forked from mindspore-Ecosystem/mindspore
130 lines
5.2 KiB
Python
130 lines
5.2 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.nn as nn
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import mindspore.ops as ops
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import mindspore.context as context
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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="GPU")
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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="GPU")
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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_gpu_training
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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 = [[[14, 19, 18, 11, 6], [1, 4, 18, 6, 1], [15, 13, 12, 9, 19]],
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[[16, 16, 17, 10, 15], [9, 7, 10, 9, 4], [6, 14, 16, 3, 2]],
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[[1, 13, 15, 1, 6], [20, 6, 8, 19, 19], [3, 14, 20, 18, 19]],
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[[20, 1, 14, 9, 3], [13, 11, 2, 17, 14], [0, 15, 13, 7, 10]]]
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cummin_output = (
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[[[14, 19, 18, 11, 6], [1, 4, 18, 6, 1], [1, 4, 12, 6, 1]],
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[[16, 16, 17, 10, 15], [9, 7, 10, 9, 4], [6, 7, 10, 3, 2]],
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[[1, 13, 15, 1, 6], [1, 6, 8, 1, 6], [1, 6, 8, 1, 6]], [[20, 1, 14, 9, 3], [13, 1, 2, 9, 3], [0, 1, 2, 7, 3]]],
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[[[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [1, 1, 2, 1, 1]], [[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 1, 1, 2, 2]],
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[[0, 0, 0, 0, 0], [0, 1, 1, 0, 0], [0, 1, 1, 0, 0]], [[0, 0, 0, 0, 0], [1, 0, 1, 0, 0], [2, 0, 1, 2, 0]]])
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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_gpu_training
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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 = [[[11, 11, 1, 7, 11], [1, 8, 18, 0, 9], [12, 1, 16, 11, 8]],
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[[18, 8, 10, 17, 14], [4, 20, 8, 20, 11], [14, 1, 8, 5, 16]],
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[[6, 13, 19, 14, 8], [17, 19, 11, 0, 7], [18, 4, 13, 14, 16]],
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[[10, 7, 7, 7, 19], [15, 0, 15, 5, 14], [9, 7, 10, 4, 14]]]
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cummax_output = ([[[11, 11, 1, 7, 11], [11, 11, 18, 7, 11], [12, 11, 18, 11, 11]],
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[[18, 8, 10, 17, 14], [18, 20, 10, 20, 14], [18, 20, 10, 20, 16]],
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[[6, 13, 19, 14, 8], [17, 19, 19, 14, 8], [18, 19, 19, 14, 16]],
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[[10, 7, 7, 7, 19], [15, 7, 15, 7, 19], [15, 7, 15, 7, 19]]],
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[[[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [2, 0, 1, 2, 0]],
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[[0, 0, 0, 0, 0], [0, 1, 0, 1, 0], [0, 1, 0, 1, 2]],
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[[0, 0, 0, 0, 0], [1, 1, 0, 0, 0], [2, 1, 0, 2, 2]],
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[[0, 0, 0, 0, 0], [1, 0, 1, 0, 0], [1, 2, 1, 0, 0]]])
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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_gpu_training
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@pytest.mark.parametrize("data_type", [np.float16, np.float32])
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def test_cumminmax_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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