Source code for

# 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.
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import warnings
from import Callable
from copy import deepcopy
from typing import TYPE_CHECKING, Any

import numpy as np
import torch

from monai.config.type_definitions import NdarrayOrTensor
from import DataLoader
from import Dataset
from import decollate_batch, pad_list_data_collate
from monai.transforms.compose import Compose
from monai.transforms.croppad.batch import PadListDataCollate
from monai.transforms.inverse import InvertibleTransform
from import Invertd
from monai.transforms.transform import Randomizable
from monai.transforms.utils_pytorch_numpy_unification import mode, stack
from monai.utils import CommonKeys, PostFix, optional_import

    from tqdm import tqdm

    has_tqdm = True
    tqdm, has_tqdm = optional_import("tqdm", name="tqdm")

__all__ = ["TestTimeAugmentation"]

DEFAULT_POST_FIX = PostFix.meta()

def _identity(x):
    return x

[docs] class TestTimeAugmentation: """ Class for performing test time augmentations. This will pass the same image through the network multiple times. The user passes transform(s) to be applied to each realization, and provided that at least one of those transforms is random, the network's output will vary. Provided that inverse transformations exist for all supplied spatial transforms, the inverse can be applied to each realization of the network's output. Once in the same spatial reference, the results can then be combined and metrics computed. Test time augmentations are a useful feature for computing network uncertainty, as well as observing the network's dependency on the applied random transforms. Reference: Wang et al., Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks, Args: transform: transform (or composed) to be applied to each realization. At least one transform must be of type `RandomizableTrait` (i.e. `Randomizable`, `RandomizableTransform`, or `RandomizableTrait`). . All random transforms must be of type `InvertibleTransform`. batch_size: number of realizations to infer at once. num_workers: how many subprocesses to use for data. inferrer_fn: function to use to perform inference. device: device on which to perform inference. image_key: key used to extract image from input dictionary. orig_key: the key of the original input data in the dict. will get the applied transform information for this input data, then invert them for the expected data with `image_key`. orig_meta_keys: the key of the metadata of original input data, will get the `affine`, `data_shape`, etc. the metadata is a dictionary object which contains: filename, original_shape, etc. if None, will try to construct meta_keys by `{orig_key}_{meta_key_postfix}`. meta_key_postfix: use `key_{postfix}` to fetch the metadata according to the key data, default is `meta_dict`, the metadata is a dictionary object. For example, to handle key `image`, read/write affine matrices from the metadata `image_meta_dict` dictionary's `affine` field. this arg only works when `meta_keys=None`. to_tensor: whether to convert the inverted data into PyTorch Tensor first, default to `True`. output_device: if converted the inverted data to Tensor, move the inverted results to target device before `post_func`, default to "cpu". post_func: post processing for the inverted data, should be a callable function. return_full_data: normally, metrics are returned (mode, mean, std, vvc). Setting this flag to `True` will return the full data. Dimensions will be same size as when passing a single image through `inferrer_fn`, with a dimension appended equal in size to `num_examples` (N), i.e., `[N,C,H,W,[D]]`. progress: whether to display a progress bar. Example: .. code-block:: python model = UNet(...).to(device) transform = Compose([RandAffined(keys, ...), ...]) transform.set_random_state(seed=123) # ensure deterministic evaluation tt_aug = TestTimeAugmentation( transform, batch_size=5, num_workers=0, inferrer_fn=model, device=device ) mode, mean, std, vvc = tt_aug(test_data) """ def __init__( self, transform: InvertibleTransform, batch_size: int, num_workers: int = 0, inferrer_fn: Callable = _identity, device: str | torch.device = "cpu", image_key=CommonKeys.IMAGE, orig_key=CommonKeys.LABEL, nearest_interp: bool = True, orig_meta_keys: str | None = None, meta_key_postfix=DEFAULT_POST_FIX, to_tensor: bool = True, output_device: str | torch.device = "cpu", post_func: Callable = _identity, return_full_data: bool = False, progress: bool = True, ) -> None: self.transform = transform self.batch_size = batch_size self.num_workers = num_workers self.inferrer_fn = inferrer_fn self.device = device self.image_key = image_key self.return_full_data = return_full_data self.progress = progress self._pred_key = CommonKeys.PRED self.inverter = Invertd( keys=self._pred_key, transform=transform, orig_keys=orig_key, orig_meta_keys=orig_meta_keys, meta_key_postfix=meta_key_postfix, nearest_interp=nearest_interp, to_tensor=to_tensor, device=output_device, post_func=post_func, ) # check that the transform has at least one random component, and that all random transforms are invertible self._check_transforms() def _check_transforms(self): """Should be at least 1 random transform, and all random transforms should be invertible.""" ts = [self.transform] if not isinstance(self.transform, Compose) else self.transform.transforms randoms = np.array([isinstance(t, Randomizable) for t in ts]) invertibles = np.array([isinstance(t, InvertibleTransform) for t in ts]) # check at least 1 random if sum(randoms) == 0: warnings.warn( "TTA usually has at least a `Randomizable` transform or `Compose` contains `Randomizable` transforms." ) # check that whenever randoms is True, invertibles is also true for r, i in zip(randoms, invertibles): if r and not i: warnings.warn( f"Not all applied random transform(s) are invertible. Problematic transform: {type(r).__name__}" ) def __call__( self, data: dict[str, Any], num_examples: int = 10 ) -> tuple[NdarrayOrTensor, NdarrayOrTensor, NdarrayOrTensor, float] | NdarrayOrTensor: """ Args: data: dictionary data to be processed. num_examples: number of realizations to be processed and results combined. Returns: - if `return_full_data==False`: mode, mean, std, vvc. The mode, mean and standard deviation are calculated across `num_examples` outputs at each voxel. The volume variation coefficient (VVC) is `std/mean` across the whole output, including `num_examples`. See original paper for clarification. - if `return_full_data==False`: data is returned as-is after applying the `inferrer_fn` and then concatenating across the first dimension containing `num_examples`. This allows the user to perform their own analysis if desired. """ d = dict(data) # check num examples is multiple of batch size if num_examples % self.batch_size != 0: raise ValueError("num_examples should be multiple of batch size.") # generate batch of data of size == batch_size, dataset and dataloader data_in = [deepcopy(d) for _ in range(num_examples)] ds = Dataset(data_in, self.transform) dl = DataLoader(ds, num_workers=self.num_workers, batch_size=self.batch_size, collate_fn=pad_list_data_collate) outs: list = [] for b in tqdm(dl) if has_tqdm and self.progress else dl: # do model forward pass b[self._pred_key] = self.inferrer_fn(b[self.image_key].to(self.device)) outs.extend([self.inverter(PadListDataCollate.inverse(i))[self._pred_key] for i in decollate_batch(b)]) output: NdarrayOrTensor = stack(outs, 0) if self.return_full_data: return output # calculate metrics _mode = mode(output, dim=0) mean = output.mean(0) std = output.std(0) vvc = (output.std() / output.mean()).item() return _mode, mean, std, vvc