Source code for catalyst.dl.meters.apmeter

import math

import torch

from . import meter

[docs]class APMeter(meter.Meter): """ The APMeter measures the average precision per class. The APMeter is designed to operate on `NxK` Tensors `output` and `target`, and optionally a `Nx1` Tensor weight where (1) the `output` contains model output scores for `N` examples and `K` classes that ought to be higher when the model is more convinced that the example should be positively labeled, and smaller when the model believes the example should be negatively labeled (for instance, the output of a sigmoid function); (2) the `target` contains only values 0 (for negative examples) and 1 (for positive examples); and (3) the `weight` ( > 0) represents weight for each sample. """ def __init__(self): super(APMeter, self).__init__() self.reset()
[docs] def reset(self): """Resets the meter with empty member variables""" self.scores = torch.FloatTensor(torch.FloatStorage()) self.targets = torch.LongTensor(torch.LongStorage()) self.weights = torch.FloatTensor(torch.FloatStorage())
[docs] def add(self, output, target, weight=None): """Add a new observation Args: output (Tensor): NxK 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. The probabilities should sum to one over all classes target (Tensor): binary NxK tensort that encodes which of the K classes are associated with the N-th input (eg: a row [0, 1, 0, 1] indicates that the example is associated with classes 2 and 4) weight (optional, Tensor): Nx1 tensor representing the weight for each example (each weight > 0) """ if not torch.is_tensor(output): output = torch.from_numpy(output) if not torch.is_tensor(target): target = torch.from_numpy(target) if weight is not None: if not torch.is_tensor(weight): weight = torch.from_numpy(weight) weight = weight.squeeze() if output.dim() == 1: output = output.view(-1, 1) else: assert output.dim() == 2, \ "wrong output size (should be 1D or 2D with one column \ per class)" if target.dim() == 1: target = target.view(-1, 1) else: assert target.dim() == 2, \ "wrong target size (should be 1D or 2D with one column \ per class)" if weight is not None: assert weight.dim() == 1, "Weight dimension should be 1" assert weight.numel() == target.size(0), \ "Weight dimension 1 should be the same as that of target" assert torch.min(weight) >= 0, "Weight should be non-negative only" assert torch.equal(target ** 2, target), \ "targets should be binary (0 or 1)" if self.scores.numel() > 0: assert target.size(1) == self.targets.size(1), \ "dimensions for output should match previously added examples." # make sure storage is of sufficient size if < self.scores.numel() + output.numel(): new_size = math.ceil( * 1.5) new_weight_size = math.ceil( * 1.5) + output.numel())) + output.numel())) if weight is not None: int(new_weight_size + output.size(0))) # store scores and targets offset = self.scores.size(0) if self.scores.dim() > 0 else 0 self.scores.resize_(offset + output.size(0), output.size(1)) self.targets.resize_(offset + target.size(0), target.size(1)) self.scores.narrow(0, offset, output.size(0)).copy_(output) self.targets.narrow(0, offset, target.size(0)).copy_(target) if weight is not None: self.weights.resize_(offset + weight.size(0)) self.weights.narrow(0, offset, weight.size(0)).copy_(weight)
[docs] def value(self): """Returns the model"s average precision for each class Return: ap (FloatTensor): 1xK tensor, with avg precision for each class k """ if self.scores.numel() == 0: return 0 ap = torch.zeros(self.scores.size(1)) if hasattr(torch, "arange"): rg = torch.arange(1, self.scores.size(0) + 1).float() else: rg = torch.range(1, self.scores.size(0)).float() if self.weights.numel() > 0: weight = weighted_truth = # compute average precision for each class for k in range(self.scores.size(1)): # sort scores scores = self.scores[:, k] targets = self.targets[:, k] _, sortind = torch.sort(scores, 0, True) truth = targets[sortind] if self.weights.numel() > 0: weight = self.weights[sortind] weighted_truth = truth.float() * weight rg = weight.cumsum(0) # compute true positive sums if self.weights.numel() > 0: tp = weighted_truth.cumsum(0) else: tp = truth.float().cumsum(0) # compute precision curve precision = tp.div(rg) # compute average precision ap[k] = precision[truth.byte()].sum() / max(float(truth.sum()), 1) return ap