mindspore2022/mindspore/dataset/transforms/validators.py

# Copyright 2019 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.
# ==============================================================================
"""Validators for TensorOps.
"""
from functools import wraps
import numpy as np

from mindspore._c_expression import typing
from ..core.validator_helpers import parse_user_args, type_check, check_pos_int64, check_value, check_positive, \
    check_tensor_op

# POS_INT_MIN is used to limit values from starting from 0
POS_INT_MIN = 1
UINT8_MAX = 255
UINT8_MIN = 0
UINT32_MAX = 4294967295
UINT32_MIN = 0
UINT64_MAX = 18446744073709551615
UINT64_MIN = 0
INT32_MAX = 2147483647
INT32_MIN = -2147483648
INT64_MAX = 9223372036854775807
INT64_MIN = -9223372036854775808
FLOAT_MAX_INTEGER = 16777216
FLOAT_MIN_INTEGER = -16777216
DOUBLE_MAX_INTEGER = 9007199254740992
DOUBLE_MIN_INTEGER = -9007199254740992


def check_fill_value(method):
    """Wrapper method to check the parameters of fill_value."""

    @wraps(method)
    def new_method(self, *args, **kwargs):
        [fill_value], _ = parse_user_args(method, *args, **kwargs)
        type_check(fill_value, (str, float, bool, int, bytes), "fill_value")

        return method(self, *args, **kwargs)

    return new_method


def check_one_hot_op(method):
    """Wrapper method to check the parameters of one_hot_op."""

    @wraps(method)
    def new_method(self, *args, **kwargs):
        [num_classes, smoothing_rate], _ = parse_user_args(method, *args, **kwargs)

        type_check(num_classes, (int,), "num_classes")
        check_positive(num_classes)

        if smoothing_rate is not None:
            check_value(smoothing_rate, [0., 1.], "smoothing_rate")

        return method(self, *args, **kwargs)

    return new_method


def check_num_classes(method):
    """Wrapper method to check the parameters of number of classes."""

    @wraps(method)
    def new_method(self, *args, **kwargs):
        [num_classes], _ = parse_user_args(method, *args, **kwargs)

        type_check(num_classes, (int,), "num_classes")
        check_positive(num_classes)

        return method(self, *args, **kwargs)

    return new_method


def check_de_type(method):
    """Wrapper method to check the parameters of data type."""

    @wraps(method)
    def new_method(self, *args, **kwargs):
        [data_type], _ = parse_user_args(method, *args, **kwargs)

        type_check(data_type, (typing.Type,), "data_type")

        return method(self, *args, **kwargs)

    return new_method


def check_slice_op(method):
    """Wrapper method to check the parameters of slice."""

    @wraps(method)
    def new_method(self, *args):
        for _, arg in enumerate(args):
            type_check(arg, (int, slice, list, type(None), type(Ellipsis)), "arg")
            if isinstance(arg, list):
                for a in arg:
                    type_check(a, (int,), "a")
        return method(self, *args)

    return new_method


def check_mask_op(method):
    """Wrapper method to check the parameters of mask."""

    @wraps(method)
    def new_method(self, *args, **kwargs):
        [operator, constant, dtype], _ = parse_user_args(method, *args, **kwargs)

        from .c_transforms import Relational
        type_check(operator, (Relational,), "operator")
        type_check(constant, (str, float, bool, int, bytes), "constant")
        type_check(dtype, (typing.Type,), "dtype")

        return method(self, *args, **kwargs)

    return new_method


def check_pad_end(method):
    """Wrapper method to check the parameters of PadEnd."""

    @wraps(method)
    def new_method(self, *args, **kwargs):

        [pad_shape, pad_value], _ = parse_user_args(method, *args, **kwargs)

        if pad_value is not None:
            type_check(pad_value, (str, float, bool, int, bytes), "pad_value")
        type_check(pad_shape, (list,), "pad_end")

        for dim in pad_shape:
            if dim is not None:
                if isinstance(dim, int):
                    check_pos_int64(dim)
                else:
                    raise TypeError("a value in the list is not an integer.")

        return method(self, *args, **kwargs)

    return new_method


def check_concat_type(method):
    """Wrapper method to check the parameters of concatenation op."""

    @wraps(method)
    def new_method(self, *args, **kwargs):

        [axis, prepend, append], _ = parse_user_args(method, *args, **kwargs)

        if axis is not None:
            type_check(axis, (int,), "axis")
            if axis not in (0, -1):
                raise ValueError("only 1D concatenation supported.")

        if prepend is not None:
            type_check(prepend, (np.ndarray,), "prepend")
            if len(prepend.shape) != 1:
                raise ValueError("can only prepend 1D arrays.")

        if append is not None:
            type_check(append, (np.ndarray,), "append")
            if len(append.shape) != 1:
                raise ValueError("can only append 1D arrays.")

        return method(self, *args, **kwargs)

    return new_method


def check_random_transform_ops(method):
    """Wrapper method to check the parameters of RandomChoice, RandomApply and Compose."""

    @wraps(method)
    def new_method(self, *args, **kwargs):
        arg_list, _ = parse_user_args(method, *args, **kwargs)
        type_check(arg_list[0], (list,), "op_list")
        if not arg_list[0]:
            raise ValueError("op_list can not be empty.")
        for ind, op in enumerate(arg_list[0]):
            check_tensor_op(op, "op_list[{0}]".format(ind))
        if len(arg_list) == 2:  # random apply takes an additional arg
            type_check(arg_list[1], (float, int), "prob")
            check_value(arg_list[1], (0, 1), "prob")
        return method(self, *args, **kwargs)

    return new_method