
|
xi = inputs[idx, :, :]
|
|
out = self.concat((out, xi))
|
|
idx = idx + 1
|
|
return out
|
|
|
|
x = Tensor(np.arange(10 * 2 * 3).reshape(10, 2, 3).astype(np.float32))
|
|
net = Net(x)
|
|
net(x)
|
|
|
|
|
|
def test_tensor_all_construct_lack_branch():
|
|
class NetConditionLackBranch(nn.Cell):
|
|
def __init__(self):
|
|
super(NetConditionLackBranch, self).__init__()
|
|
self.logicaland = P.LogicalAnd()
|
|
self.logicalor = P.LogicalOr()
|
|
|
|
def construct(self, input1, input2):
|
|
if input1.all():
|
|
return self.logicaland(input1, input2)
|
|
while input1.any():
|
|
return self.logicalor(input1, input2)
|
|
# NOTICE: here missing return statement, default return None
|
|
|
|
input_np_1 = np.random.choice([True], size=(2, 3, 4, 5))
|
|
input_tensor_1 = Tensor(input_np_1)
|
|
input_np_2 = np.random.choice([True, False], size=(2, 3, 4, 5))
|
|
input_tensor_2 = Tensor(input_np_2)
|
|
net = NetConditionLackBranch()
|
|
with pytest.raises(Exception):
|
|
net(input_tensor_1, input_tensor_2)
|
|
|
|
|
|
def test_parser_switch_layer_func_primitive():
|
|
class FinalNet(nn.Cell):
|
|
def __init__(self, funcs):
|
|
super().__init__()
|
|
self.funcs = funcs
|
|
|
|
def construct(self, i, input1):
|
|
x = self.funcs[i](input1)
|
|
return x
|
|
|
|
func1 = P.ReLU()
|
|
func2 = P.Softmax()
|
|
funcs = (func1, func2)
|
|
net = FinalNet(funcs)
|
|
|
|
input1 = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
|
|
i = Tensor(1, mstype.int32)
|
|
|
|
with pytest.raises(ValueError):
|
|
net(i, input1)
|
|
|
|
|
|
def test_recursive_call():
|
|
class Net(nn.Cell):
|
|
""" Net definition """
|
|
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.fc = nn.Dense(10, 10) # padding=0
|
|
# self.net2 = Net2()
|
|
|
|
def construct(self, x):
|
|
net2 = Net2()
|
|
x = net2(x)
|
|
out = self.fc(x)
|
|
return out
|
|
|
|
class Net2(nn.Cell):
|
|
def __init__(self):
|
|
super(Net2, self).__init__()
|
|
self.net = Net()
|
|
self.fc = nn.Dense(10, 10)
|
|
|
|
def construct(self, x):
|
|
x = self.net(x)
|
|
out = self.fc(x)
|
|
return out
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
|
|
old_max_call_depth = context.get_context('max_call_depth')
|
|
context.set_context(max_call_depth=80)
|
|
input_data = Tensor(np.identity(10).astype(np.float32))
|
|
net = Net2()
|
|
with pytest.raises(RuntimeError):
|
|
net(input_data)
|
|
context.set_context(max_call_depth=old_max_call_depth)
|
|
|
|
|
|
def test_switch_layer_shape_join_failed():
|
|
class AddFuncNet(nn.Cell):
|
|
def __init__(self, funcs, new_func):
|
|
super(AddFuncNet, self).__init__()
|
|
self.funcs = funcs
|
|
self.new_func = new_func
|
|
|
|
def construct(self, i, inputs):
|
|
final_funcs = self.funcs + (self.new_func,)
|
|
x = final_funcs[i](inputs)
|
|
return x
|
|
|
|
class ReLUTuple(nn.Cell):
|
|
def __init__(self):
|
|
super(ReLUTuple, self).__init__()
|
|
self.op = nn.ReLU()
|
|
|
|
def construct(self, x):
|
|
return self.op(x[0])
|
|
|
|
func1 = nn.Softmax()
|
|
func2 = nn.ReLU()
|
|
func3 = ReLUTuple()
|
|
|
|
funcs = (func1, func2)
|
|
|
|
net = AddFuncNet(funcs, func3)
|
|
|
|
inp = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
|
|
i = Tensor(1, mstype.int32)
|
|
with pytest.raises(ValueError) as err:
|
|
net(i, inp)
|
|
|
|
|
|
def test_switch_layer_dtype_join_failed():
|
|
class Cast(nn.Cell):
|
|
def __init__(self, dtype):
|
|
super(Cast, self).__init__()
|
|
self.op = P.Cast()
|
|
self.dtype = dtype
|
|
|
|
def construct(self, x):
|
|
y = self.op(x, self.dtype)
|
|
return y + y
|
|
|
|
class SwitchNegNet(nn.Cell):
|
|
def __init__(self, funcs):
|
|
super(SwitchNegNet, self).__init__()
|
|
self.funcs = funcs
|
|
self.op = P.Neg()
|
|
|
|
def construct(self, i, inputs):
|
|
x = self.funcs[i](inputs)
|
|
x = self.op(x)
|
|
return x
|
|
|
|
func1 = nn.ReLU()
|
|
func2 = Cast(mstype.int32)
|
|
funcs = (func1, func2)
|
|
net = SwitchNegNet(funcs)
|
|
|
|
inp = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
|
|
i = Tensor(0, mstype.int32)
|
|
|
|
with pytest.raises(TypeError) as err:
|
|
net(i, inp)
|
|
|
|
|
|
def test_large_for_loop_case2():
|
|
class Menet(nn.Cell):
|
|
def __init__(self, axis, flag_boottom, flag_top):
|
|
super(Menet, self).__init__()
|
|
self.squeeze = P.Squeeze(axis)
|
|
self.expanddims = P.ExpandDims()
|
|
self.flatten = nn.Flatten()
|
|
self.neg = P.Neg()
|
|
self.axis = axis
|
|
self.flag_boottom = flag_boottom
|
|
self.flag_top = flag_top
|
|
|
|
def construct(self, x):
|
|
if self.flag_boottom:
|
|
x = self.neg(x)
|
|
for i in range(0, 1500):
|
|
x = self.expanddims(x, self.axis)
|
|
x = self.squeeze(x)
|
|
x = self.flatten(x)
|
|
if self.flag_top:
|
|
x = self.neg(x)
|
|
return x
|
|
|
|
x = Tensor(np.ones([2, 3], dtype=np.float32))
|
|
net = Menet(axis=0, flag_boottom=True, flag_top=True)
|
|
old_max_call_depth = context.get_context('max_call_depth')
|
|
context.set_context(max_call_depth=80)
|
|
with pytest.raises(RuntimeError) as err:
|
|
net(x)
|
|
context.set_context(max_call_depth=old_max_call_depth)
|
|
assert 'Exceed function call depth limit 80' in str(err.value)
|