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134 lines
3.8 KiB
134 lines
3.8 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_operator """
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import numpy as np
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from mindspore.ops import operations as P
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from mindspore.nn import ReLU
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from mindspore.nn import Cell
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from mindspore import Tensor, Model, context
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from ...ut_filter import non_graph_engine
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class arithmetic_Net(Cell):
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""" arithmetic_Net definition """
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def __init__(self, symbol, loop_count=(1, 3)):
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super().__init__()
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self.symbol = symbol
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self.loop_count = loop_count
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self.relu = ReLU()
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def construct(self, x):
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a, b = self.loop_count
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y = self.symbol
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if y == 1:
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a += b
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for _ in (b, a):
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x = self.relu(x)
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elif y == 2:
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b -= a
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for _ in (a, b):
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x = self.relu(x)
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elif y == 3:
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z = a + b
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for _ in (b, z):
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x = self.relu(x)
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elif y == 4:
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z = b - a
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for _ in (z, b):
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x = self.relu(x)
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elif y == 5:
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z = a * b
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for _ in (a, z):
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x = self.relu(x)
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elif y == 6:
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z = b / a
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for _ in (a, z):
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x = self.relu(x)
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elif y == 7:
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z = b % a + 1
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for _ in (a, z):
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x = self.relu(x)
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else:
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if not a:
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x = self.relu(x)
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return x
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class logical_Net(Cell):
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""" logical_Net definition """
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def __init__(self, symbol, loop_count=(1, 3)):
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super().__init__()
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self.symbol = symbol
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self.loop_count = loop_count
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self.fla = P.Flatten()
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self.relu = ReLU()
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def construct(self, x):
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a, b = self.loop_count
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y = self.symbol
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if y == 1:
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if b and a:
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x = self.relu(x)
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else:
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x = self.fla(x)
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else:
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if b or a:
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x = self.relu(x)
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else:
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x = self.fla(x)
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return x
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def arithmetic_operator_base(symbol):
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""" arithmetic_operator_base """
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input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_me = Tensor(input_np)
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logical_operator = {"++": 1, "--": 2, "+": 3, "-": 4, "*": 5, "/": 6, "%": 7, "not": 8}
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x = logical_operator[symbol]
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net = arithmetic_Net(x)
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context.set_context(mode=context.GRAPH_MODE)
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model = Model(net)
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model.predict(input_me)
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def logical_operator_base(symbol):
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""" logical_operator_base """
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input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_me = Tensor(input_np)
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logical_operator = {"and": 1, "or": 2}
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x = logical_operator[symbol]
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net = logical_Net(x)
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context.set_context(mode=context.GRAPH_MODE)
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model = Model(net)
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model.predict(input_me)
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@non_graph_engine
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def test_ME_arithmetic_operator_0080():
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""" test_ME_arithmetic_operator_0080 """
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arithmetic_operator_base('not')
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@non_graph_engine
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def test_ME_arithmetic_operator_0070():
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""" test_ME_arithmetic_operator_0070 """
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logical_operator_base('and')
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@non_graph_engine
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def test_ME_logical_operator_0020():
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""" test_ME_logical_operator_0020 """
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logical_operator_base('or')
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