Source code for monai.transforms.compose

# Copyright 2020 - 2021 MONAI Consortium
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# you may not use this file except in compliance with the License.
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A collection of generic interfaces for MONAI transforms.

import warnings
from typing import Any, Callable, Mapping, Optional, Sequence, Union

import numpy as np

from monai.transforms.inverse import InvertibleTransform

# For backwards compatibility (so this still works: from monai.transforms.compose import MapTransform)
from monai.transforms.transform import (  # noqa: F401
from monai.utils import MAX_SEED, ensure_tuple, get_seed
from monai.utils.enums import TraceKeys

__all__ = ["Compose", "OneOf"]

[docs]class Compose(Randomizable, InvertibleTransform): """ ``Compose`` provides the ability to chain a series of callables together in a sequential manner. Each transform in the sequence must take a single argument and return a single value. ``Compose`` can be used in two ways: #. With a series of transforms that accept and return a single ndarray / tensor / tensor-like parameter. #. With a series of transforms that accept and return a dictionary that contains one or more parameters. Such transforms must have pass-through semantics that unused values in the dictionary must be copied to the return dictionary. It is required that the dictionary is copied between input and output of each transform. If some transform takes a data item dictionary as input, and returns a sequence of data items in the transform chain, all following transforms will be applied to each item of this list if `map_items` is `True` (the default). If `map_items` is `False`, the returned sequence is passed whole to the next callable in the chain. For example: A `Compose([transformA, transformB, transformC], map_items=True)(data_dict)` could achieve the following patch-based transformation on the `data_dict` input: #. transformA normalizes the intensity of 'img' field in the `data_dict`. #. transformB crops out image patches from the 'img' and 'seg' of `data_dict`, and return a list of three patch samples:: {'img': 3x100x100 data, 'seg': 1x100x100 data, 'shape': (100, 100)} applying transformB ----------> [{'img': 3x20x20 data, 'seg': 1x20x20 data, 'shape': (20, 20)}, {'img': 3x20x20 data, 'seg': 1x20x20 data, 'shape': (20, 20)}, {'img': 3x20x20 data, 'seg': 1x20x20 data, 'shape': (20, 20)},] #. transformC then randomly rotates or flips 'img' and 'seg' of each dictionary item in the list returned by transformB. The composed transforms will be set the same global random seed if user called `set_determinism()`. When using the pass-through dictionary operation, you can make use of :class:`monai.transforms.adaptors.adaptor` to wrap transforms that don't conform to the requirements. This approach allows you to use transforms from otherwise incompatible libraries with minimal additional work. Note: In many cases, Compose is not the best way to create pre-processing pipelines. Pre-processing is often not a strictly sequential series of operations, and much of the complexity arises when a not-sequential set of functions must be called as if it were a sequence. Example: images and labels Images typically require some kind of normalization that labels do not. Both are then typically augmented through the use of random rotations, flips, and deformations. Compose can be used with a series of transforms that take a dictionary that contains 'image' and 'label' entries. This might require wrapping `torchvision` transforms before passing them to compose. Alternatively, one can create a class with a `__call__` function that calls your pre-processing functions taking into account that not all of them are called on the labels. """ def __init__( self, transforms: Optional[Union[Sequence[Callable], Callable]] = None, map_items: bool = True, unpack_items: bool = False, ) -> None: if transforms is None: transforms = [] self.transforms = ensure_tuple(transforms) self.map_items = map_items self.unpack_items = unpack_items self.set_random_state(seed=get_seed())
[docs] def set_random_state(self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None) -> "Compose": super().set_random_state(seed=seed, state=state) for _transform in self.transforms: if not isinstance(_transform, Randomizable): continue _transform.set_random_state(seed=self.R.randint(MAX_SEED, dtype="uint32")) return self
[docs] def randomize(self, data: Optional[Any] = None) -> None: for _transform in self.transforms: if not isinstance(_transform, Randomizable): continue try: _transform.randomize(data) except TypeError as type_error: tfm_name: str = type(_transform).__name__ warnings.warn( f'Transform "{tfm_name}" in Compose not randomized\n{tfm_name}.{type_error}.', RuntimeWarning )
[docs] def flatten(self): """Return a Composition with a simple list of transforms, as opposed to any nested Compositions. e.g., `t1 = Compose([x, x, x, x, Compose([Compose([x, x]), x, x])]).flatten()` will result in the equivalent of `t1 = Compose([x, x, x, x, x, x, x, x])`. """ new_transforms = [] for t in self.transforms: if isinstance(t, Compose) and not isinstance(t, OneOf): new_transforms += t.flatten().transforms else: new_transforms.append(t) return Compose(new_transforms)
def __len__(self): """Return number of transformations.""" return len(self.flatten().transforms)
[docs] def __call__(self, input_): for _transform in self.transforms: input_ = apply_transform(_transform, input_, self.map_items, self.unpack_items) return input_
[docs] def inverse(self, data): invertible_transforms = [t for t in self.flatten().transforms if isinstance(t, InvertibleTransform)] if not invertible_transforms: warnings.warn("inverse has been called but no invertible transforms have been supplied") # loop backwards over transforms for t in reversed(invertible_transforms): data = apply_transform(t.inverse, data, self.map_items, self.unpack_items) return data
[docs]class OneOf(Compose): """ ``OneOf`` provides the ability to randomly choose one transform out of a list of callables with pre-defined probabilities for each. Args: transforms: sequence of callables. weights: probabilities corresponding to each callable in transforms. Probabilities are normalized to sum to one. ``OneOf`` inherits from ``Compose`` and uses args ``map_items`` and ``unpack_items`` in the same way. """ def __init__( self, transforms: Optional[Union[Sequence[Callable], Callable]] = None, weights: Optional[Union[Sequence[float], float]] = None, map_items: bool = True, unpack_items: bool = False, ) -> None: super().__init__(transforms, map_items, unpack_items) if len(self.transforms) == 0: weights = [] elif weights is None or isinstance(weights, float): weights = [1.0 / len(self.transforms)] * len(self.transforms) if len(weights) != len(self.transforms): raise AssertionError("transforms and weights should be same size if both specified as sequences.") self.weights = ensure_tuple(self._normalize_probabilities(weights)) def _normalize_probabilities(self, weights): if len(weights) == 0: return weights weights = np.array(weights) if np.any(weights < 0): raise AssertionError("Probabilities must be greater than or equal to zero.") if np.all(weights == 0): raise AssertionError("At least one probability must be greater than zero.") weights = weights / weights.sum() return list(weights)
[docs] def flatten(self): transforms = [] weights = [] for t, w in zip(self.transforms, self.weights): # if nested, probability is the current weight multiplied by the nested weights, # and so on recursively if isinstance(t, OneOf): tr = t.flatten() for t_, w_ in zip(tr.transforms, tr.weights): transforms.append(t_) weights.append(w_ * w) else: transforms.append(t) weights.append(w) return OneOf(transforms, weights, self.map_items, self.unpack_items)
def __call__(self, data): if len(self.transforms) == 0: return data index = self.R.multinomial(1, self.weights).argmax() _transform = self.transforms[index] data = apply_transform(_transform, data, self.map_items, self.unpack_items) # if the data is a mapping (dictionary), append the OneOf transform to the end if isinstance(data, Mapping): for key in data.keys(): if self.trace_key(key) in data: self.push_transform(data, key, extra_info={"index": index}) return data
[docs] def inverse(self, data): if len(self.transforms) == 0: return data if not isinstance(data, Mapping): raise RuntimeError("Inverse only implemented for Mapping (dictionary) data") # loop until we get an index and then break (since they'll all be the same) index = None for key in data.keys(): if self.trace_key(key) in data: # get the index of the applied OneOf transform index = self.get_most_recent_transform(data, key)[TraceKeys.EXTRA_INFO]["index"] # and then remove the OneOf transform self.pop_transform(data, key) if index is None: raise RuntimeError("No invertible transforms have been applied") # if applied transform is not InvertibleTransform, throw error _transform = self.transforms[index] if not isinstance(_transform, InvertibleTransform): raise RuntimeError(f"Applied OneOf transform is not invertible (applied index: {index}).") # apply the inverse return _transform.inverse(data)