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kmeans_pytorch/docs/static/tomorrow-night.css

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kmeans_pytorch/example.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "1ddgCM_qEHZa",
+    "outputId": "d8ad88e7-59b9-4097-cbd7-70668b9fec88"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: kmeans-pytorch in /usr/local/lib/python3.6/dist-packages (0.1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install kmeans-pytorch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "R8_IiKZZEU9i"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from kmeans_pytorch import kmeans, kmeans_predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "iyoljrh1FCxJ"
+   },
+   "outputs": [],
+   "source": [
+    "# set random seed\n",
+    "np.random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "tcnoTA16FHbJ"
+   },
+   "outputs": [],
+   "source": [
+    "# data\n",
+    "data_size, dims, num_clusters = 1000, 2, 3\n",
+    "x = np.random.randn(data_size, dims) / 6\n",
+    "x = torch.from_numpy(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Ar-lcW3OFTXI"
+   },
+   "outputs": [],
+   "source": [
+    "# set device\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device('cuda:0')\n",
+    "else:\n",
+    "    device = torch.device('cpu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 71
+    },
+    "colab_type": "code",
+    "id": "KsM9zQZ5FYKp",
+    "outputId": "c37d7629-560f-4191-bbee-2f5523d2cda2"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[running kmeans]: 7it [00:00, 200.48it/s, center_shift=0.000068, iteration=7, tol=0.000100]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running k-means on cuda:0..\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# k-means\n",
+    "cluster_ids_x, cluster_centers = kmeans(\n",
+    "    X=x, num_clusters=num_clusters, distance='euclidean', device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 797
+    },
+    "colab_type": "code",
+    "id": "IdzkYHBEFdja",
+    "outputId": "3bd48cc3-487c-40f7-826a-c8fccecfce89"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([2, 0, 2, 0, 1, 0, 1, 0, 1, 1, 2, 2, 0, 1, 0, 0, 0, 1, 2, 2, 0, 2, 1, 1,\n",
+      "        2, 0, 1, 2, 2, 1, 2, 0, 1, 1, 2, 1, 1, 2, 0, 0, 1, 1, 0, 0, 1, 1, 2, 2,\n",
+      "        0, 1, 0, 2, 1, 0, 0, 2, 2, 1, 0, 1, 0, 2, 1, 1, 1, 0, 2, 1, 2, 1, 2, 1,\n",
+      "        1, 2, 2, 1, 0, 2, 1, 1, 1, 2, 1, 1, 1, 0, 2, 2, 1, 2, 2, 1, 0, 0, 2, 1,\n",
+      "        1, 0, 0, 0, 1, 1, 1, 0, 2, 1, 0, 2, 1, 2, 0, 0, 1, 0, 2, 2, 2, 1, 1, 1,\n",
+      "        1, 0, 1, 0, 2, 1, 0, 1, 1, 2, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 2,\n",
+      "        2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2, 1, 0, 2, 0, 0, 1, 0, 2, 2, 0, 0,\n",
+      "        2, 1, 0, 1, 0, 2, 2, 0, 0, 0, 2, 0, 2, 2, 2, 1, 1, 0, 1, 2, 2, 0, 1, 0,\n",
+      "        2, 2, 1, 1, 0, 0, 2, 2, 1, 0, 2, 0, 2, 1, 2, 1, 1, 0, 2, 0, 0, 2, 2, 2,\n",
+      "        0, 1, 0, 1, 1, 2, 1, 2, 1, 0, 0, 2, 2, 2, 2, 0, 1, 1, 1, 2, 1, 0, 2, 0,\n",
+      "        0, 2, 2, 1, 1, 0, 0, 2, 1, 1, 1, 2, 1, 0, 0, 1, 1, 2, 2, 1, 0, 0, 2, 1,\n",
+      "        1, 0, 1, 2, 1, 2, 0, 2, 2, 0, 2, 1, 0, 1, 1, 1, 2, 0, 1, 2, 2, 1, 1, 1,\n",
+      "        0, 1, 0, 1, 2, 0, 2, 1, 2, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 0, 2, 1,\n",
+      "        0, 0, 2, 0, 2, 0, 1, 2, 1, 2, 0, 0, 2, 1, 1, 1, 1, 0, 2, 0, 2, 2, 1, 0,\n",
+      "        1, 2, 2, 1, 1, 1, 2, 2, 0, 0, 1, 2, 1, 1, 0, 1, 2, 1, 2, 0, 0, 2, 0, 1,\n",
+      "        1, 1, 2, 2, 1, 2, 0, 2, 0, 0, 2, 0, 2, 1, 2, 1, 1, 2, 2, 0, 1, 0, 0, 0,\n",
+      "        0, 1, 0, 2, 2, 1, 0, 2, 0, 0, 2, 2, 2, 0, 1, 2, 0, 2, 2, 1, 2, 1, 2, 1,\n",
+      "        0, 0, 0, 2, 0, 2, 2, 2, 0, 1, 1, 0, 2, 2, 0, 2, 2, 1, 0, 0, 2, 2, 0, 0,\n",
+      "        1, 0, 1, 2, 0, 2, 0, 1, 0, 0, 0, 1, 2, 2, 1, 1, 2, 1, 1, 1, 0, 0, 2, 0,\n",
+      "        0, 0, 2, 1, 1, 1, 2, 2, 2, 2, 0, 0, 1, 2, 0, 0, 1, 2, 1, 0, 1, 0, 2, 2,\n",
+      "        0, 0, 0, 0, 2, 1, 0, 2, 1, 1, 2, 1, 0, 2, 0, 2, 0, 2, 1, 1, 2, 1, 0, 0,\n",
+      "        0, 1, 2, 1, 1, 0, 2, 0, 2, 1, 2, 1, 1, 2, 0, 1, 0, 0, 2, 0, 2, 2, 2, 1,\n",
+      "        1, 2, 1, 1, 2, 1, 1, 1, 1, 0, 0, 2, 1, 1, 2, 1, 1, 2, 0, 0, 2, 1, 2, 1,\n",
+      "        1, 1, 1, 1, 0, 0, 2, 2, 1, 0, 1, 2, 2, 0, 1, 0, 2, 0, 2, 2, 2, 0, 1, 2,\n",
+      "        2, 0, 1, 2, 1, 1, 2, 1, 2, 1, 0, 2, 0, 2, 1, 0, 2, 0, 1, 2, 1, 2, 1, 0,\n",
+      "        1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 2, 0, 0, 2, 1, 0, 1, 1, 1, 0, 0,\n",
+      "        2, 1, 0, 2, 1, 1, 0, 2, 1, 2, 0, 2, 2, 1, 1, 0, 0, 2, 0, 2, 1, 1, 0, 1,\n",
+      "        1, 0, 2, 2, 2, 1, 0, 0, 2, 1, 1, 1, 2, 1, 0, 1, 1, 1, 2, 2, 1, 1, 2, 1,\n",
+      "        0, 1, 0, 0, 0, 2, 0, 1, 0, 0, 1, 1, 0, 1, 2, 1, 1, 1, 1, 0, 1, 0, 0, 2,\n",
+      "        1, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 2, 2, 2, 0,\n",
+      "        2, 2, 0, 2, 2, 1, 1, 1, 1, 0, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 0, 2,\n",
+      "        2, 0, 2, 2, 0, 2, 1, 0, 0, 2, 0, 0, 1, 0, 2, 2, 0, 1, 2, 0, 0, 1, 1, 2,\n",
+      "        2, 2, 0, 1, 2, 0, 0, 1, 2, 2, 0, 1, 0, 0, 2, 2, 0, 2, 1, 0, 1, 1, 2, 1,\n",
+      "        0, 2, 1, 1, 0, 1, 1, 0, 2, 2, 2, 2, 1, 0, 0, 0, 2, 1, 2, 2, 0, 0, 0, 2,\n",
+      "        1, 2, 1, 0, 2, 0, 0, 1, 1, 2, 2, 1, 2, 1, 2, 0, 0, 2, 1, 0, 1, 0, 0, 2,\n",
+      "        2, 2, 2, 0, 1, 2, 2, 2, 2, 2, 1, 0, 0, 1, 1, 0, 2, 0, 2, 0, 2, 0, 0, 1,\n",
+      "        0, 0, 0, 2, 0, 2, 1, 2, 0, 1, 0, 2, 0, 0, 0, 1, 0, 1, 1, 1, 0, 2, 2, 0,\n",
+      "        1, 2, 0, 1, 1, 2, 2, 1, 2, 1, 0, 1, 0, 2, 1, 1, 2, 1, 1, 2, 2, 0, 1, 0,\n",
+      "        2, 2, 0, 2, 2, 2, 1, 1, 0, 1, 2, 0, 2, 1, 0, 2, 1, 0, 1, 0, 2, 2, 2, 2,\n",
+      "        2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 1, 0, 0, 1, 1, 2, 1, 0, 1, 1, 1, 0, 2, 2,\n",
+      "        2, 2, 1, 2, 0, 1, 2, 0, 1, 1, 1, 1, 2, 2, 0, 0, 2, 1, 1, 1, 0, 2, 0, 2,\n",
+      "        2, 2, 0, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0, 0])\n",
+      "tensor([[-0.1075, -0.1522],\n",
+      "        [ 0.1544, -0.0137],\n",
+      "        [-0.0833,  0.1454]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# cluster IDs and cluster centers\n",
+    "print(cluster_ids_x)\n",
+    "print(cluster_centers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "32XHxknWFayP"
+   },
+   "outputs": [],
+   "source": [
+    "# more data\n",
+    "y = np.random.randn(5, dims) / 6\n",
+    "y = torch.from_numpy(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "gQB3jVTKFfsN",
+    "outputId": "1f2f634a-4a32-4b05-ca7f-231165e974ea"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicting on cuda:0..\n"
+     ]
+    }
+   ],
+   "source": [
+    "# predict cluster ids for y\n",
+    "cluster_ids_y = kmeans_predict(\n",
+    "    y, cluster_centers, 'euclidean', device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "CGoD8s6_FiDp",
+    "outputId": "7137fa6a-f5a8-4096-f5d2-fc74d45eaf2f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([1, 2, 0, 1, 2])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cluster_ids_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 481
+    },
+    "colab_type": "code",
+    "id": "5V0vxpaUEnFd",
+    "outputId": "a97b6261-034e-493a-d8a6-cf955ab5e8fd"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tpuBltPrjOeVOBupT3HsA8Be8EsE2rK8HKrmLJeWNt87gxs/Cry6B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\nl7cv9/X2Zcrljs0rc46IiIhIqrrCJIZc/rYxxQ6a2Y7AlsnbR/IO5b4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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot\n",
+    "plt.figure(figsize=(4, 3), dpi=160)\n",
+    "plt.scatter(x[:, 0], x[:, 1], c=cluster_ids_x, cmap='cool')\n",
+    "plt.scatter(y[:, 0], y[:, 1], c=cluster_ids_y, cmap='cool', marker='X')\n",
+    "plt.scatter(\n",
+    "    cluster_centers[:, 0], cluster_centers[:, 1],\n",
+    "    c='white',\n",
+    "    alpha=0.6,\n",
+    "    edgecolors='black',\n",
+    "    linewidths=2\n",
+    ")\n",
+    "plt.axis([-1, 1, -1, 1])\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "BHkQ-zw9EnsN"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "example.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

kmeans_pytorch/kmeans_pytorch/__init__.py

+from functools import partial
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+from .soft_dtw_cuda import SoftDTW
+
+
+def initialize(X, num_clusters, seed):
+    """
+    initialize cluster centers
+    :param X: (torch.tensor) matrix
+    :param num_clusters: (int) number of clusters
+    :param seed: (int) seed for kmeans
+    :return: (np.array) initial state
+    """
+    num_samples = len(X)
+    if seed == None:
+        indices = np.random.choice(num_samples, num_clusters, replace=False)
+    else:
+        np.random.seed(seed) ; indices = np.random.choice(num_samples, num_clusters, replace=False)
+    initial_state = X[indices]
+    return initial_state
+
+
+def kmeans(
+        X,
+        num_clusters,
+        distance='euclidean',
+        cluster_centers=[],
+        tol=1e-4,
+        tqdm_flag=True,
+        iter_limit=0,
+        device=torch.device('cpu'),
+        gamma_for_soft_dtw=0.001,
+        seed=None,
+):
+    """
+    perform kmeans
+    :param X: (torch.tensor) matrix
+    :param num_clusters: (int) number of clusters
+    :param distance: (str) distance [options: 'euclidean', 'cosine'] [default: 'euclidean']
+    :param seed: (int) seed for kmeans
+    :param tol: (float) threshold [default: 0.0001]
+    :param device: (torch.device) device [default: cpu]
+    :param tqdm_flag: Allows to turn logs on and off
+    :param iter_limit: hard limit for max number of iterations
+    :param gamma_for_soft_dtw: approaches to (hard) DTW as gamma -> 0
+    :return: (torch.tensor, torch.tensor) cluster ids, cluster centers
+    """
+    if tqdm_flag:
+        print(f'running k-means on {device}..')
+
+    if distance == 'euclidean':
+        pairwise_distance_function = partial(pairwise_distance, device=device, tqdm_flag=tqdm_flag)
+    elif distance == 'cosine':
+        pairwise_distance_function = partial(pairwise_cosine, device=device)
+    elif distance == 'soft_dtw':
+        sdtw = SoftDTW(use_cuda=device.type == 'cuda', gamma=gamma_for_soft_dtw)
+        pairwise_distance_function = partial(pairwise_soft_dtw, sdtw=sdtw, device=device)
+    else:
+        raise NotImplementedError
+
+    # convert to float
+    X = X.float()
+
+    # transfer to device
+    X = X.to(device)
+
+    # initialize
+    if type(cluster_centers) == list:  # ToDo: make this less annoyingly weird
+        initial_state = initialize(X, num_clusters, seed=seed)
+    else:
+        if tqdm_flag:
+            print('resuming')
+        # find data point closest to the initial cluster center
+        initial_state = cluster_centers
+        dis = pairwise_distance_function(X, initial_state)
+        choice_points = torch.argmin(dis, dim=0)
+        initial_state = X[choice_points]
+        initial_state = initial_state.to(device)
+
+    iteration = 0
+    if tqdm_flag:
+        tqdm_meter = tqdm(desc='[running kmeans]')
+    while True:
+
+        dis = pairwise_distance_function(X, initial_state)
+
+        choice_cluster = torch.argmin(dis, dim=1)
+
+        initial_state_pre = initial_state.clone()
+
+        for index in range(num_clusters):
+            selected = torch.nonzero(choice_cluster == index).squeeze().to(device)
+
+            selected = torch.index_select(X, 0, selected)
+
+            # https://github.com/subhadarship/kmeans_pytorch/issues/16
+            if selected.shape[0] == 0:
+                selected = X[torch.randint(len(X), (1,))]
+
+            initial_state[index] = selected.mean(dim=0)
+
+        center_shift = torch.sum(
+            torch.sqrt(
+                torch.sum((initial_state - initial_state_pre) ** 2, dim=1)
+            ))
+
+        # increment iteration
+        iteration = iteration + 1
+
+        # update tqdm meter
+        if tqdm_flag:
+            tqdm_meter.set_postfix(
+                iteration=f'{iteration}',
+                center_shift=f'{center_shift ** 2:0.6f}',
+                tol=f'{tol:0.6f}'
+            )
+            tqdm_meter.update()
+        if center_shift ** 2 < tol:
+            break
+        if iter_limit != 0 and iteration >= iter_limit:
+            break
+
+    return choice_cluster.cpu(), initial_state.cpu()
+
+
+def kmeans_predict(
+        X,
+        cluster_centers,
+        distance='euclidean',
+        device=torch.device('cpu'),
+        gamma_for_soft_dtw=0.001,
+        tqdm_flag=True
+):
+    """
+    predict using cluster centers
+    :param X: (torch.tensor) matrix
+    :param cluster_centers: (torch.tensor) cluster centers
+    :param distance: (str) distance [options: 'euclidean', 'cosine'] [default: 'euclidean']
+    :param device: (torch.device) device [default: 'cpu']
+    :param gamma_for_soft_dtw: approaches to (hard) DTW as gamma -> 0
+    :return: (torch.tensor) cluster ids
+    """
+    if tqdm_flag:
+        print(f'predicting on {device}..')
+
+    if distance == 'euclidean':
+        pairwise_distance_function = partial(pairwise_distance, device=device, tqdm_flag=tqdm_flag)
+    elif distance == 'cosine':
+        pairwise_distance_function = partial(pairwise_cosine, device=device)
+    elif distance == 'soft_dtw':
+        sdtw = SoftDTW(use_cuda=device.type == 'cuda', gamma=gamma_for_soft_dtw)
+        pairwise_distance_function = partial(pairwise_soft_dtw, sdtw=sdtw, device=device)
+    else:
+        raise NotImplementedError
+
+    # convert to float
+    X = X.float()
+
+    # transfer to device
+    X = X.to(device)
+
+    dis = pairwise_distance_function(X, cluster_centers)
+    choice_cluster = torch.argmin(dis, dim=1)
+
+    return choice_cluster.cpu()
+
+
+def pairwise_distance(data1, data2, device=torch.device('cpu'), tqdm_flag=True):
+    if tqdm_flag:
+        print(f'device is :{device}')
+    
+    # transfer to device
+    data1, data2 = data1.to(device), data2.to(device)
+
+    # N*1*M
+    A = data1.unsqueeze(dim=1)
+
+    # 1*N*M
+    B = data2.unsqueeze(dim=0)
+
+    dis = (A - B) ** 2.0
+    # return N*N matrix for pairwise distance
+    dis = dis.sum(dim=-1).squeeze()
+    return dis
+
+
+def pairwise_cosine(data1, data2, device=torch.device('cpu')):
+    # transfer to device
+    data1, data2 = data1.to(device), data2.to(device)
+
+    # N*1*M
+    A = data1.unsqueeze(dim=1)
+
+    # 1*N*M
+    B = data2.unsqueeze(dim=0)
+
+    # normalize the points  | [0.3, 0.4] -> [0.3/sqrt(0.09 + 0.16), 0.4/sqrt(0.09 + 0.16)] = [0.3/0.5, 0.4/0.5]
+    A_normalized = A / A.norm(dim=-1, keepdim=True)
+    B_normalized = B / B.norm(dim=-1, keepdim=True)
+
+    cosine = A_normalized * B_normalized
+
+    # return N*N matrix for pairwise distance
+    cosine_dis = 1 - cosine.sum(dim=-1).squeeze()
+    return cosine_dis
+
+
+def pairwise_soft_dtw(data1, data2, sdtw=None, device=torch.device('cpu')):
+    if sdtw is None:
+        raise ValueError('sdtw is None - initialize it with SoftDTW')
+
+    # transfer to device
+    data1, data2 = data1.to(device), data2.to(device)
+
+    # (batch_size, seq_len, feature_dim=1)
+    A = data1.unsqueeze(dim=2)
+
+    # (cluster_size, seq_len, feature_dim=1)
+    B = data2.unsqueeze(dim=2)
+
+    distances = []
+    for b in B:
+        # (1, seq_len, 1)
+        b = b.unsqueeze(dim=0)
+        A, b = torch.broadcast_tensors(A, b)
+        # (batch_size, 1)
+        sdtw_distance = sdtw(b, A).view(-1, 1)
+        distances.append(sdtw_distance)
+
+    # (batch_size, cluster_size)
+    dis = torch.cat(distances, dim=1)
+    return dis

kmeans_pytorch/kmeans_pytorch/main.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+__version__ = "0.3"
+
+
+def main():
+    print("TODO")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file

kmeans_pytorch/kmeans_pytorch/soft_dtw_cuda.py

+# Code by Maghoumi/pytorch-softdtw-cuda
+# https://github.com/Maghoumi/pytorch-softdtw-cuda
+
+# MIT License
+#
+# Copyright (c) 2020 Mehran Maghoumi
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+# ----------------------------------------------------------------------------------------------------------------------
+
+import numpy as np
+import torch
+import torch.cuda
+from numba import jit
+from torch.autograd import Function
+from numba import cuda
+import math
+
+# ----------------------------------------------------------------------------------------------------------------------
+@cuda.jit
+def compute_softdtw_cuda(D, gamma, bandwidth, max_i, max_j, n_passes, R):
+    """
+    :param seq_len: The length of the sequence (both inputs are assumed to be of the same size)
+    :param n_passes: 2 * seq_len - 1 (The number of anti-diagonals)
+    """
+    # Each block processes one pair of examples
+    b = cuda.blockIdx.x
+    # We have as many threads as seq_len, because the most number of threads we need
+    # is equal to the number of elements on the largest anti-diagonal
+    tid = cuda.threadIdx.x
+
+    # Compute I, J, the indices from [0, seq_len)
+
+    # The row index is always the same as tid
+    I = tid
+
+    inv_gamma = 1.0 / gamma
+
+    # Go over each anti-diagonal. Only process threads that fall on the current on the anti-diagonal
+    for p in range(n_passes):
+
+        # The index is actually 'p - tid' but need to force it in-bounds
+        J = max(0, min(p - tid, max_j - 1))
+
+        # For simplicity, we define i, j which start from 1 (offset from I, J)
+        i = I + 1
+        j = J + 1
+
+        # Only compute if element[i, j] is on the current anti-diagonal, and also is within bounds
+        if I + J == p and (I < max_i and J < max_j):
+            # Don't compute if outside bandwidth
+            if not (abs(i - j) > bandwidth > 0):
+                r0 = -R[b, i - 1, j - 1] * inv_gamma
+                r1 = -R[b, i - 1, j] * inv_gamma
+                r2 = -R[b, i, j - 1] * inv_gamma
+                rmax = max(max(r0, r1), r2)
+                rsum = math.exp(r0 - rmax) + math.exp(r1 - rmax) + math.exp(r2 - rmax)
+                softmin = -gamma * (math.log(rsum) + rmax)
+                R[b, i, j] = D[b, i - 1, j - 1] + softmin
+
+        # Wait for other threads in this block
+        cuda.syncthreads()
+
+# ----------------------------------------------------------------------------------------------------------------------
+@cuda.jit
+def compute_softdtw_backward_cuda(D, R, inv_gamma, bandwidth, max_i, max_j, n_passes, E):
+    k = cuda.blockIdx.x
+    tid = cuda.threadIdx.x
+
+    # Indexing logic is the same as above, however, the anti-diagonal needs to
+    # progress backwards
+    I = tid
+
+    for p in range(n_passes):
+        # Reverse the order to make the loop go backward
+        rev_p = n_passes - p - 1
+
+        # convert tid to I, J, then i, j
+        J = max(0, min(rev_p - tid, max_j - 1))
+
+        i = I + 1
+        j = J + 1
+
+        # Only compute if element[i, j] is on the current anti-diagonal, and also is within bounds
+        if I + J == rev_p and (I < max_i and J < max_j):
+
+            if math.isinf(R[k, i, j]):
+                R[k, i, j] = -math.inf
+
+            # Don't compute if outside bandwidth
+            if not (abs(i - j) > bandwidth > 0):
+                a = math.exp((R[k, i + 1, j] - R[k, i, j] - D[k, i + 1, j]) * inv_gamma)
+                b = math.exp((R[k, i, j + 1] - R[k, i, j] - D[k, i, j + 1]) * inv_gamma)
+                c = math.exp((R[k, i + 1, j + 1] - R[k, i, j] - D[k, i + 1, j + 1]) * inv_gamma)
+                E[k, i, j] = E[k, i + 1, j] * a + E[k, i, j + 1] * b + E[k, i + 1, j + 1] * c
+
+        # Wait for other threads in this block
+        cuda.syncthreads()
+
+# ----------------------------------------------------------------------------------------------------------------------
+class _SoftDTWCUDA(Function):
+    """
+    CUDA implementation is inspired by the diagonal one proposed in https://ieeexplore.ieee.org/document/8400444:
+    "Developing a pattern discovery method in time series data and its GPU acceleration"
+    """
+
+    @staticmethod
+    def forward(ctx, D, gamma, bandwidth):
+        dev = D.device
+        dtype = D.dtype
+        gamma = torch.cuda.FloatTensor([gamma])
+        bandwidth = torch.cuda.FloatTensor([bandwidth])
+
+        B = D.shape[0]
+        N = D.shape[1]
+        M = D.shape[2]
+        threads_per_block = max(N, M)
+        n_passes = 2 * threads_per_block - 1
+
+        # Prepare the output array
+        R = torch.ones((B, N + 2, M + 2), device=dev, dtype=dtype) * math.inf
+        R[:, 0, 0] = 0
+
+        # Run the CUDA kernel.
+        # Set CUDA's grid size to be equal to the batch size (every CUDA block processes one sample pair)
+        # Set the CUDA block size to be equal to the length of the longer sequence (equal to the size of the largest diagonal)
+        compute_softdtw_cuda[B, threads_per_block](cuda.as_cuda_array(D.detach()),
+                                                   gamma.item(), bandwidth.item(), N, M, n_passes,
+                                                   cuda.as_cuda_array(R))
+        ctx.save_for_backward(D, R, gamma, bandwidth)
+        return R[:, -2, -2]
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        dev = grad_output.device
+        dtype = grad_output.dtype
+        D, R, gamma, bandwidth = ctx.saved_tensors
+
+        B = D.shape[0]
+        N = D.shape[1]
+        M = D.shape[2]
+        threads_per_block = max(N, M)
+        n_passes = 2 * threads_per_block - 1
+
+        D_ = torch.zeros((B, N + 2, M + 2), dtype=dtype, device=dev)
+        D_[:, 1:N + 1, 1:M + 1] = D
+
+        R[:, :, -1] = -math.inf
+        R[:, -1, :] = -math.inf
+        R[:, -1, -1] = R[:, -2, -2]
+
+        E = torch.zeros((B, N + 2, M + 2), dtype=dtype, device=dev)
+        E[:, -1, -1] = 1
+
+        # Grid and block sizes are set same as done above for the forward() call
+        compute_softdtw_backward_cuda[B, threads_per_block](cuda.as_cuda_array(D_),
+                                                            cuda.as_cuda_array(R),
+                                                            1.0 / gamma.item(), bandwidth.item(), N, M, n_passes,
+                                                            cuda.as_cuda_array(E))
+        E = E[:, 1:N + 1, 1:M + 1]
+        return grad_output.view(-1, 1, 1).expand_as(E) * E, None, None
+
+
+# ----------------------------------------------------------------------------------------------------------------------
+#
+# The following is the CPU implementation based on https://github.com/Sleepwalking/pytorch-softdtw
+# Credit goes to Kanru Hua.
+# I've added support for batching and pruning.
+#
+# ----------------------------------------------------------------------------------------------------------------------
+@jit(nopython=True)
+def compute_softdtw(D, gamma, bandwidth):
+    B = D.shape[0]
+    N = D.shape[1]
+    M = D.shape[2]
+    R = np.ones((B, N + 2, M + 2)) * np.inf
+    R[:, 0, 0] = 0
+    for b in range(B):
+        for j in range(1, M + 1):
+            for i in range(1, N + 1):
+
+                # Check the pruning condition
+                if 0 < bandwidth < np.abs(i - j):
+                    continue
+
+                r0 = -R[b, i - 1, j - 1] / gamma
+                r1 = -R[b, i - 1, j] / gamma
+                r2 = -R[b, i, j - 1] / gamma
+                rmax = max(max(r0, r1), r2)
+                rsum = np.exp(r0 - rmax) + np.exp(r1 - rmax) + np.exp(r2 - rmax)
+                softmin = - gamma * (np.log(rsum) + rmax)
+                R[b, i, j] = D[b, i - 1, j - 1] + softmin
+    return R
+
+# ----------------------------------------------------------------------------------------------------------------------
+@jit(nopython=True)
+def compute_softdtw_backward(D_, R, gamma, bandwidth):
+    B = D_.shape[0]
+    N = D_.shape[1]
+    M = D_.shape[2]
+    D = np.zeros((B, N + 2, M + 2))
+    E = np.zeros((B, N + 2, M + 2))
+    D[:, 1:N + 1, 1:M + 1] = D_
+    E[:, -1, -1] = 1
+    R[:, :, -1] = -np.inf
+    R[:, -1, :] = -np.inf
+    R[:, -1, -1] = R[:, -2, -2]
+    for k in range(B):
+        for j in range(M, 0, -1):
+            for i in range(N, 0, -1):
+
+                if np.isinf(R[k, i, j]):
+                    R[k, i, j] = -np.inf
+
+                # Check the pruning condition
+                if 0 < bandwidth < np.abs(i - j):
+                    continue
+
+                a0 = (R[k, i + 1, j] - R[k, i, j] - D[k, i + 1, j]) / gamma
+                b0 = (R[k, i, j + 1] - R[k, i, j] - D[k, i, j + 1]) / gamma
+                c0 = (R[k, i + 1, j + 1] - R[k, i, j] - D[k, i + 1, j + 1]) / gamma
+                a = np.exp(a0)
+                b = np.exp(b0)
+                c = np.exp(c0)
+                E[k, i, j] = E[k, i + 1, j] * a + E[k, i, j + 1] * b + E[k, i + 1, j + 1] * c
+    return E[:, 1:N + 1, 1:M + 1]
+
+# ----------------------------------------------------------------------------------------------------------------------
+class _SoftDTW(Function):
+    """
+    CPU implementation based on https://github.com/Sleepwalking/pytorch-softdtw
+    """
+
+    @staticmethod
+    def forward(ctx, D, gamma, bandwidth):
+        dev = D.device
+        dtype = D.dtype
+        gamma = torch.Tensor([gamma]).to(dev).type(dtype)  # dtype fixed
+        bandwidth = torch.Tensor([bandwidth]).to(dev).type(dtype)
+        D_ = D.detach().cpu().numpy()
+        g_ = gamma.item()
+        b_ = bandwidth.item()
+        R = torch.Tensor(compute_softdtw(D_, g_, b_)).to(dev).type(dtype)
+        ctx.save_for_backward(D, R, gamma, bandwidth)
+        return R[:, -2, -2]
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        dev = grad_output.device
+        dtype = grad_output.dtype
+        D, R, gamma, bandwidth = ctx.saved_tensors
+        D_ = D.detach().cpu().numpy()
+        R_ = R.detach().cpu().numpy()
+        g_ = gamma.item()
+        b_ = bandwidth.item()
+        E = torch.Tensor(compute_softdtw_backward(D_, R_, g_, b_)).to(dev).type(dtype)
+        return grad_output.view(-1, 1, 1).expand_as(E) * E, None, None
+
+# ----------------------------------------------------------------------------------------------------------------------
+class SoftDTW(torch.nn.Module):
+    """
+    The soft DTW implementation that optionally supports CUDA
+    """
+
+    def __init__(self, use_cuda, gamma=1.0, normalize=False, bandwidth=None, dist_func=None):
+        """
+        Initializes a new instance using the supplied parameters
+        :param use_cuda: Flag indicating whether the CUDA implementation should be used
+        :param gamma: sDTW's gamma parameter
+        :param normalize: Flag indicating whether to perform normalization
+                          (as discussed in https://github.com/mblondel/soft-dtw/issues/10#issuecomment-383564790)
+        :param bandwidth: Sakoe-Chiba bandwidth for pruning. Passing 'None' will disable pruning.
+        :param dist_func: Optional point-wise distance function to use. If 'None', then a default Euclidean distance function will be used.
+        """
+        super(SoftDTW, self).__init__()
+        self.normalize = normalize
+        self.gamma = gamma
+        self.bandwidth = 0 if bandwidth is None else float(bandwidth)
+        self.use_cuda = use_cuda
+
+        # Set the distance function
+        if dist_func is not None:
+            self.dist_func = dist_func
+        else:
+            self.dist_func = SoftDTW._euclidean_dist_func
+
+    def _get_func_dtw(self, x, y):
+        """
+        Checks the inputs and selects the proper implementation to use.
+        """
+        bx, lx, dx = x.shape
+        by, ly, dy = y.shape
+        # Make sure the dimensions match
+        assert bx == by  # Equal batch sizes
+        assert dx == dy  # Equal feature dimensions
+
+        use_cuda = self.use_cuda
+
+        if use_cuda and (lx > 1024 or ly > 1024):  # We should be able to spawn enough threads in CUDA
+                print("SoftDTW: Cannot use CUDA because the sequence length > 1024 (the maximum block size supported by CUDA)")
+                use_cuda = False
+
+        # Finally, return the correct function
+        return _SoftDTWCUDA.apply if use_cuda else _SoftDTW.apply
+
+    @staticmethod
+    def _euclidean_dist_func(x, y):
+        """
+        Calculates the Euclidean distance between each element in x and y per timestep
+        """
+        n = x.size(1)
+        m = y.size(1)
+        d = x.size(2)
+        x = x.unsqueeze(2).expand(-1, n, m, d)
+        y = y.unsqueeze(1).expand(-1, n, m, d)
+        return torch.pow(x - y, 2).sum(3)
+
+    def forward(self, X, Y):
+        """
+        Compute the soft-DTW value between X and Y
+        :param X: One batch of examples, batch_size x seq_len x dims
+        :param Y: The other batch of examples, batch_size x seq_len x dims
+        :return: The computed results
+        """
+
+        # Check the inputs and get the correct implementation
+        func_dtw = self._get_func_dtw(X, Y)
+
+        if self.normalize:
+            # Stack everything up and run
+            x = torch.cat([X, X, Y])
+            y = torch.cat([Y, X, Y])
+            D = self.dist_func(x, y)
+            out = func_dtw(D, self.gamma, self.bandwidth)
+            out_xy, out_xx, out_yy = torch.split(out, X.shape[0])
+            return out_xy - 1 / 2 * (out_xx + out_yy)
+        else:
+            D_xy = self.dist_func(X, Y)
+            return func_dtw(D_xy, self.gamma, self.bandwidth)
+
+# ----------------------------------------------------------------------------------------------------------------------
+def timed_run(a, b, sdtw):
+    """
+    Runs a and b through sdtw, and times the forward and backward passes.
+    Assumes that a requires gradients.
+    :return: timing, forward result, backward result
+    """
+    from timeit import default_timer as timer
+
+    # Forward pass
+    start = timer()
+    forward = sdtw(a, b)
+    end = timer()
+    t = end - start
+
+    grad_outputs = torch.ones_like(forward)
+
+    # Backward
+    start = timer()
+    grads = torch.autograd.grad(forward, a, grad_outputs=grad_outputs)[0]
+    end = timer()
+
+    # Total time
+    t += end - start
+
+    return t, forward, grads
+
+# ----------------------------------------------------------------------------------------------------------------------
+def profile(batch_size, seq_len_a, seq_len_b, dims, tol_backward):
+    sdtw = SoftDTW(False, gamma=1.0, normalize=False)
+    sdtw_cuda = SoftDTW(True, gamma=1.0, normalize=False)
+    n_iters = 6
+
+    print("Profiling forward() + backward() times for batch_size={}, seq_len_a={}, seq_len_b={}, dims={}...".format(batch_size, seq_len_a, seq_len_b, dims))
+
+    times_cpu = []
+    times_gpu = []
+
+    for i in range(n_iters):
+        a_cpu = torch.rand((batch_size, seq_len_a, dims), requires_grad=True)
+        b_cpu = torch.rand((batch_size, seq_len_b, dims))
+        a_gpu = a_cpu.cuda()
+        b_gpu = b_cpu.cuda()
+
+        # GPU
+        t_gpu, forward_gpu, backward_gpu = timed_run(a_gpu, b_gpu, sdtw_cuda)
+
+        # CPU
+        t_cpu, forward_cpu, backward_cpu = timed_run(a_cpu, b_cpu, sdtw)
+
+        # Verify the results
+        assert torch.allclose(forward_cpu, forward_gpu.cpu())
+        assert torch.allclose(backward_cpu, backward_gpu.cpu(), atol=tol_backward)
+
+        if i > 0:  # Ignore the first time we run, in case this is a cold start (because timings are off at a cold start of the script)
+            times_cpu += [t_cpu]
+            times_gpu += [t_gpu]
+
+    # Average and log
+    avg_cpu = np.mean(times_cpu)
+    avg_gpu = np.mean(times_gpu)
+    print("\tCPU:     ", avg_cpu)
+    print("\tGPU:     ", avg_gpu)
+    print("\tSpeedup: ", avg_cpu / avg_gpu)
+    print()
+
+# ----------------------------------------------------------------------------------------------------------------------
+if __name__ == "__main__":
+    from timeit import default_timer as timer
+
+    torch.manual_seed(1234)
+
+    profile(128, 17, 15, 2, tol_backward=1e-6)
+    profile(512, 64, 64, 2, tol_backward=1e-4)
+    profile(512, 256, 256, 2, tol_backward=1e-3)

kmeans_pytorch/makefile

+.PHONY: clean build publish
+
+build: clean
+	python -m pip install --upgrade --quiet setuptools wheel twine
+	python setup.py --quiet sdist bdist_wheel
+
+publish: build
+	python -m twine check dist/*
+	python -m twine upload dist/*
+
+clean:
+	rm -r build dist *.egg-info || true
\ No newline at end of file

kmeans_pytorch/setup.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# For a fully annotated version of this file and what it does, see
+# https://github.com/pypa/sampleproject/blob/master/setup.py
+
+# To upload this file to PyPI you must build it then upload it:
+# python setup.py sdist bdist_wheel  # build in 'dist' folder
+# python-m twine upload dist/*  # 'twine' must be installed: 'pip install twine'
+
+
+import ast
+import io
+import re
+import os
+from setuptools import find_packages, setup
+
+DEPENDENCIES = []
+EXCLUDE_FROM_PACKAGES = ["contrib", "docs", "tests*"]
+CURDIR = os.path.abspath(os.path.dirname(__file__))
+
+with io.open(os.path.join(CURDIR, "README.md"), "r", encoding="utf-8") as f:
+    README = f.read()
+
+
+def get_version():
+    main_file = os.path.join(CURDIR, "kmeans_pytorch", "main.py")
+    _version_re = re.compile(r"__version__\s+=\s+(?P<version>.*)")
+    with open(main_file, "r", encoding="utf8") as f:
+        match = _version_re.search(f.read())
+        version = match.group("version") if match is not None else '"unknown"'
+    return str(ast.literal_eval(version))
+
+
+setup(
+    name="kmeans_pytorch",
+    version=get_version(),
+    author="Subhadarshi",
+    author_email="subhadarshipanda08@gmail.com",
+    description="",
+    long_description=README,
+    long_description_content_type="text/markdown",
+    url="https://github.com/subhadarship/kmeans_pytorch",
+    packages=find_packages(exclude=EXCLUDE_FROM_PACKAGES),
+    include_package_data=True,
+    keywords=[],
+    scripts=[],
+    entry_points={"console_scripts": ["kmeans_pytorch=kmeans_pytorch.main:main"]},
+    zip_safe=False,
+    install_requires=DEPENDENCIES,
+    test_suite="tests.test_project",
+    python_requires=">=3.6",
+    # license and classifier list:
+    # https://pypi.org/pypi?%3Aaction=list_classifiers
+    license="License :: OSI Approved :: MIT License",
+    classifiers=[
+        "Programming Language :: Python",
+        # "Programming Language :: Python :: 3",
+        # "Operating System :: OS Independent",
+        # "Private :: Do Not Upload"
+    ],
+)
\ No newline at end of file

kmeans_pytorch/test_soft_dtw.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "R8_IiKZZEU9i"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from kmeans_pytorch import kmeans, kmeans_predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "iyoljrh1FCxJ"
+   },
+   "outputs": [],
+   "source": [
+    "# set random seed\n",
+    "np.random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "tcnoTA16FHbJ"
+   },
+   "outputs": [],
+   "source": [
+    "# data\n",
+    "data_size, dims, num_clusters = 1000, 2, 3\n",
+    "x = np.random.randn(data_size, dims) / 6\n",
+    "x = torch.from_numpy(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Ar-lcW3OFTXI"
+   },
+   "outputs": [],
+   "source": [
+    "# set device\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device('cuda:0')\n",
+    "else:\n",
+    "    device = torch.device('cpu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 71
+    },
+    "colab_type": "code",
+    "id": "KsM9zQZ5FYKp",
+    "outputId": "c37d7629-560f-4191-bbee-2f5523d2cda2"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running k-means on cuda:0..\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[running kmeans]: 6it [00:00, 73.67it/s, center_shift=0.000068, iteration=7, tol=0.000100]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "device is :cuda:0\n",
+      "device is :cuda:0\n",
+      "device is :cuda:0\n",
+      "device is :cuda:0\n",
+      "device is :cuda:0\n",
+      "device is :cuda:0\n",
+      "device is :cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# k-means\n",
+    "cluster_ids_x, cluster_centers = kmeans(\n",
+    "    X=x, num_clusters=num_clusters, distance='soft_dtw', device=device, gamma_for_soft_dtw=0.0001\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 797
+    },
+    "colab_type": "code",
+    "id": "IdzkYHBEFdja",
+    "outputId": "3bd48cc3-487c-40f7-826a-c8fccecfce89"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([2, 0, 2, 0, 1, 0, 1, 0, 1, 1, 2, 2, 0, 1, 0, 0, 0, 1, 2, 2, 0, 2, 1, 1,\n",
+      "        2, 0, 1, 2, 2, 1, 2, 0, 1, 1, 2, 1, 1, 2, 0, 0, 1, 1, 0, 0, 1, 1, 2, 2,\n",
+      "        0, 1, 0, 2, 1, 0, 0, 2, 2, 1, 0, 1, 0, 2, 1, 1, 1, 0, 2, 1, 2, 1, 2, 1,\n",
+      "        1, 2, 2, 1, 0, 2, 1, 1, 1, 2, 1, 1, 1, 0, 2, 2, 1, 2, 2, 1, 0, 0, 2, 1,\n",
+      "        1, 0, 0, 0, 1, 1, 1, 0, 2, 1, 0, 2, 1, 2, 0, 0, 1, 0, 2, 2, 2, 1, 1, 1,\n",
+      "        1, 0, 1, 0, 2, 1, 0, 1, 1, 2, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 2,\n",
+      "        2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2, 1, 0, 2, 0, 0, 1, 0, 2, 2, 0, 0,\n",
+      "        2, 1, 0, 1, 0, 2, 2, 0, 0, 0, 2, 0, 2, 2, 2, 1, 1, 0, 1, 2, 2, 0, 1, 0,\n",
+      "        2, 2, 1, 1, 0, 0, 2, 2, 1, 0, 2, 0, 2, 1, 2, 1, 1, 0, 2, 0, 0, 2, 2, 2,\n",
+      "        0, 1, 0, 1, 1, 2, 1, 2, 1, 0, 0, 2, 2, 2, 2, 0, 1, 1, 1, 2, 1, 0, 2, 0,\n",
+      "        0, 2, 2, 1, 1, 0, 0, 2, 1, 1, 1, 2, 1, 0, 0, 1, 1, 2, 2, 1, 0, 0, 2, 1,\n",
+      "        1, 0, 1, 2, 1, 2, 0, 2, 2, 0, 2, 1, 0, 1, 1, 1, 2, 0, 1, 2, 2, 1, 1, 1,\n",
+      "        0, 1, 0, 1, 2, 0, 2, 1, 2, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 0, 2, 1,\n",
+      "        0, 0, 2, 0, 2, 0, 1, 2, 1, 2, 0, 0, 2, 1, 1, 1, 1, 0, 2, 0, 2, 2, 1, 0,\n",
+      "        1, 2, 2, 1, 1, 1, 2, 2, 0, 0, 1, 2, 1, 1, 0, 1, 2, 1, 2, 0, 0, 2, 0, 1,\n",
+      "        1, 1, 2, 2, 1, 2, 0, 2, 0, 0, 2, 0, 2, 1, 2, 1, 1, 2, 2, 0, 1, 0, 0, 0,\n",
+      "        0, 1, 0, 2, 2, 1, 0, 2, 0, 0, 2, 2, 2, 0, 1, 2, 0, 2, 2, 1, 2, 1, 2, 1,\n",
+      "        0, 0, 0, 2, 0, 2, 2, 2, 0, 1, 1, 0, 2, 2, 0, 2, 2, 1, 0, 0, 2, 2, 0, 0,\n",
+      "        1, 0, 1, 2, 0, 2, 0, 1, 0, 0, 0, 1, 2, 2, 1, 1, 2, 1, 1, 1, 0, 0, 2, 0,\n",
+      "        0, 0, 2, 1, 1, 1, 2, 2, 2, 2, 0, 0, 1, 2, 0, 0, 1, 2, 1, 0, 1, 0, 2, 2,\n",
+      "        0, 0, 0, 0, 2, 1, 0, 2, 1, 1, 2, 1, 0, 2, 0, 2, 0, 2, 1, 1, 2, 1, 0, 0,\n",
+      "        0, 1, 2, 1, 1, 0, 2, 0, 2, 1, 2, 1, 1, 2, 0, 1, 0, 0, 2, 0, 2, 2, 2, 1,\n",
+      "        1, 2, 1, 1, 2, 1, 1, 1, 1, 0, 0, 2, 1, 1, 2, 1, 1, 2, 0, 0, 2, 1, 2, 1,\n",
+      "        1, 1, 1, 1, 0, 0, 2, 2, 1, 0, 1, 2, 2, 0, 1, 0, 2, 0, 2, 2, 2, 0, 1, 2,\n",
+      "        2, 0, 1, 2, 1, 1, 2, 1, 2, 1, 0, 2, 0, 2, 1, 0, 2, 0, 1, 2, 1, 2, 1, 0,\n",
+      "        1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 2, 0, 0, 2, 1, 0, 1, 1, 1, 0, 0,\n",
+      "        2, 1, 0, 2, 1, 1, 0, 2, 1, 2, 0, 2, 2, 1, 1, 0, 0, 2, 0, 2, 1, 1, 0, 1,\n",
+      "        1, 0, 2, 2, 2, 1, 0, 0, 2, 1, 1, 1, 2, 1, 0, 1, 1, 1, 2, 2, 1, 1, 2, 1,\n",
+      "        0, 1, 0, 0, 0, 2, 0, 1, 0, 0, 1, 1, 0, 1, 2, 1, 1, 1, 1, 0, 1, 0, 0, 2,\n",
+      "        1, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 2, 2, 2, 0,\n",
+      "        2, 2, 0, 2, 2, 1, 1, 1, 1, 0, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 0, 2,\n",
+      "        2, 0, 2, 2, 0, 2, 1, 0, 0, 2, 0, 0, 1, 0, 2, 2, 0, 1, 2, 0, 0, 1, 1, 2,\n",
+      "        2, 2, 0, 1, 2, 0, 0, 1, 2, 2, 0, 1, 0, 0, 2, 2, 0, 2, 1, 0, 1, 1, 2, 1,\n",
+      "        0, 2, 1, 1, 0, 1, 1, 0, 2, 2, 2, 2, 1, 0, 0, 0, 2, 1, 2, 2, 0, 0, 0, 2,\n",
+      "        1, 2, 1, 0, 2, 0, 0, 1, 1, 2, 2, 1, 2, 1, 2, 0, 0, 2, 1, 0, 1, 0, 0, 2,\n",
+      "        2, 2, 2, 0, 1, 2, 2, 2, 2, 2, 1, 0, 0, 1, 1, 0, 2, 0, 2, 0, 2, 0, 0, 1,\n",
+      "        0, 0, 0, 2, 0, 2, 1, 2, 0, 1, 0, 2, 0, 0, 0, 1, 0, 1, 1, 1, 0, 2, 2, 0,\n",
+      "        1, 2, 0, 1, 1, 2, 2, 1, 2, 1, 0, 1, 0, 2, 1, 1, 2, 1, 1, 2, 2, 0, 1, 0,\n",
+      "        2, 2, 0, 2, 2, 2, 1, 1, 0, 1, 2, 0, 2, 1, 0, 2, 1, 0, 1, 0, 2, 2, 2, 2,\n",
+      "        2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 1, 0, 0, 1, 1, 2, 1, 0, 1, 1, 1, 0, 2, 2,\n",
+      "        2, 2, 1, 2, 0, 1, 2, 0, 1, 1, 1, 1, 2, 2, 0, 0, 2, 1, 1, 1, 0, 2, 0, 2,\n",
+      "        2, 2, 0, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0, 0])\n",
+      "tensor([[-0.1075, -0.1522],\n",
+      "        [ 0.1544, -0.0137],\n",
+      "        [-0.0833,  0.1454]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# cluster IDs and cluster centers\n",
+    "print(cluster_ids_x)\n",
+    "print(cluster_centers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "32XHxknWFayP"
+   },
+   "outputs": [],
+   "source": [
+    "# more data\n",
+    "y = np.random.randn(5, dims) / 6\n",
+    "y = torch.from_numpy(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "gQB3jVTKFfsN",
+    "outputId": "1f2f634a-4a32-4b05-ca7f-231165e974ea"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[running kmeans]: 7it [09:38, 82.70s/it, center_shift=0.000068, iteration=7, tol=0.000100]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicting on cuda:0..\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# predict cluster ids for y\n",
+    "cluster_ids_y = kmeans_predict(\n",
+    "    y, cluster_centers, 'soft_dtw', device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "CGoD8s6_FiDp",
+    "outputId": "7137fa6a-f5a8-4096-f5d2-fc74d45eaf2f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([1, 2, 0, 1, 2])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cluster_ids_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 481
+    },
+    "colab_type": "code",
+    "id": "5V0vxpaUEnFd",
+    "outputId": "a97b6261-034e-493a-d8a6-cf955ab5e8fd"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot\n",
+    "plt.figure(figsize=(4, 3), dpi=160)\n",
+    "plt.scatter(x[:, 0], x[:, 1], c=cluster_ids_x, cmap='cool')\n",
+    "plt.scatter(y[:, 0], y[:, 1], c=cluster_ids_y, cmap='cool', marker='X')\n",
+    "plt.scatter(\n",
+    "    cluster_centers[:, 0], cluster_centers[:, 1],\n",
+    "    c='white',\n",
+    "    alpha=0.6,\n",
+    "    edgecolors='black',\n",
+    "    linewidths=2\n",
+    ")\n",
+    "plt.axis([-1, 1, -1, 1])\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "BHkQ-zw9EnsN"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "example.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

kmeans_pytorch/tests/test_project.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import unittest
+import kmeans_pytorch
+
+
+class UnitTests(unittest.TestCase):
+    def test_import(self):
+        self.assertIsNotNone(kmeans_pytorch)
+
+    def test_project(self):
+        self.assertTrue(False, "write more tests here")
\ No newline at end of file

laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg/README.md

+---
+tags:
+- zero-shot-image-classification
+- clip
+library_tag: open_clip
+license: mit
+library_name: open_clip
+pipeline_tag: zero-shot-image-classification
+---
+# Model card for CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg
+
+#  Table of Contents
+
+1. [Model Details](#model-details)
+2. [Uses](#uses)
+3. [Training Details](#training-details)
+4. [Evaluation](#evaluation)
+5. [Acknowledgements](#acknowledgements)
+6. [Citation](#citation)
+
+# Model Details
+
+## Model Description
+
+A series of CLIP ConvNeXt-XXLarge (a custom `timm` ConvNeXt size) models trained on LAION-2B (english), a subset of [LAION-5B](https://arxiv.org/abs/2210.08402), using [OpenCLIP](https://github.com/mlfoundations/open_clip).
+
+| Model | Dataset | Resolution | AugReg | Top-1 ImageNet Zero-Shot (%) |
+| ----- | ------- | ---------- | ------------ | --------- |
+| [convnext_xxlarge.laion2b_s34b_b82k-augreg](https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg) | LAION-2B | 256x256 |  RRC (0.33, 1.0), RE (0.35), SD (0.1) | 79.1 |
+| [convnext_xxlarge.laion2b_s34b_b82k-augreg-rewind](https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-rewind) | LAION-2B | 256x256 |  RRC (0.3, 1.0), RE (0.4), SD (0.1) | 79.3 |
+| [convnext_xxlarge.laion2b_s34b_b82k-augreg-soup](https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup) | LAION-2B | 256x256 |  N/A | 79.4 |
+RRC = Random Resize Crop (crop pcts), RE = Random Erasing (prob), SD = Stochastic Depth (prob) -- image tower only
+
+The core training run was performed in pieces over a period of ~ 2 months. The global batch size for the core run was 81920. The last ~10% of training was re-done at a 95744 global batch size w/ higher LR and aug than original finish. The two were averaged together in a 'soup'. See more details in [Training Details](#training-details).
+
+Goals:
+  * Push the size of largest convolutional CLIP image tower into the performance range of ViT-g to ViT-G w/ improved image size scaling for downstream use.
+
+Firsts:
+  * Largest released ConvNeXt model pretrained (847M params w/ 198 GMAC and 125 MActs @ 256x256 for image)
+  * A non-ViT image tower CLIP model (with no previous image tower pretrain) achieving > 79% ImageNet top-1 zero-shot
+
+The models utilize:
+  * the [timm](https://github.com/rwightman/pytorch-image-models) ConvNeXt-XXLarge model (`convnext_xxlarge`) as the image tower
+  * a standard projection at end of image tower
+  * a text tower with same size (with 1024, heads 16, depth 24) as ViT-H-14 and ViT-g-14 models
+
+
+The models are trained at 256x256 image resolution. The size of the combined image + text CLIP model is 1.2B params w/ 222 GMAC and 146 MActs. At 256x256, the ConvNext-XXLarge sits just above a ViT-H-14 CLIP configuration in FLOPS and params while being lower in activation counts. It is well under both g-14 and G-14 while being between them in capabilities.
+
+|model                     |image_size|embed_dim|gmacs |macts |mparams|image_gmacs|image_macts|image_mparams|text_gmacs|text_macts|text_mparams|
+|--------------------------|----------|---------|------|------|-------|-----------|-----------|-------------|----------|----------|------------|
+|ViT-H-16                  |224       |1024     |150.96|122.01|986.26 |127.4      |100.81     |632.23       |23.57     |21.2      |354.03      |
+|ViT-H-14                  |224       |1024     |190.97|160.61|986.11 |167.4      |139.41     |632.08       |23.57     |21.2      |354.03      |
+|ViT-L-14-336              |336       |768      |197.76|278.19|427.94 |191.1      |270.24     |304.29       |6.66      |7.95      |123.65      |
+|convnext_xxlarge          |256       |1024     |221.66|145.66|1200.58|198.09     |124.45     |846.54       |23.57     |21.2      |354.03      |
+|RN50x64                   |448       |1024     |276.8 |249.73|623.26 |265.02     |239.13     |420.38       |11.78     |10.6      |202.88      |
+|ViT-g-14                  |224       |1024     |290.74|213.84|1366.68|267.18     |192.64     |1012.65      |23.57     |21.2      |354.03      |
+|convnext_xxlarge_320      |320       |1024     |333.08|215.66|1200.58|309.52     |194.46     |846.54       |23.57     |21.2      |354.03      |
+|ViT-H-14-336              |336       |1024     |414.53|428.74|986.52 |390.97     |407.54     |632.49       |23.57     |21.2      |354.03      |
+|ViT-bigG-14               |224       |1280     |532.92|310.71|2539.57|483.96     |275.37     |1844.91      |48.96     |35.34     |694.66      |
+
+
+Model training done by Ross Wightman across both the [stability.ai](https://stability.ai/) cluster and the [JUWELS Booster](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html) supercomputer. See acknowledgements below.
+
+# Uses
+
+As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. 
+
+The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. 
+
+## Direct Use
+
+Zero-shot image classification, image and text retrieval, among others.
+
+## Downstream Use
+
+Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others.
+
+## Out-of-Scope Use
+
+As per the OpenAI models,
+
+**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 
+
+Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
+
+Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
+
+Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below.
+
+# Training Details
+
+## Training Data
+
+This model was trained with LAION-2B -- A 2 billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/).
+
+**IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.
+
+## Training Procedure
+
+The main training run was done at global batch size of 81920 for 256 checkpoint intervals of 135.6M samples for a total of ~34B samples seen over training.
+
+Many difficulties w/ both model numerical stability and cluster stability and performance were encountered while training this model. Initial attempts to train with float16 AMP and default adam beta2 resulted in loss spikes and eventually NaN blow ups. `beta2` was reduced to 0.97 which helped, but the loss / zs curves were not tracking as expected. After switching to PyTorch nightlies, it was possible to use bfloat16 + AMP for training (as with rececnt H/14, g/14, and G/14 models), beta2 was returned to 0.98 and metrics improved.
+
+|Checkpoint Interval |Cluster   |# GPUs|# Nodes|GPU       |local BS|sample/s|sample/s/gpu|precision |adam beta2 |
+|--------------------|----------|------|-------|----------|--------|--------|------------|----------|-----------|
+|1 - 2               |Stability |1024  |128    |A100 40GB | 80     |37-40k  | 36-39      |amp + fp16|0.97       |
+|3 - 32              |Stability |512   |64     |A100 80GB | 160    |27-32k  | 52-62      |amp + fp16|0.97       |
+|33 - 75             |Booster   |1024  |256    |A100 40GB | 80     |48k     | 47         |amp + fp16|0.97       |
+|76 - 165            |Booster   |1024  |256    |A100 40GB | 80     |51k     | 50         |amp + bf16|0.98       |
+|166 - 232           |Stability |320   |40     |A100 80GB | 256    |18-19k  | 56-59      |amp + bf16|0.98       |
+|233 - 249           |Booster   |1024  |256    |A100 40GB | 80     |51k     | 50         |amp + bf16|0.98       |
+|250 - 256           |Stability |1024  |128    |A100 40GB | 80     |27-31k  | 26-30      |amp + bf16|0.98       |
+
+JUWELS Booster has 4x A100 GPU per node w/ 4x HDR-200 IB adapters per node (200Gbit/sec per GPU). Stability setup used was 8x A100 GPU per node w/ 400Gbit/sec EFA networking per node (50 GBit/sec per GPU). Significant variation in training efficiency (throughput per GPU) as observed across the various configurations. The 1024 GPU configurations across both clusters were particularly prone to crashing (or very difficult to get running w/ a 'good' set of GPUs).
+
+A slurm srun command line below for a 128 8-GPU (40GB A100) configuration:
+
+```
+srun --cpu_bind=v --accel-bind=gn python -m training.main \
+    --save-frequency 1 \
+    --name "xxlarge-2b-81920-bf16" \
+    --resume "latest" \
+    --logs "/runs" \
+    --log-every-n-steps 50 \
+    --train-data="pipe:aws s3 cp s3://laion5b/laion2B-data/{000000..231349}.tar -" \
+    --train-num-samples 135646078 \
+    --dataset-type webdataset \
+    --warmup 10000 \
+    --batch-size=80 \
+    --epochs=256 \
+    --dataset-resampled \
+    --aug-cfg use_timm=True scale='(0.33, 1.0)' re_prob=0.35 \
+    --precision amp_bfloat16 \
+    --grad-clip-norm 5.0 \
+    --lr 1e-3 \
+    --workers=6 \
+    --beta2 0.98 \
+    --model "convnext_xxlarge" \
+    --seed 0 \
+    --ddp-static-graph \
+    --local-loss \
+    --gather-with-grad \
+    --grad-checkpointing \
+    --report-to "tensorboard"
+```
+
+For the rewind of last 10%, a higher global batch size of 95744 was used w/ a higher LR and slightly increased augmentation strength.
+
+|Checkpoint Interval |Cluster  |# GPUs|# Nodes|GPU       |local BS|sample/s|sample/s/gpu|precision |adam beta2 |
+|--------------------|---------|------|-------|----------|--------|--------|------------|----------|-----------|
+|231 - 256           |stability|1088  |136    |A100 40GB | 88     |32-35k  | 29-32      |amp + bf16|0.98       |
+
+The slurm srun command line for 136 8-GPU (40GB A100) nodes:
+```
+srun --cpu_bind=v --accel-bind=gn python -m training.main \
+    --save-frequency 1 \
+    --name "xxlarge-2b-81920-r-bf16" \
+    --resume "latest" \
+    --logs "/runs" \
+    --log-every-n-steps 50 \
+    --train-data="pipe:aws s3 cp s3://laion5b/laion2B-data/{000000..231349}.tar -" \
+    --train-num-samples 135646078 \
+    --dataset-type webdataset \
+    --warmup 10000 \
+    --batch-size=88 \
+    --epochs=256 \
+    --dataset-resampled \
+    --aug-cfg use_timm=True scale='(0.3, 1.0)' re_prob=0.4 \
+    --precision amp_bfloat16 \
+    --grad-clip-norm 5.0 \
+    --lr 2e-3 \
+    --workers=6 \
+    --beta2 0.98 \
+    --model "convnext_xxlarge" \
+    --seed 0 \
+    --ddp-static-graph \
+    --local-loss \
+    --gather-with-grad \
+    --grad-checkpointing \
+    --report-to "tensorboard"
+```
+
+# Evaluation
+
+Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark).
+
+## Testing Data, Factors & Metrics
+
+### Testing Data
+
+The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval.
+
+## Results
+
+These models achieve between 79.1 and 79.4 top-1 zero-shot accuracy on ImageNet-1k.
+
+![](convnext_xxlarge_zero_shot.png)
+
+A zoom-in on final 10% w/ rewind:
+
+![](convnext_xxlarge_zero_shot_zoom.png)
+
+An initial round of benchmarks have been performed on a wider range of datasets, to be viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb
+
+# Acknowledgements
+
+Acknowledging [stability.ai](https://stability.ai/) and the Gauss Centre for Supercomputing e.V. (http://gauss-centre.eu) for funding this part of work by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS Booster at Jülich Supercomputing Centre (JSC).
+
+# Citation
+
+**BibTeX:**
+
+LAION-5B
+```bibtex
+@inproceedings{schuhmann2022laionb,
+  title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
+  author={Christoph Schuhmann and
+          Romain Beaumont and
+          Richard Vencu and
+          Cade W Gordon and
+          Ross Wightman and
+          Mehdi Cherti and
+          Theo Coombes and
+          Aarush Katta and
+          Clayton Mullis and
+          Mitchell Wortsman and
+          Patrick Schramowski and
+          Srivatsa R Kundurthy and
+          Katherine Crowson and
+          Ludwig Schmidt and
+          Robert Kaczmarczyk and
+          Jenia Jitsev},
+  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
+  year={2022},
+  url={https://openreview.net/forum?id=M3Y74vmsMcY}
+}
+```
+
+OpenCLIP software
+```bibtex
+@software{ilharco_gabriel_2021_5143773,
+  author       = {Ilharco, Gabriel and
+                  Wortsman, Mitchell and
+                  Wightman, Ross and
+                  Gordon, Cade and
+                  Carlini, Nicholas and
+                  Taori, Rohan and
+                  Dave, Achal and
+                  Shankar, Vaishaal and
+                  Namkoong, Hongseok and
+                  Miller, John and
+                  Hajishirzi, Hannaneh and
+                  Farhadi, Ali and
+                  Schmidt, Ludwig},
+  title        = {OpenCLIP},
+  month        = jul,
+  year         = 2021,
+  note         = {If you use this software, please cite it as below.},
+  publisher    = {Zenodo},
+  version      = {0.1},
+  doi          = {10.5281/zenodo.5143773},
+  url          = {https://doi.org/10.5281/zenodo.5143773}
+}
+```
+
+OpenAI CLIP paper
+```bibtex
+@inproceedings{Radford2021LearningTV,
+  title={Learning Transferable Visual Models From Natural Language Supervision},
+  author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
+  booktitle={ICML},
+  year={2021}
+}
+```
+
+```bibtex
+@Article{liu2022convnet,
+  author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
+  title   = {A ConvNet for the 2020s},
+  journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year    = {2022},
+}
+```
+
+```bibtex
+@misc{rw2019timm,
+  author = {Ross Wightman},
+  title = {PyTorch Image Models},
+  year = {2019},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  doi = {10.5281/zenodo.4414861},
+  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
+}
+```
+
+```
+@InProceedings{pmlr-v162-wortsman22a,
+  title = 	 {Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time},
+  author =       {Wortsman, Mitchell and Ilharco, Gabriel and Gadre, Samir Ya and Roelofs, Rebecca and Gontijo-Lopes, Raphael and Morcos, Ari S and Namkoong, Hongseok and Farhadi, Ali and Carmon, Yair and Kornblith, Simon and Schmidt, Ludwig},
+  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
+  pages = 	 {23965--23998},
+  year = 	 {2022},
+  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
+  volume = 	 {162},
+  series = 	 {Proceedings of Machine Learning Research},
+  month = 	 {17--23 Jul},
+  publisher =    {PMLR},
+  pdf = 	 {https://proceedings.mlr.press/v162/wortsman22a/wortsman22a.pdf},
+  url = 	 {https://proceedings.mlr.press/v162/wortsman22a.html}
+}
+```
\ No newline at end of file