Я работаю над моделью обнаружения изображений и следую инструкциям, приведенным ниже. Ссылка https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/?completed=/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/
Хотя я обучил свою модель и также скачал frozen_inference_graph.pb, но при компиляции в блокнот jupyter я столкнулсяошибка "" graph. "% (repr (name), repr (op_name))) KeyError:" Имя 'image_tensor: 0' относится к Tensor, который не существует.Операция 'image_tensor' не существует на графике. "" пожалуйста, сообщите, поскольку я не могу найти решение.ниже код, который я использую:
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import io
import pandas as pd
sys.path.append("C:\\...\\tensorflow\\models\\research\\")
sys.path.append("C:\\...\\tensorflow\\models\\research\\object_detection\\utils")
from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
from object_detection.utils import ops as utils_ops
from object_detection.utils import dataset_util
if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')
# This is needed to display the images.
%matplotlib inline
from utils import label_map_util
from utils import visualization_utils as vis_util
MODEL_NAME = 'Trained_inference_graph'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_FROZEN_GRAPH = 'C:\\...\\Tensorflow\\models\\research\\object_detection\\inference_graph\\frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = 'C:\\...\\Tensorflow\\models\\research\\object_detection\\Training\\label_map.pbtxt'
Num_classes = 5
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
category_index =
label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
PATH_TO_TEST_IMAGES_DIR = 'C:\\...\\Tensorflow\\models\\research\\object_detection\\test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(3, 7) ]
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
def run_inference_for_single_image(image, graph):
with graph.as_default():
with tf.Session() as sess:
# Get handles to input and output tensors
ops = tf.get_default_graph().get_operations()
all_tensor_names = {output.name for op in ops for output in op.outputs}
tensor_dict = {}
for key in [
'num_detections', 'detection_boxes', 'detection_scores',
'detection_classes', 'detection_masks'
]:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
tensor_name)
if 'detection_masks' in tensor_dict:
# The following processing is only for single image
detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes, image.shape[0], image.shape[1])
detection_masks_reframed = tf.cast(
tf.greater(detection_masks_reframed, 0.5), tf.uint8)
# Follow the convention by adding back the batch dimension
tensor_dict['detection_masks'] = tf.expand_dims(
detection_masks_reframed, 0)
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
# Run inference
output_dict = sess.run(tensor_dict,
feed_dict={image_tensor: np.expand_dims(image, 0)})
# all outputs are float32 numpy arrays, so convert types as appropriate
output_dict['num_detections'] = int(output_dict['num_detections'][0])
output_dict['detection_classes'] = output_dict[
'detection_classes'][0].astype(np.uint8)
output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
output_dict['detection_scores'] = output_dict['detection_scores'][0]
if 'detection_masks' in output_dict:
output_dict['detection_masks'] = output_dict['detection_masks'][0]
return output_dict
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
image_np = load_image_into_numpy_array(image)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
output_dict = run_inference_for_single_image(image_np, detection_graph)
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=8)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np)
ОШИБКА
KeyError
Traceback (most recent call last) <ipython-input-66-b19082c2666b> in <module>
7 image_np_expanded = np.expand_dims(image_np, axis=0)
8 # Actual detection.
----> 9 output_dict = run_inference_for_single_image(image_np, detection_graph)
10 # Visualization of the results of a detection.
11 vis_util.visualize_boxes_and_labels_on_image_array(
<ipython-input-65-f22e65a052c1> in run_inference_for_single_image(image, graph)
29 tensor_dict['detection_masks'] = tf.expand_dims(
30 detection_masks_reframed, 0)
---> 31 image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
32
33 # Run inference
D:\anaconda\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\framework\ops.py in get_tensor_by_name(self, name) 3664 raise TypeError("Tensor names are strings (or similar), not %s." % 3665 type(name).__name__)
-> 3666 return self.as_graph_element(name, allow_tensor=True, allow_operation=False) 3667 3668 def
_get_tensor_by_tf_output(self, tf_output):
D:\anaconda\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\framework\ops.py in as_graph_element(self, obj, allow_tensor, allow_operation) 3488 3489 with self._lock:
-> 3490 return self._as_graph_element_locked(obj, allow_tensor, allow_operation) 3491 3492 def _as_graph_element_locked(self, obj, allow_tensor, allow_operation):
D:\anaconda\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\framework\ops.py in _as_graph_element_locked(self, obj, allow_tensor, allow_operation) 3530 raise KeyError("The name %s refers to a Tensor which does not " 3531 "exist. The operation, %s, does not exist in the "
-> 3532 "graph." % (repr(name), repr(op_name))) 3533 try: 3534 return op.outputs[out_n]
KeyError: "The name 'image_tensor:0' refers to a Tensor which does not exist. The operation, 'image_tensor', does not exist in the graph."