YuriHead
GitHub
commit
fd6031b855
|
|
@ -1,55 +1,80 @@
|
||||||
import time,pdb
|
import time,pdb
|
||||||
import tqdm
|
import tqdm
|
||||||
from time import time as ttime
|
from time import time as ttime
|
||||||
import os
|
import os
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
import logging
|
import logging
|
||||||
import argparse
|
import argparse
|
||||||
from cluster.kmeans import KMeansGPU
|
from cluster.kmeans import KMeansGPU
|
||||||
import torch
|
import torch
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from sklearn.cluster import KMeans
|
from sklearn.cluster import KMeans,MiniBatchKMeans
|
||||||
|
|
||||||
logging.basicConfig(level=logging.INFO)
|
logging.basicConfig(level=logging.INFO)
|
||||||
logger = logging.getLogger(__name__)
|
logger = logging.getLogger(__name__)
|
||||||
from time import time as ttime
|
from time import time as ttime
|
||||||
import pynvml,torch
|
import pynvml,torch
|
||||||
|
|
||||||
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
|
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
|
||||||
logger.info(f"Loading features from {in_dir}")
|
logger.info(f"Loading features from {in_dir}")
|
||||||
features = []
|
features = []
|
||||||
nums = 0
|
nums = 0
|
||||||
for path in tqdm.tqdm(in_dir.glob("*.soft.pt")):
|
for path in tqdm.tqdm(in_dir.glob("*.soft.pt")):
|
||||||
# for name in os.listdir(in_dir):
|
# for name in os.listdir(in_dir):
|
||||||
# path="%s/%s"%(in_dir,name)
|
# path="%s/%s"%(in_dir,name)
|
||||||
features.append(torch.load(path,map_location="cpu").squeeze(0).numpy().T)
|
features.append(torch.load(path,map_location="cpu").squeeze(0).numpy().T)
|
||||||
# print(features[-1].shape)
|
# print(features[-1].shape)
|
||||||
features = np.concatenate(features, axis=0)
|
features = np.concatenate(features, axis=0)
|
||||||
print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype)
|
print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype)
|
||||||
features = features.astype(np.float32)
|
features = features.astype(np.float32)
|
||||||
logger.info(f"Clustering features of shape: {features.shape}")
|
logger.info(f"Clustering features of shape: {features.shape}")
|
||||||
t = time.time()
|
t = time.time()
|
||||||
if(use_gpu==False):
|
if(use_gpu==False):
|
||||||
if use_minibatch:
|
if use_minibatch:
|
||||||
kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features)
|
kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features)
|
||||||
else:
|
else:
|
||||||
kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features)
|
kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features)
|
||||||
else:
|
else:
|
||||||
kmeans = KMeansGPU(n_clusters=n_clusters, mode='euclidean', verbose=2 if verbose else 0,max_iter=500,tol=1e-2)#
|
kmeans = KMeansGPU(n_clusters=n_clusters, mode='euclidean', verbose=2 if verbose else 0,max_iter=500,tol=1e-2)#
|
||||||
features=torch.from_numpy(features)#.to(device)
|
features=torch.from_numpy(features)#.to(device)
|
||||||
labels = kmeans.fit_predict(features)#
|
labels = kmeans.fit_predict(features)#
|
||||||
|
|
||||||
print(time.time()-t, "s")
|
print(time.time()-t, "s")
|
||||||
|
|
||||||
x = {
|
x = {
|
||||||
"n_features_in_": kmeans.n_features_in_ if use_gpu==False else features.shape[0],
|
"n_features_in_": kmeans.n_features_in_ if use_gpu==False else features.shape[0],
|
||||||
"_n_threads": kmeans._n_threads if use_gpu==False else 4,
|
"_n_threads": kmeans._n_threads if use_gpu==False else 4,
|
||||||
"cluster_centers_": kmeans.cluster_centers_ if use_gpu==False else kmeans.centroids.cpu().numpy(),
|
"cluster_centers_": kmeans.cluster_centers_ if use_gpu==False else kmeans.centroids.cpu().numpy(),
|
||||||
}
|
}
|
||||||
print("end")
|
print("end")
|
||||||
|
|
||||||
return x
|
return x
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
res=train_cluster("/data/docker/dataset/12b-co256tensor",1000,use_minibatch=False,verbose=False,use_gpu=True)
|
parser = argparse.ArgumentParser()
|
||||||
pdb.set_trace()
|
parser.add_argument('--dataset', type=Path, default="./dataset/44k",
|
||||||
|
help='path of training data directory')
|
||||||
|
parser.add_argument('--output', type=Path, default="logs/44k",
|
||||||
|
help='path of model output directory')
|
||||||
|
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
checkpoint_dir = args.output
|
||||||
|
dataset = args.dataset
|
||||||
|
n_clusters = 1000
|
||||||
|
|
||||||
|
ckpt = {}
|
||||||
|
for spk in os.listdir(dataset):
|
||||||
|
if os.path.isdir(dataset/spk):
|
||||||
|
print(f"train kmeans for {spk}...")
|
||||||
|
in_dir = dataset/spk
|
||||||
|
x = train_cluster(in_dir, n_clusters,use_minibatch=False,verbose=False,use_gpu=True)
|
||||||
|
ckpt[spk] = x
|
||||||
|
|
||||||
|
checkpoint_path = checkpoint_dir / f"kmeans_{n_clusters}.pt"
|
||||||
|
checkpoint_path.parent.mkdir(exist_ok=True, parents=True)
|
||||||
|
torch.save(
|
||||||
|
ckpt,
|
||||||
|
checkpoint_path,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue