forked from mindspore-Ecosystem/mindspore
171 lines
7.0 KiB
Python
171 lines
7.0 KiB
Python
# Copyright 2021 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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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import PrimitiveWithInfer, prim_attr_register
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from mindspore._checkparam import Validator as validator
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from mindspore.common import dtype as mstype
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class Shift(PrimitiveWithInfer):
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"""
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Shift op frontend implementation
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"""
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@prim_attr_register
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def __init__(self, periods=1, axis=-1):
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"""Initialize Sort"""
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self.periods = validator.check_value_type("periods", periods, [int], self.name)
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self.axis = validator.check_value_type("axis", axis, [int], self.name)
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self.init_prim_io_names(inputs=['x', 'fill_value'], outputs=['output'])
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def __infer__(self, x, fill_value):
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out_shapes = x['shape']
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return {
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'shape': tuple(out_shapes),
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'dtype': x['dtype'],
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'value': None
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}
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def infer_dtype(self, x_dtype, fill_value_type):
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validator.check_scalar_or_tensor_types_same({"x_dtype": x_dtype, "fill_value": fill_value_type},
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[mstype.float32, mstype.float64, mstype.int32, mstype.int64,
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mstype.bool_],
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self.name, True)
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return x_dtype
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class ShiftNet(nn.Cell):
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def __init__(self, periods=1, axis=-1):
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super(ShiftNet, self).__init__()
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self.shift = Shift(periods, axis)
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def construct(self, x, fill_value):
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return self.shift(x, fill_value)
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def numpy_shift(array: np.ndarray, periods: int, axis: int, fill_value=np.nan) -> np.ndarray:
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"""
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numpy implementation for validation
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"""
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assert axis in range(-array.ndim, array.ndim)
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copy_src_indices = [slice(None)] * array.ndim
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copy_dst_indices = [slice(None)] * array.ndim
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fill_indices = [slice(None)] * array.ndim
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if periods > 0:
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fill_indices[axis] = slice(None, periods)
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copy_src_indices[axis] = slice(None, -periods)
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copy_dst_indices[axis] = slice(periods, None)
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elif periods < 0:
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fill_indices[axis] = slice(periods, None)
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copy_src_indices[axis] = slice(-periods, None)
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copy_dst_indices[axis] = slice(None, periods)
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else:
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return array.copy()
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result = np.empty_like(array)
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result[tuple(fill_indices)] = fill_value
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result[tuple(copy_dst_indices)] = array[tuple(copy_src_indices)]
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return result
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def compare(arr: np.ndarray, periods: int, axis: int, fill_value=np.nan):
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numpy_result = numpy_shift(arr, periods=periods, axis=axis, fill_value=fill_value)
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shift = ShiftNet(periods=periods, axis=axis)
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mindspore_result = shift(Tensor(arr), fill_value=fill_value).asnumpy()
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print('numpy:\n')
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print(numpy_result)
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print('mindspore:\n')
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print(mindspore_result)
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assert np.allclose(numpy_result, mindspore_result, equal_nan=True)
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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, fill_value',
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[(np.float32, 0.0), (np.float32, 5.3), (np.float32, -5.5), (np.float32, np.nan),
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(np.float64, 0.0), (np.float64, 5.3), (np.float64, -5.5), (np.float64, np.nan),
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(np.int32, 0), (np.int32, 1), (np.int32, 5), (np.int32, -4),
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(np.int64, 0), (np.int64, 1), (np.int64, 5), (np.int64, -4),
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(np.bool_, True), (np.bool_, False)])
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@pytest.mark.parametrize('axis', [0, 1, 2, 3])
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def test_no_shift(fill_value, dtype, axis):
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arr = np.random.random((4, 6, 5, 3)).astype(dtype)
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compare(arr, axis=axis, periods=0, fill_value=fill_value)
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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, fill_value',
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[(np.float32, 0.0), (np.float32, 5.3), (np.float32, -5.5), (np.float32, np.nan),
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(np.float64, 0.0), (np.float64, 5.3), (np.float64, -5.5), (np.float64, np.nan),
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(np.int32, 0), (np.int32, 1), (np.int32, 5), (np.int32, -4),
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(np.int64, 0), (np.int64, 1), (np.int64, 5), (np.int64, -4),
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(np.bool_, True), (np.bool_, False)])
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@pytest.mark.parametrize('periods', [-35, 18, 25])
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def test_fancy_1d(fill_value, dtype, periods):
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arr = np.random.random((1, 1, 20, 1)).astype(dtype)
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compare(arr, axis=2, periods=periods, fill_value=fill_value)
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arr = np.random.random((30, 1, 1, 1)).astype(dtype)
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compare(arr, axis=0, periods=periods, fill_value=fill_value)
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arr = np.random.random((1, 1, 1, 30)).astype(dtype)
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compare(arr, axis=3, periods=periods, fill_value=fill_value)
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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, fill_value',
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[(np.float32, 0.0), (np.float32, 5.3), (np.float32, -5.5), (np.float32, np.nan),
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(np.float64, 0.0), (np.float64, 5.3), (np.float64, -5.5), (np.float64, np.nan),
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(np.int32, 0), (np.int32, 1), (np.int32, 5), (np.int32, -4),
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(np.int64, 0), (np.int64, 1), (np.int64, 5), (np.int64, -4),
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(np.bool_, True), (np.bool_, False)])
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@pytest.mark.parametrize('axis', [0, 1])
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@pytest.mark.parametrize('periods', [-3, 7, -5, 8, 9])
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def test_2d(fill_value, dtype, axis, periods):
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arr = np.random.random((10, 10)).astype(dtype)
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compare(arr, axis=axis, periods=periods, fill_value=fill_value)
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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, fill_value',
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[(np.float32, 0.0), (np.float32, 5.3), (np.float32, -5.5), (np.float32, np.nan),
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(np.float64, 0.0), (np.float64, 5.3), (np.float64, -5.5), (np.float64, np.nan),
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(np.int32, 0), (np.int32, 1), (np.int32, 5), (np.int32, -4),
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(np.int64, 0), (np.int64, 1), (np.int64, 5), (np.int64, -4),
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(np.bool_, True), (np.bool_, False)])
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@pytest.mark.parametrize('axis', [0, 1, 2, 3])
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@pytest.mark.parametrize('periods', [-30, 30, -45, 55])
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def test_4d(fill_value, dtype, axis, periods):
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arr = np.random.random((30, 40, 10, 20)).astype(dtype)
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compare(arr, axis=axis, periods=periods, fill_value=fill_value)
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