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642 lines
17 KiB
642 lines
17 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test control ops """
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import numpy as np
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import pytest
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import mindspore as ms
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from mindspore import Tensor
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from mindspore import context
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from mindspore import nn
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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from mindspore.ops import operations as P
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from mindspore.common.parameter import Parameter, ParameterTuple
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from mindspore.common import ms_function
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context.set_context(mode=context.GRAPH_MODE)
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def cond_data_test(x_init, y_init):
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class Net(nn.Cell):
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def __init__(self):
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""""""
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super(Net, self).__init__()
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self.square = P.Square()
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self.add = P.TensorAdd()
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self.value = Tensor(3, dtype=ms.float32)
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self.switch = P.GeSwitch()
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self.merge = P.Merge()
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self.less = P.Less()
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def construct(self, x, y):
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cond = self.less(x, y)
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st1, _ = self.switch(x, cond)
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st2, _ = self.switch(y, cond)
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add_ret = self.add(st1, st2)
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_, sf3 = self.switch(self.value, cond)
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sq_ret = self.square(sf3)
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ret = self.merge((add_ret, sq_ret))
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return ret[0]
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x = Tensor(x_init, dtype=ms.float32)
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y = Tensor(y_init, dtype=ms.float32)
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net = Net()
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output = net(x, y)
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return output
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def test_cond_data_true():
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output = cond_data_test(3, 8)
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print("test_cond_data_true:", output)
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def test_cond_data_false():
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output = cond_data_test(8, 3)
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print("test_cond_data_false:", output)
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def if_compile_test(x_init, y_init):
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class Net(nn.Cell):
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def __init__(self):
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""""""
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super(Net, self).__init__()
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self.square = P.Square()
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self.add = P.TensorAdd()
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self.value = Tensor(3, dtype=ms.float32)
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self.switch = P.GeSwitch()
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self.merge = P.Merge()
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self.less = P.Less()
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def construct(self, x, y):
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cond = self.less(x, y)
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ret = self.value
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if cond:
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ret = self.add(x, ret)
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ret = self.add(y, ret)
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else:
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ret = self.square(self.value)
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return ret
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x = Tensor(x_init, dtype=ms.float32)
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y = Tensor(y_init, dtype=ms.float32)
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net = Net()
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output = net(x, y)
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return output
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def test_if_none():
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class Net(nn.Cell):
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def __init__(self, z: None):
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""""""
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = None
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net = Net(z)
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assert net(x, y) == y
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def test_if_str_is_not_none_right():
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class Net(nn.Cell):
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def __init__(self, z: str):
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""""""
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z is None:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = "ok"
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net = Net(z)
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assert net(x, y) == y
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def test_if_str_is_not_none_left():
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class Net(nn.Cell):
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def __init__(self, z: str):
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""""""
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z is None:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = "ok"
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net = Net(z)
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assert net(x, y) == y
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def test_if_none_equal_none():
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class Net(nn.Cell):
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def __init__(self, z: None):
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""""""
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z is None:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = None
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net = Net(z)
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assert net(x, y) == x
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def test_if_str_is_null():
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class Net(nn.Cell):
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def __init__(self, z: str):
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""""""
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = ""
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net = Net(z)
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assert net(x, y) == y
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def test_if_str_is_true():
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class Net(nn.Cell):
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def __init__(self, z: str):
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""""""
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 9, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = "ok"
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net = Net(z)
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assert net(x, y) == x
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def test_if_str_equal():
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class Net(nn.Cell):
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def __init__(self, z: str):
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""""""
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z == "ok":
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = "ok"
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net = Net(z)
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assert net(x, y) == x
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def test_if_tuple_is_null():
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class Net(nn.Cell):
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def __init__(self, z: tuple):
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""""""
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = ()
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net = Net(z)
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assert net(x, y) == y
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def test_if_tuple_is_not_null():
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class Net(nn.Cell):
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def __init__(self, z: tuple):
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""""""
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = (1, 2, 3)
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net = Net(z)
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assert net(x, y) == x
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def test_if_dict_is_null():
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class Net(nn.Cell):
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def __init__(self, z: dict):
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""""""
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = {}
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net = Net(z)
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assert net(x, y) == y
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def test_if_dict_is_not_null():
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class Net(nn.Cell):
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def __init__(self, z: dict):
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""""""
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = {"one": 1, "two": 2}
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net = Net(z)
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assert net(x, y) == x
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def test_if_else_assign():
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class Net(nn.Cell):
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def __init__(self, m: list):
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""""""
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super(Net, self).__init__()
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self.m = m
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self.n = [4, 5, 6]
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def construct(self, x, y):
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exp_1 = self.m if self.m else self.n
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exp_2 = self.m if exp_1 == self.n else self.n
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if exp_2 == self.m:
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if self.m:
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ret = x
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else:
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ret = y
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else:
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if self.m:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = [1, 2]
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net = Net(z)
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assert net(x, y) == x
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def test_if_compile_true():
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output = if_compile_test(3, 8)
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print("test_if_compile_true:", output)
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def test_if_compile_false():
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output = if_compile_test(8, 3)
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print("test_if_compile_false:", output)
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def test_switch_layer():
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class Layer1(nn.Cell):
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def __init__(self):
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super(Layer1, self).__init__()
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self.z1 = Parameter(
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Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z1')
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def construct(self, x):
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return x * self.z1
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class Layer2(nn.Cell):
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def __init__(self):
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super(Layer2, self).__init__()
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self.z2 = Parameter(
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Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z2')
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def construct(self, x):
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return x * self.z2
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class SwitchLayerCell(nn.Cell):
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def __init__(self):
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super(SwitchLayerCell, self).__init__()
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self.layers = (Layer1(), Layer2())
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self.z3 = Parameter(
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Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z3')
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def construct(self, index, x):
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ret = F.switch_layer(index, self.layers)(x) * self.z3
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return ret
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index = Tensor(0)
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net = SwitchLayerCell()
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net(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
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C.grad_by_list(net, ParameterTuple(net.trainable_params()))(index,
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Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
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C.grad_all(net)(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
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def test_index_to_switch_layer():
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class Layer1(nn.Cell):
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def __init__(self):
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super(Layer1, self).__init__()
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self.z1 = Parameter(
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Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z1')
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def construct(self, x):
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return x * self.z1
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class Layer2(nn.Cell):
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def __init__(self):
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super(Layer2, self).__init__()
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self.z2 = Parameter(
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Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z2')
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def construct(self, x):
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return x * self.z2
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class SwitchLayerCell(nn.Cell):
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def __init__(self):
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super(SwitchLayerCell, self).__init__()
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self.layers = (Layer1(), Layer2())
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self.z3 = Parameter(
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Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z3')
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def construct(self, index, x):
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ret = self.layers[index](x) * self.z3
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return ret
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index = Tensor(0)
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net = SwitchLayerCell()
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net(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
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C.grad_by_list(net, ParameterTuple(net.trainable_params()))(index,
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Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
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C.grad_all(net)(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
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def test_control_depend_check():
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with pytest.raises(TypeError) as e:
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P.ControlDepend(0.0)
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print(e)
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with pytest.raises(ValueError) as e:
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P.ControlDepend(2)
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print(e)
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with pytest.raises(TypeError) as e:
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P.ControlDepend((2,))
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print(e)
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def test_if_nested_compile():
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class Net(nn.Cell):
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def __init__(self, auto_prefix=True):
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super().__init__(auto_prefix=auto_prefix)
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self.squre = P.Square()
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self.value = Tensor(3, dtype=ms.float32)
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def construct(self, x, y):
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res = self.value
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if x <= y:
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res = x + res
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res = y + res
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else:
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if x == y:
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res = self.squre(self.value * y)
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else:
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res = self.squre(self.value)
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return res
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x = Tensor(1.0, dtype=ms.float32)
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y = Tensor(2.0, dtype=ms.float32)
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net = Net()
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net(x, y)
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def test_if_inside_for():
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class Net(nn.Cell):
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def __init__(self, auto_prefix=True):
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super().__init__(auto_prefix=auto_prefix)
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self.squre = P.Square()
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self.value = Tensor(3, dtype=ms.float32)
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self.count = 4
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def construct(self, x, y):
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res = 0
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for i in range(self.count):
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if i == x:
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res = res + x
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else:
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res = res - y
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return res
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c1 = Tensor(1, dtype=ms.int32)
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c2 = Tensor(1, dtype=ms.int32)
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net = Net()
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net(c1, c2)
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def test_while_in_while():
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c1 = Tensor(1, dtype=ms.int32)
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c2 = Tensor(2, dtype=ms.int32)
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c3 = Tensor(3, dtype=ms.int32)
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c4 = Tensor(4, dtype=ms.int32)
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@ms_function
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def while_in_while(x, y, z, u):
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out = c4
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while x < y:
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z = c4 + c4
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while z < y:
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z = z + 1
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out = out + 1
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x = x + 1
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out = out + 3
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return out
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while_in_while(c1, c2, c3, c4)
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def test_tensor_cond():
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.t = Tensor(np.array(0, np.bool))
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self.t1 = Tensor(np.array([True], np.bool))
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def construct(self, x, y):
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t = 0
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if self.t:
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t = t - x * y
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else:
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t = t - x / y
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if self.t1:
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t = t + x / y
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else:
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t = t + x * y
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return t
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.ones([6, 8, 10], np.int32))
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net = Net()
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out = net(x, y)
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def test_tensor_cond_exception():
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class Net(nn.Cell):
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def __init__(self):
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|
super(Net, self).__init__()
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|
self.t = Tensor(np.array([True, False], np.bool))
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|
def construct(self, x, y):
|
|
t = 0
|
|
if self.t:
|
|
t = t - x * y
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|
else:
|
|
t = t - x / y
|
|
return t
|
|
|
|
|
|
x = Tensor(np.ones([6, 8, 10], np.int32))
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|
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, 19000):
|
|
x = self.flatten(x + elem)
|
|
return x
|
|
|
|
t = Tensor(np.ones([2, 3], dtype=np.float32))
|
|
net = Net()
|
|
net(t)
|
|
|
|
|
|
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
|
|
|
|
t = Tensor(np.ones([2, 3], dtype=np.float32))
|
|
net = Net()
|
|
net(t)
|