Module facetorch.analyzer.predictor.post
Expand source code
from abc import abstractmethod
from typing import List, Optional, Tuple, Union
import torch
from codetiming import Timer
from itertools import compress
from facetorch.base import BaseProcessor
from facetorch.datastruct import Prediction
from facetorch.logger import LoggerJsonFile
from torchvision import transforms
logger = LoggerJsonFile().logger
class BasePredPostProcessor(BaseProcessor):
@Timer(
"BasePredPostProcessor.__init__",
"{name}: {milliseconds:.2f} ms",
logger=logger.debug,
)
def __init__(
self,
transform: transforms.Compose,
device: torch.device,
optimize_transform: bool,
labels: List[str],
):
"""Base class for predictor post processors.
All predictor post processors should subclass it.
All subclass should overwrite:
- Methods:``run``, used for running the processing
Args:
device (torch.device): Torch device cpu or cuda.
transform (transforms.Compose): Transform compose object to be applied to the image.
optimize_transform (bool): Whether to optimize the transform.
labels (List[str]): List of labels.
"""
super().__init__(transform, device, optimize_transform)
self.labels = labels
def create_pred_list(
self, preds: torch.Tensor, indices: List[int]
) -> List[Prediction]:
"""Create a list of predictions.
Args:
preds (torch.Tensor): Tensor of predictions, shape (batch, _).
indices (List[int]): List of label indices, one for each sample.
Returns:
List[Prediction]: List of predictions.
"""
assert (
len(indices) == preds.shape[0]
), "Predictions and indices must have the same length."
pred_labels = [self.labels[indx] for indx in indices]
pred_list = []
for i, label in enumerate(pred_labels):
pred = Prediction(label, preds[i])
pred_list.append(pred)
return pred_list
@abstractmethod
def run(self, preds: Union[torch.Tensor, Tuple[torch.Tensor]]) -> List[Prediction]:
"""Abstract method that runs the predictor post processing functionality and returns a list of prediction data structures, one for each face in the batch.
Args:
preds (Union[torch.Tensor, Tuple[torch.Tensor]]): Output of the predictor model.
Returns:
List[Prediction]: List of predictions.
"""
class PostArgMax(BasePredPostProcessor):
@Timer("PostArgMax.__init__", "{name}: {milliseconds:.2f} ms", logger=logger.debug)
def __init__(
self,
transform: transforms.Compose,
device: torch.device,
optimize_transform: bool,
labels: List[str],
dim: int,
):
"""Initialize the predictor postprocessor that runs argmax on the prediction tensor and returns a list of prediction data structures.
Args:
transform (Compose): Composed Torch transform object.
device (torch.device): Torch device cpu or cuda.
optimize_transform (bool): Whether to optimize the transform using TorchScript.
labels (List[str]): List of labels.
dim (int): Axis along which to apply the argmax.
"""
super().__init__(transform, device, optimize_transform, labels)
self.dim = dim
@Timer("PostArgMax.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug)
def run(self, preds: torch.Tensor) -> List[Prediction]:
"""Post-processes the prediction tensor using argmax and returns a list of prediction data structures, one for each face.
Args:
preds (torch.Tensor): Batch prediction tensor.
Returns:
List[Prediction]: List of prediction data structures containing the predicted labels and confidence scores for each face in the batch.
"""
indices = torch.argmax(preds, dim=self.dim).cpu().numpy().tolist()
pred_list = self.create_pred_list(preds, indices)
return pred_list
class PostSigmoidBinary(BasePredPostProcessor):
@Timer(
"PostSigmoidBinary.__init__",
"{name}: {milliseconds:.2f} ms",
logger=logger.debug,
)
def __init__(
self,
transform: transforms.Compose,
device: torch.device,
optimize_transform: bool,
labels: List[str],
threshold: float = 0.5,
):
"""Initialize the predictor postprocessor that runs sigmoid on the prediction tensor and returns a list of prediction data structures.
Args:
transform (Compose): Composed Torch transform object.
device (torch.device): Torch device cpu or cuda.
optimize_transform (bool): Whether to optimize the transform using TorchScript.
labels (List[str]): List of labels.
threshold (float): Probability threshold for positive class.
"""
super().__init__(transform, device, optimize_transform, labels)
self.threshold = threshold
@Timer(
"PostSigmoidBinary.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug
)
def run(self, preds: torch.Tensor) -> List[Prediction]:
"""Post-processes the prediction tensor using argmax and returns a list of prediction data structures, one for each face.
Args:
preds (torch.Tensor): Batch prediction tensor.
Returns:
List[Prediction]: List of prediction data structures containing the predicted labelsand confidence scores for each face in the batch.
"""
preds = torch.sigmoid(preds.squeeze(1))
preds_thresh = preds.where(preds >= self.threshold, torch.zeros_like(preds))
indices = torch.round(preds_thresh)
indices = indices.cpu().numpy().astype(int).tolist()
pred_list = self.create_pred_list(preds, indices)
return pred_list
class PostEmbedder(BasePredPostProcessor):
def __init__(
self,
transform: transforms.Compose,
device: torch.device,
optimize_transform: bool,
labels: List[str],
):
"""Initialize the predictor postprocessor that extracts the embedding from the prediction tensor and returns a list of prediction data structures.
Args:
transform (Compose): Composed Torch transform object.
device (torch.device): Torch device cpu or cuda.
optimize_transform (bool): Whether to optimize the transform using TorchScript.
labels (List[str]): List of labels.
"""
super().__init__(transform, device, optimize_transform, labels)
@Timer("PostEmbedder.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug)
def run(self, preds: torch.Tensor) -> List[Prediction]:
"""Extracts the embedding from the prediction tensor and returns a list of prediction data structures, one for each face.
Args:
preds (torch.Tensor): Batch prediction tensor.
Returns:
List[Prediction]: List of prediction data structures containing the predicted embeddings.
"""
if isinstance(preds, tuple):
preds = preds[0]
indices = [0] * preds.shape[0]
pred_list = self.create_pred_list(preds, indices)
return pred_list
class PostMultiLabel(BasePredPostProcessor):
def __init__(
self,
transform: transforms.Compose,
device: torch.device,
optimize_transform: bool,
labels: List[str],
dim: int,
threshold: float = 0.5,
):
"""Initialize the predictor postprocessor that extracts multiple labels from the confidence scores.
Args:
transform (Compose): Composed Torch transform object.
device (torch.device): Torch device cpu or cuda.
optimize_transform (bool): Whether to optimize the transform using TorchScript.
labels (List[str]): List of labels.
dim (int): Axis along which to apply the softmax.
threshold (float): Probability threshold for including a label. Only labels with a confidence score above the threshold are included. Defaults to 0.5.
"""
super().__init__(transform, device, optimize_transform, labels)
self.dim = dim
self.threshold = threshold
@Timer("PostMultiLabel.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug)
def run(self, preds: torch.Tensor) -> List[Prediction]:
"""Extracts multiple labels and puts them in other[multi] predictions. The most likely label is put in the label field. Confidence scores are returned in the logits field.
Args:
preds (torch.Tensor): Batch prediction tensor.
Returns:
List[Prediction]: List of prediction data structures containing the most prevailing label, confidence scores, and multiple labels for each face.
"""
if isinstance(preds, tuple):
preds = preds[0]
indices = torch.argmax(preds, dim=self.dim).cpu().numpy().tolist()
pred_list = []
for i in range(preds.shape[0]):
preds_sample = preds[i]
label_filter = (preds_sample > self.threshold).cpu().numpy().tolist()
labels_true = list(compress(self.labels, label_filter))
pred = Prediction(
label=self.labels[indices[i]],
logits=preds_sample,
other={"multi": labels_true},
)
pred_list.append(pred)
return pred_list
class PostLabelConfidencePairs(BasePredPostProcessor):
def __init__(
self,
transform: transforms.Compose,
device: torch.device,
optimize_transform: bool,
labels: List[str],
offsets: Optional[List[float]] = [0.25, 0.05],
):
"""Initialize the predictor postprocessor that zips the confidence scores with the labels.
Args:
transform (Compose): Composed Torch transform object.
device (torch.device): Torch device cpu or cuda.
optimize_transform (bool): Whether to optimize the transform using TorchScript.
labels (List[str]): List of labels.
offsets (List[float]): Offsets for the confidence scores. Defaults to [0.25, 0.05].
"""
super().__init__(transform, device, optimize_transform, labels)
if offsets is None:
offsets = [0] * len(labels)
self.offsets = offsets
@Timer(
"PostLabelConfidencePairs.run",
"{name}: {milliseconds:.2f} ms",
logger=logger.debug,
)
def run(self, preds: torch.Tensor) -> List[Prediction]:
"""Extracts the confidence scores and puts them in other[label] predictions.
Args:
preds (torch.Tensor): Batch prediction tensor.
Returns:
List[Prediction]: List of prediction data structures containing the logits and label logit pairs.
"""
if isinstance(preds, tuple):
preds = preds[0]
pred_list = []
for i in range(preds.shape[0]):
preds_sample = preds[i]
other_labels = {
label: preds_sample.cpu().numpy().tolist()[j] + self.offsets[j]
for j, label in enumerate(self.labels)
}
pred = Prediction(
label="other",
logits=preds_sample,
other=other_labels,
)
pred_list.append(pred)
return pred_list
Classes
class BasePredPostProcessor (transform: torchvision.transforms.transforms.Compose, device: torch.device, optimize_transform: bool, labels: List[str])
-
Base class for predictor post processors.
All predictor post processors should subclass it. All subclass should overwrite:
- Methods:
run
, used for running the processing
Args
device
:torch.device
- Torch device cpu or cuda.
transform
:transforms.Compose
- Transform compose object to be applied to the image.
optimize_transform
:bool
- Whether to optimize the transform.
labels
:List[str]
- List of labels.
Expand source code
class BasePredPostProcessor(BaseProcessor): @Timer( "BasePredPostProcessor.__init__", "{name}: {milliseconds:.2f} ms", logger=logger.debug, ) def __init__( self, transform: transforms.Compose, device: torch.device, optimize_transform: bool, labels: List[str], ): """Base class for predictor post processors. All predictor post processors should subclass it. All subclass should overwrite: - Methods:``run``, used for running the processing Args: device (torch.device): Torch device cpu or cuda. transform (transforms.Compose): Transform compose object to be applied to the image. optimize_transform (bool): Whether to optimize the transform. labels (List[str]): List of labels. """ super().__init__(transform, device, optimize_transform) self.labels = labels def create_pred_list( self, preds: torch.Tensor, indices: List[int] ) -> List[Prediction]: """Create a list of predictions. Args: preds (torch.Tensor): Tensor of predictions, shape (batch, _). indices (List[int]): List of label indices, one for each sample. Returns: List[Prediction]: List of predictions. """ assert ( len(indices) == preds.shape[0] ), "Predictions and indices must have the same length." pred_labels = [self.labels[indx] for indx in indices] pred_list = [] for i, label in enumerate(pred_labels): pred = Prediction(label, preds[i]) pred_list.append(pred) return pred_list @abstractmethod def run(self, preds: Union[torch.Tensor, Tuple[torch.Tensor]]) -> List[Prediction]: """Abstract method that runs the predictor post processing functionality and returns a list of prediction data structures, one for each face in the batch. Args: preds (Union[torch.Tensor, Tuple[torch.Tensor]]): Output of the predictor model. Returns: List[Prediction]: List of predictions. """
Ancestors
Subclasses
Methods
def create_pred_list(self, preds: torch.Tensor, indices: List[int]) ‑> List[Prediction]
-
Create a list of predictions.
Args
preds
:torch.Tensor
- Tensor of predictions, shape (batch, _).
indices
:List[int]
- List of label indices, one for each sample.
Returns
List[Prediction]
- List of predictions.
Expand source code
def create_pred_list( self, preds: torch.Tensor, indices: List[int] ) -> List[Prediction]: """Create a list of predictions. Args: preds (torch.Tensor): Tensor of predictions, shape (batch, _). indices (List[int]): List of label indices, one for each sample. Returns: List[Prediction]: List of predictions. """ assert ( len(indices) == preds.shape[0] ), "Predictions and indices must have the same length." pred_labels = [self.labels[indx] for indx in indices] pred_list = [] for i, label in enumerate(pred_labels): pred = Prediction(label, preds[i]) pred_list.append(pred) return pred_list
def run(self, preds: Union[torch.Tensor, Tuple[torch.Tensor]]) ‑> List[Prediction]
-
Abstract method that runs the predictor post processing functionality and returns a list of prediction data structures, one for each face in the batch.
Args
preds
:Union[torch.Tensor, Tuple[torch.Tensor]]
- Output of the predictor model.
Returns
List[Prediction]
- List of predictions.
Expand source code
@abstractmethod def run(self, preds: Union[torch.Tensor, Tuple[torch.Tensor]]) -> List[Prediction]: """Abstract method that runs the predictor post processing functionality and returns a list of prediction data structures, one for each face in the batch. Args: preds (Union[torch.Tensor, Tuple[torch.Tensor]]): Output of the predictor model. Returns: List[Prediction]: List of predictions. """
Inherited members
- Methods:
class PostArgMax (transform: torchvision.transforms.transforms.Compose, device: torch.device, optimize_transform: bool, labels: List[str], dim: int)
-
Initialize the predictor postprocessor that runs argmax on the prediction tensor and returns a list of prediction data structures.
Args
transform
:Compose
- Composed Torch transform object.
device
:torch.device
- Torch device cpu or cuda.
optimize_transform
:bool
- Whether to optimize the transform using TorchScript.
labels
:List[str]
- List of labels.
dim
:int
- Axis along which to apply the argmax.
Expand source code
class PostArgMax(BasePredPostProcessor): @Timer("PostArgMax.__init__", "{name}: {milliseconds:.2f} ms", logger=logger.debug) def __init__( self, transform: transforms.Compose, device: torch.device, optimize_transform: bool, labels: List[str], dim: int, ): """Initialize the predictor postprocessor that runs argmax on the prediction tensor and returns a list of prediction data structures. Args: transform (Compose): Composed Torch transform object. device (torch.device): Torch device cpu or cuda. optimize_transform (bool): Whether to optimize the transform using TorchScript. labels (List[str]): List of labels. dim (int): Axis along which to apply the argmax. """ super().__init__(transform, device, optimize_transform, labels) self.dim = dim @Timer("PostArgMax.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug) def run(self, preds: torch.Tensor) -> List[Prediction]: """Post-processes the prediction tensor using argmax and returns a list of prediction data structures, one for each face. Args: preds (torch.Tensor): Batch prediction tensor. Returns: List[Prediction]: List of prediction data structures containing the predicted labels and confidence scores for each face in the batch. """ indices = torch.argmax(preds, dim=self.dim).cpu().numpy().tolist() pred_list = self.create_pred_list(preds, indices) return pred_list
Ancestors
Methods
def run(self, preds: torch.Tensor) ‑> List[Prediction]
-
Post-processes the prediction tensor using argmax and returns a list of prediction data structures, one for each face.
Args
preds
:torch.Tensor
- Batch prediction tensor.
Returns
List[Prediction]
- List of prediction data structures containing the predicted labels and confidence scores for each face in the batch.
Expand source code
@Timer("PostArgMax.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug) def run(self, preds: torch.Tensor) -> List[Prediction]: """Post-processes the prediction tensor using argmax and returns a list of prediction data structures, one for each face. Args: preds (torch.Tensor): Batch prediction tensor. Returns: List[Prediction]: List of prediction data structures containing the predicted labels and confidence scores for each face in the batch. """ indices = torch.argmax(preds, dim=self.dim).cpu().numpy().tolist() pred_list = self.create_pred_list(preds, indices) return pred_list
Inherited members
class PostSigmoidBinary (transform: torchvision.transforms.transforms.Compose, device: torch.device, optimize_transform: bool, labels: List[str], threshold: float = 0.5)
-
Initialize the predictor postprocessor that runs sigmoid on the prediction tensor and returns a list of prediction data structures.
Args
transform
:Compose
- Composed Torch transform object.
device
:torch.device
- Torch device cpu or cuda.
optimize_transform
:bool
- Whether to optimize the transform using TorchScript.
labels
:List[str]
- List of labels.
threshold
:float
- Probability threshold for positive class.
Expand source code
class PostSigmoidBinary(BasePredPostProcessor): @Timer( "PostSigmoidBinary.__init__", "{name}: {milliseconds:.2f} ms", logger=logger.debug, ) def __init__( self, transform: transforms.Compose, device: torch.device, optimize_transform: bool, labels: List[str], threshold: float = 0.5, ): """Initialize the predictor postprocessor that runs sigmoid on the prediction tensor and returns a list of prediction data structures. Args: transform (Compose): Composed Torch transform object. device (torch.device): Torch device cpu or cuda. optimize_transform (bool): Whether to optimize the transform using TorchScript. labels (List[str]): List of labels. threshold (float): Probability threshold for positive class. """ super().__init__(transform, device, optimize_transform, labels) self.threshold = threshold @Timer( "PostSigmoidBinary.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug ) def run(self, preds: torch.Tensor) -> List[Prediction]: """Post-processes the prediction tensor using argmax and returns a list of prediction data structures, one for each face. Args: preds (torch.Tensor): Batch prediction tensor. Returns: List[Prediction]: List of prediction data structures containing the predicted labelsand confidence scores for each face in the batch. """ preds = torch.sigmoid(preds.squeeze(1)) preds_thresh = preds.where(preds >= self.threshold, torch.zeros_like(preds)) indices = torch.round(preds_thresh) indices = indices.cpu().numpy().astype(int).tolist() pred_list = self.create_pred_list(preds, indices) return pred_list
Ancestors
Methods
def run(self, preds: torch.Tensor) ‑> List[Prediction]
-
Post-processes the prediction tensor using argmax and returns a list of prediction data structures, one for each face.
Args
preds
:torch.Tensor
- Batch prediction tensor.
Returns
List[Prediction]
- List of prediction data structures containing the predicted labelsand confidence scores for each face in the batch.
Expand source code
@Timer( "PostSigmoidBinary.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug ) def run(self, preds: torch.Tensor) -> List[Prediction]: """Post-processes the prediction tensor using argmax and returns a list of prediction data structures, one for each face. Args: preds (torch.Tensor): Batch prediction tensor. Returns: List[Prediction]: List of prediction data structures containing the predicted labelsand confidence scores for each face in the batch. """ preds = torch.sigmoid(preds.squeeze(1)) preds_thresh = preds.where(preds >= self.threshold, torch.zeros_like(preds)) indices = torch.round(preds_thresh) indices = indices.cpu().numpy().astype(int).tolist() pred_list = self.create_pred_list(preds, indices) return pred_list
Inherited members
class PostEmbedder (transform: torchvision.transforms.transforms.Compose, device: torch.device, optimize_transform: bool, labels: List[str])
-
Initialize the predictor postprocessor that extracts the embedding from the prediction tensor and returns a list of prediction data structures.
Args
transform
:Compose
- Composed Torch transform object.
device
:torch.device
- Torch device cpu or cuda.
optimize_transform
:bool
- Whether to optimize the transform using TorchScript.
labels
:List[str]
- List of labels.
Expand source code
class PostEmbedder(BasePredPostProcessor): def __init__( self, transform: transforms.Compose, device: torch.device, optimize_transform: bool, labels: List[str], ): """Initialize the predictor postprocessor that extracts the embedding from the prediction tensor and returns a list of prediction data structures. Args: transform (Compose): Composed Torch transform object. device (torch.device): Torch device cpu or cuda. optimize_transform (bool): Whether to optimize the transform using TorchScript. labels (List[str]): List of labels. """ super().__init__(transform, device, optimize_transform, labels) @Timer("PostEmbedder.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug) def run(self, preds: torch.Tensor) -> List[Prediction]: """Extracts the embedding from the prediction tensor and returns a list of prediction data structures, one for each face. Args: preds (torch.Tensor): Batch prediction tensor. Returns: List[Prediction]: List of prediction data structures containing the predicted embeddings. """ if isinstance(preds, tuple): preds = preds[0] indices = [0] * preds.shape[0] pred_list = self.create_pred_list(preds, indices) return pred_list
Ancestors
Methods
def run(self, preds: torch.Tensor) ‑> List[Prediction]
-
Extracts the embedding from the prediction tensor and returns a list of prediction data structures, one for each face.
Args
preds
:torch.Tensor
- Batch prediction tensor.
Returns
List[Prediction]
- List of prediction data structures containing the predicted embeddings.
Expand source code
@Timer("PostEmbedder.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug) def run(self, preds: torch.Tensor) -> List[Prediction]: """Extracts the embedding from the prediction tensor and returns a list of prediction data structures, one for each face. Args: preds (torch.Tensor): Batch prediction tensor. Returns: List[Prediction]: List of prediction data structures containing the predicted embeddings. """ if isinstance(preds, tuple): preds = preds[0] indices = [0] * preds.shape[0] pred_list = self.create_pred_list(preds, indices) return pred_list
Inherited members
class PostMultiLabel (transform: torchvision.transforms.transforms.Compose, device: torch.device, optimize_transform: bool, labels: List[str], dim: int, threshold: float = 0.5)
-
Initialize the predictor postprocessor that extracts multiple labels from the confidence scores.
Args
transform
:Compose
- Composed Torch transform object.
device
:torch.device
- Torch device cpu or cuda.
optimize_transform
:bool
- Whether to optimize the transform using TorchScript.
labels
:List[str]
- List of labels.
dim
:int
- Axis along which to apply the softmax.
threshold
:float
- Probability threshold for including a label. Only labels with a confidence score above the threshold are included. Defaults to 0.5.
Expand source code
class PostMultiLabel(BasePredPostProcessor): def __init__( self, transform: transforms.Compose, device: torch.device, optimize_transform: bool, labels: List[str], dim: int, threshold: float = 0.5, ): """Initialize the predictor postprocessor that extracts multiple labels from the confidence scores. Args: transform (Compose): Composed Torch transform object. device (torch.device): Torch device cpu or cuda. optimize_transform (bool): Whether to optimize the transform using TorchScript. labels (List[str]): List of labels. dim (int): Axis along which to apply the softmax. threshold (float): Probability threshold for including a label. Only labels with a confidence score above the threshold are included. Defaults to 0.5. """ super().__init__(transform, device, optimize_transform, labels) self.dim = dim self.threshold = threshold @Timer("PostMultiLabel.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug) def run(self, preds: torch.Tensor) -> List[Prediction]: """Extracts multiple labels and puts them in other[multi] predictions. The most likely label is put in the label field. Confidence scores are returned in the logits field. Args: preds (torch.Tensor): Batch prediction tensor. Returns: List[Prediction]: List of prediction data structures containing the most prevailing label, confidence scores, and multiple labels for each face. """ if isinstance(preds, tuple): preds = preds[0] indices = torch.argmax(preds, dim=self.dim).cpu().numpy().tolist() pred_list = [] for i in range(preds.shape[0]): preds_sample = preds[i] label_filter = (preds_sample > self.threshold).cpu().numpy().tolist() labels_true = list(compress(self.labels, label_filter)) pred = Prediction( label=self.labels[indices[i]], logits=preds_sample, other={"multi": labels_true}, ) pred_list.append(pred) return pred_list
Ancestors
Methods
def run(self, preds: torch.Tensor) ‑> List[Prediction]
-
Extracts multiple labels and puts them in other[multi] predictions. The most likely label is put in the label field. Confidence scores are returned in the logits field.
Args
preds
:torch.Tensor
- Batch prediction tensor.
Returns
List[Prediction]
- List of prediction data structures containing the most prevailing label, confidence scores, and multiple labels for each face.
Expand source code
@Timer("PostMultiLabel.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug) def run(self, preds: torch.Tensor) -> List[Prediction]: """Extracts multiple labels and puts them in other[multi] predictions. The most likely label is put in the label field. Confidence scores are returned in the logits field. Args: preds (torch.Tensor): Batch prediction tensor. Returns: List[Prediction]: List of prediction data structures containing the most prevailing label, confidence scores, and multiple labels for each face. """ if isinstance(preds, tuple): preds = preds[0] indices = torch.argmax(preds, dim=self.dim).cpu().numpy().tolist() pred_list = [] for i in range(preds.shape[0]): preds_sample = preds[i] label_filter = (preds_sample > self.threshold).cpu().numpy().tolist() labels_true = list(compress(self.labels, label_filter)) pred = Prediction( label=self.labels[indices[i]], logits=preds_sample, other={"multi": labels_true}, ) pred_list.append(pred) return pred_list
Inherited members
class PostLabelConfidencePairs (transform: torchvision.transforms.transforms.Compose, device: torch.device, optimize_transform: bool, labels: List[str], offsets: Optional[List[float]] = [0.25, 0.05])
-
Initialize the predictor postprocessor that zips the confidence scores with the labels.
Args
transform
:Compose
- Composed Torch transform object.
device
:torch.device
- Torch device cpu or cuda.
optimize_transform
:bool
- Whether to optimize the transform using TorchScript.
labels
:List[str]
- List of labels.
offsets
:List[float]
- Offsets for the confidence scores. Defaults to [0.25, 0.05].
Expand source code
class PostLabelConfidencePairs(BasePredPostProcessor): def __init__( self, transform: transforms.Compose, device: torch.device, optimize_transform: bool, labels: List[str], offsets: Optional[List[float]] = [0.25, 0.05], ): """Initialize the predictor postprocessor that zips the confidence scores with the labels. Args: transform (Compose): Composed Torch transform object. device (torch.device): Torch device cpu or cuda. optimize_transform (bool): Whether to optimize the transform using TorchScript. labels (List[str]): List of labels. offsets (List[float]): Offsets for the confidence scores. Defaults to [0.25, 0.05]. """ super().__init__(transform, device, optimize_transform, labels) if offsets is None: offsets = [0] * len(labels) self.offsets = offsets @Timer( "PostLabelConfidencePairs.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug, ) def run(self, preds: torch.Tensor) -> List[Prediction]: """Extracts the confidence scores and puts them in other[label] predictions. Args: preds (torch.Tensor): Batch prediction tensor. Returns: List[Prediction]: List of prediction data structures containing the logits and label logit pairs. """ if isinstance(preds, tuple): preds = preds[0] pred_list = [] for i in range(preds.shape[0]): preds_sample = preds[i] other_labels = { label: preds_sample.cpu().numpy().tolist()[j] + self.offsets[j] for j, label in enumerate(self.labels) } pred = Prediction( label="other", logits=preds_sample, other=other_labels, ) pred_list.append(pred) return pred_list
Ancestors
Methods
def run(self, preds: torch.Tensor) ‑> List[Prediction]
-
Extracts the confidence scores and puts them in other[label] predictions.
Args
preds
:torch.Tensor
- Batch prediction tensor.
Returns
List[Prediction]
- List of prediction data structures containing the logits and label logit pairs.
Expand source code
@Timer( "PostLabelConfidencePairs.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug, ) def run(self, preds: torch.Tensor) -> List[Prediction]: """Extracts the confidence scores and puts them in other[label] predictions. Args: preds (torch.Tensor): Batch prediction tensor. Returns: List[Prediction]: List of prediction data structures containing the logits and label logit pairs. """ if isinstance(preds, tuple): preds = preds[0] pred_list = [] for i in range(preds.shape[0]): preds_sample = preds[i] other_labels = { label: preds_sample.cpu().numpy().tolist()[j] + self.offsets[j] for j, label in enumerate(self.labels) } pred = Prediction( label="other", logits=preds_sample, other=other_labels, ) pred_list.append(pred) return pred_list
Inherited members