OSError：无法在保存 tensorflow yolov4 模型时创建文件

如何解决OSError：无法在保存 tensorflow yolov4 模型时创建文件

我正在尝试保存 tensorflow yolov4 模型，但它生成 OSError: Unable to create file (unable to open file: name = './checkpoints/yolov4-416',errno = 2,error message = '没有这样的文件或目录'，flags = 13，o_flags = 302)

我在命令提示符中使用 python save_model.py --weights ./data/yolov4.weights --output ./checkpoints/yolov4-416 --input_size 416 --model yolov4 命令

这是完整的错误

save_model.py 如下：

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
#import tensorflow as tf
from absl import app,flags,logging
from absl.flags import FLAGS
from core.yolov4 import YOLO,decode,filter_Boxes
import core.utils as utils
from core.config import cfg

flags.DEFINE_string('weights','./data/yolov4.weights','path to weights file')
flags.DEFINE_string('output','./checkpoints/yolov4-416','path to output')
flags.DEFINE_boolean('tiny',False,'is yolo-tiny or not')
flags.DEFINE_integer('input_size',416,'define input size of export model')
flags.DEFINE_float('score_thres',0.2,'define score threshold')
flags.DEFINE_string('framework','tf','define what framework do you want to convert (tf,trt,tflite)')
flags.DEFINE_string('model','yolov4','yolov3 or yolov4')

def save_tf():
  STRIDES,ANCHORS,NUM_CLASS,XYSCALE = utils.load_config(FLAGS)

  input_layer = tf.keras.layers.Input([FLAGS.input_size,FLAGS.input_size,3])
  feature_maps = YOLO(input_layer,FLAGS.model,FLAGS.tiny)
  bBox_tensors = []
  prob_tensors = []
  if FLAGS.tiny:
    for i,fm in enumerate(feature_maps):
      if i == 0:
        output_tensors = decode(fm,FLAGS.input_size // 16,STRIDES,i,XYSCALE,FLAGS.framework)
      else:
        output_tensors = decode(fm,FLAGS.input_size // 32,FLAGS.framework)
      bBox_tensors.append(output_tensors[0])
      prob_tensors.append(output_tensors[1])
  else:
    for i,FLAGS.input_size // 8,FLAGS.framework)
      elif i == 1:
        output_tensors = decode(fm,FLAGS.framework)
      bBox_tensors.append(output_tensors[0])
      prob_tensors.append(output_tensors[1])
  pred_bBox = tf.concat(bBox_tensors,axis=1)
  pred_prob = tf.concat(prob_tensors,axis=1)
  if FLAGS.framework == 'tflite':
    pred = (pred_bBox,pred_prob)
  else:
    Boxes,pred_conf = filter_Boxes(pred_bBox,pred_prob,score_threshold=FLAGS.score_thres,input_shape=tf.constant([FLAGS.input_size,FLAGS.input_size]))
    pred = tf.concat([Boxes,pred_conf],axis=-1)
  model = tf.keras.Model(input_layer,pred)
  utils.load_weights(model,FLAGS.weights,FLAGS.tiny)
  model.summary()
  model.save(FLAGS.output)

def main(_argv):
  save_tf()

if __name__ == '__main__':
    try:
        app.run(main)
    except SystemExit:
        pass