inference

nndet/inference/__init__.py

+from nndet.inference.ensembler import BaseEnsemblerType, BaseEnsembler, BoxEnsembler, SegmentationEnsembler
+from nndet.inference.predictor import PredictorType, Predictor
+from nndet.inference.sweeper import SweeperType, Sweeper, BoxSweeper
+from nndet.inference.restore import restore_detection, restore_fmap

nndet/inference/detection/__init__.py

+from nndet.inference.detection.wbc import batched_wbc, wbc
+from nndet.inference.detection.model import batched_nms_model
+from nndet.inference.detection.ensemble import batched_wbc_ensemble, batched_nms_ensemble, \
+    wbc_nms_no_label_ensemble

nndet/inference/detection/ensemble.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+from typing import Tuple
+
+from torch import Tensor
+
+from nndet.detection.boxes import batched_nms, nms
+from nndet.inference.detection import batched_wbc
+
+
+def batched_nms_ensemble(
+        boxes: Tensor,
+        scores: Tensor,
+        labels: Tensor,
+        weights: Tensor,
+        iou_thresh: float,
+        *args, **kwargs,
+        ) -> Tuple[Tensor, Tensor, Tensor]:
+    """
+    Ensemble nms for ensembler (same as batched nms with adjusted signature)
+
+    Args:
+        boxes: predicted boxes
+        scores: predicted scores
+        labels: predicted labels
+        weights: weight per box (ignored in this function)
+        iou_thresh: IoU threshold for nms
+        *args: kept for compatibility
+        **kwargs: kept for compatibility
+
+    Returns:
+        Tensor: boxes
+        Tensor: scores
+        Tensor: labels
+    """
+    keep = batched_nms(boxes=boxes, scores=scores,
+                       idxs=labels, iou_threshold=iou_thresh,
+                       )
+    return boxes[keep], scores[keep], labels[keep]
+
+
+def batched_wbc_ensemble(
+        boxes: Tensor,
+        scores: Tensor,
+        labels: Tensor,
+        weights: Tensor,
+        iou_thresh: float,
+        n_exp_preds: Tensor,
+        score_thresh: float,
+        *args, **kwargs) -> Tuple[Tensor, Tensor, Tensor]:
+    """
+    Ensemble wbc for ensembler (same as batched nms with adjusted signature)
+
+    Args:
+        boxes: predicted boxes
+        scores: predicted scores
+        labels: predicted labels
+        weights: weight per box (ignored in this function)
+        iou_thresh: IoU threshold for nms
+        n_exp_preds: number of expected predictions per box
+        score_thresh: minimum score
+        *args: kept for compatibility
+        **kwargs: kept for compatibility
+
+    Returns:
+        Tensor: boxes
+        Tensor: scores
+        Tensor: labels
+    """
+    boxes, scores, labels = batched_wbc(
+        boxes, scores, labels, weights=weights,
+        n_exp_preds=n_exp_preds,
+        iou_thresh=iou_thresh,
+        score_thresh=score_thresh,
+    )
+    return boxes, scores, labels
+
+
+def wbc_nms_no_label_ensemble(
+        boxes: Tensor,
+        scores: Tensor,
+        labels: Tensor,
+        weights: Tensor,
+        iou_thresh: float,
+        n_exp_preds: Tensor,
+        score_thresh: float,
+        *args, **kwargs) -> Tuple[Tensor, Tensor, Tensor]:
+    """
+    Normal wbc -> nms without class labels
+    This results in a single prediction per position regardless of the class
+
+    Args:
+        boxes: predicted boxes
+        scores: predicted scores
+        labels: predicted labels
+        weights: weight per box (ignored in this function)
+        iou_thresh: IoU threshold for nms
+        n_exp_preds: number of expected predictions per box
+        score_thresh: minimum score
+        *args: kept for compatibility
+        **kwargs: kept for compatibility
+
+    Returns:
+        Tensor: boxes
+        Tensor: scores
+        Tensor: labels
+    """
+    boxes, scores, labels = batched_wbc(
+        boxes, scores, labels, weights=weights,
+        n_exp_preds=n_exp_preds,
+        iou_thresh=iou_thresh,
+        score_thresh=score_thresh,
+    )
+    keep = nms(boxes, scores, iou_thresh)
+    return boxes[keep], scores[keep], labels[keep]

nndet/inference/detection/model.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+from typing import Tuple
+
+from torch import Tensor
+import torch
+
+from nndet.detection.boxes import batched_nms
+
+
+def batched_nms_model(
+        boxes: Tensor,
+        scores: Tensor,
+        labels: Tensor,
+        weights: Tensor,
+        iou_thresh: float,
+        *args, **kwargs,
+        ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
+    """
+    Model nms for ensembler (same as batched nms with adjusted signature)
+
+    Args:
+        boxes: predicted boxes
+        scores: predicted scores
+        labels: predicted labels
+        weights: weight per box
+        iou_thresh: IoU threshold for nms
+        *args: kept for compatibility
+        **kwargs: kept for compatibility
+
+    Returns:
+        Tensor: sorted boxes
+        Tensor: sorted scores (descending)
+        Tensor: sorted labels
+        Tensor: sorted weights
+    """
+    keep = batched_nms(boxes=boxes, scores=scores,
+                       idxs=labels, iou_threshold=iou_thresh,
+                       )
+    return boxes[keep], scores[keep], labels[keep], weights[keep]
+
+
+def batched_weighted_nms_model(
+        boxes: Tensor,
+        scores: Tensor,
+        labels: Tensor,
+        weights: Tensor,
+        iou_thresh: float,
+        *args, **kwargs,
+        ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
+    """
+    Model nms for ensembler (same as batched nms with adjusted signature)
+
+    Args:
+        boxes: predicted boxes
+        scores: predicted scores
+        labels: predicted labels
+        weights: weight per box
+        iou_thresh: IoU threshold for nms
+        *args: kept for compatibility
+        **kwargs: kept for compatibility
+
+    Returns:
+        Tensor: sorted boxes
+        Tensor: sorted scores (descending)
+        Tensor: sorted labels
+        Tensor: sorted weights
+    """
+    new_scores = scores * weights
+    keep = batched_nms(boxes=boxes, scores=new_scores, idxs=labels, iou_threshold=iou_thresh)
+    new_weights = torch.ones_like(weights)
+    return boxes[keep], scores[keep], labels[keep], new_weights[keep]

nndet/inference/detection/postprocessing.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+from typing import Tuple
+
+import torch
+from torch import Tensor
+
+from nndet.detection.boxes import batched_nms, nms
+from nndet.inference.detection import batched_wbc
+
+
+def batched_nms_model(
+        boxes: Tensor,
+        scores: Tensor,
+        labels: Tensor,
+        weights: Tensor,
+        iou_thresh: float,
+        *args, **kwargs,
+        ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
+    keep = batched_nms(boxes=boxes, scores=scores,
+                       idxs=labels, iou_threshold=iou_thresh,
+                       )
+    return boxes[keep], scores[keep], labels[keep], weights[keep]
+
+
+def batched_nms_ensemble(
+        boxes: Tensor,
+        scores: Tensor,
+        labels: Tensor,
+        weights: Tensor,
+        iou_thresh: float,
+        *args, **kwargs,
+        ) -> Tuple[Tensor, Tensor, Tensor]:
+    keep = batched_nms(boxes=boxes, scores=scores,
+                       idxs=labels, iou_threshold=iou_thresh,
+                       )
+    return boxes[keep], scores[keep], labels[keep]
+
+
+def batched_wbc_ensemble(
+        boxes: Tensor,
+        scores: Tensor,
+        labels: Tensor,
+        weights: Tensor,
+        iou_thresh: float,
+        n_exp_preds: Tensor,
+        score_thresh: float,
+        *args, **kwargs) -> Tuple[Tensor, Tensor, Tensor]:
+    boxes, scores, labels = batched_wbc(
+        boxes, scores, labels, weights=weights,
+        n_exp_preds=n_exp_preds,
+        iou_thresh=iou_thresh,
+        score_thresh=score_thresh,
+    )
+    return boxes, scores, labels
+
+
+def wbc_nms_no_label_ensemble(
+        boxes: Tensor,
+        scores: Tensor,
+        labels: Tensor,
+        weights: Tensor,
+        iou_thresh: float,
+        n_exp_preds: Tensor,
+        score_thresh: float,
+        *args, **kwargs) -> Tuple[Tensor, Tensor, Tensor]:
+    boxes, scores, labels = batched_wbc(
+        boxes, scores, labels, weights=weights,
+        n_exp_preds=n_exp_preds,
+        iou_thresh=iou_thresh,
+        score_thresh=score_thresh,
+    )
+    keep = nms(boxes, scores, iou_thresh)
+    return boxes[keep], scores[keep], labels[keep]

nndet/inference/detection/wbc.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+import torch
+
+from torch import Tensor
+from typing import Tuple
+
+from torch._C import device
+
+from nndet.detection.boxes import box_iou, box_area
+
+
+__all__ = ["batched_wbc", "wbc"]
+
+
+def batched_wbc(
+    boxes: Tensor,
+    scores: Tensor,
+    labels: Tensor,
+    weights: Tensor,
+    iou_thresh: float,
+    n_exp_preds: Tensor,
+    score_thresh: float,
+    use_area: bool = False,
+    missing_weight: float = 1.,
+    ) -> Tuple[Tensor, Tensor, Tensor]:
+    """
+    Computed weighted box clustering per class
+
+    Args:
+        boxes: predicted boxes (x1, y1, x2, y2, (z1, z2)) [N, dims * 2]
+        scores: predicted scores [N]
+        labels: predicted labels [N]
+        weights: weight for each box [N] (gaussian weighting of boxes near
+            corners need to be included in this weight)
+        iou_thresh: iou threshold used for clustering boxes
+        n_exp_preds: number of expected predictions per box (computed as the
+            mean number predictions inside the bounding box)
+        score_thresh: minimum score of predictions after clustering
+        use_area: assigns higher weights to larger boxes based on
+            empirical observations indicating an increase in image
+            evidence from larger areas.
+        missing_weight: weight for score dampening when predictions are missing
+
+    Returns:
+        Tensor: clustered boxes
+        Tensor: clustered scores
+        Tensor: labels
+    """
+    clustered_boxes = []
+    clustered_scores = []
+    clustered_labels = []
+    for label in labels.unique():
+        _labels_mask = labels == label
+        _boxes = boxes[_labels_mask]
+        _scores = scores[_labels_mask]
+        _weights = weights[_labels_mask]
+        _n_exp_preds = n_exp_preds[_labels_mask]
+
+        b, s = wbc(_boxes, _scores,
+                   weights=_weights, n_exp_preds=_n_exp_preds,
+                   iou_thresh=iou_thresh, score_thresh=score_thresh,
+                   use_area=use_area,
+                   missing_weight=missing_weight,
+                   )
+
+        clustered_boxes.append(b)
+        clustered_scores.append(s)
+        clustered_labels.append(torch.empty_like(s).fill_(label))
+    if clustered_boxes:
+        return (torch.cat(clustered_boxes, dim=0),
+                torch.cat(clustered_scores, dim=0),
+                torch.cat(clustered_labels, dim=0))
+    else:
+        return (torch.tensor([]).view(-1, boxes.shape[1]),
+                torch.tensor([]).view(-1),
+                torch.tensor([]).view(-1))
+
+
+def wbc(
+    boxes: Tensor,
+    scores: Tensor,
+    weights: Tensor,
+    n_exp_preds: Tensor,
+    iou_thresh: float,
+    score_thresh: float,
+    use_area: bool = True,
+    missing_weight: float = 1.,
+    ) -> Tuple[Tensor, Tensor]:
+    """
+    Weighted box clustering
+
+    Args:
+        boxes: tensor with boxes (x1, y1, x2, y2, (z1, z2))[N, dim * 2]
+        scores: score for each box [N]
+        weights: additional weights for boxes [N]
+        n_exp_preds: expected number of predictions per box
+        iou_thresh: iou threshold for determining clusters of boxes which are
+            combined
+        score_thresh: minimum scores of boxes after consolidation
+        use_area: assigns higher weights to larger boxes based on
+            empirical observations indicating an increase in image
+            evidence from larger areas.
+        missing_weight: weight for score dampening when predictions are missing
+
+    Returns:
+        Tensor: consolidated boxes
+        Tensor: consolidated scores
+    """
+    ious = box_iou(boxes, boxes)
+
+    if use_area:
+        areas = box_area(boxes)
+        weights = weights * areas
+
+    _, idx_pool = torch.sort(scores, descending=True)
+
+    new_boxes, new_scores = [], []
+    while idx_pool.nelement() > 0:
+        # build cluster
+        highest_scoring_id = idx_pool[0]
+        matches = torch.where(ious[highest_scoring_id][idx_pool] > iou_thresh)[0].flatten()
+        box_idx = idx_pool[matches]
+
+        # compute new scores
+        n_expected = n_exp_preds[box_idx].float().mean()
+        new_box, new_score = compute_cluster_consolidation(
+            boxes[box_idx], scores[box_idx],
+            weights=weights[box_idx],
+            ious=ious[highest_scoring_id][box_idx],
+            n_expected=n_expected,
+            n_found=len(box_idx),
+            missing_weight=missing_weight,
+            )
+
+        if new_score > score_thresh:
+            new_boxes.append(new_box)
+            new_scores.append(new_score)
+
+        # get all elements that were not matched and discard all others.
+        non_matches = torch.where(ious[highest_scoring_id][idx_pool] <= iou_thresh)[0].flatten()
+        idx_pool = idx_pool[non_matches]
+    if new_boxes:
+        return torch.stack(new_boxes, dim=0), torch.cat(new_scores, dim=0)
+    else:
+        return torch.tensor([]).view(-1, boxes.shape[1]).to(boxes), torch.tensor([]).view(-1).to(scores)
+
+
+def compute_cluster_consolidation(
+    boxes: Tensor,
+    scores: Tensor,
+    weights: Tensor,
+    ious: Tensor,
+    n_expected: Tensor,
+    n_found: int,
+    missing_weight: float,
+    ) -> Tuple[Tensor, Tensor]:
+    """
+    Consolidate predictions of a single cluster
+
+    Args:
+        boxes: boxes of a single cluster (x1, y1, x2, y2, (z1, z2) [N, dims * 2]
+        scores: scores of a single cluster [N]
+        weights: weights for boxes of a single cluster [N]
+        ious: ious with recard to highest scoring box in a single cluster [N]
+        n_expected: expected number of predictions
+        n_found: number of predictions
+        missing_weight: weight for score dampening when predictions are missing
+
+    Returns:
+        Tensor: new boxes (x1, y1, x2, y2, (z1, z2) [N, dims * 2]
+        Tensor: new scores [N]
+    """
+    # compute new score
+    match_score_weights = ious * weights
+    match_scores = match_score_weights * scores
+
+    n_missing_preds = torch.max(torch.tensor([0.], device=n_expected.device),
+                                (n_expected - n_found).float())
+    denom = match_score_weights.sum() + n_missing_preds * match_score_weights.mean() * missing_weight
+    consolidated_score = match_scores.sum() / denom
+
+    consolidated_boxes = (boxes * match_scores.reshape(-1, 1)).sum(dim=0) / match_scores.sum()
+    return consolidated_boxes, consolidated_score
+
+
+def compute_cluster_consolidation2(
+    boxes: Tensor,
+    scores: Tensor,
+    weights: Tensor,
+    ious: Tensor,
+    n_expected: Tensor,
+    n_found: int,
+    missing_weight: float,
+    ) -> Tuple[Tensor, Tensor]:
+    """
+    Consolidate predictions of a single cluster
+
+    Args:
+        boxes: boxes of a single cluster (x1, y1, x2, y2, (z1, z2) [N, dims * 2]
+        scores: scores of a single cluster [N]
+        weights: weights for boxes of a single cluster [N]
+        ious: ious with recard to highest scoring box in a single cluster [N]
+        n_expected: expected number of predictions
+        n_found: number of predictions
+        missing_weight: weight for score dampening when predictions are missing
+
+    Returns:
+        Tensor: new boxes (x1, y1, x2, y2, (z1, z2) [N, dims * 2]
+        Tensor: new scores [N]
+    """
+    # select num expected predictions from ious & score weihted score
+    topk_score = ious * weights * scores
+    topk_weighted_scores, topk_idx = topk_score.topk(min(len(scores), int(n_expected)))
+
+    boxes = boxes[topk_idx]
+    scores = scores[topk_idx]
+    n_missing_preds = torch.max(torch.tensor([0.], device=n_expected.device),
+                            (n_expected - n_found).float())
+    
+    # weigh predictions with high ious higher, penalty term for missing predictions
+    consolidated_score = scores.mean() * (1 - missing_weight * n_missing_preds / n_expected)
+    consolidated_boxes = (boxes * topk_weighted_scores.reshape(-1, 1)).sum(dim=0) / topk_weighted_scores.sum()
+
+    return consolidated_boxes, consolidated_score

nndet/inference/ensembler/__init__.py

+from nndet.inference.ensembler.base import BaseEnsembler, BaseEnsemblerType, OverlapMap
+from nndet.inference.ensembler.detection import BoxEnsembler
+from nndet.inference.ensembler.segmentation import SegmentationEnsembler

nndet/inference/ensembler/base.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+from os import PathLike
+from pathlib import Path
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union, TypeVar
+
+import torch
+
+from nndet.io.load import save_pickle
+from nndet.utils.tensor import to_numpy
+from nndet.utils.info import maybe_verbose_iterable
+
+
+class BaseEnsembler(ABC):
+    ID = "abstract"
+
+    def __init__(self,
+                 properties: Dict[str, Any],
+                 parameters: Dict[str, Any],
+                 device: Optional[Union[torch.device, str]] = None,
+                 **kwargs):
+        """
+        Base class to containerize and ensemble the predictions of a single case.
+        Call :method:`process_batch` to add batched predictions of a case
+        to the ensembler and :method:`add_model` to signal the next model
+        if multiple models are used.
+
+        Args:
+            properties: properties of the patient/case (e.g. tranpose axes)
+            parameters: parameters for ensembling
+            device: device to use for internal computations
+            **kwargs: parameters for ensembling
+
+        Notes:
+            Call :method:`add_model` before adding predictions.
+        """
+        self.model_current = None
+        self.model_results = {}
+        self.model_weights = {}
+
+        self.properties = properties
+        self.case_result: Optional[Dict] = None
+
+        self.parameters = parameters
+        self.parameters.update(kwargs)
+        
+        if device is None:
+            self.device = torch.device("cpu")
+        elif isinstance(device, str):
+            self.device = torch.device(device)
+        elif isinstance(device, torch.device):
+            self.device = device
+        else:
+            raise ValueError(f"Wrong type {type(device)} for device argument.")
+
+    @classmethod
+    def from_case(cls,
+                  case: Dict,
+                  properties: Optional[Dict] = None,
+                  parameters: Optional[Dict] = None,
+                  **kwargs,
+                  ):
+        """
+        Primary way to instantiate this class. Automatically extracts all
+        properties and uses a default set of parameters for ensembling.
+
+        Args:
+            case: case which is predicted
+            properties: Additional properties. Defaults to None.
+            parameters: Additional parameters. Defaults to None.
+        """
+        return cls(properties=properties, parameters=parameters, **kwargs)
+
+    def add_model(self,
+                  name: Optional[str] = None,
+                  model_weight: Optional[float] = None,
+                  ) -> str:
+        """
+        This functions signales the ensembler to add a new model for internal
+        processing
+
+        Args:
+            name: Name of the model. If None, uses counts the models.
+            model_weight: Optional weight for this model. Defaults to None.
+        """
+        if name is None:
+            name = len(self.model_weights) + 1
+        if name in self.model_results:
+            raise ValueError(f"Invalid model name, model {name} is already present")
+
+        if model_weight is None:
+            model_weight = 1.0
+
+        self.model_weights[name] = model_weight
+        self.model_results[name] = defaultdict(list)
+        self.model_current = name
+        return name
+
+    @abstractmethod
+    @torch.no_grad()
+    def process_batch(self, result: Dict, batch: Dict):
+        """
+        Process a single batch
+
+        Args:
+            result: predictions to save and ensemble
+            batch: input batch used for predictions (for additional meta data)
+
+        Raises:
+            NotImplementedError: Overwrite this function in subclasses for the
+            specific use case.
+
+        Warnings:
+            Make sure to move cached values to the CPU after they have been
+            processed.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    @torch.no_grad()
+    def get_case_result(self, restore: bool = False) -> Dict[str, torch.Tensor]:
+        """
+        Retrieve the results of a single case
+
+        Args:
+            restore: restores predictions in original image space
+
+        Raises:
+            NotImplementedError: Overwrite this function in subclasses for the
+            specific use case.
+
+        Returns:
+            Dict[str, torch.Tensor]: the result of a single case
+        """
+        raise NotImplementedError
+
+    def update_parameters(self, **parameters: Dict):
+        """
+        Update internal parameters used for ensembling the results
+
+        Args:
+            parameters: parameters to update
+        """
+        self.parameters.update(parameters)
+
+    @classmethod
+    @abstractmethod
+    def sweep_parameters(cls) -> Tuple[Dict[str, Any], Dict[str, Sequence[Any]]]:
+        """
+        Return a set of parameters which can be used to sweep ensembling
+        parameters in a postprocessing step
+
+        Returns:
+            Dict[str, Any]: default state to start with
+            Dict[str, Sequence[Any]]]: Defines the values to search for each
+                parameter
+        """
+        raise NotImplementedError
+
+    def save_state(self,
+                   target_dir: Path,
+                   name: str,
+                   **kwargs,
+                   ):
+        """
+        Save case result as pickle file. Identifier of ensembler will
+        be added to the name
+
+        Args:
+            target_dir: folder to save result to
+            name: name of case
+            **kwargs: data to save
+        """
+        kwargs["properties"] = self.properties
+        kwargs["parameters"] = self.parameters
+
+        kwargs["model_current"] = self.model_current
+        kwargs["model_results"] = self.model_results
+        kwargs["model_weights"] = self.model_weights
+
+        kwargs["case_result"] = self.case_result
+
+        with open(Path(target_dir) / f"{name}_{self.ID}.pt", "wb") as f:
+            torch.save(kwargs, f)
+
+    def load_state(self, base_dir: PathLike, case_id: str) -> Dict:
+        """
+        Path to result file
+        """
+        ckp = torch.load(str(Path(base_dir) / f"{case_id}_{self.ID}.pt"))
+        self._load(ckp)
+        return ckp
+
+    def _load(self, state: Dict):
+        for key, item in state.items():
+            setattr(self, key, item)
+
+    @classmethod
+    def from_checkpoint(cls, base_dir: PathLike, case_id: str):
+        ckp = torch.load(str(Path(base_dir) / f"{case_id}_{cls.ID}.pt"))
+        t = cls(
+            properties=ckp["properties"],
+            parameters=ckp["parameters"],
+        )
+        t._load(ckp)
+        return t
+
+    @classmethod
+    def get_case_ids(cls, base_dir: PathLike):
+        return [c.stem.rsplit(f"_{cls.ID}", 1)[0] 
+                for c in Path(base_dir).glob(f"*_{cls.ID}.pt")]
+
+
+class OverlapMap:
+    def __init__(self, data_shape: Sequence[int]):
+        """
+        Handler for overlap map
+
+        Args:
+            data_shape: spatial dimensions of data (
+                no batch dim and no channel dim!)
+        """
+        self.overlap_map: torch.Tensor = \
+            torch.zeros(*data_shape, requires_grad=False, dtype=torch.float)
+
+    def add_overlap(self, crop: Sequence[slice]):
+        """
+        Increase values of :param:`self.overlap_map` inside of crop
+
+        Args:
+            crop: defines crop. Negative values are assumed to be outside
+                of the data and thus discarded
+        """
+        # discard leading indexes which could be due to batches and channels
+        if len(crop) > self.overlap_map.ndim:
+            crop = crop[-self.overlap_map.ndim:]
+
+        # clip crop to data shape
+        slicer = []
+        for data_shape, crop_dim in zip(tuple(self.overlap_map.shape), crop):
+            start = max(0, crop_dim.start)
+            stop = min(data_shape, crop_dim.stop)
+            slicer.append(slice(start, stop, crop_dim.step))
+        self.overlap_map[slicer] += 1
+
+    def mean_num_overlap_of_box(self, box: Sequence[int]) -> float:
+        """
+        Extract mean number of overlaps from a bounding box area
+
+        Args:
+            box: defines bounding box (x1, y1, x2, y2, (z1, z2))
+
+        Returns:
+            int: mean number of overlaps
+        """
+        slicer = [slice(int(box[0]), int(box[2])), slice(int(box[1]), int(box[3]))]
+        if len(box) == 6:
+            slicer.append(slice(int(box[4]), int(box[5])))
+        return torch.mean(self.overlap_map[slicer].float()).item()
+
+    def mean_num_overlap_of_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
+        """
+        Extract mean number of overlaps from a bounding box area
+
+        Args:
+            boxes: defines multiple bounding boxes (x1, y1, x2, y2, (z1, z2))
+                [N, dim * 2]
+
+        Returns:
+            Tensor: mean number of overlaps per box [N]
+        """
+        return torch.tensor(
+            [self.mean_num_overlap_of_box(box) for box in boxes]).to(
+            dtype=torch.float, device=boxes.device)
+
+    def avg(self) -> torch.Tensor:
+        """
+        Compute mean over all overlaps
+        """
+        return self.overlap_map.float().median()
+
+    def restore_mean(self, val):
+        """
+        Generate a new overlap map filled with the specified value
+        """
+        self.overlap_map = torch.zeros_like(self.overlap_map)
+        self.overlap_map = float(val)
+
+
+def extract_results(source_dir: PathLike,
+                    target_dir: PathLike,
+                    ensembler_cls: Callable,
+                    restore: bool,
+                    **params,
+                    ) -> None:
+    """
+    Compute case result from ensembler and save it
+
+    Args:
+        source_dir: directory which contains the saved predictions/state from
+            the ensembler class
+        target_dir: directory to save results
+        ensembler_cls: ensembler class for prediction
+        restore: if true, the results are converted into the opriginal image
+            space
+    """
+    Path(target_dir).mkdir(parents=True, exist_ok=True)
+    for case_id in maybe_verbose_iterable(ensembler_cls.get_case_ids(source_dir)):
+        ensembler = ensembler_cls.from_checkpoint(base_dir=source_dir, case_id=case_id)
+        ensembler.update_parameters(**params)
+
+        pred = to_numpy(ensembler.get_case_result(restore=restore))
+
+        save_pickle(pred, Path(target_dir) / f"{case_id}_{ensembler_cls.ID}.pkl")
+
+
+BaseEnsemblerType = TypeVar('BaseEnsemblerType', bound=BaseEnsembler)

nndet/inference/ensembler/segmentation.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+from os import PathLike
+from pathlib import Path
+from typing import Dict, Optional, Tuple, Sequence, Any
+
+import torch
+import numpy as np
+from loguru import logger
+from scipy.ndimage import gaussian_filter
+from torch import Tensor
+
+from nndet.inference.ensembler.base import BaseEnsembler
+from nndet.inference.restore import restore_fmap
+
+
+class SegmentationEnsembler(BaseEnsembler):
+    ID = "seg"
+
+    def __init__(self,
+                 seg_key: str = 'pred_seg',
+                 data_key: str = 'data',
+                 **kwargs,
+                 ):
+        """
+        Ensemble segmentation predictions from tta and model ensembling
+
+        Args:
+            seg_key: key where segmentation is located inside prediction dict
+            data_key: key where data is located inside batch dict
+            use_gaussian: apply gaussian weighting to individual crops
+            non_lin: non linearity to apply to convert logits to probabilities
+                (will be applied after consolidation)
+            argmax: apply argmax to output
+            kwargs: passed to super class
+        """
+        super().__init__(**kwargs)
+        self.seg_key = seg_key
+        self.data_key = data_key
+
+        self.model_results: Optional[Tensor] = None
+        self.overlap = torch.zeros(self.properties["shape"])
+        self.cache_crop_weight: Dict[Tuple, torch.Tensor] = {}
+
+    @classmethod
+    def from_case(cls,
+                  case: Dict,
+                  properties: Dict,
+                  parameters: Optional[Dict] = None,
+                  seg_key: str = 'pred_seg',
+                  data_key: str = 'data',
+                  **kwargs,
+                  ):
+        """
+        Primary way to instantiate this class. Automatically extracts all
+        properties and uses a default set of parameters for ensembling.
+
+        Args:
+            case: case which is predicted.
+            mode: operation mode of ensembler (defines which network was used)
+                e.g. '2d' | '3d'
+            properties: Additional properties.
+                Required keys:
+                    `transpose_backward`
+                    `spacing_after_resampling`
+                    `crop_bbox`
+            parameters: Additional parameters. Defaults to None.
+            seg_key: key where segmentation is located inside prediction dict
+            data_key: key where data is located inside batch dict
+        """
+        parameters = parameters if parameters is not None else {}
+        _parameters = {"use_gaussian": True, "argmax": True}
+        _parameters.update(parameters)
+
+        _properties = {
+            "shape": case[data_key].shape[1:],  # remove channel dim
+            "transpose_backward": properties["transpose_backward"],
+            "original_spacing": properties["original_spacing"],
+            "spacing_after_resampling": properties["spacing_after_resampling"],
+            "crop_bbox": properties["crop_bbox"],
+            "size_after_cropping": properties["size_after_cropping"],
+            "original_size_before_cropping": properties["original_size_of_raw_data"],
+            "itk_origin": properties["itk_origin"],
+            "itk_spacing": properties["itk_spacing"],
+            "itk_direction": properties["itk_direction"],
+        }
+
+        return cls(
+            properties=_properties,
+            parameters=_parameters,
+            seg_key=seg_key,
+            data_key=data_key,
+            **kwargs,
+            )
+
+    def add_model(self,
+                  name: Optional[str] = None,
+                  model_weight: Optional[float] = None,
+                  ) -> str:
+        """
+        This functions signales the ensembler to add a new model for internal
+        processing
+
+        Args:
+            name: Name of the model. If None, uses counts the models.
+            model_weight: Optional weight for this model. Defaults to None.
+        """
+        if name is None:
+            name = len(self.model_weights) + 1
+        if name in self.model_weights:
+            raise ValueError(f"Invalid model name, model {name} is already present")
+
+        if model_weight is None:
+            model_weight = 1.0
+
+        self.model_weights[name] = model_weight
+        self.model_current = name
+        return name
+
+    @classmethod
+    def sweep_parameters(cls) -> Tuple[Dict[str, Any], Dict[str, Sequence[Any]]]:
+        """
+        Not available for segmentation ensembler
+
+        Returns:
+            Dict[str, Sequence[Any]]: Parameters to sweep. The keys define the
+                parameters wile the Sequences are the values to sweep.
+        """
+        return {}, {}
+
+    @torch.no_grad()
+    def process_batch(self, result: Dict, batch: Dict):
+        """
+        Process a single batch of bounding box predictions
+
+        Args:
+            result: prediction from detector. Need to provide boxes, scores
+                and class labels
+                    `self.seg_key`: [Tensor]: predicted segmentation [N, C, dims]
+            batch: input batch
+                `tile_origin: origin of crop with recard to actual data (
+                    in case of padding)
+                `crop`: Sequence[slice] original crop from data
+        """
+        seg_batch = result[self.seg_key].cpu()
+        crops = batch["crop"]
+
+        weight = self.get_weighting(tuple(seg_batch.shape[2:])).to(seg_batch)
+        seg_batch = seg_batch * weight[None].to(seg_batch) * self.model_weights[self.model_current]
+
+        if self.model_results is None:
+            self.model_results = torch.zeros(
+                (int(seg_batch.shape[1]), *self.properties["shape"])).to(seg_batch)
+
+        for seg, crop in zip(seg_batch, zip(*crops)):
+            _weight = weight.clone()
+            seg, case_crop = self.crop_to_case_boundaries(seg, crop)
+            _weight, case_crop2 = self.crop_to_case_boundaries(_weight[None], crop)
+            assert case_crop == case_crop2
+            self.model_results[case_crop] += seg
+            self.overlap[case_crop] += _weight[0]
+
+    def crop_to_case_boundaries(self, seg: torch.Tensor, crop: Sequence[slice]):
+        """
+        In case padding was used at the borders, the padding needs to be removed
+
+        Args
+            seg: predicted segmentation
+            Sequence[slice]: crop in case to save segmentation
+        """
+        if len(crop) > self.model_results.ndim - 1:
+            crop = crop[-(self.model_results.ndim - 1):]
+
+        crop_slicer = []
+        case_slicer = []
+        for dim, c in enumerate(crop):
+            case_start = max(0, c.start)
+            case_stop = min(self.model_results.shape[dim + 1], c.stop)
+
+            diff_stop = c.stop - self.model_results.shape[dim + 1]
+            crop_start = max(0, 0 - (c.start - 0))  # 0 added for completeness of pattern
+            crop_stop = min(seg.shape[dim + 1], seg.shape[dim + 1] - diff_stop)
+
+            crop_slicer.append(slice(crop_start, crop_stop, c.step))
+            case_slicer.append(slice(case_start, case_stop, c.step))
+        return seg[(..., *crop_slicer)], (..., *case_slicer)
+
+    def get_weighting(self, crop_size: Tuple[int]) -> torch.Tensor:
+        """
+        Get matrix to weight predictions inside a single patch
+
+        Args:
+            crop_size: size of crop
+
+        Returns:
+            Tensor: weight for crop
+            Tuple[int]: size of patch
+        """
+        if crop_size not in self.cache_crop_weight:
+            if self.parameters["use_gaussian"]:
+                logger.info(f"Creating new gaussian weight matrix for crop size {crop_size}")
+                tmp = np.zeros(crop_size)
+                center_coords = [i // 2 for i in crop_size]
+                sigmas = [i // 8 for i in crop_size]
+                tmp[tuple(center_coords)] = 1
+                tmp_smooth = gaussian_filter(tmp, sigmas, 0, mode='constant', cval=0)
+                tmp_smooth = tmp_smooth / tmp_smooth.max() * 1
+                weighting = tmp_smooth + 1e-8
+                self.cache_crop_weight[crop_size] = torch.from_numpy(weighting).float()
+            else:
+                logger.info(f"Creating new weight matrix for crop size {crop_size}")
+                self.cache_crop_weight[crop_size] = torch.ones(crop_size, dtype=torch.float)
+
+        return self.cache_crop_weight[crop_size]
+
+    def restore_prediction(self, logit_maps: Tensor) -> Tensor:
+        """
+        Restore predictions in the original image space
+
+        Args:
+            boxes: predicted boxes [N, dims * 2] (x1, y1, x2, y2, (z1, z2))
+
+        Returns:
+            Tensor: boxes in original image space [N, dims * 2]
+                (x1, y1, x2, y2, (z1, z2))
+        """
+        _old_dtype = logit_maps.dtype
+        logit_maps_np = restore_fmap(
+                fmap=logit_maps.detach().cpu().numpy(),
+                transpose_backward=self.properties["transpose_backward"],
+                original_spacing=self.properties["original_spacing"],
+                spacing_after_resampling=self.properties["spacing_after_resampling"],
+                original_size_before_cropping=self.properties["original_size_before_cropping"],
+                size_after_cropping=self.properties["size_after_cropping"],
+                crop_bbox=self.properties["crop_bbox"],
+                interpolation_order=1,
+                interpolation_order_z=0,
+                do_separate_z=None,
+        )
+        logit_maps = torch.from_numpy(logit_maps_np).to(dtype=_old_dtype)
+        return logit_maps
+
+    @torch.no_grad()
+    def get_case_result(self,
+                        restore: bool = False, **kwargs
+                        ) -> Dict[str, Tensor]:
+        """
+        Get final result for case after ensembling and TTA
+
+        Returns:
+            Dict: results
+                `pred_seg`: [C, dims] if :param:`self.argmax`
+                    if False and [dims] if True
+                `restore`: indicate whether predictions were restored in
+                    original image space
+                `itk_origin`: itk origin of image before preprocessing
+                `itk_spacing`: itk spacing of image before preprocessing
+                `itk_direction`: itk direction of image before preprocessing
+        """
+        result = self.model_results / self.overlap[None]
+
+        if restore:
+            result = self.restore_prediction(result)
+
+        if self.parameters["argmax"]:
+            result = result.argmax(dim=0).to(dtype=torch.uint8)
+
+        self.case_result = result
+        return {
+            "pred_seg": self.case_result,
+            "restore": restore,
+            "itk_origin": self.properties["itk_origin"],
+            "itk_spacing": self.properties["itk_spacing"],
+            "itk_direction": self.properties["itk_direction"],
+            }
+
+    def save_state(self,
+                   target_dir: Path,
+                   name: str,
+                   **kwargs,
+                   ):
+        """
+        Save case result as pickle file. Identifier of ensembler will
+        be added to the name
+
+        Args:
+            target_dir: folder to save result to
+            name: name of case
+        """
+        super().save_state(
+            target_dir=target_dir,
+            name=name,
+            seg_key=self.seg_key,
+            data_key=self.data_key,
+            case_crop_weight=self.cache_crop_weight,
+            **kwargs,
+        )
+
+    @classmethod
+    def from_checkpoint(cls, base_dir: PathLike, case_id: str, **kwargs):
+        ckp = torch.load(str(Path(base_dir) / f"{case_id}_{cls.ID}.pt"))
+        t = cls(
+            properties=ckp["properties"],
+            parameters=ckp["parameters"],
+            seg_key=ckp["seg_key"],
+            data_key=ckp["data_key"],
+        )
+        t._load(ckp)
+        return t

nndet/inference/helper.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+from pathlib import Path
+from typing import Sequence, List, Dict, Callable, Optional
+
+import numpy as np
+from loguru import logger
+
+from nndet.utils.tensor import to_numpy
+from nndet.io.load import load_pickle, save_pickle
+from nndet.io.paths import Pathlike, get_case_id_from_path
+from nndet.inference.loading import load_time_ensemble
+
+
+def predict_dir(
+    source_dir: Pathlike,
+    target_dir: Pathlike,
+    cfg: dict,
+    plan: dict,
+    source_models: Path,
+    model_fn: Callable[[Path, dict, dict, int], Sequence[dict]] = load_time_ensemble,
+    num_models: int = None,
+    num_tta_transforms: int = None,
+    restore: bool = False,
+    case_ids: Optional[Sequence[str]] = None,
+    save_state: bool = False,
+    **kwargs
+    ):
+    """
+    Predict all preprocessed(!) cases inside a directory
+
+    Args:
+        source_dir: directory where preprocessed cases are located
+        target_dir: directory to save predictions to
+        cfg: config
+            `predictor`: define predictor to use
+        plan: plan
+        source_models: directory where models for prediction are located
+        model_fn: function to load model from directory
+        num_models: number of models to use for prediction; None = all
+        num_tta_transforms: number of tta transforms to use for
+            prediction; None = all
+        stage: current stage to predict
+        restore: restore predictions in original image space
+        case_ids: case ids to predict. If None the whole folder will be
+            predicted
+        save_state: If `true` the state of the ensembler is saved. If
+            `false` only the final result is saved.
+        kwargs: passed to :method:'get_predictor' method of module
+    """
+    logger.info("Running inference")
+
+    source_dir = Path(source_dir)
+    target_dir = Path(target_dir)
+
+    models = model_fn(source_models, cfg, plan, num_models)
+    predictor = models[0]["model"].get_predictor(
+        plan=plan,
+        models=[m["model"] for m in models],
+        num_tta_transforms=num_tta_transforms,
+        **kwargs,
+    )
+
+    if case_ids is None:
+        case_paths = list(source_dir.glob('*.npz'))
+        case_paths = [cp for cp in case_paths if "_gt.npz" not in str(cp)]
+    else:
+        case_paths = [source_dir / f"{cid}.npz" for cid in case_ids]
+    logger.info(f"Found {len(case_paths)} files for inference.")
+
+    for idx, path in enumerate(case_paths, start=1):
+        logger.info(f"Predicting case {idx} of {len(case_paths)}.")
+        case_id = get_case_id_from_path(str(path), remove_modality=False)
+        if path.is_file():
+            case = np.load(str(path), allow_pickle=True)['data']
+        else:
+            case = np.load(str(path)[:-4] + ".npy", allow_pickle=True)
+        properties = load_pickle(path.parent / f"{case_id}.pkl")
+        properties["transpose_backward"] = plan["transpose_backward"]
+
+        if save_state:
+            _ = predictor.predict_case({"data": case},
+                                       properties,
+                                       save_dir=target_dir,
+                                       case_id=case_id,
+                                       restore=restore,
+                                       )
+        else:
+            result = predictor.predict_case({"data": case},
+                                            properties,
+                                            save_dir=None,
+                                            case_id=None,
+                                            restore=restore,
+                                            )
+            for key, item in to_numpy(result).items():
+                save_pickle(item, target_dir / f"{case_id}_{key}.pkl")
+    return predictor

nndet/inference/loading.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+import os
+from functools import partial
+from pathlib import Path
+from typing import Sequence, Optional
+
+import torch
+from loguru import logger
+
+from nndet.ptmodule import MODULE_REGISTRY
+from nndet.io.paths import Pathlike
+
+
+def get_loader_fn(mode: str, **kwargs):
+    if mode == "best":
+        load_fn = partial(load_time_ensemble, **kwargs)
+    elif mode == "final":
+        load_fn = partial(load_final_model, **kwargs)
+    elif mode == "latest":
+        load_fn = partial(load_final_model, identifier="latest", **kwargs)
+    else:
+        raise ValueError(f"Unknown mode {mode}")
+    return load_fn
+
+
+def get_latest_model(base_dir: Pathlike, fold: int = 0) -> Optional[Path]:
+    """
+    Get the latest training dir in a given base dir
+    E.g. ../RetinaUNetV0/fold0__0, ../RetinaUNetV0/fold0__1
+    -> would select fold0__1
+
+    Args:
+        base_dir: path to base dir
+        fold: fold to look for
+
+    Returns:
+        Optional[Path]: If no model for specified fold is found, None this
+            will return None
+    """
+    base_dir = Path(base_dir)
+    m = [m for m in base_dir.iterdir() if m.is_dir()]
+    m = [_m for _m in m if f"fold{fold}" in _m.stem]
+    if m:
+        return sorted(m, key=lambda x: x.stem, reverse=True)[0]
+    else:
+        return None
+
+
+# TODO: update
+def load_time_ensemble(
+    source_models: Path,
+    cfg: dict,
+    plan: dict,
+    num_models: int = None,
+    ) -> Sequence[dict]:
+    """
+    Load time ensembled models
+
+    Args:
+        source_models: path to directory where models are saved
+        cfg: config used for experiment
+            `model`: name of model in DETECTION_REGISTRY
+        plan: plan used for training
+        num_models: number of models to load
+
+    Returns:
+        Sequence[dict]: loaded models
+            `model`: loaded model
+            `rank`: rank of model
+    """
+    logger.info("Loading time ensemble")
+    model_names = list(source_models.glob('model_best*.ckpt'))
+    if not model_names:
+        raise RuntimeError(f"Did not find any models in {source_models}")
+
+    models = []
+    for path in model_names:
+        model = MODULE_REGISTRY[cfg["module"]](
+            model_cfg=cfg["model_cfg"],
+            trainer_cfg=cfg["trainer_cfg"],
+            plan=plan,
+        )
+        state_dict = torch.load(path, map_location="cpu")["state_dict"]
+        t = model.load_state_dict(state_dict)
+        
+        logger.info(f"Loaded {path} with {t}")
+        model.float()
+        model.eval()
+        rank = int(str(path).rsplit(os.sep, 1)[-1][10])
+        models.append({"model": model.cpu(), "rank": rank})
+    if num_models is not None:
+        models = models[:num_models]
+        logger.info(f"Using {len(models)} models for for inference.")
+    return models
+
+
+def load_final_model(
+    source_models: Path,
+    cfg: dict,
+    plan: dict,
+    num_models: int = 1,
+    identifier: str = "last",
+    ) -> Sequence[dict]:
+    """
+    Load final model from training
+
+    Args:
+        source_models: path to directory where models are saved
+        cfg: config used for experiment
+            `model`: name of model in DETECTION_REGISTRY
+        plan: plan used for training
+        num_models: Only supports one model
+        identifier: looks for identifier inside of model name
+
+    Returns:
+        Sequence[dict]: loaded models
+            `model`: loaded model
+            `rank`: rank is always 0
+    """
+    assert num_models == 1, f"load_final_model only supports num_models=1, found {num_models}"
+    logger.info(f"Loading {identifier} model")
+
+    model_names = list(source_models.glob('*.ckpt'))
+    model_names = [m for m in model_names if identifier in str(m.stem)]
+    assert len(model_names) == 1, f"Found wrong number of models, {model_names} in {source_models} with {identifier}"
+
+    path = model_names[0]
+    model = MODULE_REGISTRY[cfg["module"]](
+        model_cfg=cfg["model_cfg"],
+        trainer_cfg=cfg["trainer_cfg"],
+        plan=plan,
+        )
+    state_dict = torch.load(path, map_location="cpu")["state_dict"]
+    t = model.load_state_dict(state_dict)
+    logger.info(f"Loaded {path} with {t}")
+    model.float()
+    model.eval()
+    return [{"model": model, "rank": 0}]
+
+
+def load_all_models(
+    source_models: Path, 
+    cfg: dict, 
+    plan: dict,
+    *args, 
+    **kwargs,
+    ):
+    """
+    Load all models to ensemble
+
+    Args:
+        source_models: path to directory where models are saved
+        cfg: config used for experiment
+            `model`: name of model in DETECTION_REGISTRY
+        plan: plan used for training
+        kwargs: not used
+
+    Returns:
+        Sequence[dict]: loaded models
+            `model`: loaded model
+            `rank`: rank of model
+    """
+    model_names = list(source_models.glob('*.ckpt'))
+    if not model_names:
+        raise RuntimeError(f"Did not find any models in {source_models}")
+    logger.info(f"Found {len(model_names)} models to ensemble")
+
+    models = []
+    for path in model_names:
+        model = MODULE_REGISTRY[cfg["module"]](
+            model_cfg=cfg["model_cfg"],
+            trainer_cfg=cfg["trainer_cfg"],
+            plan=plan,
+            )
+        state_dict = torch.load(path, map_location="cpu")["state_dict"]
+        t = model.load_state_dict(state_dict)
+        logger.info(f"Loaded {path} with {t}")
+        model.float()
+        model.eval()
+        models.append({"model": model.cpu()})
+    return models

nndet/inference/patching.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+import typing
+import itertools
+import numpy as np
+
+from loguru import logger
+
+from skimage.measure import regionprops
+import SimpleITK as sitk
+
+
+def center_crop_object_mask(mask: np.ndarray, cshape: typing.Union[tuple, int],
+                            ) -> typing.List[tuple]:
+    """
+    Creates indices to crop patches around individual objects in mask
+
+    Args
+        mask: mask where objects have diffrent numbers. Objects need to be numbered
+            consequtively from one to n, with 0 as background.
+        cshape: size of individual crops. Needs to be divisible by two.
+            Otherwise crops do not have the expected size.
+            If cshape is a int, crops will have the same size in every dimension.
+
+    Returns
+        list[tuple]: each crop generates one tuple with indices
+
+    Raises
+        TypeError: raised if mask and patches define different dimensionalities
+        TypeError: raised if `cshape` is larger than mask
+
+    See Also
+        :func:`save_get_crop`
+
+    Warnings
+        The returned crops are not checked for image boundaries. Slices
+        with negative indices and indices which extend over the mask boundaries
+        are possible! To correct for this, use `save_get_crop` which handles
+        this exceptions.
+    """
+    if isinstance(cshape, int):
+        cshape = tuple([cshape] * mask.ndim)
+
+    if mask.ndim != len(cshape):
+        raise TypeError("Size of crops needs to be defined for "
+                        "every dimension")
+    if any(np.subtract(mask.shape, cshape) < 0):
+        raise TypeError("Patches must be smaller than data.")
+
+    if mask.max() == 0:
+        # no objects in mask
+        return []
+
+    all_centroids = [i['centroid'] for i in regionprops(mask.astype(np.int32))]
+    crops = []
+    for centroid in all_centroids:
+        crops.append(tuple(slice(int(c) - (s // 2), int(c) + (s // 2))
+                           for c, s in zip(centroid, cshape)))
+    return crops
+
+
+def center_crop_object_seg(seg: np.ndarray, cshape: typing.Union[tuple, int],
+                           **kwargs) -> typing.List[tuple]:
+    """
+    Creates indices to crop patches around individual objects in segmentation.
+    Objects are determined by region growing with connected threshold.
+
+    Args
+        seg: semantic segmentation of objects.
+        cshape: size of individual crops. Needs to be divisible by two.
+            Otherwise crops do not have the expected size.
+            If cshape is a int, crops will have the same size in every dimension.
+        kwargs: additional keyword arguments passed to `center_crop_objects_mask`
+
+    Returns
+        list[tuple]: each crop generates one tuple with indices
+
+    See Also
+        :func:`save_get_crop`
+
+    Warnings
+        The returned crops are not checked for image boundaries. Slices
+        with negative indices and indices which extend over the mask boundaries
+        are possible! To correct for this, use `save_get_crop` which handles
+        this exceptions.
+    """
+    _mask, _ = create_mask_from_seg(seg)
+    return center_crop_object_mask(_mask, cshape=cshape, **kwargs)
+
+
+def create_mask_from_seg(seg: np.ndarray) -> typing.Tuple[np.ndarray, list]:
+    """
+    Create a mask where objects are enumerated from 1, ..., n.
+    Objects are determined by region growing with connected threshold.
+
+    Args
+        seg: semantic segmentation array
+
+    Returns
+        np.ndarray: mask with objects
+        list: classes to objects (ascending order)
+    """
+    _seg = np.copy(seg).astype(np.int32)
+    _seg_sitk = sitk.GetImageFromArray(_seg)
+    _mask = np.zeros_like(seg).astype(np.int32)
+    _obj_cls = []
+    _obj = 1
+
+    while _seg.max() > 0:
+        # choose one seed in segmentation
+        seed = np.transpose(np.nonzero(_seg))[0]
+        # invert coordinates for sitk
+        seed_sitk = tuple(seed[:: -1].tolist())
+        seed = tuple(seed)
+        # region growing
+        seg_con = sitk.ConnectedThreshold(_seg_sitk,
+                                          seedList=[seed_sitk],
+                                          lower=int(_seg[seed]),
+                                          upper=int(_seg[seed]))
+        seg_con = sitk.GetArrayFromImage(seg_con).astype(bool)
+
+        # add object to mask
+        _mask[seg_con] = _obj
+        _obj_cls.append(_seg[seed])
+        # remove object from segmentation
+        _seg[seg_con] = 0
+
+        _obj += 1
+
+        # objects should never overlap
+        assert _mask.max() < _obj
+    return _mask, _obj_cls
+
+
+def create_grid(cshape: typing.Union[typing.Sequence[int], int],
+                dshape: typing.Sequence[int],
+                overlap: typing.Union[typing.Sequence[int], int] = 0,
+                mode='fixed',
+                center_boarder: bool = False,
+                **kwargs,
+                ) -> typing.List[typing.Tuple[slice]]:
+    """
+    Create indices for a grid
+
+    Args
+        cshape: size of individual patches
+        dshape: shape of data
+        overlap: overlap between patches. If `overlap` is an integer is is applied
+            to all dimensions.
+        mode: defines how borders should be handled, by default 'fixed'.
+            `fixed` created patches without special handling of borders, thus
+            the last patch might exceed `dshape`
+            `symmetric` moves patches such that the the first and last patch are
+            equally overlapping of dshape (when combined with padding, the last and
+            first patch would have the same amount of padding)
+        center_boarder: adds additional crops at the boarders which have the
+            boarder as their center
+
+    Returns
+        typing.List[slice]: slices to extract patches
+
+    Raises
+        TypeError: raised if `cshape` and `dshape` do not have the same length
+        TypeError: raised if `overlap` and `dshape` do not have the same length
+        TypeError: raised if `cshape` is larger than `dshape`
+        TypeError: raised if `overlap` is larger than `cshape`
+
+    Warnings
+        The returned crops are can exceed the image boundaries. Slices
+        with negative indices and indices which extend over the image
+        boundary at the start. To correct for this, use `save_get_crop`
+        which handles exceptions at borders.
+    """
+    _mode_fn = {
+        "fixed": _fixed_slices,
+        "symmetric": _symmetric_slices,
+    }
+
+    if len(dshape) == 3 and len(cshape) == 2:
+        logger.info("Creating 2d grid.")
+        slices_3d = dshape[0]
+        dshape = dshape[1:]
+    else:
+        slices_3d = None
+
+    # create tuples from shapes
+    if isinstance(cshape, int):
+        cshape = tuple([cshape] * len(dshape))
+    if isinstance(overlap, int):
+        overlap = tuple([overlap] * len(dshape))
+
+    # check shapes
+    if len(cshape) != len(dshape):
+        raise TypeError(
+            "cshape and dshape must be defined for same dimensionality.")
+    if len(overlap) != len(dshape):
+        raise TypeError(
+            "overlap and dshape must be defined for same dimensionality.")
+    if any(np.subtract(dshape, cshape) < 0):
+        axes = np.nonzero(np.subtract(dshape, cshape) < 0)
+        logger.warning(f"Found patch size which is bigger than data: data {dshape} patch {cshape}")
+    if any(np.subtract(cshape, overlap) < 0):
+        raise TypeError("Overlap must be smaller than size of patches.")
+
+    grid_slices = [_mode_fn[mode](psize, dlim, ov, **kwargs)
+                   for psize, dlim, ov in zip(cshape, dshape, overlap)]
+
+    if center_boarder:
+        for idx, (psize, dlim, ov) in enumerate(zip(cshape, dshape, overlap)):
+            lower_bound_start = int(-0.5 * psize)
+            upper_bound_start = dlim - int(0.5 * psize)
+            grid_slices[idx] = tuple([
+                slice(lower_bound_start, lower_bound_start + psize),
+                *grid_slices[idx],
+                slice(upper_bound_start, upper_bound_start + psize),
+            ])
+
+    if slices_3d is not None:
+        grid_slices = [tuple([slice(i, i + 1) for i in range(slices_3d)])] + grid_slices
+    grid = list(itertools.product(*grid_slices))
+    return grid
+
+
+def _fixed_slices(psize: int, dlim: int, overlap: int, start: int = 0) -> typing.Tuple[slice]:
+    """
+    Creates fixed slicing of a single axis. Only last patch exceeds dlim.
+
+    Args
+        psize: size of patch
+        dlim: size of data
+        overlap: overlap between patches
+        start: where to start patches, by default 0
+
+    Returns
+        typing.List[slice]: ordered slices for a single axis
+    """
+    upper_limit = 0
+    lower_limit = start
+    idx = 0
+    crops = []
+
+    while upper_limit < dlim:
+        if idx != 0:
+            lower_limit = lower_limit - overlap
+
+        upper_limit = lower_limit + psize
+        crops.append(slice(lower_limit, upper_limit))
+        lower_limit = upper_limit
+        idx += 1
+    return tuple(crops)
+
+
+def _symmetric_slices(psize: int, dlim: int, overlap: int) -> typing.Tuple[slice]:
+    """
+    Creates symmetric slicing of a single axis. First and last patch exceed
+    data borders.
+
+    Args
+        psize: size of patch
+        dlim: size of data
+        overlap: overlap between patches
+        start: where to patches, by default 0
+
+    Returns
+        typing.List[slice]: ordered slices for a single axis
+    """
+    if psize >= dlim:
+        return _fixed_slices(psize, dlim, overlap, start=-(psize - dlim) // 2)
+
+    pmod = dlim % (psize - overlap)
+    start = (pmod - psize) // 2
+    return _fixed_slices(psize, dlim, overlap, start=start)
+
+
+def save_get_crop(data: np.ndarray,
+                  crop: typing.Sequence[slice],
+                  mode: str = "shift",
+                  **kwargs,
+                  ) -> typing.Tuple[np.ndarray,
+                                    typing.Tuple[int],
+                                    typing.Tuple[slice]]:
+    """
+    Safely extract crops from data
+
+    Args
+        data: list or tuple with data where patches are extracted from
+        crop: contains the coordiates of a single crop as slices
+        mode: Handling of borders when crops are outside of data, by default "shift".
+            Following modes are supported: "shift" crops are shifted inside the
+            data | other modes are identical to `np.pad`
+        kwargs: additional keyword arguments passed to `np.pad`
+
+    Returns
+        list[np.ndarray]: crops from data
+        Tuple[int]: origin offset of crop with regard to data origin (can be
+            used to offset bounding boxes)
+        Tuple[slice]: crop from data used to extract information
+
+    See Also
+        :func:`center_crop_objects_mask`, :func:`center_crop_objects_seg`
+
+    Warnings
+        This functions only supports positive indexing. Negative indices are
+        interpreted like they were outside the lower boundary!
+    """
+    if len(crop) > data.ndim:
+        raise TypeError(
+            "crop must have smaller or same dimensionality as data.")
+    if mode == 'shift':
+        # move slices if necessary
+        return _shifted_crop(data, crop)
+    else:
+        # use np.pad if necessary
+        return _padded_crop(data, crop, mode, **kwargs)
+
+
+def _shifted_crop(data: np.ndarray,
+                  crop: typing.Sequence[slice],
+                  ) -> typing.Tuple[np.ndarray,
+                                    typing.Tuple[int],
+                                    typing.Tuple[slice]]:
+    """
+    Created shifted crops to handle borders
+
+    Args
+        data: crop is extracted from data
+        crop: defines boundaries of crops
+
+    Returns
+        List[np.ndarray]: list of crops
+        Tuple[int]: origin offset of crop with regard to data origin (can be
+            used to offset bounding boxes)
+        Tuple[slice]: crop from data used to extract information
+
+    Raises
+        TypeError: raised if patchsize is bigger than data
+
+    Warnings
+        This functions only supports positive indexing. Negative indices are
+        interpreted like they were outside the lower boundary!
+    """
+    shifted_crop = []
+    dshape = tuple(data.shape)
+
+    # index from back, so batch and channel dimensions must not be defined
+    axis = data.ndim - len(crop)
+
+    for idx, crop_dim in enumerate(crop):
+        if crop_dim.start < 0:
+            # start is negative, thus it is subtracted from stop
+            new_slice = slice(0, crop_dim.stop - crop_dim.start, crop_dim.step)
+            if new_slice.stop > dshape[axis + idx]:
+                raise RuntimeError(
+                    "Patch is bigger than entire data. shift "
+                    "is not supported in this case.")
+            shifted_crop.append(new_slice)
+        elif crop_dim.stop > dshape[axis + idx]:
+            new_slice = \
+                slice(crop_dim.start - (crop_dim.stop - dshape[axis + idx]),
+                      dshape[axis + idx], crop_dim.step)
+            if new_slice.start < 0:
+                raise RuntimeError(
+                    "Patch is bigger than entire data. shift "
+                    "is not supported in this case.")
+            shifted_crop.append(new_slice)
+        else:
+            shifted_crop.append(crop_dim)
+    origin = [int(x.start) for x in shifted_crop]
+    return data[tuple([..., *shifted_crop])], origin, shifted_crop
+
+
+def _padded_crop(data: np.ndarray,
+                 crop: typing.Sequence[slice],
+                 mode: str,
+                 **kwargs,
+                 ) -> typing.Tuple[np.ndarray,
+                                   typing.Tuple[int],
+                                   typing.Tuple[slice]]:
+    """
+    Extract patch from data and pad accordingly
+
+    Args
+        data: crop is extracted from data
+        crop: defines boundaries of crops
+        mode: mode for padding. See `np.pad` for more details
+        kwargs: additional keyword arguments passed to :func:`np.pad`
+
+    Returns
+        typing.List[np.ndarray]: list of crops
+        Tuple[int]: origin offset of crop with regard to data origin (can be
+            used to offset bounding boxes)
+        Tuple[slice]: crop from data used to extract information
+    """
+    clipped_crop = []
+    dshape = tuple(data.shape)
+
+    # index from back, so batch and channel dimensions must not be defined
+    axis = data.ndim - len(crop)
+    padding = [(0, 0)] * axis if axis > 0 else []
+
+    for idx, crop_dim in enumerate(crop):
+        lower_pad = 0
+        upper_pad = 0
+        lower_bound = crop_dim.start
+        upper_bound = crop_dim.stop
+
+        # handle lower bound
+        if lower_bound < 0:
+            lower_pad = -lower_bound
+            lower_bound = 0
+        # handle upper bound
+        if upper_bound > dshape[axis + idx]:
+            upper_pad = upper_bound - dshape[axis + idx]
+            upper_bound = dshape[axis + idx]
+        padding.append((lower_pad, upper_pad))
+        clipped_crop.append(slice(lower_bound, upper_bound, crop_dim.step))
+    origin = [int(x.start) for x in crop]
+    return (np.pad(data[tuple([..., *clipped_crop])], pad_width=padding, mode=mode, **kwargs),
+            origin,
+            clipped_crop,
+            )
+

nndet/inference/predictor.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+import time
+import torch
+import copy
+import collections
+import numpy as np
+
+from loguru import logger
+from typing import Hashable, List, Sequence, Dict, Union, Any, Optional, Callable, TypeVar
+from pathlib import Path
+
+from nndet.io.load import save_pickle
+from nndet.models.abstract import AbstractModel
+from nndet.io.transforms import NoOp
+from nndet.inference.patching import save_get_crop, create_grid
+from nndet.utils import to_device, maybe_verbose_iterable
+
+from rising.transforms import AbstractTransform
+from rising.loading import DataLoader
+
+
+__all__ = ["Predictor"]
+torch_device = Union[torch.device, str]
+
+
+class Predictor:
+    def __init__(self,
+                 ensembler: Dict[str, Callable],
+                 models: Sequence[AbstractModel],
+                 crop_size: Sequence[int],
+                 overlap: float = 0.5, 
+                 tile_keys: Sequence[str] = ('data',),
+                 model_keys: Sequence[str] = ('data',),
+                 tta_transforms: Sequence[AbstractTransform] = (NoOp(),),
+                 tta_inverse_transforms: Sequence[AbstractTransform] = (NoOp(),),
+                 pre_transform: AbstractTransform = None,
+                 post_transform: AbstractTransform = None,
+                 batch_size: int = 4,
+                 model_weights: Sequence[float] = None,
+                 device: torch_device = "cuda:0",
+                 ensemble_on_device: bool = True,
+                 ):
+        """
+        Predict entire cases with TTA and Model-Ensembling
+
+        Workflow
+        - Load whole patient
+        -> create predictor from patient
+        - tile patient
+        * for each model:
+            * for each batch (batches of tiles):
+                * for each tta transform:
+                    - pre transform
+                    - tta transform
+                    - post transform
+                    - predict batch
+                    - inverse tta transform
+                    - forward predictions and batch to ensembler classes
+        <- return patient result
+
+        Args:
+            ensembler: Callable to instantiate ensembler from case and
+                properties
+            models: models to ensemble
+            crop_size: size of each crop (for most cases this should be
+                the same as in training)
+            overlap: overlap of crops
+            tile_keys: keys which are tiles
+            model_keys: this kyes are passed as positional arugments to the
+                model
+            tta_transforms: tta transformations
+            tta_inverse_transforms: inverse tta transformation
+            pre_transform: transform which is performed before every tta
+                transform
+            post_transform: transform which is performed after every tta
+                transform
+            batch_size: batch size to use for prediction
+            model_weights: additional weighting of individual models
+            device: device used for prediction
+            ensemble_on_device: The results will be passed to the ensembler
+                class with the current device. The ensembler needs to make
+                sure to avoid memory leaks!
+        """
+        self.ensemble_on_device = ensemble_on_device
+        self.device = device
+        self.ensembler_fns = ensembler
+        self.ensembler = {}
+
+        self.models = models
+        self.model_weights = [1.] * len(models) if model_weights is None else model_weights
+
+        self.crop_size = crop_size
+        self.overlap = overlap
+        self.tile_keys = tile_keys
+        self.model_keys = model_keys
+        
+        self.batch_size = batch_size
+
+        if len(tta_transforms) != len(tta_inverse_transforms):
+            raise ValueError("Every tta transform needs a reverse transform")
+        self.tta_transforms = tta_transforms
+        self.tta_inverse_transforms = tta_inverse_transforms
+        self.post_transform = post_transform
+        self.pre_transform = pre_transform
+        
+        self.grid_mode = 'symmetric'
+        self.save_get_mode = 'shift'
+
+    @classmethod
+    def create(cls, *args, **kwargs):
+        """
+        Create predictor object with specific ensembler objects
+
+        Raises:
+            NotImplementedError: Need to be overwritten in subclasses
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def get_ensembler(cls, key: Hashable, dim: int) -> Callable:
+        """
+        Return ensembler class for specific keys
+        Typically: `boxes`, `seg`, `instances`
+
+        Args:
+            key: Key to return
+            dim: number of spatial dimensions the network expects
+
+        Raises:
+            NotImplementedError: Need to be overwritten in subclasses
+
+        Returns:
+            Callable: Ensembler class
+        """
+        raise NotImplementedError
+
+    def predict_case(self,
+                     case: Dict,
+                     properties: Optional[Dict] = None,
+                     save_dir: Optional[Union[Path, str]] = None,
+                     case_id: Optional[str] = None,
+                     restore: bool = False,
+                     ) -> dict:
+        """
+        Load and predict a single case.
+
+        Args:
+            case: data of a single case
+            properties: additional properties of the case. E.g. to
+                restore prediction in original image space
+            save_dir: directory to save predictions
+            case_id: used for saving
+            restore: restore prediction in original image space
+                ("revert" preprocessing)
+
+        Returns:
+            dict: result of each ensembler (converted to numpy)
+        """
+        tic = time.perf_counter()
+        for name, fn in self.ensembler_fns.items():
+            self.ensembler[name] = fn(case, properties=properties)
+
+        tiles = self.tile_case(case)
+        self.predict_tiles(tiles)
+
+        result = {key: value.get_case_result(restore=restore) for key, value in self.ensembler.items()}
+        if save_dir is not None:
+            save_dir = Path(save_dir)
+            save_dir.mkdir(parents=True, exist_ok=True)
+            for ensembler in self.ensembler.values():
+                ensembler.save_state(save_dir, name=case_id)
+            save_pickle(properties, save_dir / f"{case_id}_properties.pkl")
+        toc = time.perf_counter()
+        logger.info(f"Prediction took {toc - tic} s")
+        return result
+
+    def tile_case(self, case: dict, update_remaining: bool = True) -> \
+            Sequence[Dict[str, np.ndarray]]:
+        """
+        Create patches from whole patient for prediction
+
+        Args:
+            case: data of a single case
+            update_remaining: properties from case which are not tiles
+                are saved into all patches
+
+        Returns:
+            Sequence[Dict[str, np.ndarray]]: extracted crops from case
+                and added new key:
+                    `tile_origin`: Sequence[int] offset of tile relative
+                        to case origin
+        """
+        dshape = case[self.tile_keys[0]].shape
+        overlap = [int(c * self.overlap) for c in self.crop_size]
+        crops = create_grid(
+            cshape=self.crop_size,
+            dshape=dshape[1:],
+            overlap=overlap,
+            mode=self.grid_mode,
+            )
+
+        tiles = []
+        for crop in crops:
+            try:
+                # try selected extraction mode
+                tile = {key: save_get_crop(case[key], crop, mode=self.save_get_mode)[0]
+                        for key in self.tile_keys}
+                _, tile["tile_origin"], tile["crop"] = save_get_crop(
+                    case[self.tile_keys[0]], crop, mode=self.save_get_mode)
+            except RuntimeError:
+                # fallback to symmetric
+                logger.warning("Path size is bigger than whole case, padding case to match patch size")
+                tile = {key: save_get_crop(case[key], crop, mode="symmetric")[0]
+                        for key in self.tile_keys}
+                _, tile["tile_origin"], tile["crop"] = save_get_crop(
+                    case[self.tile_keys[0]], crop, mode="symmetric")
+
+            if update_remaining:
+                tile.update({key: item for key, item in case.items()
+                             if key not in self.tile_keys})
+            tiles.append(tile)
+        return tiles
+
+    @torch.no_grad()
+    def predict_tiles(self, tiles: Sequence[Dict]) -> None:
+        """
+        Predict tiles of a single case with ensembling and tta. Results
+        are saved inside ensemblers
+
+        Args:
+            tiles: tiles from single case
+        """
+        dataloader = DataLoader(tiles,
+                                batch_size=self.batch_size,
+                                shuffle=False,
+                                collate_fn=slice_collate,
+                                )
+        for model_idx, (model, model_weight) in enumerate(
+            zip(self.models, self.model_weights)):
+            logger.info(f"Predicting model {model_idx + 1} of "
+                        f"{len(self.models)} with weight {model_weight}.")
+
+            model.to(device=self.device)
+            model.eval()
+
+            for t, (transform, inverse_transform) in enumerate(maybe_verbose_iterable(
+                    list(zip(self.tta_transforms, self.tta_inverse_transforms)),
+                    desc="Transform", position=0)):
+                for ensembler in self.ensembler.values():
+                    ensembler.add_model(name=f"model{model_idx}_t{t}", model_weight=model_weight)
+
+                for batch_num, batch in enumerate(maybe_verbose_iterable(
+                    dataloader, desc="Crop", position=1)):
+                    self.predict_with_transformation(
+                        model=model,
+                        batch=batch,
+                        batch_num=batch_num,
+                        transform=transform,
+                        inverse_transform=inverse_transform,
+                    )
+
+            model.cpu()
+            torch.cuda.empty_cache()
+
+    def predict_with_transformation(self,
+                                    model: AbstractModel,
+                                    batch: Dict,
+                                    batch_num: int,
+                                    transform: Callable,
+                                    inverse_transform: Callable,
+                                    ):
+        """
+        Run prediction with the specified transformations
+
+        Args:
+            model: model to predict
+            batch: input batch to model
+            batch_num: batch index
+            transform: transform to apply to batch.
+            inverse_transform: inverse transform to apply to batch and resuls
+        """
+        batch = to_device(batch, device=self.device)
+        if self.pre_transform is not None:
+            batch = self.pre_transform(**batch)
+
+        transformed = transform(**batch)
+
+        if self.post_transform is not None:
+            transformed = self.post_transform(**transformed)
+
+        inp = [transformed[key] for key in self.model_keys]
+        with torch.cuda.amp.autocast():
+            result = model.inference_step(*inp, batch_num=batch_num)
+        result = inverse_transform(**result)
+
+        if not self.ensemble_on_device:
+            result = to_device(result, device="cpu")
+
+        for ensembler in self.ensembler.values():
+            ensembler.process_batch(result=result, batch=batch)
+
+
+def slice_collate(batch: List[Any]):
+    """
+    Add support for slices to collate function
+    
+    Args:
+        batch: batch to collate
+    
+    Returns:
+        Any: collated items
+    """
+    elem = batch[0]
+    elem_type = type(elem)
+    if isinstance(batch[0], slice):
+        return batch
+    elif isinstance(elem, collections.abc.Mapping):
+        return {key: slice_collate([d[key] for d in batch]) for key in elem}
+    elif isinstance(elem, tuple) and hasattr(elem, '_fields'):  # namedtuple
+        return elem_type(*(slice_collate(samples) for samples in zip(*batch)))
+    elif isinstance(elem, collections.abc.Sequence):
+        transposed = zip(*batch)
+        return [slice_collate(samples) for samples in transposed]
+    else:
+        return torch.utils.data._utils.collate.default_collate(batch)
+
+
+PredictorType = TypeVar('PredictorType', bound=Predictor)

nndet/inference/restore.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+from typing import Sequence, Tuple, Union, Optional
+
+import numpy as np
+from loguru import logger
+
+from nndet.detection.boxes.utils import permute_boxes, expand_to_boxes
+from nndet.preprocessing.resampling import resample_data_or_seg, get_do_separate_z, get_lowres_axis
+
+
+def restore_detection(boxes: np.ndarray,
+                      transpose_backward: Sequence[int],
+                      original_spacing: Sequence[float],
+                      spacing_after_resampling: Sequence[float],
+                      crop_bbox: Sequence[Tuple[int, int]],
+                      **kwargs,
+                      ) -> np.ndarray:
+    """
+    Restore boxes from preprocessed space into original space
+
+    Args:
+        boxes: predicted boxes in preprocessing space,
+            (x1, y1, x2, y2, (z1, z2))[N, dims * 2]
+        transpose_backward: backward transposing
+        original_spacing: spacing of the original image
+        spacing_after_resampling: spacing in the preprocessed space
+            (forward transposed order)
+        crop_bbox: bounding box crop in the original spacing [(min_0, max_0), ...]
+        **kwargs: ignored
+
+    Returns:
+        np.ndarray: predicted bounding boxes in the original image space
+    """
+    boxes_transposed = permute_boxes(boxes, transpose_backward)
+
+    original_spacing = np.asarray(original_spacing)
+    spacing_after_resampling = np.asarray(spacing_after_resampling)
+    resampled_spacing = spacing_after_resampling[transpose_backward]
+    scaling = resampled_spacing / original_spacing
+    scaling_expanded = expand_to_boxes(scaling[None])
+    boxes_scaled = boxes_transposed * scaling_expanded
+
+    offset = np.asarray([i[0] for i in crop_bbox])
+    offset_expanded = expand_to_boxes(offset[None])
+    boxes_original = boxes_scaled + offset_expanded
+    return boxes_original
+
+
+def restore_fmap(fmap: np.ndarray,
+                 transpose_backward: Sequence[int],
+                 original_spacing: Sequence[float],
+                 spacing_after_resampling: Sequence[float],
+                 original_size_before_cropping: Sequence[int],
+                 size_after_cropping: Sequence[int],
+                 crop_bbox: Optional[Sequence[Tuple[int, int]]] = None,
+                 interpolation_order: int = 3,
+                 interpolation_order_z: int = 0,
+                 do_separate_z: bool = None,
+                 ) -> np.ndarray:
+    """
+    Restore feature map from preprocessed space into original space
+
+    Args:
+        fmap: feature map to resample [C, dims], where C is the number of
+            channels
+        transpose_backward: backward transposing
+        original_spacing: spacing of the original image
+        spacing_after_resampling: spacing in the preprocessed space
+            (forward transposed order)
+        original_size_before_cropping: original image size before cropping
+        size_after_cropping: image size after cropping
+        crop_bbox: bounding box of crop
+        interpolation_order: interpolation order for inplane axis
+        interpolation_order_z: interpolation order for anisotropic axis
+        do_separate_z: if None then we dynamically decide how to resample
+            along z, if True/False then always/never resample along z
+            separately. Do not touch unless you know what you are doin
+
+    Returns:
+        np.ndarray: resampled feature map [C, new_dims]
+    """
+    fmap_transposed = np.transpose(fmap, [0] + [i + 1 for i in transpose_backward])
+
+    original_spacing = np.asarray(original_spacing)
+    spacing_after_resampling = np.asarray(spacing_after_resampling)
+    resampled_spacing = spacing_after_resampling[transpose_backward]
+
+    if np.any([i != j for i, j in zip(fmap.shape[1:], size_after_cropping)]):
+        lowres_axis = _get_lowres_axes(original_spacing, resampled_spacing,
+                                       do_separate_z=do_separate_z)
+        logger.info(f"Resampling: do separate z: {do_separate_z}; lowres axis: {lowres_axis}")
+        fmap_old_spacing = resample_data_or_seg(fmap, size_after_cropping, is_seg=False,
+                                                axis=lowres_axis, order=interpolation_order,
+                                                do_separate_z=do_separate_z, cval=0,
+                                                order_z=interpolation_order_z)
+    else:
+        logger.info(f"Resampling: no resampling necessary")
+        fmap_old_spacing = fmap_transposed
+
+    if crop_bbox is not None:
+        crop_bbox = [list(cb) for cb in crop_bbox]
+        tmp = np.zeros((fmap_old_spacing.shape[0], *original_size_before_cropping))
+
+        for c in range(len(crop_bbox)):
+            crop_bbox[c][1] = np.min(
+                (crop_bbox[c][0] + fmap_old_spacing.shape[c + 1], original_size_before_cropping[c]))
+
+        _slices = [...] + [slice(b[0], b[1]) for b in crop_bbox]
+        tmp[_slices] = fmap_old_spacing
+        fmap_original = tmp
+    else:
+        fmap_original = fmap_old_spacing
+    return fmap_original
+
+
+def _get_lowres_axes(original_spacing: Sequence[float],
+                     resampled_spacing: Sequence[float],
+                     do_separate_z: bool) -> Union[Sequence[int], None]:
+    """
+    Dynamically determine lowres axes
+
+    Args:
+        original_spacing: original spacing (not transposed!)
+        resampled_spacing: resampled sapcing (not transposed!)
+        do_separate_z: force sepearte
+
+    Returns:
+        Union[Sequence[int], None]: Lowres axes. If None, no lowres axes
+            is present
+    """
+    if do_separate_z is None:
+        if get_do_separate_z(original_spacing):  # original spacing was anisotropic
+            do_separate_z = True
+            lowres_axis = get_lowres_axis(original_spacing)
+        elif get_do_separate_z(resampled_spacing):  # resampled spacing was anisotropic
+            do_separate_z = True
+            lowres_axis = get_lowres_axis(resampled_spacing)
+        else:  # no separate z
+            do_separate_z = False
+            lowres_axis = None
+    else:
+        if do_separate_z:
+            lowres_axis = get_lowres_axis(original_spacing)
+        else:
+            lowres_axis = None
+    return lowres_axis

nndet/inference/sweeper.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+from abc import ABC, abstractmethod
+from pathlib import Path
+import time
+from typing import Callable, Tuple, Dict, Sequence, Any, Optional, TypeVar
+
+import numpy as np
+from loguru import logger
+
+from nndet.io.paths import Pathlike
+from nndet.io.load import save_json
+from nndet.utils.info import maybe_verbose_iterable
+from nndet.utils import to_numpy
+from nndet.evaluator.registry import BoxEvaluator
+
+
+class Sweeper(ABC):
+    def __init__(self,
+                 classes: Sequence[str],
+                 pred_dir: Pathlike,
+                 gt_dir: Pathlike,
+                 target_metric: str,
+                 save_dir: Optional[Pathlike] = None,
+                 ):
+        """
+        Sweep multiple parameters and compute evaluation metrics
+        to determine the best set of parameters
+
+        Args:
+            evaluation: reference to an evaluation objects
+            pred_dir: directory where predicted data is saved
+            device: device to use for internal computations
+        """
+        self.classes = classes
+        self.save_dir = save_dir if save_dir is None else Path(save_dir)
+        if self.save_dir is not None:
+            self.save_dir.mkdir(parents=True, exist_ok=True)
+        self.target_metric = target_metric
+
+        self.device = "cpu"
+
+        self.pred_dir = Path(pred_dir)
+        self.gt_dir = Path(gt_dir)
+
+    @abstractmethod
+    def run_postprocessing_sweep(self,
+                                 restore: bool = True,
+                                 ) -> Tuple[Dict, Dict]:
+        """
+        Run parameter sweeps to determine best parameters
+        accoring to target metric
+
+        Args:
+            target_metric: metric to optimize
+
+        Returns:
+            Dict: determined parameters
+            Dict: final results with parameters
+        """
+        raise NotImplementedError
+
+
+class BoxSweeper(Sweeper):
+    def __init__(self,
+                 classes: Sequence[str],
+                 pred_dir: Pathlike,
+                 gt_dir: Pathlike,
+                 target_metric: str,
+                 ensembler_cls: Callable,
+                 save_dir: Optional[Pathlike] = None,
+                 ) -> None:
+        """
+        Run sweep over parameters and select the best
+
+        Args:
+            classes: classes present in dataset 
+            pred_dir: directory where predictions are saved
+            gt_dir: directory where ground truth is saved
+            target_metric: metric to optimize
+            ensembler_cls: ensembler class used during prediction
+            save_dir: Directory to save results. Defaults to None.
+        """
+        super().__init__(classes=classes,
+                         pred_dir=pred_dir,
+                         gt_dir=gt_dir,
+                         target_metric=target_metric,
+                         save_dir=save_dir,
+                         )
+
+        self.evaluator_cls = BoxEvaluator
+        self.ensembler_cls = ensembler_cls
+
+    def run_postprocessing_sweep(self):
+        """
+        Sequentially search for the best parameters
+
+        Returns:
+            Dict: final parameters to run inference on new cases
+            Dict:
+                `det_scores`: detection score metrics
+                `det_curves`: detection curves
+        """
+        state, sweep_params = self.ensembler_cls.sweep_parameters()
+        num_cases = self.ensembler_cls.get_case_ids(self.pred_dir)
+        logger.info(f"Running parameter sweep on {num_cases} cases to optimize "
+                    f"{self.target_metric} with initial state {state}.")
+
+        best_score = float('-inf')
+        for param_name, values in sweep_params.items():
+            best_value, _best_score = self.run_parameter(
+                values=values,
+                param_name=param_name,
+                state=state,
+                )
+            state[param_name] = best_value
+
+            if _best_score < best_score:
+                logger.error("ERROR: Something went wrong during sweeping. "
+                             "Results were modified inplace! "
+                             f"Previous: {best_score} now {_best_score}")
+            best_score = _best_score
+
+        logger.info(f"\n\n Determined {state} with best sweeping score {best_score} {self.target_metric}\n\n")
+        return state
+
+    def run_parameter(self,
+                      values: Sequence[Any],
+                      param_name: str,
+                      state: Dict[str, Any],
+                      ):
+        """
+        Evaluate parameters and select the best
+        
+        Args:
+            values: values to evaluate
+            param_name: name of parameter
+            state: different state parameters
+        """
+        cache = []
+        overview = {}
+        for value in values:
+            logger.info(f"Running sweep {param_name}={value}")
+            tic = time.perf_counter()
+            metric_scores = self._evaluate_value(state=state, **{param_name: value})
+            overview[f"{param_name}_{value}".replace(".", "_")] = {
+                "state": str(state),
+                "overwrite": {param_name: str(value)},
+                "scores": str(metric_scores),
+            }
+            cache.append(metric_scores[self.target_metric])
+            toc = time.perf_counter()
+            logger.info(f"Sweep took {toc - tic} s")
+
+        best_idx = np.argmax(cache)
+        best_value = values[best_idx]
+        best_score = cache[best_idx]
+
+        if self.save_dir is not None:
+            overview[f"best_{param_name}"] = {"value": str(best_value), "score": str(best_score)}
+            save_json(overview, self.save_dir / f"sweep_{param_name}.json")
+        return best_value, best_score
+
+    def _evaluate_value(self,
+                        state: Dict[str, Any],
+                        **overwrite,
+                        ):
+        """
+        Evalaute a single value
+
+        Args:
+            state: state for ensembler
+            overwrite: state overwrites
+
+        Returns:
+            Dict: scalar metrics
+        """
+        evaluator = self.evaluator_cls.create(classes=self.classes,
+                                              fast=True,
+                                              verbose=False,
+                                              save_dir=None,
+                                              )
+
+        for case_id in maybe_verbose_iterable(self.ensembler_cls.get_case_ids(self.pred_dir)):
+            ensembler = self.ensembler_cls.from_checkpoint(
+                base_dir=self.pred_dir, case_id=case_id, device=self.device,
+                )
+            ensembler.update_parameters(**state)
+            ensembler.update_parameters(**overwrite)
+
+            pred = to_numpy(ensembler.get_case_result(restore=False))
+            gt = np.load(str(self.gt_dir / f"{case_id}_boxes_gt.npz"), allow_pickle=True)
+
+            evaluator.run_online_evaluation(
+                pred_boxes=[pred["pred_boxes"]], pred_classes=[pred["pred_labels"]],
+                pred_scores=[pred["pred_scores"]], gt_boxes=[gt["boxes"]],
+                gt_classes=[gt["classes"]], gt_ignore=None,
+            )
+
+        metric_scores, _ = evaluator.finish_online_evaluation()
+        return metric_scores
+
+
+SweeperType = TypeVar('SweeperType', bound=Sweeper)

nndet/inference/transforms.py

+"""
+Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+
+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.
+"""
+
+from loguru import logger
+from typing import List, Tuple, Sequence
+
+from nndet.io.transforms import Mirror, NoOp
+
+from rising.transforms import AbstractTransform
+
+
+def get_tta_transforms(num_tta_transforms: int, seg: bool = True) -> Tuple[
+        List[AbstractTransform], List[AbstractTransform]]:
+    """
+    Get tta transformations
+
+    Args:
+        num_tta_transforms: number of tta transformations; 0: no tta, 4: augments
+            all directions in 2D, 8: augments all directions in 3D
+
+    Returns:
+        List[AbstractTransform]: transforms for TTA
+        List[AbstractTransform]: inverted transformations for TTA
+    """
+    transforms = [NoOp()]
+    inverse_transforms = [NoOp()]
+    mirror_keys = ["data"]
+    pred_mirror_keys = ["pred_seg"] if seg else ["pred_seg"]
+    boxes_mirror_keys = ["pred_boxes"]
+
+    if num_tta_transforms >= 4:
+        logger.info("Adding 2D Mirror TTA for prediction.")
+        transforms.append(Mirror(keys=mirror_keys, dims=(0,)))
+        transforms.append(Mirror(keys=mirror_keys, dims=(1,)))
+        transforms.append(Mirror(keys=mirror_keys, dims=(0, 1)))
+
+        inverse_transforms.append(Mirror(keys=pred_mirror_keys,
+                                         box_keys=boxes_mirror_keys, dims=(0,)))
+        inverse_transforms.append(Mirror(keys=pred_mirror_keys,
+                                         box_keys=boxes_mirror_keys, dims=(1,)))
+        inverse_transforms.append(Mirror(keys=pred_mirror_keys,
+                                         box_keys=boxes_mirror_keys, dims=(0, 1)))
+
+    if num_tta_transforms >= 8:
+        logger.info("Adding 3D Mirror TTA for prediction.")
+        transforms.append(Mirror(keys=mirror_keys, dims=(2,)))
+        transforms.append(Mirror(keys=mirror_keys, dims=(0, 2)))
+        transforms.append(Mirror(keys=mirror_keys, dims=(1, 2)))
+        transforms.append(Mirror(keys=mirror_keys, dims=(0, 1, 2)))
+
+        inverse_transforms.append(Mirror(keys=pred_mirror_keys,
+                                         box_keys=boxes_mirror_keys, dims=(2,)))
+        inverse_transforms.append(Mirror(keys=pred_mirror_keys,
+                                         box_keys=boxes_mirror_keys, dims=(0, 2)))
+        inverse_transforms.append(Mirror(keys=pred_mirror_keys,
+                                         box_keys=boxes_mirror_keys, dims=(1, 2)))
+        inverse_transforms.append(Mirror(keys=pred_mirror_keys,
+                                         box_keys=boxes_mirror_keys, dims=(0, 1, 2)))
+    return transforms, inverse_transforms
+
+
+class Inference2D(AbstractTransform):
+    def __init__(self,
+                 keys: Sequence[str],
+                 ):
+        """
+        Helper transform to run inference with 2d models
+
+        Args:
+            keys: data keys to remove dimension from for inference
+        """
+        super().__init__(grad=False)
+        self.keys = keys
+
+    def forward(self, **data) -> dict:
+        """
+        Removes first spatial dimension (N, C, [removed], ax1, ax2)
+
+        Args:
+            **data: intput batch
+
+        Returns:
+            dict: transformed batch
+        """
+        for key in self.keys:
+            data[key] = data[key][:, :, 0]
+        return data