Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.
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Memory leakage during image feature extraction(OOM) #258
def pipline(img):
p_search = (
pipe.input('img')
.map('img', 'vec', ops.image_embedding.timm('lambda_resnet50ts'))
.output('vec')
)
res = pipline(img).get()
del p_search
return res
if __name__ =="__main__":
from glob import glob
path = 'mypath'
inputFiles = glob(path+"/*.*")
print(len(inputFiles))
for idx in range(len(inputFiles)):
input_file_path = inputFiles[idx]
pipline(input_file_path)
There are a large number of images in the folder. When calling the feature extraction interface in a loop, the memory gradually increases, ultimately leading to OOM. What is the reason for this
There are a large number of images in the folder. When calling the feature extraction interface in a loop, the memory gradually increases, ultimately leading to OOM. What is the reason for this
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