安装Tensorflow object detection API之后运行model_builder_test.py报错?
Traceback (most recent call last):
  File "G:\python\models\research\object_detection\builders\model_builder_test.py", line 23, in <module>
    from object_detection.builders import model_builder
  File "G:\python\models\research\object_detection\builders\model_builder.py", line 20, in <module>
    from object_detection.builders import anchor_generator_builder
  File "G:\python\models\research\object_detection\builders\anchor_generator_builder.py", line 22, in <module>
    from object_detection.protos import anchor_generator_pb2
  File "G:\python\models\research\object_detection\protos\anchor_generator_pb2.py", line 29, in <module>
    dependencies=[object__detection_dot_protos_dot_flexible__grid__anchor__generator__pb2.DESCRIPTOR,object__detection_dot_protos_dot_grid__anchor__generator__pb2.DESCRIPTOR,object__detection_dot_protos_dot_multiscale__anchor__generator__pb2.DESCRIPTOR,object__detection_dot_protos_dot_ssd__anchor__generator__pb2.DESCRIPTOR,])
  File "G:\python\python setup\lib\site-packages\google\protobuf\descriptor.py", line 879, in __new__
    return _message.default_pool.AddSerializedFile(serialized_pb)
TypeError: Couldn't build proto file into descriptor pool!
Invalid proto descriptor for file "object_detection/protos/anchor_generator.proto":
  object_detection/protos/flexible_grid_anchor_generator.proto: Import "object_detection/protos/flexible_grid_anchor_generator.proto" has not been loaded.
  object_detection/protos/multiscale_anchor_generator.proto: Import "object_detection/protos/multiscale_anchor_generator.proto" has not been loaded.
  object_detection.protos.AnchorGenerator.multiscale_anchor_generator: "object_detection.protos.MultiscaleAnchorGenerator" seems to be defined in "protos/multiscale_anchor_generator.proto", which is not imported by "object_detection/protos/anchor_generator.proto".  To use it here, please add the necessary import.
  object_detection.protos.AnchorGenerator.flexible_grid_anchor_generator: "object_detection.protos.FlexibleGridAnchorGenerator" seems to be defined in "protos/flexible_grid_anchor_generator.proto", which is not imported by "object_detection/protos/anchor_generator.proto".  To use it here, please add the necessary import.
网上找了各种方法都没用,有些可能有用的但是不够详细。

2个回答

既然是人家的例程,最好找和人家一样的软件版本,tf各个版本兼容性都有问题。

楼上正解。和楼主遇到同样的问题。报错除了路径不同,其他一模一样。

最后解决方案是换用了V3.4的protoc

https://github.com/protocolbuffers/protobuf/releases/tag/v3.4.0

注意:更换版本后记得添加protoc.exe到环境变量中,重新protoc "\research\object_detection\protos" 路径下的所有*.proto 文件

具体操作可以参考这篇博文
https://blog.csdn.net/qq_34809033/article/details/80533347

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For more information, please see: https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md https://github.com/tensorflow/addons If you depend on functionality not listed there, please file an issue. WARNING:tensorflow:Forced number of epochs for all eval validations to be 1. WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered eval_on_train_input_config.num_epochs = 0. Overwriting num_epochs to 1. WARNING:tensorflow:Estimator's model_fn (<function create_model_fn..model_fn at 0x0000027CBAB7BB70>) includes params argument, but params are not passed to Estimator. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. 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Instructions for updating: Use standard file APIs to check for files with this prefix. creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=2.43s). Accumulating evaluation results... DONE (t=0.14s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.287 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.529 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.162 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.061 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384 (default) D:\gitcode\models\research>
Tensorflow object-detection api 报错
我尝试使用ssd_mobilenet_v1模型,报错TypeError: `pred` must be a Tensor, or a Python bool, or 1 or 0. Found instead: None 不知道是什么原因引起的,is_training改成true的方法我已经试过了,没有用
在jupyter notebook上运行tensorflow目标识别官方测试代码object_detection_tutorial.ipynb,每次都是最后一个模块运行时出现“服务器挂了”,如何解决?
在annaconda中创建了tensorflow-gpu的环境,代码可以跑通,没有报错,但是每次到最后一块检测test_image 的时候就服务器挂了。 创建tensorflowcpu环境可以正常跑下来(最后显示那个输出结果),请问是为什么?如何解决呢? 对该环境用代码测试过,pycharm里,可以显示应用的显卡信息,算力等信息,应该是没有问题的。
在使用Tensorflow object_detection API训练自己的数据集时,无法将自己的数据转换为tfrecord文件,但是给的VOC2012可以转换,老板给的任务,急。。。?
win10, Tensorflow-gpu==1.14.0, python3.7 按照要求已将相关文件放至相应位置: ![图片说明](https://img-ask.csdn.net/upload/201911/24/1574584428_312195.png) 数据集准备: ![图片说明](https://img-ask.csdn.net/upload/201911/24/1574584780_579019.png) 标签准备: ![图片说明](https://img-ask.csdn.net/upload/201911/24/1574584792_727796.png) xml文件格式: ![图片说明](https://img-ask.csdn.net/upload/201911/24/1574584811_141648.png) imageset文件准备: ![图片说明](https://img-ask.csdn.net/upload/201911/24/1574584825_425924.png) train.txt文件: ![图片说明](https://img-ask.csdn.net/upload/201911/24/1574584847_363800.png) pascal_label_map也改了:我的只有这两个标签 ![图片说明](https://img-ask.csdn.net/upload/201911/24/1574584895_190927.png) 命令行运行create_pascal_tf_record文件结果: ![图片说明](https://img-ask.csdn.net/upload/201911/24/1574585056_500783.png) 运行完之后只生成一个0kb的record文件 有人说降级到tf1.12.0也许可以,但是现在已经下载不到这个版本了 而且这个程序是可以转换VOC2012的数据集的,也许不是版本问题,卡了三天啦,求大佬指导【抱拳】
Tensorflow+GPU做物体检测,CPU和内存都高占用?
如题, 我在用Tensorflow Object Detection做物体检测的时候, 用mobilenetV1模型, 然后在session运行的时候发现占用的CPU很高, i7的占到了80%, 很不解用到CPU做了什么, 请大神解答...
使用tesorflow中model_main.py遇到的问题!
直接上代码了 ``` D:\tensorflow\models\research\object_detection>python model_main.py --pipeline_config_path=E:\python_demo\pedestrian_demo\pedestrian_train\models\pipeline.config --model_dir=E:\python_demo\pedestrian_demo\pedestrian_train\models\train --num_train_steps=5000 --sample_1_of_n_eval_examples=1 --alsologstderr Traceback (most recent call last): File "model_main.py", line 109, in <module> tf.app.run() File "C:\anaconda\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run _sys.exit(main(argv)) File "model_main.py", line 71, in main FLAGS.sample_1_of_n_eval_on_train_examples)) File "D:\ssd-detection\models-master\research\object_detection\model_lib.py", line 589, in create_estimator_and_inputs pipeline_config_path, config_override=config_override) File "D:\ssd-detection\models-master\research\object_detection\utils\config_util.py", line 98, in get_configs_from_pipeline_file text_format.Merge(proto_str, pipeline_config) File "C:\anaconda\lib\site-packages\google\protobuf\text_format.py", line 574, in Merge descriptor_pool=descriptor_pool) File "C:\anaconda\lib\site-packages\google\protobuf\text_format.py", line 631, in MergeLines return parser.MergeLines(lines, message) File "C:\anaconda\lib\site-packages\google\protobuf\text_format.py", line 654, in MergeLines self._ParseOrMerge(lines, message) File "C:\anaconda\lib\site-packages\google\protobuf\text_format.py", line 676, in _ParseOrMerge self._MergeField(tokenizer, message) File "C:\anaconda\lib\site-packages\google\protobuf\text_format.py", line 801, in _MergeField merger(tokenizer, message, field) File "C:\anaconda\lib\site-packages\google\protobuf\text_format.py", line 875, in _MergeMessageField self._MergeField(tokenizer, sub_message) File "C:\anaconda\lib\site-packages\google\protobuf\text_format.py", line 801, in _MergeField merger(tokenizer, message, field) File "C:\anaconda\lib\site-packages\google\protobuf\text_format.py", line 875, in _MergeMessageField self._MergeField(tokenizer, sub_message) File "C:\anaconda\lib\site-packages\google\protobuf\text_format.py", line 801, in _MergeField merger(tokenizer, message, field) File "C:\anaconda\lib\site-packages\google\protobuf\text_format.py", line 875, in _MergeMessageField self._MergeField(tokenizer, sub_message) File "C:\anaconda\lib\site-packages\google\protobuf\text_format.py", line 768, in _MergeField (message_descriptor.full_name, name)) google.protobuf.text_format.ParseError: 35:7 : Message type "object_detection.protos.SsdFeatureExtractor" has no field named "batch_norm_trainable". ``` 这个错误怎么解决,求大神指导~
关于object detection运行视频检测代码出现报错:ValueError:assignment destination is read-only
我参考博主 withzheng的博客:https://blog.csdn.net/xiaoxiao123jun/article/details/76605928 在视频物体识别的部分中,我用的是Anaconda自带的spyder(python3.6)来运行他给的视频检测代码,出现了如下报错,![图片说明](https://img-ask.csdn.net/upload/201904/20/1555752185_448895.jpg) 具体报错: Moviepy - Building video video1_out.mp4. Moviepy - Writing video video1_out.mp4 t: 7%|▋ | 7/96 [00:40<09:17, 6.26s/it, now=None]Traceback (most recent call last): File "", line 1, in runfile('C:/models-master1/research/object_detection/object_detection_tutorial (1).py', wdir='C:/models-master1/research/object_detection') File "C:\Users\Administrator\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 710, in runfile execfile(filename, namespace) File "C:\Users\Administrator\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 101, in execfile exec(compile(f.read(),filename,'exec'), namespace) File "C:/models-master1/research/object_detection/object_detection_tutorial (1).py", line 273, in white_clip.write_videofile(white_output, audio=False) File "", line 2, in write_videofile File "C:\Users\Administrator\Anaconda3\lib\site-packages\moviepy\decorators.py", line 54, in requires_duration return f(clip, *a, **k) File "", line 2, in write_videofile File "C:\Users\Administrator\Anaconda3\lib\site-packages\moviepy\decorators.py", line 137, in use_clip_fps_by_default return f(clip, *new_a, **new_kw) File "", line 2, in write_videofile File "C:\Users\Administrator\Anaconda3\lib\site-packages\moviepy\decorators.py", line 22, in convert_masks_to_RGB return f(clip, *a, **k) File "C:\Users\Administrator\Anaconda3\lib\site-packages\moviepy\video\VideoClip.py", line 326, in write_videofile logger=logger) File "C:\Users\Administrator\Anaconda3\lib\site-packages\moviepy\video\io\ffmpeg_writer.py", line 216, in ffmpeg_write_video fps=fps, dtype="uint8"): File "C:\Users\Administrator\Anaconda3\lib\site-packages\moviepy\Clip.py", line 475, in iter_frames frame = self.get_frame(t) File "", line 2, in get_frame File "C:\Users\Administrator\Anaconda3\lib\site-packages\moviepy\decorators.py", line 89, in wrapper return f(*new_a, **new_kw) File "C:\Users\Administrator\Anaconda3\lib\site-packages\moviepy\Clip.py", line 95, in get_frame return self.make_frame(t) File "C:\Users\Administrator\Anaconda3\lib\site-packages\moviepy\Clip.py", line 138, in newclip = self.set_make_frame(lambda t: fun(self.get_frame, t)) File "C:\Users\Administrator\Anaconda3\lib\site-packages\moviepy\video\VideoClip.py", line 511, in return self.fl(lambda gf, t: image_func(gf(t)), apply_to) File "C:/models-master1/research/object_detection/object_detection_tutorial (1).py", line 267, in process_image image_process=detect_objects(image,sess,detection_graph) File "C:/models-master1/research/object_detection/object_detection_tutorial (1).py", line 258, in detect_objects line_thickness=8) File "C:\models-master1\research\object_detection\utils\visualization_utils.py", line 743, in visualize_boxes_and_labels_on_image_array use_normalized_coordinates=use_normalized_coordinates) File "C:\models-master1\research\object_detection\utils\visualization_utils.py", line 129, in draw_bounding_box_on_image_array np.copyto(image, np.array(image_pil)) ValueError: assignment destination is read-only 想问问各位大神有遇到过类似的问题吗。。如何解决?
用create_pascal_tf_record.py时候出现的问题!
这是我用create_pascal_tf_record.py出现的错误 ``` D:\tensorflow\models\research\object_detection>python dataset_tools\create_pascal_tf_record.py --label_map=D:\tensorflow\pedestrain_train\data\label_map.pbtxt --data_dir=D:\pedestrain_data --year=VOC2012 --set=train --output_path=D:\pascal_train.record Traceback (most recent call last): File "dataset_tools\create_pascal_tf_record.py", line 185, in <module> tf.app.run() File "C:\anaconda\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run _sys.exit(main(argv)) File "dataset_tools\create_pascal_tf_record.py", line 167, in main examples_list = dataset_util.read_examples_list(examples_path) File "D:\ssd-detection\models-master\research\object_detection\utils\dataset_util.py", line 59, in read_examples_list lines = fid.readlines() File "C:\anaconda\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 188, in readlines self._preread_check() File "C:\anaconda\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 85, in _preread_check compat.as_bytes(self.__name), 1024 * 512, status) File "C:\anaconda\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 519, in __exit__ c_api.TF_GetCode(self.status.status)) tensorflow.python.framework.errors_impl.NotFoundError: NewRandomAccessFile failed to Create/Open: D:\pedestrain_data\VOC2012\ImageSets\Main\aeroplane_train.txt : \u03f5\u0373\udcd5\u04b2\udcbb\udcb5\udcbd\u05b8\udcb6\udca8\udcb5\udcc4\udcce\u013c\udcfe\udca1\udca3 ; No such file or directory ``` 可是我的main文件夹里面是pedestrain_train.txt和pedestrain_val.txt为什么他要去找aeroplane_train.txt这个文件呢
'Datasets' object has no attribute 'train_step'
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import mnist_forward import os BATAH_SIZE = 200 LEARNING_RATE_BASE = 0.1 LEARNING_RATE_DECAY = 0.99 REGULARIZER = 0.0001 STEPS = 50000 MOVING_AVERAGE_DECAY = 0.99 MODEL_SAVE_PATH = "./model/" MODEL_NAME = "mnist_model" def backward(mnist): x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE]) y_ = tf.placeholder(tf.float32, [None, mnist_forward.OUTPUT_NODE]) y = mnist_forward.forward(x, REGULARIZER) global_step = tf.Variable(0, trainable=False) ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.arg_max(y_, 1)) cem = tf.reduce_mean(ce) loss = cem + tf.add_n(tf.get_collection('losses')) learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, global_step, mnist.train.num_examples / BATAH_SIZE, LEARNING_RATE_DECAY, staircase=True) train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step) ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step) ema_op = ema.apply(tf.trainable_variables()) with tf.control_dependencies([train_step, ema_op]): train_op = tf.no_op(name='train') saver = tf.train.Saver() with tf.Session() as sess: init_op = tf.global_variables_initializer() sess.run(init_op) for i in range(STEPS): xs, ys = mnist.train_step.next_batch(BATAH_SIZE) _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys}) if i % 1000 == 0: print("After %d training step(s), loss on training batch is %g." % (step, loss_value)) saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step) def main(): mnist = input_data.read_data_sets("./data/", one_hot=True) backward(mnist) if __name__ == '__main__': main() 运行程序后报错: File "C:/Users/98382/PycharmProjects/minst/mnist_backward.py", line 54, in <module> main() File "C:/Users/98382/PycharmProjects/minst/mnist_backward.py", line 51, in main backward(mnist) File "C:/Users/98382/PycharmProjects/minst/mnist_backward.py", line 43, in backward xs, ys = mnist.train_step.next_batch(BATAH_SIZE) AttributeError: 'Datasets' object has no attribute 'train_step'
用TensorFlow 训练mask rcnn时,总是在执行训练语句时报错,进行不下去了,求大神
用TensorFlow 训练mask rcnn时,总是在执行训练语句时报错,进行不下去了,求大神 执行语句是: ``` python model_main.py --model_dir=C:/Users/zoyiJiang/Desktop/mask_rcnn_test-master/training --pipeline_config_path=C:/Users/zoyiJiang/Desktop/mask_rcnn_test-master/training/mask_rcnn_inception_v2_coco.config ``` 报错信息如下: ``` WARNING:tensorflow:Forced number of epochs for all eval validations to be 1. WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1. WARNING:tensorflow:Estimator's model_fn (<function create_model_fn.<locals>.model_fn at 0x000001C1EA335C80>) includes params argument, but params are not passed to Estimator. WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards. Traceback (most recent call last): File "model_main.py", line 109, in <module> tf.app.run() File "E:\Python3.6\lib\site-packages\tensorflow\python\platform\app.py", line 126, in run _sys.exit(main(argv)) File "model_main.py", line 105, in main tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0]) File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\training.py", line 439, in train_and_evaluate executor.run() File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\training.py", line 518, in run self.run_local() File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\training.py", line 650, in run_local hooks=train_hooks) File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\estimator.py", line 363, in train loss = self._train_model(input_fn, hooks, saving_listeners) File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\estimator.py", line 843, in _train_model return self._train_model_default(input_fn, hooks, saving_listeners) File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\estimator.py", line 853, in _train_model_default input_fn, model_fn_lib.ModeKeys.TRAIN)) File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\estimator.py", line 691, in _get_features_and_labels_from_input_fn result = self._call_input_fn(input_fn, mode) File "E:\Python3.6\lib\site-packages\tensorflow\python\estimator\estimator.py", line 798, in _call_input_fn return input_fn(**kwargs) File "D:\Tensorflow\tf\models\research\object_detection\inputs.py", line 525, in _train_input_fn batch_size=params['batch_size'] if params else train_config.batch_size) File "D:\Tensorflow\tf\models\research\object_detection\builders\dataset_builder.py", line 149, in build dataset = data_map_fn(process_fn, num_parallel_calls=num_parallel_calls) File "E:\Python3.6\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 853, in map return ParallelMapDataset(self, map_func, num_parallel_calls) File "E:\Python3.6\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 1870, in __init__ super(ParallelMapDataset, self).__init__(input_dataset, map_func) File "E:\Python3.6\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 1839, in __init__ self._map_func.add_to_graph(ops.get_default_graph()) File "E:\Python3.6\lib\site-packages\tensorflow\python\framework\function.py", line 484, in add_to_graph self._create_definition_if_needed() File "E:\Python3.6\lib\site-packages\tensorflow\python\framework\function.py", line 319, in _create_definition_if_needed self._create_definition_if_needed_impl() File "E:\Python3.6\lib\site-packages\tensorflow\python\framework\function.py", line 336, in _create_definition_if_needed_impl outputs = self._func(*inputs) File "E:\Python3.6\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 1804, in tf_map_func ret = map_func(nested_args) File "D:\Tensorflow\tf\models\research\object_detection\builders\dataset_builder.py", line 130, in process_fn processed_tensors = transform_input_data_fn(processed_tensors) File "D:\Tensorflow\tf\models\research\object_detection\inputs.py", line 515, in transform_and_pad_input_data_fn tensor_dict=transform_data_fn(tensor_dict), File "D:\Tensorflow\tf\models\research\object_detection\inputs.py", line 129, in transform_input_data tf.expand_dims(tf.to_float(image), axis=0)) File "D:\Tensorflow\tf\models\research\object_detection\meta_architectures\faster_rcnn_meta_arch.py", line 543, in preprocess parallel_iterations=self._parallel_iterations) File "D:\Tensorflow\tf\models\research\object_detection\utils\shape_utils.py", line 237, in static_or_dynamic_map_fn outputs = [fn(arg) for arg in tf.unstack(elems)] File "D:\Tensorflow\tf\models\research\object_detection\utils\shape_utils.py", line 237, in <listcomp> outputs = [fn(arg) for arg in tf.unstack(elems)] File "D:\Tensorflow\tf\models\research\object_detection\core\preprocessor.py", line 2264, in resize_to_range lambda: _resize_portrait_image(image)) File "E:\Python3.6\lib\site-packages\tensorflow\python\util\deprecation.py", line 432, in new_func return func(*args, **kwargs) File "E:\Python3.6\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2063, in cond orig_res_t, res_t = context_t.BuildCondBranch(true_fn) File "E:\Python3.6\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 1913, in BuildCondBranch original_result = fn() File "D:\Tensorflow\tf\models\research\object_detection\core\preprocessor.py", line 2263, in <lambda> lambda: _resize_landscape_image(image), File "D:\Tensorflow\tf\models\research\object_detection\core\preprocessor.py", line 2245, in _resize_landscape_image align_corners=align_corners, preserve_aspect_ratio=True) TypeError: resize_images() got an unexpected keyword argument 'preserve_aspect_ratio' ``` 根据提示的最后一句,是说没有一个有效参数 我用的是TensorFlow1.8 python3.6,下载的最新的TensorFlow-models-master
ImportError:cannot import name 'cloud' from 'tensorflow.contrib'求助
使用Tensorflow Object_Detection, 配好了Tensorflow 1.14.0 和 Protocobuf 3.10.0 然后路径也配好了,就是运行测试文件时会报错 ImportError:cannot import name 'cloud' from 'tensorflow.contrib' 请问各位大神,这是缺少了什么库?
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