forked from mindspore-Ecosystem/mindspore
86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
# Copyright 2021-2022 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 list control flow """
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import pytest
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import mindspore.context as context
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from mindspore import Tensor, dtype
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from mindspore.nn import Cell
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import mindspore.ops.operations as P
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@pytest.mark.skip(reason='Not support list as parameter in while function yet')
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_while_list():
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"""
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Feature: list in while.
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Description: Infer list in while.
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Expectation: Null.
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"""
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class Net(Cell):
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def __init__(self):
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super().__init__()
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self.addn = P.AddN()
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def construct(self, x):
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y = []
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for _ in range(3):
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while x < 10:
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y.append(x)
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x = self.addn(y)
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return x
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True, save_graphs_path="./listir")
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net = Net()
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x = Tensor([1], dtype.float32)
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print(net(x))
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_for_list():
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"""
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Feature: list for.
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Description: Infer list in for.
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Expectation: Null.
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"""
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def convert_points_to_homogeneous(points):
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padding = [[0, 0] for _ in range(len(points.shape))]
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padding[-1][-1] = 1
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return padding
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class Net(Cell):
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def construct(self, x1, x2):
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y1 = convert_points_to_homogeneous(x1)
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y2 = convert_points_to_homogeneous(x2)
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return y1, y2
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context.set_context(mode=context.GRAPH_MODE)
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x1 = Tensor([[[-1, -1], # left top
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[1, -1], # right top
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[-1, 5], # left bottom
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[1, 5]]], dtype.float32) # right bottom
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x2 = Tensor([[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]], dtype.float32)
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net = Net()
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print(net(x1, x2))
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