tensorflow / tensorflow/models
google.protobuf.text_format.ParseError: 33:7: Message type "object_detection.protos.SsdFeatureExtractor" has no field named "bifpn"
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models:research:odapi
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Description
I was trying to train an efficientdet_d0 object detection model, but i got this error:
2020-10-11 11:45:11.217063: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-10-11 11:45:11.218063: 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.
C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\numpy\_distributor_init.py:32: UserWarning: loaded more than 1 DLL from .libs:
C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\numpy\.libs\libopenblas.NOIJJG62EMASZI6NYURL6JBKM4EVBGM7.gfortran-win_amd64.dll
C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\numpy\.libs\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dll
stacklevel=1)
2020-10-11 11:45:14.750265: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2020-10-11 11:45:14.751265: W tensorflow/stream_executor/cuda/cuda_driver.cc:312] failed call to cuInit: UNKNOWN ERROR (303)
2020-10-11 11:45:14.754265: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: AMMAR
2020-10-11 11:45:14.754265: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: AMMAR
2020-10-11 11:45:14.816269: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x17892160 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-10-11 11:45:14.817269: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
WARNING:tensorflow:There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
W1011 11:45:14.837270 16416 cross_device_ops.py:1202] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)
I1011 11:45:14.837270 16416 mirrored_strategy.py:341] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)
Traceback (most recent call last):
File "model_main_tf2.py", line 113, in <module>
tf.compat.v1.app.run()
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\tensorflow\python\platform\app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\absl\app.py", line 299, in run
_run_main(main, args)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\absl\app.py", line 250, in _run_main
sys.exit(main(argv))
File "model_main_tf2.py", line 110, in main
record_summaries=FLAGS.record_summaries)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\object_detection\model_lib_v2.py", line 443, in train_loop
pipeline_config_path, config_override=config_override)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\object_detection\utils\config_util.py", line 139, in get_configs_from_pipeline_file
text_format.Merge(proto_str, pipeline_config)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\google\protobuf\text_format.py", line 734, in Merge
allow_unknown_field=allow_unknown_field)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\google\protobuf\text_format.py", line 802, in MergeLines
return parser.MergeLines(lines, message)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\google\protobuf\text_format.py", line 827, in MergeLines
self._ParseOrMerge(lines, message)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\google\protobuf\text_format.py", line 849, in _ParseOrMerge
self._MergeField(tokenizer, message)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\google\protobuf\text_format.py", line 974, in _MergeField
merger(tokenizer, message, field)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\google\protobuf\text_format.py", line 1048, in _MergeMessageField
self._MergeField(tokenizer, sub_message)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\google\protobuf\text_format.py", line 974, in _MergeField
merger(tokenizer, message, field)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\google\protobuf\text_format.py", line 1048, in _MergeMessageField
self._MergeField(tokenizer, sub_message)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\google\protobuf\text_format.py", line 974, in _MergeField
merger(tokenizer, message, field)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\google\protobuf\text_format.py", line 1048, in _MergeMessageField
self._MergeField(tokenizer, sub_message)
File "C:\Users\user\Anaconda3\envs\tensorflow_obj\lib\site-packages\google\protobuf\text_format.py", line 941, in _MergeField
(message_descriptor.full_name, name))
google.protobuf.text_format.ParseError: 33:7 : Message type "object_detection.protos.SsdFeatureExtractor" has no field named "bifpn".
I tried to pull the newest version of the tensorflow object detection API and reinstall google protobuf, but the issue still persists.
My directory structure:
Folder PATH listing
Volume serial number is E4EF-40D3
C:.
+---models
| +---.github
| | \---ISSUE_TEMPLATE
| +---community
| +---official
| | +---colab
| | | \---nlp
| | +---common
| | +---core
| | +---modeling
| | | +---activations
| | | +---hyperparams
| | | +---optimization
| | | | \---configs
| | | \---training
| | +---nlp
| | | +---albert
| | | +---bert
| | | +---configs
| | | +---data
| | | +---keras_nlp
| | | | +---encoders
| | | | \---layers
| | | +---modeling
| | | | +---layers
| | | | +---losses
| | | | +---models
| | | | +---networks
| | | | \---ops
| | | +---nhnet
| | | | \---testdata
| | | | \---crawled_articles
| | | | +---domain_0.com
| | | | \---domain_1.com
| | | +---projects
| | | | +---bigbird
| | | | \---triviaqa
| | | +---tasks
| | | +---transformer
| | | | \---utils
| | | \---xlnet
| | +---pip_package
| | +---recommendation
| | +---staging
| | | \---training
| | +---utils
| | | +---flags
| | | +---misc
| | | \---testing
| | | \---scripts
| | \---vision
| | +---beta
| | | +---configs
| | | | \---experiments
| | | | +---image_classification
| | | | \---retinanet
| | | +---dataloaders
| | | +---evaluation
| | | +---losses
| | | +---modeling
| | | | +---backbones
| | | | +---decoders
| | | | +---heads
| | | | \---layers
| | | +---ops
| | | +---serving
| | | \---tasks
| | +---detection
| | | +---configs
| | | +---dataloader
| | | +---evaluation
| | | +---executor
| | | +---modeling
| | | | \---architecture
| | | +---ops
| | | \---utils
| | | \---object_detection
| | +---image_classification
| | | +---configs
| | | | \---examples
| | | | +---efficientnet
| | | | | \---imagenet
| | | | \---resnet
| | | | \---imagenet
| | | +---efficientnet
| | | \---resnet
| | \---keras_cv
| | +---layers
| | +---losses
| | \---ops
| +---orbit
| | \---utils
| \---research
| +---a3c_blogpost
| +---adversarial_text
| | \---data
| +---attention_ocr
| | \---python
| | +---datasets
| | | \---testdata
| | | \---fsns
| | \---testdata
| +---audioset
| | +---vggish
| | \---yamnet
| +---autoaugment
| +---cognitive_planning
| | +---envs
| | | \---configs
| | \---preprocessing
| +---cvt_text
| | +---base
| | +---corpus_processing
| | +---model
| | +---task_specific
| | | \---word_level
| | \---training
| +---deeplab
| | +---core
| | +---datasets
| | +---deprecated
| | +---evaluation
| | | +---g3doc
| | | | \---img
| | | \---testdata
| | | +---coco_gt
| | | \---coco_pred
| | +---g3doc
| | | \---img
| | +---testing
| | | \---pascal_voc_seg
| | \---utils
| +---deep_speech
| | \---data
| +---delf
| | \---delf
| | +---protos
| | \---python
| | +---delg
| | +---detect_to_retrieve
| | +---examples
| | +---google_landmarks_dataset
| | \---training
| | +---datasets
| | \---model
| +---efficient-hrl
| | +---agents
| | +---configs
| | +---context
| | | \---configs
| | +---environments
| | | \---assets
| | +---scripts
| | \---utils
| +---lfads
| | \---synth_data
| | \---trained_itb
| +---lstm_object_detection
| | +---builders
| | +---configs
| | +---g3doc
| | +---inputs
| | +---lstm
| | +---meta_architectures
| | +---metrics
| | +---models
| | +---protos
| | +---tflite
| | | +---protos
| | | \---utils
| | \---utils
| +---marco
| +---nst_blogpost
| +---object_detection
| | +---anchor_generators
| | +---box_coders
| | +---builders
| | +---colab_tutorials
| | +---configs
| | | \---tf2
| | +---core
| | +---data
| | +---dataset_tools
| | | +---context_rcnn
| | | \---densepose
| | +---data_decoders
| | +---dockerfiles
| | | +---android
| | | +---tf1
| | | \---tf2
| | +---g3doc
| | | \---img
| | +---inference
| | +---legacy
| | +---matchers
| | +---meta_architectures
| | +---metrics
| | +---models
| | | \---keras_models
| | | \---base_models
| | +---packages
| | | +---tf1
| | | \---tf2
| | +---predictors
| | | \---heads
| | +---protos
| | +---samples
| | | +---cloud
| | | \---configs
| | +---test_data
| | +---test_images
| | | +---ducky
| | | | +---test
| | | | \---train
| | | \---snapshot_serengeti
| | +---tpu_exporters
| | | \---testdata
| | | +---faster_rcnn
| | | \---ssd
| | \---utils
| +---pcl_rl
| +---rebar
| +---sequence_projection
| | +---demo
| | +---prado
| | +---sgnn
| | +---tflite_ops
| | +---tf_ops
| | \---third_party
| | +---android
| | +---flatbuffers
| | +---py
| | \---python_runtime
| +---slim
| | +---datasets
| | +---deployment
| | +---nets
| | | +---mobilenet
| | | | \---g3doc
| | | \---nasnet
| | +---preprocessing
| | \---scripts
| \---vid2depth
| +---dataset
| | \---kitti
| +---ops
| | \---testdata
| \---third_party
+---scripts
| \---preprocessing
\---workspace
\---pengpol_face_recognition
+---annotations
+---exported-models
+---images
| +---test
| \---train
+---models
| \---efficientdet_d0
\---pre-trained-models
\---efficientdet_d0_coco17_tpu-32
+---checkpoint
\---saved_model
+---assets
\---variables
I use Windows 7.
I install TensorFlow from pip. (version 2.3.0).
Bazel version: N/A
CUDA/cuDNN version: not installed
GPU model and memory: I don't use GPU.
exact command to reproduce:
python model_main_tf2.py --model_dir=models/efficientdet_d0 --pipeline_config_path=models/efficientdet_d0/pipeline.config
from workspace/pengpol_face_recognition/
THanks!!
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