您好，我对此并不陌生，但是我想知道为什么我的代码无法正常工作。在运行我的张量流代码时收到分段错误。
import os
import pathlib
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from IPython.display import display
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
# patch tf1 into `utils.ops`
utils_ops.tf = tf.compat.v1
# Patch the location of gfile
tf.gfile = tf.io.gfile
def load_model(model_name):
model_dir = "pre-trained-model/saved_model"
model = tf.saved_model.load(str(model_dir))
model = model.signatures['serving_default']
return model
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = 'annotations/label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
#PATH_TO_TEST_IMAGES_DIR = pathlib.Path('images/test/test1/20190504_081520_900.png')
#TEST_IMAGE_PATHS = sorted(list(PATH_TO_TEST_IMAGES_DIR.glob("*.png")))
image = Image.open('images/test/test1/20190504_081520_900.png')
image = image.convert('RGB')
model_name = 'ssd_resnet50_v1_fpn'
detection_model = load_model(model_name)
detection_model.output_dtypes
detection_model.output_shapes
def run_inference_for_single_image(model, image):
image = np.asarray(image)
# The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
input_tensor = tf.convert_to_tensor(image)
# The model expects a batch of images, so add an axis with `tf.newaxis`.
input_tensor = input_tensor[tf.newaxis,...]
print("input tensor shape", input_tensor.shape)
# Run inference
output_dict = model(input_tensor)
# All outputs are batches tensors.
# Convert to numpy arrays, and take index [0] to remove the batch dimension.
# We're only interested in the first num_detections.
num_detections = int(output_dict.pop('num_detections'))
output_dict = {key:value[0, :num_detections].numpy()
for key,value in output_dict.items()}
output_dict['num_detections'] = num_detections
# detection_classes should be ints.
output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64)
# Handle models with masks:
if 'detection_masks' in output_dict:
# Reframe the the bbox mask to the image size.
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
output_dict['detection_masks'], output_dict['detection_boxes'],
image.shape[0], image.shape[1])
detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5,
tf.uint8)
output_dict['detection_masks_reframed'] = detection_masks_reframed.numpy()
return output_dict
def show_inference(model, image):
"""# Run it on each test image and show the results:
# 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 = np.array(image)
# Actual detection.
output_dict = run_inference_for_single_image(model, image_np)
# 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_reframed', None),
use_normalized_coordinates=True,
line_thickness=8)
return image_np
show_inference(detection_model, image)
我正在运行tensorflow 2.0.0并遵循https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb教程
错误：
2019-12-12 02:16:30.029707: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2019-12-12 02:16:30.029742: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2019-12-12 02:16:30.029773: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2019-12-12 02:16:30.029812: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2019-12-12 02:16:30.029915: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2019-12-12 02:16:30.029977: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2019-12-12 02:16:30.030004: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-12-12 02:16:30.032024: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-12-12 02:16:30.032077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2019-12-12 02:16:30.033422: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-12-12 02:16:30.033446: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2019-12-12 02:16:30.033458: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2019-12-12 02:16:30.035525: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9872 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:02:00.0, compute capability: 7.5)
input tensor shape (1, 1200, 4112, 3)
2019-12-12 02:16:38.847133: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
Segmentation fault (core dumped)