mmpretrain/docs/en/useful_tools/confusion_matrix.md

Confusion Matrix

MMPretrain provides tools/analysis_tools/confusion_matrix.py tool to calculate and visualize the confusion matrix. For an introduction to the confusion matrix, see link.

Command-line Usage

Command：

python tools/analysis_tools/confusion_matrix.py \
    ${CONFIG_FILE} \
    ${CHECKPOINT} \
    [--show] \
    [--show-path] \
    [--include-values] \
    [--cmap ${CMAP}] \
    [--cfg-options ${CFG-OPTIONS}]

Description of all arguments：

• config: The path of the model config file.
• checkpoint: The path of the checkpoint.
• --show: If or not to show the matplotlib visualization result of the confusion matrix, the default is False.
• --show-path: If show is True, the path where the results are saved is visualized.
• --include-values: Whether to add values to the visualization results.
• --cmap: The color map used for visualization results, cmap, which defaults to viridis.

• --cfg-options: Modifications to the configuration file, refer to Learn about Configs.

Examples of use:

python tools/analysis_tools/confusion_matrix.py \
    configs/resnet/resnet50_8xb16_cifar10.py \
    https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth \
    --show

output image:

Basic Usage

>>> import torch
>>> from mmpretrain.evaluation import ConfusionMatrix
>>> y_pred = [0, 1, 1, 3]
>>> y_true = [0, 2, 1, 3]
>>> ConfusionMatrix.calculate(y_pred, y_true, num_classes=4)
tensor([[1, 0, 0, 0],
        [0, 1, 0, 0],
        [0, 1, 0, 0],
        [0, 0, 0, 1]])
>>> # plot the confusion matrix
>>> import matplotlib.pyplot as plt
>>> y_score = torch.rand((1000, 10))
>>> y_true = torch.randint(10, (1000, ))
>>> matrix = ConfusionMatrix.calculate(y_score, y_true)
>>> ConfusionMatrix().plot(matrix)
>>> plt.show()

Use with Evalutor

>>> import torch
>>> from mmpretrain.evaluation import ConfusionMatrix
>>> from mmpretrain.structures import DataSample
>>> from mmengine.evaluator import Evaluator
>>> data_samples = [
...     DataSample().set_gt_label(i%5).set_pred_score(torch.rand(5))
...     for i in range(1000)
... ]
>>> evaluator = Evaluator(metrics=ConfusionMatrix())
>>> evaluator.process(data_samples)
>>> evaluator.evaluate(1000)
{'confusion_matrix/result': tensor([[37, 37, 48, 43, 35],
         [35, 51, 32, 46, 36],
         [45, 28, 39, 42, 46],
         [42, 40, 40, 35, 43],
         [40, 39, 41, 37, 43]])}