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[DOCS] MO Freeze Model unrecognized arguments fix for 22.3 (#19117) 

* Update Convert_EfficientDet_Models.md

* Update Convert_EfficientDet_Models.md
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@ -8,48 +8,20 @@ There are several public versions of EfficientDet model implementation available
convert models from the [repository](https://github.com/google/automl/tree/master/efficientdet)
(commit 96e1fee) to the OpenVINO format.
### Getting a Frozen TensorFlow Model
Download and extract the model checkpoint [efficientdet-d4.tar.gz](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d4.tar.gz) referenced in the **Pretrained EfficientDet Checkpoints** section of the model repository:
Follow the instructions below to get frozen TensorFlow EfficientDet model. EfficientDet-D4 model is an example:
1. Clone the repository:<br>
```sh
git clone https://github.com/google/automl
cd automl/efficientdet
```
2. (Optional) Checkout to the commit that the conversion was tested on:<br>
```sh
git checkout 96e1fee
```
3. Install required dependencies:<br>
```sh
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt
python3 -m pip install --upgrade tensorflow-model-optimization
```
4. Download and extract the model checkpoint [efficientdet-d4.tar.gz](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d4.tar.gz)
referenced in the **"Pretrained EfficientDet Checkpoints"** section of the model repository:<br>
```sh
wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d4.tar.gz
tar zxvf efficientdet-d4.tar.gz
```
5. Freeze the model:<br>
```sh
mo --runmode=saved_model --model_name=efficientdet-d4 --ckpt_path=efficientdet-d4 --saved_model_dir=savedmodeldir
```
As a result, the frozen model file `savedmodeldir/efficientdet-d4_frozen.pb` will be generated.
> **NOTE**: For custom trained models, specify `--hparams` flag to `config.yaml` which was used during training.
> **NOTE**: If you see an error *AttributeError: module 'tensorflow_core.python.keras.api._v2.keras.initializers' has no attribute 'variance_scaling'*, apply the fix from the [patch](https://github.com/google/automl/pull/846).
### Converting an EfficientDet TensorFlow Model to the IR
To generate the IR of the EfficientDet TensorFlow model, run:<br>
```sh
mo \
--input_model savedmodeldir/efficientdet-d4_frozen.pb \
--transformations_config front/tf/automl_efficientdet.json \
--input_meta_graph efficientdet-d4/model.meta \
--input_shape [1,$IMAGE_SIZE,$IMAGE_SIZE,3] \
--reverse_input_channels
```
@ -59,12 +31,6 @@ EfficientDet models were trained with different input image sizes. To determine
dictionary in the [hparams_config.py](https://github.com/google/automl/blob/96e1fee/efficientdet/hparams_config.py#L304) file.
The attribute `image_size` specifies the shape to be defined for the model conversion.
The `transformations_config` command line parameter specifies the configuration json file containing hints
for the Model Optimizer on how to convert the model and trigger transformations implemented in the
`<PYTHON_SITE_PACKAGES>/openvino/tools/mo/front/tf/AutomlEfficientDet.py`. The json file contains some parameters which must be changed if you
train the model yourself and modified the `hparams_config` file or the parameters are different from the ones used for EfficientDet-D4.
The attribute names are self-explanatory or match the name in the `hparams_config` file.
> **NOTE**: The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to the **When to Reverse Input Channels** section of the [Converting a Model to Intermediate Representation (IR)](@ref openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model) guide.
OpenVINO toolkit provides samples that can be used to infer EfficientDet model.
@ -73,21 +39,21 @@ For more information, refer to the [Open Model Zoo Demos](@ref omz_demos).
## <a name="efficientdet-ir-results-interpretation"></a>Interpreting Results of the TensorFlow Model and the IR
The TensorFlow model produces as output a list of 7-element tuples: `[image_id, y_min, x_min, y_max, x_max, confidence, class_id]`, where:
* `image_id` -- image batch index.
* `y_min` -- absolute `y` coordinate of the lower left corner of the detected object.
* `x_min` -- absolute `x` coordinate of the lower left corner of the detected object.
* `y_max` -- absolute `y` coordinate of the upper right corner of the detected object.
* `x_max` -- absolute `x` coordinate of the upper right corner of the detected object.
* `confidence` -- is the confidence of the detected object.
* `class_id` -- is the id of the detected object class counted from 1.
* `image_id` - image batch index.
* `y_min` - absolute `y` coordinate of the lower left corner of the detected object.
* `x_min` - absolute `x` coordinate of the lower left corner of the detected object.
* `y_max` - absolute `y` coordinate of the upper right corner of the detected object.
* `x_max` - absolute `x` coordinate of the upper right corner of the detected object.
* `confidence` - is the confidence of the detected object.
* `class_id` - is the id of the detected object class counted from 1.
The output of the IR is a list of 7-element tuples: `[image_id, class_id, confidence, x_min, y_min, x_max, y_max]`, where:
* `image_id` -- image batch index.
* `class_id` -- is the id of the detected object class counted from 0.
* `confidence` -- is the confidence of the detected object.
* `x_min` -- normalized `x` coordinate of the lower left corner of the detected object.
* `y_min` -- normalized `y` coordinate of the lower left corner of the detected object.
* `x_max` -- normalized `x` coordinate of the upper right corner of the detected object.
* `y_max` -- normalized `y` coordinate of the upper right corner of the detected object.
* `image_id` - image batch index.
* `class_id` - is the id of the detected object class counted from 0.
* `confidence` - is the confidence of the detected object.
* `x_min` - normalized `x` coordinate of the lower left corner of the detected object.
* `y_min` - normalized `y` coordinate of the lower left corner of the detected object.
* `x_max` - normalized `x` coordinate of the upper right corner of the detected object.
* `y_max` - normalized `y` coordinate of the upper right corner of the detected object.
The first element with `image_id = -1` means end of data.