TypeError: create_estimator_and_inputs() missing 1 required positional argument: 'hparams'

"""Binary to run train and evaluation on object detection model."""


"""Binary to run train and evaluation on object detection model."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import warnings
import os

# warnings.filterwarnings("ignore")

from absl import flags
from tensorflow import keras

import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import estimator as tf_estimator
from keras_applications.resnet import ResNet50


from object_detection import model_lib
from object_detection import model_hparams

flags.DEFINE_string(
    "model_dir",
    "D:\TensorFlow\training_demo\models\ssd_mobilenet_v1_coco\mobilenetv1.pb",
    "where event and checkpoint files will be written.",
)
flags.DEFINE_string("pipeline_config_path", "D:\TensorFlow\training_demo\annotations\pipeline.config", "D:\TensorFlow\training_demo\annotations\pipeline.config")
flags.DEFINE_integer("num_train_steps", 2000, "Number of train steps.")
flags.DEFINE_boolean(
    "eval_training_data",
    False,
    "If training data should be evaluated for this job. Note "
    "that one call only use this in eval-only mode, and "
    "`checkpoint_dir` must be supplied.",
)
flags.DEFINE_integer(
    "sample_1_of_n_eval_examples",
    1,
    "Will sample one of " "every n eval input examples, where n is provided.",
)
flags.DEFINE_integer(
    "sample_1_of_n_eval_on_train_examples",
    1,
    "Will sample "
    "one of every n train input examples for evaluation, "
    "where n is provided. This is only used if "
    "`eval_training_data` is True.",
)
flags.DEFINE_string(
    "checkpoint_dir",
    "D:\TensorFlow\training_demo\pre-trained-models\ssd_mobilenet_v1_coco",
    "Path to directory holding a checkpoint.  If "
    "`checkpoint_dir` is provided, this binary operates in eval-only mode, "
    "writing resulting metrics to `model_dir`.",
)
flags.DEFINE_boolean(
    "run_once",
    False,
    "If running in eval-only mode, whether to run just "
    "one round of eval vs running continuously (default).",
)
flags.DEFINE_integer(
    "max_eval_retries",
    0,
    "If running continuous eval, the maximum number of "
    "retries upon encountering tf.errors.InvalidArgumentError. If negative, "
    "will always retry the evaluation.",
)
FLAGS = flags.FLAGS


def main(unused_argv):
    flags.mark_flag_as_required("model_dir")
    flags.mark_flag_as_required("pipeline_config_path")
    config = tf_estimator.RunConfig(model_dir=FLAGS.model_dir)

    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config=config,
        pipeline_config_path=FLAGS.pipeline_config_path,
        train_steps=FLAGS.num_train_steps,
        sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
        sample_1_of_n_eval_on_train_examples=(
            FLAGS.sample_1_of_n_eval_on_train_examples
        ),
    )
    estimator = train_and_eval_dict["estimator"]
    train_input_fn = train_and_eval_dict["train_input_fn"]
    eval_input_fns = train_and_eval_dict["eval_input_fns"]
    eval_on_train_input_fn = train_and_eval_dict["eval_on_train_input_fn"]
    predict_input_fn = train_and_eval_dict["predict_input_fn"]
    train_steps = train_and_eval_dict["train_steps"]

    if FLAGS.checkpoint_dir:
        if FLAGS.eval_training_data:
            name = "training_data"
            input_fn = eval_on_train_input_fn
        else:
            name = "validation_data"
            # The first eval input will be evaluated.
            input_fn = eval_input_fns[0]
        if FLAGS.run_once:
            estimator.evaluate(
                input_fn,
                steps=2000,
                checkpoint_path=tf.train.latest_checkpoint(FLAGS.checkpoint_dir),
            )
        else:
            model_lib.continuous_eval(
                estimator,
                FLAGS.checkpoint_dir,
                input_fn,
                train_steps,
                name,
                FLAGS.max_eval_retries,
            )
    else:
        train_spec, eval_specs = model_lib.create_train_and_eval_specs(
            train_input_fn,
            eval_input_fns,
            eval_on_train_input_fn,
            predict_input_fn,
            train_steps,
            eval_on_train_data=False,
        )

        # Currently only a single Eval Spec is allowed.
        tf_estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])


if __name__ == "__main__":
    tf.app.run()

absl-py 1.1.0
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
astor 0.8.1
attrs 21.4.0
backcall 0.2.0
beautifulsoup4 4.11.1
bleach 5.0.1
certifi 2022.6.15
cffi 1.15.1
colorama 0.4.5
cycler 0.11.0
Cython 0.29.30
debugpy 1.6.2
decorator 5.1.1
defusedxml 0.7.1
entrypoints 0.4
fastjsonschema 2.15.3
gast 0.5.3
google-pasta 0.2.0
grpcio 1.47.0
h5py 3.7.0
importlib-metadata 4.12.0
importlib-resources 5.8.0
ipykernel 6.15.1
ipython 7.34.0
ipython-genutils 0.2.0
ipywidgets 7.7.1
jedi 0.18.1
Jinja2 3.1.2
jsonschema 4.7.2
jupyter 1.0.0
jupyter-client 7.3.4
jupyter-console 6.4.4
jupyter-core 4.11.1
jupyterlab-pygments 0.2.2
jupyterlab-widgets 1.1.1
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.2
kiwisolver 1.4.3
lxml 4.4.1
Markdown 3.4
MarkupSafe 2.1.1
matplotlib 3.1.1
matplotlib-inline 0.1.3
mistune 0.8.4
nbclient 0.6.6
nbconvert 6.5.0
nbformat 5.4.0
nest-asyncio 1.5.5
notebook 6.4.12
numpy 1.16.4
object-detection 0.1
opencv-python 4.6.0.66
packaging 21.3
pandocfilters 1.5.0
parso 0.8.3
pathlib 1.0.1
pickleshare 0.7.5
Pillow 6.2.1
pip 22.1.2
prometheus-client 0.14.1
prompt-toolkit 3.0.30
protobuf 3.20.0
psutil 5.9.1
pycocotools 2.0.4
pycparser 2.21
Pygments 2.12.0
pyparsing 3.0.9
pyrsistent 0.18.1
python-dateutil 2.8.2
pywin32 304
pywinpty 2.0.6
pyzmq 23.2.0
qtconsole 5.3.1
QtPy 2.1.0
Send2Trash 1.8.0
setuptools 61.2.0
six 1.16.0
slim 0.1
soupsieve 2.3.2.post1
tensorboard 1.14.0
tensorflow-estimator 1.14.0
tensorflow-gpu 1.14.0
termcolor 1.1.0
terminado 0.15.0
tinycss2 1.1.1
tornado 6.2
traitlets 5.3.0
typing_extensions 4.3.0
wcwidth 0.2.5
webencodings 0.5.1
Werkzeug 2.1.2
wheel 0.37.1
widgetsnbextension 3.6.1
wincertstore 0.2
wrapt 1.14.1
zipp 3.8.1