Train.py not found

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

I have the same issue

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

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

thanks RafaRuiz/fdkssdki

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

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_detectionaster_rcnn_inception_v2_coco_2018_01_28 : The filename, directory name, or volume label syntax is incorrect.flow1/modelsnsorflow1\models
; Unknown error

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

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

@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

Plz advice for the above

`(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 object__detection_dot_protos_dot_calibration__pb2
ImportError: cannot import name 'calibration_pb2'`

@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
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!

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'

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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 dataset_builder.py:72] num_readers has been reduced to 1 to match input file shards.
W0625 15:10:52.897384<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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 usetf.compat.v1.data.make_initializable_iterator(dataset)`.
W0625 15:10:53.218743<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0
I0625 15:10:58.904655<PHONE_NUMBER>47040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0
I0625 15:10:58.948891<PHONE_NUMBER>47040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0
I0625 15:10:58.993812<PHONE_NUMBER>47040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0
I0625 15:10:59.038319<PHONE_NUMBER>47040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0
I0625 15:10:59.082738<PHONE_NUMBER>47040 convolutional_box_predictor.py:151] depth of additional conv before box predictor: 0
W0625 15:11:04.966106<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 variables_helper.py:152] Variable [global_step] is not available in checkpoint
W0625 15:11:15.556331<PHONE_NUMBER>47040 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:<PHONE_NUMBER> 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 session_manager.py:500] Running local_init_op.
I0625 15:11:20.577036<PHONE_NUMBER>47040 session_manager.py:502] Done running local_init_op.
I0625 15:11:30.679445<PHONE_NUMBER>47040 learning.py:754] Starting Session.
I0625 15:11:31.052094<PHONE_NUMBER>08704 supervisor.py:1117] Saving checkpoint to path training/model.ckpt
I0625 15:11:31.070347<PHONE_NUMBER>47040 learning.py:768] Starting Queues.
I0625 15:11:38.079804<PHONE_NUMBER>28928 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<PHONE_NUMBER>16000 supervisor.py:1099] global_step/sec: 0
I0625 15:11:48.074288<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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?

Once check your path directory and also check your configuration
On Tue, 25 Jun 2019, 3:14 pm Shilpa S J<EMAIL_ADDRESS>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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 dataset_builder.py:72] num_readers
has been reduced to 1 to match input file shards.
W0625 15:10:52.897384<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040
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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 convolutional_box_predictor.py:151]
depth of additional conv before box predictor: 0
I0625 15:10:58.904655<PHONE_NUMBER>47040 convolutional_box_predictor.py:151]
depth of additional conv before box predictor: 0
I0625 15:10:58.948891<PHONE_NUMBER>47040 convolutional_box_predictor.py:151]
depth of additional conv before box predictor: 0
I0625 15:10:58.993812<PHONE_NUMBER>47040 convolutional_box_predictor.py:151]
depth of additional conv before box predictor: 0
I0625 15:10:59.038319<PHONE_NUMBER>47040 convolutional_box_predictor.py:151]
depth of additional conv before box predictor: 0
I0625 15:10:59.082738<PHONE_NUMBER>47040 convolutional_box_predictor.py:151]
depth of additional conv before box predictor: 0
W0625 15:11:04.966106<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 variables_helper.py:152] Variable
[global_step] is not available in checkpoint
W0625 15:11:15.556331<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 session_manager.py:500] Running
local_init_op.
I0625 15:11:20.577036<PHONE_NUMBER>47040 session_manager.py:502] Done running
local_init_op.
I0625 15:11:30.679445<PHONE_NUMBER>47040 learning.py:754] Starting Session.
I0625 15:11:31.052094<PHONE_NUMBER>08704 supervisor.py:1117] Saving
checkpoint to path training/model.ckpt
I0625 15:11:31.070347<PHONE_NUMBER>47040 learning.py:768] Starting Queues.
I0625 15:11:38.079804<PHONE_NUMBER>28928 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<PHONE_NUMBER>16000 supervisor.py:1099] global_step/sec:
0
I0625 15:11:48.074288<PHONE_NUMBER>47040 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<PHONE_NUMBER>47040 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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I am getting this error can someone help me with this
AttributeError: module 'tensorflow' has no attribute 'contrib'

(tensorflow1) C:\tensorflow1\models\research\object_detection>python legacy/trai
n.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_
rcnn_inception_v2_pets.config
2020-02-14 23:13:15.014676: 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 23:13:15.015676: 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 legacy/train.py:14: The name tf.logging.set_verbosity is
deprecated. Please use tf.compat.v1.logging.set_verbosity instead.
WARNING:tensorflow:From legacy/train.py:14: The name tf.logging.INFO is deprecat
ed. Please use tf.compat.v1.logging.INFO instead.
WARNING:tensorflow:From legacy/train.py:143: 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 23:13:18.257861   904 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 legacy/train.py:49: The name tf.gfile.MakeDirs is deprec
ated. Please use tf.io.gfile.makedirs instead.
W0214 23:13:18.258862   904 module_wrapper.py:139] From legacy/train.py:49: 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 23:13:18.259861   904 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 0x00000388 (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 "legacy/train.py", line 52 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 "legacy/train.py", line 143 in
(tensorflow1) C:\tensorflow1\models\research\object_detection>

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

(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

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

Yes, i can. I am listening to you.
15 Şub 2020 Cmt, saat 00:23 tarihinde ali gökkaya<EMAIL_ADDRESS>şunu yazdı:

I'm getting an error, can you help me?
—
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--
Mehmet Bütün
Math Teacher (Bolu Science and Art Center)
PhD Student (Istanbul University, Department Of Informatics)

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"<EMAIL_ADDRESS>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.

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

Sorry I couldn't solve it yet
#8143 you can also check here

no i couldn't solve it yet
#8143 (https://github.com/tensorflow/models/issues/8143)  you can take a look here

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

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 object__detection_dot_protos_dot_calibration__pb2
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

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.

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)

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 object__detection_dot_protos_dot_center__net__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)

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

@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

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\activations_init_.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

(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