mIoU

ignite.metrics.mIoU(cm, ignore_index=None)

Calculates mean Intersection over Union using ConfusionMatrix metric.

Parameters

• cm (ConfusionMatrix) – instance of confusion matrix metric

• ignore_index (Optional[int]) – index to ignore, e.g. background index

Returns

MetricsLambda

Return type

MetricsLambda

Examples

For more information on how metric works with Engine, visit Attach Engine API.

from collections import OrderedDict

import torch
from torch import nn, optim

from ignite.engine import *
from ignite.handlers import *
from ignite.metrics import *
from ignite.metrics.clustering import *
from ignite.metrics.regression import *
from ignite.utils import *

# create default evaluator for doctests

def eval_step(engine, batch):
    return batch

default_evaluator = Engine(eval_step)

# create default optimizer for doctests

param_tensor = torch.zeros([1], requires_grad=True)
default_optimizer = torch.optim.SGD([param_tensor], lr=0.1)

# create default trainer for doctests
# as handlers could be attached to the trainer,
# each test must define his own trainer using `.. testsetup:`

def get_default_trainer():

    def train_step(engine, batch):
        return batch

    return Engine(train_step)

# create default model for doctests

default_model = nn.Sequential(OrderedDict([
    ('base', nn.Linear(4, 2)),
    ('fc', nn.Linear(2, 1))
]))

manual_seed(666)

cm = ConfusionMatrix(num_classes=3)
metric = mIoU(cm, ignore_index=0)
metric.attach(default_evaluator, 'miou')
y_true = torch.tensor([0, 1, 0, 1, 2])
y_pred = torch.tensor([
    [0.0, 1.0, 0.0],
    [0.0, 1.0, 0.0],
    [1.0, 0.0, 0.0],
    [0.0, 1.0, 0.0],
    [0.0, 1.0, 0.0],
])
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics['miou'])

0.24999...