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pickle 和 savez_compressed 压缩体积对比
source link: https://segmentfault.com/a/1190000041397848
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pickle 和 savez_compressed 压缩体积对比
有的时候，我们需要把一些 numpy.ndarray 对象在网络中传输，这个时候就要寻找一种高效的、适合网络传输的序列化方式：
from mark import BASE_DIR
from numpy import ndarray
import numpy
import pickle
img1 = numpy.array([i for i in range(5120)])
# 方式一： 使用 pickle 序列化 numpy.ndarray
with open(BASE_DIR/'testing'/'001.bin', 'wb') as f:
img1: ndarray
f.write(pickle.dumps(img1))
# --- 结果 41111 bytes
# 方法二： 使用 pickle 序列化 python 的 list 对象
with open(BASE_DIR/'testing'/'002.bin', 'wb') as f:
ins: list[int] = img1.tolist()
f.write(pickle.dumps(ins))
# --- 结果 15130 bytes
# 方法三： 使用 numpy 的 savez 序列化 numpy.ndarray
numpy.savez(BASE_DIR/'testing'/'numpy_savez_test', img1)
# --- 结果 41224 bytes
# 方法四： 使用 numpy 的 savez_compressed (带压缩功能) 序列化 numpy.ndarray
numpy.savez_compressed(BASE_DIR/'testing'/'numpy_savez_compressed_test', img1)
numpy.load(BASE_DIR/'testing'/'numpy_savez_compressed_test')
# --- 结果 7982 bytes
"""
"""
结论： 方法四 < 方法二 < 方法一 < 方法三 (7982)< (15130)< (41111)< (41224)
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