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MMEval is a machine learning evaluation library that supports efficient and accurate distributed evaluation on a variety of machine learning frameworks.
Major features:
- Comprehensive metrics for various computer vision tasks (NLP will be covered soon!)
- Efficient and accurate distributed evaluation, backed by multiple distributed communication backends
- Support multiple machine learning frameworks via dynamic input dispatching mechanism
Supported distributed communication backends
| MPI4Py | torch.distributed | Horovod | paddle.distributed | oneflow.comm |
|---|---|---|---|---|
| MPI4PyDist | TorchCPUDist TorchCUDADist |
TFHorovodDist | PaddleDist | OneFlowDist |
Supported metrics and ML frameworks
NOTE: MMEval tested with PyTorch 1.6+, TensorFlow 2.4+, Paddle 2.2+ and OneFlow 0.8+.
| Metric | numpy.ndarray | torch.Tensor | tensorflow.Tensor | paddle.Tensor | oneflow.Tensor |
|---|---|---|---|---|---|
| Accuracy | ✔ | ✔ | ✔ | ✔ | ✔ |
| SingleLabelMetric | ✔ | ✔ | ✔ | ||
| MultiLabelMetric | ✔ | ✔ | ✔ | ||
| AveragePrecision | ✔ | ✔ | ✔ | ||
| MeanIoU | ✔ | ✔ | ✔ | ✔ | ✔ |
| VOCMeanAP | ✔ | ||||
| OIDMeanAP | ✔ | ||||
| CocoDetectionMetric | ✔ | ||||
| ProposalRecall | ✔ | ||||
| F1Metric | ✔ | ✔ | ✔ | ||
| HmeanIoU | ✔ | ||||
| PCKAccuracy | ✔ | ||||
| MpiiPCKAccuracy | ✔ | ||||
| JhmdbPCKAccuracy | ✔ | ||||
| EndPointError | ✔ | ✔ | ✔ | ||
| AVAMeanAP | ✔ | ||||
| SSIM | ✔ | ||||
| SNR | ✔ | ||||
| PSNR | ✔ | ||||
| MAE | ✔ | ||||
| MSE | ✔ |
MMEval requires Python 3.6+ and can be installed via pip.
pip install mmevalTo install the dependencies required for all the metrics provided in MMEval, you can install them with the following command.
pip install 'mmeval[all]'There are two ways to use MMEval's metrics, using Accuracy as an example:
from mmeval import Accuracy
import numpy as np
accuracy = Accuracy()The first way is to directly call the instantiated Accuracy object to calculate the metric.
labels = np.asarray([0, 1, 2, 3])
preds = np.asarray([0, 2, 1, 3])
accuracy(preds, labels)
# {'top1': 0.5}The second way is to calculate the metric after accumulating data from multiple batches.
for i in range(10):
labels = np.random.randint(0, 4, size=(100, ))
predicts = np.random.randint(0, 4, size=(100, ))
accuracy.add(predicts, labels)
accuracy.compute()
# {'top1': ...}Examples
- Continue to add more metrics and expand more tasks (e.g. NLP, audio).
- Support more ML frameworks and explore multiple ML framework support paradigms.
We appreciate all contributions to improve MMEval. Please refer to CONTRIBUTING.md for the contributing guideline.
This project is released under the Apache 2.0 license.
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