Shortcuts

Source code for catalyst.metrics.functional._segmentation

from typing import Callable, List, Optional, Tuple
from functools import partial

import torch


[docs]def get_segmentation_statistics( outputs: torch.Tensor, targets: torch.Tensor, class_dim: int = 1, threshold: float = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Computes true positive, false positive, false negative for a multilabel segmentation problem. Args: outputs: [N; K; ...] tensor that for each of the N examples indicates the probability of the example belonging to each of the K classes, according to the model. targets: binary [N; K; ...] tensor that encodes which of the K classes are associated with the N-th input class_dim: indicates class dimention (K) for ``outputs`` and ``targets`` tensors (default = 1) threshold: threshold for outputs binarization Returns: Segmentation stats Example: >>> size = 4 >>> half_size = size // 2 >>> shape = (1, 1, size, size) >>> empty = torch.zeros(shape) >>> full = torch.ones(shape) >>> left = torch.ones(shape) >>> left[:, :, :, half_size:] = 0 >>> right = torch.ones(shape) >>> right[:, :, :, :half_size] = 0 >>> top_left = torch.zeros(shape) >>> top_left[:, :, :half_size, :half_size] = 1 >>> pred = torch.cat([empty, left, empty, full, left, top_left], dim=1) >>> targets = torch.cat([full, right, empty, full, left, left], dim=1) >>> get_segmentation_statistics( >>> outputs=pred, >>> targets=targets, >>> class_dim=1, >>> threshold=0.5, >>> ) (tensor([ 0., 0., 0., 16., 8., 4.]), tensor([0., 8., 0., 0., 0., 0.]), tensor([16., 8., 0., 0., 0., 4.])) """ assert outputs.shape == targets.shape, ( f"targets(shape {targets.shape})" f" and outputs(shape {outputs.shape}) must have the same shape" ) if threshold is not None: outputs = (outputs > threshold).float() n_dims = len(outputs.shape) dims = list(range(n_dims)) # support negative index if class_dim < 0: class_dim = n_dims + class_dim dims.pop(class_dim) sum_per_class = partial(torch.sum, dim=dims) tp = sum_per_class(outputs * targets) class_union = sum_per_class(outputs) + sum_per_class(targets) class_union -= tp fp = sum_per_class(outputs * (1 - targets)) fn = sum_per_class(targets * (1 - outputs)) return tp, fp, fn
def _get_region_based_metrics( outputs: torch.Tensor, targets: torch.Tensor, metric_fn: Callable, class_dim=None, threshold: float = None, mode: str = "per-class", weights: Optional[List[float]] = None, ) -> torch.Tensor: """ Get aggregated metric Args: outputs: [N; K; ...] tensor that for each of the N examples indicates the probability of the example belonging to each of the K classes, according to the model. targets: binary [N; K; ...] tensor that encodes which of the K classes are associated with the N-th input metric_fn: metric function, that get statistics and return score class_dim: indicates class dimention (K) for ``outputs`` and ``targets`` tensors (default = 1), if mode = "micro" means nothing threshold: threshold for outputs binarization mode: class summation strategy. Must be one of ['micro', 'macro', 'weighted', 'per-class']. If mode='micro', classes are ignored, and metric are calculated generally. If mode='macro', metric are calculated per-class and than are averaged over all classes. If mode='weighted', metric are calculated per-class and than summed over all classes with weights. If mode='per-class', metric are calculated separately for all classes weights: class weights(for mode="weighted") Returns: computed metric """ assert mode in ["per-class", "micro", "macro", "weighted"] segmentation_stats = get_segmentation_statistics( outputs=outputs, targets=targets, class_dim=class_dim, threshold=threshold, ) if mode == "micro": segmentation_stats = [torch.sum(stats) for stats in segmentation_stats] metric = metric_fn(*segmentation_stats) metrics_per_class = metric_fn(*segmentation_stats) if mode == "macro": metric = torch.mean(metrics_per_class) elif mode == "weighted": assert len(weights) == len(segmentation_stats[0]) device = metrics_per_class.device metrics = torch.tensor(weights).to(device) * metrics_per_class metric = torch.sum(metrics) elif mode == "per-class": metric = metrics_per_class return metric def _iou(tp: torch.Tensor, fp: torch.Tensor, fn: torch.Tensor, eps: float = 1e-7) -> torch.Tensor: union = tp + fp + fn score = (tp + eps * (union == 0).float()) / (tp + fp + fn + eps) return score def _dice(tp: torch.Tensor, fp: torch.Tensor, fn: torch.Tensor, eps: float = 1e-7) -> torch.Tensor: union = tp + fp + fn score = (2 * tp + eps * (union == 0).float()) / (2 * tp + fp + fn + eps) return score def _trevsky( tp: torch.Tensor, fp: torch.Tensor, fn: torch.Tensor, alpha: float, beta: float, eps: float = 1e-7, ) -> torch.Tensor: union = tp + fp + fn score = (tp + eps * (union == 0).float()) / (tp + fp * beta + fn * alpha + eps) return score
[docs]def iou( outputs: torch.Tensor, targets: torch.Tensor, class_dim: int = 1, threshold: float = None, mode: str = "per-class", weights: Optional[List[float]] = None, eps: float = 1e-7, ) -> torch.Tensor: """ Computes the iou/jaccard score, iou score = intersection / union = tp / (tp + fp + fn) Args: outputs: [N; K; ...] tensor that for each of the N examples indicates the probability of the example belonging to each of the K classes, according to the model. targets: binary [N; K; ...] tensor that encodes which of the K classes are associated with the N-th input class_dim: indicates class dimention (K) for ``outputs`` and ``targets`` tensors (default = 1), if mode = "micro" means nothing threshold: threshold for outputs binarization mode: class summation strategy. Must be one of ['micro', 'macro', 'weighted', 'per-class']. If mode='micro', classes are ignored, and metric are calculated generally. If mode='macro', metric are calculated per-class and than are averaged over all classes. If mode='weighted', metric are calculated per-class and than summed over all classes with weights. If mode='per-class', metric are calculated separately for all classes weights: class weights(for mode="weighted") eps: epsilon to avoid zero division Returns: IoU (Jaccard) score for each class(if mode='weighted') or aggregated IOU Example: >>> size = 4 >>> half_size = size // 2 >>> shape = (1, 1, size, size) >>> empty = torch.zeros(shape) >>> full = torch.ones(shape) >>> left = torch.ones(shape) >>> left[:, :, :, half_size:] = 0 >>> right = torch.ones(shape) >>> right[:, :, :, :half_size] = 0 >>> top_left = torch.zeros(shape) >>> top_left[:, :, :half_size, :half_size] = 1 >>> pred = torch.cat([empty, left, empty, full, left, top_left], dim=1) >>> targets = torch.cat([full, right, empty, full, left, left], dim=1) >>> iou( >>> outputs=pred, >>> targets=targets, >>> class_dim=1, >>> threshold=0.5, >>> mode="per-class" >>> ) tensor([0.0000, 0.0000, 1.0000, 1.0000, 1.0000, 0.5]) """ metric_fn = partial(_iou, eps=eps) score = _get_region_based_metrics( outputs=outputs, targets=targets, metric_fn=metric_fn, class_dim=class_dim, threshold=threshold, mode=mode, weights=weights, ) return score
[docs]def dice( outputs: torch.Tensor, targets: torch.Tensor, class_dim: int = 1, threshold: float = None, mode: str = "per-class", weights: Optional[List[float]] = None, eps: float = 1e-7, ) -> torch.Tensor: """ Computes the dice score, dice score = 2 * intersection / (intersection + union)) = \ = 2 * tp / (2 * tp + fp + fn) Args: outputs: [N; K; ...] tensor that for each of the N examples indicates the probability of the example belonging to each of the K classes, according to the model. targets: binary [N; K; ...] tensor that encodes which of the K classes are associated with the N-th input class_dim: indicates class dimention (K) for ``outputs`` and ``targets`` tensors (default = 1), if mode = "micro" means nothing threshold: threshold for outputs binarization mode: class summation strategy. Must be one of ['micro', 'macro', 'weighted', 'per-class']. If mode='micro', classes are ignored, and metric are calculated generally. If mode='macro', metric are calculated per-class and than are averaged over all classes. If mode='weighted', metric are calculated per-class and than summed over all classes with weights. If mode='per-class', metric are calculated separately for all classes weights: class weights(for mode="weighted") eps: epsilon to avoid zero division Returns: Dice score for each class(if mode='weighted') or aggregated Dice Example: >>> size = 4 >>> half_size = size // 2 >>> shape = (1, 1, size, size) >>> empty = torch.zeros(shape) >>> full = torch.ones(shape) >>> left = torch.ones(shape) >>> left[:, :, :, half_size:] = 0 >>> right = torch.ones(shape) >>> right[:, :, :, :half_size] = 0 >>> top_left = torch.zeros(shape) >>> top_left[:, :, :half_size, :half_size] = 1 >>> pred = torch.cat([empty, left, empty, full, left, top_left], dim=1) >>> targets = torch.cat([full, right, empty, full, left, left], dim=1) >>> dice( >>> outputs=pred, >>> targets=targets, >>> class_dim=1, >>> threshold=0.5, >>> mode="per-class" >>> ) tensor([0.0000, 0.0000, 1.0000, 1.0000, 1.0000, 0.6667]) """ metric_fn = partial(_dice, eps=eps) score = _get_region_based_metrics( outputs=outputs, targets=targets, metric_fn=metric_fn, class_dim=class_dim, threshold=threshold, mode=mode, weights=weights, ) return score
[docs]def trevsky( outputs: torch.Tensor, targets: torch.Tensor, alpha: float, beta: Optional[float] = None, class_dim: int = 1, threshold: float = None, mode: str = "per-class", weights: Optional[List[float]] = None, eps: float = 1e-7, ) -> torch.Tensor: """ Computes the trevsky score, trevsky score = tp / (tp + fp * beta + fn * alpha) Args: outputs: [N; K; ...] tensor that for each of the N examples indicates the probability of the example belonging to each of the K classes, according to the model. targets: binary [N; K; ...] tensor that encodes which of the K classes are associated with the N-th input alpha: false negative coefficient, bigger alpha bigger penalty for false negative. Must be in (0, 1) beta: false positive coefficient, bigger alpha bigger penalty for false positive. Must be in (0, 1), if None beta = (1 - alpha) class_dim: indicates class dimention (K) for ``outputs`` and ``targets`` tensors (default = 1) threshold: threshold for outputs binarization mode: class summation strategy. Must be one of ['micro', 'macro', 'weighted', 'per-class']. If mode='micro', classes are ignored, and metric are calculated generally. If mode='macro', metric are calculated per-class and than are averaged over all classes. If mode='weighted', metric are calculated per-class and than summed over all classes with weights. If mode='per-class', metric are calculated separately for all classes weights: class weights(for mode="weighted") eps: epsilon to avoid zero division Returns: Trevsky score for each class(if mode='weighted') or aggregated score Example: >>> size = 4 >>> half_size = size // 2 >>> shape = (1, 1, size, size) >>> empty = torch.zeros(shape) >>> full = torch.ones(shape) >>> left = torch.ones(shape) >>> left[:, :, :, half_size:] = 0 >>> right = torch.ones(shape) >>> right[:, :, :, :half_size] = 0 >>> top_left = torch.zeros(shape) >>> top_left[:, :, :half_size, :half_size] = 1 >>> pred = torch.cat([empty, left, empty, full, left, top_left], dim=1) >>> targets = torch.cat([full, right, empty, full, left, left], dim=1) >>> trevsky( >>> outputs=pred, >>> targets=targets, >>> alpha=0.2, >>> class_dim=1, >>> threshold=0.5, >>> mode="per-class" >>> ) tensor([0.0000, 0.0000, 1.0000, 1.0000, 1.0000, 0.8333]) """ # assert 0 < alpha < 1 # I am not sure about this if beta is None: assert 0 < alpha < 1, "if beta=None, alpha must be in (0, 1)" beta = 1 - alpha metric_fn = partial(_trevsky, alpha=alpha, beta=beta, eps=eps) score = _get_region_based_metrics( outputs=outputs, targets=targets, metric_fn=metric_fn, class_dim=class_dim, threshold=threshold, mode=mode, weights=weights, ) return score
__all__ = ["iou", "dice", "trevsky", "get_segmentation_statistics"]