Source code for kornia.metrics.confusion_matrix

import torch

# Inspired by:
# https://github.com/pytorch/tnt/blob/master/torchnet/meter/confusionmeter.py#L68-L73


[docs]def confusion_matrix( input: torch.Tensor, target: torch.Tensor, num_classes: int, normalized: bool = False ) -> torch.Tensor: r"""Compute confusion matrix to evaluate the accuracy of a classification. Args: input: tensor with estimated targets returned by a classifier. The shape can be :math:`(B, *)` and must contain integer values between 0 and K-1. target: tensor with ground truth (correct) target values. The shape can be :math:`(B, *)` and must contain integer values between 0 and K-1, where targets are assumed to be provided as one-hot vectors. num_classes: total possible number of classes in target. normalized: whether to return the confusion matrix normalized. Returns: a tensor containing the confusion matrix with shape :math:`(B, K, K)` where K is the number of classes. Example: >>> logits = torch.tensor([[0, 1, 0]]) >>> target = torch.tensor([[0, 1, 0]]) >>> confusion_matrix(logits, target, num_classes=3) tensor([[[2., 0., 0.], [0., 1., 0.], [0., 0., 0.]]]) """ if not torch.is_tensor(input) and input.dtype is not torch.int64: raise TypeError("Input input type is not a torch.Tensor with " "torch.int64 dtype. Got {}".format(type(input))) if not torch.is_tensor(target) and target.dtype is not torch.int64: raise TypeError( "Input target type is not a torch.Tensor with " "torch.int64 dtype. Got {}".format(type(target)) ) if not input.shape == target.shape: raise ValueError( "Inputs input and target must have the same shape. " "Got: {} and {}".format(input.shape, target.shape) ) if not input.device == target.device: raise ValueError("Inputs must be in the same device. " "Got: {} - {}".format(input.device, target.device)) if not isinstance(num_classes, int) or num_classes < 2: raise ValueError("The number of classes must be an integer bigger " "than two. Got: {}".format(num_classes)) batch_size: int = input.shape[0] # hack for bitcounting 2 arrays together # NOTE: torch.bincount does not implement batched version pre_bincount: torch.Tensor = input + target * num_classes pre_bincount_vec: torch.Tensor = pre_bincount.view(batch_size, -1) confusion_list = [] for iter_id in range(batch_size): pb: torch.Tensor = pre_bincount_vec[iter_id] bin_count: torch.Tensor = torch.bincount(pb, minlength=num_classes ** 2) confusion_list.append(bin_count) confusion_vec: torch.Tensor = torch.stack(confusion_list) confusion_mat: torch.Tensor = confusion_vec.view(batch_size, num_classes, num_classes).to(torch.float32) # BxKxK if normalized: norm_val: torch.Tensor = torch.sum(confusion_mat, dim=1, keepdim=True) confusion_mat = confusion_mat / (norm_val + 1e-6) return confusion_mat