import numpy as np
 
##设置全部数据，不输出省略号 
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--------------------------')

import os
import numpy as np
path='./input_output' #一个文件夹下多个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' #一个文件夹下多个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')

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=',')

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

{'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],
   ...,
   ..........................................