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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
def accuracy(pred, target, topk=1, thresh=None, ignore_index=None):
"""Calculate accuracy according to the prediction and target.
Args:
pred (torch.Tensor): The model prediction, shape (N, num_class, ...)
target (torch.Tensor): The target of each prediction, shape (N, , ...)
ignore_index (int | None): The label index to be ignored. Default: None
topk (int | tuple[int], optional): If the predictions in ``topk``
matches the target, the predictions will be regarded as
correct ones. Defaults to 1.
thresh (float, optional): If not None, predictions with scores under
this threshold are considered incorrect. Default to None.
Returns:
float | tuple[float]: If the input ``topk`` is a single integer,
the function will return a single float as accuracy. If
``topk`` is a tuple containing multiple integers, the
function will return a tuple containing accuracies of
each ``topk`` number.
"""
assert isinstance(topk, (int, tuple))
if isinstance(topk, int):
topk = (topk, )
return_single = True
else:
return_single = False
maxk = max(topk)
if pred.size(0) == 0:
accu = [pred.new_tensor(0.) for i in range(len(topk))]
return accu[0] if return_single else accu
assert pred.ndim == target.ndim + 1
assert pred.size(0) == target.size(0)
assert maxk <= pred.size(1), \
f'maxk {maxk} exceeds pred dimension {pred.size(1)}'
pred_value, pred_label = pred.topk(maxk, dim=1)
# transpose to shape (maxk, N, ...)
pred_label = pred_label.transpose(0, 1)
correct = pred_label.eq(target.unsqueeze(0).expand_as(pred_label))
if thresh is not None:
# Only prediction values larger than thresh are counted as correct
correct = correct & (pred_value > thresh).t()
if ignore_index is not None:
correct = correct[:, target != ignore_index]
res = []
eps = torch.finfo(torch.float32).eps
for k in topk:
# Avoid causing ZeroDivisionError when all pixels
# of an image are ignored
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) + eps
if ignore_index is not None:
total_num = target[target != ignore_index].numel() + eps
else:
total_num = target.numel() + eps
res.append(correct_k.mul_(100.0 / total_num))
return res[0] if return_single else res
class Accuracy(nn.Module):
"""Accuracy calculation module."""
def __init__(self, topk=(1, ), thresh=None, ignore_index=None):
"""Module to calculate the accuracy.
Args:
topk (tuple, optional): The criterion used to calculate the
accuracy. Defaults to (1,).
thresh (float, optional): If not None, predictions with scores
under this threshold are considered incorrect. Default to None.
"""
super().__init__()
self.topk = topk
self.thresh = thresh
self.ignore_index = ignore_index
def forward(self, pred, target):
"""Forward function to calculate accuracy.
Args:
pred (torch.Tensor): Prediction of models.
target (torch.Tensor): Target for each prediction.
Returns:
tuple[float]: The accuracies under different topk criterions.
"""
return accuracy(pred, target, self.topk, self.thresh,
self.ignore_index)
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