如下代碼會將npy的格式數據讀出，并且輸出來到控制臺：

import numpy as np   ##設置全部數據，不輸出省略號  import sys np.set_printoptions(threshold=sys.maxsize)   boxes=np.load('./input_output/boxes.npy') print(boxes) np.savetxt('./input_output/boxes.txt',boxes,fmt='%s',newline='n') print('---------------------boxes--------------------------')

如下代碼實現(xiàn)npy格式文件轉換為txt,并且保存到當前目錄相同文件名

實現(xiàn)轉換整個文件夾下面多個文件：

import os import numpy as np path='./input_output' #一個文件夾下多個npy文件， txtpath='./input_output' namelist=[x for x in os.listdir(path)] for i in range( len(namelist) ):  datapath=os.path.join(path,namelist[i]) #specific address  print(namelist[i])  data = np.load(datapath).reshape([-1, 2]) # (39, 2)  np.savetxt('%s/%s.txt'%(txtpath,namelist[i]),data) print ('over') import os import numpy as np path='./input_output' #一個文件夾下多個npy文件 txtpath='./input_output' namelist=[x for x in os.listdir(path)] for i in range( len(namelist) ):  datapath=os.path.join(path,namelist[i]) #specific address  print(namelist[i])  #data = np.load(datapath).reshape([-1, 2]) # (39, 2)  input_data = np.load(datapath) # (39, 2)  data = input_data.reshape(1, -1)  np.savetxt('%s/%s.txt'%(txtpath,namelist[i]),data) print ('over')

同樣的代碼，實現(xiàn)讀取單個npy文件，讀取并且存儲為txt :

import numpy as np input_data = np.load(r"C:test.npy") print(input_data.shape) data = input_data.reshape(1,-1) print(data.shape) print(data) np.savetxt(r"C:test.txt",data,delimiter=',')

修改pycharm的控制臺的buffer大?。?/p>

如果你是用pycharm作為Python的編輯器，那么控制臺的buf默認為1024，如果輸出數據太多，需要修改buff大小才能讓

全部數據輸出，修改方法：

找到 pycharm 安裝目錄的 bin 目錄下 idea.properties 文件， 修改 idea.cycle.buffer 值，原來默認為 1024

#——————————————————————— # This option controls console cyclic buffer: keeps the console output size not higher than the specified buffer size (Kb). # Older lines are deleted. In order to disable cycle buffer use idea.cycle.buffer.size=disabled #——————————————————————— idea.cycle.buffer.size=102400

補充知識：讀取npy格式的文件

npy文件保存的是網絡的權重

問題：Ubuntu環(huán)境下用gedit打開npy文件，是這樣的，根本看不了內容

解決方法：編寫如下代碼，使解碼后的文件內容輸出在控制臺

import numpy as np context = np.load('E:/KittiSeg_pretrained0/vgg16.npy',encoding="latin1") print(context)

文件位置依據自己的存放位置進行修改

運行代碼輸出結果為

{'conv1_2': [array([[[[ 1.66219279e-01, 1.42701820e-01, -4.02113283e-03, ...,       6.18828237e-02, -1.74057148e-02, -3.00644431e-02],      [ 9.46945231e-03, 3.87477316e-03, 5.08365929e-02, ...,      -2.77981739e-02, 1.71373668e-03, 6.82722731e-03],      [ 6.32681847e-02, 2.12877709e-02, -1.63465310e-02, ...,       8.80054955e-04, 6.68104272e-03, -1.41139806e-03],      ...,      [ 3.47490981e-03, 8.47019628e-02, -4.07223180e-02, ...,      -1.13523193e-02, -7.48998486e-03, 3.19077494e-03],      [ 5.97234145e-02, 4.97663505e-02, -3.23118735e-03, ...,       1.43114366e-02, 3.03175431e-02, -4.23925705e-02],      [ 1.33459672e-01, 4.95484173e-02, -1.78808011e-02, ...,       2.25385167e-02, 3.02020740e-02, -2.17075031e-02]],      [[ 2.12007999e-01, 2.10127644e-02, -1.47626130e-02, ...,       2.29580477e-02, 1.23102348e-02, -3.08422819e-02],      [-2.62175221e-03, 7.42094172e-03, 6.74030930e-02, ...,      -3.06594316e-02, 1.80578313e-03, 4.27369215e-03],      [ 2.27197763e-02, -1.07841045e-02, -1.31095545e-02, ...,      -1.15751950e-02, 4.18359675e-02, -1.92268589e-03],      ...,      [-2.70304317e-03, 7.41161704e-02, -3.32262330e-02, ...,      -1.10277236e-02, 1.39831286e-02, 5.34419343e-03],      [-3.20506282e-02, -2.40584910e-02, -4.52397857e-03, ...,      -6.04042644e-03, 2.01962605e-01, -5.04491515e-02],      [ 1.68114193e-02, -2.33167298e-02, -1.40886130e-02, ...,      -7.79278344e-03, 1.28428593e-01, -2.58184522e-02]],    [[-5.91698708e-03, -2.26223674e-02, 4.88128467e-03, ...,     4.13784146e-04, -4.84175496e-02, 1.63675251e-03],    [-3.93767562e-03, 9.07397643e-03, 5.36517277e-02, ...,    -2.56106984e-02, -4.17886395e-03, 2.47476017e-03],    [-3.07008922e-02, -1.09781921e-02, -3.69096454e-03, ...,    -1.19221993e-02, -1.39777903e-02, 8.52933805e-03],    ...,    ..........................................

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