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103 lines
2.5 KiB
103 lines
2.5 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_dictionary """
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import numpy as np
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from mindspore import Tensor, context
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from mindspore.nn import Cell
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context.set_context(mode=context.GRAPH_MODE)
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class Net1(Cell):
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def __init__(self):
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super().__init__()
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def construct(self, x):
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dic = {'x': 0, 'y': 1}
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output = []
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for i in dic.keys():
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output.append(i)
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for j in dic.values():
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output.append(j)
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return output
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class Net2(Cell):
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def __init__(self):
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super().__init__()
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def construct(self, x):
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dic = {'x': x, 'y': 1}
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output = []
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for i in dic.keys():
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output.append(i)
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for j in dic.values():
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output.append(j)
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return output
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class Net3(Cell):
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def __init__(self):
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super().__init__()
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def construct(self, x):
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dic = {'x': 0}
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dic['y'] = (0, 1)
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output = []
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for i in dic.keys():
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output.append(i)
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for j in dic.values():
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output.append(j)
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return output
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def test_dict1():
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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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net = Net1()
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out_me = net(input_me)
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assert out_me == ('x', 'y', 0, 1)
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def test_dict2():
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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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net = Net2()
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net(input_me)
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def test_dict3():
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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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net = Net3()
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out_me = net(input_me)
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assert out_me == ('x', 'y', 0, (0, 1))
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def test_dict4():
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class Net(Cell):
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def __init__(self):
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super().__init__()
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def construct(self, tuple_x):
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output = tuple_x + tuple_x
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return output
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x = (1, Tensor([1, 2, 3]), {"a": Tensor([1, 2, 3]), "b": 1})
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net = Net()
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out_me = net(x)
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assert out_me == x + x
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