shauviks commented 6 years ago

I am getting the following error for - D:\tensorflow\models\research\object_detection>python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config Error- python: can't open file 'train.py': [Errno 2] No such file or directory

I checked there is no such file train.py even in tensorflows github page too

petertennis commented 6 years ago

I have the same issue

RafaRuiz commented 6 years ago

They put it in the folder legacy. Now you can execute model_main.py which runs train and eval at the same time.

Apparently.

fdkssdks commented 6 years ago

Your script should become: python legacy/train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config

petertennis commented 6 years ago

thanks RafaRuiz/fdkssdki

ektavyas commented 6 years ago

i have the same issue.......but when i'm trying this script python legacy/train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config

i have another issue

ektavyas commented 6 years ago

WARNING:tensorflow:From C:\Users\ektav\Anaconda2\envs\tensorflow1\lib\site-packages\tensorflow\python\platform\app.py:125: main (from main) is deprecated and will be removed in a future version. Instructions for updating: Use object_detection/model_main.py. WARNING:tensorflow:From C:\tensorflow1\models\research\object_detection\legacy\trainer.py:262: create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Please switch to tf.train.create_global_step WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards. INFO:tensorflow:Scale of 0 disables regularizer. INFO:tensorflow:Scale of 0 disables regularizer. INFO:tensorflow:depth of additional conv before box predictor: 0 WARNING:tensorflow:From C:\tensorflow1\models\research\object_detection\predictors\mask_rcnn_heads\box_head.py:76: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version. Instructions for updating: keep_dims is deprecated, use keepdims instead INFO:tensorflow:Scale of 0 disables regularizer. INFO:tensorflow:Scale of 0 disables regularizer. WARNING:tensorflow:From C:\tensorflow1\models\research\object_detection\core\losses.py:317: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version. Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow into the labels input on backprop by default.

See @{tf.nn.softmax_cross_entropy_with_logits_v2}.

C:\Users\ektav\Anaconda2\envs\tensorflow1\lib\site-packages\tensorflow\python\ops\gradients_impl.py:108: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory. "Converting sparse IndexedSlices to a dense Tensor of unknown shape. " WARNING:tensorflow:From C:\tensorflow1\models\research\object_detection\meta_architectures\faster_rcnn_meta_arch.py:2070: get_or_create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Please switch to tf.train.get_or_create_global_step Traceback (most recent call last): File "legacy/train.py", line 184, in tf.app.run() File "C:\Users\ektav\Anaconda2\envs\tensorflow1\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run _sys.exit(main(argv)) File "C:\Users\ektav\Anaconda2\envs\tensorflow1\lib\site-packages\tensorflow\python\util\deprecation.py", line 272, in new_func return func(*args, **kwargs) File "legacy/train.py", line 180, in main graph_hook_fn=graph_rewriter_fn) File "C:\tensorflow1\models\research\object_detection\legacy\trainer.py", line 393, in train include_global_step=False)) File "C:\tensorflow1\models\research\object_detection\utils\variables_helper.py", line 126, in get_variables_available_in_checkpoint ckpt_reader = tf.train.NewCheckpointReader(checkpoint_path) File "C:\Users\ektav\Anaconda2\envs\tensorflow1\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 303, in NewCheckpointReader return CheckpointReader(compat.as_bytes(filepattern), status) File "C:\Users\ektav\Anaconda2\envs\tensorflow1\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 519, in exit c_api.TF_GetCode(self.status.status)) esearch/object_detectionaster_rcnn_inception_v2_coco_2018_01_28 : The filename, directory name, or volume label syntax is incorrect.flow1/modelsnsorflow1\models ; Unknown error

ektavyas commented 6 years ago

finally it's done ........... it's my typing mistake thanks RafaRuiz/fdkssdki

tanvirahmd1 commented 5 years ago

any one know how to get PR cures and precession values? mAP tensorboard

Itachi-Uchia commented 5 years ago

@ektavyas what was your mistake because my problem still persists. And plz provide the solution for the above error which was mentioned by you particulary.....
please As soon as possible

Itachi-Uchia commented 5 years ago

Plz advice for the above

mihuzz commented 5 years ago

I cant run Training, maybe someone can help me please

`(sdk) D:\Lesson8\models\research\object_detection>python legacy/train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config

WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0. 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.

Traceback (most recent call last): File "legacy/train.py", line 51, in from object_detection.builders import model_builder File "D:\Lesson8\models\research\object_detection\builders\model_builder.py", line 27, in from object_detection.builders import post_processing_builder File "D:\Lesson8\models\research\object_detection\builders\post_processing_builder.py", line 22, in from object_detection.protos import post_processing_pb2 File "D:\Lesson8\models\research\object_detection\protos\post_processing_pb2.py", line 16, in from object_detection.protos import calibration_pb2 as objectdetection_dot_protos_dot_calibrationpb2 ImportError: cannot import name 'calibration_pb2'`

JulianOrteil commented 5 years ago

@mihuzz It appears to be that calibration.proto was not included in the protobuff command. As mentioned in the tutorial: "(Note: TensorFlow occassionally adds new .proto files to the \protos folder. If you get an error saying ImportError: cannot import name 'something_something_pb2' , you may need to update the protoc command to include the new .proto files.)"

Run the command again, but include "calibration.proto".

protoc --python_out=. .\object_detection\protos\anchor_generator.proto .\object_detection\protos\argmax_matcher.proto .\object_detection\protos\bipartite_matcher.proto .\object_detection\protos\box_coder.proto .\object_detection\protos\box_predictor.proto .\object_detection\protos\eval.proto .\object_detection\protos\faster_rcnn.proto .\object_detection\protos\faster_rcnn_box_coder.proto .\object_detection\protos\grid_anchor_generator.proto .\object_detection\protos\hyperparams.proto .\object_detection\protos\image_resizer.proto .\object_detection\protos\input_reader.proto .\object_detection\protos\losses.proto .\object_detection\protos\matcher.proto .\object_detection\protos\mean_stddev_box_coder.proto .\object_detection\protos\model.proto .\object_detection\protos\optimizer.proto .\object_detection\protos\pipeline.proto .\object_detection\protos\post_processing.proto .\object_detection\protos\preprocessor.proto .\object_detection\protos\region_similarity_calculator.proto .\object_detection\protos\square_box_coder.proto .\object_detection\protos\ssd.proto .\object_detection\protos\ssd_anchor_generator.proto .\object_detection\protos\string_int_label_map.proto .\object_detection\protos\train.proto .\object_detection\protos\keypoint_box_coder.proto .\object_detection\protos\multiscale_anchor_generator.proto .\object_detection\protos\graph_rewriter.proto .\object_detection\protos\calibration.proto

mihuzz commented 5 years ago

@mihuzz It appears to be that calibration.proto was not included in the protobuff command. As mentioned in the tutorial: "(Note: TensorFlow occassionally adds new .proto files to the \protos folder. If you get an error saying ImportError: cannot import name 'something_something_pb2' , you may need to update the protoc command to include the new .proto files.)"

Run the command again, but include "calibration.proto".

protoc --python_out=. .\object_detection\protos\anchor_generator.proto .\object_detection\protos\argmax_matcher.proto .\object_detection\protos\bipartite_matcher.proto .\object_detection\protos\box_coder.proto .\object_detection\protos\box_predictor.proto .\object_detection\protos\eval.proto .\object_detection\protos\faster_rcnn.proto .\object_detection\protos\faster_rcnn_box_coder.proto .\object_detection\protos\grid_anchor_generator.proto .\object_detection\protos\hyperparams.proto .\object_detection\protos\image_resizer.proto .\object_detection\protos\input_reader.proto .\object_detection\protos\losses.proto .\object_detection\protos\matcher.proto .\object_detection\protos\mean_stddev_box_coder.proto .\object_detection\protos\model.proto .\object_detection\protos\optimizer.proto .\object_detection\protos\pipeline.proto .\object_detection\protos\post_processing.proto .\object_detection\protos\preprocessor.proto .\object_detection\protos\region_similarity_calculator.proto .\object_detection\protos\square_box_coder.proto .\object_detection\protos\ssd.proto .\object_detection\protos\ssd_anchor_generator.proto .\object_detection\protos\string_int_label_map.proto .\object_detection\protos\train.proto .\object_detection\protos\keypoint_box_coder.proto .\object_detection\protos\multiscale_anchor_generator.proto .\object_detection\protos\graph_rewriter.proto .\object_detection\protos\calibration.proto

Thanks a lot!

mihuzz commented 5 years ago

model_main cant run

someone can help plz models\research\object_detection>python model_main.py --logtostderr--model_dir=training/--pipeline_config_path=training/faster_rcnn_inception_v2_coco_2018_01_28 Traceback (most recent call last): File "model_main.py", line 26, in from object_detection import model_lib File "D:\Lesson8\models\research\object_detection\model_lib.py", line 27, in from object_detection import eval_util File "D:\Lesson8\models\research\object_detection\eval_util.py", line 28, in from object_detection.metrics import coco_evaluation File "D:\Lesson8\models\research\object_detection\metrics\coco_evaluation.py", line 20, in from object_detection.metrics import coco_tools File "D:\Lesson8\models\research\object_detection\metrics\coco_tools.py", line 47, in from pycocotools import coco File "D:\Lesson8\models\research\pycocotools\coco.py", line 55, in from . import mask as maskUtils File "D:\Lesson8\models\research\pycocotools\mask.py", line 3, in import pycocotools._mask as _mask ModuleNotFoundError: No module named 'pycocotools._mask'

ShilpaJagadeeshappa commented 5 years ago

shilpa.j@Theorem-TrainUP:/opt/tensorflow/models/research/object_detection/models$ cd .. shilpa.j@Theorem-TrainUP:/opt/tensorflow/models/research/object_detection$ python3 legacy/model_main.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config python3: can't open file 'legacy/model_main.py': [Errno 2] No such file or directory shilpa.j@Theorem-TrainUP:/opt/tensorflow/models/research/object_detection$ python3 legacy/train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config WARNING: Logging before flag parsing goes to stderr. W0625 15:10:52.232501 140181953247040 lazy_loader.py:50] The TensorFlow contrib module will not be included in TensorFlow 2.0. For more information, please see:

https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
https://github.com/tensorflow/addons
https://github.com/tensorflow/io (for I/O related ops) If you depend on functionality not listed there, please file an issue.

W0625 15:10:52.552797 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/slim/nets/inception_resnet_v2.py:373: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

W0625 15:10:52.636818 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/slim/nets/mobilenet/mobilenet.py:397: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.

W0625 15:10:52.715449 140181953247040 deprecation_wrapper.py:119] From legacy/train.py:55: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.

W0625 15:10:52.715638 140181953247040 deprecation_wrapper.py:119] From legacy/train.py:55: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.

W0625 15:10:52.716269 140181953247040 deprecation_wrapper.py:119] From legacy/train.py:184: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.

W0625 15:10:52.716854 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/absl/app.py:251: main (from main) is deprecated and will be removed in a future version. Instructions for updating: Use object_detection/model_main.py. W0625 15:10:52.717057 140181953247040 deprecation_wrapper.py:119] From legacy/train.py:90: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.

W0625 15:10:52.717436 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/utils/config_util.py:98: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.

W0625 15:10:52.747286 140181953247040 deprecation_wrapper.py:119] From legacy/train.py:95: The name tf.gfile.Copy is deprecated. Please use tf.io.gfile.copy instead.

W0625 15:10:52.857741 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:266: create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Please switch to tf.train.create_global_step W0625 15:10:52.863439 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/data_decoders/tf_example_decoder.py:177: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.

W0625 15:10:52.863688 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/data_decoders/tf_example_decoder.py:192: The name tf.VarLenFeature is deprecated. Please use tf.io.VarLenFeature instead.

W0625 15:10:52.879672 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/builders/dataset_builder.py:64: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead.

W0625 15:10:52.889734 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/builders/dataset_builder.py:71: The name tf.logging.warning is deprecated. Please use tf.compat.v1.logging.warning instead.

W0625 15:10:52.889871 140181953247040 dataset_builder.py:72] num_readers has been reduced to 1 to match input file shards. W0625 15:10:52.897384 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/builders/dataset_builder.py:86: parallel_interleave (from tensorflow.contrib.data.python.ops.interleave_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.data.experimental.parallel_interleave(...). W0625 15:10:52.897551 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/interleave_ops.py:77: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE) instead. If sloppy execution is desired, use tf.data.Options.experimental_determinstic. W0625 15:10:52.934068 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/builders/dataset_builder.py:155: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.data.Dataset.map() W0625 15:10:53.211105 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/builders/dataset_builder.py:43: DatasetV1.make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version. Instructions for updating: Usefor ... in dataset:to iterate over a dataset. If usingtf.estimator, return theDatasetobject directly from your input function. As a last resort, you can usetf.compat.v1.data.make_initializable_iterator(dataset)`. W0625 15:10:53.218743 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/builders/dataset_builder.py:44: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

W0625 15:10:53.223891 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/core/preprocessor.py:512: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

W0625 15:10:53.291558 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/core/preprocessor.py:188: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: seed2 arg is deprecated.Use sample_distorted_bounding_box_v2 instead. W0625 15:10:53.306091 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/core/box_list_ops.py:201: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where W0625 15:10:54.308789 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/core/batcher.py:96: batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.batch(batch_size) (or padded_batch(...) if dynamic_pad=True). W0625 15:10:54.313415 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/input.py:753: QueueRunner.init (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the tf.data module. W0625 15:10:54.314663 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/input.py:753: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the tf.data module. W0625 15:10:54.325667 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/core/prefetcher.py:58: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.

W0625 15:10:55.320201 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/core/preprocessor.py:2515: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead.

W0625 15:10:58.859347 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/predictors/convolutional_box_predictor.py:150: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.

I0625 15:10:58.859581 140181953247040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0 I0625 15:10:58.904655 140181953247040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0 I0625 15:10:58.948891 140181953247040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0 I0625 15:10:58.993812 140181953247040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0 I0625 15:10:59.038319 140181953247040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0 I0625 15:10:59.082738 140181953247040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0 W0625 15:11:04.966106 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/core/losses.py:172: The name tf.losses.huber_loss is deprecated. Please use tf.compat.v1.losses.huber_loss instead.

W0625 15:11:04.967823 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/core/losses.py:178: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.

W0625 15:11:05.765034 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:208: The name tf.losses.add_loss is deprecated. Please use tf.compat.v1.losses.add_loss instead.

W0625 15:11:05.765706 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/builders/optimizer_builder.py:95: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.

W0625 15:11:05.765954 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/utils/learning_schedules.py:61: The name tf.train.exponential_decay is deprecated. Please use tf.compat.v1.train.exponential_decay instead.

W0625 15:11:05.778445 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/builders/optimizer_builder.py:47: The name tf.train.RMSPropOptimizer is deprecated. Please use tf.compat.v1.train.RMSPropOptimizer instead.

W0625 15:11:08.371582 140181953247040 deprecation.py:506] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/rmsprop.py:119: calling Ones.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor W0625 15:11:09.939157 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/moving_averages.py:433: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. W0625 15:11:13.526557 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:353: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.

W0625 15:11:13.790925 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:355: The name tf.losses.get_losses is deprecated. Please use tf.compat.v1.losses.get_losses instead.

W0625 15:11:13.793818 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:359: The name tf.losses.get_total_loss is deprecated. Please use tf.compat.v1.losses.get_total_loss instead.

W0625 15:11:13.797688 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:368: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.

W0625 15:11:13.804011 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:376: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.

W0625 15:11:14.411106 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/utils/variables_helper.py:134: The name tf.train.NewCheckpointReader is deprecated. Please use tf.compat.v1.train.NewCheckpointReader instead.

W0625 15:11:14.445239 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[273]], model variable shape: [[6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.445385 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/biases/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[273]], model variable shape: [[6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.445502 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/biases/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[273]], model variable shape: [[6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.445610 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/biases/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[273]], model variable shape: [[6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.445716 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 273]], model variable shape: [[1, 1, 512, 6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.445822 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/weights/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 273]], model variable shape: [[1, 1, 512, 6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.445926 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/weights/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 273]], model variable shape: [[1, 1, 512, 6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446029 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/weights/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 273]], model variable shape: [[1, 1, 512, 6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446156 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446256 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/biases/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446356 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/biases/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446455 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/biases/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446555 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 1024, 546]], model variable shape: [[1, 1, 1024, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446657 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/weights/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 1024, 546]], model variable shape: [[1, 1, 1024, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446759 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/weights/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 1024, 546]], model variable shape: [[1, 1, 1024, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446861 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/weights/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 1024, 546]], model variable shape: [[1, 1, 1024, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446988 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447091 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/biases/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447191 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/biases/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447290 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/biases/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447390 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 546]], model variable shape: [[1, 1, 512, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447493 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/weights/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 546]], model variable shape: [[1, 1, 512, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447593 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/weights/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 546]], model variable shape: [[1, 1, 512, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447695 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/weights/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 546]], model variable shape: [[1, 1, 512, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447818 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447919 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/biases/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448017 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/biases/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448115 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/biases/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448216 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448317 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/weights/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448436 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/weights/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448541 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/weights/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448666 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448766 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/biases/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448865 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/biases/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448965 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/biases/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449065 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449166 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/weights/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449269 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/weights/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449370 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/weights/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449496 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449597 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/biases/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449696 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/biases/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449794 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/biases/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449894 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 546]], model variable shape: [[1, 1, 128, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449995 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/weights/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 546]], model variable shape: [[1, 1, 128, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.450096 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/weights/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 546]], model variable shape: [[1, 1, 128, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.450197 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/weights/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 546]], model variable shape: [[1, 1, 128, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.451475 140181953247040 variables_helper.py:152] Variable [global_step] is not available in checkpoint W0625 15:11:15.556331 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/contrib/slim/python/slim/learning.py:742: Supervisor.init (from tensorflow.python.training.supervisor) is deprecated and will be removed in a future version. Instructions for updating: Please switch to tf.train.MonitoredTrainingSession 2019-06-25 15:11:17.573499: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2019-06-25 15:11:17.744825: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2299995000 Hz 2019-06-25 15:11:17.745496: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xcb5cb30 executing computations on platform Host. Devices: 2019-06-25 15:11:17.745535: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): , W0625 15:11:17.757068 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix. I0625 15:11:17.758359 140181953247040 saver.py:1280] Restoring parameters from training/model.ckpt-1 2019-06-25 15:11:19.576775: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile. W0625 15:11:19.944330 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/saver.py:1066: get_checkpoint_mtimes (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file utilities to get mtimes. I0625 15:11:19.946665 140181953247040 session_manager.py:500] Running local_init_op. I0625 15:11:20.577036 140181953247040 session_manager.py:502] Done running local_init_op. I0625 15:11:30.679445 140181953247040 learning.py:754] Starting Session. I0625 15:11:31.052094 140178983208704 supervisor.py:1117] Saving checkpoint to path training/model.ckpt I0625 15:11:31.070347 140181953247040 learning.py:768] Starting Queues. I0625 15:11:38.079804 140178379228928 coordinator.py:224] Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.InvalidArgumentError'>, image_size must contain 3 elements[4] [[{{node cond_4/RandomCropImage/sample_distorted_bounding_box/SampleDistortedBoundingBoxV2}}]] I0625 15:11:39.040256 140178974816000 supervisor.py:1099] global_step/sec: 0 I0625 15:11:48.074288 140181953247040 learning.py:783] Caught OutOfRangeError. Stopping Training. FIFOQueue '_6_prefetch_queue' is closed and has insufficient elements (requested 1, current size 0) [[node prefetch_queue_Dequeue (defined at /opt/tensorflow/models/research/object_detection/core/batcher.py:111) ]]

Errors may have originated from an input operation. Input Source operations connected to node prefetch_queue_Dequeue: prefetch_queue (defined at /opt/tensorflow/models/research/object_detection/core/prefetcher.py:54)

Original stack trace for 'prefetch_queue_Dequeue': File "legacy/train.py", line 184, in tf.app.run() File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 40, in run _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef) File "/home/shilpa.j/.local/lib/python3.6/site-packages/absl/app.py", line 300, in run _run_main(main, args) File "/home/shilpa.j/.local/lib/python3.6/site-packages/absl/app.py", line 251, in _run_main sys.exit(main(argv)) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 324, in new_func return func(*args, kwargs) File "legacy/train.py", line 180, in main graph_hook_fn=graph_rewriter_fn) File "/opt/tensorflow/models/research/object_detection/legacy/trainer.py", line 291, in train clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue]) File "/opt/tensorflow/models/research/slim/deployment/model_deploy.py", line 193, in create_clones outputs = model_fn(*args, *kwargs) File "/opt/tensorflow/models/research/object_detection/legacy/trainer.py", line 181, in _create_losses train_config.use_multiclass_scores) File "/opt/tensorflow/models/research/object_detection/legacy/trainer.py", line 124, in get_inputs read_data_list = input_queue.dequeue() File "/opt/tensorflow/models/research/object_detection/core/batcher.py", line 111, in dequeue batched_tensors = self._queue.dequeue() File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/ops/data_flow_ops.py", line 445, in dequeue self._queue_ref, self._dtypes, name=name) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 4140, in queue_dequeue_v2 timeout_ms=timeout_ms, name=name) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper op_def=op_def) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func return func(args, kwargs) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3616, in create_op op_def=op_def) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2005, in init self._traceback = tf_stack.extract_stack()

I0625 15:11:48.075780 140181953247040 learning.py:785] Finished training! Saving model to disk. /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/summary/writer/writer.py:386: UserWarning: Attempting to use a closed FileWriter. The operation will be a noop unless the FileWriter is explicitly reopened. warnings.warn("Attempting to use a closed FileWriter. " Traceback (most recent call last): File "legacy/train.py", line 184, in tf.app.run() File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 40, in run _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef) File "/home/shilpa.j/.local/lib/python3.6/site-packages/absl/app.py", line 300, in run _run_main(main, args) File "/home/shilpa.j/.local/lib/python3.6/site-packages/absl/app.py", line 251, in _run_main sys.exit(main(argv)) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 324, in new_func return func(*args, *kwargs) File "legacy/train.py", line 180, in main graph_hook_fn=graph_rewriter_fn) File "/opt/tensorflow/models/research/object_detection/legacy/trainer.py", line 416, in train saver=saver) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/contrib/slim/python/slim/learning.py", line 790, in train ignore_live_threads=ignore_live_threads) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/supervisor.py", line 839, in stop ignore_live_threads=ignore_live_threads) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/coordinator.py", line 389, in join six.reraise(self._exc_info_to_raise) File "/home/shilpa.j/.local/lib/python3.6/site-packages/six.py", line 693, in reraise raise value File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/queue_runner_impl.py", line 257, in _run enqueue_callable() File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1279, in _single_operation_run self._call_tf_sessionrun(None, {}, [], target_list, None) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1429, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.InvalidArgumentError: image_size must contain 3 elements[4] [[{{node cond_4/RandomCropImage/sample_distorted_bounding_box/SampleDistortedBoundingBoxV2}}]]

Iam gettng this error. Pls help me with this?

ektavyas commented 5 years ago

Once check your path directory and also check your configuration

On Tue, 25 Jun 2019, 3:14 pm Shilpa S J, notifications@github.com wrote:

shilpa.j@Theorem-TrainUP:/opt/tensorflow/models/research/object_detection/models$ cd .. shilpa.j@Theorem-TrainUP:/opt/tensorflow/models/research/object_detection$ python3 legacy/model_main.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config python3: can't open file 'legacy/model_main.py': [Errno 2] No such file or directory shilpa.j@Theorem-TrainUP:/opt/tensorflow/models/research/object_detection$ python3 legacy/train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config WARNING: Logging before flag parsing goes to stderr. W0625 15:10:52.232501 140181953247040 lazy_loader.py:50] The TensorFlow contrib module will not be included in TensorFlow 2.0. For more information, please see:

- https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md

https://github.com/tensorflow/addons

https://github.com/tensorflow/io (for I/O related ops) If you depend on functionality not listed there, please file an issue.

W0625 15:10:52.552797 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/slim/nets/inception_resnet_v2.py:373: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

W0625 15:10:52.636818 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/slim/nets/mobilenet/mobilenet.py:397: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.

W0625 15:10:52.715449 140181953247040 deprecation_wrapper.py:119] From legacy/train.py:55: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.

W0625 15:10:52.715638 140181953247040 deprecation_wrapper.py:119] From legacy/train.py:55: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.

W0625 15:10:52.716269 140181953247040 deprecation_wrapper.py:119] From legacy/train.py:184: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.

W0625 15:10:52.716854 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/absl/app.py:251: main (from main) is deprecated and will be removed in a future version. Instructions for updating: Use object_detection/model_main.py. W0625 15:10:52.717057 140181953247040 deprecation_wrapper.py:119] From legacy/train.py:90: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.

W0625 15:10:52.717436 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/utils/config_util.py:98: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.

W0625 15:10:52.747286 140181953247040 deprecation_wrapper.py:119] From legacy/train.py:95: The name tf.gfile.Copy is deprecated. Please use tf.io.gfile.copy instead.

W0625 15:10:52.857741 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:266: create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Please switch to tf.train.create_global_step W0625 15:10:52.863439 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/data_decoders/tf_example_decoder.py:177: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.

W0625 15:10:52.863688 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/data_decoders/tf_example_decoder.py:192: The name tf.VarLenFeature is deprecated. Please use tf.io.VarLenFeature instead.

W0625 15:10:52.879672 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/builders/dataset_builder.py:64: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead.

W0625 15:10:52.889734 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/builders/dataset_builder.py:71: The name tf.logging.warning is deprecated. Please use tf.compat.v1.logging.warning instead.

W0625 15:10:52.889871 140181953247040 dataset_builder.py:72] num_readers has been reduced to 1 to match input file shards. W0625 15:10:52.897384 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/builders/dataset_builder.py:86: parallel_interleave (from tensorflow.contrib.data.python.ops.interleave_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.data.experimental.parallel_interleave(...). W0625 15:10:52.897551 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/interleave_ops.py:77: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE) instead. If sloppy execution is desired, use tf.data.Options.experimental_determinstic. W0625 15:10:52.934068 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/builders/dataset_builder.py:155: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.data.Dataset.map() W0625 15:10:53.211105 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/builders/dataset_builder.py:43: DatasetV1.make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version. Instructions for updating: Use for ... in dataset:to iterate over a dataset. If usingtf.estimator, return the Datasetobject directly from your input function. As a last resort, you can use tf.compat.v1.data.make_initializable_iterator(dataset)`. W0625 15:10:53.218743 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/builders/dataset_builder.py:44: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

W0625 15:10:53.223891 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/core/preprocessor.py:512: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

W0625 15:10:53.291558 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/core/preprocessor.py:188: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: seed2 arg is deprecated.Use sample_distorted_bounding_box_v2 instead. W0625 15:10:53.306091 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/core/box_list_ops.py:201: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where W0625 15:10:54.308789 140181953247040 deprecation.py:323] From /opt/tensorflow/models/research/object_detection/core/batcher.py:96: batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.batch(batch_size) (or padded_batch(...) if dynamic_pad=True). W0625 15:10:54.313415 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/input.py:753: QueueRunner.init (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the tf.data module. W0625 15:10:54.314663 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/input.py:753: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the tf.data module. W0625 15:10:54.325667 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/core/prefetcher.py:58: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.

W0625 15:10:55.320201 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/core/preprocessor.py:2515: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead.

W0625 15:10:58.859347 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/predictors/convolutional_box_predictor.py:150: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.

I0625 15:10:58.859581 140181953247040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0 I0625 15:10:58.904655 140181953247040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0 I0625 15:10:58.948891 140181953247040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0 I0625 15:10:58.993812 140181953247040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0 I0625 15:10:59.038319 140181953247040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0 I0625 15:10:59.082738 140181953247040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0 W0625 15:11:04.966106 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/core/losses.py:172: The name tf.losses.huber_loss is deprecated. Please use tf.compat.v1.losses.huber_loss instead.

W0625 15:11:04.967823 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/core/losses.py:178: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.

W0625 15:11:05.765034 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:208: The name tf.losses.add_loss is deprecated. Please use tf.compat.v1.losses.add_loss instead.

W0625 15:11:05.765706 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/builders/optimizer_builder.py:95: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.

W0625 15:11:05.765954 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/utils/learning_schedules.py:61: The name tf.train.exponential_decay is deprecated. Please use tf.compat.v1.train.exponential_decay instead.

W0625 15:11:05.778445 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/builders/optimizer_builder.py:47: The name tf.train.RMSPropOptimizer is deprecated. Please use tf.compat.v1.train.RMSPropOptimizer instead.

W0625 15:11:08.371582 140181953247040 deprecation.py:506] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/rmsprop.py:119: calling Ones.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor W0625 15:11:09.939157 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/moving_averages.py:433: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. W0625 15:11:13.526557 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:353: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.

W0625 15:11:13.790925 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:355: The name tf.losses.get_losses is deprecated. Please use tf.compat.v1.losses.get_losses instead.

W0625 15:11:13.793818 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:359: The name tf.losses.get_total_loss is deprecated. Please use tf.compat.v1.losses.get_total_loss instead.

W0625 15:11:13.797688 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:368: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.

W0625 15:11:13.804011 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/legacy/trainer.py:376: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.

W0625 15:11:14.411106 140181953247040 deprecation_wrapper.py:119] From /opt/tensorflow/models/research/object_detection/utils/variables_helper.py:134: The name tf.train.NewCheckpointReader is deprecated. Please use tf.compat.v1.train.NewCheckpointReader instead.

W0625 15:11:14.445239 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[273]], model variable shape: [[6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.445385 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/biases/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[273]], model variable shape: [[6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.445502 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/biases/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[273]], model variable shape: [[6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.445610 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/biases/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[273]], model variable shape: [[6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.445716 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 273]], model variable shape: [[1, 1, 512, 6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.445822 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/weights/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 273]], model variable shape: [[1, 1, 512, 6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.445926 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/weights/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 273]], model variable shape: [[1, 1, 512, 6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446029 140181953247040 variables_helper.py:149] Variable [BoxPredictor_0/ClassPredictor/weights/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 273]], model variable shape: [[1, 1, 512, 6]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446156 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446256 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/biases/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446356 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/biases/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446455 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/biases/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446555 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 1024, 546]], model variable shape: [[1, 1, 1024, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446657 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/weights/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 1024, 546]], model variable shape: [[1, 1, 1024, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446759 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/weights/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 1024, 546]], model variable shape: [[1, 1, 1024, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446861 140181953247040 variables_helper.py:149] Variable [BoxPredictor_1/ClassPredictor/weights/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 1024, 546]], model variable shape: [[1, 1, 1024, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.446988 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447091 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/biases/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447191 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/biases/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447290 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/biases/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447390 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 546]], model variable shape: [[1, 1, 512, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447493 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/weights/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 546]], model variable shape: [[1, 1, 512, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447593 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/weights/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 546]], model variable shape: [[1, 1, 512, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447695 140181953247040 variables_helper.py:149] Variable [BoxPredictor_2/ClassPredictor/weights/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 546]], model variable shape: [[1, 1, 512, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447818 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.447919 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/biases/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448017 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/biases/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448115 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/biases/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448216 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448317 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/weights/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448436 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/weights/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448541 140181953247040 variables_helper.py:149] Variable [BoxPredictor_3/ClassPredictor/weights/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448666 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448766 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/biases/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448865 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/biases/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.448965 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/biases/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449065 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449166 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/weights/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449269 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/weights/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449370 140181953247040 variables_helper.py:149] Variable [BoxPredictor_4/ClassPredictor/weights/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449496 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449597 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/biases/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449696 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/biases/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449794 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/biases/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449894 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 546]], model variable shape: [[1, 1, 128, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.449995 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/weights/ExponentialMovingAverage] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 546]], model variable shape: [[1, 1, 128, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.450096 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/weights/RMSProp] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 546]], model variable shape: [[1, 1, 128, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.450197 140181953247040 variables_helper.py:149] Variable [BoxPredictor_5/ClassPredictor/weights/RMSProp_1] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 546]], model variable shape: [[1, 1, 128, 12]]. This variable will not be initialized from the checkpoint. W0625 15:11:14.451475 140181953247040 variables_helper.py:152] Variable [global_step] is not available in checkpoint W0625 15:11:15.556331 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/contrib/slim/python/slim/learning.py:742: Supervisor.init (from tensorflow.python.training.supervisor) is deprecated and will be removed in a future version. Instructions for updating: Please switch to tf.train.MonitoredTrainingSession 2019-06-25 15:11:17.573499: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2019-06-25 15:11:17.744825: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2299995000 Hz 2019-06-25 15:11:17.745496: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xcb5cb30 executing computations on platform Host. Devices: 2019-06-25 15:11:17.745535: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): , W0625 15:11:17.757068 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix. I0625 15:11:17.758359 140181953247040 saver.py:1280] Restoring parameters from training/model.ckpt-1 2019-06-25 15:11:19.576775: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile. W0625 15:11:19.944330 140181953247040 deprecation.py:323] From /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/saver.py:1066: get_checkpoint_mtimes (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file utilities to get mtimes. I0625 15:11:19.946665 140181953247040 session_manager.py:500] Running local_init_op. I0625 15:11:20.577036 140181953247040 session_manager.py:502] Done running local_init_op. I0625 15:11:30.679445 140181953247040 learning.py:754] Starting Session. I0625 15:11:31.052094 140178983208704 supervisor.py:1117] Saving checkpoint to path training/model.ckpt I0625 15:11:31.070347 140181953247040 learning.py:768] Starting Queues. I0625 15:11:38.079804 140178379228928 coordinator.py:224] Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.InvalidArgumentError'>, image_size must contain 3 elements[4] [[{{node cond_4/RandomCropImage/sample_distorted_bounding_box/SampleDistortedBoundingBoxV2}}]] I0625 15:11:39.040256 140178974816000 supervisor.py:1099] global_step/sec: 0 I0625 15:11:48.074288 140181953247040 learning.py:783] Caught OutOfRangeError. Stopping Training. FIFOQueue '_6_prefetch_queue' is closed and has insufficient elements (requested 1, current size 0) [[node prefetch_queue_Dequeue (defined at /opt/tensorflow/models/research/object_detection/core/batcher.py:111) ]]

Errors may have originated from an input operation. Input Source operations connected to node prefetch_queue_Dequeue: prefetch_queue (defined at /opt/tensorflow/models/research/object_detection/core/prefetcher.py:54)

Original stack trace for 'prefetch_queue_Dequeue': File "legacy/train.py", line 184, in tf.app.run() File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 40, in run _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef) File "/home/shilpa.j/.local/lib/python3.6/site-packages/absl/app.py", line 300, in run _run_main(main, args) File "/home/shilpa.j/.local/lib/python3.6/site-packages/absl/app.py", line 251, in _run_main sys.exit(main(argv)) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 324, in new_func return func(*args, kwargs) File "legacy/train.py", line 180, in main graph_hook_fn=graph_rewriter_fn) File "/opt/tensorflow/models/research/object_detection/legacy/trainer.py", line 291, in train clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue]) File "/opt/tensorflow/models/research/slim/deployment/model_deploy.py", line 193, in create_clones outputs = model_fn(*args, *kwargs) File "/opt/tensorflow/models/research/object_detection/legacy/trainer.py", line 181, in _create_losses train_config.use_multiclass_scores) File "/opt/tensorflow/models/research/object_detection/legacy/trainer.py", line 124, in get_inputs read_data_list = input_queue.dequeue() File "/opt/tensorflow/models/research/object_detection/core/batcher.py", line 111, in dequeue batched_tensors = self._queue.dequeue() File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/ops/data_flow_ops.py", line 445, in dequeue self._queue_ref, self._dtypes, name=name) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 4140, in queue_dequeue_v2 timeout_ms=timeout_ms, name=name) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper op_def=op_def) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func return func(args, kwargs) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3616, in create_op op_def=op_def) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2005, in init self._traceback = tf_stack.extract_stack()

I0625 15:11:48.075780 140181953247040 learning.py:785] Finished training! Saving model to disk. /home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/summary/writer/writer.py:386: UserWarning: Attempting to use a closed FileWriter. The operation will be a noop unless the FileWriter is explicitly reopened. warnings.warn("Attempting to use a closed FileWriter. " Traceback (most recent call last): File "legacy/train.py", line 184, in tf.app.run() File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 40, in run _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef) File "/home/shilpa.j/.local/lib/python3.6/site-packages/absl/app.py", line 300, in run _run_main(main, args) File "/home/shilpa.j/.local/lib/python3.6/site-packages/absl/app.py", line 251, in _run_main sys.exit(main(argv)) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 324, in new_func return func(*args, *kwargs) File "legacy/train.py", line 180, in main graph_hook_fn=graph_rewriter_fn) File "/opt/tensorflow/models/research/object_detection/legacy/trainer.py", line 416, in train saver=saver) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/contrib/slim/python/slim/learning.py", line 790, in train ignore_live_threads=ignore_live_threads) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/supervisor.py", line 839, in stop ignore_live_threads=ignore_live_threads) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/coordinator.py", line 389, in join six.reraise(self._exc_info_to_raise) File "/home/shilpa.j/.local/lib/python3.6/site-packages/six.py", line 693, in reraise raise value File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/training/queue_runner_impl.py", line 257, in _run enqueue_callable() File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1279, in _single_operation_run self._call_tf_sessionrun(None, {}, [], target_list, None) File "/home/shilpa.j/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1429, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.InvalidArgumentError: image_size must contain 3 elements[4] [[{{node cond_4/RandomCropImage/sample_distorted_bounding_box/SampleDistortedBoundingBoxV2}}]]

Iam gettng this error. Pls help me with this?

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rahulkathet commented 4 years ago

I am getting this error can someone help me with this AttributeError: module 'tensorflow' has no attribute 'contrib'

aligokkaya commented 4 years ago

(tensorflow1) C:\tensorflow1\models\research\object_detection>python train.py -- logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_in ception_v2_pets.config 2020-02-14 23:19:38.936624: W tensorflow/stream_executor/platform/default/dso_lo ader.cc:55] Could not load dynamic library 'cudart64_100.dll'; dlerror: cudart64 _100.dll not found 2020-02-14 23:19:38.936624: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. WARNING:tensorflow: The TensorFlow contrib module will not be included in TensorFlow 2.0. For more information, please see:

https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-su nset.md
https://github.com/tensorflow/addons
https://github.com/tensorflow/io (for I/O related ops) If you depend on functionality not listed there, please file an issue.

WARNING:tensorflow:From train.py:56: The name tf.logging.set_verbosity is deprec ated. Please use tf.compat.v1.logging.set_verbosity instead.

WARNING:tensorflow:From train.py:56: The name tf.logging.INFO is deprecated. Ple ase use tf.compat.v1.logging.INFO instead.

WARNING:tensorflow:From train.py:185: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.

WARNING:tensorflow:From C:\Users\iste-pc\Anaconda3\envs\tensorflow1\lib\site-pac kages\absl\app.py:250: main (from main) is deprecated and will be removed in a future version. Instructions for updating: Use object_detection/model_main.py. W0214 23:19:42.239813 5832 deprecation.py:323] From C:\Users\iste-pc\Anaconda3\ envs\tensorflow1\lib\site-packages\absl\app.py:250: main (from main) is depr ecated and will be removed in a future version. Instructions for updating: Use object_detection/model_main.py. WARNING:tensorflow:From train.py:91: The name tf.gfile.MakeDirs is deprecated. P lease use tf.io.gfile.makedirs instead.

W0214 23:19:42.240813 5832 module_wrapper.py:139] From train.py:91: The name tf .gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.

WARNING:tensorflow:From C:\tensorflow1\models\research\object_detection\utils\co nfig_util.py:102: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile. GFile instead.

W0214 23:19:42.241813 5832 module_wrapper.py:139] From C:\tensorflow1\models\re search\object_detection\utils\config_util.py:102: The name tf.gfile.GFile is dep recated. Please use tf.io.gfile.GFile instead.

Fatal Python error: Segmentation fault

Current thread 0x000016c8 (most recent call first): File "C:\Users\iste-pc\Anaconda3\envs\tensorflow1\lib\site-packages\tensorflow _core\python\lib\io\file_io.py", line 84 in _preread_check File "C:\Users\iste-pc\Anaconda3\envs\tensorflow1\lib\site-packages\tensorflow _core\python\lib\io\file_io.py", line 122 in read File "C:\tensorflow1\models\research\object_detection\utils\config_util.py", l ine 103 in get_configs_from_pipeline_file File "train.py", line 94 in main File "C:\Users\iste-pc\Anaconda3\envs\tensorflow1\lib\site-packages\tensorflow _core\python\util\deprecation.py", line 324 in new_func File "C:\Users\iste-pc\Anaconda3\envs\tensorflow1\lib\site-packages\absl\app.p y", line 250 in _run_main File "C:\Users\iste-pc\Anaconda3\envs\tensorflow1\lib\site-packages\absl\app.p y", line 299 in run File "C:\Users\iste-pc\Anaconda3\envs\tensorflow1\lib\site-packages\tensorflow _core\python\platform\app.py", line 40 in run File "train.py", line 185 in

aligokkaya commented 4 years ago

I'm getting an error, can you help me?

tanvirahmd1 commented 4 years ago

hello sorry to get back to you late. please try to replace the train file. i.e copy train file from the orgnal model to you object detection folder, it will help. i hope this will solve your problem

2020-02-15 05:23:16"ali gökkaya" notifications@github.comwrote:

I'm getting an error, can you help me?

— You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe.

AbdullahBahi commented 4 years ago

@aligokkaya I have the same problem as you, did you solve this issue?

aligokkaya commented 4 years ago

no i couldn't solve it yet

8143) you can take a look here

dhanpalrajpurohit commented 4 years ago

run the following command for python3 python3 research/object_detection/legacy/train.py --logtostderr --train_dir=train --pipeline_config_path=ssd_mobilenet_v2_coco.config

ShilpaJagadeeshappa commented 4 years ago

I cant run Training, maybe someone can help me please

`(sdk) D:\Lesson8\models\research\object_detection>python legacy/train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config

WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0. 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.

Traceback (most recent call last): File "legacy/train.py", line 51, in from object_detection.builders import model_builder File "D:\Lesson8\models\research\object_detection\builders\model_builder.py", line 27, in from object_detection.builders import post_processing_builder File "D:\Lesson8\models\research\object_detection\builders\post_processing_builder.py", line 22, in from object_detection.protos import post_processing_pb2 File "D:\Lesson8\models\research\object_detection\protos\post_processing_pb2.py", line 16, in from object_detection.protos import calibration_pb2 as objectdetection_dot_protos_dot_calibrationpb2 ImportError: cannot import name 'calibration_pb2'`

Please refer this link to see all folder structures and .pb files generation in the step of converting csv to tf records in tensorflow. Link-https://github.com/ShilpaJagadeeshappa/TensorflowAPI #102

ShilpaJagadeeshappa commented 4 years ago

run the following command for python3 python3 research/object_detection/legacy/train.py --logtostderr --train_dir=train --pipeline_config_path=ssd_mobilenet_v2_coco.config

Please refer this to start up your training- https://github.com/ShilpaJagadeeshappa/TensorflowAPI Initially I faced the same problem, so I kept the folder structure as per the link mentioned. Keep your train.py file as per the path : python3 research/object_detection/train.py and convert all.proto files to .pb file and run it let me know the scenario.Hope this could solve your problem.

ISS88 commented 4 years ago

hello ektavyas i saw above that you had an error similar to mine, and apparently it was a typo. Can you tell me how you went about solving it

File "train.py", line 180, in main graph_hook_fn=graph_rewriter_fn)

rethamasae commented 4 years ago

I can't run the Training, maybe someone can help me (tensorflow1) C:\tensorflow1\models\research\object_detection>python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config 2020-08-10 22:08:00.754622: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll Traceback (most recent call last): File "train.py", line 53, in from object_detection.builders import model_builder File "C:\Users\ASUS\anaconda3\envs\tensorflow1\lib\site-packages\object_detection-0.1-py3.8.egg\object_detection\builders\model_builder.py", line 39, in from object_detection.protos import model_pb2 File "C:\Users\ASUS\anaconda3\envs\tensorflow1\lib\site-packages\object_detection-0.1-py3.8.egg\object_detection\protos\model_pb2.py", line 14, in from object_detection.protos import center_net_pb2 as objectdetection_dot_protos_dot_centernet__pb2 ImportError: cannot import name 'center_net_pb2' from 'object_detection.protos' (C:\Users\ASUS\anaconda3\envs\tensorflow1\lib\site-packages\object_detection-0.1-py3.8.egg\object_detection\protos__init__.py)

seppemarotta commented 3 years ago

@rethamasae I have the same issue, can't manage to solve it. Did you find any solution?

rahulkathet commented 3 years ago

@rethamasae I have the same issue, can't manage to solve it. Did you find any solution?

@rethamasae I found it is because of version issue. Just check you are not using the older version. The tf.contrib is now moved out of tf. You can upgrade your code to 2.0 or 2.3.0 using the tf_upgrade_v2 script https://www.tensorflow.org/alpha/guide/upgrade

Jayanth1812 commented 3 years ago

I have upgraded but still I am get the error. Traceback (most recent call last): File "train.py", line 54, in from object_detection.builders import model_builder File "C:\Users\Jayanth\anaconda3\envs\tensorflow\lib\site-packages\object_detection-0.1-py3.6.egg\object_detection\builders\model_builder.py", line 66, in from object_detection.models import ssd_efficientnet_bifpn_feature_extractor as ssd_efficientnet_bifpn File "C:\Users\Jayanth\anaconda3\envs\tensorflow\lib\site-packages\object_detection-0.1-py3.6.egg\object_detection\models\ssd_efficientnet_bifpn_feature_extractor.py", line 33, in from official.vision.image_classification.efficientnet import efficientnet_model File "C:\Users\Jayanth\anaconda3\envs\tensorflow\lib\site-packages\official\vision\image_classification\efficientnet\efficientnet_model.py", line 35, in from official.modeling import tf_utils File "C:\Users\Jayanth\anaconda3\envs\tensorflow\lib\site-packages\official\modeling\tf_utils.py", line 25, in from official.modeling import activations File "C:\Users\Jayanth\anaconda3\envs\tensorflow\lib\site-packages\official\modeling\activationsinit.py", line 16, in from official.modeling.activations.gelu import gelu File "C:\Users\Jayanth\anaconda3\envs\tensorflow\lib\site-packages\official\modeling\activations\gelu.py", line 26, in @tf.keras.utils.register_keras_serializable(package='Text') AttributeError: module 'tensorflow_core.keras.utils' has no attribute 'register_keras_serializable

Jayanth1812 commented 3 years ago

(tensorflow) C:\tensorflow\models\research\object_detection>python Python 3.6.12 |Anaconda, Inc.| (default, Sep 9 2020, 00:29:25) [MSC v.1916 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information.

import keras Using TensorFlow backend. import tensorflow as tf exit()

(tensorflow) C:\tensorflow\models\research\object_detection>tf_upgrade_v2 usage: tf_upgrade_v2 [-h] [--infile INPUT_FILE] [--outfile OUTPUT_FILE] [--intree INPUT_TREE] [--outtree OUTPUT_TREE] [--copyotherfiles COPY_OTHER_FILES] [--inplace] [--reportfile REPORT_FILENAME] [--mode {DEFAULT,SAFETY}] [--print_all]

Convert a TensorFlow Python file from 1.x to 2.0

Simple usage: tf_upgrade_v2.py --infile foo.py --outfile bar.py tf_upgrade_v2.py --infile foo.ipynb --outfile bar.ipynb tf_upgrade_v2.py --intree ~/code/old --outtree ~/code/new

optional arguments: -h, --help show this help message and exit --infile INPUT_FILE If converting a single file, the name of the file to convert --outfile OUTPUT_FILE If converting a single file, the output filename. --intree INPUT_TREE If converting a whole tree of files, the directory to read from (relative or absolute). --outtree OUTPUT_TREE If converting a whole tree of files, the output directory (relative or absolute). --copyotherfiles COPY_OTHER_FILES If converting a whole tree of files, whether to copy the other files. --inplace If converting a set of files, whether to allow the conversion to be performed on the input files. --reportfile REPORT_FILENAME The name of the file where the report log is stored.(default: report.txt) --mode {DEFAULT,SAFETY} Upgrade script mode. Supported modes: DEFAULT: Perform only straightforward conversions to upgrade to 2.0. In more difficult cases, switch to use compat.v1. SAFETY: Keep 1.* code intact and import compat.v1 module. Also disable 2.0 behavior to ensure code that requires 1.X behavior continues to work. --print_all Print full log to stdout instead of just printing errors

EdjeElectronics / TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10

Train.py not found #102

I cant run Training, maybe someone can help me please

model_main cant run

8143 (https://github.com/tensorflow/models/issues/8143) you can take a look here

I cant run Training, maybe someone can help me please