Source code for mindspore.ops.composite.array_ops

# Copyright 2020-2021 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""array Operations."""
from mindspore.ops.composite.multitype_ops import _constexpr_utils as const_utils
from mindspore.common import dtype as mstype
from mindspore.common._register_for_tensor import tensor_operator_registry
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from mindspore.ops.primitive import constexpr
from mindspore.ops import functional as F
from .. import operations as P


@constexpr
def _check_is_int(arg_value, arg_name, op_name):
    arg_value = validator.check_is_int(arg_value, arg_name, op_name)
    return arg_value


@constexpr
def _check_positive_int(arg_value, arg_name, op_name):
    arg_value = validator.check_positive_int(arg_value, arg_name, op_name)
    return arg_value


@constexpr
def _check_axis_range(arg_value, limit, arg_name, op_name):
    arg_value = validator.check_int_range(arg_value, -limit, limit, Rel.INC_LEFT, arg_name, op_name)
    return arg_value


@constexpr
def _cal_repeat_dims(x_rank, rep, expand_axis):
    rep_dims = [1] * (x_rank + 1)
    rep_dims[expand_axis] = rep
    return tuple(rep_dims)


@constexpr
def _cal_reshape(x_shape, rep, axis):
    x_reshape = list(x_shape)
    x_reshape[axis] *= rep
    return tuple(x_reshape)


[docs]def repeat_elements(x, rep, axis=0): """ Repeat elements of a tensor along an axis, like np.repeat. Args: x (Tensor): The tensor to repeat values for. Must be of type: float16, float32, int8, uint8, int16, int32, or int64. rep (int): The number of times to repeat, must be positive, required. axis (int): The axis along which to repeat, default 0. Outputs: One tensor with values repeated along the specified axis. If x has shape (s1, s2, ..., sn) and axis is i, the output will have shape (s1, s2, ..., si * rep, ..., sn). The output type will be the same as the type of `x`. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> # case 1 : repeat on axis 0 >>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32) >>> output = ops.repeat_elements(x, rep = 2, axis = 0) >>> print(output) [[0 1 2] [0 1 2] [3 4 5] [3 4 5]] >>> # case 2 : repeat on axis 1 >>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32) >>> output = ops.repeat_elements(x, rep = 2, axis = 1) >>> print(output) [[0 0 1 1 2 2] [3 3 4 4 5 5]] """ const_utils.check_type_valid(F.dtype(x), mstype.number_type, 'input x') rep = _check_positive_int(rep, "rep", "repeat_elements") axis = _check_is_int(axis, "axis", "repeat_elements") shape_op = P.Shape() rank_op = P.Rank() tile_op = P.Tile() expand_dims_op = P.ExpandDims() reshape_op = P.Reshape() x_rank = rank_op(x) axis = _check_axis_range(axis, x_rank, "axis", "repeat_elements") expand_axis = axis + 1 x_expand = expand_dims_op(x, expand_axis) rep_dims = _cal_repeat_dims(x_rank, rep, expand_axis) x_expand = tile_op(x_expand, rep_dims) x_shape = shape_op(x) x_reshape = _cal_reshape(x_shape, rep, axis) x_rep = reshape_op(x_expand, x_reshape) return x_rep
tensor_operator_registry.register('repeat_elements', repeat_elements) @constexpr def _check_sequence_mask_input_len(input_shape, prim_name=None): msg_prefix = f"For '{prim_name}', the" if prim_name else "The" if not input_shape: raise ValueError(f"{msg_prefix} input_shape should be greater than 0, but got {input_shape}.") # broadcast only supports 7d shape shape_size = len(input_shape) if shape_size >= 7: raise ValueError(f"{msg_prefix} dimension of input_shape should be less than 7, but got {shape_size}d.")
[docs]def sequence_mask(lengths, maxlen=None, prim_name='sequence_mask'): """ Returns a mask tensor representing the first N positions of each cell. If lengths has shape [d_1, d_2, ..., d_n], then the resulting tensor mask has type dtype and shape [d_1, d_2, ..., d_n, maxlen], with mask[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n]) Inputs: - **lengths** (Tensor) - Tensor to calculate the mask for. All values in this tensor should be less than or equal to `maxlen`. Values greater than `maxlen` will be treated as `maxlen`. Must be type int32 or int64. - **maxlen** (int) - size of the last dimension of returned tensor. Must be positive and same type as elements in `lengths`. Default is None. Outputs: One mask tensor of shape lengths.shape + (maxlen,). Raises: TypeError: If `lengths` is not a Tensor. TypeError: If `maxlen` is not an int. TypeError: If dtype of `lengths` is neither int32 nor int64. Supported Platforms: ``GPU`` Examples: >>> # case 1: When maxlen is assigned >>> x = Tensor(np.array([1, 2, 3, 4])) >>> output = ops.sequence_mask(x, 5) >>> print(output) [[ True False False False False] [ True True False False False] [ True True True False False] [ True True True True False]] >>> # case 2: When there is 0 in x >>> x = Tensor(np.array([[1, 3], [2, 0]])) >>> output = ops.sequence_mask(x, 5) >>> print(output) [[[ True False False False False] [ True True True False False]] [[ True True False False False] [False False False False False]]] >>> # case 3: when the maxlen is not assigned >>> x = Tensor(np.array([[1, 3], [2, 4]])) >>> output = ops.sequence_mask(x) >>> print(output) [[[ True False False False ] [ True True True False ]] [[ True True False False ] [ True True True True ]]] """ argmax_op = P.ArgMaxWithValue() reshape_op = P.Reshape() range_op = P.Range() expand_op = P.ExpandDims() cast_op = P.Cast() shape_op = P.Shape() to_tensor_op = P.ScalarToArray() const_utils.check_type_valid(F.dtype(lengths), [mstype.int64, mstype.int32], 'lengths') _check_sequence_mask_input_len(shape_op(lengths), prim_name) if maxlen is None: flatten_data = reshape_op(lengths, (-1,)) flatten_data = cast_op(flatten_data, mstype.float32) _, value = argmax_op(flatten_data) maxlen = cast_op(value, mstype.int32) else: maxlen = _check_positive_int(maxlen, "maxlen", "sequence_mask") maxlen = to_tensor_op(maxlen) range_vector = range_op(to_tensor_op(0), maxlen , to_tensor_op(1)) mask = expand_op(lengths, -1) result = range_vector < mask return result