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bottomup

sleap_nn.inference.bottomup

Inference modules for BottomUp models.

Classes:

Name Description
BottomUpInferenceModel

BottomUp Inference model.
BottomUpMultiClassInferenceModel

BottomUp Inference model for multi-class models.

BottomUpInferenceModel

Bases: LightningModule

BottomUp Inference model.

This model encapsulates the bottom-up approach. The images are passed to a peak detector to get the predicted instances and then fed into PAF to combine nodes belonging to the same instance.

Attributes:

Name Type Description
torch_model

A nn.Module that accepts rank-5 images as input and predicts rank-4 confidence maps as output. This should be a model that is trained on MultiInstanceConfMaps.
paf_scorer

A sleap_nn.inference.paf_grouping.PAFScorer instance configured to group instances based on peaks and PAFs produced by the model.
cms_output_stride

Output stride of the model, denoting the scale of the output confidence maps relative to the images (after input scaling). This is used for adjusting the peak coordinates to the image grid.
pafs_output_stride

Output stride of the model, denoting the scale of the output pafs relative to the images (after input scaling). This is used for adjusting the peak coordinates to the image grid.
peak_threshold

Minimum confidence map value to consider a global peak as valid.
refinement

If None, returns the grid-aligned peaks with no refinement. If "integral", peaks will be refined with integral regression. If "local", peaks will be refined with quarter pixel local gradient offset. This has no effect if the model has an offset regression head.
integral_patch_size

Size of patches to crop around each rough peak for integral refinement as an integer scalar.
return_confmaps

If True, the confidence maps will be returned together with the predicted peaks. This will result in slower inference times since the data must be copied off of the GPU, but is useful for visualizing the raw output of the model.
return_pafs

If True, the part affinity fields will be returned together with the predicted instances. This will result in slower inference times since the data must be copied off of the GPU, but is useful for visualizing the raw output of the model.
return_paf_graph

If True, the part affinity field graph will be returned together with the predicted instances. The graph is obtained by parsing the part affinity fields with the paf_scorer instance and is an intermediate representation used during instance grouping.
input_scale

Float indicating if the images should be resized before being passed to the model.

Methods:

Name Description
__init__

Initialise the model attributes.
forward

Predict confidence maps and infer peak coordinates.
Source code in sleap_nn/inference/bottomup.py 
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class BottomUpInferenceModel(L.LightningModule):
    """BottomUp Inference model.

    This model encapsulates the bottom-up approach. The images are passed to a peak detector
    to get the predicted instances and then fed into PAF to combine nodes belonging to
    the same instance.

    Attributes:
        torch_model: A `nn.Module` that accepts rank-5 images as input and predicts
            rank-4 confidence maps as output. This should be a model that is trained on
            MultiInstanceConfMaps.
        paf_scorer: A `sleap_nn.inference.paf_grouping.PAFScorer` instance configured to group
            instances based on peaks and PAFs produced by the model.
        cms_output_stride: Output stride of the model, denoting the scale of the output
            confidence maps relative to the images (after input scaling). This is used
            for adjusting the peak coordinates to the image grid.
        pafs_output_stride: Output stride of the model, denoting the scale of the output
            pafs relative to the images (after input scaling). This is used
            for adjusting the peak coordinates to the image grid.
        peak_threshold: Minimum confidence map value to consider a global peak as valid.
        refinement: If `None`, returns the grid-aligned peaks with no refinement. If
            `"integral"`, peaks will be refined with integral regression. If `"local"`,
            peaks will be refined with quarter pixel local gradient offset. This has no
            effect if the model has an offset regression head.
        integral_patch_size: Size of patches to crop around each rough peak for integral
            refinement as an integer scalar.
        return_confmaps: If `True`, the confidence maps will be returned together with
            the predicted peaks. This will result in slower inference times since
            the data must be copied off of the GPU, but is useful for visualizing the
            raw output of the model.
        return_pafs: If `True`, the part affinity fields will be returned together with
            the predicted instances. This will result in slower inference times since
            the data must be copied off of the GPU, but is useful for visualizing the
            raw output of the model.
        return_paf_graph: If `True`, the part affinity field graph will be returned
            together with the predicted instances. The graph is obtained by parsing the
            part affinity fields with the `paf_scorer` instance and is an intermediate
            representation used during instance grouping.
        input_scale: Float indicating if the images should be resized before being
            passed to the model.
    """

    def __init__(
        self,
        torch_model: L.LightningModule,
        paf_scorer: PAFScorer,
        cms_output_stride: Optional[int] = None,
        pafs_output_stride: Optional[int] = None,
        peak_threshold: float = 0.0,
        refinement: Optional[str] = "integral",
        integral_patch_size: int = 5,
        return_confmaps: Optional[bool] = False,
        return_pafs: Optional[bool] = False,
        return_paf_graph: Optional[bool] = False,
        input_scale: float = 1.0,
        max_peaks_per_node: Optional[int] = None,
    ):
        """Initialise the model attributes.

        Args:
            torch_model: A `nn.Module` that accepts images and predicts confidence maps.
            paf_scorer: A `PAFScorer` instance for grouping instances.
            cms_output_stride: Output stride of confidence maps relative to images.
            pafs_output_stride: Output stride of PAFs relative to images.
            peak_threshold: Minimum confidence map value for valid peaks.
            refinement: Peak refinement method: None, "integral", or "local".
            integral_patch_size: Size of patches for integral refinement.
            return_confmaps: If True, return confidence maps in output.
            return_pafs: If True, return PAFs in output.
            return_paf_graph: If True, return intermediate PAF graph in output.
            input_scale: Scale factor applied to input images.
            max_peaks_per_node: Maximum number of peaks allowed per node before
                skipping PAF scoring. If any node has more peaks than this limit,
                empty predictions are returned. This prevents combinatorial explosion
                during early training when confidence maps are noisy. Set to None to
                disable this check (default). Recommended value: 100.
        """
        super().__init__()
        self.torch_model = torch_model
        self.paf_scorer = paf_scorer
        self.peak_threshold = peak_threshold
        self.refinement = refinement
        self.integral_patch_size = integral_patch_size
        self.cms_output_stride = cms_output_stride
        self.pafs_output_stride = pafs_output_stride
        self.return_confmaps = return_confmaps
        self.return_pafs = return_pafs
        self.return_paf_graph = return_paf_graph
        self.input_scale = input_scale
        self.max_peaks_per_node = max_peaks_per_node

    def _generate_cms_peaks(self, cms):
        # TODO: append nans to batch them -> tensor (vectorize the initial paf grouping steps)
        peaks, peak_vals, sample_inds, peak_channel_inds = find_local_peaks(
            cms.detach(),
            threshold=self.peak_threshold,
            refinement=self.refinement,
            integral_patch_size=self.integral_patch_size,
        )
        # Adjust for stride and scale.
        peaks = peaks * self.cms_output_stride  # (n_centroids, 2)

        cms_peaks, cms_peak_vals, cms_peak_channel_inds = [], [], []

        for b in range(self.batch_size):
            cms_peaks.append(peaks[sample_inds == b])
            cms_peak_vals.append(peak_vals[sample_inds == b].to(torch.float32))
            cms_peak_channel_inds.append(peak_channel_inds[sample_inds == b])

        # cms_peaks: [(#nodes, 2), ...]
        return cms_peaks, cms_peak_vals, cms_peak_channel_inds

    def forward(self, inputs: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
        """Predict confidence maps and infer peak coordinates.

        Args:
            inputs: Dictionary with "image" as one of the keys.

        Returns:
            A dictionary of outputs with keys:

            `"pred_instance_peaks"`: The predicted peaks for each instance in the batch
                as a `torch.Tensor` of shape `(samples, nodes, 2)`.
            `"pred_peak_vals"`: The value of the confidence maps at the predicted
                peaks for each instance in the batch as a `torch.Tensor` of shape
                `(samples, nodes)`.

        """
        # Network forward pass.
        self.batch_size = inputs["image"].shape[0]
        output = self.torch_model(inputs["image"])
        cms = output["MultiInstanceConfmapsHead"]
        pafs = output["PartAffinityFieldsHead"].permute(
            0, 2, 3, 1
        )  # (batch, h, w, 2*edges)
        cms_peaks, cms_peak_vals, cms_peak_channel_inds = self._generate_cms_peaks(cms)

        # Check if too many peaks per node (prevents combinatorial explosion)
        skip_paf_scoring = False
        if self.max_peaks_per_node is not None:
            n_nodes = cms.shape[1]
            for b in range(self.batch_size):
                for node_idx in range(n_nodes):
                    n_peaks = int((cms_peak_channel_inds[b] == node_idx).sum().item())
                    if n_peaks > self.max_peaks_per_node:
                        logger.warning(
                            f"Skipping PAF scoring: node {node_idx} has {n_peaks} peaks "
                            f"(max_peaks_per_node={self.max_peaks_per_node}). "
                            f"Model may need more training."
                        )
                        skip_paf_scoring = True
                        break
                if skip_paf_scoring:
                    break

        if skip_paf_scoring:
            # Return empty predictions for each sample
            device = cms.device
            n_nodes = cms.shape[1]
            predicted_instances_adjusted = []
            predicted_peak_scores = []
            predicted_instance_scores = []
            for _ in range(self.batch_size):
                predicted_instances_adjusted.append(
                    torch.full((0, n_nodes, 2), float("nan"), device=device)
                )
                predicted_peak_scores.append(
                    torch.full((0, n_nodes), float("nan"), device=device)
                )
                predicted_instance_scores.append(torch.tensor([], device=device))
            edge_inds = [
                torch.tensor([], dtype=torch.int32, device=device)
            ] * self.batch_size
            edge_peak_inds = [
                torch.tensor([], dtype=torch.int32, device=device).reshape(0, 2)
            ] * self.batch_size
            line_scores = [torch.tensor([], device=device)] * self.batch_size
        else:
            (
                predicted_instances,
                predicted_peak_scores,
                predicted_instance_scores,
                edge_inds,
                edge_peak_inds,
                line_scores,
            ) = self.paf_scorer.predict(
                pafs=pafs,
                peaks=cms_peaks,
                peak_vals=cms_peak_vals,
                peak_channel_inds=cms_peak_channel_inds,
            )

            predicted_instances = [p / self.input_scale for p in predicted_instances]
            predicted_instances_adjusted = []
            for idx, p in enumerate(predicted_instances):
                predicted_instances_adjusted.append(
                    p / inputs["eff_scale"][idx].to(p.device)
                )

        out = {
            "pred_instance_peaks": predicted_instances_adjusted,
            "pred_peak_values": predicted_peak_scores,
            "instance_scores": predicted_instance_scores,
        }

        if self.return_confmaps:
            out["pred_confmaps"] = cms.detach()
        if self.return_pafs:
            out["pred_part_affinity_fields"] = pafs.detach()
        if self.return_paf_graph:
            out["peaks"] = cms_peaks
            out["peak_vals"] = cms_peak_vals
            out["peak_channel_inds"] = cms_peak_channel_inds
            out["edge_inds"] = edge_inds
            out["edge_peak_inds"] = edge_peak_inds
            out["line_scores"] = line_scores

        inputs.update(out)
        return [inputs]

__init__(torch_model, paf_scorer, cms_output_stride=None, pafs_output_stride=None, peak_threshold=0.0, refinement='integral', integral_patch_size=5, return_confmaps=False, return_pafs=False, return_paf_graph=False, input_scale=1.0, max_peaks_per_node=None)

Initialise the model attributes.

Parameters:

Name Type Description Default
torch_model LightningModule

A nn.Module that accepts images and predicts confidence maps.

 required
paf_scorer PAFScorer

A PAFScorer instance for grouping instances.

 required
cms_output_stride Optional[int]

Output stride of confidence maps relative to images.

 None
pafs_output_stride Optional[int]

Output stride of PAFs relative to images.

 None
peak_threshold float

Minimum confidence map value for valid peaks.

 0.0
refinement Optional[str]

Peak refinement method: None, "integral", or "local".

 'integral'
integral_patch_size int

Size of patches for integral refinement.

 5
return_confmaps Optional[bool]

If True, return confidence maps in output.

 False
return_pafs Optional[bool]

If True, return PAFs in output.

 False
return_paf_graph Optional[bool]

If True, return intermediate PAF graph in output.

 False
input_scale float

Scale factor applied to input images.

 1.0
max_peaks_per_node Optional[int]

Maximum number of peaks allowed per node before skipping PAF scoring. If any node has more peaks than this limit, empty predictions are returned. This prevents combinatorial explosion during early training when confidence maps are noisy. Set to None to disable this check (default). Recommended value: 100.

 None
Source code in sleap_nn/inference/bottomup.py 
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def __init__(
    self,
    torch_model: L.LightningModule,
    paf_scorer: PAFScorer,
    cms_output_stride: Optional[int] = None,
    pafs_output_stride: Optional[int] = None,
    peak_threshold: float = 0.0,
    refinement: Optional[str] = "integral",
    integral_patch_size: int = 5,
    return_confmaps: Optional[bool] = False,
    return_pafs: Optional[bool] = False,
    return_paf_graph: Optional[bool] = False,
    input_scale: float = 1.0,
    max_peaks_per_node: Optional[int] = None,
):
    """Initialise the model attributes.

    Args:
        torch_model: A `nn.Module` that accepts images and predicts confidence maps.
        paf_scorer: A `PAFScorer` instance for grouping instances.
        cms_output_stride: Output stride of confidence maps relative to images.
        pafs_output_stride: Output stride of PAFs relative to images.
        peak_threshold: Minimum confidence map value for valid peaks.
        refinement: Peak refinement method: None, "integral", or "local".
        integral_patch_size: Size of patches for integral refinement.
        return_confmaps: If True, return confidence maps in output.
        return_pafs: If True, return PAFs in output.
        return_paf_graph: If True, return intermediate PAF graph in output.
        input_scale: Scale factor applied to input images.
        max_peaks_per_node: Maximum number of peaks allowed per node before
            skipping PAF scoring. If any node has more peaks than this limit,
            empty predictions are returned. This prevents combinatorial explosion
            during early training when confidence maps are noisy. Set to None to
            disable this check (default). Recommended value: 100.
    """
    super().__init__()
    self.torch_model = torch_model
    self.paf_scorer = paf_scorer
    self.peak_threshold = peak_threshold
    self.refinement = refinement
    self.integral_patch_size = integral_patch_size
    self.cms_output_stride = cms_output_stride
    self.pafs_output_stride = pafs_output_stride
    self.return_confmaps = return_confmaps
    self.return_pafs = return_pafs
    self.return_paf_graph = return_paf_graph
    self.input_scale = input_scale
    self.max_peaks_per_node = max_peaks_per_node

forward(inputs)

Predict confidence maps and infer peak coordinates.

Parameters:

Name Type Description Default
inputs Dict[str, Tensor]

Dictionary with "image" as one of the keys.

 required

Returns:

Type Description
Dict[str, Tensor]

A dictionary of outputs with keys:

"pred_instance_peaks": The predicted peaks for each instance in the batch as a torch.Tensor of shape (samples, nodes, 2). "pred_peak_vals": The value of the confidence maps at the predicted peaks for each instance in the batch as a torch.Tensor of shape (samples, nodes).
Source code in sleap_nn/inference/bottomup.py 
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def forward(self, inputs: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
    """Predict confidence maps and infer peak coordinates.

    Args:
        inputs: Dictionary with "image" as one of the keys.

    Returns:
        A dictionary of outputs with keys:

        `"pred_instance_peaks"`: The predicted peaks for each instance in the batch
            as a `torch.Tensor` of shape `(samples, nodes, 2)`.
        `"pred_peak_vals"`: The value of the confidence maps at the predicted
            peaks for each instance in the batch as a `torch.Tensor` of shape
            `(samples, nodes)`.

    """
    # Network forward pass.
    self.batch_size = inputs["image"].shape[0]
    output = self.torch_model(inputs["image"])
    cms = output["MultiInstanceConfmapsHead"]
    pafs = output["PartAffinityFieldsHead"].permute(
        0, 2, 3, 1
    )  # (batch, h, w, 2*edges)
    cms_peaks, cms_peak_vals, cms_peak_channel_inds = self._generate_cms_peaks(cms)

    # Check if too many peaks per node (prevents combinatorial explosion)
    skip_paf_scoring = False
    if self.max_peaks_per_node is not None:
        n_nodes = cms.shape[1]
        for b in range(self.batch_size):
            for node_idx in range(n_nodes):
                n_peaks = int((cms_peak_channel_inds[b] == node_idx).sum().item())
                if n_peaks > self.max_peaks_per_node:
                    logger.warning(
                        f"Skipping PAF scoring: node {node_idx} has {n_peaks} peaks "
                        f"(max_peaks_per_node={self.max_peaks_per_node}). "
                        f"Model may need more training."
                    )
                    skip_paf_scoring = True
                    break
            if skip_paf_scoring:
                break

    if skip_paf_scoring:
        # Return empty predictions for each sample
        device = cms.device
        n_nodes = cms.shape[1]
        predicted_instances_adjusted = []
        predicted_peak_scores = []
        predicted_instance_scores = []
        for _ in range(self.batch_size):
            predicted_instances_adjusted.append(
                torch.full((0, n_nodes, 2), float("nan"), device=device)
            )
            predicted_peak_scores.append(
                torch.full((0, n_nodes), float("nan"), device=device)
            )
            predicted_instance_scores.append(torch.tensor([], device=device))
        edge_inds = [
            torch.tensor([], dtype=torch.int32, device=device)
        ] * self.batch_size
        edge_peak_inds = [
            torch.tensor([], dtype=torch.int32, device=device).reshape(0, 2)
        ] * self.batch_size
        line_scores = [torch.tensor([], device=device)] * self.batch_size
    else:
        (
            predicted_instances,
            predicted_peak_scores,
            predicted_instance_scores,
            edge_inds,
            edge_peak_inds,
            line_scores,
        ) = self.paf_scorer.predict(
            pafs=pafs,
            peaks=cms_peaks,
            peak_vals=cms_peak_vals,
            peak_channel_inds=cms_peak_channel_inds,
        )

        predicted_instances = [p / self.input_scale for p in predicted_instances]
        predicted_instances_adjusted = []
        for idx, p in enumerate(predicted_instances):
            predicted_instances_adjusted.append(
                p / inputs["eff_scale"][idx].to(p.device)
            )

    out = {
        "pred_instance_peaks": predicted_instances_adjusted,
        "pred_peak_values": predicted_peak_scores,
        "instance_scores": predicted_instance_scores,
    }

    if self.return_confmaps:
        out["pred_confmaps"] = cms.detach()
    if self.return_pafs:
        out["pred_part_affinity_fields"] = pafs.detach()
    if self.return_paf_graph:
        out["peaks"] = cms_peaks
        out["peak_vals"] = cms_peak_vals
        out["peak_channel_inds"] = cms_peak_channel_inds
        out["edge_inds"] = edge_inds
        out["edge_peak_inds"] = edge_peak_inds
        out["line_scores"] = line_scores

    inputs.update(out)
    return [inputs]

BottomUpMultiClassInferenceModel

Bases: LightningModule

BottomUp Inference model for multi-class models.

This model encapsulates the bottom-up approach. The images are passed to a local peak detector to get the predicted instances and then grouped into instances by their identity classifications.

Attributes:

Name Type Description
torch_model

A nn.Module that accepts rank-5 images as input and predicts rank-4 confidence maps as output. This should be a model that is trained on MultiInstanceConfMaps.
cms_output_stride

Output stride of the model, denoting the scale of the output confidence maps relative to the images (after input scaling). This is used for adjusting the peak coordinates to the image grid.
class_maps_output_stride

Output stride of the model, denoting the scale of the output pafs relative to the images (after input scaling). This is used for adjusting the peak coordinates to the image grid.
peak_threshold

Minimum confidence map value to consider a global peak as valid.
refinement

If None, returns the grid-aligned peaks with no refinement. If "integral", peaks will be refined with integral regression. If "local", peaks will be refined with quarter pixel local gradient offset. This has no effect if the model has an offset regression head.
integral_patch_size

Size of patches to crop around each rough peak for integral refinement as an integer scalar.
return_confmaps

If True, the confidence maps will be returned together with the predicted peaks. This will result in slower inference times since the data must be copied off of the GPU, but is useful for visualizing the raw output of the model.
return_class_maps

If True, the class maps will be returned together with the predicted instances. This will result in slower inference times since the data must be copied off of the GPU, but is useful for visualizing the raw output of the model.
input_scale

Float indicating if the images should be resized before being passed to the model.

Methods:

Name Description
__init__

Initialise the model attributes.
forward

Predict confidence maps and infer peak coordinates.
Source code in sleap_nn/inference/bottomup.py 
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class BottomUpMultiClassInferenceModel(L.LightningModule):
    """BottomUp Inference model for multi-class models.

    This model encapsulates the bottom-up approach. The images are passed to a local peak detector
    to get the predicted instances and then grouped into instances by their identity
    classifications.

    Attributes:
        torch_model: A `nn.Module` that accepts rank-5 images as input and predicts
            rank-4 confidence maps as output. This should be a model that is trained on
            MultiInstanceConfMaps.
        cms_output_stride: Output stride of the model, denoting the scale of the output
            confidence maps relative to the images (after input scaling). This is used
            for adjusting the peak coordinates to the image grid.
        class_maps_output_stride: Output stride of the model, denoting the scale of the output
            pafs relative to the images (after input scaling). This is used
            for adjusting the peak coordinates to the image grid.
        peak_threshold: Minimum confidence map value to consider a global peak as valid.
        refinement: If `None`, returns the grid-aligned peaks with no refinement. If
            `"integral"`, peaks will be refined with integral regression. If `"local"`,
            peaks will be refined with quarter pixel local gradient offset. This has no
            effect if the model has an offset regression head.
        integral_patch_size: Size of patches to crop around each rough peak for integral
            refinement as an integer scalar.
        return_confmaps: If `True`, the confidence maps will be returned together with
            the predicted peaks. This will result in slower inference times since
            the data must be copied off of the GPU, but is useful for visualizing the
            raw output of the model.
        return_class_maps: If `True`, the class maps will be returned together with
            the predicted instances. This will result in slower inference times since
            the data must be copied off of the GPU, but is useful for visualizing the
            raw output of the model.
        input_scale: Float indicating if the images should be resized before being
            passed to the model.
    """

    def __init__(
        self,
        torch_model: L.LightningModule,
        cms_output_stride: Optional[int] = None,
        class_maps_output_stride: Optional[int] = None,
        peak_threshold: float = 0.0,
        refinement: Optional[str] = "integral",
        integral_patch_size: int = 5,
        return_confmaps: Optional[bool] = False,
        return_class_maps: Optional[bool] = False,
        input_scale: float = 1.0,
    ):
        """Initialise the model attributes."""
        super().__init__()
        self.torch_model = torch_model
        self.peak_threshold = peak_threshold
        self.refinement = refinement
        self.integral_patch_size = integral_patch_size
        self.cms_output_stride = cms_output_stride
        self.class_maps_output_stride = class_maps_output_stride
        self.return_confmaps = return_confmaps
        self.return_class_maps = return_class_maps
        self.input_scale = input_scale

    def _generate_cms_peaks(self, cms):
        # TODO: append nans to batch them -> tensor (vectorize the initial paf grouping steps)
        peaks, peak_vals, sample_inds, peak_channel_inds = find_local_peaks(
            cms.detach(),
            threshold=self.peak_threshold,
            refinement=self.refinement,
            integral_patch_size=self.integral_patch_size,
        )
        # Adjust for stride and scale.
        peaks = peaks * self.cms_output_stride  # (n_centroids, 2)

        return peaks, peak_vals, sample_inds, peak_channel_inds

    def forward(self, inputs: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
        """Predict confidence maps and infer peak coordinates.

        Args:
            inputs: Dictionary with "image" as one of the keys.

        Returns:
            A dictionary of outputs with keys:

            `"pred_instance_peaks"`: The predicted peaks for each instance in the batch
                as a `torch.Tensor` of shape `(samples, nodes, 2)`.
            `"pred_peak_vals"`: The value of the confidence maps at the predicted
                peaks for each instance in the batch as a `torch.Tensor` of shape
                `(samples, nodes)`.

        """
        # Network forward pass.
        self.batch_size = inputs["image"].shape[0]
        output = self.torch_model(inputs["image"])
        cms = output["MultiInstanceConfmapsHead"]
        class_maps = output["ClassMapsHead"]  # (batch, n_classes, h, w)
        cms_peaks, cms_peak_vals, cms_peak_sample_inds, cms_peak_channel_inds = (
            self._generate_cms_peaks(cms.detach())
        )

        cms_peaks = cms_peaks / self.class_maps_output_stride
        (
            predicted_instances,
            predicted_peak_scores,
            predicted_instance_scores,
        ) = classify_peaks_from_maps(
            class_maps.detach(),
            cms_peaks,
            cms_peak_vals,
            cms_peak_sample_inds,
            cms_peak_channel_inds,
            n_channels=cms.shape[-3],
        )
        predicted_instances = [
            p * self.class_maps_output_stride for p in predicted_instances
        ]

        # Adjust for input scaling.
        if self.input_scale != 1.0:
            predicted_instances = [p / self.input_scale for p in predicted_instances]

        predicted_instances_adjusted = []
        for idx, p in enumerate(predicted_instances):
            predicted_instances_adjusted.append(
                p / inputs["eff_scale"][idx].to(p.device)
            )
        out = {
            "pred_instance_peaks": predicted_instances_adjusted,
            "pred_peak_values": predicted_peak_scores,
            "instance_scores": predicted_instance_scores,
        }

        if self.return_confmaps:
            out["pred_confmaps"] = cms.detach()
        if self.return_class_maps:
            out["pred_class_maps"] = class_maps.detach()

        inputs.update(out)
        return [inputs]

__init__(torch_model, cms_output_stride=None, class_maps_output_stride=None, peak_threshold=0.0, refinement='integral', integral_patch_size=5, return_confmaps=False, return_class_maps=False, input_scale=1.0)

Initialise the model attributes.

Source code in sleap_nn/inference/bottomup.py 
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def __init__(
    self,
    torch_model: L.LightningModule,
    cms_output_stride: Optional[int] = None,
    class_maps_output_stride: Optional[int] = None,
    peak_threshold: float = 0.0,
    refinement: Optional[str] = "integral",
    integral_patch_size: int = 5,
    return_confmaps: Optional[bool] = False,
    return_class_maps: Optional[bool] = False,
    input_scale: float = 1.0,
):
    """Initialise the model attributes."""
    super().__init__()
    self.torch_model = torch_model
    self.peak_threshold = peak_threshold
    self.refinement = refinement
    self.integral_patch_size = integral_patch_size
    self.cms_output_stride = cms_output_stride
    self.class_maps_output_stride = class_maps_output_stride
    self.return_confmaps = return_confmaps
    self.return_class_maps = return_class_maps
    self.input_scale = input_scale

forward(inputs)

Predict confidence maps and infer peak coordinates.

Parameters:

Name Type Description Default
inputs Dict[str, Tensor]

Dictionary with "image" as one of the keys.

 required

Returns:

Type Description
Dict[str, Tensor]

A dictionary of outputs with keys:

"pred_instance_peaks": The predicted peaks for each instance in the batch as a torch.Tensor of shape (samples, nodes, 2). "pred_peak_vals": The value of the confidence maps at the predicted peaks for each instance in the batch as a torch.Tensor of shape (samples, nodes).
Source code in sleap_nn/inference/bottomup.py 
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def forward(self, inputs: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
    """Predict confidence maps and infer peak coordinates.

    Args:
        inputs: Dictionary with "image" as one of the keys.

    Returns:
        A dictionary of outputs with keys:

        `"pred_instance_peaks"`: The predicted peaks for each instance in the batch
            as a `torch.Tensor` of shape `(samples, nodes, 2)`.
        `"pred_peak_vals"`: The value of the confidence maps at the predicted
            peaks for each instance in the batch as a `torch.Tensor` of shape
            `(samples, nodes)`.

    """
    # Network forward pass.
    self.batch_size = inputs["image"].shape[0]
    output = self.torch_model(inputs["image"])
    cms = output["MultiInstanceConfmapsHead"]
    class_maps = output["ClassMapsHead"]  # (batch, n_classes, h, w)
    cms_peaks, cms_peak_vals, cms_peak_sample_inds, cms_peak_channel_inds = (
        self._generate_cms_peaks(cms.detach())
    )

    cms_peaks = cms_peaks / self.class_maps_output_stride
    (
        predicted_instances,
        predicted_peak_scores,
        predicted_instance_scores,
    ) = classify_peaks_from_maps(
        class_maps.detach(),
        cms_peaks,
        cms_peak_vals,
        cms_peak_sample_inds,
        cms_peak_channel_inds,
        n_channels=cms.shape[-3],
    )
    predicted_instances = [
        p * self.class_maps_output_stride for p in predicted_instances
    ]

    # Adjust for input scaling.
    if self.input_scale != 1.0:
        predicted_instances = [p / self.input_scale for p in predicted_instances]

    predicted_instances_adjusted = []
    for idx, p in enumerate(predicted_instances):
        predicted_instances_adjusted.append(
            p / inputs["eff_scale"][idx].to(p.device)
        )
    out = {
        "pred_instance_peaks": predicted_instances_adjusted,
        "pred_peak_values": predicted_peak_scores,
        "instance_scores": predicted_instance_scores,
    }

    if self.return_confmaps:
        out["pred_confmaps"] = cms.detach()
    if self.return_class_maps:
        out["pred_class_maps"] = class_maps.detach()

    inputs.update(out)
    return [inputs]