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* add_doc_for_confusion_matrix * add_doc_for_confusion_matrix_fix_mmcls * add_doc_for_confusion_matrix_fix_shell * add_doc_for_confusion_matrix_fix_shell * fix * update --------- Co-authored-by: fangyixiao18 <fangyx18@hotmail.com>
85 lines
2.7 KiB
Markdown
85 lines
2.7 KiB
Markdown
# Confusion Matrix
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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](https://en.wikipedia.org/wiki/Confusion_matrix).
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## Command-line Usage
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**Command**：
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```shell
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python tools/analysis_tools/confusion_matrix.py \
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${CONFIG_FILE} \
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${CHECKPOINT} \
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[--show] \
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[--show-path] \
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[--include-values] \
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[--cmap ${CMAP}] \
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[--cfg-options ${CFG-OPTIONS}]
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```
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**Description of all arguments**：
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- `config`: The path of the model config file.
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- `checkpoint`: The path of the checkpoint.
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- `--show`: If or not to show the matplotlib visualization result of the confusion matrix, the default is `False`.
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- `--show-path`: If `show` is True, the path where the results are saved is visualized.
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- `--include-values`: Whether to add values to the visualization results.
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- `--cmap`: The color map used for visualization results, `cmap`, which defaults to `viridis`.
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* `--cfg-options`: Modifications to the configuration file, refer to [Learn about Configs](../user_guides/config.md).
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**Examples of use**:
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```shell
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python tools/analysis_tools/confusion_matrix.py \
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configs/resnet/resnet50_8xb16_cifar10.py \
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https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth \
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--show
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```
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**output image**:
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<div align=center><img src="https://user-images.githubusercontent.com/26739999/210298124-49ae00f7-c8fd-488a-a4da-58c285e9c1f1.png" style=" width: auto; height: 40%; "></div>
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## **Basic Usage**
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```python
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>>> import torch
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>>> from mmpretrain.evaluation import ConfusionMatrix
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>>> y_pred = [0, 1, 1, 3]
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>>> y_true = [0, 2, 1, 3]
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>>> ConfusionMatrix.calculate(y_pred, y_true, num_classes=4)
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tensor([[1, 0, 0, 0],
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[0, 1, 0, 0],
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[0, 1, 0, 0],
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[0, 0, 0, 1]])
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>>> # plot the confusion matrix
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>>> import matplotlib.pyplot as plt
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>>> y_score = torch.rand((1000, 10))
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>>> y_true = torch.randint(10, (1000, ))
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>>> matrix = ConfusionMatrix.calculate(y_score, y_true)
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>>> ConfusionMatrix().plot(matrix)
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>>> plt.show()
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```
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## **Use with Evalutor**
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```python
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>>> import torch
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>>> from mmpretrain.evaluation import ConfusionMatrix
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>>> from mmpretrain.structures import DataSample
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>>> from mmengine.evaluator import Evaluator
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>>> data_samples = [
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... DataSample().set_gt_label(i%5).set_pred_score(torch.rand(5))
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... for i in range(1000)
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... ]
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>>> evaluator = Evaluator(metrics=ConfusionMatrix())
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>>> evaluator.process(data_samples)
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>>> evaluator.evaluate(1000)
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{'confusion_matrix/result': tensor([[37, 37, 48, 43, 35],
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[35, 51, 32, 46, 36],
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[45, 28, 39, 42, 46],
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[42, 40, 40, 35, 43],
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[40, 39, 41, 37, 43]])}
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```
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