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[Docs] Port fix convert tf crnn model docs for release 22.1 (#15466)

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* Port fix convert tf crnn model for release 22.1
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Xiake Sun
2023-02-08 15:45:43 +08:00
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docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_CRNN_From_Tensorflow.md
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# Convert TensorFlow CRNN Model {#openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_CRNN_From_Tensorflow}
# Converting a TensorFlow CRNN Model {#openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_CRNN_From_Tensorflow}
This tutorial explains how to convert a CRNN model to Intermediate Representation (IR).
This tutorial explains how to convert a CRNN model to OpenVINO™ Intermediate Representation (IR).
On GitHub*, you can find several public versions of TensorFlow\* CRNN model implementation. This tutorial explains how to convert the model from
the [https://github.com/MaybeShewill-CV/CRNN_Tensorflow](https://github.com/MaybeShewill-CV/CRNN_Tensorflow) repository to IR. If you
have another implementation of CRNN model, you can convert it to IR in similar way: you need to get inference graph and run the Model Optimizer on it.
There are several public versions of TensorFlow CRNN model implementation available on GitHub. This tutorial explains how to convert the model from
the [CRNN Tensorflow](https://github.com/MaybeShewill-CV/CRNN_Tensorflow) repository to IR, and is validated with Python 3.7, TensorFlow 1.15.0, and protobuf 3.19.0.
If you have another implementation of CRNN model, it can be converted to OpenVINO IR in a similar way. You need to get inference graph and run Model Optimizer on it.
**To convert this model to the IR:**
**To convert the model to IR:**
**Step 1.** Clone this GitHub repository and checkout the commit:
1. Clone repository:
**Step 1.** Clone this GitHub repository and check out the commit:
1. Clone the repository:
```sh
git clone https://github.com/MaybeShewill-CV/CRNN_Tensorflow.git
git clone https://github.com/MaybeShewill-CV/CRNN_Tensorflow.git
```
2. Checkout necessary commit:
2. Go to the `CRNN_Tensorflow` directory of the cloned repository:
```sh
cd path/to/CRNN_Tensorflow
```
3. Check out the necessary commit:
```sh
git checkout 64f1f1867bffaacfeacc7a80eebf5834a5726122
```
**Step 2.** Train the model using framework or use the pre-trained checkpoint provided in this repository.
**Step 2.** Train the model using the framework or the pretrained checkpoint provided in this repository.
**Step 3.** Create an inference graph:
1. Go to the `CRNN_Tensorflow` directory with the cloned repository:
```sh
cd path/to/CRNN_Tensorflow
```
2. Add `CRNN_Tensorflow` folder to `PYTHONPATH`.
* For Linux\* OS:
1. Add the `CRNN_Tensorflow` folder to `PYTHONPATH`.
* For Linux:
```sh
export PYTHONPATH="${PYTHONPATH}:/path/to/CRNN_Tensorflow/"
```
* For Windows\* OS add `/path/to/CRNN_Tensorflow/` to the `PYTHONPATH` environment variable in settings.
3. Open the `tools/test_shadownet.py` script. After `saver.restore(sess=sess, save_path=weights_path)` line, add the following code:
* For Windows, add `/path/to/CRNN_Tensorflow/` to the `PYTHONPATH` environment variable in settings.
2. Edit the `tools/demo_shadownet.py` script. After `saver.restore(sess=sess, save_path=weights_path)` line, add the following code:
```python
import tensorflow as tf
from tensorflow.python.framework import graph_io
frozen = tf.compat.v1.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['shadow/LSTMLayers/transpose_time_major'])
frozen = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['shadow/LSTMLayers/transpose_time_major'])
graph_io.write_graph(frozen, '.', 'frozen_graph.pb', as_text=False)
```
4. Run the demo with the following command:
3. Run the demo with the following command:
```sh
python tools/test_shadownet.py --image_path data/test_images/test_01.jpg --weights_path model/shadownet/shadownet_2017-10-17-11-47-46.ckpt-199999
python tools/demo_shadownet.py --image_path data/test_images/test_01.jpg --weights_path model/shadownet/shadownet_2017-10-17-11-47-46.ckpt-199999
```
If you want to use your checkpoint, replace the path in the `--weights_path` parameter with a path to your checkpoint.
5. In the `CRNN_Tensorflow` directory, you will find the inference CRNN graph `frozen_graph.pb`. You can use this graph with the OpenVINO™ toolkit
to convert the model into IR and run inference.
4. In the `CRNN_Tensorflow` directory, you will find the inference CRNN graph `frozen_graph.pb`. You can use this graph with OpenVINO
to convert the model to IR and then run inference.
**Step 4.** Convert the model into IR:
**Step 4.** Convert the model to IR:
```sh
mo --input_model path/to/your/CRNN_Tensorflow/frozen_graph.pb
```