forked from mindspore-Ecosystem/mindspore
133 lines
4.3 KiB
Python
133 lines
4.3 KiB
Python
# Copyright 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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import numpy as np
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import pytest
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import mindspore.context as context
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from mindspore import Tensor
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from mindspore.nn import Cell
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import mindspore.ops.operations as P
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class Net(Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.squaresumall = P.SquareSumAll()
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def construct(self, x0, x1):
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return self.squaresumall(x0, x1)
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def run_net(datatype, input_tensors, output_tensors):
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inp0 = Tensor(np.array(input_tensors[0]).astype(datatype))
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inp1 = Tensor(np.array(input_tensors[1]).astype(datatype))
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net = Net()
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[output0, output1] = net(inp0, inp1)
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expect0 = Tensor(output_tensors[0])
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expect1 = Tensor(output_tensors[1])
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assert output0 == expect0
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assert output1 == expect1
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assert output0.dtype == inp0.dtype
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assert output1.dtype == inp1.dtype
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('dtype', [np.float16, np.float32])
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@pytest.mark.parametrize('input_tensors, output_tensors', [
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([[1, 2, 4], [0, 1, -1]], [21, 2]),
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([[[1, 2], [3, 4]], [[0, 0], [1, 0]]], [30, 1])
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])
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def test_cpu(dtype, input_tensors, output_tensors):
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"""
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Feature: SquareSumAll cpu op.
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Description: test data type is float16 and float32.
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Expectation: success.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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run_net(dtype, input_tensors, output_tensors)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_cpu_exception_dtype_diff():
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"""
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Feature: SquareSumAll cpu op.
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Description: Test data type of two input tensors is different.
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Expectation: Throw TypeError exception.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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with pytest.raises(TypeError):
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inp0 = Tensor(np.array([1, 2, 4]).astype(np.float16))
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inp1 = Tensor(np.array([0, 1, -1]).astype(np.float32))
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net = Net()
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_ = net(inp0, inp1)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_cpu_exception_dtype_not_support():
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"""
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Feature: SquareSumAll cpu op.
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Description: Test unsupported data type.
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Expectation: Throw TypeError exception.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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with pytest.raises(TypeError):
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inp0 = Tensor(np.array([1, 2, 4]).astype(np.float64))
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inp1 = Tensor(np.array([0, 1, -1]).astype(np.float64))
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net = Net()
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_ = net(inp0, inp1)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_cpu_exception_shape_diff():
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"""
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Feature: SquareSumAll cpu op.
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Description: Test shape of two input tensors is different.
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Expectation: Throw ValueError exception.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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with pytest.raises(ValueError):
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inp0 = Tensor(np.array([1, 2, 4]).astype(np.float32))
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inp1 = Tensor(np.array([0, 1]).astype(np.float32))
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net = Net()
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_ = net(inp0, inp1)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_cpu_float16():
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"""
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Feature: SquareSumAll cpu op.
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Description: test data type is float16.
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Expectation: success.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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inp0 = Tensor(np.arange(0, 1, 0.001).astype(np.float16))
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inp1 = Tensor(np.arange(0, 1, 0.001).astype(np.float16))
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net = Net()
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[output0, output1] = net(inp0, inp1)
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expect0 = np.array(332.75).astype(np.float16)
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expect1 = np.array(332.75).astype(np.float16)
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assert output0.asnumpy() == expect0
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assert output1.asnumpy() == expect1
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