tensorflow_object_detection_api example breaks due to dependecies

Description

Running the tensorflow object detection example as mentioned in the guide (https://github.com/NVIDIA/TensorRT/blob/release/8.6/samples/python/tensorflow_object_detection_api/README.md) does not work. This includes starting from the mentioned docker image, and performing each step exactly as stated in the guide.

My current workaround is to fix the tensorflow-models and numpy versions in the setup.py from "models/research"

Environment

TensorRT Version: 8.0.3.4

NVIDIA GPU: NVIDIA GeForce GTX 1080 Ti

NVIDIA Driver Version: 510.68.02

CUDA Version: 11.6

CUDNN Version: 8.2.4

Operating System: Ubuntu 20.04.3 LTS

Python Version (if applicable): 3.8.10

Tensorflow Version (if applicable): container comes with 2.6.0nv but setup guide overwrites it to 2.13

PyTorch Version (if applicable):

Baremetal or Container (if so, version): nvcr.io/nvidia/tensorflow:21.10-tf2-py3

Steps To Reproduce

Running the exact setup stated in https://github.com/NVIDIA/TensorRT/blob/release/8.6/samples/python/tensorflow_object_detection_api/README.md is enough. The code breaks when running "exporter_main_v2.py":

root@8b3443e5f2ab:/workspace/models/research/object_detection# python exporter_main_v2.py \
>     --input_type float_image_tensor \
object_detecti>     --trained_checkpoint_dir ~/TensorRT/samples/python/tensorflow_object_detection_api/ssd_mobilenet_v2_320x320_coco17_tpu-8/checkpoint/ \
>     --pipeline_config_path ~/TensorRT/samples/python/tensorflow_object_detection_api/ssd_mobilenet_v2_320x320_coco17_tpu-8/pipeline.config  \
>     --output_directory  ~/TensorRT/samples/python/tensorflow_object_detection_api/tfod_out
2023-07-10 00:16:55.220116: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Traceback (most recent call last):
  File "exporter_main_v2.py", line 103, in <module>
    import tensorflow.compat.v2 as tf
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/__init__.py", line 38, in <module>
    from tensorflow.python.tools import module_util as _module_util
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/__init__.py", line 45, in <module>
    from tensorflow.python.feature_column import feature_column_lib as feature_column
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/feature_column_lib.py", line 18, in <module>
    from tensorflow.python.feature_column.feature_column import *
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/feature_column.py", line 143, in <module>
    from tensorflow.python.layers import base
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/base.py", line 16, in <module>
    from tensorflow.python.keras.legacy_tf_layers import base
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/__init__.py", line 25, in <module>
    from tensorflow.python.keras import models
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/models.py", line 25, in <module>
    from tensorflow.python.keras.engine import training_v1
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_v1.py", line 46, in <module>
    from tensorflow.python.keras.engine import training_arrays_v1
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_arrays_v1.py", line 37, in <module>
    from scipy.sparse import issparse  # pylint: disable=g-import-not-at-top
  File "/usr/local/lib/python3.8/dist-packages/scipy/sparse/__init__.py", line 229, in <module>
    from .base import *
  File "/usr/local/lib/python3.8/dist-packages/scipy/sparse/base.py", line 8, in <module>
    from .sputils import (isdense, isscalarlike, isintlike,
  File "/usr/local/lib/python3.8/dist-packages/scipy/sparse/sputils.py", line 17, in <module>
    supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
  File "/usr/local/lib/python3.8/dist-packages/scipy/sparse/sputils.py", line 17, in <listcomp>
    supported_dtypes = [np.typeDict[x] for x in supported_dtypes]
  File "/usr/local/lib/python3.8/dist-packages/numpy/__init__.py", line 320, in __getattr__
    raise AttributeError("module {!r} has no attribute "
AttributeError: module 'numpy' has no attribute 'typeDict'

Also, when running the installation "pip --use-deprecated=legacy-resolver install ." there are multiple conflicts:

ERROR: pip's legacy dependency resolver does not consider dependency conflicts when selecting packages. This behaviour is the source of the following dependency conflicts.
tensorflow 2.13.0 requires absl-py>=1.0.0, but you'll have absl-py 0.12.0 which is incompatible.
tensorflow 2.13.0 requires numpy<=1.24.3,>=1.22, but you'll have numpy 1.19.4 which is incompatible.
tensorflow-metadata 1.2.0 requires protobuf<4,>=3.13, but you'll have protobuf 4.23.4 which is incompatible.
tensorboard 2.13.0 requires grpcio>=1.48.2, but you'll have grpcio 1.39.0 which is incompatible.
google-auth-oauthlib 1.0.0 requires google-auth>=2.15.0, but you'll have google-auth 1.35.0 which is incompatible.
matplotlib 3.7.2 requires numpy>=1.20, but you'll have numpy 1.19.4 which is incompatible.
pandas 2.0.3 requires numpy>=1.20.3; python_version < "3.10", but you'll have numpy 1.19.4 which is incompatible.
google-api-core 2.11.1 requires google-auth<3.0.dev0,>=2.14.1, but you'll have google-auth 1.35.0 which is incompatible.
scikit-learn 1.3.0 requires scipy>=1.5.0, but you'll have scipy 1.4.1 which is incompatible.
tensorflow-model-optimization 0.7.5 requires absl-py~=1.2, but you'll have absl-py 0.12.0 which is incompatible.
tensorflow-model-optimization 0.7.5 requires numpy~=1.23, but you'll have numpy 1.19.4 which is incompatible.
tf-models-official 2.13.0 requires numpy>=1.20, but you'll have numpy 1.19.4 which is incompatible.