如下代碼會將npy
的格式數據讀出，並且輸出來到控制台：
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npy格式文件轉換為txt,並且保存到當前目錄相同文件名
實現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，實現讀取單個npy文件，讀取並且存儲為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的控制台的buffer大小：
如果你是用pycharm作為Python的編輯器，那么控制台的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台
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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