Tensorflow object detection "Use fn_output_signature instead" warning

Question

I am following this tutorial to train my own models. https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/

I followed all the steps exactly as described in this tutorial but when I start training it is throwing some warnings and got stuck at a warning. I have no GPU so I skipped the "GPU Support (Optional)" section.

(venv) C:\Users\femik\Desktop\productDetection\TensorFlow\workspace\training_demo>python model_main_tf2.py --model_dir=models/my_ssd_resnet50_v1_fpn --pipeline_config_path=models/my_ssd_resnet50_v1_fpn/pipeline.config
2021-11-06 07:58:33.841767: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll
2021-11-06 07:58:41.135367: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2021-11-06 07:58:41.135532: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)
2021-11-06 07:58:41.138487: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-Q5DQGU9
2021-11-06 07:58:41.138708: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-Q5DQGU9
2021-11-06 07:58:41.139431: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operati
ons:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
WARNING:tensorflow:There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
W1106 07:58:41.130341  4076 cross_device_ops.py:1387] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
WARNING:tensorflow:Collective ops is not configured at program startup. Some performance features may not be enabled.
W1106 07:58:41.130341  4076 mirrored_strategy.py:379] Collective ops is not configured at program startup. Some performance features may not be enabled.
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)
I1106 07:58:41.130341  4076 mirrored_strategy.py:369] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)
INFO:tensorflow:Maybe overwriting train_steps: None
I1106 07:58:41.145963  4076 config_util.py:552] Maybe overwriting train_steps: None
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I1106 07:58:41.145963  4076 config_util.py:552] Maybe overwriting use_bfloat16: False
WARNING:tensorflow:From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\object_detection\model_lib_v2.py:557: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib)
 is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
W1106 07:58:41.224069  4076 deprecation.py:330] From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\object_detection\model_lib_v2.py:557: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.pyth
on.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
INFO:tensorflow:Reading unweighted datasets: ['annotations/train.record']
I1106 07:58:41.224069  4076 dataset_builder.py:163] Reading unweighted datasets: ['annotations/train.record']
INFO:tensorflow:Reading record datasets for input file: ['annotations/train.record']
I1106 07:58:41.224069  4076 dataset_builder.py:80] Reading record datasets for input file: ['annotations/train.record']
INFO:tensorflow:Number of filenames to read: 1
I1106 07:58:41.224069  4076 dataset_builder.py:81] Number of filenames to read: 1
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W1106 07:58:41.224069  4076 dataset_builder.py:87] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\object_detection\builders\dataset_builder.py:101: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated an
d 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.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.
W1106 07:58:41.224069  4076 deprecation.py:330] From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\object_detection\builders\dataset_builder.py:101: parallel_interleave (from tensorflow.python.data.experimental.ops.int
erleave_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.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.
WARNING:tensorflow:From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\object_detection\builders\dataset_builder.py:236: 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()
W1106 07:58:41.255315  4076 deprecation.py:330] From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\object_detection\builders\dataset_builder.py:236: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.d
ataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
WARNING:tensorflow:From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\tensorflow\python\util\dispatch.py:206: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future versio
n.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
W1106 07:58:47.147608  4076 deprecation.py:330] From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\tensorflow\python\util\dispatch.py:206: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will
be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
WARNING:tensorflow:From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\tensorflow\python\util\dispatch.py:206: 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.
W1106 07:58:49.922046  4076 deprecation.py:330] From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\tensorflow\python\util\dispatch.py:206: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is de
precated and will be removed in a future version.
Instructions for updating:
`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.
WARNING:tensorflow:From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\tensorflow\python\autograph\impl\api.py:464: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W1106 07:58:51.524998  4076 deprecation.py:330] From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\tensorflow\python\autograph\impl\api.py:464: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be r
emoved in a future version.
Instructions for updating:
Use `tf.cast` instead.
2021-11-06 07:58:53.478807: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\tensorflow\python\keras\backend.py:435: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a T
rue/False value to the `training` argument of the `__call__` method of your layer or model.
  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
WARNING:tensorflow:From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\tensorflow\python\util\deprecation.py:602: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a f
uture version.
Instructions for updating:
Use fn_output_signature instead
W1106 07:59:18.849742 14976 deprecation.py:528] From C:\Users\femik\Desktop\productDetection\venv\lib\site-packages\tensorflow\python\util\deprecation.py:602: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecat
ed and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead

when installing pip install --ignore-installed --upgrade tensorflow==2.5.0

I got the following errors

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow-text 2.6.0 requires tensorflow<2.7,>=2.6.0, but you have tensorflow 2.5.0 which is incompatible.
tensorflow-metadata 1.4.0 requires absl-py<0.13,>=0.9, but you have absl-py 0.15.0 which is incompatible.
tensorflow-io 0.21.0 requires tensorflow<2.7.0,>=2.6.0, but you have tensorflow 2.5.0 which is incompatible.

So, I downgraded the modules accordingly. But still getting the same issue.

Answer 1

• For version TF_2.6.0
• in file "piplile.config"
• var batch_size = 8 -> 1-4 (using RAM)