56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
import time,pdb
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import tqdm
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from time import time as ttime
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import os
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from pathlib import Path
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import logging
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import argparse
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from cluster.kmeans import KMeansGPU
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import torch
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import numpy as np
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from sklearn.cluster import KMeans
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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from time import time as ttime
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import pynvml,torch
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def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False,use_gpu=False):#gpu_minibatch真拉，虽然库支持但是也不考虑
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logger.info(f"Loading features from {in_dir}")
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features = []
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nums = 0
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for path in tqdm.tqdm(in_dir.glob("*.soft.pt")):
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# for name in os.listdir(in_dir):
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# path="%s/%s"%(in_dir,name)
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features.append(torch.load(path,map_location="cpu").squeeze(0).numpy().T)
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# print(features[-1].shape)
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features = np.concatenate(features, axis=0)
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print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype)
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features = features.astype(np.float32)
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logger.info(f"Clustering features of shape: {features.shape}")
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t = time.time()
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if(use_gpu==False):
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if use_minibatch:
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kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features)
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else:
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kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features)
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else:
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kmeans = KMeansGPU(n_clusters=n_clusters, mode='euclidean', verbose=2 if verbose else 0,max_iter=500,tol=1e-2)#
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features=torch.from_numpy(features)#.to(device)
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labels = kmeans.fit_predict(features)#
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print(time.time()-t, "s")
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x = {
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"n_features_in_": kmeans.n_features_in_ if use_gpu==False else features.shape[0],
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"_n_threads": kmeans._n_threads if use_gpu==False else 4,
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"cluster_centers_": kmeans.cluster_centers_ if use_gpu==False else kmeans.centroids.cpu().numpy(),
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}
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print("end")
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return x
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if __name__ == "__main__":
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res=train_cluster("/data/docker/dataset/12b-co256tensor",1000,use_minibatch=False,verbose=False,use_gpu=True)
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pdb.set_trace()
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