我求助于使用云培训工作流程。鉴于我得到的产品，我希望可以直接使用与其他tflite模型兼容的代码，但是云生成的模型不起作用。当请求interpreter.get_tensor参数时，我得到“索引超出范围”。
这是我的代码，基本上是一个修改后的示例，在这里我可以摄取视频并制作带有结果的视频。
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()
这是使用固定tflite模型时打印语句的外观：
[32. 76. 56. 76. 0. 61. 74. 0. 0. 0.]
[0.609375 0.48828125 0.44921875 0.44921875 0.4140625 0.40234375
0.37890625 0.3125 0.3125 0.3125 ]
[[-0.01923192 0.17330796 0.747546 0.8384144 ]
[ 0.01866053 0.5023282 0.39603746 0.6143299 ]
[ 0.01673795 0.47382414 0.34407628 0.5580931 ]
[ 0.11588445 0.78543806 0.8778869 1.0039229 ]
[ 0.8106107 0.70675755 1.0080075 0.89248717]
[ 0.84941524 0.06391776 1.0006479 0.28792098]
[ 0.05543692 0.53557926 0.40413857 0.62823087]
[ 0.07051808 -0.00938512 0.8822515 0.28100258]
[ 0.68205094 0.33990026 0.9940187 0.6020821 ]
[ 0.08010477 0.01998334 0.6011186 0.26135433]]
这是在使用云创建的模型显示时出现的错误：
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，我想请教一个人解释如何使用TF2和Python开发TFlite模型，或者如何使云生成可用的TFlite模型。请哦，请不要将我引向要通过Internet示例进行思考的方向，除非它们是有关如何执行此操作的实际福音。，
答案 0 :(得分：-1)
在output_details[1]中，它是[1] <-列表索引超出范围。您的模型可能有1个输出，但是代码尝试访问第二个输出。
有关Python代码的更多用法，请参考https://www.tensorflow.org/lite/guide/inference#load_and_run_a_model_in_python以获得指导。