问将特征映射(3D数组)拆分为二维数组

EN

# splitting it into 96 slices in one-go!
In [659]: np.dsplit(feature_map, feature_map.shape[-1])

In [660]: np.dsplit(feature_map, feature_map.shape[-1])[10].flags
Out[660]: 
  C_CONTIGUOUS : False
  F_CONTIGUOUS : False
  OWNDATA : False   #<============== NO copy is made
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False

In [661]: np.dsplit(feature_map, feature_map.shape[-1])[10].shape
Out[661]: (32, 32, 1)

# getting rid of unitary dimension with `np.squeeze`
In [662]: np.squeeze(np.dsplit(feature_map, feature_map.shape[-1])[10]).shape
Out[662]: (32, 32)

In [663]: np.squeeze(np.dsplit(feature_map, feature_map.shape[-1])[10]).flags
Out[663]: 
  C_CONTIGUOUS : False
  F_CONTIGUOUS : False
  OWNDATA : False   #<============== NO copy is made
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False

feature_map = np.random.randint(low=0, high=255, size=(32, 32, 96))
feature_map = np.transpose(feature_map, axes=[2, 0, 1])
for i in range(feature_map.shape[0]):
  print(feature_map[i].shape)  # a view of original array. shape=(32, 32)

for i in range(feature_map.shape[2]):
  print(feature_map[:, :, i].shape)  # a view of original array. shape=(32, 32)

import numpy as np

def do_something(array_slice):
    print array_slice

feature_map = np.random.randint(low=0, high=255, size=(3, 3, 9))

# loop over the indices of the last dimension of the array (i.e. 0 to 8)
for level in range(feature_map.shape[2]):
    # now take only the 2d-slice of the first two dimensions at the height of 'level'
    do_something(feature_map[:,:,level])

# you could also take a slice from another dimension
for level in range(feature_map.shape[1]):    
    do_something(feature_map[:,level,:])