source_code/SegMamba/monai/transforms/adaptors.py

# Copyright (c) MONAI Consortium
# 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.
"""
How to use the adaptor function
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The key to using 'adaptor' lies in understanding the function that want to
adapt. The 'inputs' and 'outputs' parameters take either strings, lists/tuples
of strings or a dictionary mapping strings, depending on call signature of the
function being called.

The adaptor function is written to minimise the cognitive load on the caller.
There should be a minimal number of cases where the caller has to set anything
on the input parameter, and for functions that return a single value, it is
only necessary to name the dictionary keyword to which that value is assigned.

Use of `outputs`
----------------

`outputs` can take either a string, a list/tuple of string or a dict of string
to string, depending on what the transform being adapted returns:

    - If the transform returns a single argument, then outputs can be supplied a
      string that indicates what key to assign the return value to in the
      dictionary
    - If the transform returns a list/tuple of values, then outputs can be supplied
      a list/tuple of the same length. The strings in outputs map the return value
      at the corresponding position to a key in the dictionary
    - If the transform returns a dictionary of values, then outputs must be supplied
      a dictionary that maps keys in the function's return dictionary to the
      dictionary being passed between functions

Note, the caller is free to use a more complex way of specifying the outputs
parameter than is required. The following are synonymous and will be treated
identically:

.. code-block:: python

   # single argument
   adaptor(MyTransform(), 'image')
   adaptor(MyTransform(), ['image'])
   adaptor(MyTransform(), {'image': 'image'})

   # multiple arguments
   adaptor(MyTransform(), ['image', 'label'])
   adaptor(MyTransform(), {'image': 'image', 'label': 'label'})

Use of `inputs`
---------------

`inputs` can usually be omitted when using `adaptor`. It is only required when a
the function's parameter names do not match the names in the dictionary that is
used to chain transform calls.

.. code-block:: python

    class MyTransform1:
        def __call__(self, image):
            # do stuff to image
            return image + 1


    class MyTransform2:
        def __call__(self, img_dict):
            # do stuff to image
            img_dict["image"] += 1
            return img_dict


    xform = Compose([adaptor(MyTransform1(), "image"), MyTransform2()])
    d = {"image": 1}
    print(xform(d))

    >>> {'image': 3}

.. code-block:: python

    class MyTransform3:
        def __call__(self, img_dict):
            # do stuff to image
            img_dict["image"] -= 1
            img_dict["segment"] = img_dict["image"]
            return img_dict


    class MyTransform4:
        def __call__(self, img, seg):
            # do stuff to image
            img -= 1
            seg -= 1
            return img, seg


    xform = Compose([MyTransform3(), adaptor(MyTransform4(), ["img", "seg"], {"image": "img", "segment": "seg"})])
    d = {"image": 1}
    print(xform(d))

    >>> {'image': 0, 'segment': 0, 'img': -1, 'seg': -1}

Inputs:

- dictionary in: None | Name maps
- params in (match): None | Name list | Name maps
- params in (mismatch): Name maps
- params & `**kwargs` (match) : None | Name maps
- params & `**kwargs` (mismatch) : Name maps

Outputs:

- dictionary out: None | Name maps
- list/tuple out: list/tuple
- variable out: string

"""

from __future__ import annotations

from typing import Callable

from monai.utils import export as _monai_export

__all__ = ["adaptor", "apply_alias", "to_kwargs", "FunctionSignature"]


@_monai_export("monai.transforms")
def adaptor(function, outputs, inputs=None):

    def must_be_types_or_none(variable_name, variable, types):
        if variable is not None:
            if not isinstance(variable, types):
                raise TypeError(f"'{variable_name}' must be None or one of {types} but is {type(variable)}")

    def must_be_types(variable_name, variable, types):
        if not isinstance(variable, types):
            raise TypeError(f"'{variable_name}' must be one of {types} but is {type(variable)}")

    def map_names(ditems, input_map):
        return {input_map(k, k): v for k, v in ditems.items()}

    def map_only_names(ditems, input_map):
        return {v: ditems[k] for k, v in input_map.items()}

    def _inner(ditems):
        sig = FunctionSignature(function)

        if sig.found_kwargs:
            must_be_types_or_none("inputs", inputs, (dict,))
            # we just forward all arguments unless we have been provided an input map
            if inputs is None:
                dinputs = dict(ditems)
            else:
                # dict
                dinputs = map_names(ditems, inputs)

        else:
            # no **kwargs
            # select only items from the method signature
            dinputs = {k: v for k, v in ditems.items() if k in sig.non_var_parameters}
            must_be_types_or_none("inputs", inputs, (str, list, tuple, dict))
            if inputs is None:
                pass
            elif isinstance(inputs, str):
                if len(sig.non_var_parameters) != 1:
                    raise ValueError("if 'inputs' is a string, function may only have a single non-variadic parameter")
                dinputs = {inputs: ditems[inputs]}
            elif isinstance(inputs, (list, tuple)):
                dinputs = {k: dinputs[k] for k in inputs}
            else:
                # dict
                dinputs = map_only_names(ditems, inputs)

        ret = function(**dinputs)

        # now the mapping back to the output dictionary depends on outputs and what was returned from the function
        op = outputs
        if isinstance(ret, dict):
            must_be_types_or_none("outputs", op, (dict,))
            if op is not None:
                ret = {v: ret[k] for k, v in op.items()}
        elif isinstance(ret, (list, tuple)):
            if len(ret) == 1:
                must_be_types("outputs", op, (str, list, tuple))
            else:
                must_be_types("outputs", op, (list, tuple))

            if isinstance(op, str):
                op = [op]

            if len(ret) != len(outputs):
                raise ValueError("'outputs' must have the same length as the number of elements that were returned")

            ret = dict(zip(op, ret))
        else:
            must_be_types("outputs", op, (str, list, tuple))
            if isinstance(op, (list, tuple)):
                if len(op) != 1:
                    raise ValueError("'outputs' must be of length one if it is a list or tuple")
                op = op[0]
            ret = {op: ret}

        ditems = dict(ditems)
        for k, v in ret.items():
            ditems[k] = v

        return ditems

    return _inner


@_monai_export("monai.transforms")
def apply_alias(fn, name_map):

    def _inner(data):
        # map names
        pre_call = dict(data)
        for _from, _to in name_map.items():
            pre_call[_to] = pre_call.pop(_from)

        # execute
        post_call = fn(pre_call)

        # map names back
        for _from, _to in name_map.items():
            post_call[_from] = post_call.pop(_to)

        return post_call

    return _inner


@_monai_export("monai.transforms")
def to_kwargs(fn):

    def _inner(data):
        return fn(**data)

    return _inner


class FunctionSignature:

    def __init__(self, function: Callable) -> None:
        import inspect

        sfn = inspect.signature(function)
        self.found_args = False
        self.found_kwargs = False
        self.defaults = {}
        self.non_var_parameters = set()
        for p in sfn.parameters.values():
            if p.kind is inspect.Parameter.VAR_POSITIONAL:
                self.found_args = True
            if p.kind is inspect.Parameter.VAR_KEYWORD:
                self.found_kwargs = True
            else:
                self.non_var_parameters.add(p.name)
                self.defaults[p.name] = p.default is not p.empty

    def __repr__(self) -> str:
        s = "<class 'FunctionSignature': found_args={}, found_kwargs={}, defaults={}"
        return s.format(self.found_args, self.found_kwargs, self.defaults)

    def __str__(self) -> str:
        return self.__repr__()