Dockerfile installs two tensorflow versions when building

1. The entire URL of the file you are using

https://github.com/tensorflow/models/blob/238922e98dd0e8254b5c0921b241a1f5a151782f/research/object_detection/dockerfiles/tf2/Dockerfile

2. Describe the bug

Some of the python requirements that gets installed from the "/models/research/object_detection/packages/tf2/setup.py" leads to the installation of the latest tensorflow version (2.4.1). Since the dockerfile is building with the tensorflow/tensorflow:2.2.0-gpu image as starting point, I now have tensorflow:2.2.0-gpu AND tensorflow:2.4.1 installed.

When I try to train on a network I get the following output:

python object_detection/model_main_tf2.py --pipeline_config_path=${PIPELINE_CONFIG_PATH} --model_dir=${MODEL_DIR} --alsologtostderr

Traceback (most recent call last):
File "object_detection/model_main_tf2.py", line 31, in
import tensorflow.compat.v2 as tf
File "/home/tensorflow/.local/lib/python3.6/site-packages/tensorflow/init.py", line 436, in
_ll.load_library(_main_dir)
File "/home/tensorflow/.local/lib/python3.6/site-packages/tensorflow/python/framework/load_library.py", line 153, in load_library
py_tf.TF_LoadLibrary(lib)
tensorflow.python.framework.errors_impl.NotFoundError: /usr/local/lib/python3.6/dist-packages/tensorflow/core/kernels/libtfkernel_sobol_op.so: undefined symbol: _ZN10tensorflow8OpKernel11TraceStringEPNS_15OpKernelContextEb

3. Steps to reproduce

Clone the "models" repository and install using docker. Follow installation instructions here:
https://github.com/tensorflow/models/blob/238922e98dd0e8254b5c0921b241a1f5a151782f/research/object_detection/g3doc/tf2.md

Start training on a network:
https://github.com/tensorflow/models/blob/238922e98dd0e8254b5c0921b241a1f5a151782f/research/object_detection/g3doc/tf2_training_and_evaluation.md

 PIPELINE_CONFIG_PATH={path to pipeline config file}
 MODEL_DIR={path to model directory}
 python object_detection/model_main_tf2.py \
     --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
     --model_dir=${MODEL_DIR} \
     --alsologtostderr

4. Expected behavior

I want to use the tensorflow:2.2.0-gpu or tensorflow:X.X.X-gpu. I do not want another tensorflow version to get installed during building as this screws up the environment.

5. Additional context

After the python requirements from the https://github.com/tensorflow/models/blob/238922e98dd0e8254b5c0921b241a1f5a151782f/research/object_detection/packages/tf2/setup.py file is installed while building the dockerfile, I get the following output indicating that all tensorflow related packages has been updated to version 2.4 as well.

Successfully built object-detection avro-python3 crcmod dill future docopt pycocotools kaggle py-cpuinfo python-slugify seqeval promise
Installing collected packages: pyparsing, pytz, packaging, numpy, googleapis-common-protos, google-auth, wheel, threadpoolctl, text-unidecode, python-dateutil, pillow, kiwisolver, joblib, httplib2, grpcio, google-crc32c, google-api-core, cycler, uritemplate, typing-extensions, typeguard, tqdm, tensorflow-metadata, tensorflow-estimator, scikit-learn, python-slugify, proto-plus, promise, pbr, matplotlib, importlib-resources, google-resumable-media, google-cloud-core, google-auth-httplib2, future, flatbuffers, docopt, dm-tree, dill, dataclasses, Cython, attrs, tf-slim, tensorflow-model-optimization, tensorflow-hub, tensorflow-datasets, tensorflow-addons, tensorflow, seqeval, sentencepiece, pyyaml, pymongo, pydot, pycocotools, pyarrow, py-cpuinfo, psutil, pandas, opencv-python-headless, opencv-python, oauth2client, mock, kaggle, hdfs, google-cloud-bigquery, google-api-python-client, gin-config, fastavro, crcmod, avro-python3, tf-models-official, lvis, contextlib2, apache-beam, object-detection
Successfully installed Cython-0.29.23 apache-beam-2.28.0 attrs-20.3.0 avro-python3-1.9.2.1 contextlib2-0.6.0.post1 crcmod-1.7 cycler-0.10.0 dataclasses-0.8 dill-0.3.1.1 dm-tree-0.1.6 docopt-0.6.2 fastavro-1.3.5 flatbuffers-1.12 future-0.18.2 gin-config-0.4.0 google-api-core-1.26.3 google-api-python-client-2.2.0 google-auth-1.28.1 google-auth-httplib2-0.1.0 google-cloud-bigquery-2.13.1 google-cloud-core-1.6.0 google-crc32c-1.1.2 google-resumable-media-1.2.0 googleapis-common-protos-1.53.0 grpcio-1.32.0 hdfs-2.6.0 httplib2-0.17.4 importlib-resources-5.1.2 joblib-1.0.1 kaggle-1.5.12 kiwisolver-1.3.1 lvis-0.5.3 matplotlib-3.3.4 mock-2.0.0 numpy-1.19.5 oauth2client-4.1.3 object-detection-0.1 opencv-python-4.5.1.48 opencv-python-headless-4.5.1.48 packaging-20.9 pandas-1.1.5 pbr-5.5.1 pillow-8.2.0 promise-2.3 proto-plus-1.18.1 psutil-5.8.0 py-cpuinfo-8.0.0 pyarrow-2.0.0 pycocotools-2.0.2 pydot-1.4.2 pymongo-3.11.3 pyparsing-2.4.7 python-dateutil-2.8.1 python-slugify-4.0.1 pytz-2021.1 pyyaml-5.4.1 scikit-learn-0.24.1 sentencepiece-0.1.95 seqeval-1.2.2 tensorflow-2.4.1 tensorflow-addons-0.12.1 tensorflow-datasets-4.2.0 tensorflow-estimator-2.4.0 tensorflow-hub-0.12.0 tensorflow-metadata-0.29.0 tensorflow-model-optimization-0.5.0 text-unidecode-1.3 tf-models-official-2.4.0 tf-slim-1.1.0 threadpoolctl-2.1.0 tqdm-4.60.0 typeguard-2.12.0 typing-extensions-3.7.4.3 uritemplate-3.0.1 wheel-0.36.2

6. System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 18.04
• TensorFlow version: 2.2.0 and 2.4.1
• Python version: 3.6.9
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version: 11.2
• GPU model and memory: RTX2080i