带TypeError的TensorRT对象检测：只能将整数标量数组转换为标量索引

Question

我编写了以下代码来优化使用TensorRT的TensorFlow 1目标检测模型，然后在Jetson Nano上运行推断。但是，它运行推断，但返回TypeError：只有整数标量数组可以转换为标量索引，而不会在图像上显示识别的对象。

这是我的密码：

from PIL import Image
import sys
import os
import urllib
import tensorflow.contrib.tensorrt as trt
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import tensorflow as tf
import numpy as np
import time
from tf_trt_models.detection import download_detection_model, build_detection_graph

%matplotlib inline

config_path = '/home/luigi/Downloads/SSD_MobileNet_300000/pipeline.config'
checkpoint_path = '/home/luigi/Downloads/SSD_MobileNet_300000/model.ckpt'

DATA_DIR = '/home/luigi/Downloads'

frozen_graph, input_names, output_names = build_detection_graph(
    config=config_path,
    checkpoint=checkpoint_path,
    score_threshold=0.1,
    batch_size=1
)

print(output_names)

trt_graph = trt.create_inference_graph(
    input_graph_def=frozen_graph,
    outputs=output_names,
    max_batch_size=1,
    max_workspace_size_bytes=1 << 25,
    precision_mode='FP16',
    minimum_segment_size=50
)

with open('/home/luigi/Downloads/SSD_MobileNet_300000/frozen_inference_graph.pb', 'wb') as f:
    f.write(trt_graph.SerializeToString())

tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True

tf_sess = tf.Session(config=tf_config)

tf.import_graph_def(trt_graph, name='')

tf_input = tf_sess.graph.get_tensor_by_name(input_names[0] + ':0')
tf_scores = tf_sess.graph.get_tensor_by_name('detection_scores:0')
tf_boxes = tf_sess.graph.get_tensor_by_name('detection_boxes:0')
tf_classes = tf_sess.graph.get_tensor_by_name('detection_classes:0')
tf_num_detections = tf_sess.graph.get_tensor_by_name('num_detections:0')

image = Image.open('/home/luigi/Downloads/test/P2794.png')

plt.imshow(image)

image_resized = np.array(image.resize((320, 320)))
image = np.array(image)

scores, boxes, classes, num_detections = tf_sess.run([tf_scores, tf_boxes, tf_classes, tf_num_detections], feed_dict={
    tf_input: image_resized[None, ...]
})

boxes = boxes[0] # index by 0 to remove batch dimension
scores = scores[0]
classes = classes[0]
num_detections = num_detections[0]

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)

ax.imshow(image)

# plot boxes exceeding score threshold
for i in range(num_detections):
    # scale box to image coordinates
    box = boxes[i] * np.array([image.shape[0], image.shape[1], image.shape[0], image.shape[1]])

    # display rectangle
    patch = patches.Rectangle((box[1], box[0]), box[3] - box[1], box[2] - box[0], color='g', alpha=0.3)
    ax.add_patch(patch)

    # display class index and score
    plt.text(x=box[1] + 10, y=box[2] - 10, s='%d (%0.2f) ' % (classes[i], scores[i]), color='w')

plt.show()

num_samples = 1

t0 = time.time()
for i in range(num_samples):
    scores, boxes, classes, num_detections = tf_sess.run([tf_scores, tf_boxes, tf_classes, tf_num_detections], feed_dict={
        tf_input: image_resized[None, ...]
    })
t1 = time.time()
print('Average runtime: %f seconds' % (float(t1 - t0) / num_samples))

以下是错误的一个片段：

我该怎么解决这个问题？

谢谢