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It is faster than sklearn.cluster.KMeans.\nWhat's more, it is a differential operation which will back-propagate gradient to previous layers.\n\nYou can easily use `KMeans` as a `nn.Module`, and embed into your network structure.\n\n## Install\n\n1. From Git:\n\n```bash\ngit clone git@github.com:densechen/kmeans-gpu.git\ncd kmeans-gpu\npip install -r requirements.txt\npython setup.py install\n\n# check installation\npython -c \"import kmeans_gpu; print(kmeans_gpu.__version__)\"\n```\n\n2. From PyPI:\n\n```bash\npip install kmeans-gpu\n\n# check installation\npython -c \"import kmeans_gpu; print(kmeans_gpu.__version__)\"\n```\n\n## Demo\n\n```python\nfrom kmeans_gpu import KMeans\nimport torch\n\n# Config\nbatch_size = 128\nfeature_dim = 1024\npts_dim = 3\nnum_pts = 256\nnum_cluster = 15\n\n# Create data\nfeatures = torch.randn(batch_size, feature_dim, num_pts)\n# Pay attention to the different dimension order between features and points.\npoints = torch.randn(batch_size, num_pts, pts_dim)\n\n# Create KMeans Module\nkmeans = KMeans(\n    n_clusters=num_cluster,\n    max_iter=100,\n    tolerance=1e-4,\n    distance='euclidean',\n    sub_sampling=None,\n    max_neighbors=15,\n)\n\n# Forward\ncentroids, features = kmeans(points, features)\n\nprint(centroids.shape, features.shape)\n# output: \n# \u003e\u003e\u003e torch.Size([128, 15, 3]) torch.Size([128, 1024, 15])\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdensechen%2Fkmeans-gpu","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdensechen%2Fkmeans-gpu","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdensechen%2Fkmeans-gpu/lists"}