forked from mindspore-Ecosystem/mindspore
126 lines
5.1 KiB
Python
126 lines
5.1 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 pytest
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.ops as ops
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from mindspore import Tensor
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from mindspore.ops.operations import _inner_ops as inner
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class Net(nn.Cell):
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def __init__(self, op, axis):
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super(Net, self).__init__()
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if op == "Cummin":
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self.op = inner.Cummin(axis)
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elif op == "Cummax":
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self.op = ops.Cummax(axis)
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else:
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raise ValueError("op value error.")
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def construct(self, x):
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return self.op(x)
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def cum_minmax_compare(op, x, expected, axis, data_type):
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net = Net(op, axis)
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x = np.array(x).astype(data_type)
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expected = (np.array(expected[0]).astype(data_type), np.array(expected[1]).astype(data_type))
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# Pynative
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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output = net(Tensor(x))
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assert np.allclose(output[0].asnumpy(), expected[0], equal_nan=True)
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assert np.allclose(output[1].asnumpy(), expected[1])
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# Graph
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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output = net(Tensor(x))
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assert np.allclose(output[0].asnumpy(), expected[0], equal_nan=True)
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assert np.allclose(output[1].asnumpy(), expected[1])
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_cpu
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@pytest.mark.parametrize("data_type", [np.uint8, np.int8, np.int32, np.float16, np.float32])
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def test_cummin_multi_dims(data_type):
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"""
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Feature: Op Cummin
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Description: test Cummin operator with multiple dimension.
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Expectation: the result match expectation.
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"""
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op = "Cummin"
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axis = 1
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x = [[[9, 10, 0, 0, 2], [5, 4, 1, 9, 3], [5, 0, 3, 7, 5], [10, 4, 5, 4, 9]],
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[[5, 0, 8, 8, 10], [9, 0, 1, 5, 2], [9, 5, 8, 9, 7], [10, 9, 2, 2, 2]],
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[[8, 2, 7, 5, 6], [9, 10, 6, 0, 10], [1, 9, 0, 3, 7], [1, 6, 2, 2, 1]]]
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cummin_output = ([[[9, 10, 0, 0, 2], [5, 4, 0, 0, 2], [5, 0, 0, 0, 2], [5, 0, 0, 0, 2]],
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[[5, 0, 8, 8, 10], [5, 0, 1, 5, 2], [5, 0, 1, 5, 2], [5, 0, 1, 2, 2]],
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[[8, 2, 7, 5, 6], [8, 2, 6, 0, 6], [1, 2, 0, 0, 6], [1, 2, 0, 0, 1]]],
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[[[0, 0, 0, 0, 0], [1, 1, 0, 0, 0], [2, 2, 0, 0, 0], [2, 2, 0, 0, 0]],
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[[0, 0, 0, 0, 0], [0, 1, 1, 1, 1], [0, 1, 1, 1, 1], [0, 1, 1, 3, 3]],
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[[0, 0, 0, 0, 0], [0, 0, 1, 1, 0], [2, 0, 2, 1, 0], [3, 0, 2, 1, 3]]])
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cum_minmax_compare(op, x, cummin_output, axis, data_type)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_cpu
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@pytest.mark.parametrize("data_type", [np.uint8, np.uint32, np.int8, np.int32, np.int64, np.float16, np.float32])
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def test_cummax_multi_dims(data_type):
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"""
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Feature: Op Cummax
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Description: test Cummax operator with multiple dimension.
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Expectation: the result match expectation.
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"""
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op = "Cummax"
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axis = 1
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x = [[[6, 11, 4, 9, 15], [1, 2, 14, 13, 15], [15, 10, 6, 13, 6], [9, 4, 11, 10, 11]],
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[[5, 1, 5, 13, 7], [19, 4, 14, 11, 14], [5, 15, 6, 20, 0], [6, 2, 4, 15, 16]],
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[[17, 4, 16, 13, 3], [15, 15, 14, 9, 13], [11, 0, 2, 19, 17], [20, 18, 13, 15, 17]]]
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cummax_output = ([[[6, 11, 4, 9, 15], [6, 11, 14, 13, 15], [15, 11, 14, 13, 15], [15, 11, 14, 13, 15]],
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[[5, 1, 5, 13, 7], [19, 4, 14, 13, 14], [19, 15, 14, 20, 14], [19, 15, 14, 20, 16]],
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[[17, 4, 16, 13, 3], [17, 15, 16, 13, 13], [17, 15, 16, 19, 17], [20, 18, 16, 19, 17]]],
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[[[0, 0, 0, 0, 0], [0, 0, 1, 1, 1], [2, 0, 1, 2, 1], [2, 0, 1, 2, 1]],
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[[0, 0, 0, 0, 0], [1, 1, 1, 0, 1], [1, 2, 1, 2, 1], [1, 2, 1, 2, 3]],
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[[0, 0, 0, 0, 0], [0, 1, 0, 0, 1], [0, 1, 0, 2, 2], [3, 3, 0, 2, 3]]])
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cum_minmax_compare(op, x, cummax_output, axis, data_type)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_cpu
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@pytest.mark.parametrize("data_type", [np.float16, np.float32])
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def test_cum_minmax_nan(data_type):
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"""
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Feature: Op Cummin/Cummax
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Description: test Cummin/Cummax operator with nan input.
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Expectation: the result match expectation.
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"""
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inf = float('inf')
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nan = float('nan')
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axis = 0
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x = [4, inf, 1.5, -inf, 0, nan, 1]
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cummin_output = ([4, 4, 1.5, -inf, -inf, nan, nan], [0, 0, 2, 3, 3, 5, 5])
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cummax_output = ([4, inf, inf, inf, inf, nan, nan], [0, 1, 1, 1, 1, 5, 5])
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cum_minmax_compare("Cummin", x, cummin_output, axis, data_type)
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cum_minmax_compare("Cummax", x, cummax_output, axis, data_type)
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