如何在Python中使用Google AutoML Tensorflow容器导出

我在Google AutoML上训练了一个模型，并将其导出为容器。它给了我一个

.pb

文件。我想在Python中脱机使用它。以下是我在此代码中使用的：

import os
import cv2
import numpy as np
import tensorflow as tf
import sys

sys.path.append("..")

from utils import label_map_util
from utils import visualization_utils as vis_util

MODEL_NAME = 'inference_graph'
IMAGE_NAME = 'test8.jpg'
CWD_PATH = os.getcwd()

PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')
PATH_TO_LABELS = os.path.join(CWD_PATH,'training','optik.pbtxt')
PATH_TO_IMAGE = os.path.join(CWD_PATH,IMAGE_NAME)
NUM_CLASSES = 1

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

    sess = tf.Session(graph=detection_graph)

image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')

image = cv2.imread(PATH_TO_IMAGE)
image_expanded = np.expand_dims(image, axis=0)

(boxes, scores, classes, num) = sess.run(
    [detection_boxes, detection_scores, detection_classes, num_detections],
    feed_dict={image_tensor: image_expanded})

vis_util.visualize_boxes_and_labels_on_image_array(
    image,
    np.squeeze(boxes),
    np.squeeze(classes).astype(np.int32),
    np.squeeze(scores),
    category_index,
    use_normalized_coordinates=True,
    line_thickness=8,
    min_score_thresh=0.80)

cv2.imshow('Object detector', image)
cv2.imwrite('testres.jpg', image)

cv2.waitKey(0)
cv2.destroyAllWindows()

我已将

.pb

文件放入带有

冻结的推理图.pb

名称的推理图文件夹。我自己设置了

.pbtxt

文件。但它给出了这样一个错误：

Traceback (most recent call last):
  File "Object_detection_image.py", line 67, in <module>
    od_graph_def.ParseFromString(serialized_graph)
google.protobuf.message.DecodeError: Error parsing message

回溯（最近一次呼叫最后一次）：
文件“Object\u detection\u image.py”，第67行，在
od_图形_def.ParseFromString（序列化_图形）
google.protobuf.message.DecodeError:解析消息时出错

---

如何在python中使用Google AutoML容器模型？

试试这样：

https://stackoverflow.com/questions/58461211/saved-model-from-automl-vision-edge-not-loading-properly/58757328#58757328

尝试以下方法：

https://stackoverflow.com/questions/58461211/saved-model-from-automl-vision-edge-not-loading-properly/58757328#58757328