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kmeans_pytorch/docs/static/highlight.css 0 → 100644
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kmeans_pytorch/docs/static/highlight.js 0 → 100644
View file @ 0063a668
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kmeans_pytorch/example.ipynb 0 → 100644
View file @ 0063a668
{
"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": {
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tpuBltPrjOeVOBupT3HsA8Be8EsE2rK8HKrmLJeWNt87gxs/Cry6B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\ngg==\n",
"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 0 → 100644
View file @ 0063a668
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 0 → 100644
View file @ 0063a668
#!/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 0 → 100644
View file @ 0063a668
# 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 0 → 100644
View file @ 0063a668
.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 0 → 100644
View file @ 0063a668
#!/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 0 → 100644
View file @ 0063a668
{
"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": {
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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 0 → 100644
View file @ 0063a668
#!/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 0 → 100644
View file @ 0063a668
---
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}
}
```
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