# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ import numpy as np import pytest import mindspore.context as context import mindspore.nn as nn from mindspore import Tensor from mindspore.ops import PrimitiveWithInfer, prim_attr_register from mindspore._checkparam import Validator as validator from mindspore.common import dtype as mstype context.set_context(mode=context.GRAPH_MODE, device_target="CPU") class Shift(PrimitiveWithInfer): """ Shift op frontend implementation """ @prim_attr_register def __init__(self, periods=1, axis=-1): """Initialize Sort""" self.periods = validator.check_value_type("periods", periods, [int], self.name) self.axis = validator.check_value_type("axis", axis, [int], self.name) self.init_prim_io_names(inputs=['x', 'fill_value'], outputs=['output']) def __infer__(self, x, fill_value): out_shapes = x['shape'] return { 'shape': tuple(out_shapes), 'dtype': x['dtype'], 'value': None } def infer_dtype(self, x_dtype, fill_value_type): validator.check_scalar_or_tensor_types_same({"x_dtype": x_dtype, "fill_value": fill_value_type}, [mstype.float32, mstype.float64, mstype.int32, mstype.int64, mstype.bool_], self.name, True) return x_dtype class ShiftNet(nn.Cell): def __init__(self, periods=1, axis=-1): super(ShiftNet, self).__init__() self.shift = Shift(periods, axis) def construct(self, x, fill_value): return self.shift(x, fill_value) def numpy_shift(array: np.ndarray, periods: int, axis: int, fill_value=np.nan) -> np.ndarray: """ numpy implementation for validation """ assert axis in range(-array.ndim, array.ndim) copy_src_indices = [slice(None)] * array.ndim copy_dst_indices = [slice(None)] * array.ndim fill_indices = [slice(None)] * array.ndim if periods > 0: fill_indices[axis] = slice(None, periods) copy_src_indices[axis] = slice(None, -periods) copy_dst_indices[axis] = slice(periods, None) elif periods < 0: fill_indices[axis] = slice(periods, None) copy_src_indices[axis] = slice(-periods, None) copy_dst_indices[axis] = slice(None, periods) else: return array.copy() result = np.empty_like(array) result[tuple(fill_indices)] = fill_value result[tuple(copy_dst_indices)] = array[tuple(copy_src_indices)] return result def compare(arr: np.ndarray, periods: int, axis: int, fill_value=np.nan): numpy_result = numpy_shift(arr, periods=periods, axis=axis, fill_value=fill_value) shift = ShiftNet(periods=periods, axis=axis) mindspore_result = shift(Tensor(arr), fill_value=fill_value).asnumpy() print('numpy:\n') print(numpy_result) print('mindspore:\n') print(mindspore_result) assert np.allclose(numpy_result, mindspore_result, equal_nan=True) @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard @pytest.mark.parametrize('dtype, fill_value', [(np.float32, 0.0), (np.float32, 5.3), (np.float32, -5.5), (np.float32, np.nan), (np.float64, 0.0), (np.float64, 5.3), (np.float64, -5.5), (np.float64, np.nan), (np.int32, 0), (np.int32, 1), (np.int32, 5), (np.int32, -4), (np.int64, 0), (np.int64, 1), (np.int64, 5), (np.int64, -4), (np.bool_, True), (np.bool_, False)]) @pytest.mark.parametrize('axis', [0, 1, 2, 3]) def test_no_shift(fill_value, dtype, axis): arr = np.random.random((40, 60, 50, 30)).astype(dtype) compare(arr, axis=axis, periods=0, fill_value=fill_value) @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard @pytest.mark.parametrize('dtype, fill_value', [(np.float32, 0.0), (np.float32, 5.3), (np.float32, -5.5), (np.float32, np.nan), (np.float64, 0.0), (np.float64, 5.3), (np.float64, -5.5), (np.float64, np.nan), (np.int32, 0), (np.int32, 1), (np.int32, 5), (np.int32, -4), (np.int64, 0), (np.int64, 1), (np.int64, 5), (np.int64, -4), (np.bool_, True), (np.bool_, False)]) @pytest.mark.parametrize('periods', [-35, 28, 90]) def test_fancy_1d(fill_value, dtype, periods): arr = np.random.random((1, 1, 50, 1)).astype(dtype) compare(arr, axis=2, periods=periods, fill_value=fill_value) arr = np.random.random((70, 1, 1, 1)).astype(dtype) compare(arr, axis=0, periods=periods, fill_value=fill_value) arr = np.random.random((1, 1, 1, 80)).astype(dtype) compare(arr, axis=3, periods=periods, fill_value=fill_value) @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard @pytest.mark.parametrize('dtype, fill_value', [(np.float32, 0.0), (np.float32, 5.3), (np.float32, -5.5), (np.float32, np.nan), (np.float64, 0.0), (np.float64, 5.3), (np.float64, -5.5), (np.float64, np.nan), (np.int32, 0), (np.int32, 1), (np.int32, 5), (np.int32, -4), (np.int64, 0), (np.int64, 1), (np.int64, 5), (np.int64, -4), (np.bool_, True), (np.bool_, False)]) @pytest.mark.parametrize('axis', [0, 1]) @pytest.mark.parametrize('periods', [-24, 27, -35, 28, 100]) def test_2d(fill_value, dtype, axis, periods): arr = np.random.random((30, 40)).astype(dtype) compare(arr, axis=axis, periods=periods, fill_value=fill_value) @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard @pytest.mark.parametrize('dtype, fill_value', [(np.float32, 0.0), (np.float32, 5.3), (np.float32, -5.5), (np.float32, np.nan), (np.float64, 0.0), (np.float64, 5.3), (np.float64, -5.5), (np.float64, np.nan), (np.int32, 0), (np.int32, 1), (np.int32, 5), (np.int32, -4), (np.int64, 0), (np.int64, 1), (np.int64, 5), (np.int64, -4), (np.bool_, True), (np.bool_, False)]) @pytest.mark.parametrize('axis', [0, 1, 2, 3]) @pytest.mark.parametrize('periods', [-30, 30, -45, 55]) def test_4d(fill_value, dtype, axis, periods): arr = np.random.random((30, 40, 50, 60)).astype(dtype) compare(arr, axis=axis, periods=periods, fill_value=fill_value)