我正在使用嵌套列表来保存笛卡尔坐标类型系统中的数据。
数据是一个类别列表，可能是0,1,2,3,4,5,255（只有7个类别）。
数据保存在格式化的列表中：
stack = [[0,1,0,0],
[2,1,0,0],
[1,1,1,3]]
每个列表代表一行，行的每个元素代表一个数据点。
我很想继续使用这种格式，因为我使用它来生成图像，到目前为止它非常容易使用。
但是，我在运行以下代码时遇到了问题：
for j in range(len(stack)):
stack[j].append(255)
stack[j].insert(0, 255)
这是为了遍历每一行，在每行的开头和结尾添加一个单独的元素255。不幸的是，它在开始和结束时都添加了12个255实例！
这对我没有意义。据推测，我错过了一些非常微不足道的东西，但我看不出它可能是什么。据我所知，它与循环有关：如果我在循环之外写stack[0].append(255)，它的行为正常。
代码显然是更大脚本的一部分。该脚本运行多个For循环，其中一些是范围（12）但在调用此循环时应该关闭。
所以 - 我错过了一些微不足道的东西，还是比那更邪恶？
编辑：完整代码
step_size = 12，上面的代码是插入“左右边框”的部分
def classify(target_file, output_file):
import numpy
import cifar10_eval # want to hijack functions from the evaluation script
target_folder = "Binaries/" # finds target file in "Binaries"
destination_folder = "Binaries/Maps/" # destination for output file
# open the meta file to retrieve x,y dimensions
file = open(target_folder + target_file + "_meta" + ".txt", "r")
new_x = int(file.readline())
new_y = int(file.readline())
orig_x = int(file.readline())
orig_y = int(file.readline())
segment_dimension = int(file.readline())
step_size = int(file.readline())
file.close()
# run cifar10_eval and create predictions vector (formatted as a list)
predictions = cifar10_eval.map_interface(new_x * new_y)
del predictions[(new_x * new_y):] # get rid of excess predictions (that are an artefact of the fixed batch size)
print("# of predictions: " + str(len(predictions)))
# check that we are mapping the whole picture! (evaluation functions don't necessarily use the full data set)
if len(predictions) != new_x * new_y:
print("Error: number of predictions from cifar10_eval does not match metadata for this file")
return
# copy predictions to a nested list to make extraction of x/y data easy
# also eliminates need to keep metadata - x/y dimensions are stored via the shape of the output vector
stack = []
for j in range(new_y):
stack.append([])
for i in range(new_x):
stack[j].append(predictions[j*new_x + i])
predictions = None # clear the variable to free up memory
# iterate through map list and explode each category to cover more pixels
# assigns a step_size x step_size area to each classification input to achieve correspondance with original image
new_stack = []
for j in range(len(stack)):
row = stack[j]
new_row = []
for i in range(len(row)):
for a in range(step_size):
new_row.append(row[i])
for b in range(step_size):
new_stack.append(new_row)
stack = new_stack
new_stack = None
new_row = None # clear the variables to free up memory
# add a border to the image to indicate that some information has been lost
# border also ensures that map has 1-1 correspondance with original image which makes processing easier
# calculate border dimensions
top_and_left_thickness = int((segment_dimension - step_size) / 2)
right_thickness = int(top_and_left_thickness + (orig_x - (top_and_left_thickness * 2 + step_size * new_x)))
bottom_thickness = int(top_and_left_thickness + (orig_y - (top_and_left_thickness * 2 + step_size * new_y)))
print(top_and_left_thickness)
print(right_thickness)
print(bottom_thickness)
print(len(stack[0]))
# add the right then left borders
for j in range(len(stack)):
for b in range(right_thickness):
stack[j].append(255)
for b in range(top_and_left_thickness):
stack[j].insert(0, 255)
print(stack[0])
print(len(stack[0]))
# add the top and bottom borders
row = []
for i in range(len(stack[0])):
row.append(255) # create a blank row
for b in range(top_and_left_thickness):
stack.insert(0, row) # append the blank row to the top x many times
for b in range(bottom_thickness):
stack.append(row) # append the blank row to the bottom of the map
# we have our final output
# repackage this as a numpy array and save for later use
output = numpy.asarray(stack,numpy.uint8)
numpy.save(destination_folder + output_file + ".npy", output)
print("Category mapping complete, map saved as numpy pickle: " + output_file + ".npy")