forked from mindspore-Ecosystem/mindspore
145 lines
6.0 KiB
Python
145 lines
6.0 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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from functools import partial
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from typing import Tuple, List
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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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import numpy as np
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import pytest
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class Rolling(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, window: int, min_periods: int, center: bool, axis: int, closed: str,
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method: str):
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"""Initialize Sort"""
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self.window = validator.check_value_type("window", window, [int], self.name)
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self.min_periods = validator.check_value_type("min_periods", min_periods, [int], self.name)
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self.center = validator.check_value_type("center", center, [bool], self.name)
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self.axis = validator.check_value_type("axis", axis, [int], self.name)
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self.closed = validator.check_value_type("closed", closed, [str], self.name)
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self.method = validator.check_value_type("method", method, [str], self.name)
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self.init_prim_io_names(inputs=['x'], outputs=['output'])
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def __infer__(self, x):
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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):
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validator.check_tensor_dtype_valid(x_dtype, [mstype.float32, mstype.float64, mstype.int32, mstype.int64],
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self.name, True)
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return x_dtype
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class RollingNet(nn.Cell):
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def __init__(self, window: int, min_periods: int, center: bool, axis: int, closed: str,
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method: str):
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super(RollingNet, self).__init__()
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self.rolling = Rolling(window, min_periods, center, axis, closed, method)
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def construct(self, x):
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return self.rolling(x)
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def get_window_bounds(num_values: int, window_size: int, center: bool, closed: str = 'right') -> Tuple[List, List]:
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assert closed in {'left', 'both', 'right', 'neither'}
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offset = (window_size - 1) // 2 if center else 0
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end = np.arange(offset + 1, num_values + 1 + offset, dtype=np.int64)
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start = end - window_size
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if closed in {'left', 'both'}:
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start -= 1
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if closed in {'left', 'neither'}:
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end -= 1
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end = np.clip(end, 0, num_values)
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start = np.clip(start, 0, num_values)
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return list(start), list(end)
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def numpy_rolling(array: np.ndarray, window: int, min_periods: int, center: bool, axis: int, closed: str,
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method: str) -> np.ndarray:
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assert window > 0
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assert 0 < min_periods <= window
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assert axis in range(-array.ndim, array.ndim)
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reduce_map = {'max': np.max, 'min': np.min, 'mean': np.mean, 'sum': np.sum, 'std': partial(np.std, ddof=1),
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'var': partial(np.var, ddof=1)}
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assert method in reduce_map
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size = array.shape[axis]
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start, end = get_window_bounds(size, window, center, closed)
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rolling_indices = [[slice(None)] * array.ndim for _ in range(len(start))]
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for i, j, indice in zip(start, end, rolling_indices):
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indice[axis] = None if j - i < min_periods else slice(i, j)
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# print(f'i={i}, j={j}, index={index}, indice={rolling_indices[index][axis]}')
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shape = list(array.shape)
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shape[axis] = 1
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nan_array = np.empty(shape)
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if array.dtype == np.float32 or array.dtype == np.float64:
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nan_array[:] = np.nan
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elif array.dtype == np.int32 or array.dtype == np.int64:
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nan_array[:] = 0
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arrays = [
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nan_array.copy() if not indice[axis]
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else reduce_map[method](array[tuple(indice)], axis=axis, keepdims=True).reshape(shape)
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for indice in rolling_indices]
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return np.stack(arrays, axis=axis).reshape(array.shape).astype(array.dtype)
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@pytest.mark.parametrize('shape', [(10, 8, 15, 7), (5, 3, 8, 10)])
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@pytest.mark.parametrize('dtype', [np.float32, np.float64, np.int32, np.int64])
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@pytest.mark.parametrize('window, min_periods', [(3, 3), (5, 3)])
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@pytest.mark.parametrize('center', [True, False])
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@pytest.mark.parametrize('axis', [2, 3, -1])
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@pytest.mark.parametrize('closed', ['left', 'both', 'right', 'neither'])
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@pytest.mark.parametrize('method', ['max', 'min', 'mean', 'sum', 'std', 'var'])
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def test_two_way(shape: List[int], dtype, window: int, min_periods: int, center: bool, axis: int, closed: str,
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method: str) -> np.ndarray:
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if dtype in (np.int32, np.int64):
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arr = np.random.randint(0, 100, size=shape)
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else:
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arr = np.random.random(shape).astype(dtype)
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expect_result = numpy_rolling(arr, window=window, min_periods=min_periods, center=center, axis=axis, closed=closed,
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method=method)
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rolling = RollingNet(window=window, min_periods=min_periods, center=center, axis=axis, closed=closed,
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method=method)
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actual_result = rolling(Tensor(arr)).asnumpy()
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print('arr: \n', arr, arr.dtype, arr.shape)
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print('np: \n', expect_result, expect_result.dtype, expect_result.shape)
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print('mine: \n', actual_result, actual_result.dtype, actual_result.shape)
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print(f'center: {center}, axis: {axis}, method: {method}')
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assert np.allclose(expect_result, actual_result, equal_nan=True)
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