DL-Art-School/codes/utils/kmeans.py

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# From: https://github.com/subhadarship/kmeans_pytorch
# License: https://github.com/subhadarship/kmeans_pytorch/blob/master/LICENSE
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import random
import numpy as np
import torch
from tqdm import tqdm
# ToDo: Can't choose a cluster if two points are too close to each other, that's where the nan come from
def initialize(X, num_clusters):
"""
initialize cluster centers
:param X: (torch.tensor) matrix
:param num_clusters: (int) number of clusters
:return: (np.array) initial state
"""
num_samples = len(X)
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,
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gravity_limit_per_iter=None,
device=torch.device('cpu')
):
"""
perform kmeans
:param X: (torch.tensor) matrix
:param num_clusters: (int) number of clusters
:param distance: (str) distance [options: 'euclidean', 'cosine'] [default: 'euclidean']
: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
:return: (torch.tensor, torch.tensor) cluster ids, cluster centers
"""
print(f'running k-means on {device}..')
if distance == 'euclidean':
pairwise_distance_function = pairwise_distance
elif distance == 'cosine':
pairwise_distance_function = pairwise_cosine
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)
else:
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)
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if gravity_limit_per_iter and len(selected) > gravity_limit_per_iter:
ch = random.randint(0, len(selected)-gravity_limit_per_iter)
selected=selected[ch:ch+gravity_limit_per_iter]
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
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bins = torch.bincount(choice_cluster)
if tqdm_flag:
tqdm_meter.set_postfix(
iteration=f'{iteration}',
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center_shift=f'{center_shift ** 2}',
tol=f'{tol}',
bins=f'{bins}',
)
tqdm_meter.update()
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if tol > 0 and 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'
):
"""
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']
:return: (torch.tensor) cluster ids
"""
if distance == 'euclidean':
pairwise_distance_function = pairwise_distance
elif distance == 'cosine':
pairwise_distance_function = pairwise_cosine
else:
raise NotImplementedError
dis = pairwise_distance_function(X, cluster_centers)
choice_cluster = torch.argmin(dis, dim=1)
return choice_cluster
def pairwise_distance(data1, data2):
# 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):
# 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