Tensorflow 为什么我在云端培训时会遇到“索引器：列表索引超出范围”？

我求助于使用云培训工作流。考虑到我得到的产品，我本来希望直接进入我拥有的与其他tflite模型一起工作的代码中，但是云生成的模型不起作用。请求解释器时，我得到的索引超出范围。获取张量参数

这是我的代码，基本上是一个修改过的示例，在这里我可以摄取一个视频并生成一个带有结果的视频

import argparse
import cv2
import numpy as np
import sys
import importlib.util



# Define and parse input arguments
parser = argparse.ArgumentParser()
parser.add_argument('--modeldir', help='Folder the .tflite file is located in',
                    required=True)
parser.add_argument('--graph', help='Name of the .tflite file, if different than detect.tflite',
                    default='model.tflite')
#                    default='/tmp/detect.tflite')
parser.add_argument('--labels', help='Name of the labelmap file, if different than labelmap.txt',
            default='dict.txt')
#                    default='/tmp/coco_labels.txt')
parser.add_argument('--threshold', help='Minimum confidence threshold for displaying detected objects',
                    default=0.5)
parser.add_argument('--video', help='Name of the video file',
                    default='test.mp4')
parser.add_argument('--edgetpu', help='Use Coral Edge TPU Accelerator to speed up detection',
                    action='store_true')

args = parser.parse_args()

MODEL_NAME = args.modeldir
GRAPH_NAME = args.graph
LABELMAP_NAME = args.labels
VIDEO_NAME = args.video
min_conf_threshold = float(args.threshold)
use_TPU = args.edgetpu

# Import TensorFlow libraries
# If tensorflow is not installed, import interpreter from tflite_runtime, else import from regular tensorflow
# If using Coral Edge TPU, import the load_delegate library
pkg = importlib.util.find_spec('tensorflow')
pkg = True
if pkg is None:
    from tflite_runtime.interpreter import Interpreter
    if use_TPU:
        from tflite_runtime.interpreter import load_delegate
else:
    from tensorflow.lite.python.interpreter import Interpreter
    if use_TPU:
        from tensorflow.lite.python.interpreter import load_delegate

# If using Edge TPU, assign filename for Edge TPU model
if use_TPU:
    # If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite'
    if (GRAPH_NAME == 'detect.tflite'):
        GRAPH_NAME = 'edgetpu.tflite'   

# Get path to current working directory
CWD_PATH = os.getcwd()

# Path to video file
VIDEO_PATH = os.path.join(CWD_PATH,VIDEO_NAME)

# Path to .tflite file, which contains the model that is used for object detection
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,GRAPH_NAME)

# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,MODEL_NAME,LABELMAP_NAME)

# Load the label map
with open(PATH_TO_LABELS, 'r') as f:
    labels = [line.strip() for line in f.readlines()]

# Have to do a weird fix for label map if using the COCO "starter model" from
# https://www.tensorflow.org/lite/models/object_detection/overview
# First label is '???', which has to be removed.
if labels[0] == '???':
    del(labels[0])

# Load the Tensorflow Lite model.
# If using Edge TPU, use special load_delegate argument
if use_TPU:
    interpreter = Interpreter(model_path=PATH_TO_CKPT,
                              experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
    print(PATH_TO_CKPT)
else:
    interpreter = Interpreter(model_path=PATH_TO_CKPT)

interpreter.allocate_tensors()

# Get model details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]

floating_model = (input_details[0]['dtype'] == np.float32)

input_mean = 127.5
input_std = 127.5

# Open video file
video = cv2.VideoCapture(VIDEO_PATH)
imW = video.get(cv2.CAP_PROP_FRAME_WIDTH)
imH = video.get(cv2.CAP_PROP_FRAME_HEIGHT)
out = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc(
        'M', 'J', 'P', 'G'), 10, (1920, 1080))
while(video.isOpened()):

    # Acquire frame and resize to expected shape [1xHxWx3]
    ret, frame = video.read()
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    frame_resized = cv2.resize(frame_rgb, (width, height))
    input_data = np.expand_dims(frame_resized, axis=0)

    # Normalize pixel values if using a floating model (i.e. if model is non-quantized)
    if floating_model:
        input_data = (np.float32(input_data) - input_mean) / input_std

    # Perform the actual detection by running the model with the image as input
    interpreter.set_tensor(input_details[0]['index'],input_data)
    interpreter.invoke()

    # Retrieve detection results
    boxes = interpreter.get_tensor(output_details[0]['index'])[0] # Bounding box coordinates of detected objects
    classes = interpreter.get_tensor(output_details[1]['index'])[0] # Class index of detected objects
    scores = interpreter.get_tensor(output_details[2]['index'])[0] # Confidence of detected objects
    print (boxes)
    print (classes)
    print (scores)
    #num = interpreter.get_tensor(output_details[3]['index'])[0]  # Total number of detected objects (inaccurate and not needed)

    # Loop over all detections and draw detection box if confidence is above minimum threshold
    for i in range(len(scores)):
        if ((scores[i] > min_conf_threshold) and (scores[i] <= 1.0)):

            # Get bounding box coordinates and draw box
            # Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min()
            ymin = int(max(1,(boxes[i][0] * imH)))
            xmin = int(max(1,(boxes[i][1] * imW)))
            ymax = int(min(imH,(boxes[i][2] * imH)))
            xmax = int(min(imW,(boxes[i][3] * imW)))

            cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, 255, 0), 4)

            # Draw label
            object_name = labels[int(classes[i])] # Look up object name from "labels" array using class index
            label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%'
            labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size
            label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window
            cv2.rectangle(frame, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], 
label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in
            cv2.putText(frame, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 
0.7, (0, 0, 0), 2) # Draw label text

    # All the results have been drawn on the frame, so it's time to display it.
    cv2.imshow('Object detector', frame)
    #output_rgb = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
    out.write(frame)
    # Press 'q' to quit
    if cv2.waitKey(1) == ord('q'):
        break

# Clean up
video.release()
out.release()
cv2.destroyAllWindows()

以下是云创建的模型出现的错误：

File "tflite_vid.py", line 124, in <module>
    classes = interpreter.get_tensor(output_details[1]['index'])[0] # Class index of detected objects
IndexError: list index out of range

---

因此，我希望有人能解释一下，如何使用Python使用TF2开发TFlite模型，或者如何让云生成可用的TFlite模型。哦，请不要给我指出一个需要通过互联网示例进行思考的方向，除非它们是如何做到这一点的真正福音。

在output_details[1]中，我希望[1]在人们可能使用TF2、成功培训和使用这些模型的领域中，能够洞察到这是如何实现的。这不是一个编程问题，而是一个如何为人工智能努力培养成功有用结果的问题。TF文档显示给出的方法不起作用，stackexchange论坛上挤满了问类似问题的人，没有解决方案。我想一定是有人让它起作用了，所以让我们把问题摆在他们面前。我想没有人知道，那些知道的人是在保守这个秘密吗？

File "tflite_vid.py", line 124, in <module>
    classes = interpreter.get_tensor(output_details[1]['index'])[0] # Class index of detected objects
IndexError: list index out of range